From 58804e0d9128dc5c2d000bba5c12c7743f627ddc Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 04:21:56 -0600 Subject: [PATCH 01/26] Updated calculate peer scores function and fixed fstrings --- AI_BENCHMARKING_ANALYSIS.ipynb | 6876 +++++++++++++++------------ bootstrapped_h2h_bot_vs_pros.csv | 88 +- functions.py | 115 +- weighted_t_test_h2h_bot_vs_pros.csv | 92 +- 4 files changed, 4002 insertions(+), 3169 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 3753b54..313d580 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -182,7 +182,7 @@ "# Weighted vs unweighted breakdown for those overlapping questions?\n", "df_pro_bot_overlap = df_pro_bot_resolved_questions[~df_pro_bot_resolved_questions['pro_question_id'].isna()]\n", "print(f'Unweighted count: {df_pro_bot_overlap.shape[0]}')\n", - "print(f'Weighted count: {df_pro_bot_overlap['question_weight'].sum()}')" + "print(f'Weighted count: {df_pro_bot_overlap[\"question_weight\"].sum()}')" ] }, { @@ -503,7 +503,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Process forecasts (consolidate forecast columns; take the last forecast from each forecaster for each question) \n", + "# Process forecasts (consolidate forecast columns; take the last forecast from each forecaster for each question)\n", "df_bot_forecasts = process_forecasts(df_bot_forecasts)\n", "df_pro_forecasts = process_forecasts(df_pro_forecasts)\n", "\n", @@ -615,12 +615,12 @@ " False\n", " \n", " \n", - " 3\n", + " 5\n", " 31268\n", - " SpottedBear\n", + " darkives\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 131523\n", + " 103907\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -630,16 +630,16 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.59,0.35,0.044,0.015]\n", + " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", " \n", " \n", - " 4\n", + " 6\n", " 31268\n", - " Zaldath\n", + " datscilly\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 139161\n", + " 103777\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -649,7 +649,7 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.623,0.336,0.03,0.01]\n", + " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", " \n", " \n", @@ -657,47 +657,40 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "3 31268 SpottedBear \n", - "4 31268 Zaldath \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "1 For Q1 2025, how many banks will be listed on ... \n", - "2 For Q1 2025, how many banks will be listed on ... \n", - "3 For Q1 2025, how many banks will be listed on ... \n", - "4 For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "3 2025-01-17 19:06:22.013528+00 131523 1 \n", - "4 2025-01-17 19:06:22.013528+00 139161 1 \n", + "5 2025-01-17 19:06:22.013528+00 103907 1 \n", + "6 2025-01-17 19:06:22.013528+00 103777 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max post_id \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "\n", " forecast is_median \n", "0 [0.001,0.568,0.366,0.041,0.024] False \n", "1 [0.001,0.62,0.35,0.019,0.01] True \n", "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "3 [0.001,0.59,0.35,0.044,0.015] False \n", - "4 [0.001,0.623,0.336,0.03,0.01] False " + "5 [0.001,0.49,0.365,0.1,0.044] False \n", + "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, "execution_count": 16, @@ -740,19 +733,18 @@ { "data": { "text/plain": [ - "array(['GreeneiBot2', 'Grizeu_Bot', 'InstitutPelFutur', 'NextWorldLab',\n", - " 'acm_bot', 'metac-Gemini-Exp-1206', 'metac-Llama-3.1', 'mmBot',\n", - " 'metac-claude-3-5-sonnet-latest', 'metac-gpt-4o',\n", - " 'metac-grok-2-1212', 'metac-o1', 'metac-o1-preview',\n", - " 'metac-perplexity', 'bot_median',\n", - " 'metac-claude-3-5-sonnet-20240620', 'pgodzinai', 'jkraybill_bot',\n", - " 'metac-exa', 'manticAI', 'MWG', 'CatrachoCaster', 'twsummerbot',\n", - " 'VeritasAI', 'X_bot', 'annabot', 'minefrac1', 'metac-deepseek-r1',\n", - " 'Bot_Pepa', 'laylaps', 'ajf-bot', 'SynapseSeer', 'RPM_bot',\n", - " 'cookics_bot_TEST', 'ProfessorSP', 'wunderplumb', 'CumulativeBot',\n", - " 'pianobot', 'krm-bot', 'KevinTestBot', '4Shadower', 'swingswish',\n", - " 'jonahsingerbot', 'bean_bot', 'andrewsiah', 'cobyj-bot'],\n", - " dtype=object)" + "array(['metac-Llama-3.1', 'metac-Gemini-Exp-1206', 'acm_bot',\n", + " 'NextWorldLab', 'metac-o1-preview', 'metac-perplexity', 'mmBot',\n", + " 'metac-claude-3-5-sonnet-latest', 'Grizeu_Bot', 'GreeneiBot2',\n", + " 'InstitutPelFutur', 'metac-claude-3-5-sonnet-20240620', 'metac-o1',\n", + " 'metac-grok-2-1212', 'metac-gpt-4o', 'bot_median', 'pgodzinai',\n", + " 'metac-exa', 'jkraybill_bot', 'VeritasAI', 'MWG', 'twsummerbot',\n", + " 'CatrachoCaster', 'X_bot', 'manticAI', 'annabot', 'minefrac1',\n", + " 'metac-deepseek-r1', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", + " 'SynapseSeer', 'RPM_bot', 'cookics_bot_TEST', 'ProfessorSP',\n", + " 'wunderplumb', 'CumulativeBot', 'pianobot', 'krm-bot',\n", + " 'KevinTestBot', '4Shadower', 'swingswish', 'jonahsingerbot',\n", + " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, "execution_count": 18, @@ -801,7 +793,7 @@ " \n", " \n", " \n", - " 11\n", + " 12\n", " metac-o1\n", " 9.674740\n", " 3631.123492\n", @@ -810,7 +802,16 @@ " 1.738353\n", " \n", " \n", - " 12\n", + " 15\n", + " bot_median\n", + " 8.829587\n", + " 3337.760404\n", + " 409\n", + " 5.839419\n", + " 1.521098\n", + " \n", + " \n", + " 4\n", " metac-o1-preview\n", " 8.465638\n", " 3121.449998\n", @@ -819,16 +820,7 @@ " 2.298000\n", " \n", " \n", - " 14\n", - " bot_median\n", - " 6.926374\n", - " 2618.307732\n", - " 409\n", - " 3.779645\n", - " 1.600741\n", - " \n", - " \n", - " 19\n", + " 24\n", " manticAI\n", " 6.510835\n", " 2055.210309\n", @@ -837,7 +829,7 @@ " 3.029040\n", " \n", " \n", - " 5\n", + " 1\n", " metac-Gemini-Exp-1206\n", " 5.417367\n", " 1880.476418\n", @@ -851,18 +843,18 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "11 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "12 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "14 bot_median 6.926374 2618.307732 409 3.779645 \n", - "19 manticAI 6.510835 2055.210309 337 0.552564 \n", - "5 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", + "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "15 bot_median 8.829587 3337.760404 409 5.839419 \n", + "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "24 manticAI 6.510835 2055.210309 337 0.552564 \n", + "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", - "11 1.738353 \n", - "12 2.298000 \n", - "14 1.600741 \n", - "19 3.029040 \n", - "5 2.309106 " + "12 1.738353 \n", + "15 1.521098 \n", + "4 2.298000 \n", + "24 3.029040 \n", + "1 2.309106 " ] }, "metadata": {}, @@ -899,7 +891,7 @@ " \n", " \n", " \n", - " 23\n", + " 19\n", " VeritasAI\n", " -4.854808\n", " -1602.183635\n", @@ -917,7 +909,7 @@ " 3.096816\n", " \n", " \n", - " 1\n", + " 8\n", " Grizeu_Bot\n", " -9.743831\n", " -1882.605577\n", @@ -926,7 +918,7 @@ " 3.931500\n", " \n", " \n", - " 9\n", + " 14\n", " metac-gpt-4o\n", " -5.987786\n", " -2235.360274\n", @@ -949,17 +941,17 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "23 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", + "19 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", "26 minefrac1 -9.333648 -1757.059251 202 -15.440064 \n", - "1 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", - "9 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", + "8 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", + "14 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", "30 ajf-bot -14.000701 -3208.260547 244 -24.482548 \n", "\n", " weighted_se \n", - "23 2.036820 \n", + "19 2.036820 \n", "26 3.096816 \n", - "1 3.931500 \n", - "9 2.255950 \n", + "8 3.931500 \n", + "14 2.255950 \n", "30 5.321344 " ] }, @@ -1520,7 +1512,7 @@ " \n", " 3\n", " bot_median\n", - " 8152.574861\n", + " 8806.147044\n", " \n", " \n", " 4\n", @@ -1541,7 +1533,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8152.574861\n", + "3 bot_median 8806.147044\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1710,13 +1702,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3711.510468\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2724.680171\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -1946,8 +1938,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2724.680171\n", + "2 bot_median 3711.510468\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -2082,12 +2074,12 @@ "# Print WEIGHTED average for pro_median\n", "print(\"PRO MEDIAN\")\n", "pro_median_baseline = df_pro_baseline_long[df_pro_baseline_long['forecaster'] == 'pro_median']\n", - "print(f'Average baseline: {(pro_median_baseline['score'] * pro_median_baseline['question_weight']).sum() / pro_median_baseline['question_weight'].sum()}')\n", + "print(f'Average baseline: {(pro_median_baseline[\"score\"] * pro_median_baseline[\"question_weight\"]).sum() / pro_median_baseline[\"question_weight\"].sum()}')\n", "\n", "# Same for pgodzinai in df_bot_scores (this differs from the bot team results later on because it's on ALL his questions)\n", "print(\"pgodzinai MEDIAN\")\n", "pgodzinai_baseline = df_bot_scores[df_bot_scores['forecaster'] == 'pgodzinai']\n", - "print(f'Average baseline: {(pgodzinai_baseline['score'] * pgodzinai_baseline['question_weight']).sum() / pgodzinai_baseline['question_weight'].sum()}')" + "print(f'Average baseline: {(pgodzinai_baseline[\"score\"] * pgodzinai_baseline[\"question_weight\"]).sum() / pgodzinai_baseline[\"question_weight\"].sum()}')" ] }, { @@ -2193,12 +2185,12 @@ " False\n", " \n", " \n", - " 3\n", + " 5\n", " 31268\n", - " SpottedBear\n", + " darkives\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 131523\n", + " 103907\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2208,16 +2200,16 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.59,0.35,0.044,0.015]\n", + " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", " \n", " \n", - " 4\n", + " 6\n", " 31268\n", - " Zaldath\n", + " datscilly\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 139161\n", + " 103777\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2227,7 +2219,7 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.623,0.336,0.03,0.01]\n", + " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", " \n", " \n", @@ -2235,47 +2227,40 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "3 31268 SpottedBear \n", - "4 31268 Zaldath \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "1 For Q1 2025, how many banks will be listed on ... \n", - "2 For Q1 2025, how many banks will be listed on ... \n", - "3 For Q1 2025, how many banks will be listed on ... \n", - "4 For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "3 2025-01-17 19:06:22.013528+00 131523 1 \n", - "4 2025-01-17 19:06:22.013528+00 139161 1 \n", + "5 2025-01-17 19:06:22.013528+00 103907 1 \n", + "6 2025-01-17 19:06:22.013528+00 103777 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max post_id \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "\n", " forecast is_median \n", "0 [0.001,0.568,0.366,0.041,0.024] False \n", "1 [0.001,0.62,0.35,0.019,0.01] True \n", "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "3 [0.001,0.59,0.35,0.044,0.015] False \n", - "4 [0.001,0.623,0.336,0.03,0.01] False " + "5 [0.001,0.49,0.365,0.1,0.044] False \n", + "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, "execution_count": 28, @@ -2353,9 +2338,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.2,0.25,0.35,0.15,0.05]\n", + " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2377,7 +2362,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2427,7 +2412,7 @@ " ...\n", " [0.25,0.6,0.15]\n", " [0.2,0.6,0.2]\n", - " [0.15,0.45,0.4]\n", + " [0.15,0.55,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2449,8 +2434,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", + " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2488,24 +2473,24 @@ "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", "\n", " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.45,0.3,0.15,0.05,0.05] \n", - "1 NaN ... [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", + "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", "2 NaN ... 0.1 \n", "3 [0.16,0.47,0.37] ... [0.25,0.6,0.15] \n", - "4 NaN ... [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", "0 [0.02,0.7,0.2,0.07,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.15 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.2,0.25,0.35,0.15,0.05] NaN \n", + "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", "2 0.1 NaN \n", - "3 [0.15,0.45,0.4] NaN \n", + "3 [0.15,0.55,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2593,7 +2578,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.9\n", + " 0.95\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2617,8 +2602,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.2\n", - " 0.9\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2641,8 +2626,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.85\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2665,7 +2650,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.75\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2689,9 +2674,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.07\n", - " 0.1\n", " 0.05\n", + " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2714,18 +2699,18 @@ "98 35387 35367 no 0.85 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.9 NaN NaN ... 0.9 \n", - "95 None 0.05 0.95 NaN NaN ... 0.2 \n", - "96 None 0.97 0.85 NaN NaN ... 0.85 \n", - "97 None 0.666 0.8 NaN NaN ... 0.75 \n", - "98 None 0.03 0.3 NaN NaN ... 0.07 \n", + "94 None 0.95 0.9 NaN NaN ... 0.95 \n", + "95 None 0.05 0.95 NaN NaN ... 0.35 \n", + "96 None 0.97 0.85 NaN NaN ... 0.9 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.9 NaN NaN 0.95 0.95 NaN \n", - "95 0.9 NaN NaN 0.15 NaN NaN \n", - "96 0.9 NaN NaN 0.9 NaN NaN \n", + "95 0.4 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.05 NaN 0.15 0.05 NaN \n", + "98 0.05 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -2874,61 +2859,6 @@ "cell_type": "code", "execution_count": 34, "metadata": {}, - "outputs": [], - "source": [ - "# Simple function to parse CDF strings for numeric questions\n", - "def parse_numeric_forecasts(df):\n", - " \"\"\"\n", - " Parse CDF strings for numeric questions in-place.\n", - " \n", - " Args:\n", - " df: DataFrame with forecast data\n", - " \"\"\"\n", - " # Get numeric questions\n", - " numeric_mask = df['type'] == 'numeric'\n", - " \n", - " # List of columns to process\n", - " forecast_cols = [col for col in df.columns if col in all_bots or col in ['pro_median', 'bot_median']]\n", - " \n", - " # Process each column\n", - " for col in forecast_cols:\n", - " # Process only for numeric questions and only where the column exists\n", - " if col in df.columns:\n", - " for idx in df[numeric_mask].index:\n", - " value = df.at[idx, col]\n", - " \n", - " # Skip NaN values\n", - " if pd.isna(value):\n", - " continue\n", - " \n", - " # Process string values\n", - " if isinstance(value, str):\n", - " try:\n", - " # Parse the CDF string to an array\n", - " parsed_array = np.array([float(x) for x in value.strip('[]').split(',')])\n", - " df.at[idx, col] = parsed_array\n", - " except Exception as e:\n", - " print(f\"Warning: Could not parse {col} at index {idx}: {e}\")\n", - " \n", - " return df\n", - "\n", - "# Now parse the numeric forecasts\n", - "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, "outputs": [ { "data": { @@ -2962,6 +2892,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -2971,7 +2902,6 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", @@ -2988,169 +2918,211 @@ " NaN\n", " NaN\n", " ...\n", - " 299.573227\n", - " 529.831737\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", - " 229.263476\n", - " 270.308741\n", + " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", + " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 501.063529\n", " \n", " \n", - " 3\n", - " 31280\n", - " 31274\n", - " 5-9\n", + " 1\n", + " 31269\n", + " 31263\n", + " 86.82\n", " 1.0\n", - " multiple_choice\n", - " [0-4, 5-9, >9]\n", - " [0.16,0.44,0.4]\n", + " numeric\n", + " None\n", + " [0.0013749738, 0.0014499743, 0.001526641, 0.0016050848, 0.0016854241, 0.0017677851, 0.0018523023, 0.0019391193, 0.002028389, 0.0021202748, 0.0022149507, 0.0023126022, 0.0024134273, 0.002517637, 0.0026254563, 0.0027371251, 0.0028528992, 0.0029730514, 0.0030978724, 0.0032276722, 0.0033627814, 0.0035035523, 0.0036503604, 0.003803606, 0.0039637158, 0.0041311448, 0.0043063775, 0.0044899306, 0.0046823546, 0.0048842361, 0.0050962001, 0.0053189126, 0.0055530831, 0.0057994673, 0.0060588703, 0.0063321494, 0.0066202178, 0.0069240477, 0.0072446744, 0.0075831999, 0.0079407973, 0.0083187152, 0.0087182821, 0.0091409116, 0.0095881072, 0.0100614684, 0.0105626958, 0.0110935973, 0.0116560946, 0.0122522299, 0.0128841727, 0.0135542271, 0.0142648397, 0.0150186074, 0.0158182855, 0.0166667968, 0.0175672405, 0.0185229009, 0.0195372578, 0.0206139958, 0.0217570149, 0.0229704403, 0.0242586335, 0.0256262025, 0.027078013, 0.0286191989, 0.0302551733, 0.0319916387, 0.0338345977, 0.0357903626, 0.0378655653, 0.0400671652, 0.042402458, 0.044879082, 0.0475050233, 0.0502886206, 0.0532385667, 0.0563639085, 0.0596740451, 0.0631787221, 0.0668880234, 0.0708123591, 0.0749624495, 0.0793493045, 0.0839841985, 0.0888786389, 0.0940443298, 0.0994931287, 0.1052369965, 0.1112879404, 0.1176579487, 0.1243589183, 0.1314025737, 0.1388003774, 0.1465634324, 0.1547023763, 0.1632272673, 0.1721474631, 0.1814714929, 0.1912069234, ...]\n", + " NaN\n", " NaN\n", " NaN\n", - " 6.595797\n", " ...\n", - " 31.015493\n", - " 2.247286\n", + " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 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5 rows × 54 columns

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5 rows × 53 columns

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0.0091836287, 0.0104955756, 0.0118075226, 0.0131194695, 0.0144314165, 0.0157433634, 0.0170553104, 0.0183672573, 0.0196792043, 0.0209911512, 0.0223030982, 0.0236150451, 0.0249269921, 0.026238939, 0.027550886, 0.0288628329, 0.0301747799, 0.0314867268, 0.0327986738, 0.0341106207, 0.0354225677, 0.0367345146, 0.0380464616, 0.0393584085, 0.0406703555, 0.0419823024, 0.0432942494, 0.0446061963, 0.0459181433, 0.0472300902, 0.0485420372, 0.0498539841, 0.0511659311, 0.052477878, 0.053789825, 0.0551017719, 0.0564137189, 0.0577256658, 0.0590376128, 0.0603495597, 0.0616615067, 0.0629734536, 0.0642854006, 0.0655973475, 0.0669092945, 0.0682212414, 0.0695331884, 0.0708451353, 0.0721570823, 0.0734690292, 0.0747809762, 0.0760929231, 0.0774048701, 0.078716817, 0.080028764, 0.0813407109, 0.0826526579, 0.0839646048, 0.0852765518, 0.0865884987, 0.0879004457, 0.0902457862, 0.0933094828, 0.0978079399, 0.1023063969, 0.1068048539, 0.111303311, 0.115801768, 0.120300225, 0.124798682, 0.1292971391, 0.1338199508, 0.1388055027, 0.1440933779, 0.1496807808, 0.1571177226, 0.1652387403, 0.1753118263, 0.1904276903, 0.2058197291, 0.2212117678, 0.237030829, 0.2551785571, 0.273870758, 0.2925629589, 0.3115548313, 0.3307464845, 0.3499926649, 0.3692260274, 0.3884136416, 0.407661417, 0.4269091924, 0.4457073638, ...] \n", + "\n", + " wunderplumb \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", - " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] NaN NaN NaN \n", - "3 [0.16,0.44,0.4] NaN NaN 6.595797 \n", - "6 [0.2,0.025,0.225,0.08,0.445,0.025] NaN NaN -70.444674 \n", - "9 [0.336,0.364,0.2,0.1] NaN NaN -87.546874 \n", - "13 [0.05,0.45,0.45,0.05] NaN NaN -16.907633 \n", - "\n", - " ... metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 299.573227 529.831737 NaN 229.263476 \n", - "3 ... 31.015493 2.247286 NaN 12.783337 \n", - "6 ... 29.885537 21.184400 NaN -18.457128 \n", - "9 ... -51.879379 -121.194097 NaN -80.647587 \n", - "13 ... 44.183275 33.647224 2.197891 20.067070 \n", - "\n", - " pgodzinai pianobot swingswish twsummerbot wunderplumb \\\n", - "0 270.308741 NaN NaN NaN NaN \n", - "3 15.252598 NaN NaN -4.652002 NaN \n", - "6 11.152127 NaN NaN NaN NaN \n", - "9 -49.410118 NaN NaN -62.415431 NaN \n", - "13 25.378052 NaN NaN NaN NaN \n", - "\n", - " bot_team_median \n", - "0 501.063529 \n", - "3 31.015493 \n", - "6 11.152127 \n", - "9 -69.314718 \n", - "13 -32.542240 \n", - "\n", - "[5 rows x 54 columns]" + "[5 rows x 53 columns]" ] }, "metadata": {}, @@ -3188,6 +3160,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -3197,191 +3170,227 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", " \n", - " 81\n", - " 35169\n", - " 35119\n", - " Not in top 50\n", - " 1.0\n", - " multiple_choice\n", - " [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50]\n", - " [0.02,0.01,0.015,0.015,0.05,0.89]\n", + " 94\n", + " 35380\n", + " 35345\n", + " yes\n", + " 1.00\n", + " binary\n", + " None\n", + " 0.95\n", + " 0.9\n", " NaN\n", - " -280.223742\n", " NaN\n", " ...\n", - " -448.863637\n", - " -178.058617\n", - " -300.703183\n", - " -287.919846\n", - " -339.002408\n", + " 0.95\n", + " 0.9\n", + " NaN\n", " NaN\n", + " 0.95\n", + " 0.95\n", " NaN\n", - " -234.857021\n", - " -240.919483\n", - " -287.919846\n", + " 0.9\n", + " 0.762\n", + " 0.9\n", " \n", " \n", - " 82\n", - " 35170\n", - " 35121\n", - " 3 or more\n", - " 1.0\n", - " multiple_choice\n", - " [0, 1, 2, 3 or more]\n", - " [0.01,0.18,0.54,0.27]\n", + " 95\n", + " 35381\n", + " 35354\n", + " no\n", + " 1.00\n", + " binary\n", + " None\n", + " 0.05\n", + " 0.95\n", " NaN\n", - " -77.944110\n", " NaN\n", " ...\n", - " -99.325177\n", - " -18.677591\n", - " -52.324814\n", - " 10.536052\n", - " 25.951120\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", - " 27.650877\n", - " -64.460900\n", - " 27.650877\n", - " \n", - " \n", - " 83\n", - " 35171\n", - " 35123\n", - " ≥7.5 and ≤8.5\n", - " 1.0\n", - " multiple_choice\n", - " [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5]\n", - " [0.02,0.3,0.3,0.3,0.08]\n", + " 0.15\n", + " NaN\n", + " NaN\n", + " 0.1\n", + " 0.126\n", + " 0.95\n", + " \n", + " \n", + " 96\n", + " 35385\n", + " 35358\n", + " yes\n", + " 1.00\n", + " binary\n", + " None\n", + " 0.97\n", + " 0.85\n", " NaN\n", - " -70.227966\n", " NaN\n", " ...\n", - " -132.175584\n", - " -26.570317\n", + " 0.9\n", + " 0.95\n", " NaN\n", - " -18.232156\n", " NaN\n", + " 0.9\n", " NaN\n", " NaN\n", - " -17.832954\n", - " -56.798404\n", - " -62.860866\n", + " 0.85\n", + " 0.828\n", + " 0.85\n", " \n", " \n", - " 91\n", - " 35377\n", - " 35334\n", - " Jimmy Patronis\n", - " 1.0\n", - " multiple_choice\n", - " [Jimmy Patronis, Gay Valimont, Someone else]\n", - " [0.997,0.001,0.002]\n", - " -17.134888\n", - " -15.951442\n", + " 97\n", + " 35386\n", + " 35364\n", + " no\n", + " 0.85\n", + " binary\n", + " None\n", + " 0.666\n", + " 0.8\n", " NaN\n", - " ...\n", - " -3.781749\n", - " -4.828879\n", " NaN\n", - " -12.482886\n", - " -8.037710\n", + " ...\n", + " 0.8\n", + " 0.85\n", + " 0.3\n", " NaN\n", - " -11.352931\n", + " 0.85\n", + " 0.85\n", " NaN\n", - " -14.781838\n", - " -12.104814\n", + " 0.7\n", + " 0.132\n", + " 0.3\n", " \n", " \n", - " 92\n", - " 35378\n", - " 35336\n", - " 31-49\n", - " 1.0\n", - " multiple_choice\n", - " [0-24, 25-30, 31-49, 50-70, >70]\n", - " [0.001,0.359,0.55,0.08,0.01]\n", - " -69.314718\n", - " -87.183897\n", + " 98\n", + " 35387\n", + " 35367\n", + " no\n", + " 0.85\n", + " binary\n", + " None\n", + " 0.03\n", + " 0.3\n", " NaN\n", - " ...\n", - " -170.474809\n", - " -290.872090\n", " NaN\n", - " -170.474809\n", - " -31.845373\n", + " ...\n", + " 0.05\n", + " 0.05\n", + " 0.03\n", " NaN\n", - " -48.097266\n", + " 0.15\n", + " 0.05\n", " NaN\n", - " -74.923665\n", - " -20.067070\n", + " 0.2\n", + " 0.27\n", + " 0.2\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight \\\n", - "81 35169 35119 Not in top 50 1.0 \n", - "82 35170 35121 3 or more 1.0 \n", - "83 35171 35123 ≥7.5 and ≤8.5 1.0 \n", - "91 35377 35334 Jimmy Patronis 1.0 \n", - "92 35378 35336 31-49 1.0 \n", + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", "\n", - " type \\\n", - "81 multiple_choice \n", - "82 multiple_choice \n", - "83 multiple_choice \n", - "91 multiple_choice \n", - "92 multiple_choice \n", + " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", + "94 None 0.95 0.9 NaN NaN ... 0.95 \n", + "95 None 0.05 0.95 NaN NaN ... 0.35 \n", + "96 None 0.97 0.85 NaN NaN ... 0.9 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", - " options \\\n", - "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] \n", - "82 [0, 1, 2, 3 or more] \n", - "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] \n", - "91 [Jimmy Patronis, Gay Valimont, Someone else] \n", - "92 [0-24, 25-30, 31-49, 50-70, >70] \n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", + "94 0.9 NaN NaN 0.95 0.95 NaN \n", + "95 0.4 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", + "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "98 0.05 0.03 NaN 0.15 0.05 NaN \n", + "\n", + " swingswish twsummerbot wunderplumb \n", + "94 0.9 0.762 0.9 \n", + "95 0.1 0.126 0.95 \n", + "96 0.85 0.828 0.85 \n", + "97 0.7 0.132 0.3 \n", + "98 0.2 0.27 0.2 \n", "\n", - " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", - "81 [0.02,0.01,0.015,0.015,0.05,0.89] NaN -280.223742 NaN \n", - "82 [0.01,0.18,0.54,0.27] NaN -77.944110 NaN \n", - "83 [0.02,0.3,0.3,0.3,0.08] NaN -70.227966 NaN \n", - "91 [0.997,0.001,0.002] -17.134888 -15.951442 NaN \n", - "92 [0.001,0.359,0.55,0.08,0.01] -69.314718 -87.183897 NaN \n", - "\n", - " ... metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... -448.863637 -178.058617 -300.703183 -287.919846 \n", - "82 ... -99.325177 -18.677591 -52.324814 10.536052 \n", - "83 ... -132.175584 -26.570317 NaN -18.232156 \n", - "91 ... -3.781749 -4.828879 NaN -12.482886 \n", - "92 ... -170.474809 -290.872090 NaN -170.474809 \n", - "\n", - " pgodzinai pianobot swingswish twsummerbot wunderplumb \\\n", - "81 -339.002408 NaN NaN -234.857021 -240.919483 \n", - "82 25.951120 NaN NaN 27.650877 -64.460900 \n", - "83 NaN NaN NaN -17.832954 -56.798404 \n", - "91 -8.037710 NaN -11.352931 NaN -14.781838 \n", - "92 -31.845373 NaN -48.097266 NaN -74.923665 \n", - "\n", - " bot_team_median \n", - "81 -287.919846 \n", - "82 27.650877 \n", - "83 -62.860866 \n", - "91 -12.104814 \n", - "92 -20.067070 \n", - "\n", - "[5 rows x 54 columns]" + "[5 rows x 53 columns]" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# Simple function to parse CDF strings for numeric questions\n", + "def parse_numeric_forecasts(df):\n", + " \"\"\"\n", + " Parse CDF strings for numeric questions in-place.\n", + "\n", + " Args:\n", + " df: DataFrame with forecast data\n", + " \"\"\"\n", + " # Get numeric questions\n", + " numeric_mask = df['type'] == 'numeric'\n", + "\n", + " # List of columns to process\n", + " forecast_cols = [col for col in df.columns if col in all_bots or col in ['pro_median', 'bot_median']]\n", + "\n", + " # Process each column\n", + " for col in forecast_cols:\n", + " # Process only for numeric questions and only where the column exists\n", + " if col in df.columns:\n", + " for idx in df[numeric_mask].index:\n", + " value = df.at[idx, col]\n", + "\n", + " # Skip NaN values\n", + " if pd.isna(value):\n", + " continue\n", + "\n", + " # Process string values\n", + " if isinstance(value, str):\n", + " try:\n", + " # Parse the CDF string to an array\n", + " parsed_array = np.array([float(x) for x in value.strip('[]').split(',')])\n", + " df.at[idx, col] = parsed_array\n", + " except Exception as e:\n", + " print(f\"Warning: Could not parse {col} at index {idx}: {e}\")\n", + "\n", + " return df\n", + "\n", + "# Now parse the numeric forecasts\n", + "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)\n", + "display_head_and_tail(df_pro_bot_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", + "# @Ben: Check -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -3414,6 +3423,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -3423,165 +3433,178 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", " \n", - " 2\n", - " 31270\n", - " 31264\n", - " no\n", + " 0\n", + " 31268\n", + " 31262\n", + " 0\n", " 1.0\n", - " binary\n", - " None\n", - " 0.013\n", - " NaN\n", - " NaN\n", - " NaN\n", + " multiple_choice\n", + " [0, 1, 2-3, 4-6, >6]\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -14.943369\n", - " -9.227528\n", - " NaN\n", - " -21.005831\n", - " -5.948545\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " -14.943369\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 5\n", - " 31282\n", - " 31276\n", - " yes\n", + " 3\n", + " 31280\n", + " 31274\n", + " 5-9\n", " 1.0\n", - " binary\n", - " None\n", - " 0.45\n", - " NaN\n", - " NaN\n", - " 67.445505\n", + " multiple_choice\n", + " [0-4, 5-9, >9]\n", + " [0.16,0.44,0.4]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -25.131443\n", - " 44.183275\n", - " NaN\n", - " 51.082562\n", - " 32.047190\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 32.047190\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 8\n", - " 31294\n", - " 31288\n", - " yes\n", + " 6\n", + " 31292\n", + " 31286\n", + " Jeff Bezos\n", " 1.0\n", - " binary\n", - " None\n", - " 0.95\n", - " NaN\n", - " NaN\n", - " -19.645607\n", + " multiple_choice\n", + " [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else]\n", + " [0.2,0.025,0.225,0.08,0.445,0.025]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " 0.000000\n", - " 0.000000\n", - " NaN\n", - " -11.122564\n", - " -14.715764\n", - " NaN\n", - " NaN\n", - " -39.812370\n", - " NaN\n", - " -17.185026\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 12\n", - " 31338\n", - " 31334\n", - " yes\n", + " 9\n", + " 31321\n", + " 31370\n", + " 0\n", " 1.0\n", - " binary\n", - " None\n", - " 0.9\n", - " NaN\n", - " NaN\n", - " -0.309119\n", + " multiple_choice\n", + " [0, 1, 2, Greater than 2]\n", + " [0.336,0.364,0.2,0.1]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -18.232156\n", - " 0.000000\n", - " NaN\n", - " 5.406722\n", - " -5.715841\n", - " NaN\n", - " NaN\n", - " -49.977579\n", - " NaN\n", - " -5.715841\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 16\n", - " 33876\n", - " 33751\n", - " no\n", + " 13\n", + " 31368\n", + " 31366\n", + " ≥0% and <5%\n", " 1.0\n", - " binary\n", - " None\n", - " 0.058\n", - " NaN\n", - " NaN\n", - " NaN\n", + " multiple_choice\n", + " [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%]\n", + " [0.05,0.45,0.45,0.05]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -4.561051\n", - " 0.845671\n", - " NaN\n", - " -6.808337\n", - " NaN\n", - " NaN\n", - " NaN\n", - " -7.606972\n", - " NaN\n", - " -7.606972\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight type \\\n", - "2 31270 31264 no 1.0 binary \n", - "5 31282 31276 yes 1.0 binary \n", - "8 31294 31288 yes 1.0 binary \n", - "12 31338 31334 yes 1.0 binary \n", - "16 33876 33751 no 1.0 binary \n", + " pro_question_id bot_question_id resolution question_weight \\\n", + "0 31268 31262 0 1.0 \n", + "3 31280 31274 5-9 1.0 \n", + "6 31292 31286 Jeff Bezos 1.0 \n", + "9 31321 31370 0 1.0 \n", + "13 31368 31366 ≥0% and <5% 1.0 \n", + "\n", + " type \\\n", + "0 multiple_choice \n", + "3 multiple_choice \n", + "6 multiple_choice \n", + "9 multiple_choice \n", + "13 multiple_choice \n", + "\n", + " options \\\n", + "0 [0, 1, 2-3, 4-6, >6] \n", + "3 [0-4, 5-9, >9] \n", + "6 [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else] \n", + "9 [0, 1, 2, Greater than 2] \n", + "13 [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%] \n", + "\n", + " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 0.643473 2.597381 1.762901 \n", + "3 [0.16,0.44,0.4] 0.643473 2.597381 1.762901 \n", + "6 [0.2,0.025,0.225,0.08,0.445,0.025] 0.643473 2.597381 1.762901 \n", + "9 [0.336,0.364,0.2,0.1] 0.643473 2.597381 1.762901 \n", + "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", + "\n", + " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", + "0 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "3 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "6 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "9 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "13 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "\n", + " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", + "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "3 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "6 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "9 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "13 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... \\\n", - "2 None 0.013 NaN NaN NaN ... \n", - "5 None 0.45 NaN NaN 67.445505 ... \n", - "8 None 0.95 NaN NaN -19.645607 ... \n", - "12 None 0.9 NaN NaN -0.309119 ... \n", - "16 None 0.058 NaN NaN NaN ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -14.943369 -9.227528 NaN -21.005831 -5.948545 \n", - "5 -25.131443 44.183275 NaN 51.082562 32.047190 \n", - "8 0.000000 0.000000 NaN -11.122564 -14.715764 \n", - "12 -18.232156 0.000000 NaN 5.406722 -5.715841 \n", - "16 -4.561051 0.845671 NaN -6.808337 NaN \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "2 NaN NaN NaN NaN -14.943369 \n", - "5 NaN NaN NaN NaN 32.047190 \n", - "8 NaN NaN -39.812370 NaN -17.185026 \n", - "12 NaN NaN -49.977579 NaN -5.715841 \n", - "16 NaN NaN -7.606972 NaN -7.606972 \n", - "\n", - "[5 rows x 54 columns]" + "[5 rows x 53 columns]" ] }, "metadata": {}, @@ -3619,6 +3642,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -3628,182 +3652,183 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", " \n", - " 94\n", - " 35380\n", - " 35345\n", - " yes\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.95\n", - " -5.406722\n", - " NaN\n", - " NaN\n", + " 81\n", + " 35169\n", + " 35119\n", + " Not in top 50\n", + " 1.0\n", + " multiple_choice\n", + " [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50]\n", + " [0.02,0.01,0.015,0.015,0.05,0.89]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -5.406722\n", - " NaN\n", - " NaN\n", - " 0.000000\n", - " 0.000000\n", - " NaN\n", - " -5.406722\n", - " -22.051543\n", - " -5.406722\n", - " -5.406722\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 95\n", - " 35381\n", - " 35354\n", - " no\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.05\n", - " -294.443898\n", - " NaN\n", - " NaN\n", + " 82\n", + " 35170\n", + " 35121\n", + " 3 or more\n", + " 1.0\n", + " multiple_choice\n", + " [0, 1, 2, 3 or more]\n", + " [0.01,0.18,0.54,0.27]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -225.129180\n", - " NaN\n", - " NaN\n", - " -11.122564\n", - " NaN\n", - " NaN\n", - " -5.406722\n", - " -8.338161\n", - " -294.443898\n", - " -11.122564\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 96\n", - " 35385\n", - " 35358\n", - " yes\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.97\n", - " -13.205972\n", - " NaN\n", - " NaN\n", + " 83\n", + " 35171\n", + " 35123\n", + " ≥7.5 and ≤8.5\n", + " 1.0\n", + " multiple_choice\n", + " [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5]\n", + " [0.02,0.3,0.3,0.3,0.08]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -7.490131\n", - " NaN\n", - " NaN\n", - " -7.490131\n", - " NaN\n", - " NaN\n", - " -13.205972\n", - " -15.828292\n", - " -13.205972\n", - " -13.205972\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 97\n", - " 35386\n", - " 35364\n", - " no\n", - " 0.85\n", - " binary\n", - " None\n", - " 0.666\n", - " -51.282363\n", - " NaN\n", - " NaN\n", + " 91\n", + " 35377\n", + " 35334\n", + " Jimmy Patronis\n", + " 1.0\n", + " multiple_choice\n", + " [Jimmy Patronis, Gay Valimont, Someone else]\n", + " [0.997,0.001,0.002]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -80.050570\n", - " 73.993934\n", - " NaN\n", - " -80.050570\n", - " -80.050570\n", - " NaN\n", - " -10.735852\n", - " 95.505072\n", - " 73.993934\n", - " -10.735852\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 98\n", - " 35387\n", - " 35367\n", - " no\n", - " 0.85\n", - " binary\n", - " None\n", - " 0.03\n", - " -32.621574\n", - " NaN\n", - " NaN\n", + " 92\n", + " 35378\n", + " 35336\n", + " 31-49\n", + " 1.0\n", + " multiple_choice\n", + " [0-24, 25-30, 31-49, 50-70, >70]\n", + " [0.001,0.359,0.55,0.08,0.01]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -7.490131\n", - " -2.083409\n", - " NaN\n", - " -13.205972\n", - " -2.083409\n", - " NaN\n", - " -19.268434\n", - " -28.425154\n", - " -19.268434\n", - " -13.205972\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight type \\\n", - "94 35380 35345 yes 1.00 binary \n", - "95 35381 35354 no 1.00 binary \n", - "96 35385 35358 yes 1.00 binary \n", - "97 35386 35364 no 0.85 binary \n", - "98 35387 35367 no 0.85 binary \n", + " pro_question_id bot_question_id resolution question_weight \\\n", + "81 35169 35119 Not in top 50 1.0 \n", + "82 35170 35121 3 or more 1.0 \n", + "83 35171 35123 ≥7.5 and ≤8.5 1.0 \n", + "91 35377 35334 Jimmy Patronis 1.0 \n", + "92 35378 35336 31-49 1.0 \n", + "\n", + " type \\\n", + "81 multiple_choice \n", + "82 multiple_choice \n", + "83 multiple_choice \n", + "91 multiple_choice \n", + "92 multiple_choice \n", + "\n", + " options \\\n", + "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] \n", + "82 [0, 1, 2, 3 or more] \n", + "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] \n", + "91 [Jimmy Patronis, Gay Valimont, Someone else] \n", + "92 [0-24, 25-30, 31-49, 50-70, >70] \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... \\\n", - "94 None 0.95 -5.406722 NaN NaN ... \n", - "95 None 0.05 -294.443898 NaN NaN ... \n", - "96 None 0.97 -13.205972 NaN NaN ... \n", - "97 None 0.666 -51.282363 NaN NaN ... \n", - "98 None 0.03 -32.621574 NaN NaN ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 -5.406722 NaN NaN 0.000000 0.000000 \n", - "95 -225.129180 NaN NaN -11.122564 NaN \n", - "96 -7.490131 NaN NaN -7.490131 NaN \n", - "97 -80.050570 73.993934 NaN -80.050570 -80.050570 \n", - "98 -7.490131 -2.083409 NaN -13.205972 -2.083409 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "94 NaN -5.406722 -22.051543 -5.406722 -5.406722 \n", - "95 NaN -5.406722 -8.338161 -294.443898 -11.122564 \n", - "96 NaN -13.205972 -15.828292 -13.205972 -13.205972 \n", - "97 NaN -10.735852 95.505072 73.993934 -10.735852 \n", - "98 NaN -19.268434 -28.425154 -19.268434 -13.205972 \n", - "\n", - "[5 rows x 54 columns]" + " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", + "81 [0.02,0.01,0.015,0.015,0.05,0.89] 0.643473 2.597381 1.762901 \n", + "82 [0.01,0.18,0.54,0.27] 0.643473 2.597381 1.762901 \n", + "83 [0.02,0.3,0.3,0.3,0.08] 0.643473 2.597381 1.762901 \n", + "91 [0.997,0.001,0.002] 0.643473 2.597381 1.762901 \n", + "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", + "\n", + " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", + "81 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "82 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "83 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "91 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "92 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "\n", + " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", + "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "82 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "83 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "91 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "92 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "\n", + "[5 rows x 53 columns]" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -3825,33 +3850,455 @@ " \n", " \n", " \n", - " bot\n", - " Peer Score\n", - " \n", - " \n", - " Rank\n", - " \n", - " \n", + " pro_question_id\n", + " bot_question_id\n", + " resolution\n", + " question_weight\n", + " type\n", + " options\n", + " pro_median\n", + " 4Shadower\n", + " Bot_Pepa\n", + " CatrachoCaster\n", + " ...\n", + " metac-o1\n", + " metac-o1-preview\n", + " metac-perplexity\n", + " minefrac1\n", + " mmBot\n", + " pgodzinai\n", + " pianobot\n", + " swingswish\n", + " twsummerbot\n", + " wunderplumb\n", " \n", " \n", " \n", " \n", - " 1\n", - " metac-o1\n", - " 3864.168122\n", - " \n", - " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", - " \n", - " \n", - " 3\n", - " bot_median\n", - " 2724.680171\n", + " 31270\n", + " 31264\n", + " no\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.013\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 4\n", + " 5\n", + " 31282\n", + " 31276\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.45\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + " 8\n", + " 31294\n", + " 31288\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.95\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + " 12\n", + " 31338\n", + " 31334\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.9\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + " 16\n", + " 33876\n", + " 33751\n", + " no\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.058\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + "\n", + "

5 rows × 53 columns

\n", + "" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "2 31270 31264 no 1.0 binary \n", + "5 31282 31276 yes 1.0 binary \n", + "8 31294 31288 yes 1.0 binary \n", + "12 31338 31334 yes 1.0 binary \n", + "16 33876 33751 no 1.0 binary \n", + "\n", + " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", + "2 None 0.013 0.643473 2.597381 1.762901 ... 21.041046 \n", + "5 None 0.45 0.643473 2.597381 1.762901 ... 21.041046 \n", + "8 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", + "12 None 0.9 0.643473 2.597381 1.762901 ... 21.041046 \n", + "16 None 0.058 0.643473 2.597381 1.762901 ... 21.041046 \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "2 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "5 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "8 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "12 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "16 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb \n", + "2 -2.173212 2.411469 14.267308 2.372721 \n", + "5 -2.173212 2.411469 14.267308 2.372721 \n", + "8 -2.173212 2.411469 14.267308 2.372721 \n", + "12 -2.173212 2.411469 14.267308 2.372721 \n", + "16 -2.173212 2.411469 14.267308 2.372721 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
943538035345yes1.00binaryNone0.950.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
953538135354no1.00binaryNone0.050.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
963538535358yes1.00binaryNone0.970.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
973538635364no0.85binaryNone0.6660.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
983538735367no0.85binaryNone0.030.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
\n", + "

5 rows × 53 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", + "\n", + " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", + "94 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", + "95 None 0.05 0.643473 2.597381 1.762901 ... 21.041046 \n", + "96 None 0.97 0.643473 2.597381 1.762901 ... 21.041046 \n", + "97 None 0.666 0.643473 2.597381 1.762901 ... 21.041046 \n", + "98 None 0.03 0.643473 2.597381 1.762901 ... 21.041046 \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "95 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "96 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "97 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "98 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb \n", + "94 -2.173212 2.411469 14.267308 2.372721 \n", + "95 -2.173212 2.411469 14.267308 2.372721 \n", + "96 -2.173212 2.411469 14.267308 2.372721 \n", + "97 -2.173212 2.411469 14.267308 2.372721 \n", + "98 -2.173212 2.411469 14.267308 2.372721 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4078,8 +4525,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2724.680171\n", + "2 bot_median 3711.510468\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -4146,13 +4593,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", "mean metac-o1 forecast on questions that resolved no: 26.0%\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -4195,15 +4642,15 @@ "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", "\n", "# Plot points for \"yes\" resolution\n", - "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100, \n", + "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", " color='blue', alpha=0.6, label='Resolved Yes')\n", - "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100, \n", + "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", " color='blue', alpha=0.6)\n", "\n", "# Plot points for \"no\" resolution\n", - "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100, \n", + "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", " color='red', alpha=0.6, label='Resolved No')\n", - "plt.scatter(x_pro_no, resolved_no[top_bot] * 100, \n", + "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", " color='red', alpha=0.6)\n", "\n", "# Customize the plot\n", @@ -4228,7 +4675,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_322865/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_739597/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -4353,20 +4800,20 @@ "
\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4711,8 +5158,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2481.552010 97 \n", - "4 5 acm_bot 2239.058675 85 \n", + "3 4 acm_bot 2239.058675 85 \n", + "4 5 bot_median 2196.323052 97 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -4760,8 +5207,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 93.10 \n", - "4 81.25 \n", + "3 81.25 \n", + "4 93.10 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -4956,20 +5403,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4984,6 +5417,20 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5580,8 +6027,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2481.6 93.1 26.7 55.791339 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", + "bot_median 2196.3 93.1 23.6 59.192687 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -5629,8 +6076,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 5.782185 4.609796 1.985277 38.1 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", + "bot_median 6.134698 3.845505 1.985277 35.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -5678,8 +6125,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 15.2 0.999994 0.000013 \n", "acm_bot 15.3 0.999987 0.000025 \n", + "bot_median 11.4 0.999889 0.000221 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -5749,6 +6196,38 @@ "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", + " t_statistic = (weighted_average - 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botPeer Score
Rank
1metac-o13864.168122
2bot_median3711.510468
3metac-o1-preview3162.155445
4manticAI2142.538438
34bot_median2481.5520109793.10
45acm_bot2239.0586758581.25
45bot_median2196.3230529793.10
56metac-claude-3-5-sonnet-202406200.000036
bot_median2481.693.126.755.7913395.7821854.6097961.98527738.115.20.9999940.000013
acm_bot2239.181.20.000025
bot_median2196.393.123.659.1926876.1346983.8455051.98527735.811.40.9998890.000221
metac-claude-3-5-sonnet-202406202018.191.5
Grizeu_Bot487.940.012.2123.49852319.5390470.6251002.02031451.7-27.30.7322250.535551metac-o11998.995.021.03.570999e-153.663768e-165.743007e+161.9847521.021.01.00.000000
acm_bot149.763.82.3123.16721915.4139760.1521161.99701833.1-28.40.5602090.879583metac-perplexity1927.095.020.30.000000e+000.000000e+00inf1.9847520.320.31.00.000000
RPM_bot145.06.024.231.46890712.8471271.8809962.57058257.2-8.90.9406380.118725bot_median1698.895.017.90.000000e+000.000000e+00inf1.9847517.917.91.00.000000
X_bot20.75.04.119.7562378.8352580.4688972.77644528.7-20.40.6682210.663558acm_bot1680.695.017.73.570999e-153.663768e-164.828449e+161.9847517.717.71.00.000000
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNAmanticAI1378.295.014.50.000000e+000.000000e+00inf1.9847514.514.51.00.000000
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNAtwsummerbot1355.495.014.31.785500e-151.831884e-167.788325e+161.9847514.314.31.00.000000
jonahsingerbot-61.34.7-13.05.4853692.530212-5.1548422.784843-6.0-20.10.0041410.008283jkraybill_bot1354.595.014.31.785500e-151.831884e-167.783286e+161.9847514.314.31.00.000000
bean_bot-70.74.7-15.18.8131374.065197-3.7022222.784843-3.7-26.40.0119250.023851metac-claude-3-5-sonnet-202406201136.795.012.03.570999e-153.663768e-163.265969e+161.9847512.012.01.00.000000
jkraybill_bot-76.138.2-2.067.06547910.858048-0.1837062.02336020.0-24.00.4276220.855243GreeneiBot21115.495.011.75.356499e-155.495652e-162.136428e+161.9847511.711.71.00.000000
CumulativeBot-97.010.2-9.530.1210609.408238-1.0055352.231848metac-claude-3-5-sonnet-latest1091.695.011.55.356499e-155.495652e-162.090764e+161.9847511.511.5-30.50.1701090.3402181.00.000000
swingswish-109.06.7-16.315.1455315.851229-2.7797012.450387-1.9-30.60.0168960.033793NextWorldLab1050.395.011.11.785500e-151.831884e-166.035038e+161.9847511.111.11.00.000000
SynapseSeer-128.527.1-4.847.0810459.052373-0.5249592.04956913.8-23.30.3020260.604052metac-grok-2-12121047.495.011.00.000000e+000.000000e+00inf1.9847511.011.01.00.000000
KevinTestBot-148.38.4-17.759.36966920.484482-0.8619382.31149629.7-65.00.2078890.415777metac-gpt-4o1002.095.010.53.570999e-153.663768e-162.878879e+161.9847510.510.51.00.000000
twsummerbot-237.247.0-5.079.50269011.596659-0.4351342.01121518.3-28.40.3327500.665500metac-Llama-3.1973.095.010.20.000000e+000.000000e+00inf1.9847510.210.21.00.000000
pianobot-272.24.7-57.992.18716542.522768-1.3617862.79898661.1-176.90.1251370.250274Grizeu_Bot966.495.010.20.000000e+000.000000e+00inf1.9847510.210.21.00.000000
annabot-316.024.8-12.743.7374108.782683-1.4506142.0613075.4-30.80.0799700.159940SynapseSeer964.795.010.21.785500e-151.831884e-165.543440e+161.9847510.210.21.00.000000
CatrachoCaster-331.319.7-16.852.31505911.786737-1.4269802.0887777.8-41.40.0850350.170071metac-o1-preview962.895.010.11.785500e-151.831884e-165.532510e+161.9847510.110.11.00.000000
cookics_bot_TEST-413.324.6-16.872.42669414.602631-1.1504362.06084513.3-46.90.1307440.261488mmBot924.895.09.70.000000e+000.000000e+00inf1.984759.79.71.00.000000
GreeneiBot2-446.645.8-9.888.55320713.092083-0.7457052.01234016.6-36.10.2298720.459745metac-exa919.995.09.71.785500e-151.831884e-165.285939e+161.984759.79.71.00.000000
metac-o1-500.374.7-6.7111.25524212.872419-0.5203391.99159718.9-32.30.3021940.604387annabot854.495.09.01.785500e-151.831884e-164.909363e+161.984759.09.01.00.000000
krm-bot-521.09.5-54.850.62785616.425846-3.3389622.264709-17.6-92.00.0047000.009400metac-deepseek-r1802.095.08.41.785500e-151.831884e-164.608683e+161.984758.48.41.00.000000
4Shadower-527.812.2-43.380.79118223.130448-1.8702732.1816957.2-93.70.0438960.087792VeritasAI802.095.08.41.785500e-151.831884e-164.608352e+161.984758.48.41.00.000000
MWG-766.429.5-26.087.75333816.156699-1.6080772.0435277.0-59.00.0594210.118842laylaps723.495.07.68.927498e-169.159420e-178.313180e+161.984757.67.61.00.000000
bot_median-780.675.7-10.385.1138919.782560-1.0541471.9911819.2-29.80.1476070.295213cookics_bot_TEST612.495.06.41.785500e-151.831884e-163.518949e+161.984756.46.41.00.000000
Bot_Pepa-814.937.2-21.993.06728515.269248-1.4365512.0250989.0-52.90.0797220.159444metac-Gemini-Exp-1206548.095.05.80.000000e+000.000000e+00inf1.984755.85.81.00.000000
ajf-bot-843.131.4-26.9104.85473318.727046-1.4360202.03766711.3-65.10.0806120.161224MWG520.895.05.58.927498e-169.159420e-175.985647e+161.984755.55.51.00.000000
manticAI-861.555.0-15.782.87386511.169634-1.4011472.0030646.7-38.00.0834430.166886
ajf-bot481.295.05.11.785500e-151.831884e-162.764898e+161.984755.15.11.00.000000
ProfessorSP-997.216.8-59.496.91948823.645934-2.5102932.112371-9.4-109.30.0116720.023345pgodzinai336.095.03.58.927498e-169.159420e-173.861639e+161.984753.53.51.00.000000
metac-perplexity-1072.972.7-14.8105.31560712.351666-1.1948081.9924629.9-39.40.1180500.236099KevinTestBot314.595.03.38.927498e-169.159420e-173.614852e+161.984753.33.31.00.000000
wunderplumb-1159.023.8-48.890.74010618.619477-2.6209902.065034-10.4-87.30.0076770.015353InstitutPelFutur256.095.02.78.927498e-169.159420e-172.941623e+161.984752.72.71.00.000000
laylaps-1214.552.2-23.348.0199296.646397-3.5005872.005359-9.9-36.60.0004860.000971Bot_Pepa246.895.02.60.000000e+000.000000e+00inf1.984752.62.61.00.000000
NextWorldLab-1224.163.8-19.298.66262212.347306-1.5526991.9970185.5-43.80.0627580.125517CumulativeBot241.195.02.54.463749e-164.579710e-175.542703e+161.984752.52.51.00.000000
metac-Gemini-Exp-1206-1250.565.1-19.294.99321111.773405-1.6315191.9963774.3-42.70.0538420.107685swingswish229.195.02.44.463749e-164.579710e-175.265549e+161.984752.42.41.00.000000
minefrac1-1289.443.5-29.6123.19979118.679504-1.5868582.0149188.0-67.30.0599790.119958wunderplumb225.495.02.44.463749e-164.579710e-175.180942e+161.984752.42.41.00.000000
pgodzinai-1330.462.0-21.598.40405312.497327-1.7169531.9981743.5-46.40.0455310.091062jonahsingerbot212.995.02.24.463749e-164.579710e-174.894511e+161.984752.22.21.00.000000
metac-deepseek-r1-1360.348.2-28.2108.35980215.607908-1.8082482.0091123.1-59.60.0384710.076941bean_bot200.095.02.10.000000e+000.000000e+00inf1.984752.12.11.00.000000
metac-Llama-3.1-1412.173.7-19.297.48349911.355267-1.6873751.9920243.5-41.80.0479090.095818X_bot181.495.01.90.000000e+000.000000e+00inf1.984751.91.91.00.000000
metac-claude-3-5-sonnet-latest-1463.974.7-19.696.85591111.206393-1.7487371.9915972.7-41.90.0422500.084500CatrachoCaster167.595.01.84.463749e-164.579710e-173.849373e+161.984751.81.81.00.000000
metac-claude-3-5-sonnet-20240620-1649.975.1-22.0105.32409412.153679-1.8076161.9915362.2-46.20.0373620.0747254Shadower61.195.00.62.231875e-162.289855e-172.810106e+161.984750.60.61.00.000000
metac-o1-preview-1830.674.7-24.5107.51540912.439714-1.9699551.9915970.3-49.30.0263010.052601krm-bot60.895.00.61.115937e-161.144927e-175.586129e+161.984750.60.61.00.000000
mmBot-2006.475.7-26.578.5323519.026111-2.9364461.991181-8.5-44.50.0022050.004411RPM_bot52.695.00.61.115937e-161.144927e-174.834420e+161.984750.60.61.00.000000
VeritasAI-2024.567.7-29.963.2821037.691066-3.8881871.994849-14.6-45.20.0001180.000235andrewsiah0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
metac-grok-2-1212-2154.674.7-28.8106.09460612.275325-2.3496851.991597-4.4-53.30.0107350.021470cobyj-bot0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
metac-gpt-4o-2196.674.7-29.4100.42168411.618958-2.5308441.991597-6.3-52.50.0067560.013513pianobot-206.595.0-2.24.463749e-164.579710e-17-4.745305e+161.98475-2.2-2.20.00.000000
metac-exa-2249.172.7-30.991.72329010.757526-2.8758531.992462-9.5-52.40.0026510.005302ProfessorSP-280.495.0-3.08.927498e-169.159420e-17-3.222942e+161.98475-3.0-3.00.00.000000
InstitutPelFutur-2477.372.8-34.0102.04145411.959443-2.8453911.992461-10.2-57.90.0028880.005777minefrac1-283.995.0-3.04.463749e-164.579710e-17-6.524424e+161.98475-3.0-3.00.00.000000
\n", "
" ], "text/plain": [ - " W_score W_count W_ave W_stdev \\\n", - "Grizeu_Bot 487.9 40.0 12.2 123.498523 \n", - "acm_bot 149.7 63.8 2.3 123.167219 \n", - "RPM_bot 145.0 6.0 24.2 31.468907 \n", - "X_bot 20.7 5.0 4.1 19.756237 \n", - "cobyj-bot 0.0 0.0 NaN NaN \n", - "andrewsiah 0.0 0.0 NaN NaN \n", - "jonahsingerbot -61.3 4.7 -13.0 5.485369 \n", - "bean_bot -70.7 4.7 -15.1 8.813137 \n", - "jkraybill_bot -76.1 38.2 -2.0 67.065479 \n", - "CumulativeBot -97.0 10.2 -9.5 30.121060 \n", - "swingswish -109.0 6.7 -16.3 15.145531 \n", - "SynapseSeer -128.5 27.1 -4.8 47.081045 \n", - "KevinTestBot -148.3 8.4 -17.7 59.369669 \n", - "twsummerbot -237.2 47.0 -5.0 79.502690 \n", - "pianobot -272.2 4.7 -57.9 92.187165 \n", - "annabot -316.0 24.8 -12.7 43.737410 \n", - "CatrachoCaster -331.3 19.7 -16.8 52.315059 \n", - "cookics_bot_TEST -413.3 24.6 -16.8 72.426694 \n", - "GreeneiBot2 -446.6 45.8 -9.8 88.553207 \n", - "metac-o1 -500.3 74.7 -6.7 111.255242 \n", - "krm-bot -521.0 9.5 -54.8 50.627856 \n", - "4Shadower -527.8 12.2 -43.3 80.791182 \n", - "MWG -766.4 29.5 -26.0 87.753338 \n", - "bot_median -780.6 75.7 -10.3 85.113891 \n", - "Bot_Pepa -814.9 37.2 -21.9 93.067285 \n", - "ajf-bot -843.1 31.4 -26.9 104.854733 \n", - "manticAI -861.5 55.0 -15.7 82.873865 \n", - "ProfessorSP -997.2 16.8 -59.4 96.919488 \n", - "metac-perplexity -1072.9 72.7 -14.8 105.315607 \n", - "wunderplumb -1159.0 23.8 -48.8 90.740106 \n", - "laylaps -1214.5 52.2 -23.3 48.019929 \n", - "NextWorldLab -1224.1 63.8 -19.2 98.662622 \n", - "metac-Gemini-Exp-1206 -1250.5 65.1 -19.2 94.993211 \n", - "minefrac1 -1289.4 43.5 -29.6 123.199791 \n", - "pgodzinai -1330.4 62.0 -21.5 98.404053 \n", - "metac-deepseek-r1 -1360.3 48.2 -28.2 108.359802 \n", - "metac-Llama-3.1 -1412.1 73.7 -19.2 97.483499 \n", - "metac-claude-3-5-sonnet-latest -1463.9 74.7 -19.6 96.855911 \n", - "metac-claude-3-5-sonnet-20240620 -1649.9 75.1 -22.0 105.324094 \n", - "metac-o1-preview -1830.6 74.7 -24.5 107.515409 \n", - "mmBot -2006.4 75.7 -26.5 78.532351 \n", - "VeritasAI -2024.5 67.7 -29.9 63.282103 \n", - "metac-grok-2-1212 -2154.6 74.7 -28.8 106.094606 \n", - "metac-gpt-4o -2196.6 74.7 -29.4 100.421684 \n", - "metac-exa -2249.1 72.7 -30.9 91.723290 \n", - "InstitutPelFutur -2477.3 72.8 -34.0 102.041454 \n", - "\n", - " std_err t_stat t_crit upper_bound \\\n", - "Grizeu_Bot 19.539047 0.625100 2.020314 51.7 \n", - "acm_bot 15.413976 0.152116 1.997018 33.1 \n", - "RPM_bot 12.847127 1.880996 2.570582 57.2 \n", - "X_bot 8.835258 0.468897 2.776445 28.7 \n", - "cobyj-bot NaN NaN NaN NaN \n", - "andrewsiah NaN NaN NaN NaN \n", - "jonahsingerbot 2.530212 -5.154842 2.784843 -6.0 \n", - "bean_bot 4.065197 -3.702222 2.784843 -3.7 \n", - "jkraybill_bot 10.858048 -0.183706 2.023360 20.0 \n", - "CumulativeBot 9.408238 -1.005535 2.231848 11.5 \n", - "swingswish 5.851229 -2.779701 2.450387 -1.9 \n", - "SynapseSeer 9.052373 -0.524959 2.049569 13.8 \n", - "KevinTestBot 20.484482 -0.861938 2.311496 29.7 \n", - "twsummerbot 11.596659 -0.435134 2.011215 18.3 \n", - "pianobot 42.522768 -1.361786 2.798986 61.1 \n", - "annabot 8.782683 -1.450614 2.061307 5.4 \n", - "CatrachoCaster 11.786737 -1.426980 2.088777 7.8 \n", - "cookics_bot_TEST 14.602631 -1.150436 2.060845 13.3 \n", - "GreeneiBot2 13.092083 -0.745705 2.012340 16.6 \n", - "metac-o1 12.872419 -0.520339 1.991597 18.9 \n", - "krm-bot 16.425846 -3.338962 2.264709 -17.6 \n", - "4Shadower 23.130448 -1.870273 2.181695 7.2 \n", - "MWG 16.156699 -1.608077 2.043527 7.0 \n", - "bot_median 9.782560 -1.054147 1.991181 9.2 \n", - "Bot_Pepa 15.269248 -1.436551 2.025098 9.0 \n", - "ajf-bot 18.727046 -1.436020 2.037667 11.3 \n", - "manticAI 11.169634 -1.401147 2.003064 6.7 \n", - "ProfessorSP 23.645934 -2.510293 2.112371 -9.4 \n", - "metac-perplexity 12.351666 -1.194808 1.992462 9.9 \n", - "wunderplumb 18.619477 -2.620990 2.065034 -10.4 \n", - "laylaps 6.646397 -3.500587 2.005359 -9.9 \n", - "NextWorldLab 12.347306 -1.552699 1.997018 5.5 \n", - "metac-Gemini-Exp-1206 11.773405 -1.631519 1.996377 4.3 \n", - "minefrac1 18.679504 -1.586858 2.014918 8.0 \n", - "pgodzinai 12.497327 -1.716953 1.998174 3.5 \n", - "metac-deepseek-r1 15.607908 -1.808248 2.009112 3.1 \n", - "metac-Llama-3.1 11.355267 -1.687375 1.992024 3.5 \n", - "metac-claude-3-5-sonnet-latest 11.206393 -1.748737 1.991597 2.7 \n", - "metac-claude-3-5-sonnet-20240620 12.153679 -1.807616 1.991536 2.2 \n", - "metac-o1-preview 12.439714 -1.969955 1.991597 0.3 \n", - "mmBot 9.026111 -2.936446 1.991181 -8.5 \n", - "VeritasAI 7.691066 -3.888187 1.994849 -14.6 \n", - "metac-grok-2-1212 12.275325 -2.349685 1.991597 -4.4 \n", - "metac-gpt-4o 11.618958 -2.530844 1.991597 -6.3 \n", - "metac-exa 10.757526 -2.875853 1.992462 -9.5 \n", - "InstitutPelFutur 11.959443 -2.845391 1.992461 -10.2 \n", - "\n", - " lower_bound cdf p_value \n", - "Grizeu_Bot -27.3 0.732225 0.535551 \n", - "acm_bot -28.4 0.560209 0.879583 \n", - "RPM_bot -8.9 0.940638 0.118725 \n", - "X_bot -20.4 0.668221 0.663558 \n", - "cobyj-bot NaN NaN NA \n", - "andrewsiah NaN NaN NA \n", - "jonahsingerbot -20.1 0.004141 0.008283 \n", - "bean_bot -26.4 0.011925 0.023851 \n", - "jkraybill_bot -24.0 0.427622 0.855243 \n", - "CumulativeBot -30.5 0.170109 0.340218 \n", - "swingswish -30.6 0.016896 0.033793 \n", - "SynapseSeer -23.3 0.302026 0.604052 \n", - "KevinTestBot -65.0 0.207889 0.415777 \n", - "twsummerbot -28.4 0.332750 0.665500 \n", - "pianobot -176.9 0.125137 0.250274 \n", - "annabot -30.8 0.079970 0.159940 \n", - "CatrachoCaster -41.4 0.085035 0.170071 \n", - "cookics_bot_TEST -46.9 0.130744 0.261488 \n", - "GreeneiBot2 -36.1 0.229872 0.459745 \n", - "metac-o1 -32.3 0.302194 0.604387 \n", - "krm-bot -92.0 0.004700 0.009400 \n", - "4Shadower -93.7 0.043896 0.087792 \n", - "MWG -59.0 0.059421 0.118842 \n", - "bot_median -29.8 0.147607 0.295213 \n", - "Bot_Pepa -52.9 0.079722 0.159444 \n", - "ajf-bot -65.1 0.080612 0.161224 \n", - "manticAI -38.0 0.083443 0.166886 \n", - "ProfessorSP -109.3 0.011672 0.023345 \n", - "metac-perplexity -39.4 0.118050 0.236099 \n", - "wunderplumb -87.3 0.007677 0.015353 \n", - "laylaps -36.6 0.000486 0.000971 \n", - "NextWorldLab -43.8 0.062758 0.125517 \n", - "metac-Gemini-Exp-1206 -42.7 0.053842 0.107685 \n", - "minefrac1 -67.3 0.059979 0.119958 \n", - "pgodzinai -46.4 0.045531 0.091062 \n", - "metac-deepseek-r1 -59.6 0.038471 0.076941 \n", - "metac-Llama-3.1 -41.8 0.047909 0.095818 \n", - "metac-claude-3-5-sonnet-latest -41.9 0.042250 0.084500 \n", - "metac-claude-3-5-sonnet-20240620 -46.2 0.037362 0.074725 \n", - "metac-o1-preview -49.3 0.026301 0.052601 \n", - "mmBot -44.5 0.002205 0.004411 \n", - "VeritasAI -45.2 0.000118 0.000235 \n", - "metac-grok-2-1212 -53.3 0.010735 0.021470 \n", - "metac-gpt-4o -52.5 0.006756 0.013513 \n", - "metac-exa -52.4 0.002651 0.005302 \n", - "InstitutPelFutur -57.9 0.002888 0.005777 " + " W_score W_count W_ave W_stdev \\\n", + "metac-o1 1998.9 95.0 21.0 3.570999e-15 \n", + "metac-perplexity 1927.0 95.0 20.3 0.000000e+00 \n", + "bot_median 1698.8 95.0 17.9 0.000000e+00 \n", + "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", + "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", + "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", + "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", + "metac-claude-3-5-sonnet-20240620 1136.7 95.0 12.0 3.570999e-15 \n", + "GreeneiBot2 1115.4 95.0 11.7 5.356499e-15 \n", + "metac-claude-3-5-sonnet-latest 1091.6 95.0 11.5 5.356499e-15 \n", + "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", + "metac-grok-2-1212 1047.4 95.0 11.0 0.000000e+00 \n", + "metac-gpt-4o 1002.0 95.0 10.5 3.570999e-15 \n", + "metac-Llama-3.1 973.0 95.0 10.2 0.000000e+00 \n", + "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", + "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", + "metac-o1-preview 962.8 95.0 10.1 1.785500e-15 \n", + "mmBot 924.8 95.0 9.7 0.000000e+00 \n", + "metac-exa 919.9 95.0 9.7 1.785500e-15 \n", + "annabot 854.4 95.0 9.0 1.785500e-15 \n", + "metac-deepseek-r1 802.0 95.0 8.4 1.785500e-15 \n", + "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", + "laylaps 723.4 95.0 7.6 8.927498e-16 \n", + "cookics_bot_TEST 612.4 95.0 6.4 1.785500e-15 \n", + "metac-Gemini-Exp-1206 548.0 95.0 5.8 0.000000e+00 \n", + "MWG 520.8 95.0 5.5 8.927498e-16 \n", + "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", + "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", + "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", + "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", + "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", + "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", + "swingswish 229.1 95.0 2.4 4.463749e-16 \n", + "wunderplumb 225.4 95.0 2.4 4.463749e-16 \n", + "jonahsingerbot 212.9 95.0 2.2 4.463749e-16 \n", + "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", + "X_bot 181.4 95.0 1.9 0.000000e+00 \n", + "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", + "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", + "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", + "RPM_bot 52.6 95.0 0.6 1.115937e-16 \n", + "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", + "cobyj-bot 0.0 95.0 0.0 0.000000e+00 \n", + "pianobot -206.5 95.0 -2.2 4.463749e-16 \n", + "ProfessorSP -280.4 95.0 -3.0 8.927498e-16 \n", + "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", + "\n", + " std_err t_stat t_crit \\\n", + "metac-o1 3.663768e-16 5.743007e+16 1.98475 \n", + "metac-perplexity 0.000000e+00 inf 1.98475 \n", + "bot_median 0.000000e+00 inf 1.98475 \n", + "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", + "manticAI 0.000000e+00 inf 1.98475 \n", + "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", + "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", + "metac-claude-3-5-sonnet-20240620 3.663768e-16 3.265969e+16 1.98475 \n", + "GreeneiBot2 5.495652e-16 2.136428e+16 1.98475 \n", + "metac-claude-3-5-sonnet-latest 5.495652e-16 2.090764e+16 1.98475 \n", + "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", + "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", + "metac-gpt-4o 3.663768e-16 2.878879e+16 1.98475 \n", + "metac-Llama-3.1 0.000000e+00 inf 1.98475 \n", + "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", + "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", + "metac-o1-preview 1.831884e-16 5.532510e+16 1.98475 \n", + "mmBot 0.000000e+00 inf 1.98475 \n", + "metac-exa 1.831884e-16 5.285939e+16 1.98475 \n", + "annabot 1.831884e-16 4.909363e+16 1.98475 \n", + "metac-deepseek-r1 1.831884e-16 4.608683e+16 1.98475 \n", + "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", + "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", + "cookics_bot_TEST 1.831884e-16 3.518949e+16 1.98475 \n", + "metac-Gemini-Exp-1206 0.000000e+00 inf 1.98475 \n", + "MWG 9.159420e-17 5.985647e+16 1.98475 \n", + "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", + "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", + "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", + "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", + "Bot_Pepa 0.000000e+00 inf 1.98475 \n", + "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", + "swingswish 4.579710e-17 5.265549e+16 1.98475 \n", + "wunderplumb 4.579710e-17 5.180942e+16 1.98475 \n", + "jonahsingerbot 4.579710e-17 4.894511e+16 1.98475 \n", + "bean_bot 0.000000e+00 inf 1.98475 \n", + "X_bot 0.000000e+00 inf 1.98475 \n", + "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", + "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", + "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", + "RPM_bot 1.144927e-17 4.834420e+16 1.98475 \n", + "andrewsiah 0.000000e+00 NaN 1.98475 \n", + "cobyj-bot 0.000000e+00 NaN 1.98475 \n", + "pianobot 4.579710e-17 -4.745305e+16 1.98475 \n", + "ProfessorSP 9.159420e-17 -3.222942e+16 1.98475 \n", + "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", + "\n", + " upper_bound lower_bound cdf p_value \n", + "metac-o1 21.0 21.0 1.0 0.000000 \n", + "metac-perplexity 20.3 20.3 1.0 0.000000 \n", + "bot_median 17.9 17.9 1.0 0.000000 \n", + "acm_bot 17.7 17.7 1.0 0.000000 \n", + "manticAI 14.5 14.5 1.0 0.000000 \n", + "twsummerbot 14.3 14.3 1.0 0.000000 \n", + "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-20240620 12.0 12.0 1.0 0.000000 \n", + "GreeneiBot2 11.7 11.7 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-latest 11.5 11.5 1.0 0.000000 \n", + "NextWorldLab 11.1 11.1 1.0 0.000000 \n", + "metac-grok-2-1212 11.0 11.0 1.0 0.000000 \n", + "metac-gpt-4o 10.5 10.5 1.0 0.000000 \n", + "metac-Llama-3.1 10.2 10.2 1.0 0.000000 \n", + "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", + "SynapseSeer 10.2 10.2 1.0 0.000000 \n", + "metac-o1-preview 10.1 10.1 1.0 0.000000 \n", + "mmBot 9.7 9.7 1.0 0.000000 \n", + "metac-exa 9.7 9.7 1.0 0.000000 \n", + "annabot 9.0 9.0 1.0 0.000000 \n", + "metac-deepseek-r1 8.4 8.4 1.0 0.000000 \n", + "VeritasAI 8.4 8.4 1.0 0.000000 \n", + "laylaps 7.6 7.6 1.0 0.000000 \n", + "cookics_bot_TEST 6.4 6.4 1.0 0.000000 \n", + "metac-Gemini-Exp-1206 5.8 5.8 1.0 0.000000 \n", + "MWG 5.5 5.5 1.0 0.000000 \n", + "ajf-bot 5.1 5.1 1.0 0.000000 \n", + "pgodzinai 3.5 3.5 1.0 0.000000 \n", + "KevinTestBot 3.3 3.3 1.0 0.000000 \n", + "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", + "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", + "CumulativeBot 2.5 2.5 1.0 0.000000 \n", + "swingswish 2.4 2.4 1.0 0.000000 \n", + "wunderplumb 2.4 2.4 1.0 0.000000 \n", + "jonahsingerbot 2.2 2.2 1.0 0.000000 \n", + "bean_bot 2.1 2.1 1.0 0.000000 \n", + "X_bot 1.9 1.9 1.0 0.000000 \n", + "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", + "4Shadower 0.6 0.6 1.0 0.000000 \n", + "krm-bot 0.6 0.6 1.0 0.000000 \n", + "RPM_bot 0.6 0.6 1.0 0.000000 \n", + "andrewsiah 0.0 0.0 NaN NA \n", + "cobyj-bot 0.0 0.0 NaN NA \n", + "pianobot -2.2 -2.2 0.0 0.000000 \n", + "ProfessorSP -3.0 -3.0 0.0 0.000000 \n", + "minefrac1 -3.0 -3.0 0.0 0.000000 " ] }, "execution_count": 43, @@ -7803,23 +8282,9 @@ "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", - "/tmp/ipykernel_322865/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\m'\n", - " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", - "/tmp/ipykernel_322865/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\s'\n", - " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -8344,152 +8809,144 @@ " 6.0\n", " 7.2\n", " 9.6\n", - " 12.0\n", + " 11.9\n", " 13.1\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", + " 3.7\n", " 5.2\n", " 8.3\n", " 11.2\n", - " 12.6\n", + " 12.8\n", " \n", " \n", " manticAI\n", - " -0.2\n", + " 0.2\n", " 2.1\n", " 5.5\n", - " 8.7\n", - " 10.4\n", + " 8.8\n", + " 10.5\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.9\n", - " 2.3\n", - " 5.1\n", - " 7.8\n", - " 9.1\n", + " 0.4\n", + " 1.9\n", + " 4.9\n", + " 7.5\n", + " 8.9\n", " \n", " \n", " acm_bot\n", - " 0.3\n", - " 1.9\n", - " 4.5\n", - " 7.5\n", - " 8.8\n", + " 0.2\n", + " 1.8\n", + " 4.7\n", + " 7.7\n", + " 9.1\n", " \n", " \n", " metac-perplexity\n", - " -1.7\n", - " 0.5\n", - " 4.1\n", - " 7.7\n", + " -2.2\n", + " 0.0\n", + " 4.3\n", + " 7.8\n", " 9.9\n", " \n", " \n", + " GreeneiBot2\n", + " -1.2\n", + " 0.4\n", + " 3.9\n", + " 7.0\n", + " 8.7\n", + " \n", + " \n", " twsummerbot\n", " 0.3\n", - " 1.4\n", + " 1.5\n", " 3.9\n", " 6.1\n", - " 7.5\n", + " 7.4\n", " \n", " \n", - " GreeneiBot2\n", - " -1.0\n", - " 0.7\n", - " 3.8\n", - " 7.2\n", - " 8.8\n", + " pgodzinai\n", + " -3.4\n", + " -1.2\n", + " 3.2\n", + " 7.3\n", + " 9.6\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 0.9\n", - " 3.1\n", + " -0.2\n", + " 0.8\n", + " 2.9\n", " 5.0\n", - " 6.2\n", - " \n", - " \n", - " pgodzinai\n", - " -3.1\n", - " -1.1\n", - " 2.8\n", - " 6.9\n", - " 8.7\n", + " 5.8\n", " \n", " \n", " CumulativeBot\n", - " -0.2\n", - " 0.8\n", - " 2.6\n", - " 4.4\n", + " -0.1\n", + " 0.9\n", + " 2.7\n", + " 4.6\n", " 5.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " 0.1\n", - " 2.6\n", - " 4.9\n", - " 6.2\n", - " \n", - " \n", " SynapseSeer\n", " 0.4\n", " 1.1\n", + " 2.6\n", + " 4.1\n", + " 4.8\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -1.3\n", + " -0.0\n", " 2.5\n", - " 4.0\n", - " 4.9\n", + " 5.0\n", + " 6.2\n", " \n", " \n", " jkraybill_bot\n", " -3.5\n", - " -1.6\n", + " -1.7\n", " 1.7\n", - " 4.8\n", + " 5.0\n", " 6.4\n", " \n", " \n", " metac-exa\n", - " -5.2\n", - " -2.7\n", + " -4.8\n", + " -2.2\n", " 1.7\n", - " 5.4\n", - " 7.6\n", + " 5.6\n", + " 7.8\n", " \n", " \n", " metac-deepseek-r1\n", - " -1.9\n", - " -0.6\n", - " 1.5\n", - " 3.6\n", - " 4.9\n", + " -2.0\n", + " -0.8\n", + " 1.3\n", + " 3.4\n", + " 4.6\n", " \n", " \n", " MWG\n", " -1.6\n", - " -0.9\n", + " -0.8\n", " 0.7\n", - " 2.0\n", - " 2.7\n", + " 2.1\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", - " -1.0\n", + " -0.8\n", " -0.6\n", " -0.0\n", " 0.6\n", - " 1.0\n", - " \n", - " \n", - " X_bot\n", - " -0.4\n", - " -0.2\n", - " -0.0\n", - " 0.1\n", - " 0.2\n", + " 0.9\n", " \n", " \n", " pianobot\n", @@ -8497,94 +8954,102 @@ " -0.8\n", " -0.0\n", " 0.7\n", - " 1.1\n", + " 1.0\n", " \n", " \n", " cobyj-bot\n", " -1.5\n", " -0.9\n", - " -0.1\n", - " 0.9\n", - " 1.4\n", + " -0.0\n", + " 0.8\n", + " 1.3\n", + " \n", + " \n", + " X_bot\n", + " -0.4\n", + " -0.2\n", + " -0.0\n", + " 0.1\n", + " 0.2\n", " \n", " \n", " annabot\n", - " -3.6\n", + " -3.5\n", " -2.3\n", " -0.4\n", " 1.2\n", - " 1.9\n", + " 2.1\n", " \n", " \n", " bean_bot\n", - " -3.0\n", - " -2.1\n", - " -0.4\n", - " 1.2\n", - " 2.0\n", + " -3.2\n", + " -2.3\n", + " -0.5\n", + " 1.1\n", + " 1.8\n", " \n", " \n", " KevinTestBot\n", - " -3.8\n", - " -2.7\n", - " -0.5\n", + " -4.1\n", + " -2.8\n", + " -0.6\n", " 1.6\n", - " 2.5\n", + " 2.7\n", " \n", " \n", " CatrachoCaster\n", - " -2.4\n", + " -2.2\n", " -1.7\n", " -0.8\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", " -3.0\n", " -2.2\n", " -0.8\n", - " 0.4\n", + " 0.5\n", " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", + " -3.5\n", " -2.7\n", " -0.9\n", - " 0.8\n", - " 1.6\n", + " 0.7\n", + " 1.7\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", - " -3.4\n", + " -4.6\n", + " -3.3\n", " -1.0\n", - " 1.0\n", + " 1.1\n", " 2.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " -6.5\n", - " -4.7\n", - " -1.4\n", - " 1.8\n", - " 3.3\n", + " mmBot\n", + " -7.5\n", + " -5.4\n", + " -1.5\n", + " 2.4\n", + " 4.7\n", " \n", " \n", - " mmBot\n", - " -7.1\n", - " -5.2\n", - " -1.6\n", - " 2.2\n", - " 4.1\n", + " metac-grok-2-1212\n", + " -6.6\n", + " -4.8\n", + " -1.5\n", + " 1.9\n", + " 3.6\n", " \n", " \n", " 4Shadower\n", - " -4.7\n", + " -4.6\n", " -3.6\n", " -1.6\n", - " 0.3\n", + " 0.2\n", " 1.2\n", " \n", " \n", @@ -8592,112 +9057,112 @@ " -5.2\n", " -4.0\n", " -1.9\n", - " -0.1\n", - " 0.7\n", + " -0.2\n", + " 0.5\n", " \n", " \n", - " RPM_bot\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.2\n", " -4.9\n", - " -3.9\n", " -2.0\n", - " -0.7\n", - " -0.1\n", + " 0.9\n", + " 2.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -5.0\n", + " RPM_bot\n", + " -4.9\n", + " -3.8\n", " -2.1\n", - " 0.9\n", - " 2.4\n", + " -0.7\n", + " -0.2\n", " \n", " \n", " InstitutPelFutur\n", - " -9.2\n", - " -6.7\n", - " -2.5\n", - " 1.8\n", - " 3.6\n", + " -9.1\n", + " -6.4\n", + " -2.4\n", + " 1.7\n", + " 4.0\n", " \n", " \n", - " metac-Llama-3.1\n", - " -6.6\n", - " -5.5\n", - " -2.5\n", - " 0.2\n", - " 1.4\n", + " wunderplumb\n", + " -6.2\n", + " -4.9\n", + " -2.4\n", + " -0.2\n", + " 1.1\n", " \n", " \n", - " wunderplumb\n", - " -6.3\n", - " -5.2\n", - " -2.6\n", - " -0.3\n", - " 1.0\n", + " metac-Llama-3.1\n", + " -6.8\n", + " -5.3\n", + " -2.7\n", + " 0.0\n", + " 1.5\n", " \n", " \n", " NextWorldLab\n", - " -8.3\n", - " -6.7\n", + " -8.8\n", + " -6.8\n", " -3.4\n", - " -0.4\n", - " 1.2\n", - " \n", - " \n", - " laylaps\n", - " -9.9\n", - " -7.7\n", - " -3.8\n", - " -0.1\n", - " 2.2\n", + " -0.3\n", + " 1.5\n", " \n", " \n", " Bot_Pepa\n", " -7.0\n", - " -6.0\n", + " -5.9\n", " -3.9\n", - " -1.8\n", - " -0.9\n", + " -2.0\n", + " -1.1\n", + " \n", + " \n", + " laylaps\n", + " -10.1\n", + " -7.9\n", + " -4.0\n", + " -0.1\n", + " 2.1\n", " \n", " \n", " VeritasAI\n", - " -7.8\n", - " -6.6\n", - " -4.3\n", - " -1.9\n", - " -0.4\n", + " -8.0\n", + " -6.8\n", + " -4.4\n", + " -2.0\n", + " -0.7\n", " \n", " \n", " minefrac1\n", - " -8.0\n", - " -6.7\n", + " -7.9\n", + " -6.8\n", " -4.6\n", - " -2.5\n", - " -1.3\n", + " -2.7\n", + " -1.5\n", " \n", " \n", " Grizeu_Bot\n", - " -8.8\n", - " -7.6\n", + " -9.3\n", + " -7.7\n", " -5.1\n", - " -2.4\n", - " -0.9\n", + " -2.5\n", + " -1.0\n", " \n", " \n", " metac-gpt-4o\n", - " -10.6\n", + " -10.4\n", " -9.0\n", - " -5.8\n", - " -2.9\n", + " -6.1\n", + " -3.0\n", " -1.4\n", " \n", " \n", " ajf-bot\n", " -15.0\n", - " -13.0\n", - " -8.6\n", - " -4.4\n", - " -2.0\n", + " -12.6\n", + " -8.4\n", + " -4.2\n", + " -2.2\n", " \n", " \n", "\n", @@ -8705,51 +9170,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.2 9.6 12.0 13.1\n", - "metac-o1-preview 3.9 5.2 8.3 11.2 12.6\n", - "manticAI -0.2 2.1 5.5 8.7 10.4\n", - "metac-Gemini-Exp-1206 0.9 2.3 5.1 7.8 9.1\n", - "acm_bot 0.3 1.9 4.5 7.5 8.8\n", - "metac-perplexity -1.7 0.5 4.1 7.7 9.9\n", - "twsummerbot 0.3 1.4 3.9 6.1 7.5\n", - "GreeneiBot2 -1.0 0.7 3.8 7.2 8.8\n", - "cookics_bot_TEST 0.0 0.9 3.1 5.0 6.2\n", - "pgodzinai -3.1 -1.1 2.8 6.9 8.7\n", - "CumulativeBot -0.2 0.8 2.6 4.4 5.4\n", - "metac-claude-3-5-sonnet-latest -1.3 0.1 2.6 4.9 6.2\n", - "SynapseSeer 0.4 1.1 2.5 4.0 4.9\n", - "jkraybill_bot -3.5 -1.6 1.7 4.8 6.4\n", - "metac-exa -5.2 -2.7 1.7 5.4 7.6\n", - "metac-deepseek-r1 -1.9 -0.6 1.5 3.6 4.9\n", - "MWG -1.6 -0.9 0.7 2.0 2.7\n", - "andrewsiah -1.0 -0.6 -0.0 0.6 1.0\n", + "metac-o1 6.0 7.2 9.6 11.9 13.1\n", + "metac-o1-preview 3.7 5.2 8.3 11.2 12.8\n", + "manticAI 0.2 2.1 5.5 8.8 10.5\n", + "metac-Gemini-Exp-1206 0.4 1.9 4.9 7.5 8.9\n", + "acm_bot 0.2 1.8 4.7 7.7 9.1\n", + "metac-perplexity -2.2 0.0 4.3 7.8 9.9\n", + "GreeneiBot2 -1.2 0.4 3.9 7.0 8.7\n", + "twsummerbot 0.3 1.5 3.9 6.1 7.4\n", + "pgodzinai -3.4 -1.2 3.2 7.3 9.6\n", + "cookics_bot_TEST -0.2 0.8 2.9 5.0 5.8\n", + "CumulativeBot -0.1 0.9 2.7 4.6 5.4\n", + "SynapseSeer 0.4 1.1 2.6 4.1 4.8\n", + "metac-claude-3-5-sonnet-latest -1.3 -0.0 2.5 5.0 6.2\n", + "jkraybill_bot -3.5 -1.7 1.7 5.0 6.4\n", + "metac-exa -4.8 -2.2 1.7 5.6 7.8\n", + "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.6\n", + "MWG -1.6 -0.8 0.7 2.1 2.8\n", + "andrewsiah -0.8 -0.6 -0.0 0.6 0.9\n", + "pianobot -1.2 -0.8 -0.0 0.7 1.0\n", + "cobyj-bot -1.5 -0.9 -0.0 0.8 1.3\n", "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "pianobot -1.2 -0.8 -0.0 0.7 1.1\n", - "cobyj-bot -1.5 -0.9 -0.1 0.9 1.4\n", - "annabot -3.6 -2.3 -0.4 1.2 1.9\n", - "bean_bot -3.0 -2.1 -0.4 1.2 2.0\n", - "KevinTestBot -3.8 -2.7 -0.5 1.6 2.5\n", - "CatrachoCaster -2.4 -1.7 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", - "krm-bot -3.6 -2.7 -0.9 0.8 1.6\n", - "ProfessorSP -4.5 -3.4 -1.0 1.0 2.1\n", - "metac-grok-2-1212 -6.5 -4.7 -1.4 1.8 3.3\n", - "mmBot -7.1 -5.2 -1.6 2.2 4.1\n", - "4Shadower -4.7 -3.6 -1.6 0.3 1.2\n", - "swingswish -5.2 -4.0 -1.9 -0.1 0.7\n", - "RPM_bot -4.9 -3.9 -2.0 -0.7 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -6.5 -5.0 -2.1 0.9 2.4\n", - "InstitutPelFutur -9.2 -6.7 -2.5 1.8 3.6\n", - "metac-Llama-3.1 -6.6 -5.5 -2.5 0.2 1.4\n", - "wunderplumb -6.3 -5.2 -2.6 -0.3 1.0\n", - "NextWorldLab -8.3 -6.7 -3.4 -0.4 1.2\n", - "laylaps -9.9 -7.7 -3.8 -0.1 2.2\n", - "Bot_Pepa -7.0 -6.0 -3.9 -1.8 -0.9\n", - "VeritasAI -7.8 -6.6 -4.3 -1.9 -0.4\n", - "minefrac1 -8.0 -6.7 -4.6 -2.5 -1.3\n", - "Grizeu_Bot -8.8 -7.6 -5.1 -2.4 -0.9\n", - "metac-gpt-4o -10.6 -9.0 -5.8 -2.9 -1.4\n", - "ajf-bot -15.0 -13.0 -8.6 -4.4 -2.0" + "annabot -3.5 -2.3 -0.4 1.2 2.1\n", + "bean_bot -3.2 -2.3 -0.5 1.1 1.8\n", + "KevinTestBot -4.1 -2.8 -0.6 1.6 2.7\n", + "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", + "jonahsingerbot -3.0 -2.2 -0.8 0.5 1.0\n", + "krm-bot -3.5 -2.7 -0.9 0.7 1.7\n", + "ProfessorSP -4.6 -3.3 -1.0 1.1 2.1\n", + "mmBot -7.5 -5.4 -1.5 2.4 4.7\n", + "metac-grok-2-1212 -6.6 -4.8 -1.5 1.9 3.6\n", + "4Shadower -4.6 -3.6 -1.6 0.2 1.2\n", + "swingswish -5.2 -4.0 -1.9 -0.2 0.5\n", + "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.0 0.9 2.4\n", + "RPM_bot -4.9 -3.8 -2.1 -0.7 -0.2\n", + "InstitutPelFutur -9.1 -6.4 -2.4 1.7 4.0\n", + "wunderplumb -6.2 -4.9 -2.4 -0.2 1.1\n", + "metac-Llama-3.1 -6.8 -5.3 -2.7 0.0 1.5\n", + "NextWorldLab -8.8 -6.8 -3.4 -0.3 1.5\n", + "Bot_Pepa -7.0 -5.9 -3.9 -2.0 -1.1\n", + "laylaps -10.1 -7.9 -4.0 -0.1 2.1\n", + "VeritasAI -8.0 -6.8 -4.4 -2.0 -0.7\n", + "minefrac1 -7.9 -6.8 -4.6 -2.7 -1.5\n", + "Grizeu_Bot -9.3 -7.7 -5.1 -2.5 -1.0\n", + "metac-gpt-4o -10.4 -9.0 -6.1 -3.0 -1.4\n", + "ajf-bot -15.0 -12.6 -8.4 -4.2 -2.2" ] }, "execution_count": 50, @@ -8828,372 +9293,372 @@ " \n", " \n", " \n", - " Grizeu_Bot\n", - " -9.7\n", - " -5.4\n", - " 4.4\n", - " 15.9\n", - " 22.2\n", + " metac-o1\n", + " 21.0\n", + " 21.0\n", + " 21.0\n", + " 21.0\n", + " 21.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", - " 0.3\n", - " 1.4\n", - " 2.8\n", - " 3.7\n", + " metac-perplexity\n", + " 20.3\n", + " 20.3\n", + " 20.3\n", + " 20.3\n", + " 20.3\n", " \n", " \n", - " X_bot\n", - " -0.4\n", - " -0.3\n", - " 0.2\n", - " 0.7\n", - " 1.2\n", + " bot_median\n", + " 17.9\n", + " 17.9\n", + " 17.9\n", + " 17.9\n", + " 17.9\n", " \n", " \n", - " andrewsiah\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " acm_bot\n", + " 17.7\n", + " 17.7\n", + " 17.7\n", + " 17.7\n", + " 17.7\n", " \n", " \n", - " cobyj-bot\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " manticAI\n", + " 14.5\n", + " 14.5\n", + " 14.5\n", + " 14.5\n", + " 14.5\n", " \n", " \n", - " acm_bot\n", - " -16.3\n", - " -11.3\n", - " -0.2\n", - " 14.8\n", - " 22.5\n", + " twsummerbot\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", " \n", " \n", - " jonahsingerbot\n", - " -1.4\n", - " -1.1\n", - " -0.6\n", - " -0.3\n", - " -0.1\n", + " jkraybill_bot\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", " \n", " \n", - " bean_bot\n", - " -1.6\n", - " -1.3\n", - " -0.7\n", - " -0.3\n", - " -0.1\n", + " metac-claude-3-5-sonnet-20240620\n", + " 12.0\n", + " 12.0\n", + " 12.0\n", + " 12.0\n", + " 12.0\n", " \n", " \n", - " CumulativeBot\n", - " -2.9\n", - " -2.3\n", - " -1.0\n", - " 0.2\n", - " 1.0\n", + " GreeneiBot2\n", + " 11.7\n", + " 11.7\n", + " 11.7\n", + " 11.7\n", + " 11.7\n", " \n", " \n", - " swingswish\n", - " -2.4\n", - " -1.9\n", - " -1.1\n", - " -0.5\n", - " -0.3\n", + " metac-claude-3-5-sonnet-latest\n", + " 11.5\n", + " 11.5\n", + " 11.5\n", + " 11.5\n", + " 11.5\n", " \n", " \n", - " jkraybill_bot\n", - " -8.5\n", - " -6.2\n", - " -1.1\n", - " 4.6\n", - " 7.5\n", + " NextWorldLab\n", + " 11.1\n", + " 11.1\n", + " 11.1\n", + " 11.1\n", + " 11.1\n", " \n", " \n", - " KevinTestBot\n", - " -5.8\n", - " -3.9\n", - " -1.4\n", - " 0.4\n", - " 1.1\n", + " metac-grok-2-1212\n", + " 11.0\n", + " 11.0\n", + " 11.0\n", + " 11.0\n", + " 11.0\n", " \n", " \n", - " SynapseSeer\n", - " -6.3\n", - " -4.6\n", - " -1.5\n", - " 1.9\n", - " 3.9\n", + " metac-gpt-4o\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", " \n", " \n", - " pianobot\n", - " -8.0\n", - " -5.9\n", - " -2.6\n", - " -0.2\n", - " 0.1\n", + " metac-Llama-3.1\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", " \n", " \n", - " twsummerbot\n", - " -13.4\n", - " -10.3\n", - " -2.9\n", - " 4.6\n", - " 9.2\n", + " Grizeu_Bot\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", " \n", " \n", - " CatrachoCaster\n", - " -8.6\n", - " -6.8\n", - " -3.4\n", - " -0.3\n", - " 1.1\n", + " SynapseSeer\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", " \n", " \n", - " annabot\n", - " -8.4\n", - " -6.5\n", - " -3.4\n", - " -0.6\n", - " 0.9\n", + " metac-o1-preview\n", + " 10.1\n", + " 10.1\n", + " 10.1\n", + " 10.1\n", + " 10.1\n", " \n", " \n", - " cookics_bot_TEST\n", - " -12.1\n", - " -9.7\n", - " -4.2\n", - " 0.1\n", - " 2.1\n", + " mmBot\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", " \n", " \n", - " GreeneiBot2\n", - " -17.4\n", - " -13.2\n", - " -4.9\n", - " 3.6\n", - " 7.4\n", + " metac-exa\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", " \n", " \n", - " krm-bot\n", - " -10.6\n", - " -8.6\n", - " -5.3\n", - " -2.6\n", - " -1.6\n", + " annabot\n", + " 9.0\n", + " 9.0\n", + " 9.0\n", + " 9.0\n", + " 9.0\n", " \n", " \n", - " 4Shadower\n", - " -12.8\n", - " -9.8\n", - " -5.3\n", - " -1.8\n", - " -1.1\n", + " metac-deepseek-r1\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", " \n", " \n", - " metac-o1\n", - " -22.7\n", - " -18.5\n", - " -6.7\n", - " 8.5\n", - " 16.1\n", + " VeritasAI\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", " \n", " \n", - " MWG\n", - " -18.3\n", - " -14.9\n", - " -8.3\n", - " -2.2\n", - " 1.3\n", + " laylaps\n", + " 7.6\n", + " 7.6\n", + " 7.6\n", + " 7.6\n", + " 7.6\n", " \n", " \n", - " ajf-bot\n", - " -22.3\n", - " -17.2\n", - " -8.8\n", - " -1.4\n", - " 2.5\n", + " cookics_bot_TEST\n", + " 6.4\n", + " 6.4\n", + " 6.4\n", + " 6.4\n", + " 6.4\n", " \n", " \n", - " bot_median\n", - " -22.7\n", - " -18.3\n", - " -9.0\n", - " 2.1\n", - " 8.9\n", + " metac-Gemini-Exp-1206\n", + " 5.8\n", + " 5.8\n", + " 5.8\n", + " 5.8\n", + " 5.8\n", " \n", " \n", - " Bot_Pepa\n", - " -20.9\n", - " -16.3\n", - " -9.0\n", - " -1.2\n", - " 2.7\n", + " MWG\n", + " 5.5\n", + " 5.5\n", + " 5.5\n", + " 5.5\n", + " 5.5\n", " \n", " \n", - " manticAI\n", - " -22.1\n", - " -17.7\n", - " -9.5\n", - " -0.7\n", - " 4.9\n", + " ajf-bot\n", + " 5.1\n", + " 5.1\n", + " 5.1\n", + " 5.1\n", + " 5.1\n", " \n", " \n", - " ProfessorSP\n", - " -20.7\n", - " -16.8\n", - " -10.1\n", - " -4.7\n", - " -2.4\n", + " pgodzinai\n", + " 3.5\n", + " 3.5\n", + " 3.5\n", + " 3.5\n", + " 3.5\n", " \n", " \n", - " wunderplumb\n", - " -22.4\n", - " -19.1\n", - " -12.0\n", - " -5.8\n", - " -3.3\n", + " KevinTestBot\n", + " 3.3\n", + " 3.3\n", + " 3.3\n", + " 3.3\n", + " 3.3\n", " \n", " \n", - " metac-perplexity\n", - " -29.1\n", - " -24.0\n", - " -12.0\n", - " 0.8\n", - " 8.0\n", + " InstitutPelFutur\n", + " 2.7\n", + " 2.7\n", + " 2.7\n", + " 2.7\n", + " 2.7\n", " \n", " \n", - " laylaps\n", - " -21.0\n", - " -17.8\n", - " -12.8\n", - " -8.1\n", - " -5.8\n", + " Bot_Pepa\n", + " 2.6\n", + " 2.6\n", + " 2.6\n", + " 2.6\n", + " 2.6\n", " \n", " \n", - " NextWorldLab\n", - " -28.4\n", - " -24.0\n", - " -13.6\n", - " -2.8\n", - " 4.0\n", + " CumulativeBot\n", + " 2.5\n", + " 2.5\n", + " 2.5\n", + " 2.5\n", + " 2.5\n", " \n", " \n", - " pgodzinai\n", - " -31.7\n", - " -25.6\n", - " -14.0\n", - " -4.1\n", - " 1.9\n", + " swingswish\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " -28.1\n", - " -23.3\n", - " -14.0\n", - " -2.7\n", - " 3.2\n", + " wunderplumb\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", " \n", " \n", - " metac-deepseek-r1\n", - " -30.7\n", - " -25.2\n", - " -14.6\n", - " -4.9\n", - " 0.5\n", + " jonahsingerbot\n", + " 2.2\n", + " 2.2\n", + " 2.2\n", + " 2.2\n", + " 2.2\n", " \n", " \n", - " minefrac1\n", - " -29.8\n", - " -24.8\n", - " -14.9\n", - " -3.1\n", - " 4.1\n", + " bean_bot\n", + " 2.1\n", + " 2.1\n", + " 2.1\n", + " 2.1\n", + " 2.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -32.9\n", - " -26.8\n", - " -15.1\n", - " -3.3\n", - " 3.2\n", + " X_bot\n", + " 1.9\n", + " 1.9\n", + " 1.9\n", + " 1.9\n", + " 1.9\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -32.6\n", - " -26.6\n", - " -15.9\n", - " -3.5\n", - " 3.2\n", + " CatrachoCaster\n", + " 1.8\n", + " 1.8\n", + " 1.8\n", + " 1.8\n", + " 1.8\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -35.3\n", - " -29.9\n", - " -18.2\n", - " -4.3\n", - " 2.8\n", + " 4Shadower\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", " \n", " \n", - " metac-o1-preview\n", - " -38.9\n", - " -32.4\n", - " -19.3\n", - " -6.9\n", - " 0.3\n", + " krm-bot\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", " \n", " \n", - " mmBot\n", - " -36.2\n", - " -30.9\n", - " -21.1\n", - " -11.7\n", - " -7.1\n", + " RPM_bot\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", " \n", " \n", - " VeritasAI\n", - " -33.5\n", - " -28.9\n", - " -21.3\n", - " -14.4\n", - " -11.1\n", + " andrewsiah\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " metac-grok-2-1212\n", - " -41.8\n", - " -35.2\n", - " -23.4\n", - " -10.4\n", - " -3.8\n", + " cobyj-bot\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " metac-exa\n", - " -40.4\n", - " -34.4\n", - " -23.4\n", - " -13.8\n", - " -7.9\n", + " pianobot\n", + " -2.2\n", + " -2.2\n", + " -2.2\n", + " -2.2\n", + " -2.2\n", " \n", " \n", - " metac-gpt-4o\n", - " -41.7\n", - " -34.7\n", - " -23.8\n", - " -11.3\n", - " -5.3\n", + " ProfessorSP\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", " \n", " \n", - " InstitutPelFutur\n", - " -43.6\n", - " -37.9\n", - " -26.5\n", - " -14.9\n", - " -6.6\n", + " minefrac1\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", " \n", " \n", "\n", @@ -9201,52 +9666,52 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "Grizeu_Bot -9.7 -5.4 4.4 15.9 22.2\n", - "RPM_bot -0.1 0.3 1.4 2.8 3.7\n", - "X_bot -0.4 -0.3 0.2 0.7 1.2\n", + "metac-o1 21.0 21.0 21.0 21.0 21.0\n", + "metac-perplexity 20.3 20.3 20.3 20.3 20.3\n", + "bot_median 17.9 17.9 17.9 17.9 17.9\n", + "acm_bot 17.7 17.7 17.7 17.7 17.7\n", + "manticAI 14.5 14.5 14.5 14.5 14.5\n", + "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", + "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", + "metac-claude-3-5-sonnet-20240620 12.0 12.0 12.0 12.0 12.0\n", + "GreeneiBot2 11.7 11.7 11.7 11.7 11.7\n", + "metac-claude-3-5-sonnet-latest 11.5 11.5 11.5 11.5 11.5\n", + "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", + "metac-grok-2-1212 11.0 11.0 11.0 11.0 11.0\n", + "metac-gpt-4o 10.5 10.5 10.5 10.5 10.5\n", + "metac-Llama-3.1 10.2 10.2 10.2 10.2 10.2\n", + "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", + "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", + "metac-o1-preview 10.1 10.1 10.1 10.1 10.1\n", + "mmBot 9.7 9.7 9.7 9.7 9.7\n", + "metac-exa 9.7 9.7 9.7 9.7 9.7\n", + "annabot 9.0 9.0 9.0 9.0 9.0\n", + "metac-deepseek-r1 8.4 8.4 8.4 8.4 8.4\n", + "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", + "laylaps 7.6 7.6 7.6 7.6 7.6\n", + "cookics_bot_TEST 6.4 6.4 6.4 6.4 6.4\n", + "metac-Gemini-Exp-1206 5.8 5.8 5.8 5.8 5.8\n", + "MWG 5.5 5.5 5.5 5.5 5.5\n", + "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", + "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", + "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", + "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", + "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", + "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", + "swingswish 2.4 2.4 2.4 2.4 2.4\n", + "wunderplumb 2.4 2.4 2.4 2.4 2.4\n", + "jonahsingerbot 2.2 2.2 2.2 2.2 2.2\n", + "bean_bot 2.1 2.1 2.1 2.1 2.1\n", + "X_bot 1.9 1.9 1.9 1.9 1.9\n", + "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", + "4Shadower 0.6 0.6 0.6 0.6 0.6\n", + "krm-bot 0.6 0.6 0.6 0.6 0.6\n", + "RPM_bot 0.6 0.6 0.6 0.6 0.6\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", - "acm_bot -16.3 -11.3 -0.2 14.8 22.5\n", - "jonahsingerbot -1.4 -1.1 -0.6 -0.3 -0.1\n", - "bean_bot -1.6 -1.3 -0.7 -0.3 -0.1\n", - "CumulativeBot -2.9 -2.3 -1.0 0.2 1.0\n", - "swingswish -2.4 -1.9 -1.1 -0.5 -0.3\n", - "jkraybill_bot -8.5 -6.2 -1.1 4.6 7.5\n", - "KevinTestBot -5.8 -3.9 -1.4 0.4 1.1\n", - "SynapseSeer -6.3 -4.6 -1.5 1.9 3.9\n", - "pianobot -8.0 -5.9 -2.6 -0.2 0.1\n", - "twsummerbot -13.4 -10.3 -2.9 4.6 9.2\n", - "CatrachoCaster -8.6 -6.8 -3.4 -0.3 1.1\n", - "annabot -8.4 -6.5 -3.4 -0.6 0.9\n", - "cookics_bot_TEST -12.1 -9.7 -4.2 0.1 2.1\n", - "GreeneiBot2 -17.4 -13.2 -4.9 3.6 7.4\n", - "krm-bot -10.6 -8.6 -5.3 -2.6 -1.6\n", - "4Shadower -12.8 -9.8 -5.3 -1.8 -1.1\n", - "metac-o1 -22.7 -18.5 -6.7 8.5 16.1\n", - "MWG -18.3 -14.9 -8.3 -2.2 1.3\n", - "ajf-bot -22.3 -17.2 -8.8 -1.4 2.5\n", - "bot_median -22.7 -18.3 -9.0 2.1 8.9\n", - "Bot_Pepa -20.9 -16.3 -9.0 -1.2 2.7\n", - "manticAI -22.1 -17.7 -9.5 -0.7 4.9\n", - "ProfessorSP -20.7 -16.8 -10.1 -4.7 -2.4\n", - "wunderplumb -22.4 -19.1 -12.0 -5.8 -3.3\n", - "metac-perplexity -29.1 -24.0 -12.0 0.8 8.0\n", - "laylaps -21.0 -17.8 -12.8 -8.1 -5.8\n", - "NextWorldLab -28.4 -24.0 -13.6 -2.8 4.0\n", - "pgodzinai -31.7 -25.6 -14.0 -4.1 1.9\n", - "metac-Gemini-Exp-1206 -28.1 -23.3 -14.0 -2.7 3.2\n", - "metac-deepseek-r1 -30.7 -25.2 -14.6 -4.9 0.5\n", - "minefrac1 -29.8 -24.8 -14.9 -3.1 4.1\n", - "metac-Llama-3.1 -32.9 -26.8 -15.1 -3.3 3.2\n", - "metac-claude-3-5-sonnet-latest -32.6 -26.6 -15.9 -3.5 3.2\n", - "metac-claude-3-5-sonnet-20240620 -35.3 -29.9 -18.2 -4.3 2.8\n", - "metac-o1-preview -38.9 -32.4 -19.3 -6.9 0.3\n", - "mmBot -36.2 -30.9 -21.1 -11.7 -7.1\n", - "VeritasAI -33.5 -28.9 -21.3 -14.4 -11.1\n", - "metac-grok-2-1212 -41.8 -35.2 -23.4 -10.4 -3.8\n", - "metac-exa -40.4 -34.4 -23.4 -13.8 -7.9\n", - "metac-gpt-4o -41.7 -34.7 -23.8 -11.3 -5.3\n", - "InstitutPelFutur -43.6 -37.9 -26.5 -14.9 -6.6" + "pianobot -2.2 -2.2 -2.2 -2.2 -2.2\n", + "ProfessorSP -3.0 -3.0 -3.0 -3.0 -3.0\n", + "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" ] }, "execution_count": 51, @@ -9294,7 +9759,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -9909,505 +10374,511 @@ "output_type": "stream", "text": [ " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.65]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.3]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.01]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.3]\n", " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " >>> Collected 2 forecasts: [0.15, 0.1]\n", - " >>> 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0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.35, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.6, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.8, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.85, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.2, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", " >>> Collected 6 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.35, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.65, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.3, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", " >>> Collected 7 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", - " >>> Collected 7 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8]\n", + " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1]\n", - " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", + " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", " >>> Collected 8 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", + " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513]\n", + " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", + " >>> Collected 8 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", + " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", " >>> Collected 9 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65]\n", " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", - " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1, 0.6485000000000001, 0.75]\n", - " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95]\n", + " >>> Collected 9 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", " >>> Collected 10 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3, nan]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65, 0.293]\n", " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1, 0.6485000000000001, 0.75, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10490,7 +10961,7 @@ " 0\n", " [0.02,0.7,0.2,0.07,0.01]\n", " 0.017463\n", - " 0.085\n", + " 0.1\n", " \n", " \n", " 1\n", @@ -10498,8 +10969,8 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", " \n", " \n", " 2\n", @@ -10524,9 +10995,9 @@ " numeric\n", " NaN\n", " 119.2\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0, 0.00161112178, 0.0032277004800000003, 0....\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 0.0...\n", + " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", + " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", + " [0.0, 0.0028936984428571426, 0.005791294657142...\n", " \n", " \n", " ...\n", @@ -10543,7 +11014,7 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.9\n", + " 0.905\n", " 0.9025\n", " \n", " \n", @@ -10551,18 +11022,18 @@ " binary\n", " NaN\n", " no\n", - " 0.9\n", - " 0.2\n", - " 0.1335\n", + " 0.4\n", + " 0.35\n", + " 0.2085\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", + " 0.95\n", " 0.9\n", - " 0.85\n", - " 0.775\n", + " 0.772\n", " \n", " \n", " 361\n", @@ -10570,16 +11041,16 @@ " NaN\n", " no\n", " 0.85\n", - " 0.75\n", - " 0.73\n", + " 0.8\n", + " 0.755\n", " \n", " \n", " 364\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.052\n", + " 0.05\n", + " 0.05\n", " 0.046\n", " \n", " \n", @@ -10606,38 +11077,38 @@ "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.15 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", ".. ... \n", "342 0.9 \n", - "351 0.9 \n", - "355 0.9 \n", + "351 0.4 \n", + "355 0.95 \n", "361 0.85 \n", - "364 0.1 \n", + "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", "2 0.085 \n", "3 0.6 \n", - "4 [0.0, 0.00161112178, 0.0032277004800000003, 0.... \n", + "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", ".. ... \n", - "342 0.9 \n", - "351 0.2 \n", - "355 0.85 \n", - "361 0.75 \n", - "364 0.052 \n", + "342 0.905 \n", + "351 0.35 \n", + "355 0.9 \n", + "361 0.8 \n", + "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.085 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.125 \n", "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", ".. ... \n", "342 0.9025 \n", - "351 0.1335 \n", - "355 0.775 \n", - "361 0.73 \n", + "351 0.2085 \n", + "355 0.772 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -10712,52 +11183,52 @@ " \n", " 0\n", " 1\n", - " 16.68\n", + " 15.75\n", " \n", " \n", " 1\n", " 2\n", - " 26.29\n", + " 26.31\n", " \n", " \n", " 2\n", " 3\n", - " 28.21\n", + " 27.15\n", " \n", " \n", " 3\n", " 4\n", - " 26.98\n", + " 27.65\n", " \n", " \n", " 4\n", " 5\n", - " 27.65\n", + " 27.58\n", " \n", " \n", " 5\n", " 6\n", - " 26.39\n", + " 27.57\n", " \n", " \n", " 6\n", " 7\n", - " 26.89\n", + " 27.05\n", " \n", " \n", " 7\n", " 8\n", - " 27.15\n", + " 27.45\n", " \n", " \n", " 8\n", " 9\n", - " 27.29\n", + " 26.23\n", " \n", " \n", " 9\n", " 10\n", - " 26.71\n", + " 26.47\n", " \n", " \n", "\n", @@ -10765,16 +11236,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 16.68\n", - "1 2 26.29\n", - "2 3 28.21\n", - "3 4 26.98\n", - "4 5 27.65\n", - "5 6 26.39\n", - "6 7 26.89\n", - "7 8 27.15\n", - "8 9 27.29\n", - "9 10 26.71" + "0 1 15.75\n", + "1 2 26.31\n", + "2 3 27.15\n", + "3 4 27.65\n", + "4 5 27.58\n", + "5 6 27.57\n", + "6 7 27.05\n", + "7 8 27.45\n", + "8 9 26.23\n", + "9 10 26.47" ] }, "execution_count": 61, @@ -10814,7 +11285,7 @@ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2']" ] }, "execution_count": 62, @@ -10927,19 +11398,19 @@ " NaN\n", " NaN\n", " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", " ...\n", " 0.02\n", - " 0.235\n", + " 0.21\n", " 0.02\n", " 0.017463\n", " 0.017463\n", " 0.02\n", - " 0.085\n", - " 0.085\n", - " 0.15\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.02\n", + " 0.02\n", " \n", " \n", " 1\n", @@ -10951,19 +11422,19 @@ " 60.0\n", " 100.0\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.03366666666666667, 0.0341314028, 0.03460208...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", - " [0.041833333333333333, 0.042403191266666675, 0...\n", - " [0.041833333333333333, 0.042403191266666675, 0...\n", + " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", + " [0.03366666666666667, 0.03409436576666667, 0.0...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.041833333333333333, 0.04238467275, 0.042938...\n", + " [0.041833333333333333, 0.04238467275, 0.042938...\n", " \n", " \n", " 2\n", @@ -11010,7 +11481,7 @@ " 0.55625\n", " 0.5125\n", " 0.5125\n", - " 0.53125\n", + " 0.55625\n", " 0.5125\n", " \n", " \n", @@ -11022,20 +11493,20 @@ " NaN\n", " 0.0\n", " 400.0\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " ...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.0017047194333333333, 0.0034148989, 0.0...\n", - " [0.0, 0.001733085025, 0.003470265075, 0.005210...\n", - " [0.0, 0.00161112178, 0.0032277004800000003, 0....\n", - " [0.0, 0.0016497910333333336, 0.003304129483333...\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 0.0...\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 0.0...\n", - " [0.0, 0.0019069861375000002, 0.003817382825, 0...\n", - " [0.0, 0.0018408706777777778, 0.003684772944444...\n", + " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07...\n", + " [0.0, 0.00642857145, 0.01285714285, 0.01928571...\n", + " [0.0, 0.004323767066666667, 0.0086529941333333...\n", + " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", + " [0.0, 0.00295931485, 0.0059231771, 0.008890847...\n", + " [0.0, 0.0028936984428571426, 0.005791294657142...\n", + " [0.0, 0.0028936984428571426, 0.005791294657142...\n", + " [0.0, 0.0028097639124999995, 0.005622938375, 0...\n", + " [0.0, 0.0026433398111111108, 0.005289711211111...\n", " \n", " \n", "\n", @@ -11062,14 +11533,14 @@ "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.15 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", "\n", " metac-o1 \\\n", - "0 [0.45,0.3,0.15,0.05,0.05] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", "2 0.1 \n", "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " pgodzinai ... \\\n", "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", @@ -11083,70 +11554,70 @@ "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", "2 0.15 \n", "3 0.6 \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07... \n", "\n", " median_forecast_2_bots \\\n", - "0 0.235 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "0 0.21 \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", "2 0.125 \n", "3 0.6 \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "4 [0.0, 0.00642857145, 0.01285714285, 0.01928571... \n", "\n", " median_forecast_3_bots \\\n", "0 0.02 \n", - "1 [0.03366666666666667, 0.0341314028, 0.03460208... \n", + "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", "2 0.1 \n", "3 0.6 \n", - "4 [0.0, 0.0017047194333333333, 0.0034148989, 0.0... \n", + "4 [0.0, 0.004323767066666667, 0.0086529941333333... \n", "\n", " median_forecast_4_bots \\\n", "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", "2 0.085 \n", "3 0.6 \n", - "4 [0.0, 0.001733085025, 0.003470265075, 0.005210... \n", + "4 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", "\n", " median_forecast_5_bots \\\n", "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", "2 0.085 \n", "3 0.6 \n", - "4 [0.0, 0.00161112178, 0.0032277004800000003, 0.... \n", + "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", "\n", " median_forecast_6_bots \\\n", "0 0.02 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.1 \n", "3 0.55625 \n", - "4 [0.0, 0.0016497910333333336, 0.003304129483333... \n", + "4 [0.0, 0.00295931485, 0.0059231771, 0.008890847... \n", "\n", " median_forecast_7_bots \\\n", - "0 0.085 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.125 \n", "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", "\n", " median_forecast_8_bots \\\n", - "0 0.085 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.125 \n", "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", "\n", " median_forecast_9_bots \\\n", - "0 0.15 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", + "0 0.02 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", "2 0.15 \n", - "3 0.53125 \n", - "4 [0.0, 0.0019069861375000002, 0.003817382825, 0... \n", + "3 0.55625 \n", + "4 [0.0, 0.0028097639124999995, 0.005622938375, 0... \n", "\n", " median_forecast_10_bots \n", - "0 0.15 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", + "0 0.02 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", "2 0.15 \n", "3 0.5125 \n", - "4 [0.0, 0.0018408706777777778, 0.003684772944444... \n", + "4 [0.0, 0.0026433398111111108, 0.005289711211111... \n", "\n", "[5 rows x 27 columns]" ] @@ -11210,14 +11681,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -14.9893\n" + "Weighted Total Score: -15.1905\n" ] } ], @@ -11227,7 +11698,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11239,7 +11710,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -11251,7 +11722,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -14.97\n" + "The average of 'head_to_head' is: -15.16\n" ] } ], @@ -11261,7 +11732,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 69, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11307,17 +11778,17 @@ " \n", " \n", " head_to_head\n", - " -1424.0\n", + " -1443.1\n", " 93.1\n", - " -15.3\n", - " 90.635958\n", - " 9.393462\n", - " -1.628277\n", + " -15.5\n", + " 86.181587\n", + " 8.931813\n", + " -1.735425\n", " 1.985277\n", - " 3.4\n", - " -33.9\n", - " 0.053441\n", - " 0.106882\n", + " 2.2\n", + " -33.2\n", + " 0.043005\n", + " 0.086010\n", " \n", " \n", "\n", @@ -11325,13 +11796,13 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1424.0 93.1 -15.3 90.635958 9.393462 -1.628277 \n", + "head_to_head -1443.1 93.1 -15.5 86.181587 8.931813 -1.735425 \n", "\n", " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 3.4 -33.9 0.053441 0.106882 " + "head_to_head 1.985277 2.2 -33.2 0.043005 0.086010 " ] }, - "execution_count": 70, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -11342,24 +11813,317 @@ "df_bot_team_h2h" ] }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0I0myCHpl7FT", + "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
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titlebot_team_medianpro_medianresolutionhead_to_head
335How many cubic meters of water produced and su...[0.146083333325, 0.1540953797, 0.1622041748, 0...[0.0346238299,0.0364286012,0.0383259676,0.0403...130027.0-265.7
279What will Kalshi's rank in the iPhone Top Free...0.063[0.02,0.01,0.015,0.015,0.05,0.89]Not in top 50-264.8
121How many movies will be new on Netflix's top 1...0.14[0.005,0.017,0.157,0.821]3 or more-176.9
151How many earthquakes of magnitude ≥ 4 will hap...[0.0, 0.0032810261, 0.0065908451250000005, 0.0...[0.0,0.0158237002,0.0235315723,0.0279864362,0....0.0-157.3
47What will be Donald Trump's net worth, accordi...0.17[0.6,0.2,0.1,0.075,0.025]0-$6 billion, inclusive-126.1
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "335 How many cubic meters of water produced and su... \n", + "279 What will Kalshi's rank in the iPhone Top Free... \n", + "121 How many movies will be new on Netflix's top 1... \n", + "151 How many earthquakes of magnitude ≥ 4 will hap... \n", + "47 What will be Donald Trump's net worth, accordi... \n", + "\n", + " bot_team_median \\\n", + "335 [0.146083333325, 0.1540953797, 0.1622041748, 0... \n", + "279 0.063 \n", + "121 0.14 \n", + "151 [0.0, 0.0032810261, 0.0065908451250000005, 0.0... \n", + "47 0.17 \n", + "\n", + " pro_median \\\n", + "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] \n", + "121 [0.005,0.017,0.157,0.821] \n", + "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... \n", + "47 [0.6,0.2,0.1,0.075,0.025] \n", + "\n", + " resolution head_to_head \n", + "335 130027.0 -265.7 \n", + "279 Not in top 50 -264.8 \n", + "121 3 or more -176.9 \n", + "151 0.0 -157.3 \n", + "47 0-$6 billion, inclusive -126.1 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('display.max_colwidth', 50)\n", + "\n", + "df_sorted = df_top_bot_pro_forecasts.sort_values(by='head_to_head')\n", + "df_sorted['head_to_head'] = df_sorted['head_to_head'].round(1)\n", + "#df_sorted['resolution'] = df_sorted['resolution'].map({1: 'yes', 0: 'no'})\n", + "\n", + "df_top5 = df_sorted.head(5)\n", + "df_bottom5 = df_sorted.tail(5)\n", + "\n", + "print(\"Top 5:\")\n", + "\n", + "df_top5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bottom 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
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titlebot_team_medianpro_medianresolutionhead_to_head
85Will Elon Musk attend the Super Bowl in 2025?0.16850.755no122.2
0For Q1 2025, how many banks will be listed on ...0.017463[0.001,0.62,0.35,0.019,0.01]0286.0
189What will the highest rank of metac-GPT4o or m...[0.0, 0.051569126225, 0.10695714615, 0.1599563...[0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...34.0491.5
211Will Nikola Corporation file for bankruptcy be...0.990.999annulledNaN
214Will the state of Rhode Island have any recrea...0.9720.95annulledNaN
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "85 Will Elon Musk attend the Super Bowl in 2025? \n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "189 What will the highest rank of metac-GPT4o or m... \n", + "211 Will Nikola Corporation file for bankruptcy be... \n", + "214 Will the state of Rhode Island have any recrea... \n", + "\n", + " bot_team_median \\\n", + "85 0.1685 \n", + "0 0.017463 \n", + "189 [0.0, 0.051569126225, 0.10695714615, 0.1599563... \n", + "211 0.99 \n", + "214 0.972 \n", + "\n", + " pro_median resolution \\\n", + "85 0.755 no \n", + "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", + "211 0.999 annulled \n", + "214 0.95 annulled \n", + "\n", + " head_to_head \n", + "85 122.2 \n", + "0 286.0 \n", + "189 491.5 \n", + "211 NaN \n", + "214 NaN " + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"\\nBottom 5:\")\n", + "\n", + "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "head_to_head float64\n", + "weighted_score float64\n", + "dtype: object" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", + "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", + "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].map({'yes': 1, 'no': 0})\n", + "df_top_bot_pro_forecasts.dtypes" + ] + }, { "cell_type": "code", "execution_count": 73, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0I0myCHpl7FT", - "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" - }, + "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 5:\n" - ] - }, { "data": { "text/html": [ @@ -11381,86 +12145,160 @@ " \n", " \n", " \n", + " bot_question_id\n", " title\n", + " resolution\n", + " scheduled_close_time\n", + " actual_close_time\n", + " type\n", + " options\n", + " range_min\n", + " range_max\n", + " pro_question_id\n", + " question_weight\n", " bot_team_median\n", " pro_median\n", - " resolution\n", " head_to_head\n", + " weighted_score\n", " \n", " \n", " \n", " \n", - " 279\n", - " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.03\n", - " [0.02,0.01,0.015,0.015,0.05,0.89]\n", - " Not in top 50\n", - " -339.0\n", + " 0\n", + " 31262\n", + " For Q1 2025, how many banks will be listed on ...\n", + " NaN\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " multiple_choice\n", + " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " NaN\n", + " NaN\n", + " 31268\n", + " 1.0\n", + " 0.017463\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 286.007699\n", + " 286.007699\n", " \n", " \n", - " 121\n", - " How many movies will be new on Netflix's top 1...\n", - " 0.1\n", - " [0.005,0.017,0.157,0.821]\n", - " 3 or more\n", - " -210.5\n", + " 1\n", + " 31263\n", + " What percentage of the vote will Alexander Luk...\n", + " NaN\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " numeric\n", + " NaN\n", + " 60.0\n", + " 100.0\n", + " 31269\n", + " 1.0\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", + " -76.357515\n", + " -76.357515\n", " \n", " \n", - " 335\n", - " How many cubic meters of water produced and su...\n", - " [0.12255555556666668, 0.1304049507, 0.13838334...\n", - " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", - " 130027.0\n", - " -158.7\n", + " 2\n", + " 31264\n", + " Will the bubble in the Magnificent Seven pop b...\n", + " 0.0\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31270\n", + " 1.0\n", + " 0.085\n", + " 0.013\n", + " -7.574597\n", + " -7.574597\n", " \n", " \n", - " 12\n", - " What will be the monthly cargo volumes at the ...\n", - " [0.03366666666666667, 0.034913915633333334, 0....\n", - " [0.001714054,0.0017985406,0.0018846914,0.00197...\n", - " 720283.0\n", - " -130.3\n", + " 3\n", + " 31274\n", + " How many arms sales globally will the US State...\n", + " NaN\n", + " 2025-01-21 11:42:00\n", + " 2025-01-21 11:42:00\n", + " multiple_choice\n", + " [\"0-4\",\"5-9\",\">9\"]\n", + " NaN\n", + " NaN\n", + " 31280\n", + " 1.0\n", + " 0.6\n", + " [0.16,0.44,0.4]\n", + " 31.015493\n", + " 31.015493\n", " \n", " \n", - " 71\n", - " Will OpenAI, Anthropic, or Perplexity run an a...\n", - " 0.15\n", - " 0.55\n", - " yes\n", - " -129.9\n", + " 4\n", + " 31275\n", + " How much will it rain in Brasília, Brazil in F...\n", + " NaN\n", + " 2025-01-21 11:42:00\n", + " 2025-01-21 11:42:00\n", + " numeric\n", + " NaN\n", + " 0.0\n", + " 400.0\n", + " 31281\n", + " 1.0\n", + " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", + " 28.578581\n", + " 28.578581\n", " \n", " \n", "\n", "" ], "text/plain": [ - " title \\\n", - "279 What will Kalshi's rank in the iPhone Top Free... \n", - "121 How many movies will be new on Netflix's top 1... \n", - "335 How many cubic meters of water produced and su... \n", - "12 What will be the monthly cargo volumes at the ... \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", "\n", - " bot_team_median \\\n", - "279 0.03 \n", - "121 0.1 \n", - "335 [0.12255555556666668, 0.1304049507, 0.13838334... \n", - "12 [0.03366666666666667, 0.034913915633333334, 0.... \n", - "71 0.15 \n", - "\n", - " pro_median resolution \\\n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more \n", - "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... 130027.0 \n", - "12 [0.001714054,0.0017985406,0.0018846914,0.00197... 720283.0 \n", - "71 0.55 yes \n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", "\n", - " head_to_head \n", - "279 -339.0 \n", - "121 -210.5 \n", - "335 -158.7 \n", - "12 -130.3 \n", - "71 -129.9 " + " options range_min range_max pro_question_id \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", + "1 NaN 60.0 100.0 31269 \n", + "2 NaN NaN NaN 31270 \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", + "4 NaN 0.0 400.0 31281 \n", + "\n", + " question_weight bot_team_median \\\n", + "0 1.0 0.017463 \n", + "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 1.0 0.085 \n", + "3 1.0 0.6 \n", + "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", + "2 0.013 -7.574597 \n", + "3 [0.16,0.44,0.4] 31.015493 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "\n", + " weighted_score \n", + "0 286.007699 \n", + "1 -76.357515 \n", + "2 -7.574597 \n", + "3 31.015493 \n", + "4 28.578581 " ] }, "execution_count": 73, @@ -11469,33 +12307,92 @@ } ], "source": [ - "pd.set_option('display.max_colwidth', 50)\n", - "\n", - "df_sorted = df_top_bot_pro_forecasts.sort_values(by='head_to_head')\n", - "df_sorted['head_to_head'] = df_sorted['head_to_head'].round(1)\n", - "#df_sorted['resolution'] = df_sorted['resolution'].map({1: 'yes', 0: 'no'})\n", - "\n", - "df_top5 = df_sorted.head(5)\n", - "df_bottom5 = df_sorted.tail(5)\n", - "\n", - "print(\"Top 5:\")\n", - "\n", - "df_top5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + "df_top_bot_pro_forecasts.head()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, + "outputs": [], + "source": [ + "# Make binary-only df_top_bot_pro_forecasts for calibration curves etc\n", + "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['type'] == 'binary'].copy()\n", + "\n", + "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all[df_top_bot_pro_forecasts_all['type'] == 'binary'].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "BjNQ4IND6Ct7", + "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" + }, "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Bottom 5:\n" + "Number of pro forecasts: 50\n" ] - }, + } + ], + "source": [ + "# Set up the plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", + "\n", + "# Plot calibration curves for bot_team_median and pro_median\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", + "\n", + "# Customize the plot\n", + "plt.xlabel('Assigned Probability', fontsize=12)\n", + "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", + "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(only overlap: {len(df_top_bot_pro_forecasts_binary)} questions)', fontsize=14)\n", + "plt.legend(fontsize=10)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set axis limits\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Map resolution to 0 and 1\n", + "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -11517,141 +12414,206 @@ " \n", " \n", " \n", + " bot_question_id\n", " title\n", + " resolution\n", + " scheduled_close_time\n", + " actual_close_time\n", + " type\n", + " options\n", + " range_min\n", + " range_max\n", + " pro_question_id\n", + " question_weight\n", " bot_team_median\n", " pro_median\n", - " resolution\n", - " head_to_head\n", " \n", " \n", " \n", " \n", - " 170\n", - " In its March update, will Similarweb report de...\n", - " 0.7\n", - " 0.144\n", - " yes\n", - " 158.1\n", + " 2\n", + " 31264\n", + " Will the bubble in the Magnificent Seven pop b...\n", + " 0.0\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31270\n", + " 1.0\n", + " 0.085\n", + " 0.013\n", " \n", " \n", - " 0\n", - " For Q1 2025, how many banks will be listed on ...\n", - " 0.02\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " 0\n", - " 299.6\n", + " 5\n", + " 31276\n", + " Will the USDA-posted recall by Pork Dynasty In...\n", + " 1.0\n", + " 2025-01-21 11:42:00\n", + " 2025-01-21 11:42:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31282\n", + " 1.0\n", + " 0.66\n", + " 0.45\n", " \n", " \n", - " 189\n", - " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.05003188076666667, 0.11135575903333333...\n", - " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", - " 34.0\n", - " 502.6\n", + " 8\n", + " 31288\n", + " Will Eric Adams be Mayor of New York City on t...\n", + " 1.0\n", + " 2025-01-22 20:19:00\n", + " 2025-01-22 20:19:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31294\n", + " 1.0\n", + " 0.86\n", + " 0.95\n", " \n", " \n", - " 211\n", - " Will Nikola Corporation file for bankruptcy be...\n", - " 0.99\n", - " 0.999\n", - " annulled\n", + " 10\n", + " 31318\n", + " Will the S&P 500 index go up in January 2025?\n", + " 1.0\n", + " 2025-01-23 23:23:00\n", + " 2025-01-23 23:23:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " <NA>\n", + " 1.0\n", + " NaN\n", " NaN\n", " \n", " \n", - " 214\n", - " Will the state of Rhode Island have any recrea...\n", - " 0.923333\n", - " 0.95\n", - " annulled\n", + " 13\n", + " 31334\n", + " At the end of March 2025, will Wikipedia still...\n", + " 1.0\n", + " 2025-01-24 14:23:00\n", + " 2025-01-24 14:23:00\n", + " binary\n", " NaN\n", + " NaN\n", + " NaN\n", + " 31338\n", + " 1.0\n", + " 0.85\n", + " 0.9\n", " \n", " \n", "\n", "" ], "text/plain": [ - " title \\\n", - "170 In its March update, will Similarweb report de... \n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "189 What will the highest rank of metac-GPT4o or m... \n", - "211 Will Nikola Corporation file for bankruptcy be... \n", - "214 Will the state of Rhode Island have any recrea... \n", + " bot_question_id title \\\n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", + "8 31288 Will Eric Adams be Mayor of New York City on t... \n", + "10 31318 Will the S&P 500 index go up in January 2025? \n", + "13 31334 At the end of March 2025, will Wikipedia still... \n", "\n", - " bot_team_median \\\n", - "170 0.7 \n", - "0 0.02 \n", - "189 [0.0, 0.05003188076666667, 0.11135575903333333... \n", - "211 0.99 \n", - "214 0.923333 \n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", + "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", + "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", + "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", + "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", "\n", - " pro_median resolution \\\n", - "170 0.144 yes \n", - "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", - "211 0.999 annulled \n", - "214 0.95 annulled \n", + " range_min range_max pro_question_id question_weight bot_team_median \\\n", + "2 NaN NaN 31270 1.0 0.085 \n", + "5 NaN NaN 31282 1.0 0.66 \n", + "8 NaN NaN 31294 1.0 0.86 \n", + "10 NaN NaN 1.0 NaN \n", + "13 NaN NaN 31338 1.0 0.85 \n", "\n", - " head_to_head \n", - "170 158.1 \n", - "0 299.6 \n", - "189 502.6 \n", - "211 NaN \n", - "214 NaN " + " pro_median \n", + "2 0.013 \n", + "5 0.45 \n", + "8 0.95 \n", + "10 NaN \n", + "13 0.9 " ] }, - "execution_count": 74, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"\\nBottom 5:\")\n", - "\n", - "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + "df_top_bot_pro_forecasts_all_binary.head()" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 78, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "bot_question_id Int64\n", - "title object\n", - "resolution float64\n", - "scheduled_close_time datetime64[ns]\n", - "actual_close_time datetime64[ns]\n", - "type object\n", - "options object\n", - "range_min float64\n", - "range_max float64\n", - "pro_question_id Int64\n", - "question_weight float64\n", - "bot_team_median object\n", - "pro_median object\n", - "head_to_head float64\n", - "weighted_score float64\n", - "dtype: object" + "
" ] }, - "execution_count": 75, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pro forecasts: 50\n", + "Number of bot forecasts: 241\n" + ] } ], "source": [ - "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", - "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", - "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].map({'yes': 1, 'no': 0})\n", - "df_top_bot_pro_forecasts.dtypes" + "# Set up the plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", + "\n", + "# Plot calibration curves for bot_team_median and pro_median\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_all_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", + "\n", + "# Customize the plot\n", + "plt.xlabel('Assigned Probability', fontsize=12)\n", + "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", + "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(all questions)', fontsize=14)\n", + "plt.legend(fontsize=10)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set axis limits\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")\n", + "print(f\"Number of bot forecasts: {len(df_top_bot_pro_forecasts_all_binary)}\")" ] }, { "cell_type": "code", - "execution_count": 76, - "metadata": {}, + "execution_count": 80, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lPPgorXB7omi", + "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" + }, "outputs": [ { "data": { @@ -11705,10 +12667,10 @@ " NaN\n", " 31268\n", " 1.0\n", - " 0.02\n", + " 0.017463\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 299.573227\n", - " 299.573227\n", + " 286.007699\n", + " 286.007699\n", " \n", " \n", " 1\n", @@ -11723,10 +12685,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.0341314028, 0.03460208...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -57.286904\n", - " -57.286904\n", + " -76.357515\n", + " -76.357515\n", " \n", " \n", " 2\n", @@ -11741,10 +12703,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.085\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -7.574597\n", + " -7.574597\n", " \n", " \n", " 3\n", @@ -11777,151 +12739,62 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.0017047194333333333, 0.0034148989, 0.0...\n", + " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 56.082092\n", - " 56.082092\n", + " 28.578581\n", + " 28.578581\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bot_question_id title \\\n", - "0 31262 For Q1 2025, how many banks will be listed on ... \n", - "1 31263 What percentage of the vote will Alexander Luk... \n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "3 31274 How many arms sales globally will the US State... \n", - "4 31275 How much will it rain in Brasília, Brazil in F... \n", - "\n", - " resolution scheduled_close_time actual_close_time type \\\n", - "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", - "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", - "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", - "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", - "\n", - " options range_min range_max pro_question_id \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", - "1 NaN 60.0 100.0 31269 \n", - "2 NaN NaN NaN 31270 \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", - "4 NaN 0.0 400.0 31281 \n", - "\n", - " question_weight bot_team_median \\\n", - "0 1.0 0.02 \n", - "1 1.0 [0.03366666666666667, 0.0341314028, 0.03460208... \n", - "2 1.0 0.1 \n", - "3 1.0 0.6 \n", - "4 1.0 [0.0, 0.0017047194333333333, 0.0034148989, 0.0... \n", - "\n", - " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 299.573227 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -57.286904 \n", - "2 0.013 -9.227528 \n", - "3 [0.16,0.44,0.4] 31.015493 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 56.082092 \n", - "\n", - " weighted_score \n", - "0 299.573227 \n", - "1 -57.286904 \n", - "2 -9.227528 \n", - "3 31.015493 \n", - "4 56.082092 " - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_top_bot_pro_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "# Make binary-only df_top_bot_pro_forecasts for calibration curves etc\n", - "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['type'] == 'binary'].copy()\n", - "\n", - "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all[df_top_bot_pro_forecasts_all['type'] == 'binary'].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 807 - }, - "id": "BjNQ4IND6Ct7", - "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max pro_question_id \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", + "1 NaN 60.0 100.0 31269 \n", + "2 NaN NaN NaN 31270 \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", + "4 NaN 0.0 400.0 31281 \n", + "\n", + " question_weight bot_team_median \\\n", + "0 1.0 0.017463 \n", + "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 1.0 0.085 \n", + "3 1.0 0.6 \n", + "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", + "2 0.013 -7.574597 \n", + "3 [0.16,0.44,0.4] 31.015493 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "\n", + " weighted_score \n", + "0 286.007699 \n", + "1 -76.357515 \n", + "2 -7.574597 \n", + "3 31.015493 \n", + "4 28.578581 " ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of pro forecasts: 50\n" - ] - } - ], - "source": [ - "# Set up the plot\n", - "plt.figure(figsize=(10, 8))\n", - "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", - "\n", - "# Plot calibration curves for bot_team_median and pro_median\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", - "\n", - "# Customize the plot\n", - "plt.xlabel('Assigned Probability', fontsize=12)\n", - "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", - "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(only overlap: {len(df_top_bot_pro_forecasts_binary)} questions)', fontsize=14)\n", - "plt.legend(fontsize=10)\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set axis limits\n", - "plt.xlim(0, 1)\n", - "plt.ylim(0, 1)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()\n", - "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Map resolution to 0 and 1\n", - "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ { "data": { "text/html": [ @@ -11956,194 +12829,138 @@ " question_weight\n", " bot_team_median\n", " pro_median\n", + " head_to_head\n", + " weighted_score\n", " \n", " \n", " \n", " \n", - " 2\n", - " 31264\n", - " Will the bubble in the Magnificent Seven pop b...\n", - " 0.0\n", - " 2025-01-20 03:27:00\n", - " 2025-01-20 03:27:00\n", + " 342\n", + " 35345\n", + " Will the US Citizenship and Immigration Servic...\n", + " 1.0\n", + " 2025-03-12 22:00:00\n", + " 2025-03-12 22:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31270\n", - " 1.0\n", - " 0.1\n", - " 0.013\n", + " 35380\n", + " 1.00\n", + " 0.9275\n", + " 0.95\n", + " -2.396919\n", + " -2.396919\n", " \n", " \n", - " 5\n", - " 31276\n", - " Will the USDA-posted recall by Pork Dynasty In...\n", - " 1.0\n", - " 2025-01-21 11:42:00\n", - " 2025-01-21 11:42:00\n", + " 351\n", + " 35354\n", + " Will the United States impose any new tariffs ...\n", + " 0.0\n", + " 2025-03-13 03:00:00\n", + " 2025-03-13 03:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31282\n", - " 1.0\n", - " 0.6\n", - " 0.45\n", + " 35381\n", + " 1.00\n", + " 0.375\n", + " 0.05\n", + " -41.871033\n", + " -41.871033\n", " \n", " \n", - " 8\n", - " 31288\n", - " Will Eric Adams be Mayor of New York City on t...\n", + " 355\n", + " 35358\n", + " Will ChatGPT rank in the top 10 global website...\n", " 1.0\n", - " 2025-01-22 20:19:00\n", - " 2025-01-22 20:19:00\n", + " 2025-03-13 03:00:00\n", + " 2025-03-13 03:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31294\n", - " 1.0\n", - " 0.9\n", - " 0.95\n", + " 35385\n", + " 1.00\n", + " 0.925\n", + " 0.97\n", + " -4.750233\n", + " -4.750233\n", " \n", " \n", - " 10\n", - " 31318\n", - 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" bot_question_id title \\\n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", - "8 31288 Will Eric Adams be Mayor of New York City on t... \n", - "10 31318 Will the S&P 500 index go up in January 2025? \n", - "13 31334 At the end of March 2025, will Wikipedia still... \n", - "\n", - " resolution scheduled_close_time actual_close_time type options \\\n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", - "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", - "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", - "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", - "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", - "\n", - " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.1 \n", - "5 NaN NaN 31282 1.0 0.6 \n", - "8 NaN NaN 31294 1.0 0.9 \n", - "10 NaN NaN 1.0 NaN \n", - "13 NaN NaN 31338 1.0 0.75 \n", - "\n", - " pro_median \n", - "2 0.013 \n", - "5 0.45 \n", - "8 0.95 \n", - "10 NaN \n", - "13 0.9 " - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_top_bot_pro_forecasts_all_binary.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" + " bot_question_id title \\\n", + "342 35345 Will the US Citizenship and Immigration Servic... \n", + "351 35354 Will the United States impose any new tariffs ... \n", + "355 35358 Will ChatGPT rank in the top 10 global website... \n", + "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", + "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", + "\n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "342 1.0 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", + "351 0.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "355 1.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "361 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "\n", + " range_min range_max pro_question_id question_weight bot_team_median \\\n", + "342 NaN NaN 35380 1.00 0.9275 \n", + "351 NaN NaN 35381 1.00 0.375 \n", + "355 NaN NaN 35385 1.00 0.925 \n", + "361 NaN NaN 35386 0.85 0.825 \n", + "364 NaN NaN 35387 0.85 0.05 \n", + "\n", + " pro_median head_to_head weighted_score \n", + "342 0.95 -2.396919 -2.396919 \n", + "351 0.05 -41.871033 -41.871033 \n", + "355 0.97 -4.750233 -4.750233 \n", + "361 0.666 -64.635502 -54.940177 \n", + "364 0.03 -2.083409 -1.770897 " ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of pro forecasts: 50\n", - "Number of bot forecasts: 241\n" - ] - } - ], - "source": [ - "# Set up the plot\n", - "plt.figure(figsize=(10, 8))\n", - "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", - "\n", - "# Plot calibration curves for bot_team_median and pro_median\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_all_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", - "\n", - "# Customize the plot\n", - "plt.xlabel('Assigned Probability', fontsize=12)\n", - "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", - "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(all questions)', fontsize=14)\n", - "plt.legend(fontsize=10)\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set axis limits\n", - "plt.xlim(0, 1)\n", - "plt.ylim(0, 1)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()\n", - "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")\n", - "print(f\"Number of bot forecasts: {len(df_top_bot_pro_forecasts_all_binary)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lPPgorXB7omi", - "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" - }, - "outputs": [ { "ename": "ValueError", "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", @@ -12151,21 +12968,22 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[80], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/metaculus/aib-analysis/functions.py:824\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 814\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 815\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 823\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 824\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 826\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 827\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/_core/_methods.py:49\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 48\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[80], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" ] } ], "source": [ "# Calculate confidence scores for bot_team_median and pro_median\n", + "display_head_and_tail(df_top_bot_pro_forecasts)\n", "bot_confidence = calculate_confidence(df_top_bot_pro_forecasts['bot_team_median'], df_top_bot_pro_forecasts['resolution'])\n", "pro_confidence = calculate_confidence(df_top_bot_pro_forecasts['pro_median'], df_top_bot_pro_forecasts['resolution'])\n", "\n", @@ -12277,7 +13095,7 @@ "cp.rename(columns={'post_id': 'cp_post_id', 'question_id': 'cp_question_id'}, inplace=True)\n", "\n", "bot_cp_id = pd.read_csv('bot_to_main_feed_ids.csv')\n", - " \n", + "\n", "# Merge these on cp_question_id\n", "df_bot_cp = pd.merge(bot_cp_id, cp, on='cp_post_id', how='right') # ahh?\n", "\n", @@ -12400,10 +13218,10 @@ "for bot_question_id in groups_exploded['bot_question_id'].unique():\n", " # Get all rows for this bot_question_id\n", " question_group = groups_exploded[groups_exploded['bot_question_id'] == bot_question_id]\n", - " \n", + "\n", " # Get the question title\n", " question_title = question_group['question_title'].iloc[0]\n", - " \n", + "\n", " # Function to check if option matches question title\n", " def option_matches(row):\n", " option = row['options']\n", @@ -12415,16 +13233,16 @@ " or_format = f\"{start} or {end}\"\n", " return or_format in question_title\n", " return False\n", - " \n", + "\n", " # Find rows where the question title contains the option (with format handling)\n", " matching_rows = question_group[question_group.apply(option_matches, axis=1)]\n", - " \n", + "\n", " filtered_rows = []\n", "\n", " # If we found a matching row, add the first one to our filtered rows, EXCEPT... Biden\n", " if not matching_rows.empty and 'Biden' not in question_title:\n", " filtered_rows.append(matching_rows.iloc[0])\n", - " \n", + "\n", " # If Biden in question_title, we mustn't just take the first row - we must sum the rows that meet the threshold\n", " if 'Biden' in question_title:\n", " # Get first row for each unique option to avoid duplicates\n", @@ -12433,7 +13251,7 @@ " # Drop option='1' - we don't ask about 1 or more\n", " first_rows = first_rows[first_rows['options'] != '1']\n", " biden_interp = first_rows.copy()\n", - " \n", + "\n", " # Now for each row in biden_interp\n", " for idx, row in biden_interp.iterrows():\n", " threshold = int(row['threshold'])\n", @@ -12444,10 +13262,10 @@ " forecast_value = first_rows[first_rows['options'].isin(['3', '4 or more'])]['forecast_values'].sum()\n", " elif threshold == 4:\n", " forecast_value = first_rows[first_rows['options'] == '4 or more']['forecast_values'].sum()\n", - " \n", + "\n", " # Update this row's forecast value\n", " biden_interp.at[idx, 'forecast_value'] = forecast_value\n", - " \n", + "\n", " filtered_rows.append(biden_interp.iloc[0])\n", "\n", "# Combine all filtered rows into a DataFrame\n", @@ -12502,7 +13320,7 @@ "thresholds = {\n", " 29163: ('less', 2.0), # COVID hospitalizations\n", " 29349: ('greater', 100), # Brasilia rain\n", - " 29350: ('greater', 150), # Brasilia rain \n", + " 29350: ('greater', 150), # Brasilia rain\n", " 29351: ('greater', 200), # Brasilia rain\n", " 29353: ('greater', 20), # Arms sales\n", " 29354: ('greater', 25), # Arms sales\n", @@ -12591,7 +13409,7 @@ "# 29567: China youth unemployment > 17.0 and less than 18.0\n", "row = numerics[numerics['bot_question_id'] == 29567].iloc[0]\n", "numerics.loc[numerics['bot_question_id'] == row['bot_question_id'], 'forecast_values'] = cdf_between(row, row['cdf'], 17.0, 18.0)\n", - " \n", + "\n", "# 29568: China youth unemployment > 18.0 and less than 19.0\n", "row = numerics[numerics['bot_question_id'] == 29568].iloc[0]\n", "numerics.loc[numerics['bot_question_id'] == row['bot_question_id'], 'forecast_values'] = cdf_between(row, row['cdf'], 18.0, 19.0)\n", @@ -12701,7 +13519,7 @@ "if True:\n", " # Filter rows where the months do not match\n", " df_bot_cp_exploded = df_bot_cp_exploded[\n", - " (df_bot_cp_exploded['bot_version_month'] == df_bot_cp_exploded['cp_version_month']) | \n", + " (df_bot_cp_exploded['bot_version_month'] == df_bot_cp_exploded['cp_version_month']) |\n", " (df_bot_cp_exploded['bot_version_month'].isnull())\n", "]\n", "\n", @@ -13234,7 +14052,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -13248,7 +14066,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/bootstrapped_h2h_bot_vs_pros.csv b/bootstrapped_h2h_bot_vs_pros.csv index c536929..5811dc4 100644 --- a/bootstrapped_h2h_bot_vs_pros.csv +++ b/bootstrapped_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -Grizeu_Bot,-9.7,-5.4,4.4,15.9,22.2 -RPM_bot,-0.1,0.3,1.4,2.8,3.7 -X_bot,-0.4,-0.3,0.2,0.7,1.2 +metac-o1,21.0,21.0,21.0,21.0,21.0 +metac-perplexity,20.3,20.3,20.3,20.3,20.3 +bot_median,17.9,17.9,17.9,17.9,17.9 +acm_bot,17.7,17.7,17.7,17.7,17.7 +manticAI,14.5,14.5,14.5,14.5,14.5 +twsummerbot,14.3,14.3,14.3,14.3,14.3 +jkraybill_bot,14.3,14.3,14.3,14.3,14.3 +metac-claude-3-5-sonnet-20240620,12.0,12.0,12.0,12.0,12.0 +GreeneiBot2,11.7,11.7,11.7,11.7,11.7 +metac-claude-3-5-sonnet-latest,11.5,11.5,11.5,11.5,11.5 +NextWorldLab,11.1,11.1,11.1,11.1,11.1 +metac-grok-2-1212,11.0,11.0,11.0,11.0,11.0 +metac-gpt-4o,10.5,10.5,10.5,10.5,10.5 +metac-Llama-3.1,10.2,10.2,10.2,10.2,10.2 +Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 +SynapseSeer,10.2,10.2,10.2,10.2,10.2 +metac-o1-preview,10.1,10.1,10.1,10.1,10.1 +mmBot,9.7,9.7,9.7,9.7,9.7 +metac-exa,9.7,9.7,9.7,9.7,9.7 +annabot,9.0,9.0,9.0,9.0,9.0 +metac-deepseek-r1,8.4,8.4,8.4,8.4,8.4 +VeritasAI,8.4,8.4,8.4,8.4,8.4 +laylaps,7.6,7.6,7.6,7.6,7.6 +cookics_bot_TEST,6.4,6.4,6.4,6.4,6.4 +metac-Gemini-Exp-1206,5.8,5.8,5.8,5.8,5.8 +MWG,5.5,5.5,5.5,5.5,5.5 +ajf-bot,5.1,5.1,5.1,5.1,5.1 +pgodzinai,3.5,3.5,3.5,3.5,3.5 +KevinTestBot,3.3,3.3,3.3,3.3,3.3 +InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 +Bot_Pepa,2.6,2.6,2.6,2.6,2.6 +CumulativeBot,2.5,2.5,2.5,2.5,2.5 +swingswish,2.4,2.4,2.4,2.4,2.4 +wunderplumb,2.4,2.4,2.4,2.4,2.4 +jonahsingerbot,2.2,2.2,2.2,2.2,2.2 +bean_bot,2.1,2.1,2.1,2.1,2.1 +X_bot,1.9,1.9,1.9,1.9,1.9 +CatrachoCaster,1.8,1.8,1.8,1.8,1.8 +4Shadower,0.6,0.6,0.6,0.6,0.6 +krm-bot,0.6,0.6,0.6,0.6,0.6 +RPM_bot,0.6,0.6,0.6,0.6,0.6 andrewsiah,0.0,0.0,0.0,0.0,0.0 cobyj-bot,0.0,0.0,0.0,0.0,0.0 -acm_bot,-16.3,-11.3,-0.2,14.8,22.5 -jonahsingerbot,-1.4,-1.1,-0.6,-0.3,-0.1 -bean_bot,-1.6,-1.3,-0.7,-0.3,-0.1 -CumulativeBot,-2.9,-2.3,-1.0,0.2,1.0 -swingswish,-2.4,-1.9,-1.1,-0.5,-0.3 -jkraybill_bot,-8.5,-6.2,-1.1,4.6,7.5 -KevinTestBot,-5.8,-3.9,-1.4,0.4,1.1 -SynapseSeer,-6.3,-4.6,-1.5,1.9,3.9 -pianobot,-8.0,-5.9,-2.6,-0.2,0.1 -twsummerbot,-13.4,-10.3,-2.9,4.6,9.2 -CatrachoCaster,-8.6,-6.8,-3.4,-0.3,1.1 -annabot,-8.4,-6.5,-3.4,-0.6,0.9 -cookics_bot_TEST,-12.1,-9.7,-4.2,0.1,2.1 -GreeneiBot2,-17.4,-13.2,-4.9,3.6,7.4 -krm-bot,-10.6,-8.6,-5.3,-2.6,-1.6 -4Shadower,-12.8,-9.8,-5.3,-1.8,-1.1 -metac-o1,-22.7,-18.5,-6.7,8.5,16.1 -MWG,-18.3,-14.9,-8.3,-2.2,1.3 -ajf-bot,-22.3,-17.2,-8.8,-1.4,2.5 -bot_median,-22.7,-18.3,-9.0,2.1,8.9 -Bot_Pepa,-20.9,-16.3,-9.0,-1.2,2.7 -manticAI,-22.1,-17.7,-9.5,-0.7,4.9 -ProfessorSP,-20.7,-16.8,-10.1,-4.7,-2.4 -wunderplumb,-22.4,-19.1,-12.0,-5.8,-3.3 -metac-perplexity,-29.1,-24.0,-12.0,0.8,8.0 -laylaps,-21.0,-17.8,-12.8,-8.1,-5.8 -NextWorldLab,-28.4,-24.0,-13.6,-2.8,4.0 -pgodzinai,-31.7,-25.6,-14.0,-4.1,1.9 -metac-Gemini-Exp-1206,-28.1,-23.3,-14.0,-2.7,3.2 -metac-deepseek-r1,-30.7,-25.2,-14.6,-4.9,0.5 -minefrac1,-29.8,-24.8,-14.9,-3.1,4.1 -metac-Llama-3.1,-32.9,-26.8,-15.1,-3.3,3.2 -metac-claude-3-5-sonnet-latest,-32.6,-26.6,-15.9,-3.5,3.2 -metac-claude-3-5-sonnet-20240620,-35.3,-29.9,-18.2,-4.3,2.8 -metac-o1-preview,-38.9,-32.4,-19.3,-6.9,0.3 -mmBot,-36.2,-30.9,-21.1,-11.7,-7.1 -VeritasAI,-33.5,-28.9,-21.3,-14.4,-11.1 -metac-grok-2-1212,-41.8,-35.2,-23.4,-10.4,-3.8 -metac-exa,-40.4,-34.4,-23.4,-13.8,-7.9 -metac-gpt-4o,-41.7,-34.7,-23.8,-11.3,-5.3 -InstitutPelFutur,-43.6,-37.9,-26.5,-14.9,-6.6 +pianobot,-2.2,-2.2,-2.2,-2.2,-2.2 +ProfessorSP,-3.0,-3.0,-3.0,-3.0,-3.0 +minefrac1,-3.0,-3.0,-3.0,-3.0,-3.0 diff --git a/functions.py b/functions.py index 29a05b2..00efd06 100644 --- a/functions.py +++ b/functions.py @@ -27,12 +27,12 @@ def process_forecasts(df): 2. Sorting by created_at to get chronological order 3. Taking the last forecast for each (forecaster, question_id) pair 4. Dropping unused columns - + Parameters: ----------- df : pandas DataFrame DataFrame containing forecast data - + Returns: -------- pandas DataFrame @@ -44,22 +44,22 @@ def process_forecasts(df): df['continuous_cdf'] ) ) - + # Sort by created_at to ensure chronological order df = df.sort_values(by='created_at') - + # Take the last forecast for each (forecaster, question_id) pair df = df.groupby(['question_id', 'forecaster']).last().reset_index() - + # Drop the original forecast columns as they're now redundant df = df.drop(['probability_yes', 'probability_yes_per_category', 'continuous_cdf'], axis=1) - + return df def add_is_median(df): """ Marks exactly one row per question_id as the median. - Guarantees one median per question by taking the forecaster with + Guarantees one median per question by taking the forecaster with the actual median value for that question. Args: @@ -70,19 +70,19 @@ def add_is_median(df): """ # Initialize median column df['is_median'] = False - + # For each question_id for qid in df['question_id'].unique(): # Get just the rows for this question question_mask = df['question_id'] == qid question_df = df[question_mask] - + # Get the median value index (middle position after sorting) median_idx = question_df['forecast'].sort_values().index[len(question_df)//2] - + # Mark that row df.loc[median_idx, 'is_median'] = True - + return df def add_median_rows(df, prefix): @@ -98,10 +98,10 @@ def add_median_rows(df, prefix): """ # Get the median rows median_rows = df[df['is_median']].copy() - + # Change forecaster to 'median' median_rows['forecaster'] = f'{prefix}_median' - + # Combine original and new median rows whole = pd.concat([df, median_rows], ignore_index=True).sort_values('question_id').drop_duplicates(['question_id', 'forecaster']) @@ -196,7 +196,7 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): """ Options from https://stats.stackexchange.com/questions/47325/bias-correction-in-weighted-variance I didn't think (B) beared trying, but could be wrong. - MGH -It makes very little difference here but (C) does seem to be the correct formula - corrects for +It makes very little difference here but (C) does seem to be the correct formula - corrects for the bias in the sample variance. """ @@ -216,7 +216,7 @@ def calc_weighted_std_dev(df3, bot, weighted_score, weighted_count, weight_col): """ weighted_average = weighted_score / weighted_count return np.sqrt(((df3[bot] - weighted_average) ** 2 * df3[weight_col]).sum() / (weighted_count - 1)) - + def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col): """ Calculates the weighted standard deviation using Claude (via Nikos) method - (C) from stack exchange post. @@ -233,7 +233,7 @@ def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) """ weighted_average = weighted_score / weighted_count return np.sqrt( - (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() / + (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() / (df3[weight_col].sum() * (1 - (df3[weight_col] ** 2).sum() / (df3[weight_col].sum() ** 2))) ) @@ -319,16 +319,16 @@ def get_median_forecast(row, bots): @BEN: Check Calculates the median forecast for a given set of bots, handling different question types properly. - + Args: df (pandas.DataFrame): DataFrame with bot forecast columns and question metadata. bots (list): List of bot column names. - + Returns: pandas.Series: Median forecast for each row. """ q_type = row['type'] - + forecasts = [] for bot in bots: f_raw = row.get(bot) @@ -341,7 +341,7 @@ def get_median_forecast(row, bots): continue else: forecasts.append(f_raw) # Already parsed float or list - + if not forecasts: return np.nan @@ -380,6 +380,19 @@ def get_median_forecast(row, bots): raise ValueError(f"Unknown question type: {q_type}") +def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.DataFrame: + """ + Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value + Changes the df so that the forecast is now the score for that question + """ + score_df = df_bot_team_forecasts.copy() + team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) + for team in teams: + score_for_team = team_scores[team] + score_df[team] = score_for_team + return score_df + + def calculate_weighted_scores(df_bot_team_forecasts, teams): """ @BEN: check @@ -470,15 +483,15 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): Calculates weighted statistics, including t-test and p-values, for multiple bots. Args: - df_input (pandas.DataFrame): + df_input (pandas.DataFrame): DataFrame with peer scores, such as `df_bot_vs_pro_peer`, comparing each bot to the pro median. - bot_list (list): + bot_list (list): List of column names corresponding to bot scores. - weight_col (str, optional): + weight_col (str, optional): Name of the column containing weights. Defaults to 'question_weight'. Returns: - pandas.DataFrame: + pandas.DataFrame: Leaderboard DataFrame with calculated statistics for each bot, including: - W_score: Weighted score. - W_count: Weighted count. @@ -494,33 +507,33 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): """ # Initialize results dataframe df_W_leaderboard = pd.DataFrame(index=bot_list) - + for bot in bot_list: # Create working copy with just needed columns df3 = df_input[[bot, weight_col]].copy() df3 = df3.dropna() df3 = df3.reset_index(drop=True) - + # Calculate weighted statistics weighted_score = (df3[bot] * df3[weight_col]).sum() weighted_count = df3[weight_col].sum() - + if weighted_count > 2: # Only calculate if we have enough data weighted_average = weighted_score / weighted_count weighted_std_dev = calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) std_error = weighted_std_dev / np.sqrt(weighted_count) t_statistic = (weighted_average - 0) / std_error - + # Get t-critical value and confidence bounds effective_n = (df3[weight_col].sum() ** 2) / (df3[weight_col] ** 2).sum() t_crit = stats.t.ppf(0.975, df=effective_n - 1) # 95% confidence level upper_bound = weighted_average + t_crit * std_error lower_bound = weighted_average - t_crit * std_error - + # Calculate CDF and p-value cdf = stats.t.cdf(t_statistic, df=weighted_count-1) p_value = 2 * min(cdf, 1 - cdf) # Two-tailed p-value - + else: # Not enough data weighted_average = weighted_score / weighted_count if weighted_count > 0 else np.nan weighted_std_dev = np.nan @@ -531,7 +544,7 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): lower_bound = np.nan cdf = np.nan p_value = np.nan - + # Store results df_W_leaderboard.loc[bot, 'W_score'] = weighted_score df_W_leaderboard.loc[bot, 'W_count'] = weighted_count @@ -544,34 +557,36 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): df_W_leaderboard.loc[bot, 'lower_bound'] = lower_bound df_W_leaderboard.loc[bot, 'cdf'] = cdf df_W_leaderboard.loc[bot, 'p_value'] = p_value - + # Format and round the results df_W_leaderboard['W_score'] = df_W_leaderboard['W_score'].round(1) # Store numerical p-values temporarily for sorting df_W_leaderboard['_p_value_sort'] = df_W_leaderboard['p_value'] - + # Format p-values as percentages df_W_leaderboard['p_value'] = df_W_leaderboard['p_value'].apply( lambda x: f"{x:.6f}" if pd.notnull(x) else "NA" ) - + # Round other columns df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']] = \ df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']].round(1) - + # Sort by the numerical p-values df_W_leaderboard = df_W_leaderboard.sort_values( by='W_score', ascending=False, na_position='last' ) - + # Drop the temporary sorting column df_W_leaderboard = df_W_leaderboard.drop('_p_value_sort', axis=1) - + return df_W_leaderboard + + def calculate_head_to_head(row, a, b): """ @BEN: Check... @@ -866,10 +881,10 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): """ # Create figure and axes fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12)) - + # Define bin edges bins = np.linspace(0, 1, 6) - + # Top plot: Questions that resolved 1 ax1.hist([df[df[resolution_col] == 1][bot_col], df[df[resolution_col] == 1][pro_col]], @@ -882,7 +897,7 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): # Set integer y-ticks for top plot ymax1 = int(np.ceil(ax1.get_ylim()[1])) ax1.set_yticks(range(0, ymax1 + 1, 2)) - + # Bottom plot: Questions that resolved 0 ax2.hist([df[df[resolution_col] == 0][bot_col], df[df[resolution_col] == 0][pro_col]], @@ -895,7 +910,7 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): # Set integer y-ticks for bottom plot ymax2 = int(np.ceil(ax2.get_ylim()[1])) ax2.set_yticks(range(0, ymax2 + 1, 10)) - + # Adjust layout and display plt.tight_layout() plt.show() @@ -1134,24 +1149,24 @@ def compute_bucket_forecast_value(row): # Handle binary_version_tuple gracefully if pd.isna(row['binary_version_tuple']) or not isinstance(row['binary_version_tuple'], (list, tuple)): return None - + # Extract the first and second elements of the tuple comparison_type = row['binary_version_tuple'][0] string_location = row['binary_version_tuple'][1] - + # Skip if comparison_type is 'complicated' if comparison_type == 'complicated': return None - + # Compute forecast_value using the extracted string_location forecast_value = get_cdf_at(row['cdf'], nominal_location_to_cdf_location(string_location, row)) - + # Apply logic based on comparison_type if comparison_type == 'less': return forecast_value elif comparison_type == 'greater': return 1 - forecast_value - + return None # Apply the function to each row and overwrite forecast_value (currently contains cdf, which we no longer need) @@ -1161,16 +1176,16 @@ def compute_bucket_forecast_value(row): def parse_options_array(options_str): """ Parse options string that looks like an array into an actual array. - + Args: options_str: String representation of options array (e.g. '["0","1","2-3","4-6",">6"]') - + Returns: List of option strings """ if not isinstance(options_str, str): return options_str # Already parsed or None - + try: # First try using eval (safer than literal_eval for this specific case) options_array = eval(options_str) @@ -1185,6 +1200,6 @@ def parse_options_array(options_str): parts = re.findall(r'"([^"]*)"', cleaned) if parts: return parts - + # Simple fallback: just split by comma and strip quotes return [p.strip().strip('"\'') for p in cleaned.split(',')] diff --git a/weighted_t_test_h2h_bot_vs_pros.csv b/weighted_t_test_h2h_bot_vs_pros.csv index 96cf6b7..b364ee5 100644 --- a/weighted_t_test_h2h_bot_vs_pros.csv +++ b/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value 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-minefrac1,-1289.4,43.5,-29.6,123.19979122882201,18.679504139979862,-1.5868575895194426,2.0149178012042084,8.0,-67.3,0.05997902931188052,0.119958 -pgodzinai,-1330.4,62.0,-21.5,98.40405336166643,12.497327274265158,-1.7169528181446574,1.998173547416901,3.5,-46.4,0.04553088385451872,0.091062 -metac-deepseek-r1,-1360.3,48.2,-28.2,108.35980238796017,15.607907596292135,-1.808247915950853,2.0091123850303423,3.1,-59.6,0.038470700886698884,0.076941 -metac-Llama-3.1,-1412.1,73.7,-19.2,97.48349885250519,11.355267367831132,-1.687375000139217,1.9920236390185833,3.5,-41.8,0.04790881765000651,0.095818 -metac-claude-3-5-sonnet-latest,-1463.9,74.7,-19.6,96.8559111558961,11.206392518452509,-1.7487367238291156,1.9915966480791545,2.7,-41.9,0.04225009834107552,0.084500 -metac-claude-3-5-sonnet-20240620,-1649.9,75.1,-22.0,105.32409379053074,12.153679026757276,-1.8076157533135497,1.9915359040496325,2.2,-46.2,0.03736236035591808,0.074725 -metac-o1-preview,-1830.6,74.7,-24.5,107.51540873641419,12.439714393299266,-1.9699554012840843,1.9915966480791545,0.3,-49.3,0.026300611526952466,0.052601 -mmBot,-2006.4,75.7,-26.5,78.53235084186326,9.026110757840675,-2.9364459612521934,1.991180868356605,-8.5,-44.5,0.0022054969593251583,0.004411 -VeritasAI,-2024.5,67.7,-29.9,63.28210251110541,7.691066484341371,-3.88818660370801,1.9948486063528272,-14.6,-45.2,0.00011762351540143696,0.000235 -metac-grok-2-1212,-2154.6,74.7,-28.8,106.09460633753015,12.275325155894583,-2.3496848937723014,1.9915966480791545,-4.4,-53.3,0.01073504583547352,0.021470 -metac-gpt-4o,-2196.6,74.7,-29.4,100.42168394988849,11.618958453605197,-2.53084357359069,1.9915966480791545,-6.3,-52.5,0.006756252860737068,0.013513 -metac-exa,-2249.1,72.7,-30.9,91.72328991140397,10.757526338903716,-2.875853188346894,1.9924623002180712,-9.5,-52.4,0.002651041040011998,0.005302 -InstitutPelFutur,-2477.3,72.8,-34.0,102.04145421493415,11.959442897860137,-2.8453905383922216,1.992460623985373,-10.2,-57.9,0.002888355174527779,0.005777 +metac-o1,1998.9,95.0,21.0,3.570999300115835e-15,3.663767977230083e-16,5.743007173754146e+16,1.9847501794262088,21.0,21.0,1.0,0.000000 +metac-perplexity,1927.0,95.0,20.3,0.0,0.0,inf,1.9847501794262088,20.3,20.3,1.0,0.000000 +bot_median,1698.8,95.0,17.9,0.0,0.0,inf,1.9847501794262088,17.9,17.9,1.0,0.000000 +acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 +manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 +twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 +jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 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+laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 +cookics_bot_TEST,612.4,95.0,6.4,1.7854996500579174e-15,1.8318839886150415e-16,3.5189490119492424e+16,1.9847501794262088,6.4,6.4,1.0,0.000000 +metac-Gemini-Exp-1206,548.0,95.0,5.8,0.0,0.0,inf,1.9847501794262088,5.8,5.8,1.0,0.000000 +MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 +ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 +pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 +KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 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+CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 +4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 +krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 +RPM_bot,52.6,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,4.834419627569585e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 +andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA +cobyj-bot,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA +pianobot,-206.5,95.0,-2.2,4.463749125144793e-16,4.579709971537604e-17,-4.745304957283875e+16,1.9847501794262088,-2.2,-2.2,0.0,0.000000 +ProfessorSP,-280.4,95.0,-3.0,8.927498250289587e-16,9.159419943075207e-17,-3.2229421543642156e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 +minefrac1,-283.9,95.0,-3.0,4.463749125144793e-16,4.579709971537604e-17,-6.524423956604449e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 From 455a676bc827a0e11436056a3d13ec0377d55fad Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 07:01:51 -0600 Subject: [PATCH 02/26] reorganized data files into subdirectories --- AI_BENCHMARKING_ANALYSIS.ipynb | 521 +++++++++++++++++- .../pgodzinai_comments.csv | 0 .../pgodzinai_comments.ipynb | 0 functions.py | 1 + main.py | 0 .../bot_to_main_feed_ids.csv | 0 .../bootstrapped_h2h_bot_vs_pros.csv | 0 .../df_top_bot_pro_cp_forecasts.csv | 0 .../weighted_baseline_bot_cp.csv | 0 ...ghted_bot_ONLY_peer_leaderboard_t_test.csv | 0 .../weighted_bot_peer_leaderboard_t_test.csv | 0 .../weighted_t_test_h2h_bot_vs_cp.csv | 0 .../weighted_t_test_h2h_bot_vs_pros.csv | 0 13 files changed, 512 insertions(+), 10 deletions(-) rename pgodzinai_comments.csv => archived/pgodzinai_comments.csv (100%) rename pgodzinai_comments.ipynb => archived/pgodzinai_comments.ipynb (100%) create mode 100644 main.py rename bot_to_main_feed_ids.csv => misc_data/bot_to_main_feed_ids.csv (100%) rename bootstrapped_h2h_bot_vs_pros.csv => notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv (100%) rename df_top_bot_pro_cp_forecasts.csv => notebook_outputs/df_top_bot_pro_cp_forecasts.csv (100%) rename weighted_baseline_bot_cp.csv => notebook_outputs/weighted_baseline_bot_cp.csv (100%) rename weighted_bot_ONLY_peer_leaderboard_t_test.csv => notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv (100%) rename weighted_bot_peer_leaderboard_t_test.csv => notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv (100%) rename weighted_t_test_h2h_bot_vs_cp.csv => notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv (100%) rename weighted_t_test_h2h_bot_vs_pros.csv => notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv (100%) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 313d580..0f510e4 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -7088,7 +7088,7 @@ "outputs": [], "source": [ "# Write to csv\n", - "df_W_leaderboard.to_csv('weighted_t_test_h2h_bot_vs_pros.csv', index=True)" + "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv', index=True)" ] }, { @@ -8051,7 +8051,7 @@ "outputs": [], "source": [ "# Write to csv\n", - "df_W_leaderboard_print.to_csv('weighted_bot_peer_leaderboard_t_test.csv', index=False)" + "df_W_leaderboard_print.to_csv('notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv', index=False)" ] }, { @@ -9740,7 +9740,7 @@ "outputs": [], "source": [ "# Write df_rounded (bootstrapping h2h) to csv\n", - "df_rounded.to_csv('bootstrapped_h2h_bot_vs_pros.csv')" + "df_rounded.to_csv('notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv')" ] }, { @@ -10319,7 +10319,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_W_bot_only_peer_leaderboard.to_csv('weighted_bot_ONLY_peer_leaderboard_t_test.csv', index=True)" + "df_W_bot_only_peer_leaderboard.to_csv('notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv', index=True)" ] }, { @@ -11145,7 +11145,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 81, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11154,6 +11154,507 @@ "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, "outputs": [ + { + "data": { + "text/html": [ + "
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"execution_count": 61, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -13094,7 +13595,7 @@ "cp = pd.read_csv('https://data.heroku.com/dataclips/xwbtczmsuszvlbrhdifhsilplfxf.csv')\n", "cp.rename(columns={'post_id': 'cp_post_id', 'question_id': 'cp_question_id'}, inplace=True)\n", "\n", - "bot_cp_id = pd.read_csv('bot_to_main_feed_ids.csv')\n", + "bot_cp_id = pd.read_csv('misc_data/bot_to_main_feed_ids.csv')\n", "\n", "# Merge these on cp_question_id\n", "df_bot_cp = pd.merge(bot_cp_id, cp, on='cp_post_id', how='right') # ahh?\n", @@ -13828,9 +14329,9 @@ "outputs": [], "source": [ "# Write both leaderboards to csv\n", - "weighted_leaderboard.to_csv('weighted_baseline_bot_cp.csv', index=False)\n", + "weighted_leaderboard.to_csv('notebook_outputs/weighted_baseline_bot_cp.csv', index=False)\n", "\n", - "df_W_leaderboard.to_csv('weighted_t_test_h2h_bot_vs_cp.csv', index=True)" + "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv', index=True)" ] }, { @@ -14024,7 +14525,7 @@ "df_top_bot_pro_cp_forecasts = df_top_bot_pro_cp_forecasts.rename(columns={'forecast_values': 'community_prediction'})\n", "\n", "# Write df_top_bot_pro_cp_forecasts to csv, but only the columns bot question id, cp post id, cp question id, title, resolution, cp_reveal_time, forecast_values, bot_team_median, pro_median\n", - "df_top_bot_pro_cp_forecasts[['bot_question_id', 'cp_post_id', 'cp_question_id', 'title', 'resolution', 'cp_reveal_time', 'community_prediction', 'bot_team_median', 'pgodzinai', 'pro_median']].to_csv('df_top_bot_pro_cp_forecasts.csv', index=False)" + "df_top_bot_pro_cp_forecasts[['bot_question_id', 'cp_post_id', 'cp_question_id', 'title', 'resolution', 'cp_reveal_time', 'community_prediction', 'bot_team_median', 'pgodzinai', 'pro_median']].to_csv('notebook_outputs/df_top_bot_pro_cp_forecasts.csv', index=False)" ] }, { diff --git a/pgodzinai_comments.csv b/archived/pgodzinai_comments.csv similarity index 100% rename from pgodzinai_comments.csv rename to archived/pgodzinai_comments.csv diff --git a/pgodzinai_comments.ipynb b/archived/pgodzinai_comments.ipynb similarity index 100% rename from pgodzinai_comments.ipynb rename to archived/pgodzinai_comments.ipynb diff --git a/functions.py b/functions.py index 00efd06..d0bf79f 100644 --- a/functions.py +++ b/functions.py @@ -385,6 +385,7 @@ def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[s Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value Changes the df so that the forecast is now the score for that question """ + raise NotImplementedError("I accidentally implemented baseline scoring here unfortunately") score_df = df_bot_team_forecasts.copy() team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) for team in teams: diff --git a/main.py b/main.py new file mode 100644 index 0000000..e69de29 diff --git a/bot_to_main_feed_ids.csv b/misc_data/bot_to_main_feed_ids.csv similarity index 100% rename from bot_to_main_feed_ids.csv rename to misc_data/bot_to_main_feed_ids.csv diff --git a/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv similarity index 100% rename from bootstrapped_h2h_bot_vs_pros.csv rename to notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv diff --git a/df_top_bot_pro_cp_forecasts.csv b/notebook_outputs/df_top_bot_pro_cp_forecasts.csv similarity index 100% rename from df_top_bot_pro_cp_forecasts.csv rename to notebook_outputs/df_top_bot_pro_cp_forecasts.csv diff --git a/weighted_baseline_bot_cp.csv b/notebook_outputs/weighted_baseline_bot_cp.csv similarity index 100% rename from weighted_baseline_bot_cp.csv rename to notebook_outputs/weighted_baseline_bot_cp.csv diff --git a/weighted_bot_ONLY_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv similarity index 100% rename from weighted_bot_ONLY_peer_leaderboard_t_test.csv rename to notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv diff --git a/weighted_bot_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv similarity index 100% rename from weighted_bot_peer_leaderboard_t_test.csv rename to notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv diff --git a/weighted_t_test_h2h_bot_vs_cp.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv similarity index 100% rename from weighted_t_test_h2h_bot_vs_cp.csv rename to notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv diff --git a/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv similarity index 100% rename from weighted_t_test_h2h_bot_vs_pros.csv rename to notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv From 222d88338e2ec828e70294849d77cb3f49b6cbe6 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 07:49:31 -0600 Subject: [PATCH 03/26] Added pseudocode that for a refactor, and data models that can be used in creating test data --- .gitignore | 1 + AI_BENCHMARKING_ANALYSIS.ipynb | 7 +- ... 2024 FAB - questions list - FINAL BOT.csv | 0 .../scores}/bots_score_data_q3.csv | 0 .../scores}/bots_score_data_q4.csv | 0 .../scores}/luke_baseline_cp_scores.csv | 0 .../scores}/pros_score_data_q3.csv | 0 .../scores}/pros_score_data_q4.csv | 0 main.py | 0 pytest.ini | 13 ++ refactored_notebook/data_models.py | 52 ++++++ refactored_notebook/pseudocode_for_main.py | 162 ++++++++++++++++++ refactored_notebook/simulated_tournament.py | 64 +++++++ tests/generate_test_data.py | 3 + test_functions.py => tests/test_functions.py | 0 15 files changed, 296 insertions(+), 6 deletions(-) rename {scores => archived/scores}/Q4 2024 FAB - questions list - FINAL BOT.csv (100%) rename {scores => archived/scores}/bots_score_data_q3.csv (100%) rename {scores => archived/scores}/bots_score_data_q4.csv (100%) rename {scores => archived/scores}/luke_baseline_cp_scores.csv (100%) rename {scores => archived/scores}/pros_score_data_q3.csv (100%) rename {scores => archived/scores}/pros_score_data_q4.csv (100%) delete mode 100644 main.py create mode 100644 pytest.ini create mode 100644 refactored_notebook/data_models.py create mode 100644 refactored_notebook/pseudocode_for_main.py create mode 100644 refactored_notebook/simulated_tournament.py create mode 100644 tests/generate_test_data.py rename test_functions.py => tests/test_functions.py (100%) diff --git a/.gitignore b/.gitignore index 39111a6..14cb0d1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .venv/ .env __pycache__ +.personal/ \ No newline at end of file diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 0f510e4..482161d 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -2074,12 +2074,7 @@ "# Print WEIGHTED average for pro_median\n", "print(\"PRO MEDIAN\")\n", "pro_median_baseline = df_pro_baseline_long[df_pro_baseline_long['forecaster'] == 'pro_median']\n", - "print(f'Average baseline: {(pro_median_baseline[\"score\"] * pro_median_baseline[\"question_weight\"]).sum() / pro_median_baseline[\"question_weight\"].sum()}')\n", - "\n", - "# Same for pgodzinai in df_bot_scores (this differs from the bot team results later on because it's on ALL his questions)\n", - "print(\"pgodzinai MEDIAN\")\n", - "pgodzinai_baseline = df_bot_scores[df_bot_scores['forecaster'] == 'pgodzinai']\n", - "print(f'Average baseline: {(pgodzinai_baseline[\"score\"] * pgodzinai_baseline[\"question_weight\"]).sum() / pgodzinai_baseline[\"question_weight\"].sum()}')" + "print(f'Average baseline: {(pro_median_baseline[\"score\"] * pro_median_baseline[\"question_weight\"]).sum() / pro_median_baseline[\"question_weight\"].sum()}')" ] }, { diff --git a/scores/Q4 2024 FAB - questions list - FINAL BOT.csv b/archived/scores/Q4 2024 FAB - questions list - FINAL BOT.csv similarity index 100% rename from scores/Q4 2024 FAB - questions list - FINAL BOT.csv rename to archived/scores/Q4 2024 FAB - questions list - FINAL BOT.csv diff --git a/scores/bots_score_data_q3.csv b/archived/scores/bots_score_data_q3.csv similarity index 100% rename from scores/bots_score_data_q3.csv rename to archived/scores/bots_score_data_q3.csv diff --git a/scores/bots_score_data_q4.csv b/archived/scores/bots_score_data_q4.csv similarity index 100% rename from scores/bots_score_data_q4.csv rename to archived/scores/bots_score_data_q4.csv diff --git a/scores/luke_baseline_cp_scores.csv b/archived/scores/luke_baseline_cp_scores.csv similarity index 100% rename from scores/luke_baseline_cp_scores.csv rename to archived/scores/luke_baseline_cp_scores.csv diff --git a/scores/pros_score_data_q3.csv b/archived/scores/pros_score_data_q3.csv similarity index 100% rename from scores/pros_score_data_q3.csv rename to archived/scores/pros_score_data_q3.csv diff --git a/scores/pros_score_data_q4.csv b/archived/scores/pros_score_data_q4.csv similarity index 100% rename from scores/pros_score_data_q4.csv rename to archived/scores/pros_score_data_q4.csv diff --git a/main.py b/main.py deleted file mode 100644 index e69de29..0000000 diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..531adbb --- /dev/null +++ b/pytest.ini @@ -0,0 +1,13 @@ +[pytest] +python_files = test_*.py +pythonpath="./" +# log_level=DEBUG +# log_cli=true +# asyncio_mode = auto +# addopts=-nauto --durations=10 + +# log_file = logs/latest-pytest-outputs.log +# log_cli_format = %(threadName)s - %(asctime)s - %(levelname)s - %(name)s - %(funcName)s - %(message)s +# log_cli_date_format = %Y-%m-%d %H:%M:%S +# log_file_format = %(threadName)s - %(asctime)s - %(levelname)s - %(name)s - %(funcName)s - %(message)s +# log_file_date_format = %Y-%m-%d %H:%M:%S diff --git a/refactored_notebook/data_models.py b/refactored_notebook/data_models.py new file mode 100644 index 0000000..6452dae --- /dev/null +++ b/refactored_notebook/data_models.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +from datetime import datetime +from typing import Literal + +from pydantic import BaseModel +from enum import Enum + +class ResolutionType(Enum): + YES = "yes" + NO = "no" + ANNULLED = "annulled" + AMBIGUOUS = "ambiguous" + +class Forecast(BaseModel): + question: Question + user: User + prediction: list[float] # binary, MC, or numeric + predcition_for_correct_answer: float + prediction_time: datetime + comment: str | None = None + + def get_spot_baseline_score(self, resolution: ResolutionType) -> Score: + raise NotImplementedError("Not implemented") + + def get_spot_peer_score(self, resolution: ResolutionType, other_users_forecasts: list[Forecast]) -> Score: + # assert only one forecast per user + # assert that forecasts are in time range of question + raise NotImplementedError("Not implemented") + +class Score(BaseModel): + score: float + type: Literal["spot_peer", "spot_baseline"] + forecast: Forecast + users_used_in_scoring: list[User] | None# Empty if baseline + +class Question(BaseModel): + question_text: str + resolution: ResolutionType + weight: float + spot_scoring_time: datetime + +class User(BaseModel): + name: str + type: Literal["pro", "bot", "cp"] + is_aggregate: bool + aggregated_users: list[User] + + @property + def is_metac_bot(self) -> bool: + return "metac-" in self.name + diff --git a/refactored_notebook/pseudocode_for_main.py b/refactored_notebook/pseudocode_for_main.py new file mode 100644 index 0000000..3df7559 --- /dev/null +++ b/refactored_notebook/pseudocode_for_main.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +from typing import Literal, Callable +from datetime import datetime +from pydantic import BaseModel +from refactored_notebook.data_models import User, Forecast, Question, Score +from refactored_notebook.simulated_tournament import SimulatedTournament + +# TODO: Since I'm already creating spot score calculations, +# I might as well just input forecasts rather than scores into the tournament +# Though I will also need to check for spot scoring timing/ +# I should check that the scoring matches the original scoring though +# TODO: Rather than the seperate tournament creation for pros and bots, create a +# "Create tournament from tournament" function that takes in a tournament and +# a function that returns users. The function uses the tournament to make the new users +# a new tournament with full scores is created. + + +def set_up_data(path_to_data: str) -> dict[str, SimulatedTournament]: + + def load_initial_tournament(path_to_data: str) -> dict[str, SimulatedTournament]: + # Load the data + # Match questions between the tournaments + # Raise errors (or require manual matching) if there are differences in the questions + bot_tournament = None + pro_tournament = None + return { + "bot_tournament": bot_tournament, + "pro_tournament": pro_tournament, + } + + def caculate_spot_peer_score_for_user(all_forecasts_for_question: list[Forecast], user: User) -> Score: + # Assert forecasts are all for the same question + # Assert that there is only one forecast per user + # Filter for last forecast of each user that is before the spot scoring time (possibly do in previous step) + # Calculate the score for the user (weighted by question weight) + raise NotImplementedError("Not implemented") + + def caculate_spot_baseline_score(forecasts_for_user: list[Forecast]) -> Score: + # Find last forecast for user that is before the spot scoring time + # Calculate the score for the user (weighted by question weight) + raise NotImplementedError("Not implemented") + + def caculate_all_scores_for_forecasts(forecasts: list[Forecast]) -> list[Score]: + # Find questions + # For each question + # For each user + # Calculate spot peer score + # Calculate spot baseline score + raise NotImplementedError("Not implemented") + + def get_bot_team_user_with_size(original_tournament: SimulatedTournament, team_size: int) -> tuple[User, list[Forecast]]: + # Create a new user for the team + # Create forecasts for the team + # Calculate the scores for the user + raise NotImplementedError("Not implemented") + + def get_all_bot_teams_as_users(original_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + users_and_forecasts = [] + for team_size in range(1, len(original_tournament.users)): + users_and_forecasts.extend(get_bot_team_user_with_size(original_tournament, team_size)) + return users_and_forecasts + + def get_best_bot_team_user(bot_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + # Simulate bot team tournament + # Grab the user and forecasts for the best bot team + raise NotImplementedError("Not implemented") + + def get_pro_median_user(pro_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + # Create new user + # Create forecasts for the median + raise NotImplementedError("Not implemented") + + def get_pro_median_and_bot_median_users(bot_tournament: SimulatedTournament, pro_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + # Get the pro median user + # Get the bot median user + # Return the two users and their forecasts + raise NotImplementedError("Not implemented") + + def create_tournament( + original_tournament: SimulatedTournament, + new_users: list[tuple[User, list[Forecast]]], + remove_all_old_users: bool = False + ) -> SimulatedTournament: + # TODO: Also add parameter for filtering questions (or choosing new ones like only binaries) + # assert that the forecasts given each have a corresonding question and vise versa for each user + # Create scores for the new users and recaculate for old users + # Make a new tournament with all the new scores + raise NotImplementedError("Not implemented") + + original_tournament = load_initial_tournament(path_to_data) + original_bot_tournament = original_tournament["bot_tournament"] + original_pro_tournament = original_tournament["pro_tournament"] + bot_team_only_tournament = create_tournament( + original_bot_tournament, + get_all_bot_teams_as_users(original_bot_tournament), + remove_all_old_users=True + ) + pro_v_bot_head_to_head_tournament = create_tournament( + original_bot_tournament, + get_pro_median_and_bot_median_users(original_bot_tournament, original_pro_tournament), + remove_all_old_users=True + ) + + return { + "original_bot_tournament": original_bot_tournament, + "original_pro_tournament": original_pro_tournament, + "bot_team_only_tournament": bot_team_only_tournament, + "pro_v_bot_head_to_head_tournament": pro_v_bot_head_to_head_tournament, + } + + +def display_everything(score_sets: dict[str, SimulatedTournament]) -> None: + + forecasts_to_display = score_sets["original_bot_tournament"].forecasts + + def display_calibration_curve(forecasts: list[Forecast]) -> None: + # Each user has its own line and a 90% confidence interval + raise NotImplementedError("Not implemented") + + def display_discrimination_curve(forecasts: list[Forecast]) -> None: + # Each user has its own bar + raise NotImplementedError("Not implemented") + + def display_spot_peer_score_table(tournament: SimulatedTournament, users_to_display: list[User] | None = None) -> None: + # Filter for peer scores + # make sure all scores are peer scores + # make sure that all scores use the same users for calculation + + # Add these stats as a property of the simulated tournament scores + # Caculate average spot peer score + # Caculate sum of spot peer scores + # Find confidence interval + # Weighted question count (sum of weights) + # Show in table with a row for each user + # Filter by users_to_display if provided + raise NotImplementedError("Not implemented") + + def display_best_and_worse_scoring_questions(tournament: SimulatedTournament) -> None: + # Assert there are only 2 users + # Find the score differences between each question + # Show the top 5 and bottom 5 questions, forecasts for those questions, the resolution, and the score difference + raise NotImplementedError("Not implemented") + + def display_general_tournament_stats(bot_tournament: SimulatedTournament, pro_tournament: SimulatedTournament) -> None: + # Display num pro questions + # Display num bot questions + # Display num pro users + # Display num bot users + raise NotImplementedError("Not implemented") + + + metac_bots = [user for user in score_sets["original_bot_tournament"].users if user.is_metac_bot] + + display_calibration_curve(forecasts_to_display) + display_discrimination_curve(forecasts_to_display) + display_spot_peer_score_table(score_sets["original_bot_tournament"]) + display_spot_peer_score_table(score_sets["original_bot_tournament"], users_to_display=metac_bots) + display_spot_peer_score_table(score_sets["bot_team_only_tournament"]) + display_spot_peer_score_table(score_sets["pro_v_bot_head_to_head_tournament"]) + display_best_and_worse_scoring_questions(score_sets["pro_v_bot_head_to_head_tournament"]) + display_general_tournament_stats(score_sets["original_bot_tournament"], score_sets["original_pro_tournament"]) \ No newline at end of file diff --git a/refactored_notebook/simulated_tournament.py b/refactored_notebook/simulated_tournament.py new file mode 100644 index 0000000..eddbfc5 --- /dev/null +++ b/refactored_notebook/simulated_tournament.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +from pydantic import BaseModel +from refactored_notebook.data_models import User, Question, Forecast, Score + + +class SimulatedTournament(BaseModel): + forecasts: list[Forecast] + + @property + def users(self) -> set[User]: + users = set() + for forecast in self.forecasts: + users.add(forecast.user) + return users + + @property + def questions(self) -> set[Question]: + questions = set() + for forecast in self.forecasts: + questions.add(forecast.question) + return questions + + @property + def scores(self) -> list[Score]: + spot_peer_scores = [] + spot_baseline_scores = [] + for forecast in self.forecasts: + forecasts_from_other_users = [ + f + for f in self.forecasts + if f.question == forecast.question and f.user != forecast.user + ] + spot_peer_scores.append( + forecast.get_spot_peer_score( + forecast.question.resolution, forecasts_from_other_users + ) + ) + spot_baseline_scores.append( + forecast.get_spot_baseline_score(forecast.question.resolution) + ) + return spot_peer_scores + spot_baseline_scores + + def get_ranking_by_spot_peer_score_lower_t_bound( + self, confidence_level: float + ) -> list[tuple[User, float]]: + # Get all spot peer scores + # create a confidence interval for the spot peer score + # Sort by lower bound + raise NotImplementedError("Not implemented") + + def get_ranking_by_spot_peer_score_sum(self) -> list[tuple[User, float]]: + # Get all spot peer scores + # Sort by spot peer score + raise NotImplementedError("Not implemented") + + def get_ranking_by_spot_peer_score_bootstrap_lower_bound( + self, confidence_level: float + ) -> list[tuple[User, float]]: + # Get all spot peer scores + # bootstrap the spot peer scores + # create a confidence interval for the spot peer score + # Sort by lower bound + raise NotImplementedError("Not implemented") diff --git a/tests/generate_test_data.py b/tests/generate_test_data.py new file mode 100644 index 0000000..8ef6257 --- /dev/null +++ b/tests/generate_test_data.py @@ -0,0 +1,3 @@ +from refactored_notebook.data_models import User, Question, Forecast, Score + + diff --git a/test_functions.py b/tests/test_functions.py similarity index 100% rename from test_functions.py rename to tests/test_functions.py From 28edce0346c63f41208ee77649a7a64740dac2fd Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 08:29:43 -0600 Subject: [PATCH 04/26] Converted baseline scoring function to be independent of dataframes --- refactored_notebook/scoring.py | 87 ++++++++++++++++++++++++++++++++++ tests/generate_test_data.py | 2 + tests/test_scoring.py | 14 ++++++ 3 files changed, 103 insertions(+) create mode 100644 refactored_notebook/scoring.py create mode 100644 tests/test_scoring.py diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py new file mode 100644 index 0000000..4054f3c --- /dev/null +++ b/refactored_notebook/scoring.py @@ -0,0 +1,87 @@ +from typing import Literal + +import numpy as np + + +def calculate_spot_peer_score( + forecast_for_correct_answer: float, + other_users_forecasts_for_correct_answer: list[float], +) -> float: + raise NotImplementedError("Not implemented") + + +def calculate_spot_baseline_score( + forecast: list[float] | None, # binary: [p_yes, p_no], multiple choice: [p_a, p_b, p_c], numeric: [p_0, p_1, p_2, ...] + resolution: bool | str | float | None, # binary: bool, multiple choice: str, numeric: float + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, +) -> float: + """ + Question type can be infered from resolution type + """ + + if forecast is None or resolution is None: + raise NotImplementedError("Havent decided how to handle null forecasts and resolutions") + + if len(forecast) == 0: + raise ValueError("Forecast is empty") + + baseline_score = None + + if isinstance(resolution, bool): + if len(forecast) != 1 or len(forecast) != 2: + raise ValueError("Binary questions must have exactly one forecast and two options (for yes or 'yes and no')") + + forecast_val = float(forecast[0]) + baseline_prob = 0.5 + if resolution: + prob_for_resolution = forecast_val + else: + prob_for_resolution = 1 - forecast_val + elif isinstance(resolution, str): + if options is None: + raise ValueError("Options are required for multiple choice questions") + + if len(forecast) != len(options): + raise ValueError("Forecast and options have different lengths") + + pmf = [float(p) for p in forecast] + options = [str(opt) for opt in options] + resolution_idx = options.index(str(resolution)) + prob_for_resolution = pmf[resolution_idx] + baseline_prob = 1 / len(pmf) + elif isinstance(resolution, float): + if range_min is None or range_max is None: + raise ValueError("Range min and range max are required for numeric questions") + + cdf = [float(p) for p in forecast] + pmf = [cdf[0]] + [cdf[i] - cdf[i-1] for i in range(1, len(cdf))] # @Ben check: is this a correct conversion? + pmf.append(1 - cdf[-1]) + + resolution = float(resolution) + + bin_edges = np.linspace(range_min, range_max, 200) + resolution_idx = np.searchsorted(bin_edges, resolution, side='right') + + if resolution_idx >= len(pmf): + raise ValueError("Resolution is out of bounds") + + prob_for_resolution = pmf[resolution_idx] + baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin + + else: + raise ValueError("Unknown question type") + + if prob_for_resolution <= 0 or baseline_prob <= 0: + raise ValueError("Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue") + + baseline_score = np.log2(prob_for_resolution / baseline_prob) + + if isinstance(resolution, float): + baseline_score /= 2 # Numeric scores are halved + + weighted_score = baseline_score * question_weight + + return weighted_score diff --git a/tests/generate_test_data.py b/tests/generate_test_data.py index 8ef6257..5b68133 100644 --- a/tests/generate_test_data.py +++ b/tests/generate_test_data.py @@ -1,3 +1,5 @@ from refactored_notebook.data_models import User, Question, Forecast, Score +# TODO: Things to test: +# - peer rankings \ No newline at end of file diff --git a/tests/test_scoring.py b/tests/test_scoring.py new file mode 100644 index 0000000..ce27003 --- /dev/null +++ b/tests/test_scoring.py @@ -0,0 +1,14 @@ + +# TODO: +# For each of Multiple Choice, Binary, and Numeric questions +# - Test spot peer score +# - forecast this is further away than others gets worse scores (with 1-5 forecasts) +# - forecast this is closer to the resolution gets better scores (with 1-5 forecasts) +# - If everyone has the same forecast, the score is 0 +# - The sum (average?) of everyone's scores is 0 +# - The score for a weighted question is weighted by the question weight +# - Test spot baseline score +# - 0 with 50% forecast, ? for a uniform distribution, and 0 for uniform multiple choice questions +# - better score when closer to resolution, and worse when further away (for forecasts on both sides of 50% forecast) +# - The score for a weighted question is weighted by the question weight +# - Run a test of some forecasts from the site, and make sure the score generated matches the score the site gives From 956097a62431616f4cd66c3448a87fa07b44bac1 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 12:56:08 -0600 Subject: [PATCH 05/26] Added baseline scoring tests --- AI_BENCHMARKING_ANALYSIS.ipynb | 2 +- functions.py | 519 +++++++++++---------- refactored_notebook/data_models.py | 12 +- refactored_notebook/pseudocode_for_main.py | 3 +- refactored_notebook/scoring.py | 71 ++- tests/generate_test_data.py | 5 - tests/test_end_to_end.py | 18 + tests/test_scoring.py | 186 ++++++++ 8 files changed, 546 insertions(+), 270 deletions(-) delete mode 100644 tests/generate_test_data.py create mode 100644 tests/test_end_to_end.py diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 482161d..619d445 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -3378,7 +3378,7 @@ "outputs": [], "source": [ "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", - "# @Ben: Check -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" + "# @Check: -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" ] }, { diff --git a/functions.py b/functions.py index d0bf79f..42c14de 100644 --- a/functions.py +++ b/functions.py @@ -1,25 +1,27 @@ -import pandas as pd -import numpy as np +import ast +import math +import random +import re +from datetime import datetime + import matplotlib.pyplot as plt -from scipy.stats import norm +import numpy as np +import pandas as pd from scipy import stats from scipy.optimize import minimize_scalar -from scipy.stats import binom -import re -from datetime import datetime -import random -import math -import ast +from scipy.stats import binom, norm + +from refactored_notebook.scoring import calculate_spot_baseline_score + def extract_forecast(df): # Extract the forecast from whichever column it's in - df['forecast'] = df['probability_yes'].combine_first( - df['probability_yes_per_category'].combine_first( - df['continuous_cdf'] - ) + df["forecast"] = df["probability_yes"].combine_first( + df["probability_yes_per_category"].combine_first(df["continuous_cdf"]) ) return df + def process_forecasts(df): """ Process a dataframe of forecasts by: @@ -39,23 +41,24 @@ def process_forecasts(df): Processed DataFrame with last forecasts """ # Extract the forecast value - df['forecast'] = df['probability_yes'].combine_first( - df['probability_yes_per_category'].combine_first( - df['continuous_cdf'] - ) + df["forecast"] = df["probability_yes"].combine_first( + df["probability_yes_per_category"].combine_first(df["continuous_cdf"]) ) # Sort by created_at to ensure chronological order - df = df.sort_values(by='created_at') + df = df.sort_values(by="created_at") # Take the last forecast for each (forecaster, question_id) pair - df = df.groupby(['question_id', 'forecaster']).last().reset_index() + df = df.groupby(["question_id", "forecaster"]).last().reset_index() # Drop the original forecast columns as they're now redundant - df = df.drop(['probability_yes', 'probability_yes_per_category', 'continuous_cdf'], axis=1) + df = df.drop( + ["probability_yes", "probability_yes_per_category", "continuous_cdf"], axis=1 + ) return df + def add_is_median(df): """ Marks exactly one row per question_id as the median. @@ -69,22 +72,23 @@ def add_is_median(df): pandas.DataFrame: DataFrame with an additional 'is_median' column. """ # Initialize median column - df['is_median'] = False + df["is_median"] = False # For each question_id - for qid in df['question_id'].unique(): + for qid in df["question_id"].unique(): # Get just the rows for this question - question_mask = df['question_id'] == qid + question_mask = df["question_id"] == qid question_df = df[question_mask] # Get the median value index (middle position after sorting) - median_idx = question_df['forecast'].sort_values().index[len(question_df)//2] + median_idx = question_df["forecast"].sort_values().index[len(question_df) // 2] # Mark that row - df.loc[median_idx, 'is_median'] = True + df.loc[median_idx, "is_median"] = True return df + def add_median_rows(df, prefix): """ For each row where is_median=True, creates a duplicate row with forecaster='median'. @@ -97,16 +101,21 @@ def add_median_rows(df, prefix): pandas.DataFrame: Original DataFrame plus duplicate rows for medians. """ # Get the median rows - median_rows = df[df['is_median']].copy() + median_rows = df[df["is_median"]].copy() # Change forecaster to 'median' - median_rows['forecaster'] = f'{prefix}_median' + median_rows["forecaster"] = f"{prefix}_median" # Combine original and new median rows - whole = pd.concat([df, median_rows], ignore_index=True).sort_values('question_id').drop_duplicates(['question_id', 'forecaster']) + whole = ( + pd.concat([df, median_rows], ignore_index=True) + .sort_values("question_id") + .drop_duplicates(["question_id", "forecaster"]) + ) return whole + def calculate_weighted_stats(df): """ Calculates weighted statistics (mean, sum, standard error, confidence intervals) for each forecaster. @@ -120,12 +129,12 @@ def calculate_weighted_stats(df): results = [] # For each forecaster - for forecaster in df['forecaster'].unique(): - forecaster_data = df[df['forecaster'] == forecaster] + for forecaster in df["forecaster"].unique(): + forecaster_data = df[df["forecaster"] == forecaster] # Get scores and weights - scores = forecaster_data['score'] - weights = forecaster_data['question_weight'] + scores = forecaster_data["score"] + weights = forecaster_data["question_weight"] # Calculate weighted mean weighted_mean = np.average(scores, weights=weights) @@ -133,26 +142,28 @@ def calculate_weighted_stats(df): # Calculate weighted standard error # Using weighted variance formula - weighted_var = np.average((scores - weighted_mean)**2, weights=weights) + weighted_var = np.average((scores - weighted_mean) ** 2, weights=weights) n = len(scores) weighted_se = np.sqrt(weighted_var / n) # Calculate t-statistic for 95% confidence interval - t_value = stats.t.ppf(0.975, n-1) + t_value = stats.t.ppf(0.975, n - 1) ci_lower = weighted_mean - (t_value * weighted_se) - results.append({ - 'forecaster': forecaster, - 'weighted_mean': weighted_mean, - 'weighted_sum': weighted_sum, - 'n_questions': n, - 'ci_lower': ci_lower, - 'weighted_se': weighted_se - }) + results.append( + { + "forecaster": forecaster, + "weighted_mean": weighted_mean, + "weighted_sum": weighted_sum, + "n_questions": n, + "ci_lower": ci_lower, + "weighted_se": weighted_se, + } + ) # Convert to dataframe and sort by lower bound results_df = pd.DataFrame(results) - return results_df.sort_values('weighted_sum', ascending=False) + return results_df.sort_values("weighted_sum", ascending=False) def make_wide(df_bot_peer, df_pro_bot_resolved_questions): @@ -166,33 +177,38 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): Returns: pandas.DataFrame: Wide-format DataFrame with question weights merged. """ - df_pivoted = df_bot_peer.pivot(index='bot_question_id', columns='forecaster', values='score') + df_pivoted = df_bot_peer.pivot( + index="bot_question_id", columns="forecaster", values="score" + ) df_pivoted = df_pivoted.reset_index() df_pivoted = df_pivoted.reindex(sorted(df_pivoted.columns), axis=1) # Step 4: Move 'question_id' to be the first column cols = df_pivoted.columns.tolist() - cols = ['bot_question_id'] + [col for col in cols if col != 'bot_question_id'] + cols = ["bot_question_id"] + [col for col in cols if col != "bot_question_id"] df_pivoted = df_pivoted[cols] all_columns = df_pivoted.columns.tolist() ## Remove 'question_id' and 'bot_median' from the list if they exist - all_columns = [col for col in all_columns if col not in ['bot_question_id']] - new_column_order = ['bot_question_id'] + all_columns + all_columns = [col for col in all_columns if col not in ["bot_question_id"]] + new_column_order = ["bot_question_id"] + all_columns df_pivoted = df_pivoted[new_column_order] df_bot_peer_wide = df_pivoted - df_bot_peer_wide['bot_question_id'] = pd.to_numeric(df_bot_peer_wide['bot_question_id'], errors='coerce') + df_bot_peer_wide["bot_question_id"] = pd.to_numeric( + df_bot_peer_wide["bot_question_id"], errors="coerce" + ) # Join with df_pro_bot_resolved_questions to get question weights df_bot_peer_wide = pd.merge( df_bot_peer_wide, - df_pro_bot_resolved_questions[['bot_question_id', 'question_weight']], - on='bot_question_id', - how='left' + df_pro_bot_resolved_questions[["bot_question_id", "question_weight"]], + on="bot_question_id", + how="left", ) return df_bot_peer_wide + """ Options from https://stats.stackexchange.com/questions/47325/bias-correction-in-weighted-variance I didn't think (B) beared trying, but could be wrong. - MGH @@ -200,6 +216,7 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): the bias in the sample variance. """ + def calc_weighted_std_dev(df3, bot, weighted_score, weighted_count, weight_col): """ Calculates the weighted standard deviation using Molly's method - (A) from stack exchange post. @@ -215,7 +232,11 @@ def calc_weighted_std_dev(df3, bot, weighted_score, weighted_count, weight_col): float: Weighted standard deviation. """ weighted_average = weighted_score / weighted_count - return np.sqrt(((df3[bot] - weighted_average) ** 2 * df3[weight_col]).sum() / (weighted_count - 1)) + return np.sqrt( + ((df3[bot] - weighted_average) ** 2 * df3[weight_col]).sum() + / (weighted_count - 1) + ) + def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col): """ @@ -233,10 +254,14 @@ def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) """ weighted_average = weighted_score / weighted_count return np.sqrt( - (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() / - (df3[weight_col].sum() * (1 - (df3[weight_col] ** 2).sum() / (df3[weight_col].sum() ** 2))) + (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() + / ( + df3[weight_col].sum() + * (1 - (df3[weight_col] ** 2).sum() / (df3[weight_col].sum() ** 2)) + ) ) + def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): """ Performs weighted bootstrap analysis to calculate confidence intervals and medians. @@ -250,10 +275,11 @@ def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): Returns: pandas.DataFrame: DataFrame with confidence intervals and medians for each bot. """ + # Function to perform a single bootstrap iteration def single_bootstrap(df): # Weighted sampling of questions - sampled_df = df.sample(n=NUM, weights='question_weight', replace=True) + sampled_df = df.sample(n=NUM, weights="question_weight", replace=True) # Calculate total weighted score for each bot return sampled_df[bots].sum() @@ -271,32 +297,35 @@ def single_bootstrap(df): median = results_df.median() # Create output DataFrame - output_df = pd.DataFrame({ - '2.5% CI': ci_low, - '10% CI': ci_10, - 'Median': median, - '90% CI': ci_90, - '97.5% CI': ci_high - }) + output_df = pd.DataFrame( + { + "2.5% CI": ci_low, + "10% CI": ci_10, + "Median": median, + "90% CI": ci_90, + "97.5% CI": ci_high, + } + ) # Sort by median descending - output_df = output_df.sort_values('Median', ascending=False) + output_df = output_df.sort_values("Median", ascending=False) return output_df + def get_median_forecast_multiple_choice(row, forecasts): """ Given a row (with 'options' and 'resolution') and a list of forecasts (each a list of floats), returns the median probability assigned to the resolution option. """ - options = row['options'] - resolution = row['resolution'] + options = row["options"] + resolution = row["resolution"] try: resolution_idx = options.index(resolution) - #print(f"Resolution '{resolution}' found at index {resolution_idx} in options {options}") + # print(f"Resolution '{resolution}' found at index {resolution_idx} in options {options}") except ValueError: - #print(f"Resolution '{resolution}' not found in options {options} — returning np.nan") + # print(f"Resolution '{resolution}' not found in options {options} — returning np.nan") return np.nan # Resolution not found in options probs = [] @@ -309,14 +338,15 @@ def get_median_forecast_multiple_choice(row, forecasts): continue if not probs: - #print(f"NO PROBS collected for multiple-choice question {row.get('bot_question_id')} — returning np.nan") + # print(f"NO PROBS collected for multiple-choice question {row.get('bot_question_id')} — returning np.nan") return np.nan return np.nanmedian(probs) + def get_median_forecast(row, bots): """ - @BEN: Check + @Check: Calculates the median forecast for a given set of bots, handling different question types properly. @@ -327,7 +357,7 @@ def get_median_forecast(row, bots): Returns: pandas.Series: Median forecast for each row. """ - q_type = row['type'] + q_type = row["type"] forecasts = [] for bot in bots: @@ -345,7 +375,7 @@ def get_median_forecast(row, bots): if not forecasts: return np.nan - if q_type == 'numeric': + if q_type == "numeric": forecasts = [f for f in forecasts if isinstance(f, list)] if not forecasts: @@ -356,36 +386,42 @@ def get_median_forecast(row, bots): return mean_cdf - elif q_type == 'binary': + elif q_type == "binary": probs = [] for f in forecasts: try: val = float(f) probs.append(val) except (ValueError, TypeError): - print(f' Invalid forecast: {f} — error {e}') + print(f" Invalid forecast: {f} — error {e}") continue if not probs: - print(f" >>> NO PROBS collected for binary question {row.get('bot_question_id')} — returning np.nan") + print( + f" >>> NO PROBS collected for binary question {row.get('bot_question_id')} — returning np.nan" + ) return np.nan print(f" >>> Collected {len(probs)} forecasts: {probs}") return np.nanmedian(probs) - elif q_type == 'multiple_choice': + elif q_type == "multiple_choice": return get_median_forecast_multiple_choice(row, forecasts) else: raise ValueError(f"Unknown question type: {q_type}") -def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.DataFrame: +def calculate_all_peer_scores( + df_bot_team_forecasts: pd.DataFrame, teams: list[str] +) -> pd.DataFrame: """ Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value Changes the df so that the forecast is now the score for that question """ - raise NotImplementedError("I accidentally implemented baseline scoring here unfortunately") + raise NotImplementedError( + "I accidentally implemented baseline scoring here unfortunately" + ) score_df = df_bot_team_forecasts.copy() team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) for team in teams: @@ -396,7 +432,7 @@ def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[s def calculate_weighted_scores(df_bot_team_forecasts, teams): """ - @BEN: check + @Check: Calculates weighted scores for each team based on their forecasts and question weights. @@ -410,76 +446,30 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - q_type = row['type'] - resolution = row['resolution'] - options = row.get('options') - range_min = row.get('range_min') - range_max = row.get('range_max') - question_weight = row['question_weight'] + q_type = row["type"] + resolution = row["resolution"] + options = row.get("options") + range_min = row.get("range_min") + range_max = row.get("range_max") + question_weight = row["question_weight"] for team in teams: forecast = row[team] - if forecast is None or (isinstance(forecast, float) and np.isnan(forecast)): - continue - - baseline_score = None - try: - if q_type == 'binary': - forecast_val = float(forecast) - baseline_prob = 0.5 - if resolution == 'yes': - p_team = forecast_val - elif resolution == 'no': - p_team = 1 - forecast_val - else: - continue # Skip if invalid resolution - - elif q_type == 'multiple_choice': - pmf = [float(p) for p in forecast] - options = [str(opt) for opt in options] - resolution_idx = options.index(str(resolution)) - p_team = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) - - elif q_type == 'numeric': - cdf = [float(p) for p in forecast] - pmf = [cdf[0]] + [cdf[i] - cdf[i-1] for i in range(1, len(cdf))] - pmf.append(1 - cdf[-1]) - - resolution = float(resolution) - if range_min is None or range_max is None: - continue - bin_edges = np.linspace(range_min, range_max, 200) - resolution_idx = np.searchsorted(bin_edges, resolution, side='right') - - if resolution_idx >= len(pmf): - continue # Skip if out of bounds - - p_team = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin - - else: - continue # Unknown question type - - if p_team <= 0 or baseline_prob <= 0: - continue # Avoid log(0) issues - - baseline_score = np.log2(p_team / baseline_prob) - - if q_type == 'numeric': - baseline_score /= 2 # Numeric scores are halved - - weighted_score = baseline_score * question_weight + weighted_score = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) team_scores[team] += weighted_score except (ValueError, TypeError, IndexError): + # @Ben: Does skipping introduce any problems? continue # Be robust to bad/missing data return pd.Series(team_scores) -def calculate_t_test(df_input, bot_list, weight_col='question_weight'): + +def calculate_t_test(df_input, bot_list, weight_col="question_weight"): """ Calculates weighted statistics, including t-test and p-values, for multiple bots. @@ -521,7 +511,9 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): if weighted_count > 2: # Only calculate if we have enough data weighted_average = weighted_score / weighted_count - weighted_std_dev = calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) + weighted_std_dev = calc_weighted_std_dev2( + df3, bot, weighted_score, weighted_count, weight_col + ) std_error = weighted_std_dev / np.sqrt(weighted_count) t_statistic = (weighted_average - 0) / std_error @@ -532,11 +524,13 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): lower_bound = weighted_average - t_crit * std_error # Calculate CDF and p-value - cdf = stats.t.cdf(t_statistic, df=weighted_count-1) + cdf = stats.t.cdf(t_statistic, df=weighted_count - 1) p_value = 2 * min(cdf, 1 - cdf) # Two-tailed p-value else: # Not enough data - weighted_average = weighted_score / weighted_count if weighted_count > 0 else np.nan + weighted_average = ( + weighted_score / weighted_count if weighted_count > 0 else np.nan + ) weighted_std_dev = np.nan std_error = np.nan t_statistic = np.nan @@ -547,50 +541,48 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): p_value = np.nan # Store results - df_W_leaderboard.loc[bot, 'W_score'] = weighted_score - df_W_leaderboard.loc[bot, 'W_count'] = weighted_count - df_W_leaderboard.loc[bot, 'W_ave'] = weighted_average - df_W_leaderboard.loc[bot, 'W_stdev'] = weighted_std_dev - df_W_leaderboard.loc[bot, 'std_err'] = std_error - df_W_leaderboard.loc[bot, 't_stat'] = t_statistic - df_W_leaderboard.loc[bot, 't_crit'] = t_crit - df_W_leaderboard.loc[bot, 'upper_bound'] = upper_bound - df_W_leaderboard.loc[bot, 'lower_bound'] = lower_bound - df_W_leaderboard.loc[bot, 'cdf'] = cdf - df_W_leaderboard.loc[bot, 'p_value'] = p_value + df_W_leaderboard.loc[bot, "W_score"] = weighted_score + df_W_leaderboard.loc[bot, "W_count"] = weighted_count + df_W_leaderboard.loc[bot, "W_ave"] = weighted_average + df_W_leaderboard.loc[bot, "W_stdev"] = weighted_std_dev + df_W_leaderboard.loc[bot, "std_err"] = std_error + df_W_leaderboard.loc[bot, "t_stat"] = t_statistic + df_W_leaderboard.loc[bot, "t_crit"] = t_crit + df_W_leaderboard.loc[bot, "upper_bound"] = upper_bound + df_W_leaderboard.loc[bot, "lower_bound"] = lower_bound + df_W_leaderboard.loc[bot, "cdf"] = cdf + df_W_leaderboard.loc[bot, "p_value"] = p_value # Format and round the results - df_W_leaderboard['W_score'] = df_W_leaderboard['W_score'].round(1) + df_W_leaderboard["W_score"] = df_W_leaderboard["W_score"].round(1) # Store numerical p-values temporarily for sorting - df_W_leaderboard['_p_value_sort'] = df_W_leaderboard['p_value'] + df_W_leaderboard["_p_value_sort"] = df_W_leaderboard["p_value"] # Format p-values as percentages - df_W_leaderboard['p_value'] = df_W_leaderboard['p_value'].apply( + df_W_leaderboard["p_value"] = df_W_leaderboard["p_value"].apply( lambda x: f"{x:.6f}" if pd.notnull(x) else "NA" ) # Round other columns - df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']] = \ - df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']].round(1) + df_W_leaderboard[["W_ave", "W_count", "lower_bound", "upper_bound"]] = ( + df_W_leaderboard[["W_ave", "W_count", "lower_bound", "upper_bound"]].round(1) + ) # Sort by the numerical p-values df_W_leaderboard = df_W_leaderboard.sort_values( - by='W_score', - ascending=False, - na_position='last' + by="W_score", ascending=False, na_position="last" ) # Drop the temporary sorting column - df_W_leaderboard = df_W_leaderboard.drop('_p_value_sort', axis=1) + df_W_leaderboard = df_W_leaderboard.drop("_p_value_sort", axis=1) return df_W_leaderboard - def calculate_head_to_head(row, a, b): """ - @BEN: Check... + @Check:... Calculates the head-to-head score for two forecasters. Positive if 'a' did better than 'b', negative if 'b' did better than 'a'. @@ -603,42 +595,50 @@ def calculate_head_to_head(row, a, b): Returns: float: Head-to-head score. """ - q_type = row['type'] - resolution = row['resolution'] - options = row['options'] - range_min = row.get('range_min') - range_max = row.get('range_max') + q_type = row["type"] + resolution = row["resolution"] + options = row["options"] + range_min = row.get("range_min") + range_max = row.get("range_max") forecast_a = row[a] forecast_b = row[b] - if q_type == 'binary': - if (resolution == 'yes') or (resolution == 1): + if q_type == "binary": + if (resolution == "yes") or (resolution == 1): return 100 * np.log(forecast_a / forecast_b) - elif (resolution == 'no') or (resolution == 0): + elif (resolution == "no") or (resolution == 0): return 100 * np.log((1 - forecast_a) / (1 - forecast_b)) else: return np.nan - elif q_type == 'multiple_choice': + elif q_type == "multiple_choice": # Parse forecast_a if it's a string if isinstance(forecast_a, str): forecast_a = ast.literal_eval(forecast_a) - options = ast.literal_eval(row['options']) if isinstance(row['options'], str) else row['options'] - resolution_idx = options.index(str(row['resolution'])) + options = ( + ast.literal_eval(row["options"]) + if isinstance(row["options"], str) + else row["options"] + ) + resolution_idx = options.index(str(row["resolution"])) forecast_a = forecast_a[resolution_idx] # Parse forecast_b if it's a string if isinstance(forecast_b, str): forecast_b = ast.literal_eval(forecast_b) - options = ast.literal_eval(row['options']) if isinstance(row['options'], str) else row['options'] - resolution_idx = options.index(str(row['resolution'])) + options = ( + ast.literal_eval(row["options"]) + if isinstance(row["options"], str) + else row["options"] + ) + resolution_idx = options.index(str(row["resolution"])) forecast_b = forecast_b[resolution_idx] # Now both are floats with the prob assigned to the correct bin return 100 * np.log(forecast_a / forecast_b) - elif q_type == 'numeric': + elif q_type == "numeric": # Ensure both forecasts are Python lists if isinstance(forecast_a, str): forecast_a = ast.literal_eval(forecast_a) @@ -656,10 +656,10 @@ def calculate_head_to_head(row, a, b): cdf_a = forecast_a cdf_b = forecast_b - pmf_a = [cdf_a[0]] + [cdf_a[i] - cdf_a[i-1] for i in range(1, len(cdf_a))] + pmf_a = [cdf_a[0]] + [cdf_a[i] - cdf_a[i - 1] for i in range(1, len(cdf_a))] pmf_a.append(1 - cdf_a[-1]) - pmf_b = [cdf_b[0]] + [cdf_b[i] - cdf_b[i-1] for i in range(1, len(cdf_b))] + pmf_b = [cdf_b[0]] + [cdf_b[i] - cdf_b[i - 1] for i in range(1, len(cdf_b))] pmf_b.append(1 - cdf_b[-1]) bin_edges = np.linspace(range_min, range_max, 200) @@ -671,7 +671,9 @@ def calculate_head_to_head(row, a, b): else: try: resolution_val = float(resolution) - resolution_idx = np.searchsorted(bin_edges, resolution_val, side='right') + resolution_idx = np.searchsorted( + bin_edges, resolution_val, side="right" + ) except ValueError: print(f"Bad resolution value: {resolution}") return np.nan @@ -685,7 +687,10 @@ def calculate_head_to_head(row, a, b): return 100 * np.log(p_a / p_b) -def plot_head_to_head_distribution(df_forecasts, col='head_to_head', vs=('Bot Team', 'Pros')): + +def plot_head_to_head_distribution( + df_forecasts, col="head_to_head", vs=("Bot Team", "Pros") +): """ Plots the distribution of head-to-head scores and fits a Gaussian curve. @@ -706,23 +711,23 @@ def plot_head_to_head_distribution(df_forecasts, col='head_to_head', vs=('Bot Te # Create the histogram plt.figure(figsize=(10, 6)) - n, bins, patches = plt.hist(data, bins=30, density=True, alpha=0.7, color='skyblue') + n, bins, patches = plt.hist(data, bins=30, density=True, alpha=0.7, color="skyblue") # Generate points for the fitted Gaussian curve x = np.linspace(min(data), max(data), 100) y = norm.pdf(x, mean, std) # Plot the fitted Gaussian curve - plt.plot(x, y, 'r-', linewidth=2, label='Fitted Gaussian') + plt.plot(x, y, "r-", linewidth=2, label="Fitted Gaussian") # Customize the plot - plt.title(f'{vs[0]} Head-to-Head Scores vs {vs[1]}') - plt.xlabel('Head-to-Head Score') - plt.ylabel('Density') + plt.title(f"{vs[0]} Head-to-Head Scores vs {vs[1]}") + plt.xlabel("Head-to-Head Score") + plt.ylabel("Density") plt.legend() # Add text annotation for the mean - #plt.text(0.95, 0.95, f'Mean: {mean:.2f}', transform=plt.gca().transAxes, verticalalignment='top', horizontalalignment='right') + # plt.text(0.95, 0.95, f'Mean: {mean:.2f}', transform=plt.gca().transAxes, verticalalignment='top', horizontalalignment='right') # Display the plot plt.show() @@ -730,6 +735,7 @@ def plot_head_to_head_distribution(df_forecasts, col='head_to_head', vs=('Bot Te # Print the average print(f"The average of 'head_to_head' is: {mean:.2f}") + def calculate_calibration_curve(forecasts, resolutions, weights): """ Calculates a calibration curve for forecasts. @@ -787,6 +793,7 @@ def calculate_calibration_curve(forecasts, resolutions, weights): "calibration_curve": calibration_curve, } + def plot_calibration_curve(df, column_name, label, color): """ Plots a calibration curve with confidence intervals. @@ -801,29 +808,36 @@ def plot_calibration_curve(df, column_name, label, color): None """ # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) - df = df[df['resolution'].isin(['yes', 'no', 1, 0])] + df = df[df["resolution"].isin(["yes", "no", 1, 0])] - y_true = df['resolution'] + y_true = df["resolution"] y_pred = df[column_name] weights = [1.0 for _ in y_true] - calibration_curve = calculate_calibration_curve(y_pred, y_true, weights)['calibration_curve'] - prob_true = [item['average_resolution'] for item in calibration_curve] - bin_center = [(item['bin_lower'] + item['bin_upper']) / 2 for item in calibration_curve] - ci_lower = [item['lower_confidence_interval'] for item in calibration_curve] - ci_upper = [item['upper_confidence_interval'] for item in calibration_curve] - - plt.plot(bin_center, prob_true, marker='o', linewidth=2, label=label, color=color) + calibration_curve = calculate_calibration_curve(y_pred, y_true, weights)[ + "calibration_curve" + ] + prob_true = [item["average_resolution"] for item in calibration_curve] + bin_center = [ + (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve + ] + ci_lower = [item["lower_confidence_interval"] for item in calibration_curve] + ci_upper = [item["upper_confidence_interval"] for item in calibration_curve] + + plt.plot(bin_center, prob_true, marker="o", linewidth=2, label=label, color=color) plt.fill_between(bin_center, ci_lower, ci_upper, alpha=0.2, color=color) for x, y in zip(bin_center, prob_true): if x is None or y is None: continue - plt.annotate(f'({x:.2f}, {y:.2f})', - (x, y), - textcoords="offset points", - xytext=(0,10), - ha='center', - color=color, - fontsize=8) + plt.annotate( + f"({x:.2f}, {y:.2f})", + (x, y), + textcoords="offset points", + xytext=(0, 10), + ha="center", + color=color, + fontsize=8, + ) + def calculate_confidence(predictions, outcomes): """ @@ -840,9 +854,11 @@ def calculate_confidence(predictions, outcomes): bins = pd.cut(predictions, bins=10) # Calculate mean prediction and actual outcome for each bin - grouped = pd.DataFrame({'prediction': predictions, 'outcome': outcomes}).groupby(bins) - mean_prediction = grouped['prediction'].mean() - mean_outcome = grouped['outcome'].mean() + grouped = pd.DataFrame({"prediction": predictions, "outcome": outcomes}).groupby( + bins + ) + mean_prediction = grouped["prediction"].mean() + mean_outcome = grouped["outcome"].mean() # Calculate the difference between mean prediction and mean outcome confidence_diff = mean_prediction - mean_outcome @@ -850,6 +866,7 @@ def calculate_confidence(predictions, outcomes): # Return the average difference (excluding NaN values) return np.nanmean(confidence_diff) + def interpret_confidence(score): """ Interprets the confidence score. @@ -867,6 +884,7 @@ def interpret_confidence(score): else: return "Perfectly calibrated" + def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): """ Creates histograms to compare discrimination between bot and pro teams. @@ -887,12 +905,15 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): bins = np.linspace(0, 1, 6) # Top plot: Questions that resolved 1 - ax1.hist([df[df[resolution_col] == 1][bot_col], - df[df[resolution_col] == 1][pro_col]], - bins=bins, label=['Bot Team', 'Pro Team'], alpha=0.7) - ax1.set_title('Questions that Resolved \'Yes\'') - ax1.set_xlabel('Assigned Probability') - ax1.set_ylabel('Frequency') + ax1.hist( + [df[df[resolution_col] == 1][bot_col], df[df[resolution_col] == 1][pro_col]], + bins=bins, + label=["Bot Team", "Pro Team"], + alpha=0.7, + ) + ax1.set_title("Questions that Resolved 'Yes'") + ax1.set_xlabel("Assigned Probability") + ax1.set_ylabel("Frequency") ax1.legend() # Set integer y-ticks for top plot @@ -900,12 +921,15 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): ax1.set_yticks(range(0, ymax1 + 1, 2)) # Bottom plot: Questions that resolved 0 - ax2.hist([df[df[resolution_col] == 0][bot_col], - df[df[resolution_col] == 0][pro_col]], - bins=bins, label=['Bot Team', 'Pro Team'], alpha=0.7) - ax2.set_title('Questions that Resolved \'No\'') - ax2.set_xlabel('Assigned Probability') - ax2.set_ylabel('Frequency') + ax2.hist( + [df[df[resolution_col] == 0][bot_col], df[df[resolution_col] == 0][pro_col]], + bins=bins, + label=["Bot Team", "Pro Team"], + alpha=0.7, + ) + ax2.set_title("Questions that Resolved 'No'") + ax2.set_xlabel("Assigned Probability") + ax2.set_ylabel("Frequency") ax2.legend() # Set integer y-ticks for bottom plot @@ -916,6 +940,7 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): plt.tight_layout() plt.show() + def get_weighted_score(df_forecasts): """ Calculates the weighted total score for forecasts. @@ -927,13 +952,15 @@ def get_weighted_score(df_forecasts): float: Weighted total score. """ # Calculate the weighted score for each row - df_forecasts['weighted_score'] = df_forecasts['head_to_head'] * df_forecasts['question_weight'] + df_forecasts["weighted_score"] = ( + df_forecasts["head_to_head"] * df_forecasts["question_weight"] + ) # Calculate the total weighted score - total_weighted_score = df_forecasts['weighted_score'].sum() + total_weighted_score = df_forecasts["weighted_score"].sum() # Calculate the sum of weights - total_weight = df_forecasts['question_weight'].sum() + total_weight = df_forecasts["question_weight"].sum() # Calculate the weighted total score weighted_total_score = total_weighted_score / total_weight @@ -942,8 +969,10 @@ def get_weighted_score(df_forecasts): return weighted_total_score + # ====== CODE FROM LUKE, REFACTORED BY CHATGPT ======= + def string_location_to_scaled_location(string_location, question_row): """ Converts a string location to a scaled location based on question type. @@ -978,6 +1007,7 @@ def string_location_to_scaled_location(string_location, question_row): # question.type == "numeric" return float(string_location) + def scaled_location_to_unscaled_location(scaled_location, question_row): """ Converts a scaled location to an unscaled location based on question type. @@ -1001,12 +1031,16 @@ def scaled_location_to_unscaled_location(scaled_location, question_row): if zero_point is not None: deriv_ratio = (range_max - zero_point) / max((range_min - zero_point), 1e-7) return ( - np.log((scaled_location - range_min) * (deriv_ratio - 1) + (range_max - range_min)) + np.log( + (scaled_location - range_min) * (deriv_ratio - 1) + + (range_max - range_min) + ) - np.log(range_max - range_min) ) / np.log(deriv_ratio) return (scaled_location - range_min) / (range_max - range_min) + def nominal_location_to_cdf_location(nominal_location, question_data): """ Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to @@ -1042,6 +1076,7 @@ def nominal_location_to_cdf_location(nominal_location, question_data): unscaled_location = (scaled_location - range_min) / (range_max - range_min) return unscaled_location + def get_cdf_at(cdf, unscaled_location): """ Retrieves the CDF value at a given unscaled location. @@ -1061,7 +1096,7 @@ def get_cdf_at(cdf, unscaled_location): if index_scaled_location.is_integer(): return cdf[int(index_scaled_location)] # linear interpolation step - left_index = int(index_scaled_location) # This is the floor, which is what we want + left_index = int(index_scaled_location) # This is the floor, which is what we want right_index = left_index + 1 left_value = cdf[left_index] right_value = cdf[right_index] @@ -1069,8 +1104,10 @@ def get_cdf_at(cdf, unscaled_location): index_scaled_location - left_index ) + # ======== END OF LUKE'S CODE ========== + def cdf_between(row, cdf, lower_bound, upper_bound): """ Calculates the probability between two bounds using the CDF. @@ -1086,7 +1123,8 @@ def cdf_between(row, cdf, lower_bound, upper_bound): """ a = get_cdf_at(cdf, nominal_location_to_cdf_location(lower_bound, row)) b = get_cdf_at(cdf, nominal_location_to_cdf_location(upper_bound, row)) - return (b - a) + return b - a + def extract_year(title): """ @@ -1098,9 +1136,10 @@ def extract_year(title): Returns: int or None: Extracted year or None if not found. """ - match = re.search(r'\b(19|20)\d{2}\b', title) + match = re.search(r"\b(19|20)\d{2}\b", title) return int(match.group(0)) if match else None + def extract_month(title): """ Extracts the month from a title string. @@ -1111,9 +1150,13 @@ def extract_month(title): Returns: str or None: Extracted month or None if not found. """ - match = re.search(r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\b', title) + match = re.search( + r"\b(January|February|March|April|May|June|July|August|September|October|November|December)\b", + title, + ) return match.group(0) if match else None + def compute_cp_baseline_score(value): """ Gracefully computes the cp_baseline_score. @@ -1134,6 +1177,7 @@ def compute_cp_baseline_score(value): # Handle any unexpected errors return np.nan + def process_forecast_values(df): """ Adds a 'bucket_forecast_value' column to the DataFrame (for interpreting CP distribution as a @@ -1146,34 +1190,40 @@ def process_forecast_values(df): Returns: pandas.DataFrame: Updated DataFrame with 'bucket_forecast_value' column added. """ + def compute_bucket_forecast_value(row): # Handle binary_version_tuple gracefully - if pd.isna(row['binary_version_tuple']) or not isinstance(row['binary_version_tuple'], (list, tuple)): + if pd.isna(row["binary_version_tuple"]) or not isinstance( + row["binary_version_tuple"], (list, tuple) + ): return None # Extract the first and second elements of the tuple - comparison_type = row['binary_version_tuple'][0] - string_location = row['binary_version_tuple'][1] + comparison_type = row["binary_version_tuple"][0] + string_location = row["binary_version_tuple"][1] # Skip if comparison_type is 'complicated' - if comparison_type == 'complicated': + if comparison_type == "complicated": return None # Compute forecast_value using the extracted string_location - forecast_value = get_cdf_at(row['cdf'], nominal_location_to_cdf_location(string_location, row)) + forecast_value = get_cdf_at( + row["cdf"], nominal_location_to_cdf_location(string_location, row) + ) # Apply logic based on comparison_type - if comparison_type == 'less': + if comparison_type == "less": return forecast_value - elif comparison_type == 'greater': + elif comparison_type == "greater": return 1 - forecast_value return None # Apply the function to each row and overwrite forecast_value (currently contains cdf, which we no longer need) - df['forecast_values'] = df.apply(compute_bucket_forecast_value, axis=1) + df["forecast_values"] = df.apply(compute_bucket_forecast_value, axis=1) return df + def parse_options_array(options_str): """ Parse options string that looks like an array into an actual array. @@ -1194,13 +1244,14 @@ def parse_options_array(options_str): except: # If that fails, try custom parsing # Strip brackets and split by comma - cleaned = options_str.strip('[]') + cleaned = options_str.strip("[]") # Split by comma, but respect quotes import re + # Match items in quotes with commas inside parts = re.findall(r'"([^"]*)"', cleaned) if parts: return parts # Simple fallback: just split by comma and strip quotes - return [p.strip().strip('"\'') for p in cleaned.split(',')] + return [p.strip().strip("\"'") for p in cleaned.split(",")] diff --git a/refactored_notebook/data_models.py b/refactored_notebook/data_models.py index 6452dae..1aaa3f4 100644 --- a/refactored_notebook/data_models.py +++ b/refactored_notebook/data_models.py @@ -4,18 +4,14 @@ from typing import Literal from pydantic import BaseModel -from enum import Enum -class ResolutionType(Enum): - YES = "yes" - NO = "no" - ANNULLED = "annulled" - AMBIGUOUS = "ambiguous" +ResolutionType = bool | str | float | None # binary, MC, numeric, or 'annulled/ambiguous' +ForecastType = list[float] | None # binary: [p_yes, p_no], multiple choice: [p_a, p_b, p_c], numeric: [p_0, p_1, p_2, ...] class Forecast(BaseModel): question: Question user: User - prediction: list[float] # binary, MC, or numeric + prediction: ForecastType predcition_for_correct_answer: float prediction_time: datetime comment: str | None = None @@ -32,7 +28,7 @@ class Score(BaseModel): score: float type: Literal["spot_peer", "spot_baseline"] forecast: Forecast - users_used_in_scoring: list[User] | None# Empty if baseline + users_used_in_scoring: list[User] | None # Empty if baseline class Question(BaseModel): question_text: str diff --git a/refactored_notebook/pseudocode_for_main.py b/refactored_notebook/pseudocode_for_main.py index 3df7559..6660cc4 100644 --- a/refactored_notebook/pseudocode_for_main.py +++ b/refactored_notebook/pseudocode_for_main.py @@ -130,7 +130,8 @@ def display_spot_peer_score_table(tournament: SimulatedTournament, users_to_disp # Add these stats as a property of the simulated tournament scores # Caculate average spot peer score # Caculate sum of spot peer scores - # Find confidence interval + # Find confidence interval w/ t test + # find confidence interval with bootstrapping # Weighted question count (sum of weights) # Show in table with a row for each user # Filter by users_to_display if provided diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 4054f3c..62a2c3f 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,7 +1,7 @@ -from typing import Literal - import numpy as np +from refactored_notebook.data_models import ForecastType, ResolutionType + def calculate_spot_peer_score( forecast_for_correct_answer: float, @@ -11,28 +11,42 @@ def calculate_spot_peer_score( def calculate_spot_baseline_score( - forecast: list[float] | None, # binary: [p_yes, p_no], multiple choice: [p_a, p_b, p_c], numeric: [p_0, p_1, p_2, ...] - resolution: bool | str | float | None, # binary: bool, multiple choice: str, numeric: float - options: list[str] | None, - range_min: float | None, - range_max: float | None, - question_weight: float, + forecast: ForecastType, + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, + question_weight: float = 1.0, ) -> float: """ Question type can be infered from resolution type + Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ + + is_binary = isinstance(resolution, bool) + is_multiple_choice = isinstance(resolution, str) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) + + if forecast is None or resolution is None: - raise NotImplementedError("Havent decided how to handle null forecasts and resolutions") + raise NotImplementedError( + "Havent decided how to handle null forecasts or anulled resolutions" + ) if len(forecast) == 0: raise ValueError("Forecast is empty") - baseline_score = None + if not is_numeric and any(p <= 0 or p >= 1 for p in forecast): + # @Check: Is it valid to have a numeric forecast with 0 probability for a number? + raise ValueError("Forecast contains probabilities outside of 0 to 1 range") + - if isinstance(resolution, bool): - if len(forecast) != 1 or len(forecast) != 2: - raise ValueError("Binary questions must have exactly one forecast and two options (for yes or 'yes and no')") + if is_binary: + if len(forecast) != 1 and len(forecast) != 2: + raise ValueError( + "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" + ) forecast_val = float(forecast[0]) baseline_prob = 0.5 @@ -40,7 +54,7 @@ def calculate_spot_baseline_score( prob_for_resolution = forecast_val else: prob_for_resolution = 1 - forecast_val - elif isinstance(resolution, str): + elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") @@ -52,32 +66,47 @@ def calculate_spot_baseline_score( resolution_idx = options.index(str(resolution)) prob_for_resolution = pmf[resolution_idx] baseline_prob = 1 / len(pmf) - elif isinstance(resolution, float): + elif is_numeric: if range_min is None or range_max is None: - raise ValueError("Range min and range max are required for numeric questions") + raise ValueError( + "Range min and range max are required for numeric questions" + ) + if len(forecast) != 201: + raise ValueError("CDF should have 201 bins") + previous_prob = 0 + for current_prob in forecast: + if current_prob < previous_prob: + raise ValueError("CDF should be in increasing order") + previous_prob = current_prob cdf = [float(p) for p in forecast] - pmf = [cdf[0]] + [cdf[i] - cdf[i-1] for i in range(1, len(cdf))] # @Ben check: is this a correct conversion? + pmf = [cdf[0]] + [ + cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) + ] # @Check: is this a correct conversion? pmf.append(1 - cdf[-1]) resolution = float(resolution) bin_edges = np.linspace(range_min, range_max, 200) - resolution_idx = np.searchsorted(bin_edges, resolution, side='right') + resolution_idx = np.searchsorted(bin_edges, resolution, side="right") if resolution_idx >= len(pmf): raise ValueError("Resolution is out of bounds") prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin + baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins else: raise ValueError("Unknown question type") if prob_for_resolution <= 0 or baseline_prob <= 0: - raise ValueError("Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue") + raise ValueError( + "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" + ) - baseline_score = np.log2(prob_for_resolution / baseline_prob) + baseline_score = np.log2( + prob_for_resolution / baseline_prob + ) * 100 # @Check: check correctness (also shouldn't this be natural log?) if isinstance(resolution, float): baseline_score /= 2 # Numeric scores are halved diff --git a/tests/generate_test_data.py b/tests/generate_test_data.py deleted file mode 100644 index 5b68133..0000000 --- a/tests/generate_test_data.py +++ /dev/null @@ -1,5 +0,0 @@ -from refactored_notebook.data_models import User, Question, Forecast, Score - - -# TODO: Things to test: -# - peer rankings \ No newline at end of file diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py new file mode 100644 index 0000000..233e769 --- /dev/null +++ b/tests/test_end_to_end.py @@ -0,0 +1,18 @@ +from refactored_notebook.data_models import User, Question, Forecast, Score + + +# Generate test csvs to input into the notebook, and assert the below tests pass + +# Things that could go wrong: +# - bad math in scoring +# - didn't load in data correctly +# - bad filtering/manipulation of scoring data (did we take out the right people) +# - make sure to determine the bot team only by the bot-only questions +# - make sure best bot team is decided by baseline score comparison to each other +# - make sure best bots for bot team are decided by lower bound of t test +# - Confidence interval code is wrong +# - make sure that there are large intervals if only a few forecasts, and small intervals if many forecasts +# - make sure bootstrap and t tests indicate the same things generally +# ... continue through and consider other final outputs (e.g. calibration curve) + + diff --git a/tests/test_scoring.py b/tests/test_scoring.py index ce27003..6195e67 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -1,3 +1,6 @@ +import pytest + +from refactored_notebook.scoring import calculate_spot_baseline_score # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -12,3 +15,186 @@ # - better score when closer to resolution, and worse when further away (for forecasts on both sides of 50% forecast) # - The score for a weighted question is weighted by the question weight # - Run a test of some forecasts from the site, and make sure the score generated matches the score the site gives + + +def generate_uniform_cdf(num_points: int) -> list[float]: + return [(i + 1) / num_points for i in range(num_points)] + + +def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[float]: + assert correct_index >= 0 and correct_index <= 201 + length_of_cdf = 201 + cdf = [] + for i in range(length_of_cdf): + if i < correct_index: + cdf.append(float(i / length_of_cdf)) + else: + cdf.append(0.99) + + if inverse_cdf: + cdf = [1 - c for c in cdf] + + return cdf + + + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight,expected", + [ + # Binary: uniform forecast, should be 0 + ([0.5], True, None, None, None, 1.0, 0.0), + ([0.5], False, None, None, None, 1.0, 0.0), + ([0.5, 0.5], False, None, None, None, 1.0, 0.0), + # Multiple Choice: uniform forecast, should be 0 + ([1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None, 1.0, 0.0), + ([0.25, 0.25, 0.25, 0.25], "B", ["A", "B", "C", "D"], None, None, 1.0, 0.0), + # Numeric: uniform CDF, should be 0 + (generate_uniform_cdf(201), 0.5, None, 0.0, 1.0, 1.0, 0.0), + ], +) +def test_baseline_score_is_0_with_uniform_prediction( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, + expected: float, +): + score = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score - expected) == pytest.approx(0) + + +def test_binary_baseline_score_when_perfect_forecast(): + score = calculate_spot_baseline_score( + forecast=[0.99999999], + resolution=True, + ) + assert score == pytest.approx(100) + + +def test_binary_baseline_if_completly_incorrect_forecast(): + score = calculate_spot_baseline_score( + forecast=[0.0000001], + resolution=True, + ) + assert score == pytest.approx(-897) + + +def test_numeric_baseline_when_perfect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=generate_perfect_cdf(correct_index), + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(183) + + +def test_numeric_baseline_if_completly_incorrect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=generate_perfect_cdf(correct_index), + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(-230) + + +def test_multiple_choice_perfect_forecast(): + forecast_for_answer_a = 0.999999999 + num_other_forecasts = 7 + other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts + score = calculate_spot_baseline_score( + forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, + resolution="A", + options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], + ) + assert score == pytest.approx(100) + + +def test_multiple_choice_if_completly_incorrect_forecast(): + forecast_for_answer_c = 0.999999999 + other_forecasts = (1 - forecast_for_answer_c) / 2 + score = calculate_spot_baseline_score( + forecast=[other_forecasts, other_forecasts, forecast_for_answer_c], + resolution="C", + options=["A", "B", "C"], + ) + assert score == pytest.approx(-232) + + +@pytest.mark.parametrize( + "forecast_closer,forecast_further,resolution,options,range_min,range_max", + [ + # Binary: closer to True + ([0.8], [0.2], True, None, None, None), + # Binary: closer to False + ([0.2], [0.8], False, None, None, None), + # Multiple Choice: closer to "A" + ([0.7, 0.2, 0.1], [0.1, 0.2, 0.7], "A", ["A", "B", "C"], None, None), + # Numeric: CDF with more mass near 0.5 vs near 0.0 + ([0.1] * 52 + [0.9] * 149, [0.9] * 52 + [0.1] * 149, 0.5, None, 0.0, 1.0), + ], +) +def test_baseline_score_better_when_closer( + forecast_closer: list[float], + forecast_further: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + score_closer = calculate_spot_baseline_score( + forecast_closer, resolution, options, range_min, range_max, 1.0 + ) + score_further = calculate_spot_baseline_score( + forecast_further, resolution, options, range_min, range_max, 1.0 + ) + assert score_closer > score_further + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight", + [ + # Binary + ([0.8], True, None, None, None, 2.0), + # Multiple Choice + ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), + # Numeric + ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), + ], +) +def test_baseline_score_weighted( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, +): + score_unweighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 + + + From 2575e6cc4aa6e1b356bbd15ac1138b4030e579b6 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 13:27:27 -0600 Subject: [PATCH 06/26] Added peer scoring tests --- refactored_notebook/scoring.py | 61 ++++++---- tests/test_scoring.py | 208 +++++++++++++++++++++++++++++++-- 2 files changed, 241 insertions(+), 28 deletions(-) diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 62a2c3f..080aea4 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -4,8 +4,13 @@ def calculate_spot_peer_score( - forecast_for_correct_answer: float, - other_users_forecasts_for_correct_answer: list[float], + forecast: ForecastType, + forecast_for_other_users: list[ForecastType], + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, + question_weight: float = 1.0, ) -> float: raise NotImplementedError("Not implemented") @@ -23,12 +28,41 @@ def calculate_spot_baseline_score( Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ + prob_for_resolution, baseline_prob = ( + _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + ) + + if prob_for_resolution <= 0 or baseline_prob <= 0: + raise ValueError( + "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" + ) + + baseline_score = ( + np.log2(prob_for_resolution / baseline_prob) * 100 + ) # @Check: check correctness (also shouldn't this be natural log?) + + if isinstance(resolution, float): + baseline_score /= 2 # Numeric scores are halved + + weighted_score = baseline_score * question_weight + + return weighted_score + + +def _determine_probability_for_resolution_and_baseline( + forecast: ForecastType, + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, +) -> tuple[float, float]: is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - if forecast is None or resolution is None: raise NotImplementedError( "Havent decided how to handle null forecasts or anulled resolutions" @@ -41,7 +75,6 @@ def calculate_spot_baseline_score( # @Check: Is it valid to have a numeric forecast with 0 probability for a number? raise ValueError("Forecast contains probabilities outside of 0 to 1 range") - if is_binary: if len(forecast) != 1 and len(forecast) != 2: raise ValueError( @@ -94,23 +127,11 @@ def calculate_spot_baseline_score( raise ValueError("Resolution is out of bounds") prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + baseline_prob = 1 / len( + pmf + ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins else: raise ValueError("Unknown question type") - if prob_for_resolution <= 0 or baseline_prob <= 0: - raise ValueError( - "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" - ) - - baseline_score = np.log2( - prob_for_resolution / baseline_prob - ) * 100 # @Check: check correctness (also shouldn't this be natural log?) - - if isinstance(resolution, float): - baseline_score /= 2 # Numeric scores are halved - - weighted_score = baseline_score * question_weight - - return weighted_score + return prob_for_resolution, baseline_prob diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 6195e67..3a4dd7c 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -1,6 +1,11 @@ +import numpy as np import pytest -from refactored_notebook.scoring import calculate_spot_baseline_score +from refactored_notebook.data_models import ForecastType +from refactored_notebook.scoring import ( + calculate_spot_baseline_score, + calculate_spot_peer_score, +) # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -10,12 +15,10 @@ # - If everyone has the same forecast, the score is 0 # - The sum (average?) of everyone's scores is 0 # - The score for a weighted question is weighted by the question weight -# - Test spot baseline score -# - 0 with 50% forecast, ? for a uniform distribution, and 0 for uniform multiple choice questions -# - better score when closer to resolution, and worse when further away (for forecasts on both sides of 50% forecast) -# - The score for a weighted question is weighted by the question weight # - Run a test of some forecasts from the site, and make sure the score generated matches the score the site gives +################################### HELPER FUNCTIONS ################################### + def generate_uniform_cdf(num_points: int) -> list[float]: return [(i + 1) / num_points for i in range(num_points)] @@ -37,6 +40,198 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ return cdf +################################### PEER SCORES ################################### + + +@pytest.mark.parametrize( + "forecasts,resolution,options,range_min,range_max", + [ + # Binary: forecast closer to resolution gets better score + ( + [[0.9], [0.7], [0.5], [0.3], [0.1]], + True, + None, + None, + None, + ), + # Multiple Choice: forecast closer to resolution gets better score + ( + [ + [0.9, 0.1, 0.0], + [0.7, 0.2, 0.1], + [0.5, 0.3, 0.2], + [0.3, 0.4, 0.3], + [0.1, 0.2, 0.7], + ], + "A", + ["A", "B", "C"], + None, + None, + ), + # Numeric: forecast CDFs with more mass near resolution get better score + ( + [ + [0.1] * 100 + [0.9] * 101, # most mass above 0.5 + [0.2] * 100 + [0.8] * 101, + [0.5] * 201, + [0.8] * 100 + [0.2] * 101, + [0.9] * 100 + [0.1] * 101, # most mass below 0.5 + ], + 0.5, + None, + 0.0, + 1.0, + ), + ], +) +def test_better_forecast_means_better_peer_score( + forecasts: list[list[float]], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + expected_order: list[int], +): + scores = [ + calculate_spot_peer_score( + forecast, + [f for i, f in enumerate(forecasts) if i != idx], + resolution, + options, + range_min, + range_max, + 1.0, + ) + for idx, forecast in enumerate(forecasts) + ] + # Scores should be ordered as expected (descending) + sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) + assert sorted_indices == expected_order + + +@pytest.mark.parametrize( + "question_type,forecast,resolution,options,range_min,range_max", + [ + ("binary", [0.5], True, None, None, None), + ("mc", [1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None), + ("numeric", [0.5] * 201, 0.5, None, 0.0, 1.0), + ], +) +def test_peer_score_zero_when_all_same( + question_type: str, + forecast: list[float], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + forecasts = [forecast for _ in range(5)] + scores = [ + calculate_spot_peer_score( + f, + [f2 for i2, f2 in enumerate(forecasts) if i2 != i], + resolution, + options, + range_min, + range_max, + 1.0, + ) + for i, f in enumerate(forecasts) + ] + for score in scores: + assert score == pytest.approx(0) + + +@pytest.mark.parametrize( + "forecasts,resolution,options,range_min,range_max", + [ + # Binary + ([[0.7], [0.3], [0.5]], True, None, None, None), + # Multiple Choice + ( + [[0.7, 0.2, 0.1], [0.1, 0.7, 0.2], [0.2, 0.1, 0.7]], + "A", + ["A", "B", "C"], + None, + None, + ), + # Numeric + ( + [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], + 0.5, + None, + 0.0, + 1.0, + ), + ], +) +def test_peer_score_average_zero( + forecasts: list[list[float]], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + scores = [ + calculate_spot_peer_score( + forecast, + [f for i, f in enumerate(forecasts) if i != idx], + resolution, + options, + range_min, + range_max, + 1.0, + ) + for idx, forecast in enumerate(forecasts) + ] + assert np.mean(scores) == pytest.approx(0) + + +@pytest.mark.parametrize( + "forecasts,resolution,options,range_min,range_max,weight", + [ + # Binary + ([[0.7], [0.3], [0.5]], True, None, None, None, 2.0), + # Multiple Choice + ( + [[0.7, 0.2, 0.1], [0.1, 0.7, 0.2], [0.2, 0.1, 0.7]], + "A", + ["A", "B", "C"], + None, + None, + 0.5, + ), + # Numeric + ( + [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], + 0.5, + None, + 0.0, + 1.0, + 3.0, + ), + ], +) +def test_peer_score_weighted( + forecasts: list[ForecastType], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + weight: float, +): + for idx, forecast in enumerate(forecasts): + other_forecasts = [f for i, f in enumerate(forecasts) if i != idx] + score_unweighted = calculate_spot_peer_score( + forecast, other_forecasts, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_spot_peer_score( + forecast, other_forecasts, resolution, options, range_min, range_max, weight + ) + assert score_weighted == pytest.approx(score_unweighted * weight) + + +################################### BASELINE SCORES ################################### @pytest.mark.parametrize( @@ -195,6 +390,3 @@ def test_baseline_score_weighted( forecast, resolution, options, range_min, range_max, question_weight ) assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 - - - From c20f0eb891cc4a54c7fe1ed0f8b54a97ba98a735 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 13:46:40 -0600 Subject: [PATCH 07/26] Minor updates --- tests/test_scoring.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 3a4dd7c..7080b1f 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -27,12 +27,13 @@ def generate_uniform_cdf(num_points: int) -> list[float]: def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[float]: assert correct_index >= 0 and correct_index <= 201 length_of_cdf = 201 + perfect_forecast = 0.99999 cdf = [] for i in range(length_of_cdf): if i < correct_index: - cdf.append(float(i / length_of_cdf)) + cdf.append(1 - perfect_forecast) else: - cdf.append(0.99) + cdf.append(perfect_forecast) if inverse_cdf: cdf = [1 - c for c in cdf] @@ -90,7 +91,6 @@ def test_better_forecast_means_better_peer_score( options: list[str] | None, range_min: float | None, range_max: float | None, - expected_order: list[int], ): scores = [ calculate_spot_peer_score( @@ -104,9 +104,8 @@ def test_better_forecast_means_better_peer_score( ) for idx, forecast in enumerate(forecasts) ] - # Scores should be ordered as expected (descending) sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) - assert sorted_indices == expected_order + assert sorted_indices == list(range(len(scores))), "Scores should be ordered as expected (descending)" @pytest.mark.parametrize( @@ -230,6 +229,12 @@ def test_peer_score_weighted( ) assert score_weighted == pytest.approx(score_unweighted * weight) +# TODO: Test the below +# Best score for MC and binary is 996 +# Worst score for MC and binary is -996 +# Best score for numeric is 408 +# Worst score for numeric is -408 +# @Check: Can we even validate this (won't we need infinite other forecasters to get max score?) ################################### BASELINE SCORES ################################### From a7e7d5e4c9bd39e663a85fea0c024fc33949c2fd Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 04:35:49 -0600 Subject: [PATCH 08/26] Added some comments --- AI_BENCHMARKING_ANALYSIS.ipynb | 2 +- tests/test_end_to_end.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 619d445..864dc28 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -3378,7 +3378,7 @@ "outputs": [], "source": [ "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", - "# @Check: -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" + "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." ] }, { diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 233e769..76bbe91 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -10,6 +10,7 @@ # - make sure to determine the bot team only by the bot-only questions # - make sure best bot team is decided by baseline score comparison to each other # - make sure best bots for bot team are decided by lower bound of t test +# - make sure that worse bots come out on bottom # - Confidence interval code is wrong # - make sure that there are large intervals if only a few forecasts, and small intervals if many forecasts # - make sure bootstrap and t tests indicate the same things generally From 4498342390345be7299b797953103a602b93b3b2 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 04:45:41 -0600 Subject: [PATCH 09/26] Added peer score function from previous versions --- AI_BENCHMARKING_ANALYSIS.ipynb | 3773 +++++++---------- functions.py | 174 +- .../bootstrapped_h2h_bot_vs_pros.csv | 30 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 30 +- 4 files changed, 1816 insertions(+), 2191 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 864dc28..510d463 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 82, "metadata": { "id": "ISzIoto4hnoG" }, @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -358,7 +358,7 @@ " dtype='object')" ] }, - "execution_count": 9, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ "dtype: object" ] }, - "execution_count": 11, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 16, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -704,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -747,7 +747,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -759,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -802,15 +802,6 @@ " 1.738353\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.829587\n", - " 3337.760404\n", - " 409\n", - " 5.839419\n", - " 1.521098\n", - " \n", - " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -820,6 +811,15 @@ " 2.298000\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 8.215149\n", + " 3105.490478\n", + " 409\n", + " 5.145245\n", + " 1.561660\n", + " \n", + " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -844,15 +844,15 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.829587 3337.760404 409 5.839419 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "15 bot_median 8.215149 3105.490478 409 5.145245 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.521098 \n", "4 2.298000 \n", + "15 1.561660 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -968,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 101, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1017,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -1441,7 +1441,7 @@ " np.int64(35705)}" ] }, - "execution_count": 21, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -1462,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 103, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1512,7 +1512,7 @@ " \n", " 3\n", " bot_median\n", - " 8806.147044\n", + " 8671.898307\n", " \n", " \n", " 4\n", @@ -1533,7 +1533,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8806.147044\n", + "3 bot_median 8671.898307\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1639,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -1658,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 105, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1703,7 +1703,7 @@ " \n", " 2\n", " bot_median\n", - " 3711.510468\n", + " 3347.538115\n", " \n", " \n", " 3\n", @@ -1938,7 +1938,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3711.510468\n", + "2 bot_median 3347.538115\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -1986,7 +1986,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 24, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -2028,7 +2028,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -2047,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -2056,7 +2056,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -2064,9 +2064,7 @@ "output_type": "stream", "text": [ "PRO MEDIAN\n", - "Average baseline: 44.964801909223056\n", - "pgodzinai MEDIAN\n", - "Average baseline: 16.482817250003514\n" + "Average baseline: 44.964801909223056\n" ] } ], @@ -2079,7 +2077,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -2258,7 +2256,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 28, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2269,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 110, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2334,7 +2332,7 @@ " NaN\n", " ...\n", " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", @@ -2357,7 +2355,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2382,8 +2380,8 @@ " NaN\n", " ...\n", " 0.1\n", - " 0.15\n", " 0.1\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2405,7 +2403,7 @@ " NaN\n", " [0.16,0.47,0.37]\n", " ...\n", - " [0.25,0.6,0.15]\n", + " [0.3,0.55,0.15]\n", " [0.2,0.6,0.2]\n", " [0.15,0.55,0.3]\n", " NaN\n", @@ -2429,8 +2427,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", - " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2469,22 +2467,22 @@ "\n", " CatrachoCaster ... metac-o1 \\\n", "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", - "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 NaN ... 0.1 \n", - "3 [0.16,0.47,0.37] ... [0.25,0.6,0.15] \n", - "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "3 [0.16,0.47,0.37] ... [0.3,0.55,0.15] \n", + "4 NaN ... [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.15 \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", + "2 0.15 NaN \n", "3 [0.15,0.55,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", @@ -2574,7 +2572,7 @@ " NaN\n", " ...\n", " 0.95\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2597,8 +2595,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2621,7 +2619,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.9\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -2645,9 +2643,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", - " 0.3\n", + " 0.25\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -2695,16 +2693,16 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.35 \n", - "96 None 0.97 0.85 NaN NaN ... 0.9 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.7 \n", "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.9 NaN NaN 0.95 0.95 NaN \n", - "95 0.4 NaN NaN 0.15 NaN NaN \n", + "94 0.95 NaN NaN 0.95 0.95 NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "97 0.85 0.25 NaN 0.85 0.85 NaN \n", "98 0.05 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", @@ -2773,7 +2771,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -2795,7 +2793,7 @@ " dtype='object')" ] }, - "execution_count": 30, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -2806,7 +2804,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -2816,7 +2814,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 31, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -2831,7 +2829,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -2843,7 +2841,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -2852,7 +2850,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -2914,7 +2912,7 @@ " NaN\n", " ...\n", " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", @@ -2937,7 +2935,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 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0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...] \n", + "2 0.1 \n", + "3 [0.3,0.55,0.15] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...] \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", + "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", "1 [0.05, 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pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.35 \n", - "96 None 0.97 0.85 NaN NaN ... 0.9 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.7 \n", "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.9 NaN NaN 0.95 0.95 NaN \n", - "95 0.4 NaN NaN 0.15 NaN NaN \n", + "94 0.95 NaN NaN 0.95 0.95 NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "97 0.85 0.25 NaN 0.85 0.85 NaN \n", "98 0.05 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", @@ -3373,7 +3371,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -3383,7 +3381,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -3444,9 +3442,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3468,9 +3466,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3492,9 +3490,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3516,9 +3514,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3540,9 +3538,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3586,11 +3584,11 @@ "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "3 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "6 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "9 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "13 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "0 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "3 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "6 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "9 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "13 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3663,9 +3661,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3687,9 +3685,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3711,9 +3709,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3735,9 +3733,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3759,9 +3757,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3805,11 +3803,11 @@ "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "82 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "83 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "91 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "92 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "81 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "82 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "83 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "91 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "92 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3882,9 +3880,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3906,9 +3904,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3930,9 +3928,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3954,9 +3952,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3978,9 +3976,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4003,18 +4001,18 @@ "16 33876 33751 no 1.0 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "2 None 0.013 0.643473 2.597381 1.762901 ... 21.041046 \n", - "5 None 0.45 0.643473 2.597381 1.762901 ... 21.041046 \n", - "8 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", - "12 None 0.9 0.643473 2.597381 1.762901 ... 21.041046 \n", - "16 None 0.058 0.643473 2.597381 1.762901 ... 21.041046 \n", + "2 None 0.013 0.643473 2.597381 1.762901 ... 20.222117 \n", + "5 None 0.45 0.643473 2.597381 1.762901 ... 20.222117 \n", + "8 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", + "12 None 0.9 0.643473 2.597381 1.762901 ... 20.222117 \n", + "16 None 0.058 0.643473 2.597381 1.762901 ... 20.222117 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "5 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "8 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "12 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "16 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "2 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "5 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "8 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "12 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "16 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "2 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4087,9 +4085,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4111,9 +4109,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4135,9 +4133,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4159,9 +4157,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4183,9 +4181,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4208,18 +4206,18 @@ "98 35387 35367 no 0.85 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", - "95 None 0.05 0.643473 2.597381 1.762901 ... 21.041046 \n", - "96 None 0.97 0.643473 2.597381 1.762901 ... 21.041046 \n", - "97 None 0.666 0.643473 2.597381 1.762901 ... 21.041046 \n", - "98 None 0.03 0.643473 2.597381 1.762901 ... 21.041046 \n", + "94 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", + "95 None 0.05 0.643473 2.597381 1.762901 ... 20.222117 \n", + "96 None 0.97 0.643473 2.597381 1.762901 ... 20.222117 \n", + "97 None 0.666 0.643473 2.597381 1.762901 ... 20.222117 \n", + "98 None 0.03 0.643473 2.597381 1.762901 ... 20.222117 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "95 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "96 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "97 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "98 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "94 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "95 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "96 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "97 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "98 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4243,7 +4241,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -4285,7 +4283,7 @@ " \n", " 2\n", " bot_median\n", - " 3711.510468\n", + " 3347.538115\n", " \n", " \n", " 3\n", @@ -4520,7 +4518,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3711.510468\n", + "2 bot_median 3347.538115\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4568,7 +4566,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 37, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -4579,7 +4577,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -4588,13 +4586,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", "mean metac-o1 forecast on questions that resolved no: 26.0%\n" ] }, { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "
" ] @@ -4663,7 +4661,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -4720,7 +4718,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -4733,7 +4731,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 122, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4795,18 +4793,18 @@ " \n", " 3\n", " 4\n", - " acm_bot\n", - " 2239.058675\n", - " 85\n", - " 81.25\n", + " bot_median\n", + " 2500.508853\n", + " 97\n", + " 93.10\n", " \n", " \n", " 4\n", " 5\n", - " bot_median\n", - " 2196.323052\n", - " 97\n", - " 93.10\n", + " acm_bot\n", + " 2239.058675\n", + " 85\n", + " 81.25\n", " \n", " \n", " 5\n", @@ -5153,8 +5151,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 acm_bot 2239.058675 85 \n", - "4 5 bot_median 2196.323052 97 \n", + "3 4 bot_median 2500.508853 97 \n", + "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -5202,8 +5200,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 81.25 \n", - "4 93.10 \n", + "3 93.10 \n", + "4 81.25 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -5248,7 +5246,7 @@ "46 52.10 " ] }, - "execution_count": 41, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -5317,7 +5315,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -5398,6 +5396,20 @@ " 0.000036\n", " \n", " \n", + " bot_median\n", + " 2500.5\n", + " 93.1\n", + " 26.9\n", + " 62.117260\n", + " 6.437800\n", + " 4.171971\n", + " 1.985277\n", + " 39.6\n", + " 14.1\n", + " 0.999966\n", + " 0.000068\n", + " \n", + " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5412,20 +5424,6 @@ " 0.000025\n", " \n", " \n", - " bot_median\n", - " 2196.3\n", - " 93.1\n", - " 23.6\n", - " 59.192687\n", - " 6.134698\n", - " 3.845505\n", - " 1.985277\n", - " 35.8\n", - " 11.4\n", - " 0.999889\n", - " 0.000221\n", - " \n", - " \n", " metac-claude-3-5-sonnet-20240620\n", " 2018.1\n", " 91.5\n", @@ -6022,8 +6020,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "bot_median 2500.5 93.1 26.9 62.117260 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", - "bot_median 2196.3 93.1 23.6 59.192687 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -6071,8 +6069,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "bot_median 6.437800 4.171971 1.985277 39.6 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", - "bot_median 6.134698 3.845505 1.985277 35.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -6120,8 +6118,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", + "bot_median 14.1 0.999966 0.000068 \n", "acm_bot 15.3 0.999987 0.000025 \n", - "bot_median 11.4 0.999889 0.000221 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -6166,7 +6164,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 42, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -6182,7 +6180,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 124, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6215,8 +6213,6 @@ " t_statistic = (weighted_average - 0) / std_error\n", "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", " t_statistic = (weighted_average - 0) / std_error\n", "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", @@ -6259,44 +6255,30 @@ " \n", " \n", " \n", - " metac-o1\n", - " 1998.9\n", - " 95.0\n", - " 21.0\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 5.743007e+16\n", - " 1.98475\n", - " 21.0\n", - " 21.0\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " metac-perplexity\n", - " 1927.0\n", + " 1957.5\n", " 95.0\n", - " 20.3\n", + " 20.6\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 20.3\n", - " 20.3\n", + " 20.6\n", + " 20.6\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " bot_median\n", - " 1698.8\n", + " metac-o1\n", + " 1921.1\n", " 95.0\n", - " 17.9\n", + " 20.2\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 17.9\n", - " 17.9\n", + " 20.2\n", + " 20.2\n", " 1.0\n", " 0.000000\n", " \n", @@ -6315,6 +6297,20 @@ " 0.000000\n", " \n", " \n", + " bot_median\n", + " 1655.0\n", + " 95.0\n", + " 17.4\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.755070e+16\n", + " 1.98475\n", + " 17.4\n", + " 17.4\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " manticAI\n", " 1378.2\n", " 95.0\n", @@ -6358,99 +6354,85 @@ " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " 1136.7\n", - " 95.0\n", - " 12.0\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 3.265969e+16\n", - " 1.98475\n", - " 12.0\n", - " 12.0\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " GreeneiBot2\n", - " 1115.4\n", + " 1235.2\n", " 95.0\n", - " 11.7\n", - " 5.356499e-15\n", - " 5.495652e-16\n", - " 2.136428e+16\n", + " 13.0\n", + " 1.785500e-15\n", + " 1.831884e-16\n", + " 7.097519e+16\n", " 1.98475\n", - " 11.7\n", - " 11.7\n", + " 13.0\n", + " 13.0\n", " 1.0\n", " 0.000000\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " 1091.6\n", + " 1180.5\n", " 95.0\n", - " 11.5\n", - " 5.356499e-15\n", - " 5.495652e-16\n", - " 2.090764e+16\n", + " 12.4\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " inf\n", " 1.98475\n", - " 11.5\n", - " 11.5\n", + " 12.4\n", + " 12.4\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " NextWorldLab\n", - " 1050.3\n", + " metac-deepseek-r1\n", + " 1166.0\n", " 95.0\n", - " 11.1\n", + " 12.3\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 6.035038e+16\n", + " 6.700213e+16\n", " 1.98475\n", - " 11.1\n", - " 11.1\n", + " 12.3\n", + " 12.3\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-grok-2-1212\n", - " 1047.4\n", + " metac-Llama-3.1\n", + " 1154.9\n", " 95.0\n", - " 11.0\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", + " 12.2\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 3.318128e+16\n", " 1.98475\n", - " 11.0\n", - " 11.0\n", + " 12.2\n", + " 12.2\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-gpt-4o\n", - " 1002.0\n", + " GreeneiBot2\n", + " 1119.2\n", " 95.0\n", - " 10.5\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 2.878879e+16\n", + " 11.8\n", + " 1.785500e-15\n", + " 1.831884e-16\n", + " 6.431060e+16\n", " 1.98475\n", - " 10.5\n", - " 10.5\n", + " 11.8\n", + " 11.8\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-Llama-3.1\n", - " 973.0\n", + " NextWorldLab\n", + " 1050.3\n", " 95.0\n", - " 10.2\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", + " 11.1\n", + " 1.785500e-15\n", + " 1.831884e-16\n", + " 6.035038e+16\n", " 1.98475\n", - " 10.2\n", - " 10.2\n", + " 11.1\n", + " 11.1\n", " 1.0\n", " 0.000000\n", " \n", @@ -6483,16 +6465,16 @@ " 0.000000\n", " \n", " \n", - " metac-o1-preview\n", - " 962.8\n", + " metac-grok-2-1212\n", + " 932.3\n", " 95.0\n", - " 10.1\n", + " 9.8\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 5.532510e+16\n", + " 5.357005e+16\n", " 1.98475\n", - " 10.1\n", - " 10.1\n", + " 9.8\n", + " 9.8\n", " 1.0\n", " 0.000000\n", " \n", @@ -6511,16 +6493,16 @@ " 0.000000\n", " \n", " \n", - " metac-exa\n", - " 919.9\n", + " metac-Gemini-Exp-1206\n", + " 910.2\n", " 95.0\n", - " 9.7\n", + " 9.6\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 5.285939e+16\n", + " 5.230332e+16\n", " 1.98475\n", - " 9.7\n", - " 9.7\n", + " 9.6\n", + " 9.6\n", " 1.0\n", " 0.000000\n", " \n", @@ -6539,16 +6521,16 @@ " 0.000000\n", " \n", " \n", - " metac-deepseek-r1\n", - " 802.0\n", + " metac-exa\n", + " 836.7\n", " 95.0\n", - " 8.4\n", + " 8.8\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 4.608683e+16\n", + " 4.808056e+16\n", " 1.98475\n", - " 8.4\n", - " 8.4\n", + " 8.8\n", + " 8.8\n", " 1.0\n", " 0.000000\n", " \n", @@ -6581,30 +6563,30 @@ " 0.000000\n", " \n", " \n", - " cookics_bot_TEST\n", - " 612.4\n", + " metac-o1-preview\n", + " 640.2\n", " 95.0\n", - " 6.4\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 3.518949e+16\n", + " 6.7\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 7.357383e+16\n", " 1.98475\n", - " 6.4\n", - " 6.4\n", + " 6.7\n", + " 6.7\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 548.0\n", + " cookics_bot_TEST\n", + " 596.4\n", " 95.0\n", - " 5.8\n", + " 6.3\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 5.8\n", - " 5.8\n", + " 6.3\n", + " 6.3\n", " 1.0\n", " 0.000000\n", " \n", @@ -6665,6 +6647,20 @@ " 0.000000\n", " \n", " \n", + " metac-gpt-4o\n", + " 280.3\n", + " 95.0\n", + " 3.0\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 3.221541e+16\n", + " 1.98475\n", + " 3.0\n", + " 3.0\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " InstitutPelFutur\n", " 256.0\n", " 95.0\n", @@ -6791,6 +6787,20 @@ " 0.000000\n", " \n", " \n", + " RPM_bot\n", + " 71.4\n", + " 95.0\n", + " 0.8\n", + " 1.115937e-16\n", + " 1.144927e-17\n", + " 6.560693e+16\n", + " 1.98475\n", + " 0.8\n", + " 0.8\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " 4Shadower\n", " 61.1\n", " 95.0\n", @@ -6819,20 +6829,6 @@ " 0.000000\n", " \n", " \n", - " RPM_bot\n", - " 52.6\n", - " 95.0\n", - " 0.6\n", - " 1.115937e-16\n", - " 1.144927e-17\n", - " 4.834420e+16\n", - " 1.98475\n", - " 0.6\n", - " 0.6\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " andrewsiah\n", " 0.0\n", " 95.0\n", @@ -6908,35 +6904,35 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev \\\n", - "metac-o1 1998.9 95.0 21.0 3.570999e-15 \n", - "metac-perplexity 1927.0 95.0 20.3 0.000000e+00 \n", - "bot_median 1698.8 95.0 17.9 0.000000e+00 \n", + "metac-perplexity 1957.5 95.0 20.6 0.000000e+00 \n", + "metac-o1 1921.1 95.0 20.2 0.000000e+00 \n", "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", + "bot_median 1655.0 95.0 17.4 3.570999e-15 \n", "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", - "metac-claude-3-5-sonnet-20240620 1136.7 95.0 12.0 3.570999e-15 \n", - "GreeneiBot2 1115.4 95.0 11.7 5.356499e-15 \n", - "metac-claude-3-5-sonnet-latest 1091.6 95.0 11.5 5.356499e-15 \n", + "metac-claude-3-5-sonnet-20240620 1235.2 95.0 13.0 1.785500e-15 \n", + "metac-claude-3-5-sonnet-latest 1180.5 95.0 12.4 0.000000e+00 \n", + "metac-deepseek-r1 1166.0 95.0 12.3 1.785500e-15 \n", + "metac-Llama-3.1 1154.9 95.0 12.2 3.570999e-15 \n", + "GreeneiBot2 1119.2 95.0 11.8 1.785500e-15 \n", "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", - "metac-grok-2-1212 1047.4 95.0 11.0 0.000000e+00 \n", - "metac-gpt-4o 1002.0 95.0 10.5 3.570999e-15 \n", - "metac-Llama-3.1 973.0 95.0 10.2 0.000000e+00 \n", "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", - "metac-o1-preview 962.8 95.0 10.1 1.785500e-15 \n", + "metac-grok-2-1212 932.3 95.0 9.8 1.785500e-15 \n", "mmBot 924.8 95.0 9.7 0.000000e+00 \n", - "metac-exa 919.9 95.0 9.7 1.785500e-15 \n", + "metac-Gemini-Exp-1206 910.2 95.0 9.6 1.785500e-15 \n", "annabot 854.4 95.0 9.0 1.785500e-15 \n", - "metac-deepseek-r1 802.0 95.0 8.4 1.785500e-15 \n", + "metac-exa 836.7 95.0 8.8 1.785500e-15 \n", "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", "laylaps 723.4 95.0 7.6 8.927498e-16 \n", - "cookics_bot_TEST 612.4 95.0 6.4 1.785500e-15 \n", - "metac-Gemini-Exp-1206 548.0 95.0 5.8 0.000000e+00 \n", + "metac-o1-preview 640.2 95.0 6.7 8.927498e-16 \n", + "cookics_bot_TEST 596.4 95.0 6.3 0.000000e+00 \n", "MWG 520.8 95.0 5.5 8.927498e-16 \n", "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", + "metac-gpt-4o 280.3 95.0 3.0 8.927498e-16 \n", "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", @@ -6946,9 +6942,9 @@ "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", "X_bot 181.4 95.0 1.9 0.000000e+00 \n", "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", + "RPM_bot 71.4 95.0 0.8 1.115937e-16 \n", "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", - "RPM_bot 52.6 95.0 0.6 1.115937e-16 \n", "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", "cobyj-bot 0.0 95.0 0.0 0.000000e+00 \n", "pianobot -206.5 95.0 -2.2 4.463749e-16 \n", @@ -6956,35 +6952,35 @@ "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", "\n", " std_err t_stat t_crit \\\n", - "metac-o1 3.663768e-16 5.743007e+16 1.98475 \n", "metac-perplexity 0.000000e+00 inf 1.98475 \n", - "bot_median 0.000000e+00 inf 1.98475 \n", + "metac-o1 0.000000e+00 inf 1.98475 \n", "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", + "bot_median 3.663768e-16 4.755070e+16 1.98475 \n", "manticAI 0.000000e+00 inf 1.98475 \n", "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", - "metac-claude-3-5-sonnet-20240620 3.663768e-16 3.265969e+16 1.98475 \n", - "GreeneiBot2 5.495652e-16 2.136428e+16 1.98475 \n", - "metac-claude-3-5-sonnet-latest 5.495652e-16 2.090764e+16 1.98475 \n", + "metac-claude-3-5-sonnet-20240620 1.831884e-16 7.097519e+16 1.98475 \n", + "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", + "metac-deepseek-r1 1.831884e-16 6.700213e+16 1.98475 \n", + "metac-Llama-3.1 3.663768e-16 3.318128e+16 1.98475 \n", + "GreeneiBot2 1.831884e-16 6.431060e+16 1.98475 \n", "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", - "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", - "metac-gpt-4o 3.663768e-16 2.878879e+16 1.98475 \n", - "metac-Llama-3.1 0.000000e+00 inf 1.98475 \n", "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", - "metac-o1-preview 1.831884e-16 5.532510e+16 1.98475 \n", + "metac-grok-2-1212 1.831884e-16 5.357005e+16 1.98475 \n", "mmBot 0.000000e+00 inf 1.98475 \n", - "metac-exa 1.831884e-16 5.285939e+16 1.98475 \n", + "metac-Gemini-Exp-1206 1.831884e-16 5.230332e+16 1.98475 \n", "annabot 1.831884e-16 4.909363e+16 1.98475 \n", - "metac-deepseek-r1 1.831884e-16 4.608683e+16 1.98475 \n", + "metac-exa 1.831884e-16 4.808056e+16 1.98475 \n", "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", - "cookics_bot_TEST 1.831884e-16 3.518949e+16 1.98475 \n", - "metac-Gemini-Exp-1206 0.000000e+00 inf 1.98475 \n", + "metac-o1-preview 9.159420e-17 7.357383e+16 1.98475 \n", + "cookics_bot_TEST 0.000000e+00 inf 1.98475 \n", "MWG 9.159420e-17 5.985647e+16 1.98475 \n", "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", + "metac-gpt-4o 9.159420e-17 3.221541e+16 1.98475 \n", "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", "Bot_Pepa 0.000000e+00 inf 1.98475 \n", "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", @@ -6994,9 +6990,9 @@ "bean_bot 0.000000e+00 inf 1.98475 \n", "X_bot 0.000000e+00 inf 1.98475 \n", "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", + "RPM_bot 1.144927e-17 6.560693e+16 1.98475 \n", "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", - "RPM_bot 1.144927e-17 4.834420e+16 1.98475 \n", "andrewsiah 0.000000e+00 NaN 1.98475 \n", "cobyj-bot 0.000000e+00 NaN 1.98475 \n", "pianobot 4.579710e-17 -4.745305e+16 1.98475 \n", @@ -7004,35 +7000,35 @@ "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "metac-o1 21.0 21.0 1.0 0.000000 \n", - "metac-perplexity 20.3 20.3 1.0 0.000000 \n", - "bot_median 17.9 17.9 1.0 0.000000 \n", + "metac-perplexity 20.6 20.6 1.0 0.000000 \n", + "metac-o1 20.2 20.2 1.0 0.000000 \n", "acm_bot 17.7 17.7 1.0 0.000000 \n", + "bot_median 17.4 17.4 1.0 0.000000 \n", "manticAI 14.5 14.5 1.0 0.000000 \n", "twsummerbot 14.3 14.3 1.0 0.000000 \n", "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-20240620 12.0 12.0 1.0 0.000000 \n", - "GreeneiBot2 11.7 11.7 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-latest 11.5 11.5 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-20240620 13.0 13.0 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-latest 12.4 12.4 1.0 0.000000 \n", + "metac-deepseek-r1 12.3 12.3 1.0 0.000000 \n", + "metac-Llama-3.1 12.2 12.2 1.0 0.000000 \n", + "GreeneiBot2 11.8 11.8 1.0 0.000000 \n", "NextWorldLab 11.1 11.1 1.0 0.000000 \n", - "metac-grok-2-1212 11.0 11.0 1.0 0.000000 \n", - "metac-gpt-4o 10.5 10.5 1.0 0.000000 \n", - "metac-Llama-3.1 10.2 10.2 1.0 0.000000 \n", "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", "SynapseSeer 10.2 10.2 1.0 0.000000 \n", - "metac-o1-preview 10.1 10.1 1.0 0.000000 \n", + "metac-grok-2-1212 9.8 9.8 1.0 0.000000 \n", "mmBot 9.7 9.7 1.0 0.000000 \n", - "metac-exa 9.7 9.7 1.0 0.000000 \n", + "metac-Gemini-Exp-1206 9.6 9.6 1.0 0.000000 \n", "annabot 9.0 9.0 1.0 0.000000 \n", - "metac-deepseek-r1 8.4 8.4 1.0 0.000000 \n", + "metac-exa 8.8 8.8 1.0 0.000000 \n", "VeritasAI 8.4 8.4 1.0 0.000000 \n", "laylaps 7.6 7.6 1.0 0.000000 \n", - "cookics_bot_TEST 6.4 6.4 1.0 0.000000 \n", - "metac-Gemini-Exp-1206 5.8 5.8 1.0 0.000000 \n", + "metac-o1-preview 6.7 6.7 1.0 0.000000 \n", + "cookics_bot_TEST 6.3 6.3 1.0 0.000000 \n", "MWG 5.5 5.5 1.0 0.000000 \n", "ajf-bot 5.1 5.1 1.0 0.000000 \n", "pgodzinai 3.5 3.5 1.0 0.000000 \n", "KevinTestBot 3.3 3.3 1.0 0.000000 \n", + "metac-gpt-4o 3.0 3.0 1.0 0.000000 \n", "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", "CumulativeBot 2.5 2.5 1.0 0.000000 \n", @@ -7042,9 +7038,9 @@ "bean_bot 2.1 2.1 1.0 0.000000 \n", "X_bot 1.9 1.9 1.0 0.000000 \n", "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", + "RPM_bot 0.8 0.8 1.0 0.000000 \n", "4Shadower 0.6 0.6 1.0 0.000000 \n", "krm-bot 0.6 0.6 1.0 0.000000 \n", - "RPM_bot 0.6 0.6 1.0 0.000000 \n", "andrewsiah 0.0 0.0 NaN NA \n", "cobyj-bot 0.0 0.0 NaN NA \n", "pianobot -2.2 -2.2 0.0 0.000000 \n", @@ -7052,7 +7048,7 @@ "minefrac1 -3.0 -3.0 0.0 0.000000 " ] }, - "execution_count": 43, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -7078,7 +7074,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -7088,7 +7084,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 126, "metadata": { "cellView": "form", "colab": { @@ -8002,7 +7998,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 45, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -8041,7 +8037,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -8051,7 +8047,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -8256,7 +8252,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 47, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -8267,7 +8263,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 129, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8329,7 +8325,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -8751,7 +8747,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 131, "metadata": { "cellView": "form", "colab": { @@ -8801,131 +8797,131 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", - " 7.2\n", - " 9.6\n", - " 11.9\n", - " 13.1\n", + " 6.2\n", + " 7.4\n", + " 9.7\n", + " 11.8\n", + " 13.2\n", " \n", " \n", " metac-o1-preview\n", - " 3.7\n", - " 5.2\n", - " 8.3\n", - " 11.2\n", + " 3.9\n", + " 5.4\n", + " 8.4\n", + " 11.4\n", " 12.8\n", " \n", " \n", " manticAI\n", - " 0.2\n", - " 2.1\n", - " 5.5\n", - " 8.8\n", - " 10.5\n", + " 0.1\n", + " 2.0\n", + " 5.4\n", + " 8.6\n", + " 10.2\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.4\n", - " 1.9\n", - " 4.9\n", - " 7.5\n", - " 8.9\n", + " 0.5\n", + " 2.0\n", + " 5.0\n", + " 7.9\n", + " 9.5\n", " \n", " \n", " acm_bot\n", - " 0.2\n", + " 0.1\n", " 1.8\n", - " 4.7\n", - " 7.7\n", - " 9.1\n", + " 4.5\n", + " 7.5\n", + " 8.8\n", " \n", " \n", " metac-perplexity\n", " -2.2\n", - " 0.0\n", - " 4.3\n", + " 0.2\n", + " 4.1\n", " 7.8\n", - " 9.9\n", + " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -1.2\n", - " 0.4\n", - " 3.9\n", - " 7.0\n", + " -0.8\n", + " 0.7\n", + " 4.0\n", + " 7.2\n", " 8.7\n", " \n", " \n", " twsummerbot\n", - " 0.3\n", + " -0.1\n", " 1.5\n", " 3.9\n", - " 6.1\n", - " 7.4\n", - " \n", - " \n", - " pgodzinai\n", - " -3.4\n", - " -1.2\n", - " 3.2\n", - " 7.3\n", - " 9.6\n", + " 6.3\n", + " 7.7\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.2\n", - " 0.8\n", - " 2.9\n", - " 5.0\n", + " 0.0\n", + " 1.0\n", + " 3.0\n", + " 4.9\n", " 5.8\n", " \n", " \n", - " CumulativeBot\n", - " -0.1\n", - " 0.9\n", + " pgodzinai\n", + " -3.5\n", + " -1.1\n", + " 2.8\n", + " 6.8\n", + " 8.9\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -1.4\n", + " 0.0\n", " 2.7\n", - " 4.6\n", - " 5.4\n", + " 5.1\n", + " 6.2\n", " \n", " \n", " SynapseSeer\n", - " 0.4\n", - " 1.1\n", + " 0.3\n", + " 1.0\n", " 2.6\n", - " 4.1\n", - " 4.8\n", + " 4.0\n", + " 5.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " -0.0\n", + " CumulativeBot\n", + " -0.3\n", + " 0.7\n", " 2.5\n", - " 5.0\n", - " 6.2\n", + " 4.4\n", + " 5.4\n", " \n", " \n", " jkraybill_bot\n", - " -3.5\n", - " -1.7\n", - " 1.7\n", - " 5.0\n", + " -3.7\n", + " -1.8\n", + " 1.8\n", + " 4.9\n", " 6.4\n", " \n", " \n", " metac-exa\n", - " -4.8\n", - " -2.2\n", - " 1.7\n", - " 5.6\n", - " 7.8\n", + " -5.0\n", + " -2.4\n", + " 1.5\n", + " 5.4\n", + " 7.4\n", " \n", " \n", " metac-deepseek-r1\n", - " -2.0\n", - " -0.8\n", - " 1.3\n", + " -1.7\n", + " -0.6\n", + " 1.4\n", " 3.4\n", - " 4.6\n", + " 4.5\n", " \n", " \n", " MWG\n", @@ -8936,228 +8932,228 @@ " 2.8\n", " \n", " \n", - " andrewsiah\n", - " -0.8\n", - " -0.6\n", - " -0.0\n", - " 0.6\n", - " 0.9\n", - " \n", - " \n", " pianobot\n", - " -1.2\n", + " -1.3\n", " -0.8\n", - " -0.0\n", + " 0.0\n", " 0.7\n", - " 1.0\n", + " 1.1\n", " \n", " \n", " cobyj-bot\n", - " -1.5\n", + " -1.4\n", " -0.9\n", " -0.0\n", - " 0.8\n", - " 1.3\n", + " 0.9\n", + " 1.4\n", + " \n", + " \n", + " andrewsiah\n", + " -0.9\n", + " -0.6\n", + " -0.0\n", + " 0.5\n", + " 0.9\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.2\n", + " -0.3\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", " annabot\n", - " -3.5\n", + " -3.2\n", " -2.3\n", " -0.4\n", - " 1.2\n", - " 2.1\n", + " 1.3\n", + " 2.0\n", " \n", " \n", " bean_bot\n", " -3.2\n", " -2.3\n", - " -0.5\n", - " 1.1\n", - " 1.8\n", + " -0.4\n", + " 1.2\n", + " 1.9\n", " \n", " \n", " KevinTestBot\n", - " -4.1\n", - " -2.8\n", + " -3.9\n", + " -2.7\n", " -0.6\n", - " 1.6\n", - " 2.7\n", + " 1.3\n", + " 2.4\n", " \n", " \n", " CatrachoCaster\n", - " -2.2\n", - " -1.7\n", + " -2.3\n", + " -1.8\n", " -0.8\n", " 0.2\n", - " 0.7\n", + " 0.8\n", " \n", " \n", " jonahsingerbot\n", " -3.0\n", - " -2.2\n", + " -2.1\n", " -0.8\n", - " 0.5\n", - " 1.0\n", + " 0.4\n", + " 1.1\n", " \n", " \n", " krm-bot\n", - " -3.5\n", - " -2.7\n", - " -0.9\n", - " 0.7\n", + " -3.7\n", + " -2.8\n", + " -1.0\n", + " 0.6\n", " 1.7\n", " \n", " \n", " ProfessorSP\n", - " -4.6\n", - " -3.3\n", - " -1.0\n", + " -4.1\n", + " -3.2\n", + " -1.1\n", " 1.1\n", - " 2.1\n", - " \n", - " \n", - " mmBot\n", - " -7.5\n", - " -5.4\n", - " -1.5\n", - " 2.4\n", - " 4.7\n", + " 2.3\n", " \n", " \n", " metac-grok-2-1212\n", " -6.6\n", - " -4.8\n", + " -4.7\n", + " -1.4\n", + " 1.8\n", + " 3.5\n", + " \n", + " \n", + " mmBot\n", + " -7.2\n", + " -5.5\n", " -1.5\n", - " 1.9\n", - " 3.6\n", + " 2.2\n", + " 4.0\n", " \n", " \n", " 4Shadower\n", - " -4.6\n", - " -3.6\n", - " -1.6\n", + " -4.7\n", + " -3.8\n", + " -1.7\n", " 0.2\n", - " 1.2\n", + " 1.3\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.5\n", + " -4.5\n", + " -1.8\n", + " 0.9\n", + " 2.4\n", " \n", " \n", " swingswish\n", - " -5.2\n", + " -5.4\n", " -4.0\n", " -1.9\n", " -0.2\n", - " 0.5\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.2\n", - " -4.9\n", - " -2.0\n", - " 0.9\n", - " 2.4\n", + " 0.6\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", + " -4.8\n", " -3.8\n", " -2.1\n", " -0.7\n", - " -0.2\n", + " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -9.1\n", + " -9.0\n", " -6.4\n", - " -2.4\n", - " 1.7\n", - " 4.0\n", + " -2.5\n", + " 1.6\n", + " 3.6\n", " \n", " \n", " wunderplumb\n", - " -6.2\n", + " -6.4\n", " -4.9\n", - " -2.4\n", + " -2.7\n", " -0.2\n", - " 1.1\n", + " 0.8\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.8\n", + " -6.7\n", " -5.3\n", " -2.7\n", " 0.0\n", - " 1.5\n", + " 1.7\n", " \n", " \n", " NextWorldLab\n", - " -8.8\n", - " -6.8\n", - " -3.4\n", - " -0.3\n", - " 1.5\n", + " -8.3\n", + " -6.6\n", + " -3.6\n", + " -0.7\n", + " 1.2\n", " \n", " \n", " Bot_Pepa\n", - " -7.0\n", + " -7.2\n", " -5.9\n", - " -3.9\n", + " -4.0\n", " -2.0\n", - " -1.1\n", + " -1.3\n", " \n", " \n", " laylaps\n", - " -10.1\n", - " -7.9\n", + " -10.3\n", + " -8.0\n", " -4.0\n", - " -0.1\n", + " -0.2\n", " 2.1\n", " \n", " \n", " VeritasAI\n", - " -8.0\n", - " -6.8\n", - " -4.4\n", - " -2.0\n", - " -0.7\n", + " -7.7\n", + " -6.6\n", + " -4.2\n", + " -1.9\n", + " -0.6\n", " \n", " \n", " minefrac1\n", - " -7.9\n", - " -6.8\n", - " -4.6\n", - " -2.7\n", - " -1.5\n", + " -7.8\n", + " -6.7\n", + " -4.8\n", + " -2.8\n", + " -1.6\n", " \n", " \n", " Grizeu_Bot\n", - " -9.3\n", + " -9.2\n", " -7.7\n", - " -5.1\n", - " -2.5\n", - " -1.0\n", + " -4.9\n", + " -2.4\n", + " -1.1\n", " \n", " \n", " metac-gpt-4o\n", - " -10.4\n", - " -9.0\n", - " -6.1\n", - " -3.0\n", - " -1.4\n", + " -10.5\n", + " -8.9\n", + " -5.8\n", + " -2.8\n", + " -1.3\n", " \n", " \n", " ajf-bot\n", - " -15.0\n", - " -12.6\n", + " -15.6\n", + " -12.8\n", " -8.4\n", - " -4.2\n", - " -2.2\n", + " -4.0\n", + " -1.9\n", " \n", " \n", "\n", @@ -9165,54 +9161,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.2 9.6 11.9 13.1\n", - "metac-o1-preview 3.7 5.2 8.3 11.2 12.8\n", - "manticAI 0.2 2.1 5.5 8.8 10.5\n", - "metac-Gemini-Exp-1206 0.4 1.9 4.9 7.5 8.9\n", - "acm_bot 0.2 1.8 4.7 7.7 9.1\n", - "metac-perplexity -2.2 0.0 4.3 7.8 9.9\n", - "GreeneiBot2 -1.2 0.4 3.9 7.0 8.7\n", - "twsummerbot 0.3 1.5 3.9 6.1 7.4\n", - "pgodzinai -3.4 -1.2 3.2 7.3 9.6\n", - "cookics_bot_TEST -0.2 0.8 2.9 5.0 5.8\n", - "CumulativeBot -0.1 0.9 2.7 4.6 5.4\n", - "SynapseSeer 0.4 1.1 2.6 4.1 4.8\n", - "metac-claude-3-5-sonnet-latest -1.3 -0.0 2.5 5.0 6.2\n", - "jkraybill_bot -3.5 -1.7 1.7 5.0 6.4\n", - "metac-exa -4.8 -2.2 1.7 5.6 7.8\n", - "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.6\n", + "metac-o1 6.2 7.4 9.7 11.8 13.2\n", + "metac-o1-preview 3.9 5.4 8.4 11.4 12.8\n", + "manticAI 0.1 2.0 5.4 8.6 10.2\n", + "metac-Gemini-Exp-1206 0.5 2.0 5.0 7.9 9.5\n", + "acm_bot 0.1 1.8 4.5 7.5 8.8\n", + "metac-perplexity -2.2 0.2 4.1 7.8 9.5\n", + "GreeneiBot2 -0.8 0.7 4.0 7.2 8.7\n", + "twsummerbot -0.1 1.5 3.9 6.3 7.7\n", + "cookics_bot_TEST 0.0 1.0 3.0 4.9 5.8\n", + "pgodzinai -3.5 -1.1 2.8 6.8 8.9\n", + "metac-claude-3-5-sonnet-latest -1.4 0.0 2.7 5.1 6.2\n", + "SynapseSeer 0.3 1.0 2.6 4.0 5.0\n", + "CumulativeBot -0.3 0.7 2.5 4.4 5.4\n", + "jkraybill_bot -3.7 -1.8 1.8 4.9 6.4\n", + "metac-exa -5.0 -2.4 1.5 5.4 7.4\n", + "metac-deepseek-r1 -1.7 -0.6 1.4 3.4 4.5\n", "MWG -1.6 -0.8 0.7 2.1 2.8\n", - "andrewsiah -0.8 -0.6 -0.0 0.6 0.9\n", - "pianobot -1.2 -0.8 -0.0 0.7 1.0\n", - "cobyj-bot -1.5 -0.9 -0.0 0.8 1.3\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "annabot -3.5 -2.3 -0.4 1.2 2.1\n", - "bean_bot -3.2 -2.3 -0.5 1.1 1.8\n", - "KevinTestBot -4.1 -2.8 -0.6 1.6 2.7\n", - "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.5 1.0\n", - "krm-bot -3.5 -2.7 -0.9 0.7 1.7\n", - "ProfessorSP -4.6 -3.3 -1.0 1.1 2.1\n", - "mmBot -7.5 -5.4 -1.5 2.4 4.7\n", - "metac-grok-2-1212 -6.6 -4.8 -1.5 1.9 3.6\n", - "4Shadower -4.6 -3.6 -1.6 0.2 1.2\n", - "swingswish -5.2 -4.0 -1.9 -0.2 0.5\n", - "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.0 0.9 2.4\n", - "RPM_bot -4.9 -3.8 -2.1 -0.7 -0.2\n", - "InstitutPelFutur -9.1 -6.4 -2.4 1.7 4.0\n", - "wunderplumb -6.2 -4.9 -2.4 -0.2 1.1\n", - "metac-Llama-3.1 -6.8 -5.3 -2.7 0.0 1.5\n", - "NextWorldLab -8.8 -6.8 -3.4 -0.3 1.5\n", - "Bot_Pepa -7.0 -5.9 -3.9 -2.0 -1.1\n", - "laylaps -10.1 -7.9 -4.0 -0.1 2.1\n", - "VeritasAI -8.0 -6.8 -4.4 -2.0 -0.7\n", - "minefrac1 -7.9 -6.8 -4.6 -2.7 -1.5\n", - "Grizeu_Bot -9.3 -7.7 -5.1 -2.5 -1.0\n", - "metac-gpt-4o -10.4 -9.0 -6.1 -3.0 -1.4\n", - "ajf-bot -15.0 -12.6 -8.4 -4.2 -2.2" + "pianobot -1.3 -0.8 0.0 0.7 1.1\n", + "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", + "andrewsiah -0.9 -0.6 -0.0 0.5 0.9\n", + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "annabot -3.2 -2.3 -0.4 1.3 2.0\n", + "bean_bot -3.2 -2.3 -0.4 1.2 1.9\n", + "KevinTestBot -3.9 -2.7 -0.6 1.3 2.4\n", + "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", + "jonahsingerbot -3.0 -2.1 -0.8 0.4 1.1\n", + "krm-bot -3.7 -2.8 -1.0 0.6 1.7\n", + "ProfessorSP -4.1 -3.2 -1.1 1.1 2.3\n", + "metac-grok-2-1212 -6.6 -4.7 -1.4 1.8 3.5\n", + "mmBot -7.2 -5.5 -1.5 2.2 4.0\n", + "4Shadower -4.7 -3.8 -1.7 0.2 1.3\n", + "metac-claude-3-5-sonnet-20240620 -6.5 -4.5 -1.8 0.9 2.4\n", + "swingswish -5.4 -4.0 -1.9 -0.2 0.6\n", + "RPM_bot -4.8 -3.8 -2.1 -0.7 -0.1\n", + "InstitutPelFutur -9.0 -6.4 -2.5 1.6 3.6\n", + "wunderplumb -6.4 -4.9 -2.7 -0.2 0.8\n", + "metac-Llama-3.1 -6.7 -5.3 -2.7 0.0 1.7\n", + "NextWorldLab -8.3 -6.6 -3.6 -0.7 1.2\n", + "Bot_Pepa -7.2 -5.9 -4.0 -2.0 -1.3\n", + "laylaps -10.3 -8.0 -4.0 -0.2 2.1\n", + "VeritasAI -7.7 -6.6 -4.2 -1.9 -0.6\n", + "minefrac1 -7.8 -6.7 -4.8 -2.8 -1.6\n", + "Grizeu_Bot -9.2 -7.7 -4.9 -2.4 -1.1\n", + "metac-gpt-4o -10.5 -8.9 -5.8 -2.8 -1.3\n", + "ajf-bot -15.6 -12.8 -8.4 -4.0 -1.9" ] }, - "execution_count": 50, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -9235,7 +9231,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 132, "metadata": { "cellView": "form", "colab": { @@ -9288,28 +9284,20 @@ " \n", " \n", " \n", - " metac-o1\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " \n", - " \n", " metac-perplexity\n", - " 20.3\n", - " 20.3\n", - " 20.3\n", - " 20.3\n", - " 20.3\n", + " 20.6\n", + " 20.6\n", + " 20.6\n", + " 20.6\n", + " 20.6\n", " \n", " \n", - " bot_median\n", - " 17.9\n", - " 17.9\n", - " 17.9\n", - " 17.9\n", - " 17.9\n", + " metac-o1\n", + " 20.2\n", + " 20.2\n", + " 20.2\n", + " 20.2\n", + " 20.2\n", " \n", " \n", " acm_bot\n", @@ -9320,6 +9308,14 @@ " 17.7\n", " \n", " \n", + " bot_median\n", + " 17.4\n", + " 17.4\n", + " 17.4\n", + " 17.4\n", + " 17.4\n", + " \n", + " \n", " manticAI\n", " 14.5\n", " 14.5\n", @@ -9345,27 +9341,43 @@ " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " 12.0\n", - " 12.0\n", - " 12.0\n", - " 12.0\n", - " 12.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", " \n", " \n", - " GreeneiBot2\n", - " 11.7\n", - " 11.7\n", - " 11.7\n", - " 11.7\n", - " 11.7\n", + " metac-claude-3-5-sonnet-latest\n", + " 12.4\n", + " 12.4\n", + " 12.4\n", + " 12.4\n", + " 12.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " 11.5\n", - " 11.5\n", - " 11.5\n", - " 11.5\n", - " 11.5\n", + " metac-deepseek-r1\n", + " 12.3\n", + " 12.3\n", + " 12.3\n", + " 12.3\n", + " 12.3\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " 12.2\n", + " 12.2\n", + " 12.2\n", + " 12.2\n", + " 12.2\n", + " \n", + " \n", + " GreeneiBot2\n", + " 11.8\n", + " 11.8\n", + " 11.8\n", + " 11.8\n", + " 11.8\n", " \n", " \n", " NextWorldLab\n", @@ -9376,30 +9388,6 @@ " 11.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " 11.0\n", - " 11.0\n", - " 11.0\n", - " 11.0\n", - " 11.0\n", - " \n", - " \n", - " metac-gpt-4o\n", - " 10.5\n", - " 10.5\n", - " 10.5\n", - " 10.5\n", - " 10.5\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " 10.2\n", - " 10.2\n", - " 10.2\n", - " 10.2\n", - " 10.2\n", - " \n", - " \n", " Grizeu_Bot\n", " 10.2\n", " 10.2\n", @@ -9416,12 +9404,12 @@ " 10.2\n", " \n", " \n", - " metac-o1-preview\n", - " 10.1\n", - " 10.1\n", - " 10.1\n", - " 10.1\n", - " 10.1\n", + " metac-grok-2-1212\n", + " 9.8\n", + " 9.8\n", + " 9.8\n", + " 9.8\n", + " 9.8\n", " \n", " \n", " mmBot\n", @@ -9432,12 +9420,12 @@ " 9.7\n", " \n", " \n", - " metac-exa\n", - " 9.7\n", - " 9.7\n", - " 9.7\n", - " 9.7\n", - " 9.7\n", + " metac-Gemini-Exp-1206\n", + " 9.6\n", + " 9.6\n", + " 9.6\n", + " 9.6\n", + " 9.6\n", " \n", " \n", " annabot\n", @@ -9448,12 +9436,12 @@ " 9.0\n", " \n", " \n", - " metac-deepseek-r1\n", - " 8.4\n", - " 8.4\n", - " 8.4\n", - " 8.4\n", - " 8.4\n", + " metac-exa\n", + " 8.8\n", + " 8.8\n", + " 8.8\n", + " 8.8\n", + " 8.8\n", " \n", " \n", " VeritasAI\n", @@ -9472,20 +9460,20 @@ " 7.6\n", " \n", " \n", - " cookics_bot_TEST\n", - " 6.4\n", - " 6.4\n", - " 6.4\n", - " 6.4\n", - " 6.4\n", + " metac-o1-preview\n", + " 6.7\n", + " 6.7\n", + " 6.7\n", + " 6.7\n", + " 6.7\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 5.8\n", - " 5.8\n", - " 5.8\n", - " 5.8\n", - " 5.8\n", + " cookics_bot_TEST\n", + " 6.3\n", + " 6.3\n", + " 6.3\n", + " 6.3\n", + " 6.3\n", " \n", " \n", " MWG\n", @@ -9520,6 +9508,14 @@ " 3.3\n", " \n", " \n", + " metac-gpt-4o\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " \n", + " \n", " InstitutPelFutur\n", " 2.7\n", " 2.7\n", @@ -9592,15 +9588,15 @@ " 1.8\n", " \n", " \n", - " 4Shadower\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", + " RPM_bot\n", + " 0.8\n", + " 0.8\n", + " 0.8\n", + " 0.8\n", + " 0.8\n", " \n", " \n", - " krm-bot\n", + " 4Shadower\n", " 0.6\n", " 0.6\n", " 0.6\n", @@ -9608,7 +9604,7 @@ " 0.6\n", " \n", " \n", - " RPM_bot\n", + " krm-bot\n", " 0.6\n", " 0.6\n", " 0.6\n", @@ -9661,35 +9657,35 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 21.0 21.0 21.0 21.0 21.0\n", - "metac-perplexity 20.3 20.3 20.3 20.3 20.3\n", - "bot_median 17.9 17.9 17.9 17.9 17.9\n", + "metac-perplexity 20.6 20.6 20.6 20.6 20.6\n", + "metac-o1 20.2 20.2 20.2 20.2 20.2\n", "acm_bot 17.7 17.7 17.7 17.7 17.7\n", + "bot_median 17.4 17.4 17.4 17.4 17.4\n", "manticAI 14.5 14.5 14.5 14.5 14.5\n", "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", - "metac-claude-3-5-sonnet-20240620 12.0 12.0 12.0 12.0 12.0\n", - "GreeneiBot2 11.7 11.7 11.7 11.7 11.7\n", - "metac-claude-3-5-sonnet-latest 11.5 11.5 11.5 11.5 11.5\n", + "metac-claude-3-5-sonnet-20240620 13.0 13.0 13.0 13.0 13.0\n", + "metac-claude-3-5-sonnet-latest 12.4 12.4 12.4 12.4 12.4\n", + "metac-deepseek-r1 12.3 12.3 12.3 12.3 12.3\n", + "metac-Llama-3.1 12.2 12.2 12.2 12.2 12.2\n", + "GreeneiBot2 11.8 11.8 11.8 11.8 11.8\n", "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", - "metac-grok-2-1212 11.0 11.0 11.0 11.0 11.0\n", - "metac-gpt-4o 10.5 10.5 10.5 10.5 10.5\n", - "metac-Llama-3.1 10.2 10.2 10.2 10.2 10.2\n", "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", - "metac-o1-preview 10.1 10.1 10.1 10.1 10.1\n", + "metac-grok-2-1212 9.8 9.8 9.8 9.8 9.8\n", "mmBot 9.7 9.7 9.7 9.7 9.7\n", - "metac-exa 9.7 9.7 9.7 9.7 9.7\n", + "metac-Gemini-Exp-1206 9.6 9.6 9.6 9.6 9.6\n", "annabot 9.0 9.0 9.0 9.0 9.0\n", - "metac-deepseek-r1 8.4 8.4 8.4 8.4 8.4\n", + "metac-exa 8.8 8.8 8.8 8.8 8.8\n", "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", "laylaps 7.6 7.6 7.6 7.6 7.6\n", - "cookics_bot_TEST 6.4 6.4 6.4 6.4 6.4\n", - "metac-Gemini-Exp-1206 5.8 5.8 5.8 5.8 5.8\n", + "metac-o1-preview 6.7 6.7 6.7 6.7 6.7\n", + "cookics_bot_TEST 6.3 6.3 6.3 6.3 6.3\n", "MWG 5.5 5.5 5.5 5.5 5.5\n", "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", + "metac-gpt-4o 3.0 3.0 3.0 3.0 3.0\n", "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", @@ -9699,9 +9695,9 @@ "bean_bot 2.1 2.1 2.1 2.1 2.1\n", "X_bot 1.9 1.9 1.9 1.9 1.9\n", "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", + "RPM_bot 0.8 0.8 0.8 0.8 0.8\n", "4Shadower 0.6 0.6 0.6 0.6 0.6\n", "krm-bot 0.6 0.6 0.6 0.6 0.6\n", - "RPM_bot 0.6 0.6 0.6 0.6 0.6\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "pianobot -2.2 -2.2 -2.2 -2.2 -2.2\n", @@ -9709,7 +9705,7 @@ "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" ] }, - "execution_count": 51, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -9730,7 +9726,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -9740,7 +9736,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -9800,7 +9796,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 135, "metadata": { "cellView": "form", "colab": { @@ -10289,7 +10285,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 54, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -10310,7 +10306,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -10319,7 +10315,7 @@ }, { "cell_type": "code", - 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" >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.65, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.6, nan, nan]\n", " >>> Collected 4 forecasts: [0.7, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.35, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.6, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.1, 0.25, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.05, 0.3, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.05, 0.25, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - 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" >>> Collected 4 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.75, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.85, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.35, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.85, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.65, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.6, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 0.3, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.6, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.25, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.3, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.25, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.3, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.85, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.35, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.85, 0.3, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.65, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.6, 0.6, nan, nan, nan, 0.7]\n", " >>> Collected 6 forecasts: [0.7, 0.3, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65]\n", + " >>> Collected 7 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", + " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38]\n", - " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2]\n", + " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35]\n", + " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", - " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", - " >>> Collected 7 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", + " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513]\n", - " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", - " >>> Collected 8 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", - " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3, nan]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - 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" >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } @@ -10906,7 +10896,7 @@ }, { "cell_type": "code", - 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"print(f'Sum of weights: {a}, Number of questions: {b}')" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3-FedHpWV_1v", - "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" - }, "outputs": [ { "data": { @@ -11170,486 +10930,215 @@ " \n", " \n", " \n", - " bot_question_id\n", - " question_weight\n", - " resolution\n", " type\n", " options\n", - " range_min\n", - " range_max\n", - " metac-o1-preview\n", - " metac-o1\n", - " pgodzinai\n", - " ...\n", - " median_forecast_1_bots\n", - " median_forecast_2_bots\n", - " median_forecast_3_bots\n", - " median_forecast_4_bots\n", - " median_forecast_5_bots\n", - " median_forecast_6_bots\n", - " median_forecast_7_bots\n", - " median_forecast_8_bots\n", - " median_forecast_9_bots\n", - " median_forecast_10_bots\n", - " \n", - " \n", - " \n", - " \n", - " 0\n", - " 31262\n", - " 1.0\n", - " 0\n", - " multiple_choice\n", - " [0, 1, 2-3, 4-6, >6]\n", - " NaN\n", - 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- "\n", - " median_forecast_7_bots median_forecast_8_bots median_forecast_9_bots \\\n", - "342 0.905 0.9025 0.9 \n", - "351 0.35 0.2085 0.35 \n", - "355 0.8 0.772 0.8 \n", - "361 0.8 0.755 0.8 \n", - "364 0.05 0.046 0.05 \n", - "\n", - " median_forecast_10_bots \n", - "342 0.9 \n", - "351 0.238 \n", - "355 0.814 \n", - "361 0.755 \n", - "364 0.05 \n", + " type options resolution \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] 0 \n", + "1 numeric NaN 86.82 \n", + "2 binary NaN no \n", + "3 multiple_choice [0-4, 5-9, >9] 5-9 \n", + "4 numeric NaN 119.2 \n", + ".. ... ... ... \n", + "342 binary NaN yes \n", + "351 binary NaN no \n", + "355 binary NaN yes \n", + "361 binary NaN no \n", + "364 binary NaN no \n", "\n", - "[5 rows x 27 columns]" + " metac-o1-preview \\\n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.1 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + ".. ... \n", + "342 0.95 \n", + "351 0.85 \n", + "355 0.95 \n", + "361 0.85 \n", + "364 0.05 \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.038231012375000005, 0... \n", + "2 0.085 \n", + "3 0.55 \n", + "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", + ".. ... \n", + "342 0.95 \n", + "351 0.3 \n", + "355 0.8 \n", + "361 0.71 \n", + "364 0.05 \n", + "\n", + " median_forecast_8_bots \n", + "0 0.032463 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", + "2 0.1 \n", + "3 0.5125 \n", + "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + ".. ... \n", + "342 0.92 \n", + "351 0.1835 \n", + "355 0.775 \n", + "361 0.704 \n", + "364 0.046 \n", + "\n", + "[99 rows x 6 columns]" ] }, + "execution_count": 140, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_forecasts[['type', 'options', 'resolution', 'metac-o1-preview', 'median_forecast_5_bots', 'median_forecast_8_bots']]" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum of weights: 95.0, Number of questions: 99\n" + ] + } + ], + "source": [ + "# Sanity check\n", + "a = df_bot_team_forecasts['question_weight'].sum()\n", + "b = df_bot_team_forecasts.shape[0] # number of rows in df_bot_team_forecasts\n", + "print(f'Sum of weights: {a}, Number of questions: {b}')" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "3-FedHpWV_1v", + "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" + }, + "outputs": [ { "data": { "text/html": [ @@ -11679,52 +11168,52 @@ " \n", " 0\n", " 1\n", - " 15.75\n", + " 16.52\n", " \n", " \n", " 1\n", " 2\n", - " 26.31\n", + " 26.94\n", " \n", " \n", " 2\n", " 3\n", - " 27.15\n", + " 28.15\n", " \n", " \n", " 3\n", " 4\n", - " 27.65\n", + " 27.95\n", " 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"execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai']" ] }, - "execution_count": 62, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -11798,7 +11287,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -11807,7 +11296,7 @@ "(424, 47)" ] }, - "execution_count": 63, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -11818,7 +11307,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ @@ -11836,7 +11325,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -11893,20 +11382,20 @@ " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", - " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " [0.4,0.35,0.2,0.04,0.01]\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", " ...\n", - " 0.02\n", - " 0.21\n", - " 0.02\n", - " 0.017463\n", - " 0.017463\n", - " 0.02\n", - " 0.1\n", - " 0.1\n", - " 0.02\n", - " 0.02\n", + " 0.010417\n", + " 0.205208\n", + " 0.014926\n", + " 0.012671\n", + " 0.012671\n", + " 0.014926\n", + " 0.032463\n", + " 0.032463\n", + " 0.014926\n", + " 0.014926\n", " \n", " \n", " 1\n", @@ -11918,19 +11407,19 @@ " 60.0\n", " 100.0\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", - " [0.03366666666666667, 0.03409436576666667, 0.0...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", + " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", + " [0.03366666666666667, 0.034105259000000006, 0....\n", + " [0.037750000000000006, 0.038231012375000005, 0...\n", + " [0.037750000000000006, 0.038231012375000005, 0...\n", + " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.041833333333333333, 0.042417897133333334, 0...\n", + " [0.041833333333333333, 0.042417897133333334, 0...\n", " \n", " \n", " 2\n", @@ -11941,20 +11430,20 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.15\n", + " 0.1\n", " 0.1\n", " 0.07\n", " ...\n", - " 0.15\n", - " 0.125\n", + " 0.1\n", + " 0.1\n", " 0.1\n", " 0.085\n", " 0.085\n", " 0.1\n", - " 0.125\n", - " 0.125\n", - " 0.15\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " \n", " \n", " 3\n", @@ -11966,18 +11455,18 @@ " NaN\n", " NaN\n", " [0.2,0.6,0.2]\n", - " [0.25,0.6,0.15]\n", + " [0.3,0.55,0.15]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", " ...\n", " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.55625\n", + " 0.575\n", + " 0.55\n", + " 0.575\n", + " 0.55\n", + " 0.53125\n", " 0.5125\n", " 0.5125\n", - " 0.55625\n", + " 0.53125\n", " 0.5125\n", " \n", " \n", @@ -11989,20 +11478,20 @@ " NaN\n", " 0.0\n", " 400.0\n", - " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " ...\n", - " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07...\n", - " [0.0, 0.00642857145, 0.01285714285, 0.01928571...\n", - " [0.0, 0.004323767066666667, 0.0086529941333333...\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", - " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", - " [0.0, 0.00295931485, 0.0059231771, 0.008890847...\n", - " [0.0, 0.0028936984428571426, 0.005791294657142...\n", - " [0.0, 0.0028936984428571426, 0.005791294657142...\n", - " [0.0, 0.0028097639124999995, 0.005622938375, 0...\n", - " [0.0, 0.0026433398111111108, 0.005289711211111...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", + " [0.0, 0.0024830850250000002, 0.004970265075000...\n", + " [0.0, 0.0022111217800000003, 0.00442770048, 0....\n", + " [0.0, 0.0021497910333333338, 0.004304129483333...\n", + " [0.0, 0.002199820885714286, 0.0044035395571428...\n", + " [0.0, 0.002199820885714286, 0.0044035395571428...\n", + " [0.0, 0.0023415099375000002, 0.00468643045, 0....\n", + " [0.0, 0.002227114055555556, 0.0044572597222222...\n", " \n", " \n", "\n", @@ -12025,18 +11514,18 @@ "4 NaN 0.0 400.0 \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.15 \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-o1 \\\n", "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "3 [0.3,0.55,0.15] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " pgodzinai ... \\\n", "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", @@ -12046,79 +11535,79 @@ "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 0.02 \n", + "0 0.010417 \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.15 \n", + "2 0.1 \n", "3 0.6 \n", - "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07... \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 0.21 \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", - "2 0.125 \n", - "3 0.6 \n", - "4 [0.0, 0.00642857145, 0.01285714285, 0.01928571... \n", + "0 0.205208 \n", + "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", + "2 0.1 \n", + "3 0.575 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_3_bots \\\n", - "0 0.02 \n", - "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", + "0 0.014926 \n", + "1 [0.03366666666666667, 0.034105259000000006, 0.... \n", "2 0.1 \n", - "3 0.6 \n", - "4 [0.0, 0.004323767066666667, 0.0086529941333333... \n", + "3 0.55 \n", + "4 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", "\n", " median_forecast_4_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.038231012375000005, 0... \n", "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "3 0.575 \n", + "4 [0.0, 0.0024830850250000002, 0.004970265075000... \n", "\n", " median_forecast_5_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.038231012375000005, 0... \n", "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", + "3 0.55 \n", + "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", "\n", " median_forecast_6_bots \\\n", - "0 0.02 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "0 0.014926 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", "2 0.1 \n", - "3 0.55625 \n", - "4 [0.0, 0.00295931485, 0.0059231771, 0.008890847... \n", + "3 0.53125 \n", + "4 [0.0, 0.0021497910333333338, 0.004304129483333... \n", "\n", " median_forecast_7_bots \\\n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", + "0 0.032463 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", + "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", + "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", "\n", " median_forecast_8_bots \\\n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", + "0 0.032463 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", + "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", + "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", "\n", " median_forecast_9_bots \\\n", - "0 0.02 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.55625 \n", - "4 [0.0, 0.0028097639124999995, 0.005622938375, 0... \n", + "0 0.014926 \n", + "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "2 0.1 \n", + "3 0.53125 \n", + "4 [0.0, 0.0023415099375000002, 0.00468643045, 0.... \n", "\n", " median_forecast_10_bots \n", - "0 0.02 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", + "0 0.014926 \n", + "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.0026433398111111108, 0.005289711211111... \n", + "4 [0.0, 0.002227114055555556, 0.0044572597222222... \n", "\n", "[5 rows x 27 columns]" ] }, - "execution_count": 65, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -12129,7 +11618,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 147, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12177,14 +11666,14 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -15.1905\n" + "Weighted Total Score: -13.5599\n" ] } ], @@ -12194,7 +11683,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 149, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12206,7 +11695,7 @@ "outputs": [ { "data": { - "image/png": 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", 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X9e7d297H29tb69ev19q1a/Xpp59q9erVWrp0qW677TatWbPmqv+msl0tkP/Vn89sX68777zTPn/YmjVr9OKLL2ratGlavny5OnXqdMP7BeBchC8Axdrly5clSefPn5eUM+DAuXPnHM5+7d271749218Ht7hR2QN1lC9f/pq/oJ09e1YJCQl66qmnNGnSJHt79pmzwiYwMFC+vr7KzMz82188P/jgA918881avny5w/v614l9q1SpooSEBJ0/f97hF+p9+/Y59IuJiVF8fHyur3W1v7cqVaro+++/V1ZWlkMI+Ovf/aOPPuowSEuZMmWueWxXY+X7c72yzxgfP35cUs5ndvfu3apWrVquz8l+v/bt23fFWd19+/blOqLiX1WtWlVffPGFWrRokadg0qxZMzVr1kzPPfecFi9erLvvvltLlixxuPz2r0qXLq2uXbsqLi5OL730kpYuXapWrVopJCTEoZ+Li4vat2+v9u3b66WXXtLzzz+vxx9/XGvXri2wQPrn9/Cv9u7dq3LlyjkMx1+hQgU98MADeuCBB5SUlKSGDRvqueeeI3wBRQj3fAEotjIyMrRmzRp5eHjYLy3r3LmzMjMz9eqrrzr0nTVrlmw2m8MvMaVLl87zEN7XEhMTIz8/Pz3//PO53q9y8uRJSTlnrP56BufPl0cVJq6ururZs6eWLVum3bt3X7E9+7iy+0qOx7Z582Zt2rTJ4TmdO3fW5cuXNXfuXHtbZmam5syZ49CvQoUKio6OdliylS5dOtdLNDt37qzExEQtXbrU3nb58mXNmTNHPj4+atOmjSSpdu3aDvtt1KhRnt6Pv7Ly/bmar7/+OtfPYPa9RdmXv3Xo0EG+vr6aOnWqLl686NA3u8bGjRurfPnymjdvni5dumTf/tlnn2nPnj3q0qXL39Zz5513KjMzU88888wV2y5fvmz/93f27Nkr/l1kjwD559e+mj59+ujYsWP6z3/+o127dqlPnz4O28+cOXPFc65n/zeqQoUKioiI0KJFixx+1uzevVtr1qxR586dJZl/x3/9TJcvX14hISEFWh+A/MeZLwDFxmeffWY/i5GUlKTFixfrl19+0fjx4+33VXXr1k3t2rXT448/roMHD6p+/fpas2aNPvroI40ePdphOPlGjRrpiy++0EsvvaSQkBCFh4crMjLyuuvy8/PT3Llz1b9/fzVs2FB9+/ZVYGCgDh8+rE8//VQtWrTQq6++Kj8/P7Vu3VrTp09XRkaGKlasqDVr1tjnCiqMXnjhBa1du1aRkZG67777VLt2bZ05c0bbt2/XF198Yf+ltmvXrlq+fLl69OihLl266MCBA5o3b55q165tPyspmX8/LVq00Pjx43Xw4EHVrl1by5cvzzVMXU2jRo20dOlSjRkzRk2aNJGPj4+6deumYcOGaf78+Ro0aJC2bdumsLAwffDBB9qwYYNmz559xX2AxfH9mTZtmrZt26Y77rjDfsnj9u3b9dZbb6ls2bL2oe/9/Pw0a9Ys3XvvvWrSpInuuusulSlTRrt27dKFCxe0aNEiubu7a9q0aRo8eLDatGmjfv362YeaDwsL08MPP/y39bRp00b333+/pk6dqp07d6pDhw5yd3fXL7/8ori4OL388svq1auXFi1apNdff109evRQ1apVde7cOb355pvy8/OzB5RryZ5bbNy4cfZQ/GdPP/201q9fry5duqhKlSpKSkrS66+/rkqVKqlly5Z5em9v1IsvvqhOnTopKipKQ4cOtQ817+/vrylTpkgy5/iqVKmSevXqpfr168vHx0dffPGFvvvuuwKdww9AAbBiiEUAyE+5DTXv5eVlREREGHPnzrUPjZ3t3LlzxsMPP2yEhIQY7u7uRvXq1Y0XX3zxin579+41WrdubR+aO6/Dzv91qPlsa9euNWJiYgx/f3/Dy8vLqFq1qjFo0CBj69at9j6///670aNHDyMgIMDw9/c3evfubRw7dsyQZEyePNneL3uo+b8OjT1w4ECjdOnSV9TUpk0bo06dOn9be5UqVYwuXbrkum3t2rVXDKVuGIZx4sQJ48EHHzRCQ0MNd3d3Izg42Gjfvr3xxhtv2PtkZWUZzz//vFGlShXD09PTaNCggbFy5Upj4MCBVwwLf/r0aaN///6Gn5+f4e/vb/Tv39/YsWNHnoeaP3/+vHHXXXcZAQEBhiSH/Z84ccIYPHiwUa5cOcPDw8OoV69envaZrai/Pxs2bDAefPBBo27duoa/v7/h7u5uVK5c2Rg0aJCxf//+K/p//PHHRvPmzQ1vb2/Dz8/PaNq0qfHee+859Fm6dKnRoEEDw9PT0yhbtqxx9913G7///rtDn6t9LrO98cYbRqNGjQxvb2/D19fXqFevnvHoo48ax44dMwzDMLZv327069fPqFy5suHp6WmUL1/e6Nq1q8O/nb9z9913G5KM6OjoK7YlJCQY3bt3N0JCQgwPDw8jJCTE6Nevn/Hzzz//7X6v9ZnIlj3U/Isvvpjr9i+++MJo0aKF/X3u1q2b8dNPP9m3X7p0yXjkkUeM+vXrG76+vkbp0qWN+vXrG6+//vrf1gegcLEZRj7fgQ0AAAAAuAL3fAEAAACAExC+AAAAAMAJCF8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIBJlm9QVlaWjh07Jl9fX9lsNqvLAQAAAGARwzB07tw5hYSEyMXl6ue3CF836NixYwoNDbW6DAAAAACFxJEjR1SpUqWrbid83SBfX19J5hvs5+dncTUAAAAArJKamqrQ0FB7RrgawtcNyr7U0M/Pj/AFAAAA4G9vR2LADQAAAABwAsIXAAAAADgB4QsAAAAAnIB7vgAAAFBkZWZmKiMjw+oyUMy5urrKzc3tH08xRfgCAABAkXT+/Hn9/vvvMgzD6lJQApQqVUoVKlSQh4fHDe+D8AUAAIAiJzMzU7///rtKlSqlwMDAf3xGArgawzCUnp6ukydP6sCBA6pevfo1J1K+FsIXAAAAipyMjAwZhqHAwEB5e3tbXQ6KOW9vb7m7u+vQoUNKT0+Xl5fXDe2HATcAAABQZHHGC85yo2e7HPaRD3UAAAAAAP4G4QsAAAAAnIDwBQAAABQCbdu21ejRo532egsXLlRAQIDTXq8gOfu9u1GELwAAAMBJBg0aJJvNdsXy66+/avny5XrmmWfsfcPCwjR79myH51sRmNauXauuXbsqMDBQXl5eqlq1qvr06aP169c7tY5r+et7V1gRvgAAAAAn6tixo44fP+6whIeHq2zZsvL19bW6PAevv/662rdvr5tuuklLly7Vvn379OGHH6p58+Z6+OGHrS7PrjC+d7khfAEAAABO5OnpqeDgYIfF1dXV4dK5tm3b6tChQ3r44YftZ8e++uorDR48WCkpKfa2KVOmSJIuXbqkcePGqWLFiipdurQiIyP11VdfObzuwoULVblyZZUqVUo9evTQ6dOnr1nn4cOHNXr0aI0ePVqLFi3SbbfdpipVqujWW2/VqFGjtHXrVnvf06dPq1+/fqpYsaJKlSqlevXq6b333nPYX25n8iIiIuzHYBiGpkyZosqVK8vT01MhISF66KGH7H1ff/11Va9eXV5eXgoKClKvXr3s2/562eHbb7+txo0by9fXV8HBwbrrrruUlJRk3/7VV1/JZrMpISFBjRs3VqlSpdS8eXPt27fvmu/JP8U8XwAAACgeGjeWEhOd/7rBwdKfgkh+WL58uerXr69hw4bpvvvuk2Se3Zk9e7YmTZpkDwk+Pj6SpBEjRuinn37SkiVLFBISog8//FAdO3bUDz/8oOrVq2vz5s0aOnSopk6dqtjYWK1evVqTJ0++Zg3Lli1TRkaGHn300Vy3/3mY/4sXL6pRo0Z67LHH5Ofnp08//VT9+/dX1apV1bRp0zwd87JlyzRr1iwtWbJEderUUWJionbt2iVJ2rp1qx566CG9/fbbat68uc6cOaOvv/76qvvKyMjQM888oxo1aigpKUljxozRoEGDtGrVKod+jz/+uGbOnKnAwEANHz5cQ4YM0YYNG/JU740gfAEAAKB4SEyUjh61uoq/tXLlSntokqROnTopLi7OoU/ZsmXl6upqP3OTzd/fXzabzaHt8OHDWrBggQ4fPqyQkBBJ0rhx47R69WotWLBAzz//vF5++WV17NjRHqRuueUWbdy4UatXr75qnT///LP8/PwcXmvZsmUaOHCgfX3Tpk2qV6+eKlasqHHjxtnbR44cqc8//1zvv/9+nsPX4cOHFRwcrOjoaLm7u6ty5cr25x4+fFilS5dW165d5evrqypVqqhBgwZX3deQIUPsj2+++Wa98soratKkic6fP+/w3j/33HNq06aNJGn8+PHq0qWLLl68eMOTKP8dwhcAAACKhz+FhML8uu3atdPcuXPt66VLl/5HL//DDz8oMzNTt9xyi0P7pUuXdNNNN0mS9uzZox49ejhsj4qKumb4kq6cxDomJkY7d+7U0aNH1bZtW2VmZkqSMjMz9fzzz+v999/X0aNHlZ6erkuXLqlUqVJ5Po7evXtr9uzZuvnmm9WxY0d17txZ3bp1k5ubm26//XZVqVLFvq1jx47q0aPHVfe/bds2TZkyRbt27dLZs2eVlZUlyQxxtWvXtve79dZb7Y8rVKggSUpKSlLlypXzXPf1IHwBAACgeMjnS/8KSunSpVWtWrV829/58+fl6uqqbdu2ydXV1WHbn8/yXK/q1asrJSVFiYmJ9rNfPj4+qlatmtzcHGPEiy++qJdfflmzZ89WvXr1VLp0aY0ePVrp6en2Pi4uLjIMw+F5GRkZ9sehoaHat2+fvvjiC8XHx+uBBx7Qiy++qHXr1snX11fbt2/XV199pTVr1mjSpEmaMmWKvvvuuytGf0xLS1NMTIxiYmL07rvvKjAwUIcPH1ZMTIxDPZLk7u5uf5wdNLODWkFgwA0AAACgEPLw8LCfWbpWW4MGDZSZmamkpCRVq1bNYckOTbVq1dLmzZsdnvftt99e8/V79eold3d3TZs27W9r3bBhg7p376577rlH9evX180336yff/7ZoU9gYKCOHz9uX09NTdWBAwcc+nh7e6tbt2565ZVX9NVXX2nTpk364YcfJElubm6Kjo7W9OnT9f333+vgwYP68ssvr6hl7969On36tF544QW1atVKNWvWdBhsw0qc+QIAJ4rbn1Jg++5d1b/A9g0AcL6wsDCtX79effv2laenp8qVK6ewsDCdP39eCQkJql+/vkqVKqVbbrlFd999twYMGKCZM2eqQYMGOnnypBISEnTrrbeqS5cueuihh9SiRQvNmDFD3bt31+eff/63lxxWrlxZM2fO1KhRo3TmzBkNGjRI4eHhOnPmjN555x1Jsp9pq169uj744ANt3LhRZcqU0UsvvaQTJ044XOJ32223aeHCherWrZsCAgI0adIkhzN1CxcuVGZmpiIjI1WqVCm988478vb2VpUqVbRy5Ur99ttvat26tcqUKaNVq1YpKytLNWrUyLVuDw8PzZkzR8OHD9fu3bsLzRxgnPkCAAAACqGnn35aBw8eVNWqVRUYGChJat68uYYPH64+ffooMDBQ06dPlyQtWLBAAwYM0NixY1WjRg3Fxsbqu+++s9+71KxZM7355pt6+eWXVb9+fa1Zs0ZPPPHE39YwcuRIrVmzRidPnlSvXr1UvXp1de7cWQcOHNDq1atVr149SdITTzyhhg0bKiYmRm3btlVwcLBiY2Md9jVhwgS1adNGXbt2VZcuXRQbG6uqVavatwcEBOjNN99UixYtdOutt+qLL77QJ598optuukkBAQFavny5brvtNtWqVUvz5s3Te++9pzp16lxRc2BgoBYuXKi4uDjVrl1bL7zwgmbMmHFDfwf5zWb89cJL5Elqaqr8/f2VkpIiPz8/q8sBUERw5gsA8sfFixd14MABhYeHF9jIdMCfXeszl9dswJkvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAECRxdhxcJb8+KwRvgAAAFDkZM8PlZ6ebnElKCkuXLggSXJ3d7/hfTDJMgAAAIocNzc3lSpVSidPnpS7u7tcXDingIJhGIYuXLigpKQkBQQEOEwMfb0IXwAAAChybDabKlSooAMHDujQoUNWl4MSICAgQMHBwf9oH4QvAAAAFEkeHh6qXr06lx6iwLm7u/+jM17ZCF8AAAAoslxcXOTl5WV1GUCecHEsAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACcgfAEAAACAExC+AAAAAMAJCF8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACewPHy99tprCgsLk5eXlyIjI7Vly5Zr9o+Li1PNmjXl5eWlevXqadWqVQ7bly9frg4dOuimm26SzWbTzp07HbafOXNGI0eOVI0aNeTt7a3KlSvroYceUkpKSn4fGgAAAADYWRq+li5dqjFjxmjy5Mnavn276tevr5iYGCUlJeXaf+PGjerXr5+GDh2qHTt2KDY2VrGxsdq9e7e9T1pamlq2bKlp06bluo9jx47p2LFjmjFjhnbv3q2FCxdq9erVGjp0aIEcIwAAAABIks0wDMOqF4+MjFSTJk306quvSpKysrIUGhqqkSNHavz48Vf079Onj9LS0rRy5Up7W7NmzRQREaF58+Y59D148KDCw8O1Y8cORUREXLOOuLg43XPPPUpLS5Obm1uufS5duqRLly7Z11NTUxUaGqqUlBT5+fnl9ZABlHBx+wvuLHvvqv4Ftm8AAHB1qamp8vf3/9tsYNmZr/T0dG3btk3R0dE5xbi4KDo6Wps2bcr1OZs2bXLoL0kxMTFX7Z9X2W/S1YKXJE2dOlX+/v72JTQ09B+9JgAAAICSxbLwderUKWVmZiooKMihPSgoSImJibk+JzEx8br657WOZ555RsOGDbtmvwkTJiglJcW+HDly5IZfEwAAAEDJc/VTPSVAamqqunTpotq1a2vKlCnX7Ovp6SlPT0/nFAYAAACg2LHszFe5cuXk6uqqEydOOLSfOHFCwcHBuT4nODj4uvpfy7lz59SxY0f5+vrqww8/lLu7+3XvAwAAAADyyrLw5eHhoUaNGikhIcHelpWVpYSEBEVFReX6nKioKIf+khQfH3/V/leTmpqqDh06yMPDQx9//LG8vLyu/wAAAAAA4DpYetnhmDFjNHDgQDVu3FhNmzbV7NmzlZaWpsGDB0uSBgwYoIoVK2rq1KmSpFGjRqlNmzaaOXOmunTpoiVLlmjr1q1644037Ps8c+aMDh8+rGPHjkmS9u3bJ8k8axYcHGwPXhcuXNA777yj1NRUpaamSpICAwPl6urqzLcAAAAAQAlhafjq06ePTp48qUmTJikxMVERERFavXq1fVCNw4cPy8Ul5+Rc8+bNtXjxYj3xxBOaOHGiqlevrhUrVqhu3br2Ph9//LE9vElS3759JUmTJ0/WlClTtH37dm3evFmSVK1aNYd6Dhw4oLCwsII6XAAAAAAlmKXzfBVleR3LHwD+jHm+AAAofgr9PF8AAAAAUJIQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBNYHr5ee+01hYWFycvLS5GRkdqyZcs1+8fFxalmzZry8vJSvXr1tGrVKofty5cvV4cOHXTTTTfJZrNp586dV+zj4sWLevDBB3XTTTfJx8dHPXv21IkTJ/LzsAAAAADAgaXha+nSpRozZowmT56s7du3q379+oqJiVFSUlKu/Tdu3Kh+/fpp6NCh2rFjh2JjYxUbG6vdu3fb+6Slpally5aaNm3aVV/34Ycf1ieffKK4uDitW7dOx44d0x133JHvxwcAAAAA2WyGYRhWvXhkZKSaNGmiV199VZKUlZWl0NBQjRw5UuPHj7+if58+fZSWlqaVK1fa25o1a6aIiAjNmzfPoe/BgwcVHh6uHTt2KCIiwt6ekpKiwMBALV68WL169ZIk7d27V7Vq1dKmTZvUrFmzPNWempoqf39/paSkyM/P73oPHUAJFbc/pcD23buqf4HtGwAAXF1es4FlZ77S09O1bds2RUdH5xTj4qLo6Ght2rQp1+ds2rTJob8kxcTEXLV/brZt26aMjAyH/dSsWVOVK1e+5n4uXbqk1NRUhwUAAAAA8sqy8HXq1CllZmYqKCjIoT0oKEiJiYm5PicxMfG6+l9tHx4eHgoICLiu/UydOlX+/v72JTQ0NM+vCQAAAACWD7hRVEyYMEEpKSn25ciRI1aXBAAAAKAIcbPqhcuVKydXV9crRhk8ceKEgoODc31OcHDwdfW/2j7S09OVnJzscPbr7/bj6ekpT0/PPL8OAAAAAPyZZWe+PDw81KhRIyUkJNjbsrKylJCQoKioqFyfExUV5dBfkuLj46/aPzeNGjWSu7u7w3727dunw4cPX9d+AAAAAOB6WHbmS5LGjBmjgQMHqnHjxmratKlmz56ttLQ0DR48WJI0YMAAVaxYUVOnTpUkjRo1Sm3atNHMmTPVpUsXLVmyRFu3btUbb7xh3+eZM2d0+PBhHTt2TJIZrCTzjFdwcLD8/f01dOhQjRkzRmXLlpWfn59GjhypqKioPI90CAAAAADXy9Lw1adPH508eVKTJk1SYmKiIiIitHr1avugGocPH5aLS87JuebNm2vx4sV64oknNHHiRFWvXl0rVqxQ3bp17X0+/vhje3iTpL59+0qSJk+erClTpkiSZs2aJRcXF/Xs2VOXLl1STEyMXn/9dSccMQAAAICSytJ5vooy5vkCcCOY5wsAgOKn0M/zBQAAAAAlCeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJzA8vD12muvKSwsTF5eXoqMjNSWLVuu2T8uLk41a9aUl5eX6tWrp1WrVjlsNwxDkyZNUoUKFeTt7a3o6Gj98ssvDn1+/vlnde/eXeXKlZOfn59atmyptWvX5vuxAQAAAEA2S8PX0qVLNWbMGE2ePFnbt29X/fr1FRMTo6SkpFz7b9y4Uf369dPQoUO1Y8cOxcbGKjY2Vrt377b3mT59ul555RXNmzdPmzdvVunSpRUTE6OLFy/a+3Tt2lWXL1/Wl19+qW3btql+/frq2rWrEhMTC/yYAQAAAJRMNsMwDKtePDIyUk2aNNGrr74qScrKylJoaKhGjhyp8ePHX9G/T58+SktL08qVK+1tzZo1U0REhObNmyfDMBQSEqKxY8dq3LhxkqSUlBQFBQVp4cKF6tu3r06dOqXAwECtX79erVq1kiSdO3dOfn5+io+PV3R0dJ5qT01Nlb+/v1JSUuTn5/dP3woAJUTc/pQC23fvqv4Ftm8AAHB1ec0Glp35Sk9P17Zt2xzCjouLi6Kjo7Vp06Zcn7Np06YrwlFMTIy9/4EDB5SYmOjQx9/fX5GRkfY+N910k2rUqKG33npLaWlpunz5subPn6/y5curUaNGV6330qVLSk1NdVgAAAAAIK8sC1+nTp1SZmamgoKCHNqDgoKuevlfYmLiNftn/3mtPjabTV988YV27NghX19feXl56aWXXtLq1atVpkyZq9Y7depU+fv725fQ0NDrO2AAAAAAJZrlA244m2EYevDBB1W+fHl9/fXX2rJli2JjY9WtWzcdP378qs+bMGGCUlJS7MuRI0ecWDUAAACAos6y8FWuXDm5urrqxIkTDu0nTpxQcHBwrs8JDg6+Zv/sP6/V58svv9TKlSu1ZMkStWjRQg0bNtTrr78ub29vLVq06Kr1enp6ys/Pz2EBAAAAgLyyLHx5eHioUaNGSkhIsLdlZWUpISFBUVFRuT4nKirKob8kxcfH2/uHh4crODjYoU9qaqo2b95s73PhwgVJ5v1lf+bi4qKsrKx/fmAAAAAAkAs3K198zJgxGjhwoBo3bqymTZtq9uzZSktL0+DBgyVJAwYMUMWKFTV16lRJ0qhRo9SmTRvNnDlTXbp00ZIlS7R161a98cYbksz7uUaPHq1nn31W1atXV3h4uJ588kmFhIQoNjZWkhngypQpo4EDB2rSpEny9vbWm2++qQMHDqhLly6WvA8AAAAAij9Lw1efPn108uRJTZo0SYmJiYqIiNDq1avtA2YcPnzY4QxV8+bNtXjxYj3xxBOaOHGiqlevrhUrVqhu3br2Po8++qjS0tI0bNgwJScnq2XLllq9erW8vLwkmZc7rl69Wo8//rhuu+02ZWRkqE6dOvroo49Uv359574BAAAAAEoMS+f5KsqY5wvAjWCeLwAAip9CP88XAAAAAJQkhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATnBD4eu3337L7zoAAAAAoFi7ofBVrVo1tWvXTu+8844uXryY3zUBAAAAQLFzQ+Fr+/btuvXWWzVmzBgFBwfr/vvv15YtW/K7NgAAAAAoNm4ofEVEROjll1/WsWPH9H//9386fvy4WrZsqbp16+qll17SyZMn87tOAAAAACjS/tGAG25ubrrjjjsUFxenadOm6ddff9W4ceMUGhqqAQMG6Pjx4/lVJwAAAAAUaf8ofG3dulUPPPCAKlSooJdeeknjxo3T/v37FR8fr2PHjql79+75VScAAAAAFGluN/Kkl156SQsWLNC+ffvUuXNnvfXWW+rcubNcXMwsFx4eroULFyosLCw/awUAAACAIuuGwtfcuXM1ZMgQDRo0SBUqVMi1T/ny5fXf//73HxUHAAAAAMXFDYWv+Ph4Va5c2X6mK5thGDpy5IgqV64sDw8PDRw4MF+KBAAAAICi7obu+apatapOnTp1RfuZM2cUHh7+j4sCAAAAgOLmhsKXYRi5tp8/f15eXl7/qCAAAAAAKI6u67LDMWPGSJJsNpsmTZqkUqVK2bdlZmZq8+bNioiIyNcCAQAAAKA4uK7wtWPHDknmma8ffvhBHh4e9m0eHh6qX7++xo0bl78VAgAAAEAxcF3ha+3atZKkwYMH6+WXX5afn1+BFAUAAAAAxc0NjXa4YMGC/K4DAAAAAIq1PIevO+64QwsXLpSfn5/uuOOOa/Zdvnz5Py4MAAAAAIqTPIcvf39/2Ww2+2MAAAAAQN7lOXz9+VJDLjsEAAAAgOtzQ/N8/fHHH7pw4YJ9/dChQ5o9e7bWrFmTb4UBAAAAQHFyQ+Gre/fueuuttyRJycnJatq0qWbOnKnu3btr7ty5+VogAAAAABQHNxS+tm/frlatWkmSPvjgAwUHB+vQoUN666239Morr+RrgQAAAABQHNxQ+Lpw4YJ8fX0lSWvWrNEdd9whFxcXNWvWTIcOHcrXAgEAAACgOLih8FWtWjWtWLFCR44c0eeff64OHTpIkpKSkph4GQAAAABycUPha9KkSRo3bpzCwsIUGRmpqKgoSeZZsAYNGuRrgQAAAABQHOR5qPk/69Wrl1q2bKnjx4+rfv369vb27durR48e+VYcAAAAABQXNxS+JCk4OFjBwcEObU2bNv3HBQEAAABAcXRD4SstLU0vvPCCEhISlJSUpKysLIftv/32W74UBwAAAADFxQ2Fr3vvvVfr1q1T//79VaFCBdlstvyuCwAAAACKlRsKX5999pk+/fRTtWjRIr/rAQAAAIBi6YZGOyxTpozKli2b37UAAAAAQLF1Q+HrmWee0aRJk3ThwoX8rgcAAAAAiqUbuuxw5syZ2r9/v4KCghQWFiZ3d3eH7du3b8+X4gAAAACguLih8BUbG5vPZQAAAABA8XZD4Wvy5Mn5XQcAAAAAFGs3dM+XJCUnJ+s///mPJkyYoDNnzkgyLzc8evRovhUHAAAAAMXFDZ35+v777xUdHS1/f38dPHhQ9913n8qWLavly5fr8OHDeuutt/K7TgAAAAAo0m7ozNeYMWM0aNAg/fLLL/Ly8rK3d+7cWevXr8+34gAAAACguLih8PXdd9/p/vvvv6K9YsWKSkxM/MdFAQAAAEBxc0Phy9PTU6mpqVe0//zzzwoMDPzHRQEAAABAcXND4etf//qXnn76aWVkZEiSbDabDh8+rMcee0w9e/bM1wIBAAAAoDi4ofA1c+ZMnT9/XoGBgfrjjz/Upk0bVatWTb6+vnruuefyu0YAAAAAKPJuaLRDf39/xcfHa8OGDdq1a5fOnz+vhg0bKjo6Or/rAwAAAIBi4brDV1ZWlhYuXKjly5fr4MGDstlsCg8PV3BwsAzDkM1mK4g6AQAAAKBIu67LDg3D0L/+9S/de++9Onr0qOrVq6c6dero0KFDGjRokHr06FFQdQIAAABAkXZd4WvhwoVav369EhIStGPHDr333ntasmSJdu3apS+++EJffvnldU+w/NprryksLExeXl6KjIzUli1brtk/Li5ONWvWlJeXl+rVq6dVq1Y5bDcMQ5MmTVKFChXk7e2t6Oho/fLLL1fs59NPP1VkZKS8vb1VpkwZxcbGXlfdAAAAAHA9rit8vffee5o4caLatWt3xbbbbrtN48eP17vvvpvn/S1dulRjxozR5MmTtX37dtWvX18xMTFKSkrKtf/GjRvVr18/DR06VDt27FBsbKxiY2O1e/due5/p06frlVde0bx587R582aVLl1aMTExunjxor3PsmXL1L9/fw0ePFi7du3Shg0bdNddd13HOwEAAAAA18dmGIaR187BwcFavXq1IiIict2+Y8cOderUKc8TLUdGRqpJkyZ69dVXJZn3k4WGhmrkyJEaP378Ff379OmjtLQ0rVy50t7WrFkzRUREaN68eTIMQyEhIRo7dqzGjRsnSUpJSVFQUJAWLlyovn376vLlywoLC9NTTz2loUOH5vXQr5Camip/f3+lpKTIz8/vhvcDoGSJ259SYPvuXdW/wPYNAACuLq/Z4LrOfJ05c0ZBQUFX3R4UFKSzZ8/maV/p6enatm2bwwiJLi4uio6O1qZNm3J9zqZNm64YUTEmJsbe/8CBA0pMTHTo4+/vr8jISHuf7du36+jRo3JxcVGDBg1UoUIFderUyeHsWW4uXbqk1NRUhwUAAAAA8uq6wldmZqbc3K4+QKKrq6suX76cp32dOnVKmZmZV4S5oKCgq545S0xMvGb/7D+v1ee3336TJE2ZMkVPPPGEVq5cqTJlyqht27Y6c+bMVeudOnWq/P397UtoaGiejhMAAAAApOscat4wDA0aNEienp65br906VK+FFWQsrKyJEmPP/64evbsKUlasGCBKlWqpLi4ON1///25Pm/ChAkaM2aMfT01NZUABgAAACDPrit8DRw48G/7DBgwIE/7KleunFxdXXXixAmH9hMnTig4ODjX5wQHB1+zf/afJ06cUIUKFRz6ZN+nlt1eu3Zt+3ZPT0/dfPPNOnz48FXr9fT0vGroBAAAAIC/c13ha8GCBfn2wh4eHmrUqJESEhLsw7xnZWUpISFBI0aMyPU5UVFRSkhI0OjRo+1t8fHxioqKkiT7ZM8JCQn2sJWamqrNmzfr3//+tySpUaNG8vT01L59+9SyZUtJUkZGhg4ePKgqVark2/EBAAAAwJ9dV/jKb2PGjNHAgQPVuHFjNW3aVLNnz1ZaWpoGDx4syTyLVrFiRU2dOlWSNGrUKLVp00YzZ85Uly5dtGTJEm3dulVvvPGGJMlms2n06NF69tlnVb16dYWHh+vJJ59USEiIPeD5+flp+PDhmjx5skJDQ1WlShW9+OKLkqTevXs7/00AAAAAUCJYGr769OmjkydPatKkSUpMTFRERIRWr15tHzDj8OHDcnHJGROkefPmWrx4sZ544glNnDhR1atX14oVK1S3bl17n0cffVRpaWkaNmyYkpOT1bJlS61evVpeXl72Pi+++KLc3NzUv39//fHHH4qMjNSXX36pMmXKOO/gAQAAAJQo1zXPF3IwzxeAG8E8XwAAFD8FMs8XAAAAAODGEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJyB8AQAAAIATEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJyB8AQAAAIATEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJyB8AQAAAIATEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcwM3qAgCgMInbn2J1CTesoGvvXdW/QPcPAEBxx5kvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJ2C0QwAoYLaMDJU6ekQ+hw9IhqH0gDJK9y+j9IAyyvD1k1xdrS4RAAA4AeELAPKJLT1dgVs3ye/XvfI5uF8+hw7I5+B+lT56WC6XL+f6HMNmU4avn9L9y+h85XCdaHWbElu3V2r1WpLN5uQjAAAABYnwBQD/RFaWyn23UZU/+UChn62QR0rydT3dZhjySE2RR2qKfI4cVPCGtar/wpO6EBSixNbtldg6Wkkt2ijDL6BAygcAAM5D+AKA62UY8t/zgyp/8oEqf7JMpRKPXrXr5VKlda7KzTofVlXnq9ysLDc3eaSclUdKsjySz5qPk8/K8+xpeaTmTJJc6sQx3Rz3tm6Oe1tZrq461aiZfh46Qsdv68gZMQAAiijCFwDkVWamwj58TzXenCO//fuu2HzZu5SO3t5FSVGt7WHrYmBQ3sKSYcj3t18UvP4LBX39pcpv/kauly5KklwyM1V+ywaV37JByTXraM+/x+n3jv/iXjEAAIoYm2EYhtVFFEWpqany9/dXSkqK/Pz8rC4HQD6J25+Sa3vQ+gTdOn2SAvb+6NCe5eamxFbtdfhfvXWsfSdlliqdL3W4XPxDgVs2KvjrBFVY+7l8D+532J56c3Xt/fcYHe7aS4a7e7685t/pXdXfKa8DAEBRk9dsQPi6QYQvoHj6a/jy3/ODbn1hkoI3rHVoP9k4Sof/1Vu/d4pVepmyBVuUYajCl6tV6/UZumnXNodN50OraO/9D+tgr3tkuBXsxQyELwAAckf4KmCEL8A6Vzs7lZ+8jx9V3VnPqsqHS2T704/JM/Ua6PvHntbJZq0KvIYrGIbKb1ynWq+9qPJbNjhsOnNrQ22Z9rrOVa9ZYC9P+AIAIHd5zQZMsgwAf5aVpRrzZ6tTdCOFLX/PHrzSKlXWt7P+o4RlCdYEL0my2ZTUoq3WLf5Ua99bpcRW7e2byn6/Xbd3b6Nb3nxFysy0pj4AAHBNnPm6QZz5AqxTUGe+PE+eUOS4+xW04St7W7qfv/Y8ME6/9h+mLE/PAnndf+Km7ZvVePwI+f32i73tVIOm+m766zofXi1fX4szXwAA5I4zXwBwHYLWJ6hD15b24GXYbPp50L+16sud+vnekYUyeEnS6YaRiv94vfYNHSHjf6MqltuxRR26tlT1Ba9LWVkWVwgAALIRvgCUaLaMDNWbPlmth/SU1+mTkqQ/gipo3VsfadcTU5URUMbiCv9elpe3vp/wrNYu+UznqtwsSXK9dFERz01U27u7qNTRwxZXCAAAJMIXgBKs1JGDate3k2q+8bK97XjbDlrz8dc6GdXawspuzOlGzRS/8hv9MvB+e1vgd5vU/o72Krt9i4WVAQAAifAFoIQKWfOJbv9Xa920a6skKcvdXTsnPKtv3lii9JvKWVzdjcv0LqWdT07T2ndXKq1SZUmS1+mTantPN4V+HGdxdQAAlGyELwAlTrWFc9Xigf7yOJcqSTofGqYvl36uX4aOkFyKx4/FU5EtFb9inZL+NzKja/olNRtzn2q/PFVinCUAACxRPH7LAIC8MAzVmz5ZDZ6dYG863OUOxX+8XmdvbWhhYQUjI6CM1v/fMv3Wu7+9rc6caYp8+F65XPzDwsoAACiZCF8ASgRbRoaaPPpvh/u7fhrxiDbP/q8u+xbf6SIMDw9te/4V7Rr/jH00xMorl6ntPf+S56kki6sDAKBkIXwBKPZc086r5bC+CvtwiSRzGPltT83Uj6Mfl/4XSIo1m00/3ztSG+a+q8ulSkuSbtr5ndrfcZv8fv7J4uIAACg5CF8AijXP/w02Efx1giQp08NTm15dpN/uHmpxZc53PLqzvlzymS4EV5QklT72u9re3VX+P31vcWUAAJQMhC8AxVbpwwfV7s4Ylf1hhyQp3c9f6xd9qKMx/7K4Muuk1L5VCcsTdKZeA0mS59kzajOguwJ+3GVxZQAAFH+ELwDFks/B/WrXJ0a+h36TJF0ICtHaJZ/pVJPmFldmvYvlg7XurY90qkFTSZJn8lkzgO3eaW1hAAAUc4QvAMVOqd8PqU3/7vI+eUKSlFq1hr6MW6PUW2pbXFnhcdnXT18vWKZTjZpJkjxSkglgAAAUsEIRvl577TWFhYXJy8tLkZGR2rJlyzX7x8XFqWbNmvLy8lK9evW0atUqh+2GYWjSpEmqUKGCvL29FR0drV9++SXXfV26dEkRERGy2WzauXNnfh0SAIt4JR5Tm/7/Uqnjv0uSkmvU1toln+mPkEoWV1b4XPbx1df/jdPJxlGSJI/UFLUZ0F1lvt9ucWUAABRPloevpUuXasyYMZo8ebK2b9+u+vXrKyYmRklJuQ+BvHHjRvXr109Dhw7Vjh07FBsbq9jYWO3evdveZ/r06XrllVc0b948bd68WaVLl1ZMTIwuXrx4xf4effRRhYSEFNjxAXAez1NJajOgu3yOHJIkpVa9Revf+kjpZcpaXFnhZQ9gTXICWOuBsSqza5vFlQEAUPzYDMMwrCwgMjJSTZo00auvvipJysrKUmhoqEaOHKnx48df0b9Pnz5KS0vTypUr7W3NmjVTRESE5s2bJ8MwFBISorFjx2rcuHGSpJSUFAUFBWnhwoXq27ev/XmfffaZxowZo2XLlqlOnTrasWOHIiIi8lR3amqq/P39lZKSIj+/4jtHEFAYxe1PuaLN4+wZtbmnqwL2mUOnnw8N09r3VuliMF+u5IVr2nm1vK+Pym/ZIEnK8PHT+oXLdSaisb1P76r+VpUHAEChltdsYOmZr/T0dG3btk3R0dH2NhcXF0VHR2vTpk25PmfTpk0O/SUpJibG3v/AgQNKTEx06OPv76/IyEiHfZ44cUL33Xef3n77bZUqVepva7106ZJSU1MdFgCFg3tqsloP6mEPXhcqVNK6dz4meF2HzNI++uY/7yspsqUkyf18qloN6SnfX/ZaXBkAAMWHpeHr1KlTyszMVFBQkEN7UFCQEhMTc31OYmLiNftn/3mtPoZhaNCgQRo+fLgaN26svJg6dar8/f3tS2hoaJ6eB6BguaadV8uhd6rM/4ZK/6N8sL56+2NdqFjZ4sqKnsxSpc0A1qyVpP9dgjikl7yPH7W4MgAAigfL7/mywpw5c3Tu3DlNmDAhz8+ZMGGCUlJS7MuRI0cKsEIAeeFy6aJa3t9P5XaYg/RcKnOT1i1aobSwmy2urOjK9C6lDfMW60zdCElSqeO/q9XQ3nJPTba0LgAAigNLw1e5cuXk6uqqEydOOLSfOHFCwcHBuT4nODj4mv2z/7xWny+//FKbNm2Sp6en3NzcVK1aNUlS48aNNXDgwFxf19PTU35+fg4LAAtlZanJYw+q/LdfSzInUF731gqdq17T4sKKvss+vvrmP+/rfGiYJMn/55/U4v67pFwGLQIAAHlnafjy8PBQo0aNlJCQYG/LyspSQkKCoqKicn1OVFSUQ39Jio+Pt/cPDw9XcHCwQ5/U1FRt3rzZ3ueVV17Rrl27tHPnTu3cudM+VP3SpUv13HPP5esxAigYdV96VpVXLpMkXfYupfULliulVj2Lqyo+LpUrr68XLNPFsuUkSYHfbZT695cyMy2uDACAosvN6gLGjBmjgQMHqnHjxmratKlmz56ttLQ0DR48WJI0YMAAVaxYUVOnTpUkjRo1Sm3atNHMmTPVpUsXLVmyRFu3btUbb7whSbLZbBo9erSeffZZVa9eXeHh4XryyScVEhKi2NhYSVLlyo73gvj4+EiSqlatqkqVmAsIKOzClyxSrXkvSZIMFxd9O/u/Olu/kcVVFT/nw6rqm/+8r7Z3d5XbHxekDz6QHn5YevllyWazujwAAIocy8NXnz59dPLkSU2aNEmJiYmKiIjQ6tWr7QNmHD58WC4uOSfomjdvrsWLF+uJJ57QxIkTVb16da1YsUJ169a193n00UeVlpamYcOGKTk5WS1bttTq1avl5eXl9OMDkM8+/1wNJ4+xr+54cpqOt+9kYUHF29lbG2rTq4vUYlhfuWRmSnPmSBUrSo89ZnVpAAAUOZbP81VUMc8XYIFdu6RWraRz5yRJ+4Y8qO8ncqmwM1RZ9q6aPvZgTsOiRdKAAdYVBABAIVIk5vkCgDw7elTq0sUevH6P6abvxz9jcVElx6Ged0vPP5/TcO+90tdfW1cQAABFEOELQOF37pwZvI6a802drt9YW2bMl1z4EeZU48dLD/7v7FdGhnTHHdLBg5aWBABAUcJvLgAKt8uXpTvvNC85lKSbb9aGN95Tpncpa+sqiWw2afZs6fbbzfVTp6R//ct+NhIAAFwb4QtA4fbII9Lq1ebjMmWkVat06aZAa2sqydzcpKVLpVtuMdd/+MEcgj4ry9q6AAAoAghfAAqvt94yz7RIkru7tGKFVKOGlRVBMkPwxx9L/v7m+kcfSZMmWVsTAABFAOELQOH03XfSsGE566++KrVubV09cFSjhnkGLPu+u+eek5YssbYmAAAKOcIXgMInMVHq0UO6dMlcHz7cMYihcIiJkWbOzFkfPFjautW6egAAKOQIXwAKl/R0qVcv+8iGatlSevlla2vC1Y0aJQ0ZYj6+eFHq3l06dszamgAAKKQIXwAKl4cekjZsMB9XqiR98IHk4WFtTbg6m016/XWpRQtz/dgx86zlxYvW1gUAQCFE+AJQeMyfby6S5OkpffihFBRkbU34e56e0vLlUuXK5vqWLeYZMQAA4IDwBaBw+OYbacSInPU335QaN7auHlyf8uXNUQ+9vMz1N96Q/u//rK0JAIBChvAFwHq//y717GlOqCxJDz9szh2FoiUiIufMpSQ98IC0bZtl5QAAUNgQvgBYKz1duvNOKSnJXG/fXpo+3dqacOMGDDBDl2SOVtmzp3T6tLU1AQBQSBC+AFjrkUekTZvMx1WqmHNHublZWxP+mVmzpGbNzMeHDkl33y1lZlpbEwAAhQDhC4B1liyRXnnFfOzhYY5seNNN1taEf87DQ4qLkwIDzfXPP5eeesramgAAKAQIXwCssWePdO+9Oetz5jDARnFSqZJ5FtPlf//NPPOMtHKltTUBAGAxwhcA5zt/3rwXKC3NXB8wQLrvPmtrQv5r10564YWc9XvukX791bp6AACwGOELgHMZhhm09uwx1+vVk+bONSfrRfEzbpx0xx3m45QUM3RfuGBtTQAAWITwBcC5Xn3VvNdLkvz8pGXLpFKlrK0JBcdmkxYskGrUMNe//95xPjcAAEoQwhcA59m0SRo7Nmd94UKpenXLyoGT+PlJy5fnhOwFC8wFAIAShvAFwDlOnpR695YyMsz1Rx6RevSwtiY4T+3ajhMwP/ig9MMP1tUDAIAFCF8ACl5WljnYwtGj5nrr1tLzz1tbE5zvnnukYcPMx3/8YYbxc+esrQkAACcifAEoeM8/L61ZYz4OCmIi5ZLs5ZeliAjz8b59ZhgzDEtLAgDAWQhfAArW2rXS5MnmYxcX6b33pOBga2uCdby8zAmYfX3N9SVLHC9HBACgGCN8ASg4iYlSv37mZYeSNGWKOfcTSrZq1RwH3Bg1Stq2zbp6AABwEsIXgIKRmSnddZd04oS5fvvt0sSJ1taEwqNnTzN0SVJ6unn/V3KypSUBAFDQCF8ACsbTT5uXHEpSSIj0zjuSq6u1NaFwmT5datrUfHzggDR4MPd/AQCKNcIXgPwXHy8984z5OPs+r/Llra0JhY+Hh/T++1KZMub6ihXS7NlWVgQAQIEifAHIX8eOSXffnXMG49lnzaHlgdxUqSK9/XbO+qOPSps3W1cPAAAFiPAFIP9cvmwOsHHypLneqZP02GPW1oTCr0sXM3RJ5meoTx/pzBlrawIAoAAQvgDkn8mTpfXrzceVKklvvWVedgj8nWeflVq0MB8fOsT9XwCAYonfigDkj9WrzcmUJXNgjSVLpHLlrK0JRYe7u3lv4E03mesffyzNmmVtTQAA5DPCF4B/7vffpf79c9anTs05iwHkVWio4/1fjz0mffutdfUAAJDPCF8A/pns+7xOnTLXu3aVxo61tiYUXZ06SePHm48vX5buvJP7vwAAxQbhC8A/88QT0jffmI8rV5YWLeI+L/wzzzwjtWxpPj5yRBo4UMrKsrYmAADyAb8hAbhxn34qTZtmPnZzk5YulcqWtbYmFH1ubub9X9n3DK5cKb30krU1AQCQDwhfAG7MkSPSgAE569OnS82aWVcPipdKlRzv/xo/Xtqwwbp6AADIB4QvANcvI8NxLqbu3aXRoy0tCcVQx47ShAnm48xM8zOXPYccAABFEOELwPWbOFHatMl8HBYmLVgg2WyWloRi6umnpdatzcdHj5qjanL/FwCgiCJ8Abg+n3wizZhhPnZ3l95/XypTxtqaUHxl3/9Vvry5/vnnOfPJAQBQxBC+AOTdgQOO93nNmCE1aWJdPSgZQkKkxYtzzq5Onix9+aW1NQEAcAMIXwDy5tIlc86l5GRz/Y47pJEjLS0JJUj79tJTT5mPs7Kku+6Sjh+3tiYAAK4T4QtA3owZI23daj6uVk36v//jPi841+OPSx06mI9PnDAn97582dqaAAC4DoQvAH9v8WLp9dfNx15eUlyc5O9vbU0oeVxcpHfekSpWNNfXrTMvQQQAoIggfAG4tp9+koYNy1l/7TUpIsKyclDCBQaak3m7uprrzz8vffaZtTUBAJBHhC8AV3f+vNSrl5SWZq4PHiwNGWJtTUCLFtILL+Ss33OPdPiwdfUAAJBHhC8AuTMMafhwac8ec/3WW6VXX7W2JiDb2LHm5N6SOdl3797moDAAABRihC8AuZs/X3r3XfOxr6/0wQdSqVLW1gRks9nMyb3Dw831LVuk0aMtLQkAgL9D+AJwpa1bpVGjctb/7/+k6tWtqwfITZky0rJl5iAwkjRvnrRokbU1AQBwDYQvAI5Onzbv80pPN9dHjzbXgcKoQQNp7tyc9eHDpZ07LSsHAIBrIXwByJGZac6ddOiQud6smTRtmrU1AX9n0CDp/vvNxxcvmhOAnz1raUkAAOSG8AUgxxNPSPHx5uPy5c37vDw8rK0JyIuXX5aaNDEfHzgg9e8vZWVZWxMAAH/hZnUBAKwRtz/FYb3i6o/U/H/Dd2e5umrd7AU6ddFH+ks/oFDy9DS/LGjY0Lx09tNPpeeek5580urKAACw48wXAPn+sldNHnvQvr5rwrM61bSFhRUBN6ByZWnJEsnlf/+1TZ4srV5tbU0AAPwJ4Qso4dzOpajFA/fIPe28JOnQv+7UrwOHW1wVcIOio6VnnjEfG4Z0993SwYOWlgQAQDbCF1CSZWWp6SP/lu+BXyVJybXqattzs805lICiavx46V//Mh+fOSP16CFduGBtTQAAiPAFlGg1572kil+skiSl+/lr42vvKNObiZRRxLm4mPN9Vatmru/cKQ0ZYp4JAwDAQoUifL322msKCwuTl5eXIiMjtWXLlmv2j4uLU82aNeXl5aV69epp1apVDtsNw9CkSZNUoUIFeXt7Kzo6Wr/88ot9+8GDBzV06FCFh4fL29tbVatW1eTJk5WePa8RUAIEr4tX3VnPSZIMm03fzvqv0iqHWVsUkF8CAqSPPpJ8fMz1pUul/w0oAwCAVSwPX0uXLtWYMWM0efJkbd++XfXr11dMTIySkpJy7b9x40b169dPQ4cO1Y4dOxQbG6vY2Fjt3r3b3mf69Ol65ZVXNG/ePG3evFmlS5dWTEyMLl68KEnau3evsrKyNH/+fP3444+aNWuW5s2bp4kTJzrlmAHL/fyzIh++V7b/nQnY/fDjOtEm2uKigHxWu7b07rs5l9E+/rj0ySfW1gQAKNFshmHtdRiRkZFq0qSJXn31VUlSVlaWQkNDNXLkSI0fP/6K/n369FFaWppWrlxpb2vWrJkiIiI0b948GYahkJAQjR07VuPGjZMkpaSkKCgoSAsXLlTfvn1zrePFF1/U3Llz9dtvv+Wp7tTUVPn7+yslJUV+fn7Xe9iAdc6eNSdP/vlnSdLR6M7a+Po7OSPEAVfRu6q/1SXcmOeeM+ewkyRfX2nzZqlWLWtrAgAUK3nNBpb+tpWenq5t27YpOjrnG3cXFxdFR0dr06ZNuT5n06ZNDv0lKSYmxt7/wIEDSkxMdOjj7++vyMjIq+5TMgNa2bJlr7r90qVLSk1NdViAIufyZalPH3vwSrmltrbMmE/wQvE2caLUu7f5+Nw5qXt380sIAACczNLfuE6dOqXMzEwFBQU5tAcFBSkxMTHX5yQmJl6zf/af17PPX3/9VXPmzNH9999/1VqnTp0qf39/+xIaGnrtgwMKo7Fjpfh4SdKlMjfpm/mLddnH1+KigAJms0kLFkj165vrv/wi9esnZWZaWxcAoMQp8V93Hz16VB07dlTv3r113333XbXfhAkTlJKSYl+OHDnixCqBfPDGG9Irr5iP3d218bW3dCE0zNKSAKcpXVpasUIqV85c//xzc0h6AACcyNLwVa5cObm6uurEiRMO7SdOnFBwcHCuzwkODr5m/+w/87LPY8eOqV27dmrevLneeOONa9bq6ekpPz8/hwUoMr76SnrwwZz1uXN1qmkLy8oBLBEWJn3wgeTmZq7PmCG9/balJQEAShZLw5eHh4caNWqkhIQEe1tWVpYSEhIUFRWV63OioqIc+ktSfHy8vX94eLiCg4Md+qSmpmrz5s0O+zx69Kjatm2rRo0aacGCBXLhnhcUV7/9JvXsad7vJUkPPywNHWptTYBV2rTJOQMsSffeK339tXX1AABKFDerCxgzZowGDhyoxo0bq2nTppo9e7bS0tI0ePBgSdKAAQNUsWJFTZ06VZI0atQotWnTRjNnzlSXLl20ZMkSbd261X7mymazafTo0Xr22WdVvXp1hYeH68knn1RISIhiY2Ml5QSvKlWqaMaMGTp58qS9nqudcQOKpNRUqVs36cwZc71jR2n6dGtrQpEVtz+lwPbt1JEUhw+Xvv9emjdPSk+XYmOlTZukW25xXg0AgBLJ8vDVp08fnTx5UpMmTVJiYqIiIiK0evVq+4AZhw8fdjgr1bx5cy1evFhPPPGEJk6cqOrVq2vFihWqW7euvc+jjz6qtLQ0DRs2TMnJyWrZsqVWr14tLy8vSeaZsl9//VW//vqrKlWq5FCPxSPvA/nn8mVzUIGffjLXa9aUlizJueQKKKlsNmnOHOnAAfPerzNnpM6dzQAWGGh1dQCAYszyeb6KKub5QqFmGNK//y3Nn2+ulykjbdkiVatm71KQZzGA62XJHGKpqVLLltIPP5jrzZtLCQnS/76oAwAgr4rEPF8ACsgLL+QEL3d3c5CBPwUvAJL8/KRPP5VCQsz1jRulgQOlrCxr6wIAFFuEL6C4efttc1LZbAsWSLfdZl09QGEWGiqtXGkORS9J778vPf64tTUBAIotwhdQnMTHS0OG5KxPnSrdfbd19QBFQYMG5v2Q2fcXv/CC9Oab1tYEACiWCF9AcbFrl+OQ8g88ID32mLU1AUVF167Syy/nrP/739KaNdbVAwAolghfQHFw+LA5Wtu5c+Z69+7mXEY2m7V1AUXJiBHS6NHm48xM88uMrVstLQkAULwQvoCi7uxZqVMn6dgxcz0yUlq8WHJ1tbYuoCiaMcOc90uSzp8358bbs8fSkgAAxQfhCyjKLl6UevTImcurWjXpk0+kUqWsrQsoqlxdzS8vWrc210+flm6/XTp0yNq6AADFAuELKKrS06VevaR168z1wEBp9WomiQX+KW9v6eOPpYYNzfWjR80AlpRkbV0AgCKP8AUURZcvS/fcY85RJJnDZK9cKVWtam1dQHHh729+mVGjhrn+yy9STIyUwuTkAIAbR/gCipqsLOnee6W4OHPdy8sMXk2bWlsXUNwEBpojHlaqZK7v3Cl16yZduGBpWQCAoovwBRQlhmGOyLZokbnu7i4tXy61bWtpWUCxVbmyOX9euXLm+tdfS3feKWVkWFsXAKBIInwBRYVhSI8+Ks2da667upoTw3bqZG1dQHFXs6Z5CaKvr7n+6afSwIHmcPQAAFwHwhdQVDz9tDkMtmTO37VwoXTHHZaWBJQYjRqZg3B4eprr770nDRiQM6k5AAB5QPgCioIZM6QpU3LW580zB9wA4Dxt25r3Wrq7m+uLF0v9+xPAAAB5RvgCCruZM6VHHslZnzVLGjbMunqAkqxbN2nZspwAtmSJdNdd3AMGAMgTwhdQWBmGeanhuHE5bc88I40ebVlJAGQGsA8/lDw8zPW4OKlfPwIYAOBvEb6AwsgwpPHjpcmTc9qeflp6/HHragKQo0sXacWKnHvAli0zR0FMT7e0LABA4Ub4AgqbrCxp5Ehp+vSctpdekp580hxoA0Dh0KmT9NFH5lx7khnGeveWLl2ytCwAQOFF+AIKk8xMcwLl114z1202c3CNhx+2ti4AuYuJkT75JCeAffyx1LOn9Mcf1tYFACiUCF9AYZGRId19t7Rggbnu4mJOpnz//dbWBeDaoqPNub+8vc31Tz81286csbYuAEChQ/gCCoOLF6VevaSlS811Nzfzcf/+1tYFIG9uu01atUry8THXN26UWrSQDh2yti4AQKFC+AKsduqU+S35xx+b656e5r0jvXpZWhaA69S2rbRunRQUZK7v3Ss1by59/72lZQEACg/CF2Cln3+WmjWTNmww10uVMi9Z6tLF2roA3JiGDc2zXtWrm+vHjkmtWklr11pbFwCgUCB8AVb5+mspKkrav99cDw6W1q+X2re3ti4A/8zNN5sBLDLSXE9NlTp2zLmsGABQYhG+ACu8+67jDfn16kmbN0uNGllbF4D8Ua6clJCQcxY7PV3q21eaPdvSsgAA1iJ8Ac5kGNIzz0j33JMzGWuHDtI330iVK1tbG4D8Vbq0ef/mkCE5bQ8/LA0bxlxgAFBCEb4AZ0lPN38JmzQpp+2++6SVKyU/P+vqAlBw3Nyk//zH8d/9m2+ag3McO2ZZWQAAaxC+AGc4ckRq00ZauDCnbfp0af58yd3dsrIAOIHNJj31lPT22zmTMX/7rXmZcfZgOwCAEoHwBRS0NWukBg3MX7Yk85evuDjpkUfMX8oAlAz33GOGrexLjBMTpXbtpHnzzEuSAQDFHuELKCiZmea33R07SqdPm21hYeYoh8zhBZRMDRtKW7eaoUuSMjKkf/+b+8AAoIQgfAEF4dQpqXNnacqUnG+0u3aVtm2TGje2tDQAFgsMNM+IP/xwTtt//mNemnzwoGVlAQAKHuELyG/ffmteZrhmjbnu4iJNnSp99JFUtqy1tQEoHNzcpJdecrwPbPNmqX59cyoKAECxRPgC8ktmpjmIRuvW0u+/m21BQeZcP+PHmyEMAP4s+z6wsDBzPTXVbLvrLik52crKAAAFgN8Ggfzw889Sq1bSY4+Z93BI5vqOHeaQ0gBwNQ0bSjt3mqEr23vvSbfeKq1bZ1lZAID8R/gC/omsLGn2bPNSoU2bzDabzQxhX34pVahgaXkAigh/f/MSxPfeMx9L5hQV7dqZZ86zJ2UHABRphC/gRu3fb57Vevhh6eJFs61aNembb6QXXjDv6QCA69G3r/T99zlnzA1DmjZNatZM2rXL0tIAAP+czTCYXORGpKamyt/fXykpKfLz87O6HDhTVpY5L88jj0gXLuS0P/SQObBGqVLW1XYd4vanWF0C4DS9q/pbXcL1ycyUZs6Unngi51JmV1fz58xTT0m+vtbWBwBwkNdswJkv4Hrs2GEOB/3ggznBKzxc+uor6eWXi0zwAlDIubpKjz5qjp5aq5bZlpkpzZplri9fzsTMAFAEEb6AvDh1Sho+XGrUyLysMNvw4eYlQm3aWFcbgOKrYUPzS59nn80Zkv7oUalnT6lbN+nAAWvrAwBcF8IXcC2XL0uvvipVry7Nn5/zTfMtt0jx8dLcuZKPj7U1AijePD2lxx+XfvxR6tQpp/3TT6U6dczLnS9dsq4+AECeEb6Aq1m71pwseeTInPl2fHykF1+UfvhBio62tDwAJczNN5uB64MPpJAQs+2PP6SJE6UaNaRFi8xLEwEAhRbhC/irnTul2Fjpttuk3btz2gcONOfzGjdO8vCwqjoAJZnNZl5yuHevNHp0zuTthw5JgwaZ01589BH3gwFAIUX4ArLt2iX16GGe7froo5z2Jk3Mm94XLmTeLgCFg6+vOfjG9u2OlyL++KP55VHz5kzQDACFEOEL+P5785vkiAhpxYqc9pAQ6b//NYNXZKRV1QHA1dWvL61aZY64GhWV0/7tt+ZcYR075kwADwCwHOELJdeOHVKvXuYvL8uX57SHhEhz5piTKA8ZknNZDwAUVm3aSBs2mGft69TJaf/8c/MsWIsW5s857gkDAEvxWyVKlowMKS5OatXKHMJ52bKcbRUqmHN17d8vjRiRM6wzABQFNpv0r3+Zl1AvWiRVqZKzbeNG8wx/jRrS6687ThAPAHAawhdKhpMnpeeeMydEvvNOx7m6goOl2bPN0PXQQ4QuAEWbq6s0YIC0b5+0YIFUt27Otv37zUniK1eWnnxSOnbMujoBoAQifKH4Mgxp82ZzBLBKlaQnnjAnJ81Wq5b02mvSb79Jo0ZJ3t6WlQoA+c7T0/z59/330urVjtNjnD5tTtwcGip16WIOX89cYQBQ4AhfKH5+/VV66inz8ppmzczLb9LTzW3Zl+XEx5ujgj3wAKELQPFms0kxMebPvZ07pf79JTc3c1tWljlgR+/eUsWK5hdRO3daWS0AFGs2w2AykBuRmpoqf39/paSkyM/Pz+pyFLc/pcD23buqf4HtO98kJUlLl0rvvmue7fqLdD9/HbhzgH69e6guhIY5v74bUNDve0F+ZoDCpiD/PRX0v6UCqf3336U33zSn0Dh8+MrtDRpId99tTr9x8835//pFQIn/fxXIJ0XyZ+QNyGs2cHNiTUD+OnhQ+vRT6eOPpYSEK0fxstmUFNlSh7rfqSNdeyrTu5QlZQJAoVOpknmFwOTJ0tq10v/9nzka4sWL5vYdO8xl3Djp1lvNENajh/nYZrO2dgAowghfKDoyM825a1auNJfdu3PvV7+++Y1tv35ad8nXuTUCQFHi4iK1b28uycnSkiXmIB1btuT0+f57c3nqKXPQoh49pK5dzXnFGKAIAK4L4QuFl2GYo3WtXy+tW2fOV3P6dO59Q0Olu+4yQ1e9ejntXFoHAHkTECANH24uP/8sffihufz5Uu4DB6SXXjIXLy+pZcuc8NawoTnSIgDgqghfKDyysqQffjDDVvaSlJR7X5tNiow0v33t2pVLYQAgP91yi/TYY+Zy9Ki0YoUZxL76KucS74sXpS++MBfJDG9t25oTPkdGShERDGgEAH9B+II1srLMUQm3bTOX7dvNJeUaZ6p8fc0Ru7p2lTp1ksqXd169AFBSVaxozg324IPSmTPm6IhffGHea/v77zn9kpPNkLZihbnu5mZeBt60qblERpqj0Low0DKAkovwhYJ38qS0d6+0Z4/00085N3KfO3ft5/n5Sa1aSa1bm0ujRpK7u3NqBgBcqWxZ6Z57zMUwzC/REhLMZe1ax0vDL1/O+YJt7lyzrXRpqU4dc6lbN2epUIGrFwCUCIQv5I/kZPNegIMHzUmL9+7NCVxXu0/rr0JCzG9H27Qxl1tv5f4BACisbDapenVzGT7cvKLh++/Ne8Q2bzYH7fjpJzOkZUtLM9v/PKCHJJUpI9WuLVWteuVSrhzBDECxQfjCtRmGGayOH5eOHTP/PH7cvAfg4MGc5VqXC+amcmXz5uxGjcw/GzaUgoPzv34AgHO4uJj3eUVESPffb7adOydt3WqGrc2bzXD222+OgUySzp6VNmwwl7/y9TVHWaxUKWepWDHnz4oVJX9/AhqAIqFQhK/XXntNL774ohITE1W/fn3NmTNHTZs2vWr/uLg4Pfnkkzp48KCqV6+uadOmqXPnzvbthmFo8uTJevPNN5WcnKwWLVpo7ty5ql69ur3PmTNnNHLkSH3yySdycXFRz5499fLLL8vHx6dAj9VKtowMuZ9L/d+SIvdzqfJITZbH2bPyPHtaHmdPm38mn5XnmdPyPHNS3kknpEsXb/xFQ0KkWrXMpWZN88969aTAwPw7MABA4eTrK7VrZy7Z0tLMqyJ2785ZfvjB/IIvN+fO5Qx3fzXu7ub/K+XLO/4ZGGheKhkQYJ5dK1Mm53FAAJeyA3A6y8PX0qVLNWbMGM2bN0+RkZGaPXu2YmJitG/fPpXPZUCFjRs3ql+/fpo6daq6du2qxYsXKzY2Vtu3b1fdunUlSdOnT9crr7yiRYsWKTw8XE8++aRiYmL0008/yet/c5LcfffdOn78uOLj45WRkaHBgwdr2LBhWrx4sVOP/x/79lvpww/V4NhZuV04L9cLF+R2IU1uf6TJLS1Nbn9ckNv5c3I/lyq3i3/k/+u7uZlnscLCzG8mw8LM5ZZbzLB1jRm+AQAlUOnSUuPG5vJn58+bZ8X278/5M3s5dMi8h+xqMjLM8Ha1AHc1Xl6Sj48ZEn19HR43yXRTpncpXfbyVqZ98VKmdyllenoqy8NTmR4eyvL43+P/tWW5uyvLzV1Z7h7KcneTkf3YzU1Z7u4yXN3MM3+cqQNKJJth/PXcv3NFRkaqSZMmevXVVyVJWVlZCg0N1ciRIzV+/Pgr+vfp00dpaWlauXKlva1Zs2aKiIjQvHnzZBiGQkJCNHbsWI0bN06SlJKSoqCgIC1cuFB9+/bVnj17VLt2bX333Xdq/L8f/qtXr1bnzp31+++/KyQk5G/rTk1Nlb+/v1JSUuRnZcCYP9+81r4ApPsH6I/AYPlXqWiewapQwVyyH4eFmY8L8X1ZcUV4nq/eVf0LdP9F+b0BrldB/nsq6H9LBf2zoEjIyjIHb/r9d/Oy9z//eeyYuS0pyfzzWiGtMHFxMb/AzF5cXXMWFxfH9ey23Bab7crHf/7z7xbp6uvZ/tz+5z//blturrW9ILbBckfOZxTo/kPL+0uLFhXoa+RFXrOBpWe+0tPTtW3bNk2YMMHe5uLioujoaG3atCnX52zatEljxoxxaIuJidGK/w1te+DAASUmJio6Otq+3d/fX5GRkdq0aZP69u2rTZs2KSAgwB68JCk6OlouLi7avHmzevToccXrXrp0SZcuXbKvp/zvHqfU1NTrP/D8dI0he7NcXXW5VGldLuWjDB9fXfb1VYaPnzJ8fJXh66vLpX2V7uuv9IAySi9TRun+ZXUpoIzSA8oqwz9Axv9CVY+br/Eff1pafh9RvrpwzuK/n38gNbVg/zMpyu8NcL0K8t9TQf9bKuifBUWGt3fOAB9Xk32f8qlTOUtKitmWnOz4ODnZ/D/s/Hnz0sbz56X0dGcciSkry3w9Z74mYIGC/voo1c9Psvr3ceVkgr87r2Vp+Dp16pQyMzMVFBTk0B4UFKS9e/fm+pzExMRc+ycmJtq3Z7ddq89fL2l0c3NT2bJl7X3+aurUqXrqqaeuaA8NDb3a4VkvM1M6l2ouJ258N4PyrSBcj0FWFwAUI4OsLuAfGGR1AQBQmKWmmoPuFBLnzp2T/zXqsfyer6JiwoQJDmfcsrKydObMGd10002ycbq7SElNTVVoaKiOHDli7SWjQB7weUVRwucVRQmfV+QnwzB07ty5v719ydLwVa5cObm6uurECcfTMidOnFDwVYYdDw4Ovmb/7D9PnDihChUqOPSJiIiw90lKSnLYx+XLl3XmzJmrvq6np6c8PT0d2gICAq59gCjU/Pz8+GGLIoPPK4oSPq8oSvi8Ir9c64xXtqvfMOQEHh4eatSokRISEuxtWVlZSkhIUFRUVK7PiYqKcugvSfHx8fb+4eHhCg4OduiTmpqqzZs32/tERUUpOTlZ27Zts/f58ssvlZWVpcjIyHw7PgAAAADIZvllh2PGjNHAgQPVuHFjNW3aVLNnz1ZaWpoGDx4sSRowYIAqVqyoqVOnSpJGjRqlNm3aaObMmerSpYuWLFmirVu36o033pAk2Ww2jR49Ws8++6yqV69uH2o+JCREsbGxkqRatWqpY8eOuu+++zRv3jxlZGRoxIgR6tu3b55GOgQAAACA62V5+OrTp49OnjypSZMmKTExUREREVq9erV9wIzDhw/L5U8j+jVv3lyLFy/WE088oYkTJ6p69epasWKFfY4vSXr00UeVlpamYcOGKTk5WS1bttTq1avtc3xJ0rvvvqsRI0aoffv29kmWX3nlFecdOCzj6empyZMnX3EZKVAY8XlFUcLnFUUJn1dYwfJ5vgAAAACgJLD0ni8AAAAAKCkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhC8XSwYMHNXToUIWHh8vb21tVq1bV5MmTlZ6e7tDv+++/V6tWreTl5aXQ0FBNnz79in3FxcWpZs2a8vLyUr169bRq1SpnHQZKuNdee01hYWHy8vJSZGSktmzZYnVJKIGmTp2qJk2ayNfXV+XLl1dsbKz27dvn0OfixYt68MEHddNNN8nHx0c9e/bUiRMnHPocPnxYXbp0UalSpVS+fHk98sgjunz5sjMPBSXMCy+8YJ+CKBufVViN8IViae/evcrKytL8+fP1448/atasWZo3b54mTpxo75OamqoOHTqoSpUq2rZtm1588UVNmTLFPmecJG3cuFH9+vXT0KFDtWPHDsXGxio2Nla7d++24rBQgixdulRjxozR5MmTtX37dtWvX18xMTFKSkqyujSUMOvWrdODDz6ob7/9VvHx8crIyFCHDh2UlpZm7/Pwww/rk08+UVxcnNatW6djx47pjjvusG/PzMxUly5dlJ6ero0bN2rRokVauHChJk2aZMUhoQT47rvvNH/+fN16660O7XxWYTkDKCGmT59uhIeH29dff/11o0yZMsalS5fsbY899phRo0YN+/qdd95pdOnSxWE/kZGRxv3331/wBaNEa9q0qfHggw/a1zMzM42QkBBj6tSpFlYFGEZSUpIhyVi3bp1hGIaRnJxsuLu7G3FxcfY+e/bsMSQZmzZtMgzDMFatWmW4uLgYiYmJ9j5z5841/Pz8HH4GA/nh3LlzRvXq1Y34+HijTZs2xqhRowzD4LOKwoEzXygxUlJSVLZsWfv6pk2b1Lp1a3l4eNjbYmJitG/fPp09e9beJzo62mE/MTEx2rRpk3OKRomUnp6ubdu2OXz2XFxcFB0dzWcPlktJSZEk+8/Tbdu2KSMjw+HzWrNmTVWuXNn+ed20aZPq1aunoKAge5+YmBilpqbqxx9/dGL1KAkefPBBdenS5Yr/v/msojBws7oAwBl+/fVXzZkzRzNmzLC3JSYmKjw83KFf9g/bxMRElSlTRomJiQ4/gLP7JCYmFnzRKLFOnTqlzMzMXD97e/futagqQMrKytLo0aPVokUL1a1bV5L589LDw0MBAQEOff/8s/JqP0uztwH5ZcmSJdq+fbu+++67K7bxWUVhwJkvFCnjx4+XzWa75vLXX06PHj2qjh07qnfv3rrvvvssqhwAir4HH3xQu3fv1pIlS6wuBbjCkSNHNGrUKL377rvy8vKyuhwgV5z5QpEyduxYDRo06Jp9br75ZvvjY8eOqV27dmrevLnDQBqSFBwcfMUIR9nrwcHB1+yTvR0oCOXKlZOrqyufPRQqI0aM0MqVK7V+/XpVqlTJ3h4cHKz09HQlJyc7nFH48+c1ODj4itE6//rzFvintm3bpqSkJDVs2NDelpmZqfXr1+vVV1/V559/zmcVluPMF4qUwMBA1axZ85pL9j1cR48eVdu2bdWoUSMtWLBALi6OH/eoqCitX79eGRkZ9rb4+HjVqFFDZcqUsfdJSEhweF58fLyioqIK+EhRknl4eKhRo0YOn72srCwlJCTw2YPTGYahESNG6MMPP9SXX355xeXajRo1kru7u8Pndd++fTp8+LD98xoVFaUffvjBYbTO+Ph4+fn5qXbt2s45EBR77du31w8//KCdO3fal8aNG+vuu++2P+azCstZPeIHUBB+//13o1q1akb79u2N33//3Th+/Lh9yZacnGwEBQUZ/fv3N3bv3m0sWbLEKFWqlDF//nx7nw0bNhhubm7GjBkzjD179hiTJ0823N3djR9++MGKw0IJsmTJEsPT09NYuHCh8dNPPxnDhg0zAgICHEbgApzh3//+t+Hv72989dVXDj9LL1y4YO8zfPhwo3LlysaXX35pbN261YiKijKioqLs2y9fvmzUrVvX6NChg7Fz505j9erVRmBgoDFhwgQrDgklyJ9HOzQMPquwHuELxdKCBQsMSbkuf7Zr1y6jZcuWhqenp1GxYkXjhRdeuGJf77//vnHLLbcYHh4eRp06dYxPP/3UWYeBEm7OnDlG5cqVDQ8PD6Np06bGt99+a3VJKIGu9rN0wYIF9j5//PGH8cADDxhlypQxSpUqZfTo0cPhyy7DMIyDBw8anTp1Mry9vY1y5coZY8eONTIyMpx8NChp/hq++KzCajbDMAxrzrkBAAAAQMnBPV8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACcgfAEAAACAExC+AAAAAMAJCF8AgCLjq6++ks1mU3JystWlOE1YWJhmz55tdRkAgHxA+AIA5NmgQYMUGxt7RXthCkVTpkxRREREvuzrWsdVWELRhQsXNGHCBFWtWlVeXl4KDAxUmzZt9NFHH1ldGgDgL9ysLgAAANy44cOHa/PmzZozZ45q166t06dPa+PGjTp9+nSBvWZ6ero8PDwKbP8AUFxx5gsAUCC++eYbtWrVSt7e3goNDdVDDz2ktLQ0+/a3335bjRs3lq+vr4KDg3XXXXcpKSnJYR+rVq3SLbfcIm9vb7Vr104HDx685msuXLhQTz31lHbt2iWbzSabzaaFCxdKkg4fPqzu3bvLx8dHfn5+uvPOO3XixIl8O97k5GTde++9CgwMlJ+fn2677Tbt2rXLvn3//v3q3r27goKC5OPjoyZNmuiLL75w2EdSUpK6desmb29vhYeH69133/3b1/344481ceJEde7cWWFhYWrUqJFGjhypIUOG2PtcunRJjz32mEJDQ+Xp6alq1arpv//9r337unXr1LRpU3l6eqpChQoaP368Ll++bN/etm1bjRgxQqNHj1a5cuUUExMjSdq9e7c6deokHx8fBQUFqX///jp16tQNv4cAUNwRvgAA+W7//v3q2LGjevbsqe+//15Lly7VN998oxEjRtj7ZGRk6JlnntGuXbu0YsUKHTx4UIMGDbJvP3LkiO644w5169ZNO3fu1L333qvx48df83X79OmjsWPHqk6dOjp+/LiOHz+uPn36KCsrS927d9eZM2e0bt06xcfH67ffflOfPn3y7Zh79+6tpKQkffbZZ9q2bZsaNmyo9u3b68yZM5Kk8+fPq3PnzkpISNCOHTvUsWNHdevWTYcPH7bvY9CgQTpy5IjWrl2rDz74QK+//voVgfSvgoODtWrVKp07d+6qfQYMGKD33ntPr7zyivbs2aP58+fLx8dHknT06FF17txZTZo00a5duzR37lz997//1bPPPuuwj0WLFsnDw0MbNmzQvHnzlJycrNtuu00NGjTQ1q1btXr1ap04cUJ33nnnjb6FAFD8GQAA5NHAgQMNV1dXo3Tp0g6Ll5eXIck4e/asYRiGMXToUGPYsGEOz/36668NFxcX448//sh13999950hyTh37pxhGIYxYcIEo3bt2g59HnvsMYfXyc3kyZON+vXrO7StWbPGcHV1NQ4fPmxv+/HHHw1JxpYtW666r7Vr1xqSrjje0qVLGzabzZg1a5b92Pz8/IyLFy86PL9q1arG/Pnzr7r/OnXqGHPmzDEMwzD27dt3RT179uwxJNlfJzfr1q0zKlWqZLi7uxuNGzc2Ro8ebXzzzTf27dn7jY+Pz/X5EydONGrUqGFkZWXZ21577TXDx8fHyMzMNAzDMNq0aWM0aNDA4XnPPPOM0aFDB4e2I0eOGJKMffv2XbVeACjJOPMFALgu7dq1086dOx2W//znPw59du3apYULF8rHx8e+xMTEKCsrSwcOHJAkbdu2Td26dVPlypXl6+urNm3aSJL9TNCePXsUGRnpsN+oqCiH9T/vf/jw4Vetec+ePQoNDVVoaKi9rXbt2goICNCePXskSXXq1LHvq1OnTg7P//rrr6845pCQEIfjPX/+vG666SaHmg4cOKD9+/dLMs98jRs3TrVq1VJAQIB8fHy0Z88eh+N1c3NTo0aN7PutWbOmAgICrnpcktS6dWv99ttvSkhIUK9evfTjjz+qVatWeuaZZyRJO3fulKurq/39ze29iYqKks1ms7e1aNFC58+f1++//25v+3Nd2ce8du1ah+OtWbOmJNmPGQDgiAE3AADXpXTp0qpWrZpD259/SZfMoHH//ffroYceuuL5lStXVlpammJiYhQTE6N3331XgYGBOnz4sGJiYpSenp7nWnbu3Gl/7Ofnd30H8herVq1SRkaGJMnb29thW3h4+BUhyM0t57/Q8+fPq0KFCvrqq6+u2G/288aNG6f4+HjNmDFD1apVk7e3t3r16nVdx3s17u7uatWqlVq1aqXHHntMzz77rJ5++mk99thjVxzLjSpdurTD+vnz59WtWzdNmzbtir4VKlTIl9cEgOKG8AUAyHcNGzbUTz/9dEVIy/bDDz/o9OnTeuGFF+xno7Zu3erQp1atWvr4448d2r799luH9dz27+HhoczMzCv2deTIER05csT+ej/99JOSk5NVu3ZtSVKVKlWu4wgdNWzYUImJiXJzc1NYWFiufTZs2KBBgwapR48ekszw8ucBRGrWrKnLly9r27ZtatKkiSRp3759NzR8f+3atXX58mVdvHhR9erVU1ZWltatW6fo6Ogr+taqVUvLli2TYRj2s18bNmyQr6+vKlWqdM1jXrZsmcLCwhyCKADg6rjsEACQ7x577DFt3LhRI0aM0M6dO/XLL7/oo48+sg+4UblyZXl4eGjOnDn67bff9PHHH9svk8s2fPhw/fLLL3rkkUe0b98+LV682D5y4bWEhYXpwIED2rlzp06dOqVLly4pOjpa9erV0913363t27dry5YtGjBggNq0aaPGjRv/4+ONjo5WVFSUYmNjtWbNGh08eFAbN27U448/bg+V1atX1/Lly7Vz507t2rVLd911l7Kysuz7qFGjhjp27Kj7779fmzdv1rZt23Tvvff+7Zmrtm3bav78+dq2bZsOHjyoVatWaeLEiWrXrp38/PwUFhamgQMHasiQIVqxYoUOHDigr776Su+//74k6YEHHtCRI0c0cuRI7d27Vx999JEmT56sMWPGyMXl6r8mPPjggzpz5oz69eun7777Tvv379fnn3+uwYMHXxF+AQAmwhcAIN/deuutWrdunX7++We1atVKDRo00KRJk+z3SQUGBmrhwoWKi4tT7dq19cILL2jGjBkO+6hcubKWLVumFStWqH79+po3b56ef/75v33tnj17qmPHjmrXrp0CAwP13nvvyWaz6aOPPlKZMmXUunVrRUdH6+abb9bSpUvz5XhtNptWrVql1q1ba/DgwbrlllvUt29fHTp0SEFBQZKkl156SWXKlFHz5s3VrVs3xcTEqGHDhg77WbBggUJCQtSmTRvdcccdGjZsmMqXL3/N146JidGiRYvUoUMH1apVSyNHjlRMTIw9XEnS3Llz1atXLz3wwAOqWbOm7rvvPvuw/xUrVtSqVau0ZcsW1a9fX8OHD9fQoUP1xBNPXPN1Q0JCtGHDBmVmZqpDhw6qV6+eRo8erYCAgGuGNgAoyWyGYRhWFwEAAAAAxR1fTQEAAACAExC+AAAAAMAJCF8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACcgfAEAAACAExC+AAAAAMAJCF8AAAAA4AT/D9vcBmb35dbMAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -12218,7 +11707,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -15.16\n" + "The average of 'head_to_head' is: -13.46\n" ] } ], @@ -12228,7 +11717,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 150, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12274,17 +11763,17 @@ " \n", " \n", " head_to_head\n", - " -1443.1\n", + " -1288.2\n", " 93.1\n", - " -15.5\n", - " 86.181587\n", - " 8.931813\n", - " -1.735425\n", + " -13.8\n", + " 86.437183\n", + " 8.958303\n", + " -1.544559\n", " 1.985277\n", - " 2.2\n", - " -33.2\n", - " 0.043005\n", - " 0.086010\n", + " 3.9\n", + " -31.6\n", + " 0.062941\n", + " 0.125882\n", " \n", " \n", "\n", @@ -12292,13 +11781,13 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1443.1 93.1 -15.5 86.181587 8.931813 -1.735425 \n", + "head_to_head -1288.2 93.1 -13.8 86.437183 8.958303 -1.544559 \n", "\n", " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 2.2 -33.2 0.043005 0.086010 " + "head_to_head 1.985277 3.9 -31.6 0.062941 0.125882 " ] }, - "execution_count": 69, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -12311,7 +11800,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 151, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12357,44 +11846,44 @@ " \n", " \n", " \n", - " 335\n", - " How many cubic meters of water produced and su...\n", - " [0.146083333325, 0.1540953797, 0.1622041748, 0...\n", - " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", - " 130027.0\n", - " -265.7\n", - " \n", - " \n", " 279\n", " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.063\n", + " 0.05\n", " [0.02,0.01,0.015,0.015,0.05,0.89]\n", " Not in top 50\n", - " -264.8\n", + " -287.9\n", + " \n", + " \n", + " 335\n", + " How many cubic meters of water produced and su...\n", + " [0.167, 0.17296050626666667, 0.179050010833333...\n", + " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", + " 130027.0\n", + " -187.3\n", " \n", " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " 0.14\n", + " 0.15\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -176.9\n", + " -170.0\n", " \n", " \n", - " 151\n", - " How many earthquakes of magnitude ≥ 4 will hap...\n", - " [0.0, 0.0032810261, 0.0065908451250000005, 0.0...\n", - " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", - " 0.0\n", - " -157.3\n", + " 71\n", + " Will OpenAI, Anthropic, or Perplexity run an a...\n", + " 0.16\n", + " 0.55\n", + " yes\n", + " -123.5\n", " \n", " \n", - " 47\n", - " What will be Donald Trump's net worth, accordi...\n", - " 0.17\n", - " [0.6,0.2,0.1,0.075,0.025]\n", - " 0-$6 billion, inclusive\n", - " -126.1\n", + " 87\n", + " How many movies will be new on Netflix's globa...\n", + " 0.28\n", + " [0.01,0.064,0.926]\n", + " 2 or more\n", + " -119.6\n", " \n", " \n", "\n", @@ -12402,35 +11891,35 @@ ], "text/plain": [ " title \\\n", - "335 How many cubic meters of water produced and su... \n", "279 What will Kalshi's rank in the iPhone Top Free... \n", + "335 How many cubic meters of water produced and su... \n", "121 How many movies will be new on Netflix's top 1... \n", - "151 How many earthquakes of magnitude ≥ 4 will hap... \n", - "47 What will be Donald Trump's net worth, accordi... \n", + "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", + "87 How many movies will be new on Netflix's globa... \n", "\n", " bot_team_median \\\n", - "335 [0.146083333325, 0.1540953797, 0.1622041748, 0... \n", - "279 0.063 \n", - "121 0.14 \n", - "151 [0.0, 0.0032810261, 0.0065908451250000005, 0.0... \n", - "47 0.17 \n", - "\n", - " pro_median \\\n", - "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] \n", - "121 [0.005,0.017,0.157,0.821] \n", - "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... \n", - "47 [0.6,0.2,0.1,0.075,0.025] \n", - "\n", - " resolution head_to_head \n", - "335 130027.0 -265.7 \n", - "279 Not in top 50 -264.8 \n", - "121 3 or more -176.9 \n", - "151 0.0 -157.3 \n", - "47 0-$6 billion, inclusive -126.1 " + "279 0.05 \n", + "335 [0.167, 0.17296050626666667, 0.179050010833333... \n", + "121 0.15 \n", + "71 0.16 \n", + "87 0.28 \n", + "\n", + " pro_median resolution \\\n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", + "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... 130027.0 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more \n", + "71 0.55 yes \n", + "87 [0.01,0.064,0.926] 2 or more \n", + "\n", + " head_to_head \n", + "279 -287.9 \n", + "335 -187.3 \n", + "121 -170.0 \n", + "71 -123.5 \n", + "87 -119.6 " ] }, - "execution_count": 70, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -12452,7 +11941,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -12495,26 +11984,26 @@ " \n", " 85\n", " Will Elon Musk attend the Super Bowl in 2025?\n", - " 0.1685\n", + " 0.125\n", " 0.755\n", " no\n", - " 122.2\n", + " 127.3\n", " \n", " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " 0.017463\n", + " 0.014926\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 286.0\n", + " 270.3\n", " \n", " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.051569126225, 0.10695714615, 0.1599563...\n", + " [0.0, 0.025806875566666665, 0.0571614027666666...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 491.5\n", + " 531.1\n", " \n", " \n", " 211\n", @@ -12527,7 +12016,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.972\n", + " 0.95\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12545,11 +12034,11 @@ "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "85 0.1685 \n", - "0 0.017463 \n", - "189 [0.0, 0.051569126225, 0.10695714615, 0.1599563... \n", + "85 0.125 \n", + "0 0.014926 \n", + "189 [0.0, 0.025806875566666665, 0.0571614027666666... \n", "211 0.99 \n", - "214 0.972 \n", + "214 0.95 \n", "\n", " pro_median resolution \\\n", "85 0.755 no \n", @@ -12559,14 +12048,14 @@ "214 0.95 annulled \n", "\n", " head_to_head \n", - "85 122.2 \n", - "0 286.0 \n", - "189 491.5 \n", + "85 127.3 \n", + "0 270.3 \n", + "189 531.1 \n", "211 NaN \n", "214 NaN " ] }, - "execution_count": 71, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -12579,7 +12068,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -12603,7 +12092,7 @@ "dtype: object" ] }, - "execution_count": 72, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -12617,7 +12106,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -12672,10 +12161,10 @@ " NaN\n", " 31268\n", " 1.0\n", - " 0.017463\n", + " 0.014926\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 286.007699\n", - " 286.007699\n", + " 270.308741\n", + " 270.308741\n", " \n", " \n", " 1\n", @@ -12690,10 +12179,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.03366666666666667, 0.034105259000000006, 0....\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -76.357515\n", - " -76.357515\n", + " -79.442225\n", + " -79.442225\n", " \n", " \n", " 2\n", @@ -12708,10 +12197,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", - " -7.574597\n", - " -7.574597\n", + " -9.227528\n", + " -9.227528\n", " \n", " \n", " 3\n", @@ -12726,10 +12215,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.6\n", + " 0.55\n", " [0.16,0.44,0.4]\n", - " 31.015493\n", - " 31.015493\n", + " 22.314355\n", + " 22.314355\n", " \n", " \n", " 4\n", @@ -12744,10 +12233,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 28.578581\n", - " 28.578581\n", + " 25.971582\n", + " 25.971582\n", " \n", " \n", "\n", @@ -12776,28 +12265,28 @@ "4 NaN 0.0 400.0 31281 \n", "\n", " question_weight bot_team_median \\\n", - "0 1.0 0.017463 \n", - "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 1.0 0.085 \n", - "3 1.0 0.6 \n", - "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "0 1.0 0.014926 \n", + "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", + "2 1.0 0.1 \n", + "3 1.0 0.55 \n", + "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", - "2 0.013 -7.574597 \n", - "3 [0.16,0.44,0.4] 31.015493 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", + "2 0.013 -9.227528 \n", + "3 [0.16,0.44,0.4] 22.314355 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", "\n", " weighted_score \n", - "0 286.007699 \n", - "1 -76.357515 \n", - "2 -7.574597 \n", - "3 31.015493 \n", - "4 28.578581 " + "0 270.308741 \n", + "1 -79.442225 \n", + "2 -9.227528 \n", + "3 22.314355 \n", + "4 25.971582 " ] }, - "execution_count": 73, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -12808,7 +12297,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 155, "metadata": {}, "outputs": [], "source": [ @@ -12820,7 +12309,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 156, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12832,7 +12321,7 @@ "outputs": [ { "data": { - "image/png": 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", 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UHkm6hRBVdu211ypAHT9+vFr2d+HCBQWoqVOnWi2vStJ9+YcfpZT69NNPFaDmzJljWWb+gDZ27NgrjnfUqFHKyclJFRYWltrflSTd06dPV4BavXp1qe0/++wzBajp06dblpk/sA0aNKjU9uZ18+bNqzD+L7/8UgHqrrvuqnBbW8eoStJ98OBBm/ucOHGiJYEqacKECaUSg7lz55b6IsLMaDSqgIAA1atXL6vlsbGx6tixY1bJTnnMv0db/wwGg5ozZ45KSkqyus/zzz+vALVo0aJS+9uxY0epxKGmkm6j0ah8fX1V69atrRJus3Xr1ilAvf322xXuu6zXpPl1Zk6oSh7b0dFRAaUShLi4OAWoe++9t1KPq2TSrZRSXbt2VR06dLCs79Chg+rWrZtSStlMuvft21fqtVPSvHnzFKAOHz6slPr7y69Ro0aV2jYmJkY5ODhUOukuy+LFixWgIiMjLcvM50GrVq2sEqeS68aNG1ep/ZuTvG+++cZq+RNPPKEAtXbtWssyc9IdFRVVqX3bYn4/mzFjhpo/f77697//raZPn658fHwUoEaPHm3Z1vz7fPTRR0vtJzY2VgGqY8eOpc5Zo9Fo+VtT8susspRMupVS6rbbblO+vr6WJHnEiBEqICBAFRYW2ky6U1JSlIODQ5lJ/ltvvaUAtX79eqvYu3btWmrbrKwsy3NRmaS7LN98840C1MqVK62Wm7/EyMrKslpeVFSkDAaD6tmzZ6X2L4SoeTK9XAhR65RSrFixgpUrV3LkyBEyMjIwmUyW9efOnbvqYwwcOLDMZZdfiw1aNdmyHDhwgFdffZXt27dz/vz5UgXFUlNTr6o4mjkeW9OeIyIiLDFczjxdsqSmTZsC2pTDusbFxYUuXbrYXDd58mS++OILPv30U3r27AlAZmYm69evp0uXLlZTNP/44w8Ay/Tgyzk6OnL8+HGrZc2bN69SzOZq86BV6k9MTGTt2rU89thjbNy4kX379lmmSpf3e+zXrx8uLi42f4/VLSoqirS0NEJDQ61qKZiZ27+VfI6q+pq8/BIKvV5PYGAgubm5pZ5z82ukqq/v6dOn88gjj7Bz504Ajh07ZvOyCzPzeZKUlGSz/oD58R8/fpzOnTtbrp229d7RokULmjVrVul6CcnJybzyyiv8+OOPxMbGkpeXZ7Xe1nPQvXt39HrrUjtX+nqePHkyCxcu5NNPP7VcU20ymfj888/x9/dn5MiRlm3vuusuvv32W66//nomTZrE0KFDGThwYJUqav/3v/+1/Ozh4UGHDh24++67efDBB0tta+u91vy6GDx4cKnp6Hq9nkGDBnH8+HEOHDhAs2bNrii26dOns27dOr777jsGDRrE//73Px5++OEyL2HavXs3RqORgoICm+fNyZMnAe28ufXWWy3njbnLQUkeHh5079690tdXZ2Vl8frrr7N27VpOnz5NTk6O1Xpb5027du3w8PCwWma+dKcu/h0QorGSpFsIUWXBwcEcP36chIQE2rdvX+n7zZ07l3feeYdmzZpx2223ERISYrnm+7nnnqOgoOCqY7N1/aN5WUZGRqW2B/j9998ZMmQIADfddBNt27bFw8MDnU7H2rVrOXjw4FXHm5mZiV6vJyAgwGZcOp3Oco15SebCZiWZr702Go0VHjc4OBiAhISEKw25SgIDA8vsHX3TTTcRFBTEl19+yeuvv46DgwNff/01eXl5TJ482WrbixcvAvDSSy/VeMwl6fV6wsLCePDBB0lMTOSll17inXfe4V//+heA5Xdk61zS6XQEBQXVynNtfn6OHj3K0aNHy9yu5Af6qr4myzoHyzs3q1oF/5577uGJJ56wFFRzcnLi7rvvLnN78/OwYcMGNmzYUOZ25ufB/L5w+XX6ZkFBQZVKui9evEifPn2Ii4ujf//+DBs2DB8fHxwcHDhw4ADff/+9zefzal/PAB06dKBXr15s3LiRtLQ0fH19iYyMJD4+ngceeMAq0bzjjjtYu3Ytb7zxBh988AHvvvsuOp2OiIgIFi9efEU1KXbu3GlVvbw8tl4f5b124O8vbGy9D1bklltuISgoiOXLl3PmzBlMJhPTp08vc3vzebNjxw527NhR5nZXct5URmFhIeHh4ezbt48ePXowefJk/P39MRgMxMTE8PHHH1f6vAHt3KnseSOEqHmSdAshqqx///5ERkayadMmS2JakeTkZN599126du3Kzp07cXNzs6w7f/68zZG5qkhKSio10paUlARgs4hTWcngSy+9REFBAb/99lupkYw//vij0pWFy+Pl5YXJZCIlJaXUB7fk5GSUUmV+sLoaffr0wcnJiT179pCZmVnpY5hH42z1t7X1hYZZWc8xgIODAxMnTmTJkiX8+uuvDB8+nE8//RS9Xs+kSZOstjXHmZmZiaenZ6Virm7m4lvmyswl40pKSqJFixZW2yulSEpKqpHf4+XMxxg/fjxff/11hdvX1mvyavn7+zN69GhWr14NaIUL/f39y9ze/DxcXjSuLOb3heTkZJvrze8fFfnvf/9LXFwcL7zwAs8884zVuldeeYXvv/++UvupqsmTJ/PII4/w1VdfMXv2bD799FPL8suNHj2a0aNHk5WVxY4dOywF9UaMGMHx48fx8fGp9vhsvQ+UfO3Ycv78eavtroTBYODee+9l8eLFHD16lOuuu67c4mLmYzz22GO8/vrrFe6/us6b77//nn379jFjxgyWLVtmte7LL7/k448/rtR+hBB1k7QME0JU2dSpU3FwcOCjjz6yTFkti/kb+jNnzqCUYtiwYVYf7gF+++23aovN1r7My3r06FHp/Zw+fRo/P79SCXdubi779u0rtb25Eu+VjDCY47E1BdG87EpGnSrLzc2Nu+66i7y8PJtVcUsqLi62TDc2V3G2NWpra+p+ZZmTglWrVnH27Fm2bt1KREQEYWFhVtuZE17z9GF7SEtLA7Cagl3e7/HPP/8kPz/f6vdYlXOlMjp06ICXlxd79uyp1Khybb0mq8P06dPJysoiKyur3NFK+Ps8MU9Hr4j5EgZbjzk2NrbMtmGXO336NKAltJerjedz4sSJGAwGVq1aRV5eHt9++y1t2rQpdyTa09OTESNG8NFHHzF16lSSkpL4888/azxWM/PrYtu2baXaOiqlLG3Qqvo+OH36dMvlIRWdN3369EGn013xeWOrlVh2dnalLymx93kjhKhZknQLIaqsTZs2PPHEE6SmpnLzzTcTHR1dapv8/HzeeOMNy7Vx5hHA33//3SphiY+P56mnnqq22F544QWrUdeMjAxefPFFdDpdub1ZL9eiRQvS0tKspukajUYef/xxm180+Pn5AVT6Azpgiee5556zmj6ZkZFhGWW8kpivxEsvvURAQAAvvfQSb731ltXvxOzQoUOEh4dbYmvfvj2enp6sW7fOMhUTtBGdF198scqx9OzZk44dO/Ldd9/x4YcfopSyOTr3wAMPYDAYeOihh2z2701PTy+V/MfFxXH8+PFq6eGdn5/Pe++9B2DVmmnSpEkYDAbeeOMNq2svCwsL+ec//wlg1b/c19cXnU53RedKZRgMBu6//35iY2N5/PHHbSbeR44csYzM1dZrsjrcdNNNrF27lrVr13LjjTeWu+11111H3759+eKLLyyj4yWZTCa2bt1quT1gwABatWrFDz/8YJVAKaV4+umnK/3liPn5vDwJ+/zzz9m4cWOl9nE1AgMDuemmm9ixYwdLliwhMzOTe+65p9R227Zts/mYzOdFRS0Hq1Pz5s2JiIjg6NGjpfqxf/TRRxw7dowhQ4Zc8fXcZtdeey0//vgj3333XbmXJIB22c2ECRP4/fffee2110p9CQDal2jm95LmzZszaNAgDh06xGeffWa13csvv1zp66rLOm+2bt1q1VtdCFE/yfRyIcRVefHFF8nPz+c///kP7du3Z8iQIXTu3BlHR0eio6P59ddfuXDhgiUZCwkJYfz48XzzzTf07t2boUOHkpSUxA8//MDQoUMt3/ZfrXbt2tG5c2erPt3x8fHMmzeP3r17V3o/Dz30EP/73/8YMGAAEyZMwMXFhcjISBISEggPDy81qtmvXz9cXV1ZsmQJaWlpluu0L59mWtKgQYN46KGHePvtty0xK6UsMc+dO9dm393q0LRpU/73v/8xZswYHn74Yf7zn/8wdOhQgoKCyMzMZNeuXezevRsvLy/L9aBOTk489NBDvPzyy/Ts2dMyPXX9+vUMHjz4qn6HkydP5qmnnuLVV1/Fzc3N8vsrqXPnzrz33nvcf//9tG/fnpEjR9K6dWuysrI4c+YMW7duZerUqXzwwQeW+9x7771V6tP966+/kp+fD2hJ2vnz5/nxxx+Jj4+ne/fuPPDAA5ZtW7duzaJFi3jsscfo2rUrEyZMwN3dnfXr1xMVFcXo0aOtkh8PDw/69OnDtm3bmDx5Mm3btkWv1zN58uRS09Ov1HPPPce+fft466232LBhA4MGDSIwMJCEhAQOHz7MwYMH2blzJ4GBgbX2mqwOer3e5khgWb744gsiIiK46667WLJkCT179sTV1ZW4uDh27txJSkqK5fer1+v56KOPGDlyJMOGDbP06d68eTOJiYl07dqVQ4cOVXjMyZMns2jRIh566CG2bNlCixYtOHjwIJs2bWLcuHF8++23VX78lTV58mQ2btzI/PnzAWwm3XPnzuXcuXMMGDCAli1botPp2L59O7t27eL666+3WRisJr3//vsMGDCAWbNmsX79ejp27MjRo0dZt24dAQEBvP/++1e1/xEjRlR62/fee4+oqCieeOIJPv30U/r164ePjw9nz55lz549nDx5ksTERMvMkHfffZf+/ftz7733snbtWkuf7t27dzNw4MBKjVSPGjWKli1b8uqrr3LkyBE6d+5MVFQUP/zwA2PHjq3UpSJCiDrMPkXThRANze7du9X06dNVmzZtlKurq3J2dlYtW7ZUkyZNKtX/OisrSz322GOqZcuWytnZWbVt21a98MILqrCwUAFq8ODBVttXpWVYXl6eeuKJJ1SzZs2Uk5OTat++vXrrrbdKtaOpTMufr7/+WvXs2VO5ubmpJk2aqAkTJqjTp0/bjEsppTZs2KD69OmjXF1dS/X2Les+Sim1fPly1adPH+Xm5qbc3NxUnz591PLly0ttV5WWXRXJyclRS5YsUYMHD1ZNmjRRBoNB+fj4qH79+qmXXnqpVN9eo9GoFixYYHl+27Vrp95880115syZMluGtWjRosI44uLilF6vV4CaOHFiudvu2rVL3XXXXSo0NFQ5OjqqJk2aqJ49e6onn3xSHTt2zGrbqvbpvvyfu7u76t69u3rxxRfLbD/2/fffq8GDBytPT0/l7OysunTpohYvXmzVs9osKipKjRw5Uvn4+CidTndFMZrZ6tOtlFLFxcXqww8/VP3791deXl7K2dlZNW/eXI0YMUK9//77Vv21r/Q1aX4+y4qnrN+1rX2V5fKWYeUpq0+3Ulq/+2eeeUZ17txZubq6Kg8PD9W2bVs1adIk9e2335baftu2bWrQoEHK1dVV+fn5qTvuuEPFxsbafMxlvX8cOHBA3XTTTcrX11d5enqqwYMHq19//dXm9hW9Zq/kOTPLzc1VXl5eClD9+vWzuc2XX36pJkyYoFq3bq3c3NyUt7e36tatm1q0aFGpFlRlKatPty3m32d553dMTIyaNm2aCgkJUQaDQYWEhKhp06apmJiYSsWjVOmWYeUpq0+3Utpz+Oqrr6pevXopd3d35erqqlq1aqXGjBmjPvnkk1Kv58OHD6uRI0cqDw8P5enpqW6++WZ1+PBhm+/55fXpHj9+vAoICLD8Dfjyyy/L3L68c6Oy77lCiNqhU8rGvBkhhBBCCCGEEEJcNbmmWwghhBBCCCGEqCGSdAshhBBCCCGEEDVEkm4hhBBCCCGEEKKGSNIthBBCCCGEEELUEEm6hRBCCCGEEEKIGiJJtxBCCCGEEEIIUUMk6RZCiEZIKUWvXr246aabavW4K1euRKfTsXLlylo9bl20YMECdDodkZGR9g5F2MHUqVPR6XTExMTYOxSbBg4cSN++fe0dhhBCNAiSdAshRCP0ySefsG/fPp5//nl7hyLqgfDwcHQ6nc1/LVu2tHkfk8nE22+/TZcuXXB1dSUgIICJEydy5syZ2g3eTur7F0wLFixg165dfPnll/YORQgh6j1JuoUQopExmUwsWLCAgQMHcv3119s7HFGPzJ8/v9S/Rx55xOa2s2fPZu7cuSilmDt3LiNGjODbb7+lT58+nDx5snYDr4MWLlzIsWPHCAsLs3coNg0dOpSePXsyf/58lFL2DkeI2nPHHbBzp/azyQQPPQStW0ObNvDOO2Xfb+NG6NkTuneHzp3h44//Xrd7N/TvD926aes3b65cLMuXQ5cuYDDAkiXlb/vnn9r+27WDIUMgIaHidfn50KsXZGRULh5RZQZ7ByCEEKJ2/fjjj8TExPCvf/3L3qGIembBggWV2m7Lli0sW7aMQYMG8csvv+Dk5ATApEmTGDlyJHPmzOHnn3+uwUjrvpCQEEJCQuwdRrnuuece5s2bx+bNmxk6dKi9wxGi5u3aBRcvQr9+2u1Vq+Cvv+DECS0x7dEDIiKgUyfr+ykF99wDkZHQtSvExMC118K4ceDhAWPHwsqVMGyYtq9hwyAqClxdy4+nVy/46itYuLD87UwmuPtuWLpUi+/11+GRR2DNmvLXubjA5MmweDHIzLcaJSPdQgjRyKxYsQKdTsf48eNtro+NjWXGjBmEhYXh5ORE06ZNmTFjBnFxcaW2NU87LioqYsGCBbRs2RJnZ2fatWvHe++9V2EsGRkZuLu70+nyDzCXmEwmWrZsia+vL3l5eZV+fH379sXDwwMPDw/69u1baorvb7/9hk6nY/r06Tb3kZycjKOjI/3797danpWVxfz58+nUqROurq74+PgwfPhwtm/fXmof5ucmPz+fZ555htatW+Po6Fhh4rp8+XJGjx5Ny5YtcXFxwc/Pj+HDh7Nly5ZS20ZGRqLT6ViwYAHbt28nPDwcT09PfHx8GD9+PKdOnSr/yaohS5cuBeCFF16wJNwAN998M+Hh4fzvf/+zeT6VZdmyZXTu3BkXFxeaNWvGE088QX5+PjqdjvDwcKttW7ZsWeaUd/Pv5HJKKZYvX07//v3x8vLCzc2N3r17s3z58lLb5ufns3jxYrp164a3tzfu7u60bNmSCRMmcPDgQUC7XnvatGkATJs2zWo6vll513RX5hwG69//nj17uPHGG/H09MTb25uxY8fa3Pe+ffu4/fbbad68Oc7OzgQEBNCnTx9eeumlUtvecccdAPV2irwQV+zDD2HSpL9vr14Ns2aBgwP4+cGdd8IXX9i+r04H6enaz5mZ4O8Pzs5w4QKkpGiJNmijzT4+8OOPFcfTrRt06AD6ClK2vXu10fCICO327Nmwfr02kl3eOoC77tIScpnRUqMk6RZCiEZEKcWWLVto3749vr6+pdafOHGCPn36sHz5cnr16sVjjz1Gjx49WL58Ob179+bEiRM29ztx4kSWL1/O8OHDmTFjBhcvXuTBBx+0JF9l8fb25q677uKvv/7i999/L7X+l19+ITY2lrvvvhvXikYEgLlz5zJ9+nQSEhKYMWMGM2bMICEhgWnTpvHwww9bthswYAAtW7bkm2++Id/8waOEL774guLiYiZPnmxZdvHiRfr168fzzz+Pr68v9913H+PHj2fv3r1ERESwdu1amzGNHz+elStXEhERwcMPP0yrVq3KfQwPPvggSUlJDBs2jEcffZRbb72VnTt3MmzYML7//nub9/njjz8YOnQo3t7ePPTQQwwePJjvvvuOG264odQ11OZrjadOnVpuHLZ8/vnnvPzyyyxZsoTIyEhMJpPN7SIjI3F3dy/1pQXA8OHDAdi6dWuljvnCCy8wa9YsUlNTmTVrFnfccQerV6+2JIRXSynF3XffzYwZM0hJSWHSpEnMnDmTnJwcZsyYweOPP261/ZQpUyzLpk2bxpw5c7jhhhv47bff2L17NwBjxoxh9OjRAIwePdpqOn5FKnsOl7R7924GDRqEk5MTs2fPpnfv3qxdu5Zhw4ZZnd8HDhzghhtu4Mcff2TAgAHMmzeP22+/HTc3Nz766KNS+23atCnNmjVj06ZNlXsyhajvIiOhZAHBuDho0eLv2y1bassup9NpCfq4cdr2AwZo08udnKBJEwgJ0UasQZtqHhWljYZXl8vj9PQELy84d678dQDBwdqI+9Gj1RePKE0JIYRoNI4ePaoAdffdd9tcHxERoQD14YcfWi1/9913FaCGDBlitXzw4MEKUH379lUZGRmW5cePH1cGg0G1b9/eavsVK1YoQK1YscKy7M8//1SAmjp1aql4br/9dgWoAwcOVPjYtm7dqgDVoUMHlZ6ebll+8eJF1a5dOwWobdu2WZY/88wzClCrV68uta9evXopJycndeHCBcuySZMmKUAtXbrUatukpCTVrFkzFRAQoPLy8ko9N927d7faj9n8+fMVoLZs2WK1/MyZM6W2PXfunAoNDVVt27a1Wr5lyxYFKEB98MEHVus++OADBahbb73Varn5dzBlypRSxymL+bFc/q9du3Zq9+7dVttmZ2crQHXu3Nnmvr7++msFqGeffbbC4548eVIZDAYVFhamkpKSLMszMjJU+/btFaAGDx5sdZ8WLVqoFi1alPs4Svroo48UoKZNm6YKCwstywsKCtSoUaMUoPbs2aOUUio9PV3pdDrVq1cvVVxcbLWf4uJilZaWZrlt61wvacqUKQpQ0dHRlmVXeg6X/P1/+eWXVvufPHmyAtQXX3xhWTZv3jwFqLVr15aKJzU11WacY8eOVYDN81KIBsfJSank5L9vd+6s1O+//3373XeVmjy59P2KipQaPFiprVu127t2KRUcrFRKinb7wAGlhg9Xqnt3pe6+W6khQ5R6883KxzVlilL/+U/Z67/+WqmbbrJeFhCg1OnT5a8z69dPqR9/rHw84orJSLcQQjQi8fHxAAQFBZVaFxcXx5YtW+jYsSOzZs2yWnffffdx7bXXsnnzZs6ePVvqvgsXLsTLy8tyu3379vTv35+oqCiysrLKjem6666jR48erFmzhszMTMvylJQU1q1bR58+fejWrVuFj+3jS0VrFixYgLe3t2W5r6+vZYSx5DRZ8yj2qlWrrPZz7Ngx9u7dy8iRI/Hz8wMgNTWV1atXM2TIEGbOnGm1fWBgIP/3f/9HSkoKv/76a6m4nnvuOct+KsPWSHhISAjjx4/n5MmTxMbGllrfrl27Ur+zWbNm0bZtWzZs2EBKSopl+dixYzl27BgLK7pGsITRo0fzww8/kJCQQG5uLn/99RcPP/wwp0+f5sYbb7SaKp5xqSBPyd9BSebzJKMShXs+//xziouLmTdvHoGBgVb7eOaZZyodf3neeecd3N3deffdd3F0dLQsd3Jysky5/uLSdFKdTodSChcXF/SXTfd0cHDAx8fnqmK50nPYbNCgQdx5551Wy8yXTphH30uyNWvE39/fZkzm9wrze4cQDZqb29/TrgGaN4eS77kxMdqyyx04oI0cDxqk3e7TB5o2hf37tdvdusFPP2m3V63Sti3jsqoquTzOrCztGvTQ0PLXmeXnV3x9ubgqUkhNCCEakQsXLgDYTA4OHDgAwODBg0td96rX6xk0aBDHjx/nwIEDNGvWzGp9r169Su2vadOmAKSnp+Pp6VluXLNnz+a+++7j888/57777gO0tmaFhYWlksmy7L/04ebya3wBIi5dy2Z+jKAlqtdddx0//fQTqampNGnSBPg7CS85tXz37t0YjUYKCgpsXpNtrsZ9/Phxbr31Vqt11113XaXiNztz5gwLFy5k8+bNJCQkUFBQYLX+3LlztCg5VRDo379/qSRQr9fTv39/Tp48ycGDBxl26XpCb2/vMhPisjz66KNWtzt06MCSJUvw8vLihRde4PXXX+ett966on1Whvka6YEDB5ZaZ2vZlcrNzeXw4cOEhoayaNGiUuuLiooA7fcKWrI/cuRINm7cSM+ePbnjjjsIDw+nT58+Vgl7VV3pOWxW0evPbMKECSxZsoSxY8dy5513cuONNzJo0KByK6iX/OJJiAava1dt6rf5b9wdd2jXO99xh5aorl4NP/xQ+n7NmkFiIhw7pl2DfeoUnD4N7dtr6xMTtSnmoO3P3V2rIg5aRfSEhIqLpZWnVy8oKoItW7Rrtz/8EEaN0gqllbcOwGjUYu3SperHFxWSpFsIIRoR8wiXreuYzaPMtkbBAUul5ZKj0WYlR7nNDAbtT4zRaKwwrkmTJvH444+zbNkyS9L93//+Fw8PDyZOnFjh/c1x6fV6AgICSq0LCgpCp9OVin3y5Mns2rWL1atX8+CDD6KU4rPPPsPX15dbbrnFst3FixcB2LFjBzt27CgzhpycHJvHrqxTp05x3XXXkZmZSUREBKNGjcLLywu9Xk9kZCRbt24tlYSXdwzz8sqMKlfF7NmzeeGFF6yeE3NCX9Yxzb+DyiT+5n2UHOU2u5LntSxpaWkopUhISOC5554rc7uSv9c1a9bw8ssv8/nnn1s6AHh5eTFt2jRefvll3NzcqhxPVc5h8/EvZ+v117dvXyIjIy3xr1ixAoA+ffqwaNEiS2JfkrmA4dU8LiHqjdtvh59//rvo2eTJ2jXYbdtq123Pm/d3crpunfZv2TIICoKPPoIJE7SiZyaTlkybR8U/+gg++0wrVtahA3z3nbY/0KqjX3ON7XhWroRnnoG0NFi7Vqs8vn69VkX9gw+0EfPnn9eOuWqVViQtP18bxf70U20f5a0D2L5dG5m/ghlZ4spJ0i2EEI2I+cO8OYksyfzBPSkpyeZ9z58/b7VddfL09OTuu+/mww8/5MCBA+Tk5HDs2DFmzpyJh4dHpfbh5eWFyWQiJSWlVJKWnJyMUqpU7HfddRfz5s1j1apVPPjgg2zbto3Y2Fhmz56Ns7Oz1b4BHnvsMV5//fUremy2qmWX5T//+Q9paWl8+umn3HPPPVbr7rvvvjKLj5X1OzMvv9KR7cry9/dHp9NZJaXu7u6EhIQQHR2N0WjEwcHB6j7mWQFt27atcP/muJOTk0uN7pf1mPV6PYWFhTbXXf5FgPn32qtXL/bs2VNhPKAlny+++CIvvvgi0dHRbNmyhQ8++IA333yTvLw8Pvzww0rtx5aqnMNXauDAgfz444/k5eXx559/sn79et577z1uueUWjhw5wjWXffg3v1fY+iJAiAZn2jS44QZYsEAbjXZwgHfftb3tbbdp/8wmTtT+2TJ/vvbPlkOHwMZMGwCmTtX+2XLpC2qLfv20fdlS3rr334d//tP2OlFt5JpuIYRoRDp16oRerycqKqrUuu7duwOwbds21GWtQ5RSbNu2zWq76jZ79mxAaze1bNkygEpPLQfo0aMHoFXOvpx52eWxN2nShBEjRvDHH39w6tQpy9TyyxPePn36oNPp2LlzZ6XjqYrTp08DWCpfmymlyh1h37FjR6lK4iaTid9//x2dTlepa+KrYteuXSilSrXoGjx4MDk5OTZjNvfnHmS+9rEc5rh/++23UutsLQPt+ufk5GSKi4utlufk5FgSfjNPT086dOjAsWPHrKZhV1arVq2YPn06W7duxcPDg3Xr1lnWmb9sqMxMD7OqnMNV5erqSnh4OIsXL+bpp58mLy+PX375pdR2UVFRODo6cu2111bLcYWo0zw84D//gejo2jvm9u1aRXF7yM+HwYPhxhvtc/xGRJJuIYRoRHx8fOjatSt79uwplaQ1b96ciIgIjh49Wqo/8UcffcSxY8cYMmRIqeu5q0uPHj3o06cPn332GWvWrKFr165XdD30lClTAK1wWckpuBkZGZapw+ZtSjJfu71s2TLWrFlDq1atSrW6Cg4OZsKECfz++++89tprpb6UAPjzzz/Jzc2tdLy2mEdzL+/7/corr3DkyJEy73fixIlS7dmWLl3KiRMnuOWWW6xGKTMyMjh+/DiJiYmViik6OtrmzIiEhAQeeOABQLs8oKR//OMfADz77LNWo84//vgjkZGR3HTTTaVGrm2ZNGkSDg4OvPHGGyQnJ1uWZ2Zm8uKLL9q8T58+fSgqKuKzzz6zLFNK8dRTT9mc/j937lxyc3OZNWuWzfXR0dGWftcpKSk2fw9paWkUFBTgYr5Gkr+vhbZVeLAsVT2HK2vnzp02Ly0xzxooGT9AYWEh+/fvp3fv3jK9XDQeQ4dC5872jqJ2uLjA/ffbO4pGQaaXCyFEIzN27Fjmz5/PH3/8wQ033GC17v3332fAgAHMmjWL9evX07FjR44ePcq6desICAjg/fffr9HY7rvvPmbMmAFc2Sg3aCOnDz30EG+//TadO3dm/PjxKKX45ptviI+PZ+7cuTZHV0eNGoW3tzdvvPEGRUVFzJ071+aU8Pfee4+oqCieeOIJPv30U/r164ePjw9nz55lz549nDx5ksTExKtKTu677z5WrFjB+PHjmTBhAv7+/vzxxx/s27ePW265hQ0bNti83/Dhw5k7dy4bN26kU6dOHD16lPXr19OkSRPefPNNq22/++47pk2bxpQpU2xWwr7c1q1buf/++xk4cCCtWrXC19eX6OhoNmzYQE5ODnfffbdV0TnQin7NnDmTZcuW0bNnT2655RYSExNZvXo1fn5+vP3225V6Ptq0acO///1v5s+fT9euXZkwYQIGg4FvvvmGrl272pyxMWfOHFasWMHMmTP55ZdfCAgI4LfffiM9PZ1u3bpZirOZzZ49mz/++IOPP/6YHTt2MGzYMEJDQ0lKSuL48eP8+eeffP7557Rs2ZKEhAR69OhBt27d6Nq1K2FhYVy4cIHvv/+eoqIiq57e/fr1w9XVlSVLlpCWlmb54qO8qutVPYcra9GiRWzZsoVBgwbRqlUrXFxc2LdvH5s2beKaa65h7NixVtv/9ttvFBQUMGbMmCofUwghBNKnWwghGpuEhARlMBjU/fffb3N9TEyMmjZtmgoJCVEGg0GFhISoadOmqZiYmFLb2up7bGarD3FFvYtzcnKUs7OzcnV1tep5fCWWL1+u+vTpo9zc3JSbm5vq06ePWr58ebn3mTlzpqXfcVRUVJnb5ebmqldffVX16tVLubu7K1dXV9WqVSs1ZswY9cknn6iioiLLtuU9N0qV3ad7y5Ytqn///srT01P5+PiokSNHqr1799rc3tynef78+eq3335TgwcPVu7u7srLy0uNHTtWnTx5stRxr7RP98GDB9XkyZNVx44dlY+PjzIYDKpJkybqpptuKtUbuiSj0ajefPNN1alTJ+Xs7Kz8/f3VnXfeqU6dOlWp45a0dOlS1bFjR+Xk5KSaNm2qHn/8cZWbm2uzT7dSSm3evFn17dvXctzJkyerpKSkcn8nq1evVsOGDVO+vr7K0dFRhYWFqfDwcLV48WKVcqnXblpamlqwYIEaNGiQCgkJUU5OTio0NFSNGDFC/Wijx+2GDRtUnz59lKurq+X8MrP1+jCr7Dlc8vd/uejo6FK/559++knde++9qn379srT01N5eHiojh07qqefftryGEuaOnWqcnJyUskl+xYLIYS4YjqlbMyRE0II0aBNnjyZDRs2EBsbW2E7r9q0Z88e+vTpw+TJk/nkk0/sHU6dFxkZSUREBPPnz7fZyqyh0+l0DB482OY10OLqpKWl0aJFC26//fZSl5sIIYS4MnJNtxBCNEIvvvgieXl5lZ7mW1tee+01AO6Xa8yEsKs33ngDo9HICy+8YO9QhBCi3pNruoUQohFq0aIFH3/8cZltl2pTXFwcn3/+OUePHuWrr75i+PDh9OvXz95hCdGo+fn58cknnxAWFmbvUIQQot6TpFsIIRqpCRMm2DsEAM6cOcNTTz2Fh4cHo0aN4qOPPrJ3SEI0eo8++qi9QxBCiAajTl3TvW3bNl577TX27t1LYmIi3333XYUVMyMjI5k3bx5Hjx6lWbNmPPPMM0wtq4m8EEIIIYQQQghRi+rUNd05OTl069aNd999t1LbR0dHc8sttxAREcGBAwd45JFHmDlzJj///HMNRyqEEEIIIYQQQlSsTo10l6TT6Soc6f7nP//Jhg0bOHLkiGXZXXfdRXp6Oj/99FMtRCmEEEIIIYQQQpStXl/TvXPnToYNG2a1bPjw4TzyyCNl3qegoICCggLLbZPJxMWLF/H390en09VUqEIIIYQQQggh6jilFFlZWYSGhqLXV8/E8HqddJ8/f56goCCrZUFBQWRmZpKXl4erq2up+yxcuJDnnnuutkIUQgghhBBCCFHPnD17lqZNm1bLvup10l0VTz31FPPmzbPczsjIoHnz5kRHR+Pj42O/wISoJiaTidTUVJo0aVJt384JYU9yTouG5qrPaZMJzp2DEycgPx/l6ER6OpyNh7w8cGx0n+6EfSkKvBTOmTpAZo1WhmfsEdp/XvEgoGndOhg4sBYiEsXFxXz00UdkZWWh1+t5+eWX8fT0rLb91+u35eDg4FI9ZpOSkvDy8rI5yg3g7OyMs7NzqeU+Pj6SdIsGwWQyUVhYiI+PjyQookGQc1o0NFd1TufkwMmTEBMDXl4UhrTg7FlIyAbHAGjiDXK1nKhNCkWOQz7uRhd0knRXzrXNcf3pfQwXk20+YwodRUFNcRo5Ehwcaj28xmr06NHs2bOHIUOG8PLLL1frpcf1+tNLv3792LRpk9WyX375hX79+tkpIiGEEEKIGqAUJCbC7t1w5gwEBpKh9+XYMYiNBU8v8PGRhFuIekHvQNI9j9tcpS6l4eeeWCIJdw1LTU0lLi7OcrtTp07ce++91TrCbVanRrqzs7M5deqU5XZ0dDQHDhzAz8+P5s2b89RTT5GQkMAnn3wCwH333cc777zDE088wfTp09m8eTNfffUVGzZssNdDEEIIIYSoXgUFcOoUnD4NTk4Yw5pzPklHXBwUFUFgoHw2F6K+yb22J8rBgM5YbLW8KKgph2cswXHYODtF1jgcPnyYH374AYPBwH333WdJtGuqsHadSrr37NlDRESE5bb52uspU6awcuVKEhMTrb6NaNWqFRs2bODRRx/lzTffpGnTpixbtozhw4fXeuxCCCGEENUuNRWOH4fkZAgIIE/nRuwpbdDbw0Mb3RZC1D/e239AfynhzugzlNx23cnpNZiM/iM5n+JAMzvH11AVFRXx008/sW/fPgBCQ0Nr5bh1KukODw+nvLbhK1eutHmf/fv312BUGqPRSFFRUY0fR4irZTKZKCoqIj8/v8JrBR0dHXGQ4REhhKh7iou167ZPnACTCRXWlAvpDsTEQFYW+PuDo6O9gxRCVIlS+Gz+1nIz9fb7MBmcyOk+QKat1KDU1FS+/vprS02wQYMGMXjw4FqpF1Onku66SCnF+fPnSU9Pt3coQlSKUgqTyURWVlalpsj4+PgQHBwsfeqFEKKuyMiAqChISABfX4pcPYmPh/izoNdDUJBcuy1EfeZ2bC/O57XZuzkdelMY3BxD6nk7R9WwmaeTFxYW4u7uzrhx47jmmmtq7fiSdFfAnHAHBgbi5uYmiYmo85RSFBcXYzAYyj1flVLk5uaSnJwMQEhISG2FKIQQwhaTCeLjtYQ7NxdCQ8nKMxBzDFJStKnkZTRnEULUIz6bv7H8nDZ0vB0jaTxOnz5NYWEhLVu2ZNy4cTVSLK08knSXw2g0WhJuf39/e4cjRKVUNukGLK31kpOTCQwMlKnmQghhL7m52lTy2Fhwd8cU2pSkZG2GeUE+BASCQd6ihaj3HDIu4rVnCwDFXn5k9Qq3b0CNxMiRIwkODua6666zS/vRet0yrKaZr+F2c3OzcyRC1Bzz+S01C4QQwg7MrcB27YLoaAgIIN/Vl5MnIeo4OFyaTi4JtxANg89v6ywVy9MHjQKDFGeoCYcOHeKbb76x1AtzcnLi+uuvt0vCDTLSXSkypVw0ZHJ+CyGEnRQUaIn26dNaVbRmzbiYpiM6Wrus298fnJzsHaQQotqYTPhs+c5yMz1irB2DaZiKior48ccfLYW227VrR5cuXewclSTdQgghhBC1LzNT6719qRVYsZMbCXEQd1ZbHRSkFU0TQjQc7kd34ZScAEB2l+spCmxq54galtTUVNasWWOpVzR48GA6depk56g08nYurtiCBQsICgpCp9Oxdu3aGjtOTe+/IpGRkeh0Okvl+pUrV+JToiHqggUL6N69u11iuxKXPw4hhBB2VFysjWyfPAkXL0LTpmSb3Dh+XFvs5gpN/CXhFqIhKllALX2IFFCrTocOHeKjjz4iOTkZd3d3Jk+eTHh4uN2mk1+ubkQhqt3UqVPR6XTodDqcnJxo06YNzz//PMXFxVe132PHjvHcc8/x4YcfkpiYyM0333zVsdaX5PXOO+/kxIkTtXIsSZSFEKIBysyE/fvhyBFwckKFhJKU6sDRo5CaqhVLkzIyQjRMhrQUPPdtA6DIpwlZ3QfaOaKGY8uWLXz33XcUFRXRsmVLZs+eXavtwCpDppc3YCNGjGDFihUUFBSwceNGHnzwQRwdHXnqqaeueF9GoxGdTsfp06cBGD16dKO7FtjV1dVS7buqCgsLcZIL9IQQonExmbSe28ePa1XKQ0Ioyivm9Bk4Fw8uLtp0ciFEw+W99Xt0JiMAGYNHg0HSsOrSrl07duzYwYABAxg0aFCdGd0uqe5FJKqNs7MzwcHBtGjRgvvvv59hw4axbt06AAoKCnj88ccJCwvD3d2dvn37EhkZabmveSr1unXr6NixI87OzkyfPp1Ro0YBoNfrrZLuZcuW0aFDB1xcXLj22mt57733rGKJj49n4sSJ+Pn54e7uTu/evfnzzz9ZuXIlzz33HAcPHrSMzK9cubLUYxkyZAhz5syxWpaSkoKTkxObNm0q8zlYv349ffr0wcXFhSZNmjB27N8FKz799FN69+6Np6cnwcHBTJo0yXINiC2XTy83+/DDD2nWrBlubm5MmDCBjIwMy7qpU6cyZswYXnrpJUJDQ2nfvn2Fx46JiSEiIgIAX19fdDodU6dOBcBkMrFw4UJatWqFq6sr3bp14+uvv7aKZ+PGjXTs2BE3NzciIiKIiYkp8zEJIYSoYbm5cOgQ7Nun3W7alPRsA3HxcDZO673t7W3XCIUQNc1kxDdyLQBKpyctXAqoXa2LFy9afg4LC+Phhx+uU9PJLydfsVRRYWFhmev0ej2GEt9elbetTqfD0dGxwm2rY3TU1dWVCxcuADBnzhz++usvvvzyS0JDQ/nuu+8YMWIEhw8fpm3btgDk5uayaNEili1bhr+/PyEhIYSHhzNt2jQSExMt+/3ss8/497//zTvvvEOPHj3Yv38/s2bNwt3dnSlTppCdnc3gwYMJCwtj3bp1BAcHs2/fPkwmE3feeSdHjhzhp59+4tdffwXA28anj5kzZzJnzhwWL16Ms7MzAKtWrSIsLIwhQ4bYfLwbNmxg7Nix/Otf/+KTTz6hsLCQjRs3WtYXFRXxwgsv0L59e5KTk5k3bx5Tp0612qYip06d4quvvmL9+vVkZmYyY8YMHnjgAT777DPLNps2bcLLy4tffvmlUsdu1qwZ33zzDePHjycqKgovLy/LCPvChQtZtWoVH3zwAW3btmXbtm3cc889BAQEMHjwYM6ePcv48eO5//77mT17Nnv37uWxxx6r9OMRQghRTZSCpCRtdDstDYKCKHZwJjFea8Vd4KyNbjvUzc+HQohq5HFoJ44XzgOQ3e0GipsE2zmi+stcnfzQoUPMnDmT4GDtufT09LRzZOWTpLuKFi5cWOa6tm3bMmnSJMvt119/vcweyC1atLCMYgK8+eab5Obmltpu/vz5VY5VKcWmTZv4+eefeeihh4iLi2PFihXExcURGhoKwOOPP85PP/3EihUrePnllwHtpH7vvffo1q2bZV/mkV7zCW6ObfHixYwbNw6AVq1a8ddff/Hhhx8yZcoUPv/8c1JSUti9ezd+fn4AtGnTxnJ/Dw8PDAaD1T4vN27cOObMmcP333/PhAkTAG3k2Xztui0vvfQSd911F88995xlWcnHMn36dMvP11xzDW+99RZ9+vQhOzsbDw+Pcp7Rv+Xn5/PJJ58QFhYGwNtvv80tt9zC4sWLLY/H3d2dZcuWWX1xUtGxzc9TYGCg5TkvKCjg5Zdf5tdff6Vfv36W+27fvp0PP/yQwYMH8/7779O6dWteffVVDAYD1157LYcPH2bRokWVejxCCCGqQUGBVhXt9GltCmmzZuTk6og7o7Xk9vACZy/QG+0dqBCiNlgVUIsYZ8dI6rfLq5OfPXu23PyhLpGkuwH74Ycf8PDwoKioCJPJxKRJk1iwYAGRkZEYjUbatWtntX1BQQH+/v6W205OTnTt2rXcY+Tk5HD69GlmzJjBrFmzLMuLi4stI9YHDhygR48elkSyKlxcXJg8eTLLly9nwoQJ7Nu3jyNHjlimy9ty4MABq5gut3fvXhYsWMDBgwdJS0vDZDIBEBcXR8eOHSsVV/PmzS0JN0C/fv0wmUxERUVZ3gS6dOlSaqZCVY596tQpcnNzufHGG62WFxYW0qNHD0ArdHfddddZrTcn6EIIIWrBhQsQFQXnz0NAAMrVjdRUrR13Ti4EBICDAXLsHacQolYYLpzH48AOAIr8g8ju3t/OEdVPhw4d4ocffqCoqAh3d3fGjRtX54qllUeS7ioqrxjZ5dcSPP7442Vue/ko7cMPP3x1gZUQERHB+++/j5OTE6GhoZYp79nZ2Tg4OLB3714cHBys7lNyhNfV1bXCYmnZ2dkALF26lL59+1qtM+/7aouPmc2cOZPu3bsTHx/PihUrGDJkCC1atChz+/KOm5OTw/Dhwxk+fDifffYZAQEBxMXFMXz48HIvB6gKd3f3ajm2+bnesGGDVaIPWKbcCyGEsJPiYm3e+IkT2s9Nm1JodODsGa2GmqMjBAWCTgfK3rEKIWqNT+T36JQ2uJI+eAzoHcq/g7Bink6+f/9+QJtRO27cuErPSq0rJOmuoiu5xrqmtq2Iu7u71TRusx49emA0GklOTmbgwKtrVxAUFERoaChnzpzh7rvvtrlN165dWbZsGRcvXrQ52u3k5ITRWPEcuy5dutC7d2+WLl3K559/zjvvvFPu9l27dmXTpk1Mmzat1Lrjx49z4cIFXnnlFZo1awbAnj17KozhcnFxcZw7d84yTf+PP/5Ar9dbCqbZUpljm8+Dks+LuaBdXFwcgwcPtrnvDh06lBr9/+OPP674cQkhhLgCmZna6HZ8vFYZzcuLjAyIidEGvn39wEW+GxWi8TEW47N1LQBK70D64NH2jaceOnDggCXhHjx4cJ2tTl4RSboboXbt2nH33Xdz7733snjxYnr06EFKSgqbNm2ia9eu3HLLLVe0v+eee465c+fi7e3NiBEjKCgoYM+ePaSlpTFv3jwmTpzIyy+/zJgxY1i4cCEhISHs37+f0NBQ+vXrR8uWLYmOjubAgQM0bdoUT0/PMkduzQXV3N3drSqR2zJ//nyGDh1K69atueuuuyguLmbjxo3885//pHnz5jg5OfH2229z3333ceTIEV544YUretygTXufMmUKr7/+OpmZmcydO5cJEyaUe31JZY7dokULdDodP/zwAyNHjsTV1RVPT08ef/xxHn30UUwmEwMGDCAjI4MdO3bg5eXFlClTuO+++1i8eDFPPvkks2bNYt++fTarwQshhKgGl7cCCw3FqDNwPgHi4qCoCAIDwUEGtoRolDwObMcxLQWA7B4DKfYLtHNE9U+vXr2Ij4+ne/futGrVyt7hVFn9+5pAVIsVK1Zw77338thjj9G+fXvGjBnD7t27ad68+RXva+bMmSxbtowVK1bQpUsXBg8ezMqVKy0vDCcnJ/73v/8RGBjIyJEj6dKlC6+88opl+vn48eMZMWIEERERBAQE8MUXX5R5rIkTJ2IwGJg4cSIuLi7lxhUeHs6aNWtYt24d3bt3Z8iQIezatQuAgIAAVq5cyZo1a+jYsSOvvPIKr7/++hU/9jZt2jBu3DhGjhzJTTfdRNeuXUu1S7tcZY4dFhbGc889x5NPPklQUJClXdoLL7zAs88+y8KFC+nQoQMjRoxgw4YNlue6efPmfP3115bH/MEHH1gK4wkhhKhGNlqB5RUZOHlSm2FuMFy6flsSbiEaLd/N31p+ThsiBdQqo6ioiMjISEsRar1ez9ixY+t1wg2gU0o16kuLMjMz8fb2Ji0trVQP5vz8fKKjo2nVqlWFCZ6oHTExMbRu3Zrdu3fTs2dPe4dTJymlKC4uxmAwVHhNPsh5Luo+k8lEcnIygYGB9XJKmWhgzK3AoqLg4kUIDEQ5u3DhgjadPCsL/P21a7jL3AWKHId83I0u6Kj4fVqIukzOZ9scU87R+rHR6JSisEkopxevhbL+hpmMGFLPk9NjIEYvX86fh2bNoIJ6xg1OSkoKa9asISUlhV69enHrrbfaJY709HR8fX3JyMjAy8urWvYp08tFvVBUVMSFCxd45plnuP766yXhFkIIUfsKC+HUKatWYEXFOuJj4WycNqodFKQVSxNCNG4+kd+huzS2mR4xtuyEWwBw8OBBNmzYYKlO3qlTJ3uHVK0k6Rb1wo4dO4iIiKBdu3Z8/fXX9g5HCCFEY3NZKzDc3MjK0ka3U1K0+mnV1KxDCFHfFRfjs1UrbKscHEgfNMrOAdVdRUVFbNy4kQMHDgD1tzp5RSTpFvVCeHg4jfxKCCGEEPZgbgV28qSlFZhJ50DSeS3hLsiHgEAwyLXbQohLPPdFYsi4AEBWrwiMPk3sHFHddOHCBVavXk1KilZsLjw8nIEDBzbIS8kk6RZCCCGEsMXcCuzsWfD1hYAA8vO1HDwxEdzctOnkQghRkhRQqxyDwUB2djbu7u6MHz++3hdLK48k3UIIIYQQJZlbgUVFQU4OhIWBwcDFixAdDRkZWrE0Jyd7ByqEqGsck87iflTrllMY1IzcDr3tHFHdYjKZLCPZ3t7e3HXXXfj5+TW46eSXa3hj90IIIYQQVZWXB4cPa63AlIKmTSnGQGwsHP0LcvO00W1JuIUQtliNckeMkwJqJaSkpPDhhx8SFRVlWda8efMGn3CDjHQLIYQQQmgJdnIyHD+uFU0LCgIXF7KztWu3k5PB21ubUi6EELboigrx/m09ACaDIxlSQM2iZHXyTZs20a5du0q1tm0oJOkWQgghRONWWAhnzmjtwPR6aN4chY7kJC3hzsuTYmlCiIp57tmMISsdgKw+QzB6+tg1nrrg8urk11xzDWPHjm1UCTdI0i2EEEKIxuziRe3a7cREaNIE3N0pKIC4s5AQDy4uUixNCFE5PlYF1MbbMZK6ISUlhTVr1pCSkoJOp2Pw4MENtjp5RRrfIxaiBsXExKDT6Szf5kVGRqLT6UhPT7drXEIIIS5jNMLp07BrF6SmQtOm4O5OWjr8dQzOxmm9t7297R2oEKI+cEqIxv34PgAKQluR176HnSOyr4yMDJYuXUpKSgoeHh7ce++9DB48uFEm3CBJd60xGiEyEr74Qvu/0Vizx5s6dSo6nc7yz9/fnxEjRnDo0KEr3s+YMWPK3abkcWz9W7BgQdUfSDVasGABOp2OESNGlFr32muvodPpCA8Pr9Zj3nDDDSQmJuItn9qEEKLuyMqCAwfg0CGtIlpoKMXKgbPx8NdRyM7SRredne0dqBCivvDZUrKA2lhoZNOnL+ft7U3Xrl255pprmD17Ni1btrR3SHYl08trwbffwsMPQ3z838uaNoU334RxNdi6b8SIEaxYsQKA8+fP88wzz3DrrbcSFxdXrcdJTEy0/Lx69Wr+/e9/W1UlrEsVCUNCQtiyZQvx8fE0bdrUsnz58uU0b9682o/n5OREcHBwte9XCCFEFZhMcO6cViwtOxtCQsDRkZwciIvTZph7eoGHu70DFULUJ7rCfHy2bwDA5OhMxoBb7ByRfaSkpODq6mr57D9ixAj0en2jHd0uSZ6BGvbtt3D77dYJN2jtP2+/XVtfU5ydnQkODiY4OJju3bvz5JNPcvbsWVJSUizbHD58mCFDhuDq6oq/vz//+Mc/yM7OBrSR4Y8//pjvv//eMmodGRlZ6jjmYwQHB+Pt7Y1Op7Na9uWXX9KhQwdcXFy49tpree+996zu/89//pN27drh5ubGNddcw7PPPktRUZFl/YIFC+jevbslMfbw8OCBBx7AaDTy6quvEhwcTGBgIC+99FKFz0lgYCA33XQTH3/8sWXZ77//TmpqKrfcUvoNctmyZeXGvmvXLnr06IGLiwu9e/dm//79Vusvn15+4cIFJk6cSFhYGG5ubnTp0oUvvvjC6j7h4eHMnTuXJ554Aj8/P4KDg+vMbAEhhKi38vLgyBHYu1dLvps1QxkcSUmBo0fhfBIEBEjCLYS4cl67fsUhJxOAzL7DMHk0vhmOBw8eZOnSpXz77beYTCYADAaDJNyXyEh3DTIatRFupUqvU0qbdfLIIzB6NDjUcEXU7OxsVq1aRZs2bfD39wcgJyeH4cOH069fP3bv3k1ycjIzZ85kzpw5rFy5kscff5xjx46RmZlpGTH38/O7ouN+9tln/Pvf/+add96hR48e7N+/n1mzZuHu7s6UKVMA8PT0ZOXKlYSGhnL48GFmzZqFp6cnTzzxhGU/p0+f5scff+Snn37i9OnT3H777Zw5c4Z27dqxdetWfv/9d6ZPn86wYcPo27dvuTFNnz6dJ554gn/961+ANsp99913X3Hs2dnZ3Hrrrdx4442sWrWK6OhoHn744XKPnZ+fT69evfjnP/+Jl5cXGzZsYPLkybRu3ZrrrrvOst3HH3/MvHnz+PPPP9m5cydTp06lf//+3HjjjZV+7oUQQlySlFSqFVhhIZw9q30p7uQEQYGNfjaoEKKKShZQS4+owWmsdVBhYSE//vijpZ6RTqejsLAQFxcX+wZWx0jSXQW9e8P58xVvV1Cg1WYpi1LaH/zg4MpdNxYcDHv2VD7OH374wTK9Iycnh5CQEH744QfLN06ff/45+fn5fPLJJ7i7a1/tv/POO4waNYpFixYRFBSEq6srBQUFVZ4iPX/+fBYvXsy4S/PoW7VqxV9//cWHH35oSbqfeeYZy/YtW7bk8ccf58svv7RKuk0mE8uXL8fT05OOHTsSERFBVFQUGzduRK/X0759exYtWsSWLVsqTLpvvfVW7rvvPrZt20avXr346quv2L59O8uXL7+i2D///HNMJhP//e9/cXFxoVOnTsTHx3P//feXeeywsDAef/xxy+2HHnqIn3/+ma+++soq6e7atSvz588HoG3btrzzzjts2rRJkm4hhLgSl7cCa9YM9HoyMrRWYBcugK8fuMi120KIKnI+ewq3k1rNpPxmbchr29XOEdWey6uTh4eHM2DAABndtkGS7io4f16bHl5dykvMr0ZERATvv/8+AGlpabz33nvcfPPN7Nq1ixYtWnDs2DG6detmSbgB+vfvj8lkIioqiqCr7JGSk5PD6dOnmTFjBrNmzbIsLy4utiostnr1at566y1Onz5NdnY2xcXFeHl5We2rZcuWeHp6Wm4HBQXh4OBg9aIOCgoiOTm5wrgcHR255557WLFihWW0vGtX6zfIysR+7NgxunbtavVNXr9+/co9ttFo5OWXX+arr74iISGBwsJCCgoKcHNzs9ru8nhCQkIq9diEEEJcYqMVmNEI5xO067eLiiAwsOZnmgkhGjafzd9Yfk6PGNdopswcOHCAjRs3UlRUhIeHB+PHj2/0xdLKI0l3FVR20LeikW6zJk0qP9J9Jdzd3WnTpo3l9rJly/D29mbp0qW8+OKLV7azKjBfG7506dJSo88Olz7l7Ny5k7vvvpvnnnuO4cOH4+3tzZdffsnixYuttnd0dLS6rdPpbC4zX0NSkenTp9O3b1+OHDnC9OnTqxR7Vbz22mu8+eabLFmyhC5duuDu7s4jjzxCYWGh1XZX89iEEKJRMxohNhZOnNAy66ZNwcGBvDxtceJ57bptHx97ByqEqO90+Xl479gIgMnJhYz+I+0cUe0oLi5m+/btFBUVcc011zBu3DirQTxRmiTdVVDZKd5GI7RsqY2K27quW6fTPgtER9fON+06nQ69Xk9eXh4AHTp0YOXKleTk5FheKDt27LBM1wat+raxiv3NgoKCCA0N5cyZMzavmQatiFmLFi0s11cDxMbGVul4V6JTp0506tSJQ4cOMWnSpFLrKxN7hw4d+PTTT8nPz7eMdv/xxx/lHnfHjh2MHj2ae+65B9CmzZ84cYKOHTte5SMSQghBVpaWbMfFaQ22AwJQCi6katPJs7LA3x8u+15TCCGqxOuPn3HIywEgs99wTG51p2NPTTIYDNxxxx2cOHGCAQMGoGsko/tXQybc1yAHB60tGJSeaWK+vWRJzSXcBQUFnD9/nvPnz3Ps2DEeeughsrOzGTVqFAB33303Li4uTJkyhSNHjrBlyxYeeughJk+ebJla3rJlSw4dOkRUVBSpqalWVcUr47nnnmPhwoW89dZbnDhxgsOHD7NixQreeOMNQLteOS4uji+//JLTp0/z1ltv8d1331XvE1GGzZs3k5iYiE8Zwx0VxT5p0iR0Oh2zZs3ir7/+YuPGjbz++uvlHrNt27b88ssv/P777xw7dozZs2eTlJRU3Q9NCCEaF6W0imi7dmnFUkJCwNuboiKIiYW//tJmnwUFScIthKg+viV7cw9p2AXUDhw4wK5duyy3g4KCGDhwoCTclSRJdw0bNw6+/hrCwqyXN22qLa/JPt0//fQTISEhhISE0LdvX3bv3s2aNWsIDw8HwM3NjZ9//pmLFy/Sp08fbr/9doYOHco777xj2cesWbNo3749vXv3JiAggB07dlxRDDNnzmTZsmWsWLGCLl26MHjwYFauXEmrVq0AuO2223j00UeZM2cO3bt35/fff+fZZ5+ttuegPO7u7mUm3JWJ3cPDg/Xr13P48GF69OjBv/71LxYtWlTuMZ955hl69uzJ8OHDCQ8PJzg4mDFjxlTjoxJCiEYmLw8OH9ZagRmNWrE0R0eysrSC5dFnwMMD/PwazaWWQoha4BJzHNczfwGQ1/Ja8ls1zFmLhYWFrF27lu+//56ff/5ZBouqSKeUrYnPjUdmZibe3t6kpaWVSsDy8/OJjo6mVatWV1323miE337T6rmEhMDAgVK8RdQMpRTFxcUYDIZKfftYnee5EDXBZDKRnJxMYGCgVEQV1my0AjOZIClZm05ekA/+TcBQx/7eKhQ5Dvm4G13QId8EiPqtsZ7PwctfwneLNjszcdrTpF/NSLfJiCH1PDk9BmL08uX8ee37w652LoSenJzM119/bVWdvDGMbqenp+Pr60tGRkap4s5VJdd01xIHB7g0wCyEEEKIq2FuBXb6tDZ8fakVWH7+pWJpieDmpuXhQghR3fR5OXjt/BkAo4sbmf2G2zmi6nfgwAE2bNhAcXGxVCevBpJ0CyGEEKL+sNEKzLw4OhoyMrRiaU5Odo5TCNFgee38CYf8XAAyb7gZk2vDqty9fv169u3bByDVyauJJN1CCCGEqPvMrcBOntSqooWFgcFAcbHWJSTurLZZUBDIVQhCiBqjFL4lenM3xAJqTZo0aVTTyWuDJN1CCCGEqNsubwXWpAkA2dnatdvJydpiNzf7himEaPhczhzFJfYEAHnXdKKgRXs7R1Q98vLycHV1BeD666+nVatWBAcH2zmqhkOSbiGEEELUTUppw9hRUVriHRICjo4opSXaMTFa8fKAwLpXLE0I0TD5bi7RJmzoeDtGUj0KCwvZuHEj8fHxzJo1C2dnZ3Q6nSTc1UySbiGEEELUPXl52lTy6GhwddV6bep0FBRoU8kT4sHFRYqlCSFqjz4nC68/LhVQc/Mgs+9Ndo7o6iQnJ7NmzRpSU1PR6XTExMTQvn3DGLmvayTpFkIIIUTdkpwMx45p1dECA7XsGkhL10a30y5qfbedne0apRCikfH+fSP6wgIAMvqPRDnXz1arSikOHDjAxo0bKS4uxtPTk/Hjx9OiRQt7h9ZgSdIthBBCiLqhqEhrBXbqlHa7aVPQ6ykuhsTzEBujzTiXYmlCiFqnFD4lppanR9TPAmrm6eQHDx4EoHXr1owdO1aqk9cwSbqFEEIIYX9paXD8uNYKzN8fPDwAyMnR6qclJoKnF3jI50IhhB24njyIS/xpAHLbdaOgWRs7R1Q1P//8MwcPHkSn0xEREcGAAQOkOnktkO+JRb0QHh7OI488YrndsmVLlixZYrd4hBBCVBOjUbtue9cuSEnRWoF5eKCUdvPoUTifBAEBknALIeyn5Ch32pD6W0AtIiKCkJAQpkyZIu3AapEk3bXFaITISPjiC+3/RmONHm7q1KnodDp0Oh1OTk60adOG559/nuLi4mo9TkxMDDqdDgcHBxISEqzWJSYmYjAYLIUZqtPu3bv5xz/+Ua37FEIIUcuys+HAATh4EAwGS+/twkJtlvlff2l/LoMCtdVCCGEPDlnpeO36FYBiD2+y+gy1c0SVV1hYaJlKDuDh4cGsWbPk+u1aJkl3bfj2W2jZEiIiYNIk7f8tW2rLa9CIESNITEzk5MmTPPbYYyxYsIDXXnvN5raFhYVXdaywsDA++eQTq2Uff/wxYWFhV7XfsgQEBOAmDVmFEKJ+MrcC+/NPOHsWgoPBxweAjAythlpsLHh5a4tlIEYIYU/e2zegL9I+K2cMuAXlVD+qOCYnJ7N06VLWrl3LkSNHLMtldLv2SdJd0779Fm6/HeLjrZcnJGjLazDxdnZ2Jjg4mBYtWnD//fczbNgw1q1bB2gj4WPGjOGll14iNDTU0h7g8OHDDBkyBFdXV/z9/fnHP/5BdnZ2hceaMmUKK1assFq2YsUKpkyZUmrbI0eOcPPNN+Ph4UFQUBCTJ08mNTXVsj4nJ4d7770XDw8PQkJCWLx4cal9XD69/I033qBLly64u7vTrFkzHnjgAau4V65ciY+PDz///DMdOnTAw8PD8qWEEEKIWpSfD0eOwJ49UFysFUtzdMRo1P40Hj2qJd6BgeBSPz7XCiEaMqXw2VKigNqQul9ATSnF/v37Wbp0KampqXh6euLp6WnvsBo1SbprktEIDz+sfaN/OfOyRx6p8anmZq6urlYj2ps2bSIqKopffvmFH374gZycHIYPH46vry+7d+9mzZo1/Prrr8yZM6fCfd92222kpaWxfft2ALZv305aWhqjRo2y2i49PZ0hQ4bQo0cP9uzZw08//URSUhITJkywbPN///d/bN26le+//57//e9/REZGsm/fvnKPr9freeuttzh69Cgff/wxmzdv5oknnrDaJjc3l9dff51PP/2Ubdu2ERcXx+OPP17hYxNCCFFNkpNh926t/3aTJto/nc7SkvvESXB01K7fdnCwd7BCCAFux/finBgLQE6HXhSGtLRvQBUoLCxk7dq1rFu3juLiYlq3bs3s2bNlOrmdyRVSVdG7N5w/X/F2BQVQYgS3FKX+nlZXmWajwcHayMAVUkqxadMmfv75Zx566CHLcnd3d5YtW4aTkxMAS5cuJT8/n08++cTSNuCdd95h1KhRLFq0iKCgoDKP4ejoyD333MPy5csZMGAAy5cv55577sHR0dFqu3feeYcePXrw8ssvW5YtX76cZs2aceLECUJDQ/nvf//LqlWrGDpUu17m448/pmnTpuU+xsuLrL344ovcd999vPfee5blRUVFfPDBB7Ru3RqAOXPm8Pzzz5e7XyGEENXg8lZgzZqBXo9ScOGC1ns7K0srWn7Znw0hhLCr+tQmLDk5mTVr1pCamirVyesYSbqr4vx5bQ5cdSkvMb8KP/zwAx4eHhQVFWEymZg0aRILFiywrO/SpYsl4QY4duwY3bp1s+rT179/f0wmE1FRUeUm3QDTp0/nhhtu4OWXX2bNmjXs3LmzVOG2gwcPsmXLFjwutYIp6fTp0+Tl5VFYWEjfvn0ty/38/CzT38vy66+/snDhQo4fP05mZibFxcXk5+eTm5trufbbzc3NknADhISEkJycXO5+hRBCXKW0NIiKgnPnrFqBFRVBfAKcjdNGtYOC5NptIUTd4pBxEa/dmwEo9vQlq3eEnSMqX1pammU6+fjx42V0uw6RpLsqgoMrt11FI91mTZpUfqT7CkRERPD+++/j5OREaGgohstKv5ZMrqtDly5duPbaa5k4cSIdOnSgc+fOHDhwwGqb7Oxsy8j55UJCQjhlHgW5AjExMdx6663cf//9vPTSS/j5+bF9+3ZmzJhBYWGhJem+fNRdp9OhbE39F0IIcfWMRm02V1SU9vfwUmVy0Ea1Y2K0lmA+PuDqatdIhRDCJu/f1qMzagNI6YNGoRydKrhH7VNKWUay27dvz2233Ua7du2q/XO+uDqSdFdFZad4G41alfKEBNvXdet0WgGZ6OgauXjN3d2dNm3aVHr7Dh06sHLlSnJyciwv1B07dqDX6yscaTabPn06DzzwAO+//77N9T179uSbb76hZcuWpb4EAGjdujWOjo78+eefNG/eHNC+tTtx4gSDBw+2uc+9e/diMplYvHgxer1WpuCrr76qVLxCCCFqQHa2lmyfPQuentqXy4DJBEnJEBOt5eEBgWCQa7eFEHWRyYTvlu8sN9MjxtoxGNuSkpLYsGED48ePx9vbG4AePXrYOSphixRSq0kODvDmm9rPl8+ZM99esqTOVIu5++67cXFxYcqUKRw5coQtW7bw0EMPMXny5AqnlpvNmjWLlJQUZs6caXP9gw8+yMWLF5k4cSK7d+/m9OnT/Pzzz0ybNg2j0YiHhwczZszg//7v/9i8eTNHjhxh6tSplmTaljZt2lBUVMTbb7/NmTNn+PTTT/nggw+q9BwIIYS4Cpe3AgsKsrQCy8/XiqVFHf97Orkk3EKIusrtr904JWvdh7I796UoqJmdI/qbUop9+/axbNkyzp49y88//2zvkEQFJOmuaePGwddfa9PqSmraVFs+ru4UZHBzc+Pnn3/m4sWL9OnTh9tvv52hQ4fyzjvvVHofBoOBJk2a2BzFBggNDWXHjh0YjUZuuukmunTpwiOPPIKPj48lsX7ttdcYOHAgo0aNYtiwYQwYMIBevXqVecxu3brxxhtvsGjRIjp37sxnn33GwoULr+zBCyGEuDrmVmB79/7dCuxS3ZCLF7VWYAkJ4OurDX4LIURd5rv5G8vPdalNmLk6+fr16ykuLqZNmzbccsst9g5LVECnGvlFrZmZmXh7e5OWlobPpW/jzfLz84mOjqZVq1a4uLhc3YGMRvjtN0hMhJAQGDiwzoxwi4ZFKUVxcTEGg6FS1Sqr9TwXogaYTCaSk5MJDAwsd9aLsKPkZG06eUqKNoR96b2kuFhLtOPOapv5+YL8CkGhyHHIx93ogg6pHifqt4Z4PhvSU2nzyC3ojEaKvf05uWSDpSZFjTAZMaSeJ6fHQIxevpw/rzV56NrVerOkpCTWrFnDhQsX0Ol0DBkyhP79+0t18mqWnp6Or68vGRkZeHl5Vcs+5Zru2uLgAOHh9o5CCCGEqD5ltAID7bLumBgtH/f2hks1LYUQos7z3vo9OqMRgPTBo2s24a6k2NhYVq1aRXFxsVQnr4fsfwYJIYQQov5JT4fjx0u1AlNKS7RjYiAvT4qlCSHqGZMRn8i1ACidjrQ6UkAtNDQUPz8/vLy8GDNmjFQnr2ck6RZCCCFE5ZVsBZafb9UKrKBAm0qeEK/NMK9kDU4hhKgz3A//gVNqIgA5XW+guEmI3WK5cOECvr6+6PV6HB0duffee3Fzc5Pp5PWQXFklhBBCiMrJzoZDh2D/fi3RbtrUknCnpcNff8HZOK1g+aXuNUIIUa+ULKCWZqcCakopYmL28cEHH7B9+3bLcnd3d0m46ykZ6RZCCCFE+ZTSCoEeOwZZWRAcbKlMXlwMiechNkbbLChIiqUJIeonw8UkPPZrSW6RbyDZ3frXegzFxYVER2/g4sVDACQkJKCUkmS7npOkuxJMJpO9QxCixsj5LYQol7nBdnQ0ODtro9uXPvzl5EBcnJaPe3mBXGIohKjPfCK/R6e0z0Xp4WPAoXZTpcycVHbt/YycHK06+dChQ7nhhhsk4W4AJOkuh5OTE3q9nnPnzhEQEICTk5Oc9KLOq2zLMKUUhYWFpKSkoNfrcbo0aiWEEBYpKVqxtMtagSkFqalaHp6TCwEBdaK4rxBCVJ2xuEQBNT3p4aNr7dBKKU4VprJn/2pMJiOOjp7063c7/fs3r7UYRM2SP5Hl0Ov1tGrVisTERM6dO2fvcISoFKUUJpMJvV5fqS+J3NzcaN68ufQ/FkL8rahIy6hPntRul2gFVlio1VGLj9dmmAcFWga+hRCi3vI4uAPHtGQAsnsMoNiv9ipB5hbnsacgHhOKwMC2hISMwd9f+iw2JJJ0V8DJyYnmzZtTXFyM8VK/PiHqMpPJxIULF/D3968wkXZwcKhwRFwI0ciYW4ElJoKfn6UVGEBGhtYK7MIF8PUDF2e7RSmEENXKd/O3lp/Thoyv1WO7O7rR27kp2aHNadVxKElJ8rmsoZGkuxJ0Oh2Ojo44OjraOxQhKmQymXB0dMTFxUVGr4UQlWc0asPXx49r13GHhlrmjBuNcP48xMZqhdMCA8FBem8LIRoIx5RzuB/6HYDCJiHkdLm+Ro+nlCI66yzeTp74u/gC0MapCTnNemGUgZAGSZJuIYQQorHLyYETJ7RhbC8vrVjaJXl5WrKdeB483MHX135hCiFETfCJXItOKeBSATV9zX2rWGQqZm/KIeKyz+FucOXGpoNw0skgSUMnSbcQQgjRWJlbgR0/rs0dL9EKTCltGnlMjNYlzN8fZMKXEKLBKS7GZ+v3ACgHBzIG11wBtfSCTHYm7SWrKAcdOq7xaoGj3gBKOsk0dJJ0CyGEEI1Rfj6cPq39c3bWiqVdmtZYVATxCXA2TpthHhQkxdKEEA2T5/6tGDIuAJDVczDFPk2q/Rjm6eT7U49gVCZcHVzoF9STJq5+lzao9kOKOkaSbiGEEKKxSUmBqChIStIyaldXy6qsLG10OzlZm0peYpUQQjQ4PiUKqKVHjKv2/RtNRnanHCIuOwGAELdArgvsjrODtGptTCTpFkIIIRqLy1uBNW9uaQVmMkFSMsREQ0EBBAaBQYqlCSEaMMeks3gc+ROAwsCm5HS6rtqPodfpKTIVoUNHF7/2tPdpLV1jGiFJuoUQQojGID1dG91OSNAu0C7RCiw//1KxtERwc9MGv4UQoqHz2fKd5ee0iLGWLyGvllIKhUKv06PT6bgusDtZRdk0cfGrlv2L+keSbiGEEKIhM5ng7Fkt4c7Lg7AwSyswgIsXtcHvjAwtF3eSGY9CiEZAV1SIz7b1ACgHAxmDbquW/Zqrk+vQkm2dToezgxPODpJwN2aSdAshhBANlbkVWGwseHpatQIrKoZzCRB3VrsdHCzF0oQQjYfnni0YstIAyOwzBKPX1fdDTC/I5PekvWRfqk5+rU9rvJ29rnq/ov6TpFsIIYRoaMytwKKitGnlJVqBAWRn/10szdtbm1IuhBCNic+WEgXUhlxdATWlFGey4tifehSTuTp5cE9JuIWFJN1CCCFEQ1JQAKdOwZkzWqJdohWYUlqiHR2tXccdECjF0oQQjY/TuRjcj+0FoCCkBbnX9qryvszTyeOyzwFSnVzYJkm3EEII0VCkpsLx41pmHRho1e+roECbSp4QDy4uUixNCNF4WY1yR4yr8rU1Sil+S9xFav7FS9XJr6W9zzVSnVyUIkm3EEIIUd8VF//dCsxk0ka3S1ThTUvXWoGlpYGfHzg72y9UIYSwJ11hAd7bNwBgcnQifeCtVd+XTkcn37bsTjnE9UE9pDq5KJMk3UIIIUR9lpGhjW7baAVWXKxd2h0bq00tDwqqto44QghRL3nu3oQhOwOAzOuGYfLwvqL7F5mKySjMtCTYQW4B3Nw8HAedXKsjyiZJtxBCCFEfVdAKLCcH4uK0pNvLC9zd7RirEELUEb6bv7H8fKUF1MzVyfOLC7ix2UA8HbU3Vkm4RUUk6RZCCCHqm5KtwDw8rFqBKaVd2h0dDTm5EBBglYsLIUSj5RR/GrcTBwHID7uGvLbdKnU/pRRnMuPYf0GrTu5mcKHIWASONRmtaEjkz7AQQghRXygF589r08nT07X54iUu0C4s1Aa/4+O1wuVBgdJ7WwghzHw3lyigNnR8pd4gi0xF7Ek5zFmpTi6ugiTdQgghRH1gbgV2+nSpVmCgXdodEwMXLoCvH7hIsTQhhLDQFeTjveNSATUnZzJuGFnhfdIKMtiZtI/sohytOrn/tbT3lurk4spJ0i2EEELUdeW0AjMatcHv2FitcFpgIDjI5YVCCGHF68//4ZCbDUDm9Tdhcves8D6xWQlkF+XgZnDh+qBeNHHxrekwRQMlSbcQQghRVxUXa8PXJ07YbAWWl6cl24nnwcMdfOXzoBBC2OS76e8CamlDxlfqPl38r0Wng2t92sh0cnFVJOkWQggh6qKSrcD8/MDz71EZpbRp5DExkJWldQpzlII+Qghhk3NsFK5njgKQ36Id+dd0srldWkEGJ9Kj6RPYFb1Oj4NOTzf/jrUZqmigJOkWQggh6hKTSauEdvy4zVZgRUUQnwBn47TFQUFSLE0IIcpTsoBa2pDSBdSUUpzOjOPAperknk7udPRtW9thigZMkm4hhBCiriinFRhoo9rR0dol3j4+Vpd2CyGEsEGfl4PX7z8CYHRxI7PfCKv1tqqTt/ZqUetxioZNkm4hhBDC3sytwKKi4OJFCA62agVmMkFSMsREa0XMAwLBIMXShBCiQl47f8YhPxeAzH7DMbm6W9Zp1cn3kl2Uiw4dXf2vpZ1UJxc1QJJuIYQQwp4ubwXWvLnV1Mf8/EvF0hLBzU2bTi6EEKISlMJ3s+0Camezz/Fn8gFMyoSbwZV+QT3xl+rkooZI0i2EEELYS8lWYAEBWlZdwsWL2nTyjAytWJqTFM8VQohKc4n+C5fYKADyrulIQctrLeu8nbzQoyPYLYg+gd2kOrmoUZJ0CyGEELXN3Ars5Emt0XbTplbNtYuK4VwCxJ0FHdpsc5ntKIQQV8aqgFrEOAqMhZbk2svJg6FNB+Dl6CHTyUWNk6RbCCGEqE0ZGdq12wkJWmPtEq3AALKzIToGUpLB27vU4LcQQohK0Odm47XzZwCMru4c6Hgt+2I3MSjkOgJc/QHwdvIsbxdCVBtJuoUQQojaYG4FFhUFubkQGmrVCkwpbZZ5dLR2HbcUSxNCiKrz3rERfWE+AFE9+rA76xQAsdkJlqRbiNoiSbcQQghR03Jz/24F5u5eqhVYQQHExWmD3y4uUixNCCGuilL4bPl7avnWbh0uVSfvQDvvVnYMTDRWknQLIYQQNaVkK7C0NC2bLtEKDCAtXWsFlpYGfn6lVgshhLhCLicP4XJWG9mOa9aM7LCWDJHq5MKOJOkWQgghakJBgdYG7PRpcHSEZs2sqqEVF2ttwGJjtdw8KAj0ejvGK4QQDYTLL59bfj5zwxBubDpQqpMLu5KkWwghhKhuFy5orcCSkmy2AsvJ0aaTJyaCl5c241wIIcTV02dnELR3OwCFbu4E3TgTJOEWdiZJtxBCCFFdSrYCKy4u1QpMKa01d3Q05ORq+bhB/hILIcRVUUoRkxVPmHswQds3oC8qACBr4G3g7Grn6ISQpFsIIYSoHpmZ2rXb8fE2W4EVFsLZs9pqJycICpTe20IIcbWKTEXsST7E2ZxEEnPOc0+J3tzpEePsGJkQf5OkWwghhLgaJpNWdvz4cZutwEBrzR0To8069/UDFymWJoQQVy2tIIOd5/eSXZyLDh3tEpJwTowBIOfanhSGSaVyUTdI0i2EEEJUVclWYG5upVqBGY3addtxcdps88BAq9nmQgghqkApxenMWA6k/oUJE24GV/oF9aTLz69btkkfIqPcou6QpFsIIYS4UkppRdKOHy+zFVhenpaLJ54HD3dtxrkQQoirU2gsYk/KIeJzEgEIdQuiT2A33HJz8dy9GYBiTx+yeg+xZ5hCWJGkWwghhLgSJVuBGQylWoEppU0jj4mBrCzw99c6hgkhhLh6CsWF/DR06Ojq34F23q3Q6XR4b1uNvrgIgIyBo1COUrFc1B2SdAshhBCVdeGCVizt/HmbrcCKiiA+Ac7Gafl4UJAUSxNCiKullEJ36c3U2cGJG4J7AeDvcmkKkcmE75a/C6ilydRyUcdI0i2EEEJUpLhYmyt+4oTNVmCgjWpHR2stwXx8wFW61AghxFUzTycPcQuklVczoESyfYnbsT04JZ0FIKfTdRQFNav1OIUojyTdQgghRHnMrcDOntUuzPbyslptMmmXd8fEaDPPAwLBIMXShBDiqqUVZPD7+b3kFOeSlJdCmHswTg6lr9fx3Syj3KJuk6RbCCGEsOXyVmBhYaVageXlQ1wsnEsEdzdtOrkQQoiro5TiVGYsBy+rTm4r4XZIT8Vz7xYAir39yeoZXsvRClExSbqFEEKIy1XQCgzg4kVtOnlmJvj5gZPU7BFCiKtmqzr5dYHdcHKw/Sbrs209OqMRgPRBt5X6clSIukDOSiGEEMLM3AosKkrLqgMDwcXFapOiYjiXAHFnQYcUSxNCiOpSbDLya8JvZBflor9UnbztperkNplM+Gz5DgCl05EePqb2ghXiCkjSLYQQQgAUFsKpU2W2AgPIzoboGEhJBm/vUsXLhRBCXAWD3oFm7qHEZSdwfVDPUgXTLud+5A+cUs8BkNOlH0WBYbURphBXTJJuIYQQooJWYOYB8JgYyM+XYmlCCFFdCo1FFKti3Axay4dOfu1o79Pa5vXbl7MqoBYhBdRE3SVJtxBCiMbL3Ars5MkyW4EVFEBcnFZTzcVFiqUJIUR1uViQzs7z+3BycGRI2A046BzQ6/Q4OegrvK/hYjIe+38DoMg3gOweA2o6XCGqTJJuIYQQjdPlrcACAkptkpYOMdGQlqYVS3N2rv0whRCiodGqk8dwMPUYJkyAK3nF+Xg4uld6Hz5bv0dnulRAbfAYcJC0RtRdcnYKIYRoXMytwKKiICfHZiuw4mJITNQGwRXa6La+4oEXIYQQFdCqkx8kPuc8AGHuQfQJKLs6uU3GYnwi1wKgdHrSw0fXQKRCVB9JuoUQQjQeeXlaK7CYmDJbgeXkaMn2+fPg5QXulR94EUIIUQ7zdPKc4kpWJy+Dx8HfcbyYBEB29/4U+wfXRLhCVBtJuoUQQjR8JVuBXbigDV1f1gpMKUhN1Xpv5+Rqs82l3asQQlQPpRQHUo+SU5yLu8G1UtXJy+Kz5e8CaulDxldXiELUGPk4IYQQomErLIQzZ7R2YHo9NG9eqhVYYaF2aXd8PDg5QVCg9N4WQojqpNPpuC6wO0cvnqBHk05XNp28BENqIh4HdwBQ5B9Mdtd+1RmmEDVCkm4hhBAN18WL2uh2YiI0aWJzrnhGhjbb/MIF8PUDFymWJoQQ1eJifjqp+Rdp53MNAB6O7vQN6nFV+/SNXItOKQDSwseAXvo3irpPkm4hhBANj9GoZdInT0JRkc1WYEajlovHxWk/BwaW2kQIIUQV/F2d/C9MKLydvAhya3L1Oy4uxnvr99ox9A5kDJYCaqJ+qHO1WN99911atmyJi4sLffv2ZdeuXeVuv2TJEtq3b4+rqyvNmjXj0UcfJT8/v5aiFUIIUedkZcH+/XDokDZXPDS0VDadl6fl4ydPgaOjNgguCbcQQly9QmMRvyftZX/qUUwowtyD8XX2rpZ9ex74Dcf0VACyeg6i2Ld0q0ch6qI6NdK9evVq5s2bxwcffEDfvn1ZsmQJw4cPJyoqisDAwFLbf/755zz55JMsX76cG264gRMnTjB16lR0Oh1vvPGGHR6BEEIIuzGZ4Nw5OH4csrMhJETLqEtQSptGHhOj5eb+/qU2EUIIUUUX89PZmVSyOnlH2nq3vOLq5GXx2fyN5WcpoCbqkzqVdL/xxhvMmjWLadOmAfDBBx+wYcMGli9fzpNPPllq+99//53+/fszadIkAFq2bMnEiRP5888/azVuIYQQdmYeuo6OBldXaNas1CZFRRCfAGfjtKrkQUFSLE0IIarL6YxYDlwa3XY3uNIvqBd+Lj7Vtn/H5Hg8Dv8BQGFgGDmdrqu2fQtR0+rM9PLCwkL27t3LsGHDLMv0ej3Dhg1j586dNu9zww03sHfvXssU9DNnzrBx40ZGjhxZKzELIYSoA5KSYNcurTp5kyba8PVlsrLg2DGIPgMeHuDrKwm3EEJUJwedg2U6+Y1NB1Vrwg3gs+U7y8/pEWO1bhRC1BN1ZqQ7NTUVo9FIUFCQ1fKgoCCOHz9u8z6TJk0iNTWVAQMGoJSiuLiY++67j6effrrM4xQUFFBQUGC5nZmZCYDJZMJkMlXDIxHCvkwmE0opOZ9Fg1HmOV1YqI1sm1uBNW2q/f9SVVvtvpCcBDGxUFAATQLB4AAKIexHlfhPiPrMqIzodXoUihZeYbgYnAlybYJOp6ve87u4CJ9t6wFQDgbSBo5qYK+fS+8ISqGUCaW0P2XyUc4+auIzdJ1JuqsiMjKSl19+mffee4++ffty6tQpHn74YV544QWeffZZm/dZuHAhzz33XKnlKSkpFBYW1nTIQtQ4k8lERkYGSin08i2waABsntNZWZCQoLUE8/ICFxctCS+hoBBSUrRNXNzAwx8K0P4JYU8KRYFDEQA6ZMqFqH+UUsSmxxOTfpZ+zXuhHLTz2NPTk9waeJf13b0ZQ+ZFANL6DCLTzw1oQIWT9SYc3CC/+CKm/EKU0q6aSk62d2CNU0ZGRrXvs84k3U2aNMHBwYGkpCSr5UlJSQQHB9u8z7PPPsvkyZOZOXMmAF26dCEnJ4d//OMf/Otf/7KZcDz11FPMmzfPcjszM5NmzZoREBCAj49P9T0gIezEZDKh0+kICAiQpFs0CFbntFJaj69Tp7SLtMPCbJYdT7sI8TGQmQmBfloRc4y1HroQNplH6NyNLpJ0i3qn0FjEnpSDJORon9mT0lJo1iSsRs/nkF/WW37OjLgDd6NLjRzHbkxGDLmgM/hhdPFBp9PKk9ioIy1qgZOTU7Xvs84k3U5OTvTq1YtNmzYxZswYQPugtWnTJubMmWPzPrm5uaWSCodLH76Usj3lxNnZGWdn51LL9Xq9JCiiwdDpdHJOiwZFp9Ohz8lBf+qUlnR7e9v8NFJUDAnxcDYedECwFEsTdZSuxH9C1BdadfK95BTnoUdHtyYdae3VglwKaux8dkqMxf3YHgAKgpuT16F3A3zdXHr2dDp0Oj06nfa3Sz7G2UdNfH6uM0k3wLx585gyZQq9e/fmuuuuY8mSJeTk5Fiqmd97772EhYWxcOFCAEaNGsUbb7xBjx49LNPLn332WUaNGmVJvoUQQtRzSkFqKpw/X2YrMNBWRcdASrKWk7u51X6oQgjRECmlOJkRw6ELf12qTu5Gv6Ce+Ln41Pi11VYF1IaMk29SRb1Up5LuO++8k5SUFP79739z/vx5unfvzk8//WQprhYXF2f1zcMzzzyDTqfjmWeeISEhgYCAAEaNGsVLL71kr4cghBCiOuXlwYkTWmNtFxebrcCU0gqYx8RAfj4EXCqWJoQQonqcyIjm4IW/AAhzD6ZPQDecHEp/+VnddIUFeP+mTS03OTqRMeDWGj+mEDVBp8qah91IZGZm4u3tTVpamlzTLRoEk8lEcnIygYGBMr1c1G9JSXD8OKaLF0n29ibQywv9ZSMcBQXabPOEBC0n9/a2U6xCXAGFIschX67pFvVGobGITQk7aOPdgjZeLdGVeC+uyfPZa8ePhH2gFUfOuOFmzt3/QrXuv84wGTGknienx0CMXr6cP699x9y1q70Da5zS09Px9fUlIyMDLy+vatlnnRrpFkIIISgshDNn4PRpbRphWFipyuQAaekQEw1paeDnBzbKdQghhKgCpRSJucmEuAWi0+lwcnBkeLNB6HW1+2W+75ZvLT+nDRlfq8cWojpJ0i2EEKLuuHgRoqIgMRH8/cHDw6rvNkBxsbY6Nlbrtx0UJMVmhBCiuhQai9idcpCEnPP0bNKZNt4tAWo94XaKP41b1H4ACsKuIa9dt1o9vhDVSZJuIYQQ9mc0aln0yZPanPGwMDCU/hOVk6Ndu52cDJ6e4O5e+6EKIURDZV2dXA92vPzBt0QBtbQIKaAm6jdJuoUQQthXVpZWLM3cCqxJk1KbKKVVJY+NhZxcbRMbObkQQogq0KqTR3PowrG/q5MH98TP2ccu8egK8vHevgEAk5MzGQNG2iUOIaqLfGQRQghhH0ppFdCiorTEu4xWYIWFcD4JMuLA2QmCAmXAQwghqkuhsfDSdPIkAJq6B9O7lqqTl8Xrz19wyM0CILPvTZjcq6eYlRD2Ikm3EEKI2peXp00lj47Wyo43bWozk87IuNR7Ox+aeIOrFEsTQohqlVmYzbmcZPTo6dakI228WlhVJ7cHH6sCauPsGIkQ1UOSbiGEELUrORmOHYMLF7QqaC4upTYxGrViaXFxUGwE7wBwadQNLoUQomY0cfWjZ0BnfJ297TadvCTn2BO4nToMQH7zduS37mzniIS4epJ0CyGEqB0lW4GB1oTURtnxvDzt2u3E8+DhDj6+kKMHjLUbrhBCNESFxkL2pR6ho29bvJw8AWjt1cLOUf3Nqk2YFFATDYQk3UIIIWpeWhocP27dCuwySkHqBYiN0S7x9vfXLvGWAW4hhKgeF/LT+CNpHznFeWQV5TAsbIDdp5KXpMvPxWvHjwCYnF3J7D/CzhEJUT0k6RZCCFFzKtkKrKgI4hPgbJy2OihIBjeEEKK62KpO3iugS51KuAG8d/6MQ34OABn9RmByLf0FrRD1kSTdQgghakZ2tlaZ/OxZram2jVZgoI1qR0dDair4+ICra+2GKYQQDVmhsZBdyQc5l2uuTh5C74Cudq1OXpaSBdTSpYCaaEAk6RZCCFG9lIJz57Tp5FlZEBxssxWYyQRJSRATow2CBwSCwaH2wxVCiIYqpyiXLed2klucV6eqk9vicuYvXKOPAZDXqiP5rTrYOSIhqo8k3UIIIapPXh6cOqUVTCunFVhePsTFwrlEcHfTppMLIYSoXq4GF9wMrujQ0S+4Z52oTl4WGeUWDZkk3UIIIapHcrI2up2aWmYrMICLF7Xp5JmZ4OcHTk61HKcQQjRghcZCHPQGHHR69Do9/YJ64qBzqJPTyc30edl47/wZAKOLOxnX32TniISoXpJ0CyGEuDpFRdrI9qlT2u0yWoEVFUNCPJyNBx1SLE0IIarbhfw0dibtI8w9mB5NOgHaaHdd57XjJ/QFeQBk9B+JcnGzc0RCVC9JuoUQQlRdWppWLO3cuTJbgYFWUy06BlKSwdsb3OTzlBBCVBulFCcuVSdXKBJzkujs1x5HfT34qK8Uvpu/sdyUqeWiIaoHr0QhhBB1jtGoVSWPiiq3FZhSfxdLy8+XYmlCCFHdyqpOXi8SbsDl9BFczp4EILdNFwqat7VzREJUv/rxahRCCFF3ZGfDiRMQF1duK7CCAm2ThATt8m4pliaEENXLPJ3cXJ28e5OOtK6j1cnL4ru5ZAG18XaMRIiaI0m3EEKIyrm8FVhQUJlV0NLSISZam33u5wfOzrUbqhBCNHTFpmJ+S9xFoakID4Mb/YJ74evsbe+wrog+JxOvP/4HgNHNk8y+w+wckRA1Q5JuIYQQFcvPh5MntbLjzs5ltgIrLobERIiNBYWWl9uoqSaEEOIqGfQGegV04Wx2Ir0Dutbp6uRl8d6+EX1RAQAZA25BOdX9om9CVIUk3UIIIcqXnKxdu52SUm4rsJwc7drt5GRt1rm7e+2GKYQQDd2F/DSMykigq3ZZTzOPUJq6h9Sr6eQWlxVQS5MCaqIBk6RbCCGEbZVsBaaUlo/HxEBOrnaJt42aakIIIaqoZHVyJwdHbmo6yNIKrF4m3IDriQM4n4sGILd9DwrDrrFzRELUHPlYJIQQorT0dO3a7QpagRUWakXM4+O1y7uDAqX3thBCVKcCYyG7S1QnD3Txx6Cv/20gShZQS4uQUW7RsEnSLYQQ4m8lW4Hl55fZCgwgI0Mb3b5wAXz9wEWKpQkhRLVqCNXJbXHISsdz168AFHt4k9VniJ0jEqJmSdIthBBCk52tFUuLiQEvL61Ymg1Go1YsLS5O+zkwEBzq/6CLEELUGSWnkysUHo5u9Auqf9XJy+L92w/oi4sAyBg4CuUk39qKhk2SbiGEaOwubwUWHFxmK7C8PK0yeeJ58HAHX99ajlUIIRqJ9IJMFIpm7iH0DuyKo77+VSe3SSl8tpTozR0x1o7BCFE7JOkWQojGrJKtwJSC1AvaIHh2lnaZt2MD+fwnhBB1hVIKnU6HTqejV0Bngtya0MIjrN5PJy/J7a89OJ+PAyCnYx8KQ1rYOSIhap4k3UII0VilpGij2xW0AisqgvgEOBunXd4dFCTF0oQQojpp08nPkJqfxg1BvdDpdBj0Blp62r7Mpz7zLTHKLW3CRGMhSbcQQjQ2RUXayPbJk9rtMlqBgTbbPDoaUlPBxwdcXWsvTCGEaAwur05+LjeJMPdgO0dVMxwyLuC5ZwsAxV5+ZPUKt29AQtQSSbqFEKIxMbcCS0wEP78yW4GZTJCUpE0nLyiAgEAwSLE0IYSoVlbVyXV6uvt3ItQtyN5h1RifbevRGYsBSB90GxjkOiXROEjSLYQQjYHRqDXTPn5cu447NLTMVmB5+RAXC+cSwd1Nm04uhBCi+pinkx+6cLxBVie3yWTCZ8t3lpvpEWPsF4sQtUySbiGEaOgq2QoMtJ7bMTGQmakNhJdRxFwIIcRV2Jd6hNOZsQA08wild0CXhlOdvAzuR/7EKSUBgOwu/SgKbHjXqwtRFkm6hRCioVJKm0Z+/DhkZJTbCqyoGBLi4Ww86JBiaUIIUZNaejYlNiuBbv4duMareYOqTl4WqzZhUkBNNDKSdAshREOUnw+nTsGZM1orsGbNysyis7MhOgZSU7SBcDe32g1VCCEaOqUUGYVZ+Dh7AeDv4sutLYbg5NA4phMZ0lLw3LcNgCKfJmR1H2jniISoXZJ0CyFEQ5OSAlFRWiW0oKAyS44r9XextPx8aBIgxdKEEKK6FRgL2ZV8gKS8VIaFDbAk3o0l4Qbw2fo9OpMRgPTwMWXWFBGioZIzXgghGoqiIi2DPnFCu928eZmtwAoKIC4OEhK09txSLE0IIapfan4afyTtJbc4H71OT2aJ0e5Gw2TEJ1IroKZ0etIHj7FvPELYgSTdQgjREKSna6PbCQng719mKzCAtHSIiYa0NK1YmrNzrUUphBCNglKKqPQzHL5ork7uTr+gng27OnkZPA7txPGC1oM8u9sNFDdpmD3IhSiPJN1CCFGfmUxw9qyWcOflQVhYmdP2iou1umqxsaDQRrfLGAgXQghRRebp5Im5yUDjqU5eFp9N31h+Th8y3o6RCGE/knQLIUR9lZOjTSWPjQVPz3JbgeXkaDPPk5O1Td3day9MIYRoTGKy4knMTUav09OjSSeu8Wwc1cltMVw4j8fBHQAU+QeR3e0GO0ckhH1I0i2EEPXNFbQCU//P3p3HR1Vejx//3NkyWWcmewhhCSTsAUFBVLa4V2tVKli1au2qtYtoa7Vq1e+vWmuLS1u1dq/dwAVp61JbNlEBd0H2JYHsmUlmJslk9nt/f1wIhLAkwyST5bz74tXMcicHTGbuuc9zztH0vmqVleBrh+xs6V8jhBC9qcQ2mtZwG2MyRg7J7eRHsq9diaKpAHjmXwEG6dYphiY59RJCiIEkEIC9e/VRYBbLCUeBhUL6zvPqav2pebkye1sIIeItGA2xzb2bKZnjMRmMGBSF03PKEh1W4kUj2Ne9DIBmMOKZ97nExhOD7z5ZzI0XNzCtxIeqwkN/KWL9JzZA4/qLGrn2fOcxj1v3cQZPvlCIqkE0qnDTJfVcPqcZgC37UvjJX4poDxhAgTuvqebMCZ6TxtLeDl/+Mrz3nl4a9tBD8PnPd33eli3wxS8evu3xQEsLNOvfnt274YYbwOUCmw3++EeYNEl/bM4c+POfYfTobv8TiW6SpFsIIQaKQ6PAGhshN/e4o8BAXwCvrISmJnBkglWapQkhRNwd2Z1c1VRm5ExJdEj9RtpHb2F260lp22lziDhyEhxRz2zem4LXZ2RaiQ+Af72Tyd4aK68++imt7UYW3jOBmRNaKRke6HScpsGdz4zmT3fvYtwIPzVOC5fcOYnzT/eQYlX59uNj+PHXKjlrciuVdUnc9Egpr/7kE47f/lT3s5/pjU/37IGKCpg1CxYs0HunHmnKFPj448O3b7218wX3r38dvvY1uPFGeOEF/f/fe09/7Pbb4Uc/0hNvEV/SQkcIIfq7cFi/NP3ee/ol66Ki4ybc0ai+sr11q35lOzdXEm4hhIg3TdPY4d7Lmpp3aI8ESDOnMiZjZKLD6lcca17q+No9ABuoLV+dw6Wzmztuv7Yxk6vmuzAawJ4W5eJZbl7dkHnMYxUFWtv1rfRtfiP2tAhms4anzUhzq4mzJrcCMKogSEZKhPWb7SeNZ9ky+MY39K9Hj4b582HFihMfEwjAX/+qr5CDfs3+/ffhuuv02wsX6jvi9uzRb19yCbz2mn7hXsSXrHQLIUR/5vXqtds1Nfp8r/T04z7V79d7qtXVQ1oqOBx9GKcQQgwRR3cnH5E2jBk5ZZgNclp9iLmxhtQtGwAI5RTimzwrwRH13Hs70rnhooaO23VNFoZlhzpuF+aE+GRP166kigI//+Y+vv3EGJKTorT4TDzxnb1YTBqW9Cg59jCvbXJw8Sw3W/alUFFnpcaVBKNOHM+BAzDyiOs6o0bp953ISy9BcTFMm6bfrqqCgoLDvV0UBUaM0F9n7Fgwm/WV8vXr4dJLT/zaomfk3UEIIfqjHowC0zRwNenbydta9a1m5qE5mUYIIXqVO+jlrbr38EcD0p38BOxrX0bRNAA88y8fkPMp65vNZNkiPT4uEoVfryzgye/s5fTxbWzZl8I3l45l5cNbcaRH+eV397J0WSG/+Vc+Ywv9TC9tw2jQeuFvAL/73eFV7u7Kz9d3zIn4kqRbCCH6myNHgaWlnXAUWDgM1TVQdUDPyfPypFmaEEL0liSjhagWJc2cyll5M7AnZSQ6pP4nEsa+biUAmtGIZ95lCQ4oNskWlWD48AdqQVaIWpelo8a7xmmhICvU5bgd+1No9Jg5fXwbAFOK28nPDLG9MoWzprQyfqSfZ7+/p+P5l945kbGF/pPGM2KEflpQUKDfrqyECy44/vMrKmDjRnjx8Jh0ior04SeRiH7OoGn6KveIEYefEwicsGWMiNHAu+wkhBCD1aFRYO++q3+a5uaecI94ayts3w4V+/Tc3OGQhFsIIeItokY7vk4xJTO3YBbnD58jCfdxpH+wDlOLXgvdOmMBUVvWSY7on0qL/FTUWTtuXzjTzfNrs4mq4Gkz6lvEz2zuclx+Vginx8zeGv3Y/Q1JHGhMYlSB3nDN6Tm85vn8mmySk1TOnNgCwD9WJPHLXx47nquugmee0b+uqIC1a+Hyy48f/+9/D1dcAXb74ftyc2H6dPjLX/TbL76oX9cfO/bwc7Zvh6lTj/+6Ijay0i2EEP1BMKh3Mtm796SjwFQVGhr0vDwUgpxcMMnoUyGEiDuXv5mNjR9yWvZkClPzAci02hMbVD/XuYHalQmM5NRcMNPN21syOpqeXXZOE5/uS+HiOyajKHDjxY2UFumJ9OoPbaz50M7/fWU/2bYID9y0nyW/LMagaKiawj3XH2BYdhjQG7T9e0MmmgZjhgV48jt7Oz7u9+03MOw447q+9z246SYYMwaMRvjlLyE7W3/smWegthYefFC/rar6KLBjdSH/9a/1juUPPQQZGfCHPxx+rLJSb8gqSXf8KZqm9U4RwQDR0tKCzWbD7XZjP/JSkBADlKqqNDY2kpubi2EA1lANSS6X3iytG6PA/AE4sB9q6yA15YR91QYNDQ2fMUBq1IqCLOWLgU9+pvs/TdPY6dnLluadaGhkJdkpLzxbareP4cifZ0t9FWO/pyfawfwR7HvkhQFZzw3gCxi49sFx/O2+naRY1d79ZmoUk6ueRX/4DE/8ykRrq37tvayPx73/4Af6qvdXvtK337e/8Xg8OBwOvF4vGRnx2dEiK91CCJEokYh+WXnXLv2ydFHRCU9Omg42S2tp0RuZWyx9FqkQQgwZx+tOLgn3yTnWHJ5h5Zl/xYBNuAFSrSp3XltNjdNCSVHg5AfEwR+ebCWa6qC1tU++XRfDhumr6SL+JOkWQohE6MEosHAEaqqhqhoUpFmaEEL0Fpe/mQ0NH0p38hgo4RC2N/8JgGoy45372QRHdOpmT0pQ9psg3/52oiMYvCTpFkKIvqSq+iyOHTtOOgoMoLVNX912OfXaq5SUvgtVCCGGktZQG2tqN6ChkW5OZbZ0J++R9PdWY2rzAtB6xrlE0+2JDUiIfkSSbiGE6Cs9GAWmaYebpQUCkJ0jzdKEEKI3pVvSKM4YQVgNMyOnDLNBTpN7wrF6cDRQE6I3yLuJEEL0Nk2D+nrYuROamyE/H5KSjvv0YFCfm1lTA1arvp1cCCFE/Ln8zaSaU0g26eOdTsuehIIi28l7yFpTSerOjwAIDhuNf9xpCY5IiP5Fkm4hhOhNR48CGzHihAXZbg9UVoDbrZd6nyA3F0IIESNN09jh2cunzTvJSc5kbsGZGBQFgzJwG38lUs6qf3Z87S6/UhqPCHEUSbqFEKK3HDkKLCfnhAXZkQjU1ek7zzX01e0B3PRVCCH6rWA0xKbGj6hvdwJgNSahaioGRWp4YqGEAmS/+RoAqjkJ7zmXJDgiIfofSbqFECLeDo0C270bolG9dtt4/JM5n09/emOj3sQ8NbXPIhVCiCHF6W9m48Hu5EbFwGnZkxmdXiTbyU9BxrurMLW3AdAy63zUVGk+J8TRJOkWQoh48nr1ZmnV1eBwnHAUmKaB06kn3L52yM4+YSNzIYQQMTpyO7l0J48vx6oXO772SAO1UxaNwubN8OmnehuYOXNOeN1eDBByeieEEPFwaBTYzp3Q3g7Dhp0wgw6FoKpKP8RigbxcKYETQojeEtWiVLZWoaExIm2YdCePk6QDu0nZswWAQNFY/GOnJDiigW3Vm2Z++pS+8+2Q4cPhiSfgSrmeMaDJu40QQpyq9vbDo8BSU084Cgz0xfDKSmhqAkcmWKVZmhBC9CqTwcTsvBk0Bz2ynTyO7EePCZN/15j9Z3MB3/tTKtpR99fUwOc/Dy+8IIn3QCZJtxBCxOrIUWBut9797ATtxqNRvVnagQP617m5smVMCCF6w6Ht5EbFQKm9GAB7UoZsJ48jJeDH9s6rAESTrHjPujjBEQ1cURV+/PLkLgk36KcaigLf/S587nNy3jBQSdIthBCxiEb1ZHvvXn0beVHRCa/wB4NQUQF19ZCWqpd7CyGEiL9ANMi7DR9T73eioFCQkku6JS3RYQ06GRv/g9HvA6B59rmoKWnIOnfPRVX43SsF1HuTj/scTdNL0tavh/nz+y42ET+SdAshRCz27dOT7uzsE44CA73ce/9+qK3Vn24291GMQggxxDj9TWxs+KhTd/I0s4yE6A2OI7aWN553uSTcPdToNvPiuiyeX5tDfZOlW8fU1fVyUKLXSNIthBA9VV+v13BnZp404QZoaNAT7sxMSbiFEKI3SHfyvmWt3EFyxTYA/KPG0148ntRogoMaAFQV3vk0g2Wrs1n7kZ2o2rNLFQUFvRSY6HWSdAshRE+0tsLWrXpRVdrJtyseapqWknLCcm8hhBAx0jSNt+vfp7a9AYCRaYVMz5ki3cl7kX314TFh7gVXJDCSgcHlNfHSumxeWJtNtbPzyYBB0ZhT5mHznhTcPgscY8+Aoug9WufM6aOARdzJu5EQQnRXOAzbtumJ90k6lIM+FqyyUj/Mbu/16IQQYkhSFIWc5Cwa/E5Oy54s3cl7mcHfhu2d1wGIWlNpmX1hgiPqn1QVNm1PZ/nqbFZ94CAS7fwzmesIsXCei4XzXAzLDLBqtcK3/3R6l2Zqh36UH39cmqgNZJJ0CyFEd2ga7N6tz+4oLDzpWBRNg/0H9LFgubl9FKMQQgwRmqYRiAZJNlkBKLWNpjA1T+q3+0DGO69jCPoBaDnrItTkVCCQ2KD6EXerkRVvZrN8TTYHGqydHlMUjbMnt7Co3Mn807yYDiXRKlxYVsej9/v46VNpXeZ0P/64jAsb6CTpFkKI7qiu1pPu3Fy9W/lJNDRATbVexy1XpoUQIn4OdSf3Rdo5b/gczAYTiqJIwt0XNK1TAzV3+cIEBtN/aBq8vyON5WtyeOM9O+GIodPjWbYwV851cdV8F8NzQ8d9nXPnhplzMaxaBQYDzJqlbymX84iBT5JuIYQ4Gbcbtm/XC7OTjz/S45DWVqioBKtV6riFECKe9O7kH+KPBjEqBtxBD7nJ2YkOa8iw7t2K9cAuAPxjJhMcWQrHnC49NHjajPzzrSyWr8lmX23X84MzJ7WwaIGT8hleLKbu/TsZjVBWpk8iLSuLd8QiUSTpFkKIEwkE9MZpwSAMG3bSp4fD+jzuYADy8vogPiGEGAIOdyffgQakm9OYnTddupP3MceaIxqolQ/N/c6aBh/vTmX5mhxe3+QgGO68qm1Pi3DFXBdXLXAxKj+YoChFfyNJtxBCHI+qwo4d0NioX3I+CU2DqipwuaSOWwgh4uXQdvJ6vxOQ7uSJYvC1krHxDQCiKWm0zLogwRH1rdZ2A/96O4tlq3PYXd11Vfv0ca0sPtfJ+ad7sJiH7uq/ODZ5txJCiOOprNSXrfPz9eKqk3A69aTbIXXcQggRNx+7tlLvd2JUDEzPnsKo9OHSnTwBbG+/giGkr9x6z74ELcl6kiMGPk2DLftSWL46h9c2OvCHOn+4Z6REuHxOE1ctcDGmUJrJieOTpFsIIY6lsVFf5bbbu1WY3dam5+cWC1iljlsIIeJmatZEApEgp2VPwibbyRND07B3aqA2uLeW+/wG/r0hk+Wrc9i+P6XL46eVtLGo3MmFM91YLbKqLU5Okm4hhDiaz6fP49Y0yDj5CV44ojdO8/uljlsIIU5VIBqkuq2OsbZRACSbrMwvnJ3YoIa45F2fYK3ZB0B76TRCw8ckOKLesa0ymeVrcvj3O5m0BzqvaqclR7ns7CYWlTspLZJVbdEzknQLIcSRIhE94fZ49OGYJ6Fp+jQxZyPkSB23EEKckiO7k1uMFkaknbyBpeh9g7mBWnvAwGsbHSxfk8OWfV3Hzk0p9rGo3MnFs9ykWNUERCgGA0m6hRDiEE2DPXv0wuzCQuhGzaCrCaoO6LvQTVLHLYQQMdE0je2ePWxt3okGZJjTsFnSEx2WAIytHtLfXQVAJM1G6xnnJjii+NhVZWX56hz++XYWbf7OH+Ap1iiXntXMogVOJo7yJyhCMZhI0i2EEIfU1sKuXZCdDaaTvz36fFCxD8zmbo3vFkIIcQyBaJBNDR/T0NGdfDgzciZjku7k/YLtrX9jCIcA8M65FM0ycBuXBEIK/3nXwfLVOXy0O63L4xNGtrOo3Mmls5tJTZZVbRE/8m4mhBAAXi9s3643TUvtur3saJGI3tzc1w55sq1cCCFicuR28kPdyUdnnHxEo+gjmoZ9zYqOm54FVyQwmNjtrbGyfE02K9/KosXXOf1JtkT5zGw3i8qdTB7d3p1NbkL0mCTdQggRDOp13D5ft+q4AWpqoKFBr+OWD2ghhIhNWI3gjwbJMKcxO3+GbCnvZ1J2fEBS3X4AfBNmECoYldiAeiAUVnjjPTvLV+fw/s6uP1elRe0sWuDis2c3kZ4iq9qid0nSLYQY2lRV31JeV9fthLupCQ5Ugc0mddxCCNFTmqZ1zNkelprHmXnTGZaSK9vJ+yH7qiMbqC1MYCTdV1mfxPNrsnl5fRbuVnOnx5LMKhfNcrNogZNpJT65aC76jLy7CSGGtqoq2LdPn/VlPHkG7ffr87gNCqR0Hd0phBDiBBr9TXzk+pQ5BTNJMenNMKRDef9k9DaT8f4aACLpDlpPX5DgiI4vFFFY/YGNZatz2LSt66jP4mF+Fi1wcdk5TdjTogmIUAx1knQLIYYul0uv405LA6v1pE+PRvU67tZWmccthBA9cXR38k+bdzIzd1qiwxInYFv/L5RoBADPvMvAZD7JEX2vqtHC82uyWfFmNk0tneMzm1QuOMPN4nIXM8a1yaq2SChJuoUQQ1N7O2zdqndEy8np1iG1tfou9JwcqeMWQojuCkSCbGrs3J38tOzJCY5KnJCq4ljzUsdNz/z+00AtEoW1H9lZvjqbtz/NQNM6fyCPzA9w1XwXV8x14UiXVW3RP0jSLYQYeqJR2LkTmpu7Xcfd3AyV+yEjo1vTxIQQQqBvJ9/Y8CEB6U4+oKRufRdLYw0AbZNnEc7r3mdlb6p1mXlxXTYvrsum0W3p9JjJqHHuDH1Ve+aEVgyGBAUpxHHIqaMQYujZt08vzB42jO58MvsD+tOhW9PEhBBCAHW+Bt6qfw8NpDv5AGNffbiBmieBDdSiKqz/xMby1dm8+YkN9ahV7cLsIFct0Fe1c+yRBEUpxMl1O+l+8803u9w3d+7cuAYjhBC9rr5eX+XOygLzyevTolHYXwktLVLHLYQQPZGTnE2GJR1Hko3p2ZOlO/kAYfK4SP9QP++P2LJoPa3vz/cbmvVV7RfWZVPf1HlV22jQWDDdw6IFLs6a3CKr2mJA6Pa73/z581EUBU3TAFAUhWhU6iSEEANIS4tex20y6c3TuqGuDurq9Rxd6riFEOLE3EEvNksGBkXBZDBSXngWZkP/a8Aljs+2biWKqp/je+Z9rs9qqlQV3v40g+Wrs1n7kZ2o2vlDNz8rxFXznSyc10SuI9wnMQkRL93+Lao4tLdSCCEGolBI71Te1gaFhd06xO2B/fshLbVbi+JCCDFkqZrGjoPdySdljmOiowRAEu6BRo3iWLMCAE1RcC/o/QZqLq+Jl9Zl8/yabGpcSZ0eMygac6d6WVTuYs5UL0ZZ1RYDVLeT7pEjR/ZmHEII0Xs0DXbvhpoaPeHuxpJ1IACVFfqV924uigshxJCkdyf/iAa/CwBfuB1N01Bke9CAk7p5A+amegB8ZWcRyS7ole+jqrBpezrLVuWw+kM7kWjnn5VcR4iF81wsnOdiWLasaouBL277RTRNY82aNQSDQc455xzS06VRhhCin6iuhj17IDe3W9vkVFVf4Xa7pY5bCCFOpNHvYmPDR9KdfJA4ckyYu/zKuL9+c4uJFeuzeH5NNgcarJ0eUxSNs6e0sGiBk/mneTEZ4/7thUiYmJLuH/7wh7zzzjusWbMG0BPuCy64gNWrV6NpGiNGjGDVqlWMGTMmrsEKIUSPNTfDtm162/Hk5G4d0tAAtXV6Hbc0aBFCiK6O3E4u3ckHB1NTPWkfvQVAODOPtqlnx+V1NQ3e35HG8jU5vPGenXCk8wdrli3MlXNdXDXfxfDcUFy+pxD9TUxJ94svvsjnPve5jtsvvPACq1at4sc//jFTp07l61//Ovfffz/PPfdc3AIVQoge8/v1hDsU0seDdYPXC5WVkJoCFstJny6EEENSW9jHNvduNGBUehHTsydJd/IBzr5uJYqmAgcbqBlP7b+np83IP9/KYvmabPbVdr3ofeYkfVW7fIYXi0k7pe8lRH8X029TTU0NY8eO7bj90ksvMXHiRO666y4Abr75Zp5++un4RCiEELGIRvXRYE4nDB/erUNCIX0edzgMdnvvhieEEANZhiWN6dmTMaAwSraTD3zRCPa1KwHQFAOe+Z87yQHHpmnw0e5Ulq/O4T/vOgiGO69qO9LDXDGnic8vcDEqP3jKYQsxUMSUdJtMJoJB/RdF0zRWrVrF9ddf3/F4Xl4eLpcrPhEKIUQsKiv1P3l53dojrqqw/4C+Gz03t9ejE0KIAeXQdvL85BwyrXYAijNGJDYoETdpH7+F2d0IQNtpc4hk9qyhSVu7kZffzOH51bnsru66qn3G+FYWlTs5/3QPFrOsaouhJ6ake/LkyfzlL3/h2muvZcWKFTQ1NXHJJZd0PL5//36ys7PjFqQQQvRIYyPs2AE2GyQlnfz5Bw+pqYbMTDBK8xYhhOhwZHfyCtMBLiyaJ1vJBxnH6p43UNM02LIvheWrs3llYybBUOcPz4zUCJef08RVC1yMKQzENV4hBpqY3jHvu+8+PvvZz3Yk1meffTYLFizoePyVV17hjDPOiE+EQgjRE21teh23okBGRrcOaW2Fikq9z1o3c3QhhBgSOncnNzIpc5wk3IOM2VlL6pYNAISyh+GbcuYJn+/zG/j3hkyWrc5hx/6ULo+fVtLG4nInF8x0Y7XIqrYQEGPSff755/Phhx/y3//+F7vdzuLFizsec7vdzJ07t1OjNSGE6BPhMGzfDh5Pt+u4w2G9jjsUlG3lQghxiKppbHfvZpt7l3QnH+Tsa1egaHpy7FlwORiOvd1rW2Uyy1bn8MqGTNoDnZ+Tmhzhc+c0sXiBi5IiWdUW4mgxX6qcOHEiEydO7HK/w+HgscceO6WghBCixzQN9u6FqiooLNRXurtxyIEDeq81mccthBC6sBrhnfr3afDr/XmkO/kgFolgX/dPADSjEc/cyzo93B4w8NpGB8vX5LBlX2qXw6cU+1hU3sjcs+rINltQOPlnrxBD0Sm9e27cuJE1a9bQ2NjILbfcQklJCe3t7ezYsYPS0lLS0tLiFacQQpxYbS3s3g05OWDq3ltbY6Oeo2dmSR23EEIcYlKMGBQDRsXIjJwpjErv3s4hMfCkf7gWk7cJgNbp84na9dLRXVVWlq/O4Z9vZ9Hm7/wBmWKNculZzSxa4GTiKD8aGj6jCtE+D1+IASOmpDsUCnH11VezcuVKNE1DURQ++9nPUlJSgsFg4IILLuC2227jhz/8YbzjFUKIrjwevY47KQlSutaXHUtrm97cPMkKVqnjFkIMcaqmoWkqRoMRRVGYmTuNQDQo28kHOfsRDdQa5y5k5VuZLFuVw8d7ui6cTRjZzuJyJ5fMbiY1We3LMIUY8GJKuu+9917+/e9/8/TTT7NgwQLGjRvX8ZjVauWqq65i5cqVknQLIXpfMKjXcbe3d7+OOwKVFeD3y7ZyIYQIRIJsbPyIZGMSM3OnoSgKSUYLSUZLokMTvcjcUEXa1ncBaEgexelPfxlve+f/5smWKJ+Z7WZRuZPJo9u7U7klhDiGmJLuv//979x888187Wtfo6mpqcvjEyZM4Pnnnz/l4IQQ4oRUFXbuhLq6bifcmgbVVfrW8lxJuIUQQ1xDu4tNjUd0J4+0k2buWrsrBpdQWKH1L6923P6Z/xa8HE64S4vaWbTAxWfPbiI9RVa1hThVMSXdjY2NTJky5biPG41G2tvbYw5KCCG65cAB2LdPX67uZlG2y6UflpkJJqnjFkIMUYe6k2917wIgw5zOWfnTJeEe5Crrk3h+TTavrEvnU98KAIJY+CM3kmRWuWiWm0ULnEwr8cmqthBxFFPSXVRUxI4dO477+Ntvv83YsWNjDkoIIU7K5dK3laeng9XarUN8Pn08mMXS7UOEEGLQ8UcCbGr8iEa/vltxdHoRp2VPxnScUVFiYAtFFFZ9YGf56mw2bcsAYDH/IAe9O/0byZ/jqwsDfPbszdjTpBuaEL0hpqT7mmuuYenSpSxcuJDS0lIAlIOXw37zm9+wfPlyfvKTn8QvSiGEOFJ7O2zdqm8vt9u7dUg4ojdO87VDnszjFkIMUZqmsb7uXTyhFulOPshVNVp4fk02K97MpqnF3Omxm3mm4+spt53H2AmNfR2eEENKt5PuLVu2dGwp/+EPf8jGjRuZO3cuEyZMQFEUbrvtNpqbm6muruYzn/kMt912W68FLYQYwiIRfYW7qQlGjOj2YTXV0NAAObndGuEthBCDkqIoTM2awMdN25idN50M6U4+qIQjsO5jfVX77U8z0LTOH3gj8wPcMn0j815dB0Bw2Cj846cnIlQhhpRuJ90zZszg9ttv50c/+hFWq5XXX3+dv/71r7zwwgtEo1GCwSBlZWX8v//3//jiF7/YsfIthBBxtW+fXpQ9bFi3s2eXS5/HbbNJHbcQYujxRwK0htvITdZnMOel5HB+8lwMcq42aNS6zLy4LpsX1mbj9HTuQG4yapw7w83ichczJ7SS//ffdDzmmX+FXIkWog90O+n+8pe/zKOPPsrzzz/P008/zfnnn891113Hdddd15vxCSHEYXV1sGuX3gXNbD7589F3oldUgsHQ7RHeQggxaBzqTh5Ro5xfNIf0g43SJOEe+KIqvPmxjeVrsln/iQ31qFXt4TlBrlrg5Iq5TWTbIgAooQD29f8GQDVb8My5tM/jFmIo6nbS/fTTT/OlL32Jr3/961x00UV84Qtf4LHHHiMnJ6c34xNCCF1LC2zbpifbaWndOiQSgf37oa1N6riFEEPL0d3JbZZ0fWaiGPAamg+uaq/Lpr6p86q20aCxYLqHRQtcnDW5BYOh87Hp767C6GsBoGXmeahptr4KW4ghrUeN1GbOnMkHH3zAE088wY9+9CNee+01fvKTnzBjxoxjPn/6dKkREULEQSikJ9xtbd2exw36wnh9PWRny+45IcTQId3JBx9Vhbc/zWD56mzWfmQnqnb+UMvPCnHVfCcL5zWR6wgf93Uca17q+NpTvrDX4hVCdNbj7uUGg4HbbruNyy67jFmzZvGNb3yjy3M0TUNRFKJRGTsghDhFmga7d0NtLRQWdvuw5mao3A8ZGWCKaU6DEEIMPIe2kweiQUwHu5OPlO7kA5bTY+KlN7N5YU02Na6kTo8ZFI1507wsKndyTlkLRsNxXuSgpKo9pOz6BIDA8DH4S8p6K2whxFFiOhVdtWoVN998Mx6Ph5tvvpkzzjgj3nEJIYSuqgr27IHc3G5nz/6APo9bQeq4hRBDS217A4FoEJslndl5M8iwdK8cR/Qfqgobt6WzfHUOqz+0E4l2XtXOdYRYOM/F5+e7KMg6/qr20eydVrmvlC1gQvShHiXdTqeT2267jb///e+UlZWxYcMGSbiFEL2nuVkfD5aaCsnJ3TokGoX9leD1Qn5+74YnhBD9TVnWBJKMFkptxbKdfIBpbjGxYn0Wz6/J5kCDtdNjiqJx9pQWFi1wMv80b48ncSgBP7a3XgFAtVjxnn1JvMIWQnRDt5Pu3/zmN/zgBz8gGAzyyCOPcNttt2E0ypu5EKKX+P16HXc4rBdld1NtnV7LLXXcQoihoKHdxd6WSs7Mm45BMWBUDEx0lCQ6LNFNmgbv70hj2eoc/vu+nXCk8x7xLFuYK+e6uGq+i+G5oZi/T8amNzD6fQC0nHkBaorsgBCiL3U76T7Utfzpp59m5MiRvRmTEGKoi0Zhxw5obISiom4f5vbAgf2Qlt7tiWJCCDEgqZrGNvcutrl3A7DbW8k4e3GCoxLd5WkzsnJ9Fs+vzWZfbdedXGdOamFxuZMF071YTKfedd6x+vDWcnf5laf8ekKInul20v33v/+dxYsX92YsQgihq6zU/xQU0GXeyXEEAlBZAaoGaam9Gp0QQiSUPxJgU8NHNAYOdycfkyELIv2dpsFHu1NZvjqH1991EAp3/nxzpIe5Yk4Tn1/gYlR+MG7fN6lyB8n7tgIQGDmOQPGkuL22EKJ7up10S8IthOgTDQ36KrfDARbLyZ+P3nRm/35wuyEvr5fjE0KIBJLu5ANPi8/Iv97JZPnqHHZXd13VPmN8K4vKnZx/ugeLOf6z1I8cE+aWBmpCJES3k+7y8vIu961evTquwQghhri2Nr2O22CA9PRuH1Zfr9dyZ2V1e2FcCCEGnL0t+/nAuQVAupP3c5oGm/emsHxNDq9tzCQQ6vzhlJEa4fJzmrhqgYsxhYFei8Pg95HxzusARK0ptMy+qNe+lxDi+LqddEsdtxCiV4XDeqdyrxeGd3/VxuPRd6KnpnR7YVwIIQakHGsWJsVIUdowTsueLN3J+yGf36Cvaq/JYcf+rjMrp5e2sWiBkwtmurFa4r+qfbSMDa9jDLQD0HLWRajJUn8lRCJ0O+n+wx/+0JtxCCGGMk3TZ3FXVUFhYbe3vgWDUFEJkYi+G10IIQab9oifFJO+JTnDksaFRfNINXdN5kRibatMZtnqHF7ZkEl7oPPFkPSUCJ89u5nFC5yUFPXeqnYXmta5gdoCaaAmRKL0aE63EEL0ipoa2L0bcnLA1L23JVWFAwfA3Sx13EKIwedQd/Id7j3MG3YmOclZAJJw9yPtAQOvbXSwbHUOn1Z0XUGeUuxjUbmTi2e5SbGqfR6ftWIb1v07AfAXTyQ4anyfxyCE0EnSLYRILI9H31ZutUJK908mGxr1XD0zU+q4hRCDy9HdyevbnR1Jt0i8nQeSWb46m3+9k0Wbv/Oqdoo1ymfPamZRuZMJI/0JilDnWPVix9fu8oUJjEQIIUm3ECJxAgG9cZrfr28r76aWFn08WHIyJCX1YnxCCNHHGtqdbGz8iGA0JN3J+5FASOH1TQ6Wr87h4z1dm9dNGNnO4nInl8xuJjW571e1j2ZobyNj4xsARJNTaZl1QYIjEmJok6RbCJEYqgq7dumtx3vQOC0U0hunhUKQm9t74QkhRF86tJ18m3s3IN3J+4u9NVaWr8lm5fosWto7nzYnW6J8ZrabReVOJo9u71eTuGxvv4ohpNePe8+5BM3adVSZEKLvSNIthEiMykrYt08vyDZ2rwOvpul13C6XJNxCiMGl1lffkXAXp49gWvYk6U6eIKGwwhvv2Vm2OocPdnYdX1la1M7icheXntVEekriV7W70DTsqw9vLfdIAzUhEq7fVUL+6le/YtSoUVitVmbNmsW77757wud7PB6++c1vUlBQQFJSEqWlpbz66qt9FK0QIiZOJ+zcCRkZei13NzU2QnW1XsfdzTxdCCEGhMLUfEanFzErdxqn55ZJwp0AlXVJ/PRvhcz/dhnff7q4U8KdZFa5/BwXf7tvByt+vJ0vnOfsnwk3kLx7M9bqvQC0l5QRLBqb4IiEEP1qpXvZsmUsWbKEZ555hlmzZvH4449z4YUXsnPnTnKPsawVCoU4//zzyc3N5YUXXqCwsJD9+/djt9v7PnghRPf4fHodt6qCzdbtw1rboKJCz9GljlsIMdBpmsZOz16K00diMZpRFIUzcqcmOqwhJxRRWPWBneWrs9m0LaPL48XD/Cwud3HZOU3YUqMJiLDnjlzllgZqQvQP3Uq6DQYDSgyFKtFoz96cli5dyle/+lW+9KUvAfDMM8/wyiuv8Pvf/54f/OAHXZ7/+9//nubmZt555x3MZjMAo0aN6nGcQog+EonAjh3Q3AxFRd0+LBzWG6cFAjIeTAgx8PkjATY1fkiz30NzwMPsvBkxnWeJ2FU1Wnh+TTYr3symqcXc6TGzSeWCM9wsLncxY1xbv6rVPhlDm5eMd/8HQDQ1g9aZ5yY4IiEEdDPpvu+++7p8GKxYsYKtW7dy4YUXMm7cOAB27NjBG2+8weTJk7n88st7FEgoFOKDDz7grrvu6rjPYDBw3nnnsWHDhmMe889//pPZs2fzzW9+k5UrV5KTk8M111zDnXfeifE4e0+DwSDBYLDjdktLCwCqqqKq/XObkBA9oaoqmqb1v59nTYM9e2D/figoOHxfNw47UAWNTsjJhZMfIQYb7Yj/CTHQNbQ72dT4cUd38sLUfFCQn+8+EI4orP3IxvI1ObyzpetOq5H5Aa6a7+TyuU040iMd9w+k/zK2t/6NIRwCwHPOJaiWJHr7byDv0fFw8F9Q09A0FU3Tz3/626ncUNEb59DdSrrvv//+TrefffZZGhsb+fTTTzsS7kO2b99OeXk5w4YN61EgLpeLaDRK3lHLWHl5eezYseOYx+zbt4/Vq1dz7bXX8uqrr7Jnzx5uueUWwuEwP/rRj455zMMPP8wDDzzQ5X6n00koFOpRzEL0R6qq4vV60TQNQ38aYN3crCfdGRkQjep/usHrhapmSM6DoBmCJz9EDDIaGkFjGACFAbTkJMQRNE1jd9M+9jRXApCWlMppBZNJt6ThI5DY4Aa5elcSL6/O459r83B5OtcnGY0qC85o4opz6zl9ordjVduXgDhPmaYx+oit5bXnXULA2Ps/W/IefeoULYQhVSEQaUYNhIhE9N19jY2Jjmxo8nq9cX/NmGq6H330UW699dYuCTfAhAkTuPXWW/npT3/KV7/61VMO8ERUVSU3N5dnn30Wo9HIjBkzqKmp4dFHHz1u0n3XXXexZMmSjtstLS0UFRWRk5MjteBiUFBVFUVRyMnJ6T9Jd0sL1NaCxQI9+D1ra4P9ByAlCvZUYGCU04k4O7R6khq1ygmdGJACkQAbGz7GGWgGYHRGESW5xWRoqShR+ZnuDVEV3vzYxvNrcnjzExua1vnfeXhOkM8vcHLFXBfZtoOr2mr3G3v2RynbPyC57gAAvvHTMeaPoy/K0OU9+hRFwpidHoLDR6PaRtLYZCQlBcaM0RvHir5nsVji/poxJd3V1dUdNdTHYjabqa6u7tFrZmdnYzQaaWho6HR/Q0MD+fn5xzymoKAAs9ncaSv5hAkTqK+vJxQKHfMfLCkpiaRjdGEyGAz9J0ER4hQpitJ/fqZDIb2O2+fr0TzucETfie5vlzpuoa+eHPqfEAONQTHSFm7HpBiZkVPGiPRh+AwBlKj8TMdbQ7OZF9dl88LabOqbO58HGg0aC6Z7WFzuZPakVg5/RA6O/waONSs6vvaUX9mnP1vyHh2jSASzq47QsNEEiyfT5DFjNMK0aZCdnejghq7eOH+OKemePHkyTz31FNdccw2FhYWdHquuruapp55iypQpPXpNi8XCjBkzWLVqVUc9uKqqrFq1iltvvfWYx5x99tn87W9/Q1XVjn+cXbt2UVBQ0CtXKIQQPaRpsGsX1NT0KOHWNH00mLNRr+MWQoiBRtO0jn44SUYLZ+XPwGwwk2FJk9rXOIuq8PaWDJavzmHdxzaiaufELz8rxKL5Tq6c10SuI5ygKHuXscVNxrurAIik22k9vTzBEYmTikYwO2sI5xURKJ5Ic4sZVdUT7mMMbRIDXExJ92OPPcaFF15IaWkpV1xxBWPH6vP/du/ezcsvv4ymafzlL3/p8esuWbKEG264gdNPP52ZM2fy+OOP4/P5OrqZX3/99RQWFvLwww8DcPPNN/PLX/6S73znO3zrW99i9+7dPPTQQ3z729+O5a8lhIi3qirYuxfy88HU/bcbVxNUHdB3optkVK0QYoDxRwJsbPiQ0elFjMrQJzVkWR0JjmrwcXpMvPRmNi+syabG1XkXo0HRmDfNy6JyJ+eUtWDsBxu/epNt/b9Qovo2ee+cz6KZZfGpX1OjmBtrCOcU4h87BXd7EqEQTJ16uNesGFxiSrrPOeccNm3axL333suKFSvw+/0AJCcnc+GFF/LAAw/0eKUbYPHixTidTu677z7q6+uZNm0ar7/+ekdztQMHDnRa7i8qKuI///kPt912G2VlZRQWFvKd73yHO++8M5a/lhAinpqaYPt2SEvTh2t3U3u7Ph7MZILk5F6MTwghekF9u5NNDR8RVEO0hn0MTxuGySBXD+NFVWHjtnSWr85h9Yd2IkfVxOc6Qiyc5+Lz810UZA3OVe0uVLXT1nJ3+ZUJDEaclKpibqwlkl2Av6QMb9BKe7uecPdgU6AYYBRN68bMnhNQVRWn0wnQvxo3dVNLSws2mw232y2N1MSgoKoqjY2N5ObmJu730e+H998Hjwd6MMkgEoGdO/VunXl5DKjZqKL3aGj4jAFp0iP6NVVT2dq8i+2ePQDYLRnMzptOuiWty3PlZ7rnmltMrHgzi+Vrsqlq7HwhV1E0zp7SwuJyJ/OmeYfcDqmUTzcx8pFvAtA2aSZVP3iqT7+//Dz3gKZhbqwhYsvCP/40WqKpeL0weTIUFyc6OHGIx+PB4XDg9XrJyMiIy2vGtNJ9JIPBgNVqJS0tbcAl3EKIXhCN6o3TnE4oKurRobW10NCg13FLwi2EGCgObSc/1J18TMZIpmVNxCgr3KdE0+C9HWksX53Df9+3E450Ps/MsoX1Ve15LobnDt2xr47VL3V87ZFV7v5L0zA5a4jYHPhLp9KqpuLxwKRJMHp0ooMTvS3mpPv999/nnnvu4c033yQUCvHGG29QXl6Oy+Xiy1/+Mrfddhvz58+PY6hCiAGhogIqK/WipB5ciGtq0seD2WxSxy2EGDhC0TD/rV5PIBrEpBg5PaeMEemFJz9QHJenzcjK9VksX5NDRV3X8qTZk1pYVO6kfLoH8ykvHw1sRo+L9A/XAhCxZdE6fX5C4xHHZ3LVoaba8JdMxWdIp8kJEyfqo8FkoWHwi+mt6p133qG8vJzCwkKuu+46fvvb33Y8lp2djdfr5de//rUk3UIMNQ0N+v5wh0Ofyd1Nfr+eqytASkrvhSeEEPFmMZoZnV5EXXtjp+3k332ymBsvbmBaiQ9VhYf+UsT6T2yAxhcvauDyi6qO+XqhsMJP/zact7ZkkGTWGDeinZ/eXEkwpHD7r4rZW2vFalbJzIhw35cOMDIveNIYm7wm7vr1KA40JmExadx34wFOH9/W5Xk1TgsX3j6ZkiJ/x31PfHsvI/L0VeRal5n/96cRVNZbMRg0rj7XyXUXOHF5TXxz6Vj+et+OmC+aahp8tDuVZatz+M+7DkLhzhdtHelhrpjTxFXlrm79nYcK+5v/RInqw7g98y7rUdNS0XdMrnpUawr+0qm0m2w4G2HcOCgpkYR7qIjpN/Puu+9mwoQJbNy4kdbW1k5JN8CCBQv405/+FJcAhRADRGsrbN2qr26np3f7sGhUXxhvbZV53EKIgcEfCaBqKqlm/SrhpMxSJjhKOhqmbd6bgtdnZFqJD4B/vZPJ3horrz76Ka3tRhbeM4Epk5soK1C7vPbSZYWgwGuPbkVR9A7dh1y1wMncqS0oCvz1vznc99uR/OmHu04a79LlhZSN9fHs9/ewZV8K3358DG8s3XLMVeLU5Cgrfry9y/2aBt9+YgxfubSei2Z5AHB59RfItkWYVtLGyreyWDiv6aTxHKnFZ+Sfb2eyfHUOe2q6ds88Y3wri8udnHe6B4tZRq11okaxr3kZAE1R8My/IrHxiGMyNTegWZLwl06jPclBQ72ebI8b16MNgWKAiynpfu+993j44YdJSkqira3rldLCwkLq6+tPOTghxAARDuudyltaetx6s7YO6ushO1uu9goh+r9D3clTzMmUF56FUTFiUAwYjnj/Wr46h0tnN3fcfm1jJlfNd2E0gD0tykWz3LzxTjZlCxs7vXZ7wMCL67JZ8+TmjvfDHLs+BirJojFvWkvHc6eO8fGHV7t3pfL1TQ5e/9mnAEwpbifXEea9HemcNbm123/vDVvTsZi0joQb9GT7kM+c2cxDz43oVtKtafqFieVrcnhtYyaBUOfMIyM1oq9qL3BSPExWtY8ndctGLK5aAHxTZhPO6X7jUtE3TB4XmsGIv3QqgdQs6mv1hmkTJoBRSumGlJiSbrPZjKp2vTp7SE1NDWlpXbt1CiEGIU2D3buhuhoKC3uUObvdsL9SXxiXHXFCiP7s6O7kyZqVUDRM8jH2U7+3I50bLmrouF3XZGFY9uFGX4U5QT7Y23VVt6oxCVtahGf/WcCGrekkmVW+eWUdsyd1TY6feyOX8umek8btaTUSiSodyTvAsOwgdU3HLgHyB40sum88UVXh3Bkevv65OowG2FtjxZER4fZfjqai3kphdpDvX1NN0cEGZpNGt7OrKpk2v4G05GOfI/r8Bv71TibLVuew80DXWqLppW0sWuDkgplurBZZ1T6ZIxuoyZiw/sfobQJNw186jUB6DrU1esO0SZPknGcoiuk/+ZlnnskLL7zAd7/73S6P+Xw+/vCHPzBv3rxTjU0IMRDU1MCePZCT06NPEX8A9u0DDUhN7b3whBDiVPW0O3l9s5msI1aBuyuqQq0riTGFfpYsrmFbZTJfeaSUf/5ka6dV5V//M58DDUn8/gf7Y/sLHUeOPcyaJzaTZYvgaTNy+y+L+eOreXz50gaiqsKmben8/Uc7KBke4B+rslnyy2Kef3AHoDfAzEiN0Og2k5bceXV6W2Uyy1bn8O93MvEHO/+bpadE+OzZzSxe4KSkKBDXv89gZmpuIO3jtwAIO3Jpm3ZOgiMSRzK2uFEiYT3htudTWwsjR+oJt9mc6OhEIsSUdD/wwAPMmzePSy65hC984QsAfPLJJ+zbt4+f/exnOJ1O7r333rgGKoToh9xu2LYNkpN71AFNVeHAfn03em5uL8YnhBCn6NB28qAawqSYOD23jBFpJ97Gm2xRCYYP7/opyApR67J01HjXOJPIz+6aYBZkhTAoGpeepSf3E0f5GZ4TZFdVMtk2fbX796/k8b/37fzuzt0kJ518NdieHsVk0HB6TB2r3bWuJAqyuo7Yspi1josF9rQoV85z8co7mXz50gYKskJMGNlOyXA97svObub//jSCcISO2vBg2EDSwRXq9oCBVzc6WL46h08rul5ZnVLsY/G5Ti6a6SbFevzdk+LY7GtXoqgHG6jN/xwYZem0vzC0eVFCAfwlUwlkFVJbA8OG6Ql3D3rMikEmpvL9WbNm8eqrr7Jnzx6uv/56AG6//Xa+9rWvEY1GefXVVykrK4troEKIfiYQ0BPuQAAyM3t0aG2t/iczU5qICCH6L03T+LR5B0E1hN2SwfnDzzlpwg1QWuTvNOrqwplunl+bTVTVx2G9vsnB+We6uhznSI9y5qRW3tqcAUB1o4VqZxJjhumJ7h9fy+XVjQ5+e+duMlKjnY5dumwYf/1vzjHjuXCmm2Wr9ce27EuhwW3mjPFdt6w3eU2EDy6oh8IK/33PwYRR7QDMKWuhodlCQ7O+TPfmJxkUDwt0JNwurwlFgZY2I//3xyLmf7uM+343qlPCnWKNsrjcyYv/bxvLHtjBlXObJOGORTSCfd1KADTFgGf+5YmNR3Qw+Fox+H0Exk4hmFtEbS3k50NZGVi7Tr8TQ0jMl8XKy8vZuXMnH3/8Mbt370ZVVcaMGcOMGTNQpBuSEIObqsKOHfqIsB42TvN44MABSEuTK75CiP5NURTOzJvOHm8lUzLHH3c7+dEumOnm7S0ZHY3KLjuniU/3pXDxHZNRFLjh4gbGjmiHqJXVH9pY86Gd//uKvlX8R1/az72/HcXSZcMxKBr337SfvMww9c1mfvq3Iopyg9z4UCkAFpPGsgf07d07D6QwaXTXRB5gydU1/OCZ0Vx0xyTMJo1HvlHRkSz/4sUCcuxhrj7XxYe70vjFi8MwGjQiqsKsia18/TK9MW6KVeVHX9rPN34+FjRIS4ny82/uAyAQUnjyBf24hfdO7PL9J47ysWiBi0tmN5N6nHpv0X1pn7yDuVnvGdA27WwimTL6oz8w+Nsw+LwEiicTzBtBba3eJLasTN8QKIY2RdO0Hneq8Hq92Gy23oinz7W0tGCz2XC73djt9kSHI8QpU1WVxsZGcnNzMfTWMvK+fbB5s743PCmp24cFg7BtO7S16h9EQnSHhobPGCA1akVBLuqK3lXf3ogn2MJ4x9iYX8MXMHDtg+P42307j7mSG++f6agKX7h/PP+4f0ef7h7aU2Nl+eps/vlWFi3tnddxki1RPjPbzeJyJ5OL2/suqCGg6GffIe2TtwE4cPvj+BJczy3v0aAE2jF5mwkUTyQwfCw1tQp2O0yf3qMpqqKf8Hg8OBwOvF4vGRkZcXnNmFa6c3Nzueiii1i8eDGXXXaZdCoXYihxOvVV7oyMHiXcqgr794O7WeZxCyH6n6O7k2daHeQmZ8X0WqlWlTuvrabGaemT5mBGAyw/2NCstwVDCm+852D5mmw+2Nk1mygtamdxuYtLz2oiPUVWtePN5KojdfM7AISz8vGVzU5wREIJ+DF5mgiMGk9w+BjqGxRsNpg2TRJucVhMSfeSJUt4/vnnue6667BarVx88cVcffXVXHrppSTL/gkhBi+fT6/j1jTo4W6Xhgap4xZC9E/tET8bGz7CdUR38qwk+ym95rHGfA1klXVJLF+Tzcvrs/G0dT59TDKrXDyrmUXlLqaO9fVkcqToIcfal1EOblJ1z78culnyIHqHEgpg8jgJjCglOLKU+kYDyckwdWqPT5PEIBdT0v3www/z8MMP895777Fs2TJeeOEFVqxYQWpqKpdeeimLFy/mM5/5DBYp2BRi8IhE9BXu5mYoKurRoV4vVFbqDc57sDguhBC9rr69kU0NH3d0Jz8jt4yibjRLGwpCEYVVH9hZviqbTdu7brEsHuZncbmLy85pwnZUYzfRCyIR7GtfBkAzGPFKA7WEUsIhTE0NBItKCI4aT6PLgMWiJ9wOR6KjE/3NKc0XOOOMMzjjjDP42c9+xoYNGzoS8OXLl5ORkYHb7Y5XnEKIRNI02LtX3x8+bBg9WcYIhfSEOxwGaZsghOhPtrl382nzTgDslgxm588g3dx1vNVQU9Vo4fk1Oax4M4umls5Dhc0mlQvOcLP4XBczSttkVbsPpX/0JiZvEwCt0+cSsUtzlISJhDG56ggOLyYwegIutxGDQU+4pWeNOJa4DfWbPXs22dnZOBwOli5dSktLS7xeWgiRaHV1sGuX/kliNp/8+QdpGuw/AE1NMo9bCNH/pBj1GT5jMkYyLWtit7uTD0bhCKz9yM7yNdm8vaXrvtiR+QEWLXBy+ZwmHOmyqp0I9tUvdnztKV+YwEiGuEgEs6uW0LDRBIon0dxiQlX1Gm451xHHc8pJd0VFBcuWLWP58uV88sknGAwGFixYwOLFi+MRnxAi0bxevY7bYoHUnq0ANTRATbVex20cuueyQoh+JKxGMBv0059RGUWkW9LIsg7dvaA1Lgsvrs3mxXVZOD2dywJNRpXzTvewaIGLWRNbZVU7gcwN1aR9ugmAUG4hvkkzExzREBWNYHbWEM4rIlA8EU+bmVBIT7gLChIdnOjPYkq6q6qqWL58OcuWLeODDz5AURTmzJnDr371KxYuXEhOTk684xRCJEIwqCfcPl+P53G3tkJFJVitUscthEg8VVP5tHkXB9qqOX/4XJKMeoI5FBPuSBTWf2Jj2eoc1m/OQNM6Z9PDc4JctcDJFXObyLZFEhSlOJJ97YqOrz0LrpCOpImgRjE31hDOKcQ/dgreQBLt7fqW8sLCRAcn+ruYku6RI0eiKApnnnkmjz32GFdddRUFcnlHiMFFVfUt5XV1PU64w2GoqIBgQMaDCSES7+ju5NVtdYyxjUxwVPEVVeGDnWk4PWZy7GFmjGvDeFRe1tBs5oW12by4Lpv65s6r2kaDRvl0D4vKncye1Co5XX8SCWNf908ANKMJz5zLEhzQEKSqmBtriWQX4C8pozVspbUVpkyBESMSHZwYCGJKuh999FEWLVpEUQ87GAshBpCqKr15Wl5ej/aGaxocOAAul9Q2CSESr669kXcHeXfy/75n56G/FNFwRCKdlxni7uuqKJ/h4e0tGSxfncO6j21E1c6r2vlZIRbNd3LlvCZyHeG+Dl10Q/r7azC16s2JW05fQNSWmeCIhhhNw+ysJWLPxl9SRls0GY8HJk2CUaMSHZwYKGJKum+//fZ4xyGE6E+amvTxYOnp+v7wHnA6oboaHFLHLYRIIH07+U52ePYCg7c7+X/fs/PdJ4vRjrq/odnMd54sxpEewd3auQGmQdGYN83LonIn55S1dFkRF/2LY/VLHV9LA7U+pmmYnDVEbA78pVPxkUpzM0yYAGPG9GiYixjiupV0//nPf47pxa+//vqYjhNCJFB7u17HHQ73eO5FW5u+rdxiAavUcQshEmibe3dHwj1Yu5NHVXjoL0UHE+6jz/7120cm3LmOEAvnufj8fBcFWbKqPRBY6ipJ3f4+AMH8EbRPmJHgiIYWk6sONdWGv2Qq7cZ0nI0wbhyUlEjCLXqmW0n3jTfe2OMXVhRFkm4hBppoFHbu1PeG97SOO6I3TvP7pY5bCJF4pbZian0NTHCMHXTbyQ/5YGdapy3lxzO5uI2vX1bPvGleTIPrusOgZ19zRAO18isl0+tDJlc9qjUFf+lU/GYbDfV6sj1unPSxEz3XraS7oqKit+MQQvQH+/ZBZSXk5/foE0XT9C3lzkbIkTpuIUQCqJpKVVstI9IKURQFi9HM+cPnoAziJMXpMZ/8ScANFzVy7gxvL0cj4k0JBbGt/zcAqtmC95xLExzR0GFqbkCzJOEvnUYg2UF9rb6dfMIEKZ0TselW0j1y5ODq8CmEOIb6er1beWamvj+8B1xNUHUA7HZkFUUI0eeO7E4eViOMtY0CGNQJN0COvXtbxLv7PNG/pL+3ClObfrGkdea5RNPtiQ1oiDB5XGgGI/7SqQTTsqitgdGjYeJEMMXUDUuIGBupHWnbtm3s378f0JPziRMnnnJQQog+1toKW7fql2/T0np0qM8HFfvAbIbk5F6KTwghjqPO18imxo8IqWFMiqlj/vZgp2mwYWv6CZ+joJGXqY8PEwPPkQ3U3AukgVpfMHqbQNP0Fe70HGpqYORIvVO5uXsbS4Q4ppiT7pUrV7JkyRIqKys73T969GiWLl3KZZfJDEEhBoRwWG+c1tra4zruSETfje5rhzzZVi6E6ENHdyd3WGycmT990HUnP5ZgSOGe347ilQ1Hjo7SOLKZmnKwvdpd11VJd/IByFK9l5RdHwMQLCzGXzo1sQENAcYWN0okjL90GkFHPrU1UFioJ9w93AAoRBcxJd2vvvoqCxcuZOTIkTz00ENMmDABgO3bt/Pss89y5ZVX8u9//5uLLroorsEKIeJM02D3bqg5+MnSw62Y1dXQ0KDXcQ/yXZxCiH7kyO3kAGMzRjE1ewJGZfDXtzS3mPjW42P4aLe+K0lRNC4/p4l3tmYcNac7zF3XVXH+GZ4ERSpORadVbmmg1usMbV6UUAB/yVSC2YXU1ujtbcrKejw5VYhjUjRNO3q040nNnj2bYDDI+vXrSU3tfEXZ5/NxzjnnYLVa2bBhQ9wC7S0tLS3YbDbcbjd2uz3R4QhxylRVpbGxkdzcXAwna4ZWVQUffghZWT3eG97UBNu2Q0oypKScQsBCnISGhs8YIDVqRekyFkkMRU5/E2trN2AymDg9p2zAdSeP9Wd6b42Vm38+lmqnPpMx2RLl0W9WUD7dS1TVu5k7PWZy7PqWclnhHpiUYICSb1+Esb0N1ZLE7idfR009cSlBIg3092iDrxVDeyuBkjKC+SOpqdEnpk6bBqmDf+OMOAaPx4PD4cDr9ZKRkRGX14xppXvz5s089NBDXRJugNTUVG688UbuvvvuUw5OCNGL3G7Yvl3PmHuYcPv9+jxugyIJtxCi7+UkZ3FGzlSykzNJGwLbyUGv3/7uk8W0tuunbrmOEE8t2cPEUX4AjAaYOUFqtweDjE1vYGzX/1u2nHlBv064BzqDvw2Dz0ugeDLBvBHU1Oj9ZMvKJOEW8RXTNVCr1Upzc/NxH29ubsYqezGE6L8CAb1xWjCof7r0QDSq13G3toLD0TvhCSHEkdojftbVbqIl1Npx36iMoiGTcL+4NouvP1rSkXCPH9nOP+7f0ZFwi8FFGqj1DSXQjrHFQ3D0RELDi6mrV7DZ9IQ7Xa5ziDiLKekuLy/niSeeOOb28U2bNvHkk09y3nnnnXJwQoheoKqwYwc0NuoFSz1UWwt1dfrWKykxE0L0tjpfA29UvUmD38n7zs3EUBU3YKkqLF1WyL2/G0Ukqr/hLjjNw3P37CQ/U8aADUZJ+3eSvPdTAAIjSgmMmZTgiAYnJRjA5GkiMHIcweFjqG9QSEmBqVPBZkt0dGIwiml7+U9/+lNmz57NOeecw8yZMxk3bhwAO3fu5N133yU3N5dHHnkkroEKIeKkslLfG56fDyer+T5KczNU7oeMDJlVKYToXV26kyfZmJk7bdDP3j7EH1S469ejeeO9w1uKrr+wge9dUy212oOYNFDrfUoogMndSGBEKcGRpTQ4DSQl6TXcsoNP9JaYTptHjx7N5s2befjhh3nttddYtmwZoM/p/s53vsMPfvADcnNlfpAQ/U5jo77KbbdDUlKPDvUH9FwdpM5JCNG72iN+NjR8SFPADQyt7uQATo+JWx8by5Z9+putQdH44fVVfOE8Z4IjE71JCbST8c7rAKhJybScJVOA4k0JhzA1NRAsKiE4ajyuZgNGo76lPCsr0dGJwSzmtarc3Fwee+wxHnvssXjGI4ToLT6fPo9b0/Sl6h6IRmF/JbS0QF5e74QnhBAA3lAra2reIaSGMQ/Q7uSnYleV3qG8rkm/MJpqjbL0W/uYU9aS4MhEb7Nt+A/GgA8A7+yLUJPTEhzRIBMJY3LVERxeTGD0BJq9RlQVTjsNZK1Q9La4bhDdt28fwWCwY263EKKfiET0hNvjgeHDe3x4XR3U1etXgWWnmxCiN6WbU8mwpBHVVGbnTR8yzdIA1m/OYMkvivEF9BX9gqwgT9++h9KiQIIjE33BvvrFjq895VcmMJJBKBLB7KolNGw0geJJeNpMhEL6lvIY2tsI0WMxVQU9+eSTXH311Z3uu/HGGykpKWHy5MmcfvrpNDY2xiVAIcQp0jTYs0efyV1Q0OOs2e2B/fshLRXM5t4JUQgxtLVH/EQ1FQCDYuCs/NMpLzxrSCXcf/9fDjf/bGxHwj2l2Mc/7t8hCfcQYd23jeTKHQD4R08kMFoWsOImGsHsrCGcW0SgeCIt7Wba22HKFCgsTHRwYqiIKen+7W9/S94Re0z/85//8Oc//5mvfe1r/OIXv2Dfvn088MADcQtSCHEKamth1y693XgPu58FAlBZoXfQTZNdbkKIXlB7sDv5lqbtHfdZjUlDpn47qsJP/jKc//vTCFRNvyh6wRlu/nj3TnLskQRHJ/qKfc3hBmqyyh1HahRzYw3hnEL8Y6fQGkqitRUmT4YRIxIdnBhKYtpevn///k5byJcvX87o0aN5+umnAaivr+e5556LT4RCiNh5vbB9u940rYfdz1RVX+F2u6WOWwgRf6qmsqV5Bzs9+wBwBpqJqlGMhqGRbAP4AgbufKqYNR/ZO+77yqX1fPeqmp4OlxADmKG9DdvBBmrR5FS8Z16Q4IgGCVXF3FhLJLsAf8kU2iJWPB494R41KtHBiaEmpqT76BmZb7zxBp/73Oc6bo8aNYr6+vpTi0wIcWqCQb2O2+eLqY67oQFq6/Q6bjn5E0LEU3vEz4b6D2kKHuxObhvF1Kyh050coKHJwvd+Np4d+1MAMBk17rtxP5+f35TgyERfs73zGoaQXkbgPeszaNaUBEc0CGgaZmctEXs2/pIy2rUUmpthwgQoLpb+NKLvxXQqXVpayooVKwB9a3ltbS0XX3xxx+PV1dXY7fa4BCiEiIGqwu7dege0goIeH+716uO8U1PAYol/eEKIoevQdvKmoBuzwcRZeTOYnj15SCXc2yuTuem+qR0Jd3pKhF9/b7ck3EORpkkDtXjTNEzOGiI2B/7SqbQrqTidMG4clJRIwi0SI6aV7jvuuINrrrkGh8OBz+djwoQJXHjhhR2Pr169mmnTpsUrRiFET7lc+lDtvDww9uxENhjUDw2H9XHeQggRL6FoiE2NHxFWIziSbEOuOznA6g9tfO9Xo/GH9Pfm4Tl6h/IxhdIwbShK3rMFa9UeANrHlhEcUZLgiAY+k6sONdWGv2QqflM6DfVQWqr/kZ17IlFiSrqvvvpqsrKyePXVV7Hb7dxyyy2YDjZoam5uJjMzky9+8YtxDVQI0U1NTXqn8tRUsFp7dKiqwoEqaG6WmZVCiPizGC2cnlOGM9A85LaTaxr8+fVcfvr34WgHG6ZNK2njl9/dS2aGNEwbquyrpYFaPJlc9ajWFPylUwlYbNTXwdixMH58j9cghIgrRTu6QHuIaWlpwWaz4Xa7ZUu8GPja21HffZfG1lZyCwow9HAPVX097Nihr3AnJfVOiEL0lIaGzxggNWpFQfYFDjS1vgaMipG8lOxEh5IwkSg89FwR/1h1+GrmBWc18pMvV2GVEp4hy+BroeRbF2MIB4mmpLP7F6+hWXp2sbw/6C/v0abmBjSThfZxpxFMy6KmBkaP1hunychT0RMejweHw4HX6yUjIyMurxnTSvchNTU1vPnmmzQ2NrJw4UKGDx9ONBrF6/Vis9kwyiUlIfpONAo7d+rtxrN7fnLb2goVlZCcLAm3EOLUqZrKlqYd7PTuI8lo4YLhc0k2DbyE4lS1+Q0s+UUxb22xddx38+W13HjVPpJUK8iFpCHL9tYrGMJBALxzLh2QCXd/YfK40AxG/KVTOxLukSNh0iRJuEX/EHP38ttvv51f/vKXRCIRFEVhypQpDB8+nLa2NkaNGsWDDz7Id7/73TiHK4Q4rn379GLsggI9Ae+BUEg/NBSUbeVCiFPnC/vZ2HC4O/mItEIsxqF35lvjsnDLz8eyuzoZALNJ5f++sp/Pnt2ET3LtoU3TcByxtdy9QLaWx8robQJNw186jWBGDrU1+tCWSZOkGazoP2JqJ/Doo4/yxBNPcMcdd/Df//630wgxm83GlVdeyYsvvniCVxBCxFV9vb7KnZXV40u6mqaXgDud+uFCCHEqan0N/Le6c3fy07InDan6bYDNe1O4+v7xHQm3PS3C736wm8vObk5wZKI/SN75EUm1FQD4xk0nVDg6wRENTMZWD0okjH/sFEKZ+dTWQn4+TJnS47Y2QvSqmFa6f/Ob33D99dfz0EMP0dTUdbxFWVkZr7322ikHJ4TohpYW2LoVTCZIS9Oz6B5obITqasjMkiYjQojYaZrG5qbt7PTuAyAzycaZeTNIMw+9mcP/edfOD54ZTTCsr22Myg/w9B17GJkXTHBkor9wSAO1U2Zo86IE/fhLphLMLqS2FnJyoKxML5UToj+JKemuqqrirLPOOu7jqamptLS0xByUEKKbQiHYvh3a2qCwsMeHt7bp87gtSWCVOm4hxCkKRPWkssQ2mrKsCRiVoTWfR9Pgd6/ksXTZ8I77Zk5o5fFv78We1rOyHzF4GVs9pL+3CoBImo3WM8oTHNHAY/C1YvD7CJSUEcororYGMjP1hDt1aE0hFANETEl3bm4uVVVVx338gw8+YMSIETEHJYToBk2D3buhpkZPuHvYqTwchsoK8Pv1cd5CCBELVdMwKAqKojA9Zwoj0oZRkDr03lRCEYX/++MIXlx3uJHl5ee4uP/LB7CYhvSgGHEU25v/whAJA+CdexmaWQqPe8Lgb8Pg8xIonkwofwR1dfrUlbIySE9PdHRCHFtMl6CvvPJKnnnmGfbt29dxn3LwhP+NN97gj3/8I1dddVV8IhRCHFt1NezZo3c+M/Xs+pmm6Yc3NkLW0J3iI4Q4Baqm8knTNt6pf7+jt4vZYBqSCbfXZ+Trj47tlHB/56oafvy1/ZJwi840DfvaFR033QuuSGAwA48SaMfY4iE4eiKh4cXUNyikpOgJt8128uOFSJSYVrofeOAB1qxZw7Rp05gzZw6KovDII49w7733smHDBk477TTuvvvueMcqhDikuRm2bdP3UMVQuORywYED+lYsk9RxCyF66Oju5I3+piE7h/tAg4Wbf15CRZ3etcliVnn465VcPMud4MhEf5Sy7X2S6g8A4Jt4BuF82RnaXUowgMnTRGDUeILDx9DQqJCUBNOmgcOR6OiEOLGYVrptNhsbN27k+9//PjU1NVitVtatW4fH4+FHP/oR69evJyVl6DVOEaJP+P16wh0KxfQp4/Pp48EsFunsKYTouWN1Jx+qCfeHu1L5wgPjOxLuzPQwf7p7lyTc4rgcqw9P93GXL0xgJAOLEgpgcjcSGFFCcGQprmYDRqO+wi2TV8RAENNKN0BycjL33HMP99xzzzEfr6ioYPRoGX8gRFxFo/poMKdTH0LZQ+GI3jjN1w55Mo9bCNEDqqaypWmHdCc/6N/vOPjhb0YRjujrF2MK/Ty9ZA/Dc0MJjkz0V0ZvE+kfrAEgkpFJ64x5CY5oYFDCIUxNDQSLSgiOGk+T24Cm6SvcuXIuIwaIuLcV3bx5M9dccw3jxo2L90sLISor9T95eWDo+a9vTTU0NEB2do/7rgkhhrh3Gz/uSLhLbKNZUHj2kEy4NQ2eWlHA958u7ki4z5rcwl/v3SkJtzgh+7p/okT1LvaeuZeByZzgiAaASBiTq47g8GICoyfgaTUSDutzuPPzEx2cEN3Xo5XurVu38vTTT7N3714cDgdXXXUVV1yhN4D48MMPueeee/jPf/6D2Wzmuuuu65WAhRiyGhthxw69U0hSz+d7uVxQVaUfLnXcQoieKrUV0+B3MSN7CsPTChIdTkKEwgr3/m4k/3r78H7WqxY4uef6A5hj3jsohgRVxb72ZQA0RcGz4PKEhjMgRCKYXbWEho0mUDwJr8+E3w9Tp8Y0JVWIhOr2R8TGjRspLy8nEAh03Lds2TKWLl1KJBLhzjvvJD09ne9973t85zvfoaBgaH4gC9Er2tr0Om5FgYyMHh/e3g4VlfriuLRbEEJ0h6qpNAc9ZFszAci02rlkxLmYDEPzqp271ci3Hh/Dh7v0mUSKonHH1dXceHGj7BwSJ5X66SYszhoAfJPPJJzb8xKxISUaweysIZxXRKB4Iq1+M21teg13UVGigxOi57qddD/44INYrVZWrFjBnDlzqKio4Etf+hL33Xcffr+fJUuW8MMf/hCb9OsXIr7CYdi+HTyemOq4IxHYv1/P26WOWwjRHb5wOxsbPsQdauHcwrNxJOmf7UM14a6oS+IbPxtLVaPeMC3ZEuWRmys473RvgiMTA4U0UOsBNYq5sYZwTiH+sVNoCyfh9cLkyTByZKKDEyI23U66N23axDe/+U0uvPBCACZNmsTSpUuZO3cuS5Ys4ac//WmvBSnEkKVpsHevvi+8sDCmQuy6OqivlzpuIUT31PoaeLfxY0JqGLPBRCAaTHRICfXu9jS+/cQYWnz6KVOOPcRTS/YyaXR7giMTA4XJ7STto/UAhB05tJ12ToIj6sdUFXNDDZHsAvwlU/BFrTQ3w4QJUFws5zFi4Op20u3xeCgtLe1036Hb5eXl8Y1KCKGrrYXduyEnB0w9LxhsbobK/fqO9BgOF0IMIdGD3cl3SXfyDivezOJHvx9BJKo3TBtX1M5Tt++hICuc4MjEQGJftxJFPdhAbd7nwCgfyMekaZidtUQcOfhLyvCTgssF48dDSYkk3GJg6/ZvvaZpGI2dt5Udum2VYb9CxJ/Ho9dxJyXFVIjtD+jzuBWkjlsIcWK+cDsbGj6kOegB9O7kZVkTMCpxH3IyIKgqPPniMJ795+H+NHOnevn5N/eRmqwmMDIx4KhR7GtXAKApBjzzLk9sPP2VpmFy1hCxOfCXTsVvSKWhHsaNg9LSmAa2CNGv9OhS26uvvkp9fX3H7fb2dhRF4fnnn+fjjz/u9FxFUbjtttviEqQQQ04wqNdxt7fHVMetqnCgCrxeGakhhDi5A221NAc9mA1mZuZOpTB16L5xBEIKdz87itc3ZXbcd+35jdx5bZVMfhA9lvbJO5ibGgBom3o2keyh+7t1IiZXHWqqDX/JVALmdOrrYOxYPek2yu+dGAQUTdO07jzR0MNLTIqiED04i7A/a2lpwWaz4Xa7sdvtiQ5HCD1j/vRT2LNHT7h7+GmjahpbawI4d1vJzlIwyxhQMcBpaPiMAVKjVhRkf2FvUDWNzU3bGWsbNaS3k7u8Jm59bAyb96YBYFA0fnBdFddd4Izr95Gf6aFj+M9vI/1jvZ67asljtJ02J8ERxd+p/jybXPWoSVb846cTTHFQUwOjR+uN0+QcRiSCx+PB4XDg9XrJiGFq0LF0e6W7oqIiLt9QCHESBw7o+8Lz8mK6vOvxgLMR0tLlw0oIcWy+cDtb3buZnj0Zk8GIQVGYlj0x0WEl1O5qK7f8fCw1riQAUqxRfv7Nfcyb1pLgyMRAZXLVk/bJ2wCEs/Jom3pWgiPqf0zNDWiWJPyl0zoS7pEjYdIkOYcRg0u3k+6R0qNfiN7ncsGOHZCeDjH0SggEoLJSb3qelhr/8IQQA1+Nr553Gz8hfLA7+WnZkxIdUsK9syWd7/5iDG1+/UJnfmaIp5bsYfxIf4IjEwOZfd3LKJreA8Az/woYoiP3jsfkcaEZjPhLpxJKz6K2Rt/gN3kyWCyJjk6I+JL2iUL0F+3tsHUrRKMQw7x7VdXncbvdkF4ISK8fIcQRunYnt1NqG53gqBJv2eps/t+fRhBV9W2xk0b7eGrJHnLskQRHJga0SAT72pcB0AxGvWu56GD0NoGm4S+dRsiWQ22N3oNmyhS9f6wQg40k3UL0B5GI3jituRmKimJ6ifp6qK2DrCwIS4mgEOIIR3cnL7WNZsoQ7k4OEFXhZ/8Yzp9ey+u479wZbh75RiUpVrlqKU5N+sfrMXtcALROn0vEkZPgiPoPY6sHJRLWE+7MfGpr9cmoZWWQnJzo6IToHZJ0C9Ef7Nun13IXFMQ0iNLj0beVp6boW7JkgqwQ4pBGv4u36z84uJ1cupMDtAcMfP/p0az+0N5x35c+U8/ti2tkNJGIC/vqlzq+9iy4MoGR9C+GNi9K0I+/ZCqhnEJqayAzU0+4U6UsTgxiknQLkWh1dbBrl/6pE0PXkGAQKir1xXKHA7o1jkAIMWSkmlJR0LeTz86bTuoQ7k4O0Og2c8vSMWyr1M/wjQaNe284wKJyV4IjE4OFubGatC0bAAjlFOKbPCvBEfUPBl8rBr+PwNgphPKKqKsDux2mTtVb2QgxmEnSLUQitbTAtm16sp2W1uPDVVVfIHc3683OhRACIBQNYzHqF/FSzcnMHzabdEvakN5ODrB9fzK3/HwsDW69S1NacpTHv7WXs6a0JjgyMZjY17zc8bVnwRXI9gkw+Nsw+LwEiicTKhhJfb2+sl1WBnGayCREvxbTu8BNN93Epk2bjvv4u+++y0033RRzUEIMCaGQnnC3tUF2dkwv0dAINQe3ZslnuhAC9O7krx5YTY2vvuM+e1LGkE+4132cwXX/N64j4S7MDvK3H+2QhFvEVySM/c1/AqAZjXjmfjbBASWeEmjH2OIhOHoioeHFNDQqWK36CrfDkejohOgbMX0C//GPf2Tv3r3HfbyiooI//elPMQclxKCnabB7N9TW6u06Y9DSApUVetMR6fQphIhqKh+7tvJ2/fuE1DB7W/YnOqR+47n/5PDNpWPxB/WRTVPHtvH3+3cwtjCQ4MjEYJP+wVpMLc0AtJ5eTtSWleCIEksJBjB5mgiMHEdw+BhcTQomk55wZw3tfxoxxPTK9vLa2lqSpf2gEMdXVQV79kBuLph6/msYCumN00Ih/SWEEENb1+7kxUzJGp/YoPqBSBR+8tci/vbfw2+UF81q5qGvVWK1SAcMEX+OIxqouYd4AzUlFMDkbiQwopTgyFKa3AY0TU+4c6SZuxhiun22v3LlSlauXNlx+9lnn+V///tfl+d5PB7+97//ccYZZ8QnQiEGm+ZmfTxYampMszE0Ta/jdrkk4RZC6NvJ3238hLAaxmIwc4Z0JwegzW/g9l8Ws36zreO+r19Wx7cW1ko5jugVlrr9pG57D4Bg/gjaJ56e4IgSRwmHMDU1ECwqIThqPJ4WA+EwTJsW8wY/IQa0bifd27Zt4/nnnwdAURQ2bdrEBx980Ok5iqKQmprK3LlzWbp0aXwjFWIw8Pv1Ou5wOOY67sZGqK7W67iNxjjHJ4QYUNxBL2/Xvw9Id/Ij1brM3LJ0LLuq9H8Lk1HlwS/v5/I5zQmOTAxm9jUrOr72LLgyphGgg0IkjMlVR3B4MYHRE/C2GfH79RXuwsJEBydEYnQ76b7rrru46667ADAYDPzud7/jmmuu6bXAhBh0olHYsUPPmouKYnqJ1jaoqACrVeq4hRDgSLIxJmMkRsXIlKzxQ75ZGsCn+1K4ZelYXF69e3tGaoQnv7OXmRPaEhyZGMyUUBDb+n8BoJrMeOdcmuCIEiQSweyqJTRsNIHiSbT6TbS16V3KYzz1EWJQiKmmW1XVeMchxOBXUaEXYhcUxNRqPBzWG6cFAjIeTIihrMZXT2aSnWSTFYDp2ZNRhuqK2lH+976d7z89mkBIf48dkRfgmdv3MKogmODIxGCX/v4aTG1eAFrPOJdouj2xASVCNILZWUs4r4hA8UTaAma8Xpg8GUaOTHRwQiSWzOkWoi80NMDOnfpsDIulx4drmt57zemEHKnjFmJIimoqm5u2s9tbQW5yNnMLZmFQFEm40d8jf/9qHkuXFaJp+r/HjHGtPPmdvTjSowmOTgwFjtUvdnztPndhAiNJEE3F3FhLOKcQ/9gp+CJJuN0wcSIUFw/dnfZCHBLzPrTXXnuN888/n6ysLEwmE0ajscsfIQT6HO7t2/XV7fT0mF7C5dLruB0OMMmvlhBDTlu4nTU1b7PbWwGA3ZIBSPdtgHAE7v/9CH7+j+EdCfdnz27id3fuloRb9AlLzT5Sdn4EQHDYaPyl0xIbUF9TVUyeJiLZ+fhLpuDXrLhcMH48jB0rCbcQEONK94svvsiiRYuYNGkSV199NU8//TTXXHMNmqaxcuVKSkpKuPzyy+McqhADUDisJ9weDwwfHtNLtB2s4zab9VpuIcTQUt1Wx3vOTwirESwGMzNzpzEsVWpMAFp8Rm77RTEbtmZ03PethTV843P1cqIv+ozjiAZq7vIh1kBN0zA762jPseEfPYWAkkJDPYwbByUlMVXTCTEoxZR0P/zww8ycOZO33noLt9vN008/zU033UR5eTmVlZWceeaZjB49Ot6xCjGwaJo+i7uqSm/XGcOHcDgCFZXQ3i513EIMNUduJwfISrJzpnQn71DdaOEbPx/Lvlp99KLZpPLQ1yq5ZLY7wZGJoUQJBbCt/zcAqjkJ7zmXJDiiPqRpmJw1RGx2gsNGoBhTqa/XV7fHjZMJK0IcKabrT9u2bePqq6/GaDRiMul5ezgcBmDUqFHccsstPPLII/GLUoiBqKYGdu+GnBww9fz6lqbpW8qdjZAV23QxIcQApmoq9e2NAIyzFbOg8CxJuA/6eHcqV98/viPhdqSH+cNduyThFn0uY9P/MLa3AtAy63zU1IyTHDF4mFx1qKk2/GPLiJiTqa+H0aNhwoSYTnuEGNRi+pVISUnBcrAZlN1uJykpibq6uo7H8/LyqKioiE+EQgxEHo++rdxqhZTYTpJdTVB1AOx2qeMWYigyG0zMzptBe8Qv28mP8NpGB3c9O4pQWF83KB7m56klexiRF0pwZGIosh/RQM1TfmUCI+lbJlc9qjUFf+lUQlYbTbWNjBgBkybp5XBCiM5iWukeN24c27Zt67g9bdo0nnvuOSKRCIFAgL/97W+MGDEibkEKMaAEArBtG/j9kJUV00u0t+vjwUwmSE6Oc3xCiH4pqql85NrKTs++jvvsSRmScB+kafDMy/nc/qvijoR71sQW/nrfTkm4RUIkHdhNyp4tAASKSvCPnZLgiPqGqbkBzZKEv3QaoVQHdXX6pr5Jk2Ia0CLEkBBT0n3FFVewcuVKgkF97uUPf/hD1q5di91uJycnh/Xr1/ODH/wgroEKMSCoKuzaBfX1kJ8f00tEInrjtLY2fZVbCDH4HdmdfEvTdtoj/kSH1K+Ewgp3PzuKJ18s7Lhv4TwXz35vN7ZU6VAuEsO++qWOrz1DpIGayeNCMxjxl5QRzsiipkY/3RkxApKSEh2dEP2XomlaXGaOrF+/npdeegmj0cgll1zCggUL4vGyva6lpQWbzYbb7cYuGY44Vfv2webN+iXfGFuNHzig91/LyY1tW7mGhs8YIDVqRWHwnwCIwW+w/0xLd/IT87Qa+fYTY3h/5+GRi7cvruamSxoGbI4z2H+mhwIl0E7Jty7GGPChJiWz+xevoSanJTqsXmX0NqGoKu2l0whl5lNTA7m5MGWKis/XSG5uLgZpVy4GAY/Hg8PhwOv1kpERnz4NcWtzMGfOHObMmROvlxNi4HE6YedOyMiIOeFuaoL9B8BmkzpuIQa7rt3JHQe7k0tNySGV9Unc/POx7K/X31OTzCqP3FzBBWd4EhuYGPJsG9/AGPAB4J194eBPuFs9KJEw/tJphLPyqa3RK+jKyvTWNT5foiMUon+T3oJCxIPPp9dxq6qeMcfA79e3lSvE3HtNCDFAqJrGutoNuAJ6t+1x9mKmZI7HoMgq0SHv70jjW0+Mwdumn6pk2cL86rY9lI1pT3BkQhy1tXzB4G6gZmjzogT9+EumEsoppK5OL3+bOhXS0vRTHyHEicX06a5pGr/+9a+ZOXMm2dnZGI3GLn9MMitADBWRCOzYAc3NMQ/Tjkahcj+0tkJmZpzjE0L0OwZFoTC1AIvBzDn5ZzA1a6Ik3EdY+VYmN/2kpCPhLhnu5x/375CEW/QL1ortJFfoDYX9oycQKJ6Y4Ih6j8HXisHvIzBmMuH8IurrITVVX+GO065bIYaEmDLj73//+yxdupRp06Zx3XXX4XA44h2XEAODpsHevbB/PwwbFnMTldo6qK+D7Owh0YdFiCEpqqkEIoGOWdulttGMSBtGsim2cpTBSNPgly8V8PTLwzruO6fMy9Jb95GWLMtpon8YKqvcBn8bxjYv/jGTCRWMpKFBr56bOhXk1F+Inokp6f7Tn/7EwoULWb58ebzjEWJgqavTu5VnZcU8mNLthv2VkJ6ujwgTQgw+bWEfGxo+JKJGOG/4HMwGE4qiSMJ9hGBI4Ye/GcWrGw9v97n63Ebu/mKV9LgQ/YbB34Ztw+sARK2peGdfmOCIeocSaMfodRMYM4nQ8GKcLgWTSU+4Y5yGKsSQFtMpvt/v57zzzot3LEIMLF4vbN+uD6VMi62Bij+gNzzX0LdrCSEGn6O7k7eG2si02hMdVr/S3GLi1sfG8PEe/b1UUTTuvKaaL17YKLt/RL+S8fbrGIL6SL+Wsy9Gsw6+JixKMIDJ00Rg1HiCw8fQ1Kz/EpaV6cNZhBA9F1MB2bnnnst7770X71iEGDhCIT3hbmvT94THIBqFA/uhpQUyZZuWEINOVIvyketT3mn4gLAaISvJwQVFcyXhPsreGitX3z++I+FOToryy+/u5fqLJOEW/Yym4VhzeGu5exBuLVdCAUzuRgIjSgiOLMXTYiAc1hPu/PxERyfEwBVT0v3UU0+xceNGHnroIZqamuIdkxD9m6bpW8prak7pE6iuDmpr9cZpMtZSiMGlLexjdc077PZWAnp38gWFs0kxyTiwI23Yms41D46j2pkEQK4jxHP37GTBdG+CIxOiK+verVgP7ALAP2YywZGlCY4ovpRwCFNTA8HhYwmOGo+31YDfryfcw4ad/HghxPF1a3t5eno6ylGXmyORCPfeey/33nsvVqsVo7FzwZWiKHi98qEpBqGqKr15Wn5+zEXYHg8cOKDvSrdY4hueECLxPmnajjvoxWIwMzN3GsNSY5tsMJi9sDaLB/84kkhUP7+YMLKdp5bsIS8znODIhDg2x+oXO752ly9MYCS9IBLG5KojOLyYwOgJtLYb8flgyhQoKkp0cEIMfN3KGBYuXNgl6RZiSGpq0reVp6XpLTxjEAxCRaW+vVy6fwoxOM3IngLAadmTZHX7KKoKjy0v5HevHN4ptOA0Dz+9pYJUq3QoF/2TwddCxqY3AIimpNEy6/wERxRHkQhmVy2hYaMJFE+iLWDC64XJk2HkyEQHJ8Tg0K2k+49//GMvhyHEAOD3w7Ztej13jHXcqqqvcLtjH+kthOiH2sI+qn31jLePAcBqSuLs/NMTHFX/4w8q/OCZ0fz3/cNXHG+4qIE7vlCNUcpsRD9me+tVDKEgAN6zL0FLGiSTB6IRzM4awnlFBIon0h4y43bDpElQXCxjTIWIl5g+4h588EE+/fTT4z6+detWHnzwwZiDEqLfiUZhxw5wOk+pjruhQS8FlzpuIQaP6rY6/lu9ns1N26lqq010OP2W02Pihh+P60i4jQaN+27Yz53XSsIt+rmjG6iVD5IGamoUc2MN4ZxC/GOn4FeTcLlg/HgYM0YSbiHiKaaPufvvv5/Nmzcf9/FPP/2UBx54IOaghOh3KiqgshIKCmLOlr1e/SVSUiApKa7RCSESIKpF+fDI7uRWB1lWqRk5ll1VVhbfP4FPK/TZiKnWKE/dvoerz3MlODIhTi551yck1ewDoL10GqHhYxIcURyoKuaGGiLZBfhLphDASmMjlJZCSYksDAgRb7F1gTqJ5uZmLNIdSgwWDQ2wc6degB3jz3UopCfc4TDY7XGNTgiRAG1hHxsaPsQd1BuGjrOPYUrmOAyKnKke7c1PMrj9l8X4AnrD1YKsIE/fvofSokCCIxOiewZdAzVNw+ysJeLIwV9SRtCQQn0djB0L48bBUb2RhRBx0O2k+80332Tt2rUdt1966SX27NnT5Xkej4dly5YxZcqUuAQoREK1tsLWrfol3/T0mF5C02D/Ab0HW25unOMTQvS5Gl897zZ+TFiNYDGYmZU7jQLpTn5Mf/tvDg89V4Sq6ftUpxT7+OVte8ixRxIcmRDdY2z1kP7eKgAiaTZazyhPcESnSNMwOWuI2Bz4S6cSNKVSV6vXb0+YEPNQFiHESXT7V2vNmjUdW8YVReGll17ipZdeOuZzJ06cyC9+8Yv4RChEooTDeqfylhYYPjzml2logJpqvY5brh4LMfApKB3byWfnTZfu5McQVeGnfxvOc/85fDHigjPcPPz1CpKTtARGJkTP2N76N4ZwCADvnEvRLAO7PszkqkNNteEvmUooKZ3aGr1D+cSJYDYnOjohBq9uJ93f//73ufXWW9E0jdzcXJ555hkWLuy8xUZRFFJSUrDGOEpJiH5D02D3bqiuhsLCmLuJtLbq48GsVqnjFmIgUzW1Y+v4sNQ8zsk/g/yUHNlOfgy+gIHv/Wo0az+2d9z3lUvr+O5VtVInKgYWTcO++vACk2fBFQkM5tSZXPWo1hT8pVMJJ9uoqdZncE+eHHP1nBCim7qddCcnJ5OcrF/Nr6ioICcnh5SUlF4LTIiEqqmBPXsgJyfmvVbhsN5/LRiQ8WBCDGRVbbVsbtrBgsLZHavaw2Q7+THVN5u5ZelYduzXzw9MRo0ffWk/C+c1JTgyIXouZfsHJNUfAMA34XRCBaMSG9ApMDU3oFmS8JdOI5zmoKYahg3TE25ZFBCi98WUTYwcOTLecQjRf7jd+jzu5GS91XgMNE2fx+1ySR23EANVVIvyiWsbe1r2A7DDs5fp2ZMTHFX/ta0ymVuWjqXRrS+ZZaREeOI7e5k1sS3BkQkRG3unBmoDd0yYyeNCMxjxl5QRzsiipkZfDCgr0091hBC9T9olCHGkQEBPuAMBfVt5jJxOfWe6Q+q4hRiQ2sI+NtR/iDukdycfbx/D5MxxCY6q/1r1gY3vPzUaf0h/wyvKDfL07bspHhZMcGRCxMbobSbj/TUARNIdtJ6+IMERxcbobQJN01e4HbnU1kBWlp5wy4ZVIfqOJN1CHKKqsGOH3vnsFBqntbXp28otFrDKli0hBpyqtlred26W7uTdoGnw59dz+enfh6Md7FA+vbSNX3x3D470aIKjEyJ29vX/RInqXfY98y4D08DrMmZs9aBEwnrCnZVPba0+tnTqVEhLS3R0QgwtknQLcUhlpf4nPz/m5elwRG+c5vdLHXdfKXzyTpovvhZ/SRmoKnl/+Rlpn7wNKDRf9AXc5y8+5nFFj3wTk7cJFAOqNYX6L95BcNR4AMz1Bxj26/sxtnlQk9Oo/dqPCA0fc9JYenycpjHi4Zux7t/Brl+v7bg77aP15P79cRRVJVA0lrqv/Qg1OQ2jt4mipbdRed/vwShv373hQGsNGxs/AiDb6uBM6U5+XJEo/PjPI1i2OqfjvktmN/H/vrKfJIt0KBcDmKpiX7Oi46Zn/sBroGZo86IE/fhLphLOLaS+Tp98OnUqZGQkOjohhh7pIyoE6PvBd+7UP4li7CiiafqWcmcjZGXHOT5xTNa9n2L0tegJN2B751WSairY++hLVDzwJ7JeeQ5L9d5jHltz60+oeOgfVPz4bzRffC3Dnn2g47GC3z+EZ8EV7Hv0JZouvb7TYyfS0+MyX/8robzOuyqUQDsFv/0/qr/7c/b+bAURezbZL/8OgKgtC3/JVGxvvdKteETPDUvNx27JYLx9DPOHzZaE+zha2w3c/POxnRLuW66o5ac3V0rCLQa81K3vYmmsAaBtypmE82Lf/ZYIBl8rBr+PwJjJhPOLqK/Xa7fLyvSVbiFE35OkWwifT6/jVlWw2WJ+GVcTVB3QP9BMUsfdJxyrX8I7+8KO2xkb/4tn/uVgMKKm2WiZdT62Df855rFqanrH14b2to6xcEZvM9aK7XjPvhiA1jPOxdzcgLmh6oSx9PQ4S/Ve0j9YR9OlN3a6P+2TdwiMHEdo2CgA3OddRcYRfwfvmRfiOGKEjTh1jX4XqqYniiaDkXMLz6Ysa4KMAzuOGqeFax8cz9tb9PdLs0nlkW9UcOuVdbFOVxSiXzmygZpnwcBqoGbwt2Fs8xIYPZFQwUicTn3+dlmZXssthEiMmPcnbt++nT/84Q/s27cPt9uNpnW+sq0oCqtWrTrlAIXoVZGIXsfd3KwPq4yRzwcV+/QPNukE2ndSdnxA80XXdNw2NdUTzi7ouB3OGYZ1z5bjHl/wzH2kbv8AgKo7ngDA3NxAxJ51ePu2ohDOysPsqiecd/yfkR4dF4lQ8LsfU/eVezl6cLG5qZ5wdv7hv0P2MEweF0QjYDQRGD2epKo9GPxtqMlSlHcqomqUT5r07uSTM8cx0VECgNEgV82O55M9Kdz62FiaWvT6VntahCe/u5fTx0mHcjE4mNxO0j98E4CILYvW0+YmOKLuUwLtGL1uAmMmERpeTFOzfhWsrEyfgCqESJyYku7nnnuOL33pS5jNZsaNG4fD4ejynKOTcCH6HU2DvXth/359WGWMSzSRiF4K7muHPBkP1qdMzY1EbLFfuq/7xoMA2Nb/m9x/PEnV956MV2gnlLPiWVpPX0CocDRmZ23PDjaaiKamY3K7CEnSHbPWsI8N9R/gCbUAegIuTuz1TXbu+vVogmH9QtGo/ABP37GHkXnSoVwMHrZ1K1EOvh945l8OpoHRP0MJBjB5mgiMGk9w+BjcHoVIBKZN01vVCCESK6Z3kvvvv5/TTjuN1157jexsKV4VA1RdHezaBdnZ+hJ1jKqr9YbnObkx5+0iRprFihI+fMIfycrH7KrrqPE2O2uJZJ38bMM751Ly//AwxlYP4cw8TJ6mjpVlNA1zU0On1edj6clxKTs+xNxUj+N/y1GiUQx+H2Nu+yyVD/yZcFY+qZ9u6niu2VVLxJ7dqXGaIRxCtUhr/FhVtdXyXuNmItrB7uR5p1GQIlfMjkfT4Df/yufx5w+PUZw5oZXHv70Xe5pcrBCDiBrFsfZlADRFwT3/8oSG011KKIDJ3UhgRCnBkaV4Ww0EAnrTtGHDEh2dEAJirOmura3lpptukoRbDFxer17HbbFAamrML+NyQVW1Xgouddx9L1BUgqVuf8ftlpnnYV/7MqhRDG1eMjb9l5YzL+hynMHXisnt7Lid9v5aomk2/Y8tk8Cocdjefg2A9PdWEc7M7dgiXvDMfaQfnN16pJMdd6T99/6WPY//m72P/Yv99/4WNTmVvY/9i2iGA1/ZbKyVO7DUVgLg+N/znf4ORm8TmqIQyZT2+D0VVaN84NzChoYPiWgRsq2ZXFA0VxLuEwhFFO757chOCfflc1w8+/3dknCLQSdt8wbMTfUAtE09m8gR5Ur9lRIOYWpqIDh8LMFR42n1GfD5YMqUU6qaE0LEWUwr3WVlZdTW9nBLpBD9RTCoJ9w+3ynN4/b79W3lBgVSUuIXnui+1pnlpG3ZSPvkWQB4z/kM1n3bGHPHlaBA08XXEiwaC0Dah+tI//BN6r5yLwZ/G8N/cSdKKAiKgWiGg6rbH+vYqlB/090UPPsAWf/6A2pyKnVf/VHH90yu2I77gquPGc+Jjiv47f/ROn0ubdPnnfDvpCanUveVexj++O0o0SjB4WOo/frhLuhpmzfQNmN+l1pwcXJtYR8VrXpju/H2sUzOLJVmaSfgaTPy3SfH8O72w00Hv3tVDV/9bL3s6hGD0oBroBYJY3LVERxeTGD0BNr8RrxemDwZRoxIdHBCiCMpWgzF12+//TZXXXUVL7zwAmeddVZvxNVnWlpasNlsuN1u7DJHYfBTVdi6FXbv1hPuGOdxR6P6zvT6en0ed386AdXQ8BkDpEatKPSjwHqBEmhn1IM3UXnfH9Csvd/BztjiZthTP6TqB0/1+vc6npH/9xXqbvohocLRCYuhr8XzZ7qytZoko0VWt09if0MSN/9sLJX1VgCSzCoPf72Ci2Z5EhvYIDGU3qcHClNTPWNvuwxFUwln5rFn6cpOZT39TiSC2VVDqGA0/rGTaQ+ZaWqCSZNg7Ni+PS9RVZXGxkZyc3MxyAVhMQh4PB4cDgder5eMOA22j+nd5JFHHsFmszFnzhwmTpzIiBEjMB6VvG4sfuMAANOoSURBVCiKwsqVK+MSpBBxU1UF+/bpmXKMCTdAba1eEp6T078S7qFGs6bQcO0SLM6ajhXt3hTNcCQ04TZ6m3Cf+/khlXCfCr07+XZGpQ8n02oHYFT6wJq3mwgf7EzlW4+PxdOmnyJkZYT55W17mTrWl+DIhOg99rUrUTQVONhArT8n3NEIZmcN4bwiAsUTCUT0hHv8eBgzRs5LhOiPYnpH2bx5M4qiMGLECNra2ti2bVuX5yjyGy/6m6YmfTxYWhpYrTG/THMzVO6HjIwB09R0UGufNDPRIfSZqC2LlrMuSnQYA8KR3cnr2hu5aMR8jLKV/KT+9XYm9/x2JOGI/m81ptDPM7fvoTAnlODIhOhF0Qj2dS8DoBmMeOZ9LrHxnIgaxdxYQzinEP/YKfjVJBobobQUSkqk8kiI/iqmlKGysjLOYQjRy9rb9TrucFjvVh4jfwAqKvSvT6H/mhCiFx3ZnTzJYGF6zmRJuE9C0+BXKwp4asXhVsdnTW7hsW/tJT1FTWBkQvS+tI/fwnywuWbbtHOIZPbT8hNVxdxQQyS7AH/JFIKKlYZ6fTv5+PGntIFPCNHLZJ1ODH7RKOzcqbcaP4XGadEo7K+ElhZ9d7oQon+JqlE+btrG3ha9o322NZMz804jxdT79f4DWTCkdyh/ZcPhmfeLyp388IsHMMtZghgCHKtf6vjafe7CBEZyApqmj8F05OAvKSNkTKGuFoqLYeJESbiF6O9O6eN03bp1vPLKK+zfr5/gjBw5kksuuYR5807cnVeIPrVvn95mPD//lPZd1dVBXT1kZUm9lBD9TTAaYl3tRjyhFgAm2McySbqTn1Rzi4lvPzGGD3elAaAoGt/7QjU3XNQo73NiSDA31pC6ZQMAoexh+CafmeCIjkHTMDlriNgc+EunEjKnUlsDI0fqCbeUugnR/8X0axoKhfjCF77Ayy+/jKZpHV2/PR4PP//5z7niiiv4+9//jtlsjmesQvRcfb3eZjwzU5/JHSO3B/bvh7RUkB9rIfofi8FMssmKPxJgVt408qU7+Untq03i5p+XUNWYBECyJcojN1dw3uneBEcmRN+xr30Z5eAgH8+Cy/tlUbTJVYeaasNfMpWwNZ2aan0G9+TJp3RqI4ToQzG9szzwwAOsWLGC22+/nbq6Opqbm2lubqa+vp477riDl156iQcffDDesQrRM62t+ngwo1FvnhajQAAqK/RpY6fwMkKIOIuqUSJqBNCbd87Mncb5RXMk4e6GjVvTueaB8R0Jd449xJ/v2SUJtxhaIhHsb/4TAM1oxDP3sgQH1JXJVY9qTcFfOpVwso2aGhg2TE+4k5ISHZ0QortiSrr/9re/ccMNN/DTn/6UvCOKW3Nzc3nkkUe4/vrree655+IWpBA9Fg7rjdNaW0+pcZqq6ivcbre+WC6E6B9aQ22sqnmbD5xb0A6uUiUZLVK/3Q0vvZnF1x4toaVd3+w2bkQ7yx7YwaTR7QmOTIi+lf7hWkzeJgBap88nao/9fKE3mJob0CxJ+EunEU5zUFur95QpK4NkeasTYkCJKemuq6tj1qxZx3181qxZ1NfXxxzUr371K0aNGoXVamXWrFm8++673TruH//4B4qicPnll8f8vcUgoGn6lvLaWigoOKUC7IYGqK3T67j74Y4zkShqlJTt75Ox4XVStr8PajTREQ0pB9pq+W/1W3hCLdS3O/FHA4kOaUBQVVi6bBj3/GYUkaj+vjhvmoe/3LOT/MxwgqMTou91aqBWfmUCI+nK5HGhGYz4S8qI2LKordXXEMrKICUl0dEJIXoqppru4cOHs3btWr7xjW8c8/F169YxPMYu0cuWLWPJkiU888wzzJo1i8cff5wLL7yQnTt3kpt7/C2DlZWV3HHHHcyZMyem7ysGkepq2LMHcnJOqbuI16v3X0tNkZopcVj6e6vJ+8vPMDc3dtwXzsyl4bo7aD2jPIGRDX5RNconTdulO3kMAiGFHzwzmjfec3Tcd90FDdx5bTVGuaAohiBzQxWpW/VFnVBeEe0Tz0hwRIcZvU2gafoKtyOX2lqw2/WEW8rchBiYYvqoveGGG1i+fDnf+MY32LlzJ9FoFFVV2blzJzfffDPPP/88N954Y0wBLV26lK9+9at86UtfYuLEiTzzzDOkpKTw+9///rjHRKNRrr32Wh544AGKi4tj+r5ikHC7Yft2/TLwKey9Cgb1edzhMKSnxzE+MaClv7eawie/j+mIhBvA1NxI4ZPfJ/291QmKbPDzhdpZXfNOR8I9wT6W+cPOlIS7G1xeEzf8eFxHwm1QNH54/QHu/qIk3GLo6rTKveCKfrOdzdjqQYmE8Y+dQiQ7n/p6/Txk6lTIyEh0dEKIWMW0DHj33Xezd+9enn32WX7zm99gOPhGpaoqmqZxww03cPfdd/f4dUOhEB988AF33XVXx30Gg4HzzjuPDRs2HPe4Bx98kNzcXL785S+zfv36nv+FxOAQCOh13MGg3mUkRqoKB6qguRlOsLlCDDVqlLy//AyAowsWFEAD8v7yc1pnzAODDEyNJ03TeK/mY9rDfpIMFulO3gO7q6zcvHQstS6941KKNcrPv7mPedNaEhyZEImjhEPY1v8LANVkxjvnswmOSGdo86IE/XqX8txC6uv19YOpU/WVbiHEwBVT0m00GvnjH//IkiVLePXVVzvN6f7MZz5DWVlZTMG4XC6i0Win5mwAeXl57Nix45jHvPXWW/zud7/j448/7tb3CAaDBIPBjtstLfqJh6qqqKoaU9yiH1BVfYW7oQGGD9frumNU36DvUHdk6rlT7K+UGNoR/xPxk7Lzo05byo+mAObmBpJ3fkT7hBl9F9hQoMCk3HFUNB3gzLzTSDZZ5ee7G97anMHtvyymza9/1OdnBXlqyR7GjfDLv16Cyft0YqW/vxpTqweA1jPKiWTYSfSnvcHXiuJvwz9mCqG8QpyNKiYTTJmiJ9z9+RT10KKbnEeLwaI3fpZjL3gFysrKYk6w46G1tZUvfvGL/OY3vyG7mx2qH374YR544IEu9zudTkKhULxDFH2lvl4vwHY44BT+O7a3w4EGMDggkgyR+EXYZzQ0gka9KZLSZU1WxCqppa5bz4u21OEzSmOvU+ULtdMe9pOTmoWGRnpGOqenTEVVwIf8+57Mi//L52d/HENU1d8Dxo9uZekd28h2hPElODYh79OJNnzNCx1f1513ScLfs5VQAKPqIzh6BBGHlZZG/QLvyJF6st14/Ou9/YKqqni9XjRN69j9KsRA5vXGf3zmKSXd8ZadnY3RaKShoaHT/Q0NDeTn53d5/t69e6msrOSznz28LejQlQmTycTOnTsZM2ZMp2PuuusulixZ0nG7paWFoqIicnJysMvenYHJ6YSaGrDZTqkAOxiEuipQWvQebAzQhtSHVk5So1Y5mYsjY0ZBt56X4vURjFp7OZrBraqtlvcbNwMK5xedQ6pZb9WbqsrP9MlEVfjZ34fz59cPf2aed7qbh79eQYrVCFEpfegP5H06cSw1FWRs/xiA4LBRqCVnkhpN3H8DJdCOydNGYMxEooVjaPUoaBpMm6YPYBkIVFVFURRycnIk6RaDgqUXOih3K+k2GAwYDAba29uxWCwYDAaUk4xhUhSFSKRn64QWi4UZM2awatWqjrFfqqqyatUqbr311i7PHz9+PFu2bOl03z333ENraytPPPEERUVFXY5JSkoiKSmpy/2H/o5igPH59G3liqIn3TFSVag6AO6DddwD/RRIOeJ/Ij78405DtVgxhE68IpL/3M+wNDXgXPgNNEvX9xpxfFE1ysdN2zp1Jzcqxk4/z/IzfXy+gIHvPz2aNR/aO+676TP1LFlcc7BHlPzb9SfyM50YjjUrOr52L7gSRUncuZ8SDGDyNBMYNZ7Q8LF4WwwEg3oNd2FhwsKKiaIoci4tBo3e+DnuVtJ93333oSgKpoPjlw7d7g1Llizhhhtu4PTTT2fmzJk8/vjj+Hw+vvSlLwFw/fXXU1hYyMMPP4zVamXy5Mmdjj+0Wn30/WIQikT0xmkej17HfQrq6qCmFjIzwSgLQeIY0j9Y15Fwa3ROXw5VAioH/2S9+hxpH6+n9mv3Exgj70Xd0RpqY0PDh3hCep+NCfaxTMosxaAYpO61GxqazdyydCzb9+u7AowGjftuPMBVC1wJjkyI/kMJBbC/9QoAqtmC95xLEhqLyd1IYEQpwZGltPoMtLfrY8GOsWYkhBjgupV033///Se8HU+LFy/G6XRy3333UV9fz7Rp03j99dc7mqsdOHBArqIJvVHanj1QVaVfDj6Fi0Buj14OnpYKx9gEIQSm5kbyf//jjttqagZG3+Huz5HMPBquvQ2zs46cF5/GEA6RVFvJqAduoumzN+C6/KtoZhn2fjwHWmt437mZiBY92J38NPJTchId1oCxfX8yt/x87P9n777joyqzBo7/pqb3npCQBBJ6QFSwi11cd+2r2MCKur7qWlZsCDbcXV3XDtiwY19XVywoKnYFK0ozhZBJm0mmZvq97x8XBqKU9Ek53/2w3rkzc+cEJjP33Od5zqGxVXuPJcWHuOf/KtlvvCvKkQnRvyR/tTzy2e2cegRKYtdnyHWHLhjAaGvEX1iGv3g07jY9DgeMHw9FRVEJSQjRy3Sq2vkyz7fccgsnnnjiTkeT16xZwyuvvMLcuXO7HWBvczqdpKSk0NraKmu6B5K6Oli1SivpmZDQ5cN4ffDzGmjzQmZGz4UXTSoqHoNP1gr2FEWh6B+XkrDmKwCcex9K3V/uIH79dxjtVkKpmbSN2iPSJsxcV0n+4nnEVf4cOYSvcCSWC+fhLx4dlR+hv/vOuob1jiqyYtPZJ2cyccb2a+LlPb1zH36bwlUPluD1a++/gkw/D1+9kZEFUmyuP5P3dHQMv+Vc4jf8AED1TY/hLZ/Y90GEgpiaLfiHleIrHU9bwEhLC4wdCyNHdmsMIWoURaGpqYns7GwZGBODgt1uJy0tDYfDQXJyco8cs0u/GfPmzeOHH37Y6f0//fTTDiuEC9EjHA5tHXdMTLcS7nAYaqrB6YT0tJ4LTwwu6e88F0m4g2nZ1J97AxiMtI3ZC+e+R9M2Zq92fbkDBaVUz32cppMvRjVok4liazdSMm8mma8u1pZFCLa/3jshYwyTM8dzcP4+v0u4xY6pKjz9TjaX3jMiknBPHOlm6by1knALsQMxtRsjCbdv2Ai8ZVHovhMKYbJaCOSX4Csdhy9kxGaDUaNgxIiBmXALITqmVy5HtbS09ErVNyHw+7V13B4PdLBN3M5YLNpa7owMkAuzYkdiataT9eKDkduW2fM6Nh3RYMR23HlU3fI0vuHlAOjCYbJeW0zx/FnE1G7srZAHhE2uOlY2fIWiat0mDDo9I1OK0UexoNFAEgrD7U8VsuCZQhRVO0ufPrWFJ65bT0aKXNQRYkdSP3glsm0/9KS+z3DDIUzNdQSzC/GVjsUXMtHUBGVlUF4u5yFCDHYdbhn28ccf8+GHH0Zuv/rqq2zc+PsTR7vdzgsvvMCECRN6JEAhIhQF1q/XMuVuFk6z2aC6RuswZjL1UHxiUNEFfBQ8fCP6kNZL1zb9TNrGTenUMfxFZVTNe5LM1x8n87+Po1PCxFWvpXjuWVhPuBDbH84CQ7/q3NirQkqY72xrqHRuAqDSWcvIlOFRjmpgcXv1XPVAKSt/2Hbx56Lj6rn0RIuctAuxEzqfl5RP3wJAMcfi2P+Yvg1ACWNqqiOYVYB35AT8xNDYqCXco0dLwi3EUNDhs70VK1ZEpozrdDpeffVVXn311R0+duzYsdx///09E6EQW9XWQmUl5OR0q8R4WxtUVoFe163Z6WKQy156PzF1lQD4isppPuWSrh3IaMJ60mzckw8ib9HNxNZVog8FyX7pQZJWfYhl9nwC+cU9F3g/9bvq5GlllCZLid7OsFi1CuXra7UK5UaDwi3n1XD8gS1RjkyI/i35i3cweD0AOPc5EiU+se9eXFEwNdYRyszDWzaBgD6WeguUlsKYMdIxRYihosPX1v72t7/R3NxMU1MTqqqycOFCmpub2/2xWq20tbXx008/MXXq1N6MWww1Vqu2jjsxEWK7vuYzGIKqKnC7IE3WcYudSPj+U9LfewEAxRRD3SW3dbv6uK9kDNW3PoP12FmoW6ZRx1WuoeTGM0hf9gwo4W7H3V9tctXx3uaV2ANOYvRmDsqbyoT0UTKdvBN+rIzn1HljIgl3ckKIx67dIAm3EB2QtmLbIFHrYSf13QurKqZmC6G0LLxlFQSN8VgsUFysFU4zDp2JTkIMeR3+dY+LiyMuLg6AqqoqsrOzI7eF6FVtbbBmjVaAKqvrbYRUFTZvhqYmyMySgiVixwyOFvIfuSVyu2nG5QQKSnvk2KrJTPOpl+La82DyF88jpr4GfdBPznP/JumbD7FceDPBnME1+vtL60Z+bFkLsNPq5GLX3vs6lWsXluALaBcpinJ8LLxqI8V5/ihHJkT/F1u9NtJNwjd8FL6SsX3zwqqKsbmOUHIq3vKJBEwJ1NVpPbjHjQMpfSTE0NKlYQZFUVi+fPlO73/jjTeorq7uakxCbBMOw9q10NKiTSvvBqsVNtVASgoYZTqX2BFVJe/RWzE6bAC4J+5P6+Gn9PjL+EZOoOq2Z7FNPwN1y9Wf+PXfUXr9DNLee0GrXzBIFCTkYtQZGZtWJtXJO0lV4bE3c7j8vhGRhHvPUS6W3rxWEm4hOmj7AmqtfVhAzWitR0lIwVs+iWBsEhYL5OfDhAla8xUhxNDSpaT76quv5r777tvp/Q8++CBz5szpclBCRFRWQnU15OV1q9KIy60dymwGmaAhdib1g1dI+m4lAKGkNCwXzO21EzTVHEvT6X+l5obFBLK1woD6gI/cp/5J0Z2XYGq29Mrr9gVnwBXZTjYncszwQxgv08k7JRiCmx8v4u4XthWN/NP+Nh67dgOpSYN3KYIQPUnv9ZD8+TsAhGPjce57VJ+8rtHagBIbj7d8IqH4FCwWyM2FiopurZATQgxgXToD+vzzzzniiCN2ev9hhx3GypUruxyUEAA0NMC6dVpPr26UGA8GoboKvF5ITe258MTgYrZUk/PcPZHb9RfMJZyS0euv6x21B5W3P0/LdiPqCb98Q8n1p5H6wavacOcAEVLCfNP0A+/Ufkyz1xbZH2uQYZ3OcHoMzL6rjJc/3Lac5rKT6lgwuxqzaeC8H4SItuTP38bgawPAud/RKHG9Xz3V2NKIao7BWz5Ju3hr0TqcTpgA8fG9/vJCiH6qS0l3a2srSUlJO70/MTERm8220/uF2C2nU1vHbTRqxdO6SFVh0yZoboaM7rX1FoNZKEj+wzeiD2hTdlsOOwX3Hgf22cursXE0zryWmjkPE8jMA8DgayPviTso/OdlGFsa+yyWrnIG3Lxf9wmVrk2oqLT4HdEOaUCqbTJz+i2j+GJNMgBmk8I/L6nkouMbpA6FEJ2hqqRtP7X8kBN7/SWNdiuq3oC3rIJQSgYWi3axv6KiW6cyQohBoEtJd1FREZ9++ulO71+5ciXDutlHWQxhgYBWqdzt1i4Pd0Njo9ZpLD1d1nGLnct6ZSFx1VqxL39+MU0zLo9KHG3j9qbqjudpnXZCZF/ij59Tet2ppHz8Rr8d9a5x1bF880ocARcxBjMH501lVGrPFJ8bSr5dn8CMeaOptGhrYNKSgjwxZz1/2Lc1ypEJMfDEVq4htmY9AN7ScfiLR/fq6xkcNlBVvGUTCaVnU18PSUkwcSIkJ/fqSwshBoAuJd0zZszg+eef57777kPZruBPOBzm3nvv5YUXXuD000/vsSDFEKKqsGED1NVpC6C6MbTjdGrtweLipGiJ2Ln4X74h439PAaAajNRdfBtqTPQW3SlxiTScdwObrrmfYFo2AIY2N/mPzGfYv67EaLdGLbbf2jqd/MumbwmpYbJiMzhy2EHkxHe9y8BQ9dbnaZxzZzktLm0pTWm+l6Xz1rJHuSfKkQkxMKV9sF2bsEN7d5Tb4LKjCwXxjpxAKDOXhgZtKvnEibKsTQih0alq54dO/H4/f/jDH/jggw/Iyspi1KhRAKxbt47m5mamTZvGsmXLiBkAmY7T6SQlJYXW1lZS5ZMx+mprYfVqbR13Nyqe+f3aYLnT2a0uYwOSiorH4CMhHIsOmY+6K3qPk9LrZ2DaMn278bTLaPnD2VGOahu9x0XOM3eT+smbkX3hhGQaZv4N5z5HRb3vXY1rM182fQfA2LQyxqaVo++FmAbze1pVYdHrudz3SkFk3z7jnPz7/ypJTpCCaYPVYH5P9wd6j4uyy45GH/ATjk9kw73LUGN7p4qq3u1A72vDWzaRYG4hzc1gMMAee3R7st6AoSgKTU1NZGdno+9G0Vsh+gu73U5aWhoOh4PkHpqq0qXfjJiYGN59910ee+wxpkyZgtVqxWq1MmXKFB5//HGWL18+IBJu0c+0tMDPP0NCQrcSbkWBmhrtcBm9XwdLDFSqSt4TCyIJt2fMXrRMPzPKQbWnJCRRP3setX+9m9CWom4Gj5OCh26k4P5rMThaohpfUWIBI5KHc1De1C3VySV56IxAUMd1i4rbJdwnHWxl0dUbJOEWohtSPnsrUqPDsf8xvZdwe1zovR58I8YTzC3EatWuhVZUDJ2EWwjRMcauPlGv13POOedwzjnn9GQ8YqjyerWEOxDQGll2Q3091Fm0hFsuuIqdSfn0fyR/+R6gjR5bZs/rt28Y9+SDqSybSM7T/yRlS/ub5K8/IH7tahrOuQ7X3of1SRwhJcwvrRsYlToCs8GETqdjz6wJffLag43dZeCye0fwzbptRUmvOm0z5x7TGO0JDEIMbKpK6vvbCqjZe6mAmt7rxuB24B0xnkDecFpbIRyGSZMgJ6dXXlIIMYD1zzNMMbSEw1prsOZmbR13N7TatbbeiQlaT24hdsTUtJmcJ/8RuV1/zvWEMrr33utt4aRULJfczubL/k4oKRUAo8vOsPuuJf+hGzC47L36+lurk/9i38iq5h969bUGu+r6GE6bPzqScMeaFe697FfO+4Mk3EJ0V9yG74mtqwSgrXwi/sKRPf4aOl8bBkcrvtKxBIaVYnfo8Pu1Ee5ujhsIIQapLo90NzQ08Nhjj7F69WocDke7gmoAOp2O999/v9sBiiGgulr7k5PTrZFGrw8qfwVFldYcYhfCIfIfnhvp3Wo/8I+4ph4e5aA6zrX3YbSV70HukgUkf7MCgJTP3yHh52+oP/cG3JMP6vHXrHFtZlXzj4TUMDEGM6XJRT3+GkPF178kctl9I3C4ta/fzJQgD165kQmlbVGOTIjBIXX7Amq9MMqt8/sw2m34ikfjHzYCp0uHx6MVTZPGPUKInelS0v3DDz8wbdo0vF4vo0aN4scff2Ts2LHY7Xbq6uoYMWIEhYWFPR2rGIyammDtWkhJ6VaJ8XAYaqq1wmnZ2T0Xnhh8Mv/7BPEbtZHaQHYBjWddHeWIOi+ckk7dZf/A9cU75D75DwweJ0aHjcJ7rsR+wLE0nnkVSkLS7g+0GyElzLfWn6hy1QKQHZvB1Jw9iDNGr7r7QPb6J+nc9OhwQmHt4mJ5YRsPXbmR/MxglCMTYnAwuOwkf7Uc0JYNuab07NIbXcCHsbUJX1E5/uHluNv0OJ0wYQIUybVIIcQudGlYcc6cOSQmJrJu3TqWL1+Oqqrce++91NbW8sILL9Da2sqdd97Z07GKwcbt1tZx63TdbmJpsWhruWUdt9iV2I0/kvmfRwFQ9QYsF92KEpcQ5ai6SKfDue/RVN75Iq5JB0Z2p37yJqXXnUrCD5916/DuoIf36z6JJNxj08o4KH8fSbi7QFHgvpfzuW5RSSThPqDCwTM3rZOEW4gelPLJ/9AHAwDYDzwW1dxzn1e6YACjrRH/sJH4i0fT5tNjt8PYsVBSEvVmEkKIfq5L6cmnn37K7NmzKSoqirQG2Dq9/JRTTuGMM87gmmuu6bkoxeATDGo9vez2bg9N22xQXaPl7SZTz4QnBh+910PBwzeiU7Sq0NbjzsNbVhHlqLovlJrJ5iv/heXCeYTjtXUVptYmiv55GbmP3Y7e27U+zya9iUA4SKwhhoPz9pHq5F3kC+i45qESFr6eF9k347AmHrpyI4lxyi6eKYToFFUldcW2qeU9WkAtFMRorcc/rBRfyRi8AQM2G4waBSNGSMIthNi9LiXdiqKQs6U0Y2pqKgaDgZaWba1rJkyYwKpVq3omQjH4qCr8+its3gx5ed36tmprg8oq0OsgPr4HYxSDTs7Td2FuqgOgbWQF1uPOjXJEPUinw3HgsVQueAH3hH0iu9M+fI2S608jfs3XHTpMWN2WBMYYzByQtzdHDDuQnHjpfdMVNoeRcxaUs+zLdAB0OpXrzqzlxpm1GA1RDk6IQSZ+7Spi6msA8IzZk0B+cc8cOBTCZLUQyC/BVzoOX8hIczOUlUF5ucyuE0J0TJc+KkpKSqiqqtIOoNdTUlLC8uXLI/d/9tlnpKam9kiAYhCyWGDDBq2JpbHLtfwIhqCqSpulnpbWg/GJQSfpy+WkrnwDgHBsPJaLbwFD1997/VUoPYfaa+6n/pzrCcdqV6HM1nqG33kxOU/+HZ3Pu9PnOgNulm9eSbWzNrIvLSZFppN30ca6WE6bN5rvN2qzD+Jiwjxwxa+cdVSTjIoJ0Qu2L6DWY6Pc4RCm5jqC2YVapXLFRGMjjBwJo0dLwi2E6LgufVwceeSRvPTSS5HbF198MY8++iiHH344hx12GE8++SSnn356jwUpBhG7XVvHHRPTraFpVdUGyhsbtdxdTmLFzhhbGsl74o7I7caz/0YwexCXmNXpsB96IlV3LMUzZq/I7vTlL1F6wwzi1n33u6fUuDazfPNKHAEXa1o3oKgy7bk7PvsxidPnj6bOqhWHzEkL8PSN6zhksiPKkQkxOBkcLSR//QEAoaRUXHsd0v2DKmFMTXUEswrwjpxAQBdDfT2UlsKYMWCQ2SpCiE7o0lDPDTfcwIwZMwgGg5hMJq644go8Hg+vvPIKBoOBm266ieuvv76nYxUDnd+vreNua+t2Xw2rFTbVQGoqMk1T7JyikL/wZgweJwDOqUfgOOAPUQ6qbwSz8tk05yHS3n+J7KX3ow/4MDdtZvjtF9By1AyaT7mEoNHUvjp5XAZTs/dAr5Phm656aUUmtywpIqxoVwLHDNcqlOekS8E0IXpLyso30IVDANgP+hOqydy9AyoKpsY6Qpl5eMsmEDTEYqnTCqaNG9etSXpCiCFKp6qq2pknqKqKy+XCbDYTGzvwpx06nU5SUlJobW2VKfG9SVHgp59g40Yt4e7GJWKXG35eox1S/sl+T0XFY/CREI5Fx9CeApD+v6fIWXofAMH0HCrveB4loXuV8gciU2Mt+YvnEb/++8g+b24hr59wAutytPXG49LKGZNW1i+LpQ2E97SiwN0vFPDEW7mRfYdMtvOPi6tIiJWZA6K9gfCeHjAUhRHXnIi5aTMAG+96jWBON9rWqiqmpjpCKRl4R+9BwJRAXR0UFkJFRbe6mw5aiqLQ1NREdnZ2pMCyEAOZ3W4nLS0Nh8NBcjc7LG3V6d+MQCBAeno69913X48EIIaITZu0Bdg5Od1KuINBqKoEr1cSbrFrMdVryX7pIQBUnQ7L7PlDMuEGCOYUUnPDYhpPvwJlywhQXEMtf154P0e+v4JpmZMZl17eLxPugaDNp+fy+0rbJdwzpzdy3+W/SsItRC+L//nrSMLtHjel2wm3sbmOUHIq3vKJBM0JWCyQn6/14paEWwjRVZ1OumNiYsjNzSVGPnlER1mtsHYtJCVBN2ZHqKqWu1utkCHFlMUu6Pw+Ch6+KTLd0HbMWbSN3Ws3zxrk9AZapp9J1W3P4S0dp+1SVfZd+RFTF1xBbNUvUQ5wYGpqNTHzjnLeX6VVczToVebOquHa0zdjkAEfIXpd2gevRLbth53UrWMZrfUoCSl4yycRikvCYoHcXG2EexBM7hRCRFGXTglmzZrFU089RSAQ6Ol4xGDT1gZr1kA4DCkp3TpUYyPU1kJ6uqzjFruW/fy9xFi0Dgve4tE0n3xxlCOKPmfAjTvYRiC/mOq5j9F4yl9QjFpj+9i6SornzSLzlUUQkrXHHbW2Jo7T5o1mTVUCAIlxYRZevYHTDrNGOTIhhgaj3UrS6o8ACKVk4Nrj4K4fy9qAEhuPt3wi4YQULBatUOuECdKSVAjRfV0qBTFhwgT+85//MG7cOGbNmkVxcTFxcXG/e9yJJ/ZQywYxMIVCWuG0lhZtMVQ3OBza7PS4OJneJXYt8duVpL+vdVdQzDFYLr4NtiSXQ1WNazOrmn8kyZzIoQX7YTAYafnTOXj2OJD8RTcTW7MOnRIm6z+PkPTtR1gunI+/qCzaYfdrH32XzFUPltLm064A5mf6efjKjZQV+qIcmRBDR8pHr6MLhwGwH/ynLlc4M7Y0oppjtBHupDQsFm0JW0UFJCb2YMBCiCGrS59OM2bMiGzfdNNNO3yMTqcjvOWDUAxRlZXafPC8vG719PL7obJKW8+dldWD8YlBx+CwkffILZHbjTP+SiC/OHoBRVlICberTm7SGwkpYQxb6ir4C0dSNe9JMv/7OJn/fQxdOExszXpK5p5F8wkXYDt25qDsZ95dz76XxYKnC1FU7XOtYoSbB/76K5kpoShHJsQQooRJ/fA/gFa3wz7thC4dxmi3ouoNeMsqCKdmUG+B5GSYOFH7rxBC9IQunU2tWLGip+MQg019Paxfr80FN3V9lFFRoLoaWlu0GmxC7JSqkv/ILRhdrQC4Jh3Y7fV9A5kz4ObzxlU4Ai5gF9XJjUasJ16Ie/JB5C2eR2ztRnThENkvP0zSam3UO1BQEoWfoP8JK3DnM4U8+152ZN9RU1pYMLuaWHOnGoEIIbop4ccvMFvrAfBM2JdgVn6nj2Fw2EBVtRHu9GwaGrSp5BUVUqxVCNGzOpx0X3/99Zx22mlUVFRw8MFdXzMjhgCnE37+WUu2uzkvy2IBSz1kZIB0oRC7krb8JRK//xSAUHI69eff1K0ZFgNZtWszq5t/JKSGiTXEMDV7D3Lid1190Fc8mur5T5H5n0fIeONJdKpCXOXPlNx0Bs0nXUTL9DNAP3SLKXi8eq5+qISPvkuN7Lvgj/VcfrJFPpuEiILtC6i1Htr55YwGlx1dKKgl3Jm5NDWB2ayNcKen92SkQgjRiUJqd955Jz/99FPkts1mw2Aw8MEHH/RKYGKACgS0hNvt1iqQdEOrHWpqIDFB+yIUYmfMdZVkP39v5LblwpsJpwzNsyZFVdjgqCKkhsmOy+CIYQfuNuHeSjWZaT7lL1Tf/Dj+LdPy9cEAOUvvY/htF2Bq2NSLkfdf9TYTZ942KpJwGw0qt11QzV//LAm3ENFgbGkk8dtPAAimZeOedECnnq93O9D5vXhHVhDMLsBq1S7sV1R0+9RFCCF2qFunC6oq0+nEdlQVNmwg0mOjG7w+qPwVFFWKmIhd0wUDFDx0I/qgH4CWI/6MZ+L+UY4qevQ6PfvmTGZ8+igOytuHOGPn+9z4Royn6tZnsB1zFuqW2QLxG36g9IYZpL2zVFv3MUSsqYrntHmjWbdJK1+cHB/ikb+t58SDbFGOTIihK/XD19Gp2ueQfdpxnao9ofe40Hs9+EaMJ5hbSGur1mBlwgRZxiaE6D1yjV70nNpa2LgRsrO7XEEUtC+/mmqtYnnG0BysFJ2Q9fLDxG5aD4C/oJSm0y6LckR9r9q1mZ9bN0RuJ5oSGLuj9dudoJpjaZpxOTU3PkIgR+s+oA/4yX3mLooWXISpaXO34+7vln+Twtm3ldNs16baFGb7eH7eWqaOdUc5MiGGsHBouwJqeuzTju/wU/VeNwa3A1/JWAJ5w7HbtWKtFRWQ3/kl4UII0WGSdIue0dKitQdLSND6enVDXZ02WJ6VNWSX5IoOil/zFRlvPQ2AYjRRd/FtqObOj+wOVCElzNdN3/NV03f81LIOq6+1x1/DWz6Jytueo+XI0yL7EtaupvT6GaS+/7I2w2WQUVV44q1sLr9vBN6Ato59crmbpfPWUpLnj3J0Qgxtid9/iqm1CQD3HgcQSu/Y8LTO14bB0YqvdCyBYaU4XTo8Hhg/HoYN682IhRCik9XLq6urWb16NQAOhwOADRs2kLqTEo+TJ0/uXnRiYPB6tXXcwWC3F0NZrVCzCVJSujVYLoYAvdtB/qJ5kdvNp/wF//Dy6AXUxxwBF583rMYZ3FadPD0mtVdeS42No/Gsq3HtNY28xbdgtlrQ+73kLbmTpG9WUH/+TYQyurekpL8IhuD2p4t48YNt/QmP3c/GbefXYDYNvgsMQgw0aR+8GtluPaRjBdR0fh9Guw1f8Wj8w0bg9uhwOrUp5UVFvRWpEEJso1M7uDBbr9ej+82wo6qqv9u3/f6B0Kfb6XSSkpJCa2vrTi8eiF0Ih+GHH6CqCgoLu1VivK0N1vwMAb9UDu0OFRWPwUdCOBYdg3SqgKpScP+1JH+tFXL0jJvCpr89MGRK3Fe7NrOq+UfCW6qT75OzB9lxfVP9R+/1kL303nYnvuG4BBrPuBLHQX/qlekpffWedrXp+ev9I/jsp23Nef9ygoVLTqiXWTeiRw2Jz+leYGq2MOKq49CpKoHMPH69+z+77aqgC/gwtjThKyrHXzIGj1dPayuMGwcjRsiMup6gKApNTU1kZ2ejHyLfw2Jws9vtpKWl4XA4SE5O3v0TOqDDY4lPPPFEj7ygGGSqqrQS43l53Up4giHtUG435GTv/vFiaEtZ+UYk4Q4lpmC5cN6QSbhXN//ERmc1ANlxmeyTvQexxpg+e30lLoGGc67Htdeh5D16K6aWRgxeD/mP3kry1x9Qf96NhNKydn+gfqau2cxFd4/k1zpteYzJqHDb+TX8cf+WKEcmhNgq9cP/oNsyVmSfdvzuE+5gAKOtEX9hGf7i0Xj9elpaYPRoKC2VhFsI0Xc6nHTPnDmzN+MQA5HTqRVOS03tVk8vVYXNm6GxEbKy5UtQ7JqpsZacp++K3G4493pC6UPnSk16bCo6J4xNK2dMN4uldYdnwj5ULniBnGfvJvXjNwBtrWXpnD/TcPY1OPebPmB+mb/fmMCl94zA5jQBkJoY4v4rNrLnKE+UIxNCRIRCpH70OgCqwYDj4ON28/ggRms9/mGl+ErG4AsaaGrSEu7y8iFznVYI0U/IR47ouuZmbT13UlK3D7OpRsvdjbu+aC2GulCIgodvwuBrA8B+0J9w7X1YlIPqfb7wtuJdxUnDOKrwYMall0ct4d5KiU+k/oKbqb3yHkIpGQAY2lwULJzLsHuvweDo/221ln2Zxqw7yiMJd0mej6XzfpGEW4h+JunbjzBu+UxxTT6YUOoultSEQpisFgL5JfhKxxFQjDQ1QVkZjBolCbcQou/Jx47omlBIG57uZhNtl1ubVm42d7vouRgCMl9/jLhffwIgkFNIw1lXRzmi3hVSQnzV9B3v1a7EHw5E9iebu3ehq6e59ziQX+98Ecd+0yP7klZ9SOmcP5P05fIoRrZzqgqL/pvLVQ+U4g9qX4VTxzh57ua1FOUEdvNsIURfS92ujoR9VwXUwiFMzXUEswvxlY4lqJqor4eSEhgzBgxycV8IEQWSdIuusdnAboduFBcIBqGqUhsslxp2Ynfi1n9H5uuPAaDqDdRddCtqbHyUo+o9joCL5Zs/odq1GV/YR6PXGu2QdklJTMFy8a1svvyfhJLSADC6HQx7YA75D1yHwWWPboDbCYR03PDIcO59qSCy74SDrCz620ZSEvp/AVAhhhpTYy2JP30JQCC7AM+4KTt+oBLG1FRHMKsA78gJBPUxWCxawj1unHRFEUJEj3z8iK6pr9fWa3bxG0xVYdMmbWp5TsdabIohTO91k//wXHSqAkDzCRfgGzk+ylH1nmhWJ+8u116H0FY+idwld5L89fsApHz5Hgm/rKL+3Otx7zktqvHZ3QYuv3cEX6/dNlvgr3/ezPnHNg6UJehCDDmpK16LbLcecuKO54crCqbGOkKZeXjLJhA0xFJXpzVWGTsWTKY+DFgIIX5DRrpF53k8WtWzlJQuH6KhEWprISNDpnqJ3ct56p+YrRYA2sonYvvTOVGOqHdsnU7+VdN3hNUwOXGZHDnsoAGTcG8VTk6j7rK/s/kvdxBK1D4njM4WCv99NXkL56L3OKMSV01jDKfPHx1JuGNMCv+69Fcu+KMk3EL0V7pgIFKsUTUYcRz4x98/SFUxNVsIpWXhLasgZIrHYoGCAq0Xd0zfNXgQQogdkqRbdJ7VqiXeXVzP7XBAdZW2hlu+CMXuJH3xLqmf/A+AcGwCltm37LZNzED1U8t6ql2b0QHj0so5MG9qn7YD62mufY6kcsELuCYfFNmX+ulblF53Kgnff9qnsXyzLpHT5o2muiEWgIzkIEuuX8fRU+19GocQonOSvlmB0dUKgHOvQwinpLd/gKpibK4jlJyKt3wioZgE6uogN1dLuGNjoxC0EEL8hiTdonMURSugFt+1tbR+P1RWaeu5e6jXvBjEjNYG8p5YELndMOtagtkFu3jGwDY2rYzM2DQOzt+nX1Qn7wnh1Ew2X3E3dbPnE47XLtSZWpspuuty8h69Fb3X3esx/PfTdM67swyHW1sOM7LAy9J5a5k4sq3XX1sI0T2pK7YroHbYSb+732itR0lIwVs+iXB8EhYLZGVpCXcXT1WEEKLHSdItOqe1FVpaulT5TFGguhpaW7Rp5ULskhImf9FcDG0uABz7HKn1fh5EQkqIjY4aVFUFwGwwcUj+fgNuOvlu6XQ4D/gDlQtexF2xX2R36kevU3rdqcRvKZDU01QV7n8ljzkLSwiGtK+7/Sc4eHbuWgqypEK5EP2d2VJNwi+rAPDnDadt9J7t7jdaG1Bi4/GWTySckILFop2eTJzY7eYqQgjRoyTpFp3T2Khlz12oSGKxgKVeS7ilR6bYnYy3niZh7WoAghm5NMy6jsG08HZrdfLV1h/51VkT2a8bRD/jb4XSs6m9+l7qz7uRcGwCACZbI8P//hdyl9yJztdzI8/+gI6/PVzCw//Jj+w79dBmHr5qI0nxSo+9jhCi97Qb5T7kxHbfAcaWRlRzjDbCnZxGQ4M2g27iREjqX10VhRBCkm7RCT6fljl3YV54ayvU1EBigtaTW4hdia36hayXHwZA1emwzJ6PkjB4zqKqnLUs3/wJzqCbWENMv+u73at0OuzTjqdywVI8Y/eO7E57/2VKb5hB3JYLLd3R4jRy3t/L+d/n6VteUuXa02uZO2sTxsFZDkCIQUcX8JOypZ6HYjJjP/DYyH1GuxVVb8BbVkE4NYOGBq1OTEWFtCAVQvRPknSLjrPZwO3u9CVkrw8qK0FRZbqX2D2dz0v+wzeiC2v9km3HzqJtzJ67edbAsLU6+dfN3/+mOvnQW28Rysxj07UP0jDzWhSzVunI3FTH8Dtmk/PM3ej8vi4dt9ISw4z5o1m9XvuwiTOHue/yX5k5vWkwTZQQYtBL+vp9jG4HAK4ph6Fs6YRgcNhAVfGWTSSUnk1Tk3Yxf+JESE/f1RGFECJ6JOkWHaOqUFenTSvvxJlrOKyt43Y4IEO+DEUH5Dx/DzH12nRrb8kYmk+8MMoR9Yyt08m3Vicfnz6KgwZ4dfJu0+tpPfwUKu9YStuoPQDQqSrp7zxPyY2nE7fhh04d7os1SZw+fzS1TdrfaXZagKdvWsdhezp6PHQhRO9K++CVyHbroVoBNYPLji4UxDtyAqHMXKxWbblaRQVkDrJSGEKIwUWSbtExDgc0N3d63lZdHdRvqSQqo0xidxJXf0TaB9oaPsUci+XiW8HY+foB/VEgHMC1ZTr5wfn7MjatbFCv3+6MYM4waq5fRMMZV6KYtIQ5pmETw289n6yl96EL+Hd7jFc+yuDCf5bhbNMqlI8qamPpvLWMLfb2auxCiJ4XU7uR+PXfA+ArKMVbNhG924HO78U7soJgdgEtLVqJmYoKyMmJcsBCCLEbknSLjmluhkCgUw0vrVao2QQpKWA09mJsYlAw2K3kPXpr5HbjGVcSyCuOXkA9YGtVcoCsuAym5kwestPJd0uvp/Xo06m6/VnaRk4AQKcqZP7vKUrmnkV85dodPk1R4F8vFHDTo8WEwtpFjGmT7Dxz0zpy04N9Fr4Qoue0K6B26Ino29zovR58I8YTzC3EbtdOSSZMgLy86MUphBAdJUm32L1gUBuy7sRabo8HqqpBr5M+maIDVJX8R27B6LID4Jp8MPZDTohuTN3kCLh4v+4TnAFXZF9RYv7Qnk7eAYG8YmpuepTGU/8PZcssh9i6KsbOvUgrrhfalkh7/TqufKCUR9/Mjew766hG7v/rryTESoVyIQYind9HyqdvAaCYY3DteTAGtwNfyVgCecNxOqGtDcaPh2HDohysEEJ0kCTdYvdsNm16eQerlgdD2jputxvS0no3NDE4pL33Aok/fAZAKCWD+vNuHNDrEbTq5Ctp8Tv41vpztMMZePQGWo6dSdWtz+AtGQOATgmT9frjlMw9m5iadTTbjcy6YxTvfq19yOh1KjeevYnrztyMQb7ZhBiwkr98F0ObGwDn3oehC4bwlY4lMKwUt0eH0wnjxkFRUZQDFUKITpBTE7F79fVaAmTYfa8dVYXNm7V23pmZAzpvEn0kpnYj2Uvvi9y2XDiPcPLAvFrTvjq5Qk5cFlOzJ0U7rAErMGwE1XOfoOmk2ShbPn9iazdQPHcmX/ztVX6p1PoPxseGeeiqjZx+RHM0wxVC9IC097cVUHNNPhhf8Wj8w0bgadNht2sJd0mJnF8IIQYWSbrFrrnd0NTU4QJqzc1Qu0kb4ZZ+uGJ3dAE/+Q/fhD4YAKDlqBl4KvaNclRds+Pq5FNkOnl3GY1Yjz+fn299BF9hGQB6JcTfvLfwOftyUMp3PHvTOg6a6IxyoEKI7oqpWUdc5RoA/PklOPY/Bv/wcrx+PTYbjB4NI0ZIwi2EGHgk6Ra7ZrVqC7QTEnb7UJcbqqq0rmKdqLcmhrCslx4ktnYDAL5hI2j686VRjqhrWnx2lm9eiTPoJs4QwzSpTt7jvMVl/P3g/3E7NxBCu6K3F6tY4ZnCfj88DEo4yhEKIbpra/cKANvRZ+AvGYMvoKepCUaNgrIySbiFEAOTJN1i58JhqK3tUMIdDEJVJXi9ne4qJoaohB+/IOPt5wBQTGYsl9yOah6Yo8KpMcmkxaSQE5fFEcMOIkuqk/eosAL3PF3CvGfKuJHb2JfPqY4dBYA+FCT7hQcYfuv5mOuroxuoEKLL9F4PyZ8tAyAcE0fjGX/FHzLQ1KQl26NGaT25hRBiIJKPL7FzLS3Q2rrbLFpVoaZGm1qemdk3oYmBzeCyk7d4XuR2058vxV84MnoBdYEz4CasahWy9To9B+ROkenkvcDj03P5vSN4fllBZN/EPxTSdv/jWP9wNqpO+xqL3/gjJTecQfqyZ7U+YkKIASX502UYfG0AtEw/A398Og0N2vrtMWM6VFZGCCH6LUm6xc41NmoZ9W6abDc0asXTMjLkS1F0gKqS+/jtmOxWANwT9qH1yNOiHFTHqapKlbOW9zZ/zA+2XyL7zQaTTCfvYY0tJs6+bRQrVmuF9YwGhfnn1nDVaXXoYmNoPu0yam56FH+uVsZYH/ST89w9DL9jNqbGzdEMXQjRGaEgae+9ELnZcPzFWCxawj1u3G5PQ4QQot+TpFvsmNerVS1PSdnlwxwOqK6CuDiIkQE+0QEpH71O8jcrAAglplB/4bwBM2dQq07+faQ6uSvgRlFlVLU3/Fwdx6nzRvNLTTwAifEhFl69gVMOsbZ7nLesgqrbnsN21AzULRc94td9S+n1p5G2/CUZ9Raiv1PCJK7+mFhLFQDusXuzMXkyhYUwdqxWJ0YIIQa6gXGmK/qe1QouFyQm7vQhfj9UVmnruTvYwlsMcaaGTeQ+fVfkdv15NxFKHRhrEhx+J8s3f0KNe1t18gPzpqDXycdoT1uxOoWzbhtFU6vWEmxYlp/H5n/PvuNdO3y8GhNL05lXUXP9IgJZ2jR0fcBH7pN/p+gfl2K01vdZ7EKITlAUTI11JH33SWTXuoNnk58PEybIxXwhxOAhZ4vi91QV6uq0EuQ7mS6rKFBdDfZWbVq5ELsVClHw8I3oAz4AWqedgHuvadGNqQO2TidfXveJVCfvZaoKT72dzaX/HoHXr61VmTTSzXM3/0JJgXe3z/eOnkzlHc/TetjJkX0Ja76i9LrTSP3wP9oLCCH6B1XF1GxBiYkj6ev3AQjGJxM6+TQmTJAuKEKIwUWSbvF7djvYbLssoGaxgKUe0tMHzMxgEWVZry0mrvJnAPy5RTSecWWUI+oYfzjAd7Y1hFVFqpP3olAYbn2ykDufLURVtYsZx+zTwhPXrScjJdTh46ix8TTMmkPNtQ8SzMgFwODzkPfYbRTedTnGlsZeiV8I0QmqirG5jlByKnFVayIF1JqPPovxUxOIj49yfEII0cMkXRK/19SkzRnfybyu1latWnliApjNfRybGJDi1n1LxhtLAFANBiwX34oaGxfdoDoo1hjDXlkVjE8fJdXJe4nbq+eSu0ey9P3syL6Lj7fwj4uriDF3bXS6bfxUKhcsxX7wcZF9iT98Rul1p5LyyZsy6i1EFBmt9SgJKXjLJpLx5pOR/SnXzO5Il1IhhBhwJOkW7QUC2tTypKQd3u31QWUlKOoul3sLEaFvc5O/cC66LQXHmk+cja90XJSj2jlVVal0bqKxrTmyrzAxX6aT95I6q5kzbhnNJz9qRRuNBoUFs6v4v5Pquz2LRolLpP78m9h09b0E07IAMLS5yV80j2H/vgqD3bqbIwgheprR2oASG4+3fCJxlT8Tv/FHAEJT9iVhnwlRjk4IIXqHJN2iPZsNnM4dVkYLhbRK5U4nZKRHITYxIOU++XfMWwpZtY3aA9uxM6Mc0c4FlRBfNX3HN80/8EXTt/hC/miHNKj98Gs8p80bzYbN2qyHlMQQj8/ZwHEHtPTo63gm7k/lghew739MZF/S6o8pve5Ukj9/R0a9hegjxpZGVHMM3vJJhJPTSHx20bb7/nJRFCMTQojeJUm3aM9i0Rpi7mCIyWLRuohlZu60vpoQ7SR/9jYpny0DIByfSN1Ft4C+fzZz31advA4dOspTSokxyPqJ3vLOV6nMvH0UNofWD2h4ro/nb17LXqPdvfJ6SkIy9RfdQu0VdxFK1q4aGt0OCh66gYL752BwtvbK6wohNEa7FVVvwFtWQTg1g9ZfW8j75EXtzrQ0OOWU6AYohBC9SJJusY3Tqa3n3kEBNasVajZpbbuNxr4PTQw8Rms9uUsWRG43zJpDKDMvihHt2Nbp5MvrPsEVdBNniGVa/j6MSRsp08l7garCo2/m8Nf7R+APal9Be41y8fzctRTn9v7MAvee06i880UcU4+I7Ev++n1K5/yZpC3944UQPcvgsIGq4i2bSCg9G6sVhn3wFIYt3Sw4+2yIGxh1PoQQoisk6RbbWK3g9f7ui8/jgaoq0OuQiqKiY5QwBQvnYvB6AHDsNx3nvkdHOajfU1QlMp08rCrkxmVxROGBUp28lwRCOuY+Npx/vTAssu+4A2w8eu0GUpPCfRZHOCkVy6UL2HzpnYQSt6wld7Uy7N5ryH/4JvRuR5/FIsRgZ3DZ0YWCeEdOIJSZS0sLKGGVke9vm1rO7NnRC1AIIfqAJN1CEwpBbe3vqqMFQ1o/brdHm/0lREdkvPkk8eu+BSCQmUfDzGujHNGO6dBt+X8dE9JHc2DeFGINUp28Nzg8Bmb/cySvfJQZ2XfZyXXccWE1ZlN01lS7ph5O5Z0v4tpzWmRfymfLKL3uVBK/+yQqMQkxmOjdDnR+L96RFQSzC7DbtXqte7atxLhxrfaggw6CMWOiGqcQQvQ2SbqFpqVF68+dkhLZpaqweTM0Nso6btFxsZVryHpVG8FQdXosF92CEt9/St2rqkpY0UZVdTode2aN55CC/WQ6eS/a1Gjm9Pmj+fJnrUCj2aRw1yWVXHRcQ9Q/V8IpGWy+/J/UXXQr4Xita4PJbqXw7ivIe2Q++rbeWWMuxGCn97jQez34RownmFuI0wltbTBhAmS/unDbA2WUWwgxBEjSLTQNDdp/t1uw3dwMtZu0EW5j/6x9JfoZna+N/IdvQhfWklrbH2fhHbVHlKPaZmt18s8aV6FuqVht1BvJjJVpHL1l9foEZswfTVV9LADpSUGWXLeeY/btR4XLdDqc+0+n8s4XcU/cP7I79eM3KL3uVBJ+/CKKwQkx8Oi9bgxuB76SsQTyhuN2g8sF48ZBUbwVXnlFe2BmJpx0UnSDFUKIPiBJt9AuPTc0tCug5nJr67hNZoiNjV5oYmDJefYeYho2AeAtHUvzCRdGOaJt7H4nyzevpMZdR0NbMy1+e7RDGvTe/CyNcxaU0+rSKpSX5nt5ft5aJpV5ohzZjoXSsqi96t9YLphLOC4BAFNLI0X/uJTcJ+5A7+2fcQvRn+h8bRgcrfhKxxIYVoqnTYfdDmPHQkkJsGSJNsccYNYsiJElPUKIwU+SbqEVUHO7IUE7yQwEoKpSq6mWmrKb5wqxRdI3K0j78DUAlJg46i6+rV+Uut9anfz9uk9wBT1bqpPvS4aMbvcaVYWHXsvjbw+XEgxpXzP7jHPy3Nx1FGYHohzdbuh0OA76E5V3vIB7/NTI7rQPXqXk+hnE//xNFIMTon/T+X0Y7TZ8xaPxDxuB16fDZoPRo2HECNCpCizaroDahf3nwqwQQvQmSbqHOkWBujqtYrlOh6pqrcGsVm3WlxAdYWxtJvex2yK3G868imBuURQj0mydTr59dfIjCw8iKy492qENWoGgjjmLinng1fzIvpOnNbPo6g0kJ/RdhfLuCmXmUvu3B6ifNQclRuvoYLZaGL7gInKe+ic6nzfKEQrRv+gCPoytTfiKyvAPL8cX0NPcDKNGQVnZlrowK1bAxo3aEw47TLtDCCGGAEm6hzq7HWy2yNTyhkao2wzp6WCQddyiIxSFvEfmY9zSZsm51yE4Dj4uykFpPm9YRY27rl118hiDOdphDVqtLgPn3lnGG59qLdd0OpWrTtvM/HM3YYr+pIfO0+mwH3YylXc8j2f05Mju9PdeoPTG04lb/130YhOiH9EFAxhtjfiHjcRfPBp/UE9jI4wcqSXd+q1nm4ukTZgQYmiSpHuoa2rS2oWZzTgcUF2l9eKWJVaio9LeXUrilkJTwbQsGs69od+Uuh+XXk6CMZ5p+ftKdfJeVlUfw2nzRrN6vVYBPNas8O//q+S8PzT2l7dDlwWzh7HpuoU0nHk1iln7cDQ31jL8tgvIfu7f6AK+KEcoRBSFghit9fiHleIrGUNQMdDQAKWlWiewyAX8hgZ4TVuCRE4OHNc/Ls4KIURfkKR7KPP7tanlycn4/VBZpeXfSUnRDkwMFDGbNpD9wv2R2/UX3Ew4KTVq8QSVEI1t1sjtjNg0phdNk+nkveyrXxKZMX80tU1a1cXMlCBP3rCOI/a2RzewnqTX03rUaVTd9hxtIysA0KkqGcueoeSmM4n99acoByhEFIRCmKwWAvkl+ErHEcKIxaIVTBs37jdlPZ54QjvJADj3XDDLrCMhxNAhSfdQZrWCy4WSkER1NdhbtWnlQnSELuAn/+Eb0YeCANiOPh3PhH2iFs/W6uQrG76i1e+I7Nfr5GOuN732cQbn/70Mp0c7uy4vbGPpvF+YUNoW5ch6RyBvODU3PULjaZejmLSkIcZSTfH8c8l66UF0wX5eKE6InhIOYWquI5hdiK90LCFMWCxQVKQl3CbTdo9VFFi8WNvW6eCCC6ISshBCRIucjQ5VqgoWC5hMWBr0WCxawq2Xd4TooOwXHyB2868A+ArLaD7lL1GJQ1VVfnXWRKqTx+jNKKoSlViGEkWBf7+Uzw2PFBMKax8cB1Y4eOamdeRnBqMcXS/TG2j5w1lU3foM3tKxgFaVOfO/T1B889nEVK+NcoBC9DIljKmpjmBWAd6REwgZYrBYID8fxo/fwSD2u+9CdbW2fdRRW3qHCSHE0CEp1lDldEJzM61qKjU1kJgoM71ExyX88Dnp7zwPgGIyU3fJbajmvi8EEFRCfNn0LauafySsKuTFZ3Nk4UHSDqyX+QI6rn6ohMX/zYvsO/3wJh68ciOJcUPngkegoJTquY/TdMolqAZtpD+2diMl82aS+eribVNphRhMFAVTYx2hzDy8ZRMIm2KxWCA3FyoqIDZ2B8+RAmpCiCFuINaTFT2huRmfw0elKwdF1ZJuITrC4Gwlf/G8yO2m0y4jMGxEn8dh9zv5vHEVrqBnS3XyUYxKHSHF0nqZ1WHk0ntG8MOv2oeGXqcy58xazjyyOcqRRYnBiO1P5+KedCD5i28mtmY9unCYrNcWk7T6Iyyz5+MvHBntKIXoGaqKqdlCKC0Lb1kFSkw8ljqtxWhFhdZ99Hfq6uCNN7Tt/Hw49tg+DVkIIfoDGekeikIhQjV1bGpJwumEDFnHLTpKVcl77FaMDhsA7or9aD3i1KiEYmlrxBX0EGeI5ZD8fRkt1cl73YbNscyYNzqScMfHhnnwyo1DN+Hejr+ojKp5T9J8/AWoeq1cc2zNOkpuOpOM/z4BYRn1FgOcqmJsriOUnIq3fCLh2ATq6rSOoxMnQkLCTp732GMQDmvb55//m+pqQggxNMgn31Bks9G03o7FnUtmTr/p7iQGgNQVr5G0+mMAQkmpWC6YG7U30OjUkSiqQllKifTe7gOf/ZjEFfePwO3VEsrc9AAPXbmR0cO9UY6sHzGasJ40G/fkg8hbdDOxdZXowiGyX3qQpFUfYrlwHoECWcsqBiajtR4lIQVv+SSUhCQa6iElBSZN2kXXk1AIHnlE29brtaRbCCGGIBnpHoKsP9azuV5PUppRLjiLDjPXV5Pz7L8it+vPn0s4NbPPXt/ud/JZwypCijZiotfpGJ8+ShLuPvDiB5nMvqssknCPLfawdN5aSbh3wlcyhupbn8F67CzULdXz4yrXUHLTGaS/9QxseQ8LMVAYrQ0osfF4yyeiJKbQ0KBNJZ84UUu8d2rZMti8Wds+5hgoLOyTeIUQor+RpHuIcTV4qF3ViJqYTHx8tKMRA0YoSP7DN6EP+ABoPfQk3JMP6pOX3r46+WZPPWta1/fJ6woIK/CP5wqY98Rwwoo2o+HQyXaeumE92WmDvEJ5N6kmM82nXkr13Mfw5w0HQB8MkPP8vxl++4WYGmujHKEQHWNsaUQ1x+Atn0Q4OY2mJq3w6sSJkLa7mpXbF1C76KJejVMIIfozSbqHkGAQfv3Sir/FQ1KeVE4THZf16mLiqn4BwJ83nMbT/9onr7uj6uSjU/u+aNtQ1ObTc8V9pSxZlhvZN2t6A/de/ivxsUOnQnl3+UZOoOq2Z7FNPwN1y1KM+PXfU3r9aaS9u1TrvSZEP2W0W1H1BrxlFYRTM7BatVniEydqxdN2qaYG3npL2y4qgqOP7vV4hRCiv5Kke4hQVdi4XsH+Uy0p+Qmyjlt0WPwvq8h4cwkAqsGA5eLbUGN21BOmZ9n9Tt7bvJJNbgs6dFSkj+GA3L1lOnkfaGo1MfOOct5fpQ1jGfQqN59Tw99Or8Mg3xqdpppjaTr9r9TcsJhA9jAA9AE/uU/fRdGdl2BqtkQ5QiF+z+CwgariLZtIKD2blhbtGlFFBWRnd+AAjz6qnXwAXHABGAy9Gq8QQvRncvo0RFgsUPNtC5n6lt0swBJiG73HRf6iuei2nDg1n3wxvpIxvf66dZ4Gltd9gntrdfKCfRmdJu3A+sLamjhOmzeaNVVaKeLEuDALr97AqYdaoxzZwOcdtQeVtz9PyxF/juxL+OUbSq4/jdQPXt2WoAgRZQaXHV0oiHfkBEKZudjtEAhoCXdeXgcOEAxqVctBS7bPPbc3wxVCiH5Pku4hwOGAn3+GRG8TsWYVjKZohyQGAlUld8kCTLZGADyjJ2M75qw+eelUcwpGnYG8+GyOLDyIzFjpa9cXPvoumTNvG0VDizabID/Tz7Nz17L/BFeUIxs81Ng4Gs/+GzVzHiaQqWUvBl8beU/cQeE//w+jrSHKEYqhTu92oPN78Y6sIJhdgNMJbW0wYQIUFHTwIG+8AfX12vaf/qT15xZCiCFMku5Bzu/XEm6f3Ud20EI4MTnaIYkBIvmzZaR88S4A4fgkLBfdAvremx7oDfki2wmmOA4bdoBMJ+9Dz7ybxV/+NZI2n/ZvXDHCzdJ5aykb5tvNM0VXtI3bm6o7nqd12gmRfYk/fkHpdaeS8vEbMuotokLvcaFvc+MbMZ5gbiFuN7hcMH68tiy7w6SAmhBCtCNJ9yCmKLB+vXaxeVisFX2bCyV+Z800hdjG1FRH7pK/R27Xn3MdoYzcXTyj67ZWJ39r0wfUebaN8iWZEmQ6eR8IheG2pwq54+kiFFX7+z5qSgtLrl9PZkooytENbkpcIg3n3cCma+4nmKYtkjV4PeQ/Mp9h/7oSo12m9Iu+o/e6Mbgd+ErHEcgbjscDdjuMHQvFxZ04UGUlvKtdsKW0FA4/vBeiFUKIgUWS7kGsvl777svJVom11aGazEgFNbFb4RD5i+Zi8HkAsB/wB1z7HNkrL/Xb6uSb3fW98jpixzxePZfeM5Ln3ttWFenCP9Vz91+qtKUook94KvalcsEL2A84NrIv6buVlM75M8mfvS2j3qLX6XxtGByt+ErHEhhWiteno6UFRo+GESM6eeqwePG27Qsu0MqdCyHEECefhIOYz6edqyWEHBgdVsJJqdEOSQwAGW8sIX799wAEsgpoPPuaXnmd31UnzxjDlOxJvfJa4vfqbSbOvHUUH3+vFVY0GhRuv6CaK06xyDlyFCgJSdTPnkftX+8mlJIBgMHjpODhGym4728YHC1RjlAMVjq/D6Pdhq94NP5hI/D5dTQ3Q3k5lJV1MuEOBOCJJ7RtkwnOOadXYhZCiIHGGO0ARO8ztjajCwZRzb3f5kkMbLEbfyLrtUcAUHV6LBfdghLXsz3dVVWl0rWJb61rUFSFeGMs++RMlmJpfeinyngu+ddIrA6tqGJyQoj7Lv+VKWPcUY5MuCcfTGXZRHKe/icpn78DQPI3K4hf9y0Ns+bgmiJTdUXP0QV8GFub8BWV4x9ejj+op7FRS7ZHjerCIPV//gNNTdr2CSdATk5PhyyEEAOSjGcMcrpQEFPTZpT4nk2cxOCj87VR8PCN6JQwANbjzsNbPrHHX8fmb2VV848oqkJefDZHDJPq5H1p+TcpnH37qEjCXZjt4/mb10rC3Y+Ek1KxXHI7my/7O6EtM5SMLjvD7p9D/oPXY3DZoxqfGBx0wQBGWyP+YSPxF48mGNbT0KBNJx8zpotttRcu3LY9e3aPxSqEEAOdJN2DnNllw+B2StVysVu5T9+FuWkzAN4R47Eef16vvE5mbDojU4qpyBgj1cn7kKrC4//L4fL7RuALaB/9k8tdLJ23lpI8f5SjEzvi2vswKhe8iHOvQyL7Ur54l9LrTiVx9cdRjEwMeKEgRms9/mGl+ErGEFINWCxQUqIVTjN2ZR7k+vWwYoW2XV4Ohxyy68cLIcQQIkn3IBfTUq/ND+vFVk9i4Ev6+n1SP/4vAOHYeOouvg0MPbP6RFVVKp2b2rUEm5w5ntGpI6Q6eR8JhmDeE0XctXQY6pYK5X/c38bjczaQlhSOcnRiV8Ip6dRd9g/qLrmNcIJ28dTosFF4z5XkLZqH3iM91EUnhUKYrBYC+SX4SscRwkhdHQwfDuPGaUuxu2T7AmoXXiiFW4UQYjuSdA9iOreLWEdjZHqiEDtibGki77HbI7cbz7qaYM6wHjl2UAnyRdO3fNP8A182fYsiVZj7nNNj4KK7ynhpRVZk36UnWrhzdjVmk/x7DAg6Hc59j6byzhdxTTowsjv1kzcpve5UEn74LIrBiQElHMLUXEcwuxBf6VjCOhMWCxQUaAm3uasTj3y+bQXUzGaYObPHQhZCiMFAku5BzNBqxRBoQ42Nj3Yoor9SFPIXz8PgcQLg3PswHAf+sUcO3ep38N7mldRuqU6eG5+NjHv0rc1NZs64dRSfr9FGSE1GhX9cXMklJ9TLINQAFErNZPOV/8Jy4TzCW+p0mFqbKPrnZeQ+djt6r6zLF7ughDE11RHMKsA7cgJhYwwWC+TmQkUFxHan1uorr0DLlgr7p5wCmZk9ErIQQgwWknQPVuEwhobNhGISoh2J6MfS336OhDVfARBMy6b+3Ou7PSVQVVV+ddTwft2nuINtxBtjOaRgP5lO3se+25DAafNG82tdHABpSUGeuG49x+7XGuXIRLfodDgOPJbKBS/gnrBPZHfah69Ret1pxK/5OorBiX5LUTA11hHKzMNbNgHFHIvFAllZWsIdF9fN4y9atG1bCqgJIcTvSNI9WLW0YHC0EkxIjXYkop+KqVlP1ksPAqDqdFhmz0dJTOnWMYNKiC+avmWV9bfVydN6ImTRQcu+SGPWgnJaXNrizJI8rUL55HJPlCMTPSWUnkPtNfdTf871hLfMZjLZGhh+58XkPPl3dD5vlCMU/YaqYmq2EErLwltWgRITT10dpKdrCXdCd6/Nr1kDK1dq22PHwgEHdDtkIYQYbCTpHqwaGkBVUXuoGJYYXHQBHwUP34g+FASgZfqZtI3bu0eObfc70KGT6uRRoKqw8PVcrnqwlEBQ+3ifOsbJczevpSgnEOXoRI/T6bAfeiJVdyzFM2avyO705S9ResNpxK37NorBiX5BVTE21xFKTsVbPhElLoH6ekhJ0RLupKQeeI3tC6jNni0F1IQQYgck6R6MvF5oaCCc1L1RSzF4ZS+9j5i6SgB8w8tpPvniLh9LVVXULQXSTHoj++bsKdPJoyAQ0nHD4uHc93JBZN+JB1lZ9LeNpCRIhfLBLJiVz6Y5D9Fw9jUoZm1hrrmpjuG3X0j2s/9CF/Dt5ghisDJa61ESUvCWT0JJSKKhAeLjYeJELfHutrY2ePJJbTs2Fs46qwcOKoQQg48k3YOR1QpuN2pCYrQjEf1QwnefkP7eiwAophjqLr4d1dS10eit1cnXO6oi+1JjkmU6eR+zuw1c8Pcy/vPJtuJFV566mVvPr8FslArlQ4JeT+sRp1J5x/O0lU8EQKeqZLz9HCU3nE7sxh+jHKDoa0ZrA0psvDbCnZhCYyPExMCkSZDWUx/RL74IDoe2fdppPXhgIYQYXGTu8WCjqlBXp11xDsgoo2jP4Ggh/5FbIrebZlxOoKCkS8dq9Tv4vHEV7mAbFk8Dw5MKiDXE9FSoveaK+0qZNb2RSWUeFAXueKaQld+nACpnH93EGUc07/B55/+9DKvDiE4HCbFhrj+rlrHF2rrZ258qZMW3KVisMbxy28+MGd6x9bTVDTFcv6iYVreRpLgwt19YTdmw349KfrEmiXteLMDj06PTwcETHVx5ah16vXaMmbeX02zXLpzodSq3X1jFcQe0YnUY+cu/RvLs3LUYDV37+xIDSzCnkJobFpP+zvNkvfQQ+mCAmIZNFN9yHrY/nIX1xNldvsgmBg5jSyOqOQZv+STCyWlYrWAwaFPKMzJ68IWkgJoQQnSIJN2Djd0ONpt2tXnHuYMYqlSVvEdvxejU2rq4J+5P6+GndOEwKr86a/jO9jOKqhBvjGWfnD0HRML9w6/xODwGJpVpBcXe+CydX+tieeufP+FqM3DSjWOYMsa1w8T3X5dWkrxlmvbyb1K5YXExr93xCwBHTWnlvGMbOPPWUZ2KZ/7jRZxyiJUTDrLxzlfaMV+8Ze3vHpecEOKuv1RSmB3AH9Bx3t/Lef2TDAqz/Vz67xE4PdpHeUZKkD3LXayvjQdayUwJManMzeufZHDSwbZOxSYGML2Bluln4p54APmLbiaucg06VSHzzSdJ/HYl9bPn4ysZE+0oRS8x2q2oegPesgrCqRm0tICiaCPc2dk9+ELffw9ffKFtV1TA1Kk9eHAhhBhcZHr5YNPUBMGgNodMiO2kvv8KSd9pFWZDSWlYLpjb6YI3QSXIF42rWW39CUVVyI/PGVDVyV/8IItj922J3F72RTqnTLNi0ENqYpjpU1t56/P0HT43ebt10a42A9s3Hd9rtJvc9GCnYrE5jPxUlcAf99eS4SP3tlPfYqam8fe/u2OLvRRma4XQYswqo4va+GB1CufeWRZJuEcWeFk6by2XnlTf7mc4Zp8WXvwgq1OxicEhkF9M9dzHaPrzpShGrZJ9bF0lxfNmkfnKIgh17j0r+j+DwwaqirdsIqH0bOx2CAS0nDgvr4dfbPtR7osukgJqQgixCzLSPZgEArB5cw+VIxWDidlSTc7z90Ru1194M+GUzs0xVFSF9zd/ijPojlQnL08pGVDF0r5em8TMoxsjt+ttZvIzt1X1LsgK8P3GnffPmbOwmK9+0X6/Fl69oVuxNLSYyUoNRqZ963SQnxGg3mpmeI5/p89rajXy2soMvP5t88Vz0/08O3ctSfEKXr+OZruJUBiMBhhX0sb62jjcXj2JcUq3YhYDkMGI7Y+zcE/SRr1ja9ahU8Jk/ecRkr79CMuF8/EXlUU7StEDDC47ulAQb/kkQpm5OJ1anbOJE6GgYPfP7xS3G555RttOSIAzzujhFxBCiMFFRroHE6sVnE5ITo52JKI/CQUpeOgG9AEtkWs57BTckzrfR1Wv01OSXES8MY5DC/ZjVGrpgEq4ARpaTGSkhLr8/DsvquaDe3/kspPruHvpsB6MrGNsDgPHXTeuXcI9aaSLAyqcJMXvOKE2GrTp6U2tpr4KU/RD/sKRVM17kuYTLkQ1aO+f2Jr1lMw9i4zXH4Nw138vRPTp3Q50fi/ekRUEswtwu8HlgvHjoaioF17w+ee1FwCYMUPOO4QQYjck6R4sVBXq68FkAr38s4ptsl5+mNiadQD480tomnF5h58bVIK4gp7I7fKUEo4cdhAZA2Q6+W/FmRX8wW0XCvIyAlis24pK1TWbycvYfT/r4w9s4atfkrC7ul6dLDc9EBmRBu1X2GIzk5e549ff3GTmqKsn4NgynVynU5lzRi1nH91Eg227n8Ea024EHcAf1BNjlirmQ57RiPXEC6me9yS+wpEA6MIhsl9+mOL552Le0kZQDCx6jwt9mxvfiPEEcwvxeLTyLmPHQnFxL73ob6eWCyGE2CXJzgYLl0tbz52aGu1IRD8S//M3ZLz1NACqwUjdJbehxsR26Lmtfgfv1a7kk/qvCCraKJhOp8NsGLgjpuWFXqrqt/38R01p5aUPMwkrWtutZV+mMX2flt89z+kxtBspXv5NCqmJIVISd9//es7CYpZ/k/q7/RkpIcYWt/HGp9o0/3e/TiU3PbDDqeU/VcZz7LXjaPNpmXScOcz9V/zK2Uc3cWCFk5+r46m0aGvBly7PavczbK24npe++4sJYmjwFY+mev5TWP90DqpOOw2Iq/qZkpvOJP1/T4Eifd0HhHAIY0sj+jYXvtJxeLOH09wMra0wejSMGNFLy6y/+QZWrdK299xT+yOEEGKXZE33YNHcDF5vD5cmFQOZ3uMkf9HN6FRthLPplEvwD999de1IdXLrzygoxBvj8Ia8mMwDv1bAkVNa+fTHZPYbr02L/NMBNn6qjGf61ePR6WDW9CbKC7XK5R+sTmHF6lRuPb8Gl9fAlfeX4gvo0etU0pJDPHTVxsgJ7c2PF/HxdylYHSYu/EcZ8bFh3rl7DQA/VcVz5pFNO4xn3rk1XL+4mMVv5JIYF+b2C6oj99306HAOmWwnLkbh4rtHEAhpyZHRoJCTHmR9bRyHTnaQEKdwy/k1/N+/RxIKQ9kwHwtmb+ub/skPyRy+p10mwIh2VJOZ5lP+gmvyweQvnkeMpRp9MEDO0vtI+uZD6i+8mUDe8GiHKXZECWO029AF/ITSs/HlldCkz8Vj0ZGZCRMmaEXTem31j7QJE0KITtOpqjqk5xw6nU5SUlJobW0ldaCOEodC8Omn4Pe3a8BZuxk2boTcnCjGJvqciopH76X83ltI+Wo5AJ6xe7Pp2gd3u/QgqAT5pukHaj31AOTH57B39kRiDIOjr6/Hp+eMW0bx3Nx1xMf2flGxFqeRax4q4bE5XSu69vKHGdyyZDihsHb2PHp4Gw9dubFTldLPvLWc+eduYkTB79ugDRQqKh6Dj4RwLDoGVh2BgUAX8JP1ykLSlz0TuUinmGNo+vOltB5xqixZ6gVdek8rCkZnCzpfG6HUTPz5pdiMOdjdRtLSoLQU8vO1VWa9xunUXsTj0Yq2WiyQmNiLLygGAkVRaGpqIjs7G718XohBwG63k5aWhsPhILmHalbISPdg0NKiLeDKzY12JKKfyFj5diThDickY5k9b7cnzq1+B583rMIdahuw1cl3JyFW4dozNlPXbKassPeT0PTkUJcSbkWBe14s4LH/bfudPmQPO/+4pIqETlwssDqMnHZY84BOuEXvU80xNM24HNeeB5O/eD7mxlr0AT+5z9xN0jcrqL9gLsHsvi8cKLZQFAyuVvRtbkKpGQRKxtIak4vNaSI5eVt18tiOrRzqnmef1RJugDPPlIRbCCE6SJLuwaC+XptHZpR/TgGmxs0MX7Jde7BzrieUvvvpDj+1rMMdaiPeGMe+OZMHbLG03dl3nCvaIeyS169jzsIS3vtm29//2Uc1cs3pmzF0cgAhMyXEsfu19nCEYrDylk+i8rbnyH7pQdLfXQpAwtrVlF4/g8YZl2M/9CTpxdyXVBWDy47e4yScnI53zJ444vNodpiJN2qF0goLtY5dfRUPCxduuy1Ty4UQosMkSxvoPB5obISUlGhHIvqDcIiChXMx+LwA2A/8I66ph3foqXtlVfBTyzoqMsYMmunkA02z3chf/jWSn6q0s2i9TuWGs2uZcXhzlCMTQ4UaG0fjWVfj2msaeYtvwWy1oPd7yVtyJ0lff0D9+XMJZcqsql6lqhjcDvQeB+GEFLzlk3AlF9DsjMHkg5EjYfjwKHTp+vJL+OEHbXuffbQhdiGEEB0iCy8GOptNS7z77FK36M8yX3+c+I0/AhDILqDxrKt3+thWv4OfW7dNfY4zxg6q9dsDzfraWE6dNyaScCfEhnn46o2ScIuoaBuzF1V3PE/roSdG9iWu+YrS608l5aPXtVFP0eP0Hifmhk2gqnhHVmAfsx/V+lJaPDEUFcG++2qF0qLSFltGuYUQostkpHsgUxTYvBni4mTKnyBuww9kvv4YAKreQN3Ft6LE/f5izG+rkyebEhmWmNfX4YrtfPx9Mlc9UIpnS0uwvAw/D1+1MVJJXYhoUOISaDjnelx7HUreo7diamnE4PWQ/+itJH/9AfXn3UgoLSvaYQ4K+jY3BmcLSlwC3tJxeDOG0eRJIOTQ6paVlGh1UqP2Vd/aCi+8oG2npsKf/xylQIQQYmCSke6BrLVVG+mWqeVDnt7rIX/hTei29Ne1nDgT78gJv3tcIBzk88bVrLb+hIJCfnwO2XEZv3uc6DvPvZfFJXePjCTcE0o9LJ23VhJu0W94JuxD5YIXsB/0x8i+xO8/pXTOn0n+9C0Z9e4GvdeDqaEWXcCHb/gonOP2Y3PCKOrsCaSnw9SpWhvszMwoX1t/+mnwbflMOvtsiI+PYjBCCDHwyEj3QNbUBOEwmGU68FCX8/RdmJvqAGgrq8By3Fn8doy7xW/n84bVeLZUJ5+YMYayQVadfCAJK/CP54bx9DvbitwduXcrC2ZXERcjSYzoX5T4ROovuBnXXoeS+/jtmOxWDG0uChbO1Ua9z7mOcIpcwOsonc+LMWhDHzTjHzYCX85wbMFkPK1Eem3n5oLBEO1IkQJqQgjRAyTpHqj8fq0/ZlQWdon+JOnL5aSufAOAcGwCdRfNB4MRwtseU+ncxOpmbXR7sFcn74/CCqxal0iz3URWapDRw9uY83AJH36XGnnM+cc2cMUpddISWfRr7j0OpHLBC+Q+fRcpny0DIGnVh8St+5aGWXNwTT0iyhH2b7qAD0OrFdVoIJiXgztzNK1qGo5WSEuDyZP7oNd2Z33yCfzyi7Z94IFa2XQhhBCd0i+T7gcffJB//vOfNDQ0MHHiRO6//36mTJmyw8c+8sgjPPXUU/z0008A7Lnnntxxxx07ffygYbWC0wnDpHfqUGa0NZD3+O2R240zr9nST7f91ORYQ0xkOvmU7ImYpVhan3nv61TueKaQxpZtf+dGg0IorN+yrTJ3Vg0nT7NFK0QhOkVJTMFy8a249j6U3MfvwOhqxeh2MOyB63B+/QENM68lnJQa7TD7FV3Aj8FhBZ2eYG4R/twiHOEALntK3/fa7qxFi7Ztyyi3EEJ0Sb8bU3nhhRe48sorufnmm1m9ejUTJ07kqKOOoqmpaYeP//DDD5kxYwYrVqzg888/p7CwkCOPPJK6uro+jrwPqao2ym02I8NiQ5iikL9oHoY2re+0c+oROPb/Q+TusLJtqDs/IYdD8vdl/9y9JOHuQ+99ncoV95XS2NJ+2Gprwh1rDrPomg2ScIsBybXXIVTe+SLOvQ+L7Ev+8j1K5/yZxG8+jF5g/UkoiKnZgsFuJZhVgGfCPtgKJ1HtTCMY0jF2LOy3H4wY0U8TbqsVXnpJ287IgJNOim48QggxQPW7jO1f//oXF1xwAeeccw5jx45l4cKFxMfH8/jjj+/w8c8++yyXXHIJkyZNYvTo0Tz66KMoisL777/fx5H3IacTmpu1CqJiyEpf9gwJv3wDQDA9h/pzrgOdDlVVqbbX8tamFbSFvJHHZ8VlyPrtPhRW4I5nCtFWZ+/o710lMS7MlDGuvg1MiB4UTk6j7rK/s/kvdxBK1Ip6Gp0tFN57NfkLb0LvcUY5wigJhTBaGzC2NBJMy6Ztwj60Dt+DGncmLreOESNg9GgoL+/nNcmefBICAW171qx+emVACCH6v341vTwQCLBq1Squu+66yD69Xs/hhx/O559/3qFjtLW1EQwGSU9P3+H9fr8fv98fue10aicEiqKgKEo3ou9DTU3amu6YmF1WjVW3+yMGl9jqtWS/9BAAqk5H3UXzCSckEQwH+Lr5B+o8DQD86qxhfPqoaIY6ZH2zLqndlPLf02F1mPlmXaIk3ruhbvc/0T859zkCz+g9yH9iAUmrPwYg5dNlxP/8DfXn3oB70v5RjrCPhEMYHTZ0wRCh9Cz8eSW0JWZhbdGj80FhoUJxMSQnKzQ3q/37vENV0S1aFLlkqJx/vtaqVIjfUBQFVe3n72chOqE33sv9Kum2Wq2Ew2FycnLa7c/JyWHt2rUdOsa1115Lfn4+hx9++A7vX7BgAfPnz//d/ubmZgJbr+b2Z6EQVFdrV5t9u24p5ABC8eDpD9VPRY/R+32UPHwjunAIgIZjT6d5/Dgcvia+rf+JtqAXHTpGZ42kOLUQj05aT/W1UBjeXtWx3uebnSrjDPJvtCsqKn5DEADdDmcNiH4hIxHnVbeR8ck7FD15L8Y2N6bWZoruvoLmaX+g9sxLCccnRjvK3qEqGNpc6IJB2tKSCaXn4o9PxenWodispKdDTg4kJWkDx01NCg6HA1VV0ffTZWLmTz4hfcMGAPz7709raqp20V+I31CU/v9+FqIzHA5Hjx+zXyXd3XXnnXeydOlSPvzwQ2J3MgXquuuu48orr4zcdjqdFBYWkpWVRepAmK7d1ARtbR3qJRIAmtsgIalvQhN9I/eZe4mz1ADgLR5N6wmXUN9i4XvrL5Hq5BPzxlFgzkanSILSlzxePa9+nMnTb+dQZ43p0HOGJetICMuUzV3ZOsKdEI6VpHsA8O13PJWj9yP/sdtI/EGbpZb14f9I/fEbLOffhGf81ChH2IMUBYOzBYO3jWBaJoGiEgKpObQ6jfgdkJ0NJSWQldW+BIuiKOh0OrKysvptkqJ78cXItunSS8nOzo5iNKI/GwjvZyE6w9wL7Zj7VdKdmZmJwWCgsbGx3f7GxkZyc3N3+dy77rqLO++8k+XLl1NRUbHTx8XExBAT8/uTYb1ePzA+KBoaQKcD4+7/6XTb/RGDQ+K3K0l//2UAFHMMlotvo9LbwLfWNQAUJOSwV9ZEguYwurBOEpQ+0thi4tn3snnhg0xcbb/93VTZ0W+hDpWc9CB7jXLLv1MH6Lb7n+j/wuk51F59HykfvU7Os/dg8Hkw2RoZ/vdLaT3sZBpPuww1tj8vZt4NVcXgbEXvdRNOTqetZCyB9DxaXCY8TVqv7YqKXV8f1+l0/ffco7ER/vMfbTs7G/2JJ0rhVrFL/fr9LEQn9cb7uF/9ZpjNZvbcc892RdC2FkXbd999d/q8f/zjH9x66628/fbb7LXXXn0RanS43doX4UAYkRc9zuCwkffILZHbjaf/lUB+McMTh5EWk8KkjLHsl7MXZkN/avA6uK2tiWPOwmKOuHICj76Z2y7hPmCCg4uPt2y58NV+LfLW29edWYuhX30KC9GDdDoc046ncsFSPOO2tfFMe/9lSq+fQfwvq6IYXBepKgaXHVPDJlSDAe+oPXBP2Jfm2CI21ZswmbRe2/vso7UA282EtP7riScgqC3p4NxztW4pQgghuqxfjXQDXHnllcycOZO99tqLKVOm8O9//xuPx8M555wDwNlnn01BQQELFiwA4O9//ztz587lueeeo7i4mIYGrYBUYmIiiYmDbO2Y1apNLc/MjHYkoq+pKvmP3ILR1QpA84QptBxyInrAqDdwWMH+6HVa9ibFpnqXqsLKH5JZsiyHL9Ykt7vPZFQ4dr8WZh3dSFmhtk57dJH3d326c9KDXHdmLUfsbe/L0IWIilBmHpv+9gBpH7xC9vP3og/4MDfXMfyO2bQcNYOmU/6CGtP/l1jo3Q4MbjvhhBS8ZRMJZhfgDMTSUk//77XdGYoCixdvu33BBdGLRQghBol+l3SfeuqpNDc3M3fuXBoaGpg0aRJvv/12pLjapk2b2g35P/zwwwQCAU4++eR2x7n55puZN29eX4beu8Jh2Ly5n/cWEb0lbflLJH7/KQBticksOeogSh2/MjatDCCScIve4w/oePOzdJa8ncOvdXHt7ktOCHHaYc2ccUQTWamhdvcdsbedQ/e0s2pdIs12E1mpQfYc5ZYRbjG06PW0Hn4K7gn7kv/IfOLXfQtA+jvPk/D9p9RfcDPe8olRDnLH9B4XBlcrSlwivhETCGQX4FHisTZqX8njxkFh4SD6en7vPaiq0raPPBJKS6MbjxBCDAI6Vd1Fz6khwOl0kpKSQmtra/8upGa1wmefaVVZTB2bPly7GTZuhNyc3T9W9F/mukpKbjoLfVBrdffsGWdQWVZORcZYylNLfvd4FRWPwSdFp3qI3WVg6ftZPLs8G5uj/e9eYbaPmUc3cfyBNuJjpVVKb5H39CCjKKS9u5TsFx+MfK6pOj0t08+g+aSLUM0dK0LY2/ReNwa7DSUugUDucIK5hbTpErBata/hwkIYPlwb5e4sRVFoamoiOzu7/62BPfFEeO01bfvVV+GEE6Ibj+j3+vX7WYgusNvtpKWl4XA4SO7Kh/wO9LuRbrETjY3avNYOJtxicNAFAxQ8dEPkxPTLKVOoH1PBITmTyYhNi3J0g1t1QwxPvZ3Nf1Zm4gu0P4mYXO5m1vRGDplslxFrITpLr6f16NPxTNyPvMXzid/4IzpVIeOtp0n87hMss+fhKx0XtfB0vjaMdhtqTCy+4aMI5hbhMyVhtWp1TIuKoLgY0gbjR7DFAv/9r7adlwfHHhvdeIQQYpCQpHsg8Pmgvr5rl9PFgJb+4v3EbtL6pDZlZfHz8WdwRP7emA1S1KY3qCqsXp/AkmU5fLA6FVXdNqqq16kcsXcrs6Y3MnFkWxSjFGJwCOQVU3PTo6S/9QxZryxEHwoSY6mieP652I6dSfMJF4Cx7y406/w+jHYrqtGEf9gIAnlFBGJTsFohFIL8fK39V0aGlnwPSo89pi1nAzjvPLnQL4QQPUSS7oHAagWXS5vLJoaM+DVfkf328wCEDAbWnHsNU4fth27Qnu1FTygM732TxpK3cvixMqHdfXExYU462MrZRzUxLDsQpQiFGKT0BlqOnYl70gHkL55HXNUv6JQwmf99nMRvV2KZPQ//8FG9GoIu4MNgt4LBiD9/OMHc4QQS0mhpAV8r5ORoyXZ29iDvmhUOwyOPaNt6vRRQE0KIHiRJd3+nqloBtZiYQXxpXfyWwWUnf9G8yO1NJ55P7vhDoxfQIOXx6nnlo0yeeicbi7X9OtLstABnHtnEKYdYSUkIRylCIYaGwLARVM99gow3l5D1n0fRhUPE1m6g5OazsR5/AdZjZ4GxZ09ZdMGAlmwDwZwiArlFBJPSabXr8NRpjUImTNh1r+1B5e23obZW254+XZtHL4QQokdI0t3fORxgs0lv7iEiEA6yqvkH/vT8M5hamwBwj5uC/9jzohzZ4NLQYuKZd7J56cPMdr21AUYVtXHO9EaO3qcVs3FI15kUom8ZjdiOPx/3HgeRv+hmYms3oAuHyXplIYmrP8Jy4TwCw0Z0/3VCQYx2KygKocw8AvnFBFMycTh1ODZra7UnT9amkw+p2dULF27bnj07enEIIcQgJEl3f9fUBIHAIGj8KXanxWfn88bVlH39KZmrVwIQSkyhfvb8QT6nse/8UhPHkmU5LPsinVC4/cyRAyocnHNMI/uMdcmkEiGiyD+8nKpbniLrP4+S8cYSdEqYuKpfKLnpTKwnzcZ2zFmg78LQcyiE0WFFFwoRzMghkF9CKC0Ll0dPS+0g67XdWbW18NZb2nZhIRxzTHTjEUKIQUaS7v4sGNSmlksBtUFNVVU2Oqv53vozKTYr05cti9zXcO4NhNKyohjdwKcosPKHZJYsy+HLn9v/LpmMCn/cr4WZ0xspG+aLUoRCiN8xmmg++WJckw8if/F8Yuoq0YeCZL/wAImrPqL+wpsJ5BV37FjhEEZHC7qAn1BGDv78YkJp2Xh8BmybB2mv7c569FHtwxLg/POHyHx6IYToO5J092dWKzid2hw3MSgFwkG+bv6eOk8D+nCY015/E3NAK9ZlP/g4XHvLOu6u8gd0vPFZOkuW5VBpiWt3X0piiBmHNTPj8CayUkNRilAIsTu+0nFU3fI0ma8uIuOtZ9CpCvEbf6TkhjNoPuUSWo6asfOZQEoYo7MVna+NUGoWgbIKghm5+IIGrPXa1PGRI7vea3vQCIW0pBu0ZPs8Wc4khBA9TZLu/qy+XvsClCvOg5I35OODus/whNrQo+PPX/9M9qYqAAI5hTSceVWUIxyYWl0Gnl+ezfPLs7A52y/ILMrxMfPoJo47wEZ8rBKlCIUQnaGaY2g+7TLce04jb/E8Yho2oQ/6yXnuHpJWfYjlgpsJ5gzb9gRFweBqRe/1EEpJJ1AylmBmHgHFiLVxCPTa7qw339T6cwP88Y/a/HohhBA9SpLu/srl0tZzSwG1QSvWEEOyORGAI11myt95DQBVb6Du4ltRY4fqPMeuqa6P4cm3c3j9kwx8gfYjX5PLXcya3sghkx0YZHm8EAOSt6yCqtueI+ulB0l/dyk6VSV+3beUXn8aTaddRuuhJ2HwONF7nIST0/GOHkUwM4+gzjy0em13lhRQE0KIXidJd39ltYLXC1mynncwCYSD6HQ6THojOp2OKdmTMLR5GPXvmehUbeS1+YQL8I0YH+VIBwZVhVXrEnliWQ4ffpuCqm47i9brVI6c0sqs6Y1UjGiLYpRCiJ6ixsTSdOZVuPY6hPzF8zE316EP+Mh96h8kf7aM+plzaJuwD8HsAkKGGK3Xtm8I9drurKoqePddbbu4GI48MqrhCCHEYCVJd38UDmsF1IZsRZfBSatOvorM2HSmZE9Cp9MRYzCT9+xtmK3a1L628knY/nROlCPt/0JhePerNJYsy+GnqoR298XHhjn5YCtnHdVEQVYgShEKIXqTd/RkKu94npxn7ibto9cBiN/4I8ULLmLTFf9iw0Hn4Wkbgr22O+uRR7SrlwAXXihXJIQQopdI0t0ftbRAa6t2aV4MeKqqssFRxQ+2X1BQwddKQAkSYzCT/Pk7pH6qtWkJxyVgueiWrrXCGSLcXj2vfJjJU+9kU2+LaXdfTlqAM49s4pRDrCQnhKMUoRCiL+jb3BgcNppPuQT7wceTv2guMY21GNpclNxxAcnLX8F77yPk7DlsaPXa7oxAAB57TNs2GuEcueArhBC9RZLu/qihQbvybJR/noEuEA5sqU7eCEBBQi57Z03EbDBhtDaQu2RB5LENM+cQzJJK9TtSbzPxzLvZvLQiC7e3/UWJ0cPbmDW9kaOntmI2qlGKUAjRF/ReDwaHDSU2Hl/JWII5w1DiE9k85UQK77mS4ve1JDLjq7fh6PFw331w1lmygHtHXn9dqx0DcMIJ2nQAIYQQvUKyuv6mrU2rWp6SEu1IRDfZfK180bgaT8iLHh0TM8cyMrkYnU4HSpj8RXMxtLkBcOx7FM79p0c54v7n5+o4nngrh3e+SicUbn/SfNBEB7OmNzJ1rEvOp4UY5HQ+L0a7FdUcg6+onGBOIUpiMh4P2DZBfHwy/gcfxffzScReer5WjdvhgJkz4eWXYfFiSSp/a9GibdtSQE0IIXqVJN39jc0GHg+kp0c7EtENYVXh88ZVtIV8JBjj2TdnMumxqZH7M/73NAlrVwMQzMilYeacKEXa/ygKfPx9CkuW5fDVL0nt7jMZFf60fwszpzcyssAXpQiFEH1F5/dhsFvBaMQ/rJRgbhHhpFR8PrBu3kGv7VHTYdpPcPnl8PTT2kHeeAPGjYMHH4RTT5VRb4ANG+D997XtkSPhkEOiG48QQgxyknT3J4qiFVCLjZWTggHOoNOzd9ZEfnVuYq+sCsyGbYsKY6t+IeuVhwFQdTrqLroFJSFpZ4caMvwBHf/9NIMn386m0hLX7r7UxBAzDm9ixuHNZKaEohShEKKv6AJ+DA4r6PQEc4sI5A0nnJJOIABWy256baelwVNPwUknacXBmpq0WikzZsArr8BDD0lnkMWLt23Pni0F1IQQopdJ0t2f2O3aSLeMcg9INl8r/nCA/AStAF5OfBY58e1P7HQ+L/kP3YAurBX6sh07C+/oyX0ea3/S4jTy/PIsnl+eRYurfcWj4bk+Zh7dyHEH2IiLkfXaQgx2umBAm0auqgSzC7Yk2xmEwjqsDZ3stX3ccbD//nDppfDCC9q+l1+Gjz7SelOfeGKf/Ez9jt8PTzyhbZvNMGtWVMMRQoihQJLu/qSpSTujiInZ/WNFv7F9dXK9zsCRhQeSaErY4WNznr+HmIZNAHhLxtJ84oV9GWq/UlUfw5JlOfz3kwz8wfajLHuNcjHrmEamTXLIAIwQQ0EohNFuBSVEMCOPQH4xobQswoqOFms3em1nZsLSpdqo98UXaxe2m5u126efDvffP/QudL/yivb3ANrfQ2ZmdOMRQoghQJLu/sLvh7o6SJJpxgPJb6uT58dnYtabd/jYxFUfkvbBqwAo5lgsF98KxqHVy0ZV4Zu1iSxZlsOKb1Pb3WfQqxy5dyuzjmlkQmlbdAIUQvStcAijw4YuFCSYnkMgr5hQejYKelpbtBInPdJr+5RT4KCD4KKL4D//0fY99xx88IHWq/rYY3vqJ+r/ti+gdtFF0YtDCCGGEEm6+wubDVwuKCiIdiSig2y+Vj5vXE1byIse/Zbq5MO16uS/YbBbyXvstsjtxjOvIpA3vC/DjapgCN79Oo0ly3JYU9V+FkBCbJiTp1k586gmCjIDUYpQCNGnlDBGRws6v49QejaB/GKC6TmoegMOh1Z4PD0dRo3SppP3SK/tnBx49VUt2b70Um1JV0MD/PGP2hTrf/978HcO+eUX+PhjbXvMGDjwwOjGI4QQQ4Qk3f2BqmrtTYxGKWYyQKy3V/KD7RcUVK06ee5k0mNSd/xgRSF/8XyMLjsArskHY592fF+FGlVur56XVmTy9Ls5NNjazwDITQ9w5pFNnHJIM0nxSpQiFEL0KUXB4GxB72sjlJpJYOQEguk5YDTicmn1zpKTYeJE7Rp0bGwPv75OB2ecoVXrvuACeOstbf+SJbB8OTz2GBx5ZA+/aD+y/Sj3hRdK0VYhhOgjknT3By6XtsYsNTXakYgO8oS8KKgMS8hlr6yJ7aqT/1baey+S+OPnAIRSMqg//6ZBf6JjsZp45t1sXlqRhcfXfj7omOFtnHNMA0dNacUkn0BCDA2KgsFlR9/mIpySQVvJWIKZuWA0ab22LRAfr3X2KizUtntVfj68+aaWbF9xBTidWveQo47SktG77hp8y728XnjySW07NhbOPju68QghxBAip7z9QXOzViUmOzvakYhdUFU1MnW8ImMM6TGpFCXm73A6+VYxtRvJfuG+yG3L7PmEk1J7O9SoWVMVz5JlObz9ZRphpf3fy8GT7Mya3siUMe7Bfs1BCLGVqmJwO9B7nIQTU/GO2ZNgZh6qyaz12m7YQa/tvqLTwTnnwGGHwfnnw3vvafsXL4Z334XHHx9c/atffFGbUg/w5z8PvQJyQggRRZJ0R1sopF1dT0yMdiRiJ7ZWJ7e0NXJQ3lT0Oj0GnZ7hSbtef68L+Ml/+Eb0QW2dcstRM/BM2KcvQu5TigIffZfCkmU5fL22/ciQ2aRw3P42zj66iREFvihFKIToc6qK3uPE4LYTTkjBWzaRYFY+akxsx3pt96WiInjnHW3q9dVXa9Xbqqvh0EPh//4PFiyAhB13pBhQpICaEEJEjSTd0dbSol15zs2NdiRiBwLhAF81fY+lTatOXuu2MDxpWIeem/XSg8TWbgTAN2wETX++tNfijAZfQMfrn2Tw1Ns5VNW3X3iZlhRkxuHNzDismYyUUJQiFEJEg97jwuhsIRyfhHdkBcGsAtTYOEIhOt9ru6/odFoieuSRcO65Wi9v0FqKLVumTUPff/+ohtgtP/wAn2vLnJgwAfYZfBeAhRCiP5OkO9rq67Uve6P8U/Q3v61OPilzLEWJHasun/DjF2S8/RwAismM5ZLbUc2Do/+6zWHk+fezeH55Fq2u9mvZi3N9zJreyJ8OsBFrVqMUoRAiGvRtbgzOFpTYeLyl4wjmDEOJSyAchpbmbvTa7kulpVobsQcegDlztHXQGzdqVb6vvBJuvRXi4qIdZedtP8o9e3Y/udIhhBBDh2R60eTxaO1KBnuLkgFGVVXWO6r4wfYLKiqJxnj2zd2TtJiO/TsZXHbyFs+L3G469f/wF47spWj7TqUlhieX5fD6pxkEgu3Plvce7WLW9EYOnuTonyfSQoheo/d6MDhaUGNi8Q0fRTCnECUhCUWBVlsP9truK3o9XHYZHH20tub7s8+0LiN33w3/+5826j11arSj7DiPB555RtuOj4czz4xuPEIIMQRJ0h1NViu0tWnz60S/8YPtF9Y5KgEYlpDHXlkVu6xO3o6qkvv47ZjsVgDcE/ah9YhTeyvUXqeq8NUviSxZlsNH36W2u8+gVzl6aiszj25kfGlbdAIUQkSNzufF6LChGk34h40gkFeEkpiCqoLDvq3X9ujRkJfXQ722+1J5udbT+p574MYbwe+HtWthv/3g2mvh5pshZgDMYFq6VKvODjBjhlzoF0KIKJCkO1oUBerqtGlqMs2rXylJLqLKVcv49FGMSB6+y+rkv5X60eskf7MCgFBiCvUXzuuncyh3LRiCd75KY8myHH6ubl9AKCE2zMmHWDnryEbyM4NRilAIES26gA+D3QoGI/784QRzhxNO1iqh9Umv7b5kMGjF1Y45BmbNgq+/1r6/FyyAN97QWnBNnhztKHdt4cJt27NnRy8OIYQYwiTpjpbWVrDZtDl3IqpUVaXFbycjVjtpTDYn8ofhh2HSd+7Xw1xfQ87Td0Vu159/E6HUgfXv62rT89KKLJ55N5uGFnO7+3IzApx1ZCMnT7OSFK9EKUIhRLToAn4MDhvodARzigjkbUm2dTqt17atj3tt96WxY7Vp5v/4B8ybB8Eg/PSTNs38hhu0P/1xKH/1avjmG2178mTYa6/oxiOEEEOUJN3R0tgI4TCYzbt/rOg1W6uT17c1Mi1/X7LitKn+nU24CYXIX3gT+oDWFqv1kBNw7zmth6PtPXVWM8+8k83LH2bi8bVfcDmuxMOs6Y0cuXcrJvnEEGLoCQUx2q2gqgQz8wjkFxNO0cqO+3zaSqmtvbaLiyEpabdHHJiMRrj+ejj2WJg5E777TivFPn8+/Pe/2qj3hAnRjrI9KaAmhBD9wsCb9zoY+Hxa1fLk5GhHMqTZfK28u3kllrZGdOjxhLxdPlbWa4uJq/wZAH9uEY2nX9lTYfaqHyvjueqBEo6+ajxPvp3TLuGeNsnOk9ev48X5a/nDvpJwCzHkhEIYbQ0YWxoJpWbRNn4q3tGTCadmEgjqsFi0SVvDh8O++2r55qBNuLdXUQFffglz526rCvftt7DHHtq080BA6+89YoR2JeKBB3Z+LL8fLr0Uysq0v8CtRc58Pjj+eG1d+cSJcMQRWhX1jmhq0orAjRgBjzyi7UtM1NZzb8/thqOO0mbcpaZ2/L7GRpgyRbvgIIQQokPkNDoabDatqMmwjvV7Fj2ru9XJfytu3bdkvPGEdmyDAcvFt6HG9t+WMooCH36XwpK3cvhmXfsz5BiTwp8OsDHz6EZK8/1RilAIEVXhEEaHDV0wSCg9G39+CaF0rcdXKATWpn7aa7svmc3aCPef/gSnnAJVVdrsteuvh0cfhawsWL9eqya3xx5wyCHavPvf0F13nfaXt3699t+Ghm13XnghTJ+u7X/gATj/fPjww93HNmeO1of7+OPh4ou1fTNm/P6KiMmkFYRLT4dp0zp+X06OVkzuqae0nuZCCCF2S5LuvqaqWgE1s3lAFtga6LZOJ7e0NQJQmJDHXtkVmPRdW4un97goePgmdKrWk7r5xIvwlY7tsXh7ktev4/VPMnjq7RyqG9pXNkpPCjLj8GZmHN5MerKMXggxJClhjI4WdH4fodRMAsNKCabngMEwsHpt96U994SDDtJKtL/zjnZVs7ISNm2Cf/8brrgCTj0Vnn8ebrut3VN1bW3w+OOwefO2qxa5udp/Y2O14m1b7bMP3HUXHfLii7Bhg5awb7WjYm8xMXDooVBd3bn7QEviL7tMkm4hhOggSbr7msMBzc2/n64l+kSdpxFLWyN6nZ5JGeMYkVzUqerkv5X75N8x2bSRCc+oydiOPbunQu0xVoeR55dn8fzybOzu9r/ypfleZh7dxB/3txFrVqMUoRAiqhQFg7MFva+NUEo6gRHjCWbkgtE4cHtt96WVK+H117Xp5rNmwbp12lSAq6+G116Dww/XEvHfMFRXayPJd9wBy5dr3UzmzYPDDvv9a9x7Lxx33O5jsdm0Im+bNsH332v70tN7vn7MnnvCDz9os/ZkqZwQQuyWJN19zWrV1nsN6B4qA1dx0jCcARdFSQVdnk6+VfJnb5Py+dsAhOMTsVw0H/T950x0Y10sT7+dw38/zSAQbD8cNWWMi3OOaeDACqeMVAkxVKkqBmcr+jYX4eR02krGEMzMA6Np8PTa7gubN2vD/+PHa2u7Cwu15Bfg00/hq69g0iRtFHz7D9xQCF1NjVYZ/c47tececQSsWaMdb6s77tDWc7//fsdj2r6AWmlpt368HTIaIS0NLBZJuoUQogMk6e5LwaD25TwkKs30D/5wgJ9a1jEhfTRmgwmdTsfEzO5P/zY1W8hdsiByu2HWHEKZed0+bnepKnzxcxKPv13Kp9+lt7vPoFc5emoL5xzTyNjirheNE0IMcKqKwe1A73YQTkzBO3oywcw8VHMMMAh7bfe2+Hht3j1oo9VTp8Lee8Mzz8Cvv2rf/V9/rY1gP/64NjcfCBcUoOr16M44Q3vuHnto9/3447ak+6674NVXtZHwjvRgy8jQEuLnn9dup6RoXwxFRT38Q6P9zHH9t36JEEL0J5J09yWbTRs2yIt+cjYU2HytfN64mraQl6ASZJ+cHaxp6wolTP6imzF4PQA49p+Oc9+je+bYXRQMwbIv01myLIe1Ne1PzBLjwpxySDNnHtlEXkYwShEKIfoDvduBwW0nnJCCt2wiwewC1Bgtox70vbZ7S0WFNqW8sFC7fcop8PTTWo/sq67SCquBVgStokJLpM8/HzUjQ1s3/c472vrtqirtz5gx2uP/9S8teV6+/PdL0q67Trsacumlv49n/HhtdB20CuSffAIHH9yzP3Njo7YOfevPLIQQYpck6e5LDQ3al5QshutVWnXySn6wrdWqk5viGZU6oseOn/Hmk8Sv+xaAQGYeDWdf22PH7iynx8BLH2byzDvZNLa2X7OXl+Hn7KOaOGmalcQ4JUoRCiH6A73HhcHZghKfhG/EBALZBaixWkY9pHpt94aTT9YS58MP126fdZY2sj1pkvadf8kl8L//QU2N1orroovQvfIK+gULUB9+GN0FF2iVwvV6bVp4QYE2K+6qq7Sp4Yccoh03JkZrVQbaeu099/x9LKqqXdzfatUqbcR967qAuXO1svMXXaTdrqjQ6sxs7ahyyCHaBYPd3ff223DCCVJJTwghOkinquqQrp7kdDpJSUmhtbWV1N4sbuZ2a2u74uIgIaH3Xmc7tZu1ZWC5Obt/bH90xX2lzJreyKQyD4oCdzxTyMrvUwCVs49u4owjmn/3HH84wFWPJLLqp0IcjlRuvuIVTtyjIFKd/PC/jsdsVIkxa0nohX9sYPo+rbuNpbohhusXFWNvVci0rWMJsxirW0vNDYvxjpoUeVxds5nrFxfzS008BVl+Xrv9l3bHeeXDDB55MxdV1TF1rJObZm7CZIR1m+K4+4UCFl/TsT6sdc1mnn4nm5c/yqTN1/4izrgSDzP+UMsf93JjMgy1Pj5iMFJR8Rh8JIRj0SHv6c7Qe90YHC0osfEEcocTzBmGEp8IaOVFrFYtLxw2TOu3nZYW5YAHIrdba6H1+ec7/353OrXCalv7ZgNKUhL8+9/ozzmncz3XwmGtmvmXX/4+6f3kEzjwQG17//21273hwANh8eJto/JiSFMUhaamJrKzs9HLhRgxCNjtdtLS0nA4HCT3UN0KGenuK1brtvKvYrd++DUeh8fApDJtCvcbn6Xza10sb/3zJ1xtBk66cQxTxrgoG+aLPMfud/JJw9cUlWWxx5T3efrJC5iQMRqT3tfu2HdfWsmY4Z1b0zz/8SL+fEAdV759JG+wN7NYwrI/LWqXcAMkxIW57OQ63F4D/36poN19m5vM3PdKPi/f+guZKSEuvWcEL63I4vQjmhlV5MVsVPliTRL7jHPt8u9lybIc3v0qDUXddpKm06lM28PBOdMbmTzKRZvRhzEsizCFGKp0vjaMdhtqTCy+onKCuUUoCdrwdSikfSWFw9pqpyHba7unJCbCPfdoU8PHj9/xY5KTtST1pJPgvPOgrg69y6Vtv/aadl9Hl54ZDNpI+o5sX0Bt62h2T2ts1Pp/S8IthBAdJpej+kI4DLW1fTbCPRi8+EEWx+7bErm97It0TplmxaCH1MQw06e28tbn7QuFxRljUVWV8WVWThxXgVFv7FY7sK1sDiM/VSVwfuX1xDRs4iReYZOhhG/2/f1autTEMHuO8hAX8/vp3O98ncYhkx1kpYbQ6eDPhzbz1hfbfoY/7NvCiyt+f1EmrMDyb1I489ZyTps3hre/TI8k3DEmhVMPbeZ/f1/Dg3/9lb1Gu+XEWYghTOf3YWrcjMHjwj+sFHfFvvhHjENJSCIc1mYL19drFcmnTNFmKGdmSsLdbYcdtvOEe3tHHQU//YR69nbtJd98U1tE/9xz2vTwrrLZ4KWXtO30dG3ae2/IyYHTT++dYwshxCAlI919obVV+5MzQOd5R8HXa5OYeXRj5Ha9zUx+ZiByuyArwPcbEwgqIUx67W0cYzBzUN4U4k1xkenkO3LdomJUVceEUg9XnlpHenJol7E0tJjJiXWQ9fGrAKgxceRm66m3xzO8YOej0r9VbzOTn9H+Z7DYtq3DnjjSza1Pbqsw6/XreH1lBk++k0NNQ/tR64zkIKcf0cRphzWTlhTucAxCiMFJF/BhsFvBYMSfP5xg7nDCydpccUXRvoKk13Y/kZqK+sQT2A89lNQ5c9A1NGj/QGecAS+/DAsXQnZ254/75JPg92vbM2dKyXkhhOhHJOnuC42N2tVro/x1d1RDi4mMlF0nw/5wgHdqP2JcWjklyVoF1ZSYXa+7eOqGdeRnBgmG4L6XC7huUTGLdrOO2uBsxejY9m/XcOZVqCtiOviTdFxmagi720id1cQrH2ax9P0s7O7275nSfC+zpjfyx/1aiDEP6XIMQghAFwxoyTYQzB5GIK+YcEo66HSRmlrSa7t/8h91FOoxx6C7/PJtLb5eew1WroSHH+7cSLWqalPUt5o9u2eDFUII0S2SBfY2rxcsFq1XpuiwOLOCP7htvmNeRgCL1cykMg+qqvLj5ja8pmbaQl42OKoYnjQMfQfmR+Znai2zTEY4++hGpl+zm+mAisKkN2+lXn2BEAba9joI+0HHYXnZTN52I+8dkZcRoLZpW7Je19x+5PuX6jh0qEy/ejyhcPuVH1PHOpk1vZEDK5xSLFYIAaEgRrsVFIVQZh6B/GJCqdvmiUuv7QEiI0ObVn7SSdoabKtV+3PKKXDaafDAA9pjduejj7S2ZQDTpsGoUb0athBCiM6R0/feZrNplU0TE6MdyYBSXuilqn7bGeJRU1p56cNM2oIB3qn8gRXf5DJu/E8UJuZzSMG+HUq423x6nJ5t8yn/93k6Y4a3RW7PWVjM8m9S2z0n7d2llKx9m8ms5sn4i2k49wbe/SaN3PQAw3P8nfqZjty7lRWrU2i2G1FVbd360VNb+HxNEhf+cyQz5o9BRRdJuI0GlT/ub+PlW3/mies2cPAkSbiFGPLCIYy2Bky2RkKpmbSNn0rbmD0JpWWBTofHA5s2abOMx43TimqPGCEJd7930kmwZg2ceOK2fUuXav+I//3v7p+/cOG2bRnlFkKIfkdahvVmyzBV1Vp6tLREZT33QG4Z9sy7WdTbzFwzow7Qiond9EQ2H3+fhIrCPlO/5pJjPZQmF7Hi21RWrE7l1vNrALj58SI+/i4Fq8NEamKI+Ngw79y9htomM5ffNwJFAVXVMSzbz/Vn1lKQpY02H3vtWO6cXc34Ui0Rj9m0geKbz0YfCrKOcmbkf0SLkkpiXJjbL6imvFCrin7To8M5ZLKdQyc78Pp1HHPNeAIhHa42AxnJIf64v40rT7UA8NKKTB59MxdVhdz0AM42A+tr49v97EnxIU6ZZuXMo5rITQ92+u9O2iuJwUbe04ASxmi3oQv4CaVn4y8oIZSWHVmYvX2v7cJC6bXd3+20xZKqasn2X/6irfPe6uyz4d57YUfnKU1NWs+3/2/vvsOrKNM+jn9PSSO990boXVEBC1hQRCysiohKcVVU7KyuWIFFxYLIrg3bigoKrwouq4giousCa6FYESmJUtN7O23eP0YOhiRAIJ3f57py7Z6ZZ2buE8aTc89TbqcToqPNhVv9Gn8KlEh9VDJM2pumKBmmpLspk+7CQlizxix82gJ/ANty0l1eZeXKv3XlzQc308HfQ4WrkmW/rsKDhyCfQAbFHk+4X+MN2S8osXPXc+m8MmULYC5KlDZ1PP47twGQP/xKcq6446ivU1Ju4/9WRTH/4xhyCn1r7LPbPEwauYexw3IIDKi9+vnhUoIi7c0xfU973NhLCrFUVeAKi8aRmI4zIta7RohqbbdNh0xS9uyBiRPNlc33SUyEl1+Gc881X7vd5vzvF1/cPyf8r3+Fxx5r+jcg8gdKuqW9UZ3utiYnx/xGpCfODRbo7+HuK3eyK9eXzslVdLAH0DWsI2WuCk6I7n3Q1cmPRESIy5twA8QsesabcFcldyZ31E1Hdf6dOb68/lEM734eRWV1zSWDe3cs57xBBaTEVHHG8SVHdR0RaSc8HmylhVgrynCFReJI74EzKg7s5mefam23c/Hx5rDy11+H224zV8PbtQuGD4drr4UhQ+Cee2DnzprHpaTUfT4REWlRSrqbisNh/oFspKcjx6LOGb/hb/MFzPrmvSLMhWEao/b2wQR+t4aIjxcC4PHxY9ekhzB8fA9xVN2+3dqBV5fF8ck3Yd7a2gAWi8EZxxUz4bxs+ndRbW0R+Z1hYCstwlpegjskgsru/XFGxXs/g9xuc8ZSVZU5ayk93awupc6ldshiMUt/nXUWXHMNfPyxuf3ll82futxyi5mw/3FuuIiItDgl3U0lPx9KSiAhoaUjaXMMw2Bz0Xa+L/iZML8Qzkw8GZvF1uTJNpjlwRJenO59nXP5LTiSMhp0DrcHVq0PY96Hsaz/peYCev6+Hkaelse4c3NIi2vYQmwi0o4ZBrayYqzlxbgDQ6ns0g9nTCKGrzlSSrW2j2FJSbB8uZlo33GHeRMczO23w0UX6eYQEWlFlHQ3ld27zT94+qPXINVuB1/lbGRPRQ4AQT6BeAwDW3P0BBsG8a/MwF6cD0BZn5MpPHv0YR9eUWXlvS8ief2jGH7LrrlUcGSokyuH5jD6rFzCg92NGraItG3W8hLsJYW4A0Oo7NQHZ3Qihn8AgGpti8ligeuuM1fHGzOm/naGYS6k9sUXZukwERFpFZR0N4XSUsjNrXuVUalXXlUh/8teR4WrCqvFynFRPekYnNIsPdwAYasWE7z+PwC4gsPYfd2DhzVBMrfIzpsrYlj4aTTFZTX/k8pIrGTC8GzOH1SAn+8xvWahiBzAWlGGraQAT0AglR174ohLxvDfX81AtballsNd+3bPnqaNQ0REGkRJd1PIzYWKCrN0hxzSH4eTGxgE+QRycmx/wvyabz68754sYhfM9r7ec+2DuMOiDnrMlh3+zFsey/trInC6ak6oHNizhAnDszmtT4nma4tIDdbKcmzF+Xj8O1CV2hVnXAqeDvunopSXmzOUOnQwyzQnJ5v/X4T4+MZtJyIizUJJd2Nzu82hXUFBh24rAHgw2FG+GwODlKAE+kf3wcfajLemy0nC8w9gdZhzrAvPvISy4wfX2dQwYO0Pwcz7MJb/fl+zZJndZnDewALGD8+me2plk4ctIm2LpaoSe1Eehq8f1UmdcMSn4gna/3Dxj7W2O3VSrW2pw2mnmXO8d+2qu9d7X+24005r/thERKReSrobW34+FBWZK9zIYbFZrAyKPZ6cynzSg5ObbTj5PtHvvkBA5iYAiiPSeS1jOuGb7PTvWobt9w5sh8vCh/8LZ96yWDbvqNnlFNzBxegz87jy7BxiI5zNGruItH4WRxW2wjyw26lOSscZl4o7OMy7/4+1tlNTVWtbDsJmg7//HS691Lxh/ph47/vbOWeO1pMREWlllHQ3tuxs83/t+tXWxxxOvg2X4faWAQvyCSTIJ7DZY+mwaR2RH7wGgAMfzih4hw0v9QQgNsLB7aN2kVPow4IVMeQU1iwblhhVzbhzc7h4cB6BAZ5mj11EWjeLoxpbcR5YrDjjUnDEp+IOCfcmR6q1LUfk4ovhnXfM+t1/rNOdlGQm3CoXJiLS6igzbEwVFebiJVpArV4Hrk6eGBhHuF/oIY5qGtbyEhJeeBDL7z0FD/A3NnC8d392gQ/3vJAG1PwG3CejjAnDsxl6QhF2dSaIyIFcTnwKczEMA2dMoplsh+7PplVrW47axRebZcG++ML83hEfbw4pVw+3iEirpKS7MeXlQVmZWddFasmrLGBt9noq3ftXJw/zbb7F0mowDOJenYlPvjkyYRWnM4u7Dmj0x2TbYOgJRUwYns1xncvVEyUitblc2IvywOPCGRmPIyENV3i0N9lWrW1pVDabyoKJiLQRSrobi8djLmwSEKCxgQfYN5z8+4LNGBgE+wQyqJlXJz9QyOplhH65AoBCwhjH63io/5vvYzdkccEpBc0Vnoi0JW4X9uJ8LE4nzsgYHPHpuCL2d12r1raIiMixTUl3YykrM7swQltmqHRrtjZ7PTvLzZqhLbI6+QF8cnYR99rj3tfX8wI7ST7oMVaramyLyAE8buzFBViqKnFFxOBITMcZEVuj61q1tkVERERJd2MxDLO3Wwuo1ZLQIYbdFdkcF9WTjsEpzb46eQ1uFwlzH8BWVQ7All4X8fYPlx3ysOgwrUouIr/zeLCVFGCrLMcZHo0joxfOyLgan/+qtS0iIiL7KEOURmcYBpXuKjrYAwBIC0kmOiCKQJ+AFo4Mov49jw5bvgOgKiqRG5zPHLS9BYPYCCf9u5Y1R3gi0pp5PNhKi7BWlOIOjaQ8vTvOqHiw7x8nrlrbIiIiciAl3dKo9q1OXlRdwjnJg/GzmWW2WkPC7b/1B6KWvASAYbEywfo6n25O+n3vvuHj+3vhLb9vu+eqHd563SJyDDIMbGXFWMuKcQeHUdnteJzRCRg++8sIqta2iIiI1EdJtzSaP65ObrNYKaguIr5DTEuHBYC1spzE5+/H4nEDMMvvXhblnAlAWJCL8efuZeGnMWQX7P8SHRvh5J6rdnD2iUUtEbKItDTDwFpegq2sCHdgKJWd++KMScTw2z8pW7W2RURE5FCUdMtRa42rkx8odv6T+ObsBOBLywDuqZoKQHJMFS/ctZW0uGquvSCbdZuDyC3yITrMHFKuHm6RY5O1vBRbaSGegCCqMnrjiEnC8N8/Yke1tkVERORwKemWo1LtdvBlzgb2VuQCrWN18gMFf72SsP8sBaCUIK4wFuDGTt9OZTx7xzYiQlwA2KxwUnfN3RY5llkry7AVF+Dx70BVeg+csUl4AgK9+1VrW0RERBqq9WRG0ib9ULCZvRW52CxWjovqRXpwcsuuTn4Ae0E28a887H19C0+znQzOPqGQx27MxN9XpcBEBCxVFdiL8jH8/KlK7YozNhlP4P4V0FRrW0RERI6Ukm45Kr0julHuqqBPRPdWNZwcAI+HuLnTsZWXAPA2l/Ia4xl/bjZ3jtmpoeMigqWqEntxPobdh+qkDBzxKXiCQmu0ObDWdlIS+Pm1UMAiIiLS5ijplgapdjvILNlB17COWCwWfG0+DI4f0NJh1anD0rcI3vQVADtJ5Abmcu/YHVx1Tm4LRyYiLc3iqMJWlAc2O9UJqTjjUnGH1FxuXLW2RUREpDEo6ZbDlltZwP9+X53cbrXRKTStpUOqV9GG7XR891kAPFi41j6P6TcXclb/4haOTERaksXpMJNtwBmbgiMuBXdoRI3lxvfV2vb1hS5dICVFtbZFRETkyCnplkMyDIOfi7bxwx9WJ4/yj2jpsOr102YrveY8iB8OAJ71vYOr702kT4YSbpFjlsuJvSgPPB5cUfE4EtJwhUXVSLZVa1tERESagpJuOajaq5Mn0j+6d6tanfyPVq0PxfL3OXT1/AzAj/a+dJtxBUkJFS0cmYi0CJcLe3EeFpcLZ2ScmWyHR9eo7aVa2yIiItKUWmfmJK1CXlUBa/eaw8lb6+rkf/Tmimg2vP4972MOK6+y+OO890GSElo4MBFpfm4X9uICLI5qXJGxVCek4YqIrZFsq9a2iIiINAcl3VIvwzCoclcR7BPIoNj+rW918t95PDBrYSIffGjle/7s3Z535W34dU5twchEpNl53NhLCrFUVeIKi8LRuQ/OyJqFtFVrW0RERJqTkm6pwWMYWH/vyY4OiOSUuBOJDohstcPJqxwWpsxN5+Ovw/g3FxBLDgClfU+l9JxLWzg6EWk2Hg+20kKsleW4QiNwpPfAGRUP9v2fXaq1LSIiIi2hdWZS0iJyK/P5Jvc7Tok7gRBfc6nehMDYFo6qfoWlNm5+qhMbtgRxA89zPh8A4AqJYM91D2hCpsixwDCwlRZhLS/BHRJBZbeuOKPiMXx8azRTrW0RERFpKUq6pdbq5N8XbOaUuBNaOqyD+jXbj+uf6MRv2f50YxOzmezdt/u6B3GHRrZgdCLS5AwDW1kx1rJi3EGhVHY9Dmd0AoZvzUxatbZFRESkpSnpPsZVuav5KnsjeyvN1clTgxI5Prp3C0d1cBu3BDJpdieKyuz44GCR7QoC3FUAFAwdRXm/U1s4QhFpStbyEuwlhbgDQ6js3BdnTCKGn3+NNqq1LSIiIq2Fku5jWG5lPv/LXk+lu7pNrE4O8PHXYdz9fDrVTnN54WeC7qFP2UYAqhPSyRlzWwtGJyJNyVpRhr24AE+HICozeuGITcLwr9ltrVrbIiIi0too6T5G5VTm8fnuLzEwCPYJYlDs8a12dXIwF0B6bXkMT7yVhGGYDwUmpb3Pdb8+Ze632dk16SEMX/+DnUZE2iBrZTl2VwEWdwBV6T1wxibh6RBUo41qbYuIiEhrpaT7GBXlH0GkfxiB9g4cH9271a5ODuD2wMz5yby5Isa77aoBW3jql2uwGAYAOaNuojq1a0uFKCJNwFJVgb0oH4+vL46ERIyoLhjBYTXaqNa2iIiItHatN9OSRpdfVUiYXyg2ixWrxcrg+AHYLLZWPZy8osrKXc+ns2p9mHfbpJG7eGT3JHwLzfJg5T1OpGD4lS0UoYg0Nkt1FbaiPLDbqU7qiCM2GadPNTb/EPZ9WqnWtoiIiLQVSrqPAftXJ/+ZzqHp9IvqCYC9FfduA+QV27lpdie+3x4IgN1mMP3PvzLB8hqh730CgDswhN3XT1O3lkg7YHFUYyvOA6sNZ3wqjrgU3KERGIYHqsyHbKq1LSIiIm1N68665Kg5jGq+2LN/dfJqtwPDMFp17zbAtl3+3DCrE7vyzPI/QQFu5ty6jSHRm4i9/3Fvuz1/vhdXROutJS4ih2ZxOrAX5WEAzuhEHPGpZtm/Az6nSkvN3m3V2hYREZG2REl3O7a3LJ/1FetxGG1ndXKArzcFccucDEoqzNszLsLB3Du30CWhjISHHsBWVQFA0eALKD1paEuGKiJHw+XCXpQHHjfOqHgcCWm4wqJqJdvl5ea87YAA1doWERGRtkdJdztkGAb/3bqVVVs3YwDBPkGcHHs8oa14dfJ93l8Tzn0vpeF0mcPFu6VWMPcvW4kJdxK1+BU6bP0eAEdMEtlX3dmSoYrIkXK7sBfnY3E6cUbG4khIxxUeXWuayL5a2z4+kJgIXbtCaGgLxSwiIiJyhJR0t0MlVVWs3rYNA4ixJ3JKUutenRzMeZov/TuOOW8nered2qeYp27eTmCAh4At3xH13itmW6uNXTfOwBMQ2FLhisiR8LixFxdgqa7CFRFDdWI6rvCYWqufHVhrOyXF3BYc3EJxi4iIiByF1p2JyREJDQjgwr592ZPjwlKQhI+1dQ8nd7lhxrwU3v4s2rtt1Om5PDDhN+w2sFaWkfD8A1gMDwB5I6+lqlPvlgpXRBrK48FWUoC1qgJXWBSOTr1xRsSCveafoPpqbRsG5OS0UOwiIiIiR0lJdztgGAZfbN1KUlgYHaPNxLVHfDzBbtha2MLBHUJ5pZU7nunIf7/bP2b09lG7uO6Cvd5pnbGvz8I3dxcAFZ37kHfh1S0Rqog0lMeDrbQQa0UZ7tBIKtJ74IyKA3vNpcYPVWvbMFogdhEREZFGoqS7jSuvrmbJxo1sy80l0NeXm04/nQBf35YO67DkFPpww5Od+PlXc0UkH7uHh6/L4vyT9z8pCP5yBWH/fR8At38gu2+YATbdtiKtmmFgKy3CWl6COzicyu79cUbFY/jU/GxSrW0RERE5Fih7acOy8vN5d/16yqqrsVutDO3evc0k3L/s8OeGWZ3ZW2DGGxLo4pnbt3FCtzJvG3v+XuL/+Yj39d7xf8UZk1jrXCLSShgGtrJirOXFuANDqezSD2d0Aoaf/4HNVGtbREREjhlKutugfcPJP9tsrk4eFRTEqP79iWkjqwyt+SGY2/+RQVml2Z2VGFXNC3dtoWNC9f5GHjcJL0zFVlEKQPGAsyk55byWCFdEDoO1vAR7SSHuwBAqO/XBGZ2I4R9Qq11pqTmUXLW2RURE5FihpLuNcbrdLPrmG7bl5gLQNymJ83r1wtfeNv4pl/wnkqn/TMXlNids90ov57m/bCUq1FWjXcSy+QRuWgeAMzKWvVffU6t2r4i0PGtFGbaSAjwBgVR27IkzNqnOygLl5ZCfb9bXVq1tEREROZa0jUxNvOxWK4G+vtitVkb07k2/5OSWDumwGAY8uySe55YkeLedcXwRT9yYSQd/T422/lk/E/PO8+ZxFgu7r5+OJ7D11xgXOZZYK8uxFRfg8Q+gKrUrzrgUPB2CarXbV2vb1xe6dDHLf7WRQTkiIiIijUJJdxtgGAZOtxtfux2LxcKI3r05tVMnotvIN1eHy8K0V1J4779R3m1XnJ3DPVftwGat2dZSXUXCc/dhcZs93/kjxlHR/YTmDFdEDsJSVYm9OB/Dx5fqpAwc8al4gmo/FDuw1nZqKoSHt0DAIiIiIi1MSXcrV15dzeING7BbrVx+4olYLBZ87fY2k3CXlNu47R8d+fIn80u5xWJw15idjD83p87R4rFvzcFvz68AVKZ1I/eSG5ozXBGph8VRha0oD2x2qhNSccan4Q4Oq9Wuvlrbmh0iIiIixyol3a3YgauT55aWEhPSdoZZ787z4YZZndm6y1xMyc/Hw2M3ZnLOiUV1tg/a8AXhK98BwOPrx+4bH6pVz1dEmpfFUY2tOA8sVpyxKTjiU3GHhNfKog9Va1tERETkWKWkuxXyGAb//cPq5NFBQVzahlYnB/gpK4Abn+xEbpFZEiw82Mmzd2yjX+fyOtvbivOJf+lv3tfZV07GkZDWHKGKSF1cTnwKc83pLTGJZrIdWrvLWrW2RURERA5OSXcrs284+fa8PAD6JSUxvA2tTg7w+cYQJj/Tkcpq81t3SmwVL9y1ldTY6roPMAwSXpyOvbQQgNLjB1N0xsXNFa6I/JHLhb0oDzwunBFxOBLTcYVF1eqyVq1tERERkcPTdjK5Y4BhGCz65ht2FBa2udXJ9/m/T6OY8VoKbo/ZG9avUxnPTt5KeLC73mPCP/k/gr5bA4ArNJI91zygCaAizc3twl6cj8XpxBURQ3VCOq6IuseHq9a2iIiIyOFT0t2KWCwWhvXowfvff8+fjjuuTQ0n93hgztsJvPx+vHfbOScW8ugNmfj7GvUe57tzGzFv/cP7evfEqeZ8URFpHh439uICLNVVuMKjcSSm44yIrXN8uGpti4iIiDScku4WVl5dza6iIrrExgKQGB7OxNNOw9KGenodTgv3vpjGsv9FeLddfd5e/jJ610EXUbI4HSQ+dz9WpznsvODs0ZT3ObmpwxURAI8HW0kB1qoKXKGRODJ64YyMgzqmsqjWtoiIiMiRU9LdgrLy8nh3wwYqnU6uPeUU4kJDAdpUwl1UZuOWORms22x+A7daDO4du4Mrzs495LHRbz+H/44tAFQldiTn8luaNFYRAQwDW0kh1soy3CERVKR3xxkVX2elANXaFhERETl6SrpbQF2rk9vaYF2dHTm+3DCrM5l7/AEI8HUz66ZMzji++JDHdvjhSyI/nA+Ax+7D7kkPY/j6N2m8Isc0w8BWVoy1rBh3cBiVXY/DGRWP4Vt7MrZqbYuIiIg0HiXdzaysupolbXx1coDvtnXgptmdyC8xe8ciQ508P3krvTpWHPJYW2kRCS9O877OvexmqlM6N1WoIsc8a1kxtrIi3IGhVHbuizMmEcOv9kMut9ucs11dbdba7tgRoqNVa1tERETkaLStTO9wjBoFkyfDoEHm6l633QbLlpldNLffDjffXOdh1m3b4JZbzO6d0FCYN89cKaiqCi6/HH76CQICICYGnn8eOnU6dCwFBTB1Kns2ZhMaYaX49uuZ73Dxzr/OZOuWzgT6+fDXsQX49suFL76AOXPMmDt1gqlT2VIQyfhpafTa8zH3Fk8hPqIav+4ZMHUq1b7BTJvpy+UfTSDHJ5nVva9n2jPR5vGzZsHq1cS5LVQOHIP7ktHekLL2+nHvC2kUltnpad/MG9bxBDoK8QQEsXviVBxJGQAEfrcG+6svUVxkUEkH3uz3KONuCsHHDivXhXLH0x1xuc1v4sEdXMy//2dS4xxk/lRB5NPTOS7wFzx2H/ZOmEJlt+P3/04Mg7hXH8Gn0Bx+7vH1J3zlOwT8spE9E6fiCQgCIGjDF8S8NQeLx0NVcifvPltxPsmz7yDrwX+Crf3dviKNyVpeiq2kAE+HYKoyeuOIScTwr73y2YG1tvv0Ua1tERERkcbSvvovvvrKTHQHDTJfz59vJsu//GLue+IJ+PHHOg8NuOMOmDjRbHv33TBhwv6dEyfC5s3w7bdw0UVw7bWHF8/TT7Mr5jiu6rWBDo88QMTMmXz5ZQZFBbH8+Pa3bFywlSfeiGXTjx6YMQOefBKWLDG/9b7yCtc/ksKkEb/yovs6Nt/8DKdG/OTdN+XpRMoCYugzJJKzozdyw7TfVw1ftgwyM2HxYnKeeI2Uz97Ad+c2b0jT/5nCqDPy+PCJH5nLDcyuuJHtTywm//xxJLw4HQBreQlxzz7A6OrXyX1qPhF3X8dNP0zi7VXRvPFRNLfMyfAm3Cd0LaVvp3JWf2/ORz959RP8HHQCC67+hD0Tp5L43P3mWNXfhf5nKSFffwqAYbHw213/YNusJbjCooh67xUALFUVxL88g523P1lrnzs0ksrOfQn97weH928gcgyyVpbhs/c3LM5qqtJ7UN73ZKpTOtdKuA0Diopgxw5zkbT+/WHgQEhMVMItIiIi0ljaV9L9wgtwxRX7Xy9aBNddZ357jIiA0aPhrbdqHRYN2DduhKuuMjdccon5LXTrVvD3h/PO2z+ZceBAyMo6vHg++YRZlZO4YlgB9OyJb1wcCT96eGC8g7iwYCJC3Yw+u5CN8zZA166QlmYeN2oU7g8/4ptNgYwJeh+6duWcMZHsyPbl19OuxLP8I15ZGsXDk3ZhqaoEh4O4qN8T2xUrYORIsNnwBIeS0+9sQtd+BEB+sZ0fMgO54JR8bMUFJOR9z0uOcfya7UfpiWfhU5CNT/YOfLN3UmyLIOHERKLDXFR2O45Ezw7WLt7DzPkpgPm7GDEon5fv3sKVZ+d4Vy4P+fITKs/9E/+3Koqqjj1xhUfR4ed1APhk7yDujVneX091SmdvL3jh0FGE/B5n0LdrqErtiiMhrdY+gOKBwwj/dPHh/RuIHEMsVRX47PkNa1UlVSldKO9zMtXp3fAEBNZqW1oKv/1mJt59+5rPKlNSwKf2emoiIiIichTaV9L92WcwYMD+17/9Zi63u09amrntAMmAJzZ2f6kci8X89llHW/7+d7O3+1CKijBcLt5cn8TxPUrN0yYkEFpSSEaie39ICQ6MPXvNsZz7JCRgzc8jKaISW665z2KBlFgHmUYalrw8ooOreOTVeHb+bweO3Xl89vnvDwX27jVXPvpdZXgC9vy95q4CX6LDnNht4FOQjSssktgoD3vyfMFiwRkZi0/eXhxxKfhXFXGysQYA36/+g6+jnJCyXd7zntS9hMdvzMLXxyAx2sHufF9spUXgdtG5bwD/+zEEAGdUAj75e8HlMsuDVVcCUJHRk8qOPb3nc0YlYC/KA7cLn/y9OKPi6twHUJXeDb8dW7FWlh3630HkGGCprsIneye28lKqkzMo6zOI6oyeeAJr1/UqLzc/2qqrzRk0J58MGRngV3s9NRERERFpBO1rUuzOnebqP03lkUfM3u+VKw/azGMYrN22jQEeD/mFgfxa/AvHkdaoofy6148e6TkksQtnSAhTpvryr3fdNMa793QI4h99Xmb09zOJu7eE90tOo4oeuLBjsxqc0K2UQT1L613JOCrMRVGZnWrH/gZR/3qZgO3m0H5HbDJlxw/BJ2/PkQVos+MODMZemIfj9zngIscii6MKW1Ee2OxUJ6TijEvFHVJ3TS/V2hYRERFpGe2rp7tDB/Ob5T4pKfDrr/tfZ2WZ2w6wA7BmZ++fe2wYZlfQH9vOmgWLF8OHH5rXqUdZdTXzv/yST/bswWO1kuK7i94JHc2du3fjiYnn1z2++0Pa7YslPs7sod5n9248kVHsLAjAHW3uMwz4LduXdEsWnsgoDKuNK4cXgL8/Pp5qouNsfL81wOwx37M/mQ0o3I0r0uw1jotwkFvkg8sNzohY7EX5ZOdZiY9ygGHgk5/t7WEu7HwSt2Yso0f1esYX/4MEdvMz3XnuL1s5tU8Ju/P3v4ddub4kRDpwB4eB1YYnLx+b1cDHbuCTtxtrRTlR//qn+au12th14wwcsSn45O1/zz55u3GFRYHNjjMyrt59+1idDjx1lDoSORZYnA7subuxFRfgjEmivNdAqjr3rTPhdjhg925zobTUVHOGTM+eSrhFREREmkv7Srr79DEXPNtn1Ch46SWzDk5BgTnHe/ToWoflAu4+fcyF1wDefReSkvavUD57tjkXfMUKCAurefA998AzzwCQWVrKC2vWkJmXh4/NRtmpp/JA6FwydwaaC7jl5tLlTz146b0oM6RiG4tWhNNnXD/4+ef9c8XffhvbuedwfNcK3iodAT//zMdv5ZMU4yD1iwXYzj2Hs04s5aO1IZCWhttt8L+cDLqnV8HQofw4ezlLVgZjLS0mZuMKSgaeA0BkqIseaRX8e3Uk7tAI9kT25Frf10mNrSb465U4I2JwxiYD0CVgB8u/CmdHjj8PMIPPLGcw+PwgTutTwqScaaSvXUBukR3DgP/7NJrhAwsAKDlpKD7vv0enpEo6ZP2IvSCHiI/ewmJ4zN/1xROpyuhFeZ9B+Gf9jO9u8z2Hf/K2N86D7QOwFedjWCy4IppwVINIa+RyYs/bg60wF1dkHBW9B1LZ7XjcYbWLaLtc5rO83FzzWdzAgebc7fC6O8JFREREpIm0r+Hll14KH30EQ4ear8eOha+/hs6dzS+kkydD797mvqVLzZ/ZswGoeOopQm691RxCHhICr75qttu5E/7yF7Ng7RlnmNv8/ODLL83//+230L8/3/70E//65RcMIDooiFH9+xNx0kmcNfEhAqYsgmgLzJjBVceV8NXmUJ4d+i45tgQmXxNJz942uP9+Sm/8K5UVHmJOSIXp03kh71cmTE/jv7YXmfLMzawOr4KcjjB9OnOLfuWaGWm4M3vS1e1h7rQdJMY44bzz+Hb2Hi6ZdRY2q0HmkCtxJZsPD4LWf867wY9wwarXePHfcfSyzeX1gKsJvKsQT0Ag90XMJWN9KA6nhZB5M/nJuBo7Lr60DGTB8U/y4CW7AYjJ20T/gf25akY3AE7sVsplZ5glwHIuvwXb3x7mi/KehLxopTqxI0E/fgWYvevuQHOutycgkD3X3k/SnL9gcbupTspg9/XTD7kPIOi7tZT1P13Fg+XY4XJhL87D4nLhjIzFkZCOK7zuAtqqtS0iIiLSulgMwzBaOohGU1Zmrgq0di0E1l6tty4lJSWEhoZSWFhI2IG92IfidpvdR19+SfHu3bwwbx5d4+IY3rs3vr8vylZWYeXkP3dl7aubCQzwNPANHYarr4biYliwAAICyC20c8V96ax4bgs7dppT0OMOs0PYMOCfy2J5cmGSd9ugniXMuXUbwR1+j93jJm3a1WRNm1fnt3iHy8JlD3bj1Xt+IfWHZSQ+dx8A7oBAMh9+C2d0wtG+Y1JnXMueP9+HIzH9qM/VHhkYlNuqCHT7Y6GeiffSNnjc2IvysTiqcUXGUp2Qhis8ps56XgfW2u7Ysf3U2vZ4POTk5BATE4NVTw+kHdA9Le2J7mdpb4qKiggPD6e4uJiQkJBGOWf76ukOCoKnnjLrVPfq1eSXKywpIfzrrwEIDQ7mxh49CE5M3L8KOhDUwcNTk3eSucuXXp2q6jvVkcnPN4fLR0TArl3QqRPR4S5WPLelwadyuWHmG8m8tTLGu23kaXlM//Ov+PzxLrHayPrb6/WeZ1euL3dctovo6l3EzZvp3b53/JRGSbhtxfkUnnWpEm5p3zxu7CWFWKoqcIVF4+jcB2dEbI3Pln0Mw3zuVlxsfhR062YWMFDpLxEREZHWoX0l3QBnndXkl/B4PHzxxRd8/vnnjB49mq5duwIQ7OtbZ/uzTiptmkAiI+Hcc4/6NBVVVu58Np3PNoZ5t91yyS5uuGhvvSuU1yc9vpr02AoSHnkQW4VZ0qt40LmUnDL8qOMEcIdGUnLy0b9nkVbJ48FWWoi1ogxXWCSO9B7m4ob2ujPo0lJzuYqQEHO+dlKSSn+JiIiItDbtL+luYmVlZSxevJjMzEwAsrKyvEl3W5RbZGfS7E78mGkOx7fbDGZcm8VFpxYc8TkjP3iDwM3rAXBGxrF3/N2NEqtIu2UY2EqLsJaX4A6JoLJ7f5xR8Rg+dT/IKy83B7p06GCuRJ6cfNCiCiIiIiLSgpR0N0BmZiaLFy+mrKwMHx8fRowYQd++fVs6rCO2dZc/N8zqxO48s2ssuIOLv9+6nYE9j7xn3n/7T0S/+zwAhsXKrhtn4AlUbSKROhkGtrJirGXFuINCqezSD2dMIkY95fBUa1tERESk7VHSfRg8Hg//+c9/+PzzzwGIiYnh0ksvJTo6uoUjO3JfbQriljkZlFaYt0B8ZDVz79xK56Qjn3duqaok4fn7sbjdAORfMJ7Krsc1Srwi7Y21vAR7SSHuwBAqO/fBGZ2I4R9QZ1uHw0y2LRaz1nZqqkp/iYiIiLQVSroPQ1ZWljfhPu644xg+fDg+bXiVoqWrI7j/pVRcbnOFye6pFcy9cwvRYa6jOm/sm7Px2/sbAJXpPcj90/VHHatIe2OtKMNWUoAnIJDKjj1xxCVj+Nc9NtzlMpNtt9tcHC093VzKoaFrLYiIiIhIy1HSfRg6duzIoEGDiI2NbdPDyQ0DXvhXHP94N9G7bXDfYp68eTuB/kdXzixo3WeEr1oCgMfXn903zqhzpWWRY5W1shxbcT4e/w5UpXbFGZeCp0NQnW1Va1tERESk/VBWVAePx8OaNWvo168fQUHml+JzzjmnhaM6Ok4X/G1eKu9+HuXddtmZudw/7jfsR1nH116UR/wrD3lfZ1/1FxzxqUd3UpF2wlJVib0oD8PXj+rkzjjiUvAE1V3z8cBa2336tJ9a2yIiIiLHqlbZb/Lss8+SlpaGv78/AwYM4Kuvvjpo+7fffptu3brh7+9P7969WbZs2RFfu6ysjPnz57Ny5UoWL16MYRiHPsjthi++gM8/h3XrzNctzO2Gtd8HsXJDOJ9vDGHSk51qJNx/Gb2TqROOIuH2uOmw6RtCVn9I0uzJ2EuLACjtfzpFp4886vhF2jpLdRX27J3YyoupTkqnvM8gqjr1qjPhNgwoKoIdO8xF0vr3h4EDITFRCbeIiIhIW9fqeroXLVrE5MmTmTt3LgMGDGDOnDkMGzaMzZs3ExMTU6v9mjVrGDNmDDNnzuT888/nzTffZOTIkaxfv55evXo16Nrbt29n8eLFlJeX4+PjQ79+/bAcavLk4sVw222wc+f+bTExcOedcOaZDbp+Y1n8aRi3zUpmZ07tckM+dg+PXp/F8IGFR3z+4K8/JXb+LHwKcmpsd3UIZs8192vCqRzTLI5qbMV5YLHijEvBEZ+KOzSi3vaqtS0iIiLSvlmMw+rKbT4DBgzgxBNP5JlnngHMod7JycnccsstTJkypVb70aNHU15ezvvvv+/dNnDgQPr168fcuXMPeb2SkhJCQ0P597//zbp16wBzdfJRo0YRFRV18IMXL4ZLLzW7qery+OPNnngv/jSMS//aETOiA5Nfg1su3c2NF+094vMHf/0pif/4az1nh123Pk7piS3zsEFMBgbltioC3f5Yav0rSVOxOB3mMHLDwBmT+HuyXf+qZ3+stZ2WplrbB+PxeMjJySEmJgarJrZLO6B7WtoT3c/S3hQVFREeHk5xcTEhIXVPCWyoVtXT7XA4WLduHffcc493m9VqZejQoaxdu7bOY9auXcvkyZNrbBs2bBjvvfdeg669evVq/P39D391crfb7OE+2DOLBx6A5cubrefXMMB3dSiLDjJrwP9fHhJ+LT6yVMwwCPp2NVA74d4ndv6TlPYfAlaNiZVjhMuFvSgPPC6ckfE4EtJwhUfX+9+9am2LiIiIHFtaVdKdl5eH2+0mNja2xvbY2Fh+/vnnOo/Zu3dvne337q27N7e6uprq6mrv6+LiYsB8SnfWWWfRq1cvysvLDx3sF19g/eOQ8rovBp9+euhzNaLBh2rgBL4+8vOXHapBQTblnyynNK3nkV9EjooBVAd5KC2zqp+7iVlcbiweN47QKKpiOuMIjIISK5QU19ne4TBXIE9IMHu2w8LM53dFRc0adpvj8XgoKSnB19dXvSjSLuielvZE97O0N0W/fzFrzAHhrSrpbg4zZ85k+vTptbY//vjjPP744y0QUTv0xtSWjkBEREREROSI5efnExoa2ijnalVJd1RUFDabjezs7Brbs7OziYuLq/OYuLi4BrW/5557agxHLyoqIjU1ld9++63RfqkiLamkpITk5GR27NjRaPNQRFqS7mlpb3RPS3ui+1nam+LiYlJSUoiIqH8h3IZqVUm3r68v/fv3Z+XKlYwcORIwh6ysXLmSm2++uc5jBg0axMqVK7n99tu921asWMGgQYPqbO/n54dfHUsDh4aG6oNC2pWQkBDd09Ku6J6W9kb3tLQnup+lvWnM6RKtKukGmDx5MuPHj+eEE07gpJNOYs6cOZSXl3P11VcDMG7cOBITE5k5cyYAt912G0OGDOHJJ59kxIgRLFy4kG+++YYXX3yxJd+GiIiIiIiISOtLukePHk1ubi4PPvgge/fupV+/fixfvty7WNpvv/1W46nDySefzJtvvsn999/PvffeS+fOnXnvvfcaXKNbREREREREpLG1uqQb4Oabb653OPlnn31Wa9uoUaMYNWrUEV3Lz8+PqVOn1jnkXKQt0j0t7Y3uaWlvdE9Le6L7WdqbprinLUZjroUuIiIiIiIiIl4qpiciIiIiIiLSRJR0i4iIiIiIiDQRJd0iIiIiIiIiTeSYSLqfffZZ0tLS8Pf3Z8CAAXz11VcHbf/222/TrVs3/P396d27N8uWLWumSEUOT0Pu6ZdeeonTTjuN8PBwwsPDGTp06CH/GxBpbg39nN5n4cKFWCwWRo4c2bQBijRAQ+/noqIibrrpJuLj4/Hz86NLly767iGtSkPv6Tlz5tC1a1cCAgJITk7mjjvuoKqqqpmiFTm4//znP1xwwQUkJCRgsVh47733DnnMZ599xvHHH4+fnx+dOnVi3rx5Dbpmu0+6Fy1axOTJk5k6dSrr16+nb9++DBs2jJycnDrbr1mzhjFjxnDNNdewYcMGRo4cyciRI/nhhx+aOXKRujX0nv7ss88YM2YMq1atYu3atSQnJ3POOeewa9euZo5cpG4Nvaf3ycrK4s477+S0005rpkhFDq2h97PD4eDss88mKyuLd955h82bN/PSSy+RmJjYzJGL1K2h9/Sbb77JlClTmDp1Kps2beKVV15h0aJF3Hvvvc0cuUjdysvL6du3L88+++xhtc/MzGTEiBGcccYZbNy4kdtvv51rr72Wjz766PAvarRzJ510knHTTTd5X7vdbiMhIcGYOXNmne0vu+wyY8SIETW2DRgwwLj++uubNE6Rw9XQe/pALpfLCA4ONl577bWmClGkQY7knna5XMbJJ59svPzyy8b48eONiy66qBkiFTm0ht7Pzz//vNGxY0fD4XA0V4giDdLQe/qmm24yzjzzzBrbJk+ebJxyyilNGqfIkQCMJUuWHLTNX//6V6Nnz541to0ePdoYNmzYYV+nXfd0OxwO1q1bx9ChQ73brFYrQ4cOZe3atXUes3bt2hrtAYYNG1Zve5HmdCT39IEqKipwOp1EREQ0VZgih+1I7+m//e1vxMTEcM011zRHmCKH5Uju56VLlzJo0CBuuukmYmNj6dWrF4888ghut7u5whap15Hc0yeffDLr1q3zDkHfvn07y5Yt47zzzmuWmEUaW2Pkh/bGDqo1ycvLw+12ExsbW2N7bGwsP//8c53H7N27t872e/fubbI4RQ7XkdzTB7r77rtJSEio9eEh0hKO5J7+73//yyuvvMLGjRubIUKRw3ck9/P27dv59NNPufLKK1m2bBlbt25l0qRJOJ1Opk6d2hxhi9TrSO7pK664gry8PE499VQMw8DlcnHDDTdoeLm0WfXlhyUlJVRWVhIQEHDIc7Trnm4RqenRRx9l4cKFLFmyBH9//5YOR6TBSktLGTt2LC+99BJRUVEtHY7IUfN4PMTExPDiiy/Sv39/Ro8ezX333cfcuXNbOjSRI/LZZ5/xyCOP8Nxzz7F+/XoWL17MBx98wIwZM1o6NJEW0657uqOiorDZbGRnZ9fYnp2dTVxcXJ3HxMXFNai9SHM6knt6n1mzZvHoo4/yySef0KdPn6YMU+SwNfSe3rZtG1lZWVxwwQXebR6PBwC73c7mzZvJyMho2qBF6nEkn9Hx8fH4+Phgs9m827p3787evXtxOBz4+vo2acwiB3Mk9/QDDzzA2LFjufbaawHo3bs35eXlTJw4kfvuuw+rVX1+0rbUlx+GhIQcVi83tPOebl9fX/r378/KlSu92zweDytXrmTQoEF1HjNo0KAa7QFWrFhRb3uR5nQk9zTA448/zowZM1i+fDknnHBCc4Qqclgaek9369aN77//no0bN3p/LrzwQu+KosnJyc0ZvkgNR/IZfcopp7B161bvwyOAX375hfj4eCXc0uKO5J6uqKiolVjve6hkrlsl0rY0Sn7Y8DXe2paFCxcafn5+xrx584yffvrJmDhxohEWFmbs3bvXMAzDGDt2rDFlyhRv+9WrVxt2u92YNWuWsWnTJmPq1KmGj4+P8f3337fUWxCpoaH39KOPPmr4+voa77zzjrFnzx7vT2lpaUu9BZEaGnpPH0irl0tr0tD7+bfffjOCg4ONm2++2di8ebPx/vvvGzExMcZDDz3UUm9BpIaG3tNTp041goODjbfeesvYvn278fHHHxsZGRnGZZdd1lJvQaSG0tJSY8OGDcaGDRsMwJg9e7axYcMG49dffzUMwzCmTJlijB071tt++/btRocOHYy77rrL2LRpk/Hss88aNpvNWL58+WFfs90n3YZhGE8//bSRkpJi+Pr6GieddJLxv//9z7tvyJAhxvjx42u0/7//+z+jS5cuhq+vr9GzZ0/jgw8+aOaIRQ6uIfd0amqqAdT6mTp1avMHLlKPhn5O/5GSbmltGno/r1mzxhgwYIDh5+dndOzY0Xj44YcNl8vVzFGL1K8h97TT6TSmTZtmZGRkGP7+/kZycrIxadIko7CwsPkDF6nDqlWr6vxuvO8+Hj9+vDFkyJBax/Tr18/w9fU1OnbsaLz66qsNuqbFMDTOQ0RERERERKQptOs53SIiIiIiIiItSUm3iIiIiIiISBNR0i0iIiIiIiLSRJR0i4iIiIiIiDQRJd0iIiIiIiIiTURJt4iIiIiIiEgTUdItIiIiIiIi0kSUdIuIiIiIiIg0ESXdIiIiDTRt2jQsFktLh3FIp59+OqeffnpLh+G17/eWl5fXaOdMS0vj/PPPP2S7zz77DIvFwmeffebdNmHCBNLS0mq0s1gsTJs2rdHiExERUdItIiLtxnPPPYfFYmHAgAEtHUqbkpaWhsVi8f7ExMRw2mmnsWTJkpYOrcWtWbOGadOmUVRU1NKhiIhIG6WkW0RE2o0FCxaQlpbGV199xdatW5vsOvfffz+VlZVNdv6W0K9fP9544w3eeOMN7rzzTnbv3s3FF1/M3LlzWzq0RjF48GAqKysZPHjwQdtVVlZy//33e1+vWbOG6dOnK+kWEZEjpqRbRETahczMTNasWcPs2bOJjo5mwYIFTXYtu92Ov79/k52/JSQmJnLVVVdx1VVX8de//pXVq1cTGBjIU089Ve8xLpcLh8PRjFEeOavVir+/P1brwb/6+Pv7Y7fbmykqERE5FijpFhGRdmHBggWEh4czYsQILr300nqT7oULF9K/f3+Cg4MJCQmhd+/e/P3vf/fudzqdTJ8+nc6dO+Pv709kZCSnnnoqK1as8Lapa053ZWUlt956K1FRUQQHB3PhhReya9euWnOE9x27detWJkyYQFhYGKGhoVx99dVUVFTUinf+/Pn079+fgIAAIiIiuPzyy9mxY0etdi+++CIZGRkEBARw0kkn8cUXXzT0V1hDXFwc3bt3JzMzE4CsrCwsFguzZs1izpw5ZGRk4Ofnx08//QTAp59+ymmnnUZgYCBhYWFcdNFFbNq0qc5z5+XlcdlllxESEkJkZCS33XYbVVVVNdq8+uqrnHnmmcTExODn50ePHj14/vnn6433448/pl+/fvj7+9OjRw8WL15cY39dc7rr8sd/r2nTpnHXXXcBkJ6e7h1+n5WVxZAhQ+jbt2+d5+jatSvDhg076HVEROTYoaRbRETahQULFnDxxRfj6+vLmDFj2LJlC19//XWNNitWrGDMmDGEh4fz2GOP8eijj3L66aezevVqb5tp06Yxffp0zjjjDJ555hnuu+8+UlJSWL9+/UGvP2HCBJ5++mnOO+88HnvsMQICAhgxYkS97S+77DJKS0uZOXMml112GfPmzWP69Ok12jz88MOMGzeOzp07M3v2bG6//XZWrlzJ4MGDawx3fuWVV7j++uuJi4vj8ccf55RTTuHCCy+sMzk/XE6nkx07dhAZGVlj+6uvvsrTTz/NxIkTefLJJ4mIiOCTTz5h2LBh5OTkMG3aNCZPnsyaNWs45ZRTyMrKqvO9V1VVMXPmTM477zz+8Y9/MHHixBptnn/+eVJTU7n33nt58sknSU5OZtKkSTz77LO1zrdlyxZGjx7N8OHDmTlzJna7nVGjRtV4UHIkLr74YsaMGQPAU0895R1+Hx0dzdixY/nuu+/44Ycfahzz9ddf88svv3DVVVcd1bVFRKQdMURERNq4b775xgCMFStWGIZhGB6Px0hKSjJuu+22Gu1uu+02IyQkxHC5XPWeq2/fvsaIESMOer2pU6caf/wTum7dOgMwbr/99hrtJkyYYADG1KlTax375z//uUbbP/3pT0ZkZKT3dVZWlmGz2YyHH364Rrvvv//esNvt3u0Oh8OIiYkx+vXrZ1RXV3vbvfjiiwZgDBky5KDvxTAMIzU11TjnnHOM3NxcIzc31/j222+Nyy+/3ACMW265xTAMw8jMzDQAIyQkxMjJyalxfL9+/YyYmBgjPz/fu+3bb781rFarMW7cuFrv/cILL6xx/KRJkwzA+Pbbb73bKioqasU5bNgwo2PHjrViB4x3333Xu624uNiIj483jjvuOO+2VatWGYCxatUq77bx48cbqampNc534L/XE088YQBGZmZmjXZFRUWGv7+/cffdd9fYfuuttxqBgYFGWVlZrfhFROTYpJ5uERFp8xYsWEBsbCxnnHEGYA4RHj16NAsXLsTtdnvbhYWFUV5eftAe0LCwMH788Ue2bNly2Ndfvnw5AJMmTaqx/ZZbbqn3mBtuuKHG69NOO438/HxKSkoAWLx4MR6Ph8suu4y8vDzvT1xcHJ07d2bVqlUAfPPNN+Tk5HDDDTfg6+vrPd+ECRMIDQ097Pfw8ccfEx0dTXR0NH379uXtt99m7NixPPbYYzXaXXLJJURHR3tf79mzh40bNzJhwgQiIiK82/v06cPZZ5/NsmXLal3rpptuqvF63+/pj20DAgK8/7+4uJi8vDyGDBnC9u3bKS4urnF8QkICf/rTn7yvQ0JCGDduHBs2bGDv3r2H/TtoiNDQUC666CLeeustDMMAwO12s2jRIkaOHElgYGCTXFdERNoeJd0iItKmud1uFi5cyBlnnEFmZiZbt25l69atDBgwgOzsbFauXOltO2nSJLp06cLw4cNJSkriz3/+szdh3udvf/sbRUVFdOnShd69e3PXXXfx3XffHTSGX3/9FavVSnp6eo3tnTp1qveYlJSUGq/Dw8MBKCwsBMwh04Zh0LlzZ28yvO9n06ZN5OTkeK8N0Llz5xrn8/HxoWPHjgeN+48GDBjAihUr+OSTT1izZg15eXm8/vrrNZJfoNZ73Hf9rl271jpn9+7dycvLo7y8vMb2A2PNyMjAarXWGIq+evVqhg4d6p0jHh0dzb333gtQK+nu1KlTrTn2Xbp0AahzeHtjGTduHL/99pt3/vwnn3xCdnY2Y8eObbJriohI26PlOUVEpE379NNP2bNnDwsXLmThwoW19i9YsIBzzjkHgJiYGDZu3MhHH33Ehx9+yIcffsirr77KuHHjeO211wCztNS2bdv417/+xccff8zLL7/MU089xdy5c7n22msbLW6bzVbn9n29ph6PB4vFwocfflhn26CgoEaLBSAqKoqhQ4cest2BSXhjODBh3rZtG2eddRbdunVj9uzZJCcn4+vry7Jly3jqqafweDyNHsORGDZsGLGxscyfP5/Bgwczf/584uLiDuv3KCIixw4l3SIi0qYtWLCAmJiYOhfYWrx4MUuWLGHu3LneZNHX15cLLriACy64AI/Hw6RJk3jhhRd44IEHvD3TERERXH311Vx99dWUlZUxePBgpk2bVm/SnZqaisfjITMzs0Yv7tHUCs/IyMAwDNLT0729tvVdG8ye8TPPPNO73el0kpmZWe8K241l3/U3b95ca9/PP/9MVFRUraHWW7ZsqdFjvnXrVjweD2lpaQD8+9//prq6mqVLl9YYEbBvSP2Btm7dimEYNZL3X375BcB7ziN14AOBP7LZbFxxxRXMmzePxx57jPfee4/rrruu3gcqIiJybNLwchERabMqKytZvHgx559/Ppdeemmtn5tvvpnS0lKWLl0KQH5+fo3jrVYrffr0AaC6urrONkFBQXTq1Mm7vy77ykM999xzNbY//fTTR/zeLr74Ymw2G9OnT/f2fu9jGIY3zhNOOIHo6Gjmzp1bo2b2vHnzaqxw3lTi4+Pp168fr732Wo3r/fDDD3z88cecd955tY458AHJvt/T8OHDgf2jAP74vouLi3n11VfrjGH37t0sWbLE+7qkpITXX3+dfv36ERcXd2Rv7Hf7HhjU97scO3YshYWFXH/99ZSVlWnVchERqUU93SIi0mYtXbqU0tJSLrzwwjr3Dxw4kOjoaBYsWMDo0aO59tprKSgo4MwzzyQpKYlff/2Vp59+mn79+tG9e3cAevTowemnn07//v2JiIjgm2++4Z133uHmm2+uN47+/ftzySWXMGfOHPLz8xk4cCCff/65t7f1YL2l9cnIyOChhx7innvuISsri5EjRxIcHExmZiZLlixh4sSJ3Hnnnfj4+PDQQw9x/fXXc+aZZzJ69GgyMzN59dVXGzSn+2g88cQTDB8+nEGDBnHNNddQWVnJ008/TWhoaI0a5ftkZmZy4YUXcu6557J27Vrmz5/PFVdc4e2VP+ecc7wjEvYlsy+99BIxMTHs2bOn1vm6dOnCNddcw9dff01sbCz//Oc/yc7OrjdJb4j+/fsDcN9993H55Zfj4+PDBRdc4E3GjzvuOHr16sXbb79N9+7dOf7444/6miIi0r6op1tERNqsBQsW4O/vz9lnn13nfqvVyogRI1i+fDn5+flcddVV+Pv789xzzzFp0iRee+01Ro8ezYcffojVav5JvPXWW8nKymLmzJnceuutfP755zz00EM8+eSTB43l9ddf56abbuKDDz7g7rvvxuFwsGjRIgD8/f2P6P1NmTKFd999F6vVyvTp07nzzjtZunQp55xzTo0HDRMnTuS5555j9+7d3HXXXXzxxRcsXbqU5OTkI7puQw0dOpTly5cTGRnJgw8+yKxZsxg4cCCrV6+utfAawKJFi/Dz82PKlCl88MEH3Hzzzbzyyive/V27duWdd97BYrFw5513MnfuXCZOnMhtt91W5/U7d+7MokWLWLZsGVOmTMHpdLJo0SLvCISjceKJJzJjxgy+/fZbJkyYwJgxY8jNza3RZty4cQBaQE1EROpkMQ4csyYiIiKNYuPGjRx33HHMnz+fK6+8sqXDkSby97//nTvuuIOsrKxaq9KLiIiop1tERKQRVFZW1to2Z84crFYrgwcPboGIpDkYhsErr7zCkCFDlHCLiEidNKdbRESkETz++OOsW7eOM844A7vd7i1JNnHixGYb5i3Np7y8nKVLl7Jq1Sq+//57/vWvf7V0SCIi0kppeLmIiEgjWLFiBdOnT+enn36irKyMlJQUxo4dy3333Yfdrmfc7U1WVhbp6emEhYUxadIkHn744ZYOSUREWikl3SIiIiIiIiJNRHO6RURERERERJqIkm4RERERERGRJqKkW0RERERERKSJKOkWERERERERaSJKukVERERERESaiJJuERERERERkSaipFtERERERESkiSjpFhEREREREWkiSrpFREREREREmsj/A5L2ITiVBrapAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -12876,7 +12365,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ @@ -12886,7 +12375,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -12939,7 +12428,7 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", " \n", " \n", @@ -12955,7 +12444,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.66\n", + " 0.62\n", " 0.45\n", " \n", " \n", @@ -12971,7 +12460,7 @@ " NaN\n", " 31294\n", " 1.0\n", - " 0.86\n", + " 0.85\n", " 0.95\n", " \n", " \n", @@ -13026,9 +12515,9 @@ "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.085 \n", - "5 NaN NaN 31282 1.0 0.66 \n", - "8 NaN NaN 31294 1.0 0.86 \n", + "2 NaN NaN 31270 1.0 0.1 \n", + "5 NaN NaN 31282 1.0 0.62 \n", + "8 NaN NaN 31294 1.0 0.85 \n", "10 NaN NaN 1.0 NaN \n", "13 NaN NaN 31338 1.0 0.85 \n", "\n", @@ -13040,7 +12529,7 @@ "13 0.9 " ] }, - "execution_count": 77, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -13051,7 +12540,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 159, "metadata": {}, "outputs": [ { @@ -13102,7 +12591,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 160, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13163,10 +12652,10 @@ " NaN\n", " 31268\n", " 1.0\n", - " 0.017463\n", + " 0.014926\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 286.007699\n", - " 286.007699\n", + " 270.308741\n", + " 270.308741\n", " \n", " \n", " 1\n", @@ -13181,10 +12670,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.03366666666666667, 0.034105259000000006, 0....\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -76.357515\n", - " -76.357515\n", + " -79.442225\n", + " -79.442225\n", " \n", " \n", " 2\n", @@ -13199,10 +12688,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", - " -7.574597\n", - " -7.574597\n", + " -9.227528\n", + " -9.227528\n", " \n", " \n", " 3\n", @@ -13217,10 +12706,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.6\n", + " 0.55\n", " [0.16,0.44,0.4]\n", - " 31.015493\n", - " 31.015493\n", + " 22.314355\n", + " 22.314355\n", " \n", " \n", " 4\n", @@ -13235,10 +12724,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 28.578581\n", - " 28.578581\n", + " 25.971582\n", + " 25.971582\n", " \n", " \n", "\n", @@ -13267,25 +12756,25 @@ "4 NaN 0.0 400.0 31281 \n", "\n", " question_weight bot_team_median \\\n", - "0 1.0 0.017463 \n", - "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 1.0 0.085 \n", - "3 1.0 0.6 \n", - "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "0 1.0 0.014926 \n", + "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", + "2 1.0 0.1 \n", + "3 1.0 0.55 \n", + "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", - "2 0.013 -7.574597 \n", - "3 [0.16,0.44,0.4] 31.015493 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", + "2 0.013 -9.227528 \n", + "3 [0.16,0.44,0.4] 22.314355 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", "\n", " weighted_score \n", - "0 286.007699 \n", - "1 -76.357515 \n", - "2 -7.574597 \n", - "3 31.015493 \n", - "4 28.578581 " + "0 270.308741 \n", + "1 -79.442225 \n", + "2 -9.227528 \n", + "3 22.314355 \n", + "4 25.971582 " ] }, "metadata": {}, @@ -13343,10 +12832,10 @@ " NaN\n", " 35380\n", " 1.00\n", - " 0.9275\n", " 0.95\n", - " -2.396919\n", - " -2.396919\n", + " 0.95\n", + " 0.000000\n", + " 0.000000\n", " \n", " \n", " 351\n", @@ -13361,10 +12850,10 @@ " NaN\n", " 35381\n", " 1.00\n", - " 0.375\n", + " 0.575\n", " 0.05\n", - " -41.871033\n", - " -41.871033\n", + " -80.437282\n", + " -80.437282\n", " \n", " \n", " 355\n", @@ -13379,10 +12868,10 @@ " NaN\n", " 35385\n", " 1.00\n", - " 0.925\n", + " 0.875\n", " 0.97\n", - " -4.750233\n", - " -4.750233\n", + " -10.307219\n", + " -10.307219\n", " \n", " \n", " 361\n", @@ -13397,10 +12886,10 @@ " NaN\n", " 35386\n", " 0.85\n", - " 0.825\n", + " 0.85\n", " 0.666\n", - " -64.635502\n", - " -54.940177\n", + " -80.050570\n", + " -68.042984\n", " \n", " \n", " 364\n", @@ -13440,17 +12929,17 @@ "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "342 NaN NaN 35380 1.00 0.9275 \n", - "351 NaN NaN 35381 1.00 0.375 \n", - "355 NaN NaN 35385 1.00 0.925 \n", - "361 NaN NaN 35386 0.85 0.825 \n", + "342 NaN NaN 35380 1.00 0.95 \n", + "351 NaN NaN 35381 1.00 0.575 \n", + "355 NaN NaN 35385 1.00 0.875 \n", + "361 NaN NaN 35386 0.85 0.85 \n", "364 NaN NaN 35387 0.85 0.05 \n", "\n", " pro_median head_to_head weighted_score \n", - "342 0.95 -2.396919 -2.396919 \n", - "351 0.05 -41.871033 -41.871033 \n", - "355 0.97 -4.750233 -4.750233 \n", - "361 0.666 -64.635502 -54.940177 \n", + "342 0.95 0.000000 0.000000 \n", + "351 0.05 -80.437282 -80.437282 \n", + "355 0.97 -10.307219 -10.307219 \n", + "361 0.666 -80.050570 -68.042984 \n", "364 0.03 -2.083409 -1.770897 " ] }, @@ -13464,7 +12953,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[80], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[160], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", diff --git a/functions.py b/functions.py index 42c14de..2035207 100644 --- a/functions.py +++ b/functions.py @@ -392,7 +392,7 @@ def get_median_forecast(row, bots): try: val = float(f) probs.append(val) - except (ValueError, TypeError): + except (ValueError, TypeError) as e: print(f" Invalid forecast: {f} — error {e}") continue @@ -412,24 +412,6 @@ def get_median_forecast(row, bots): raise ValueError(f"Unknown question type: {q_type}") -def calculate_all_peer_scores( - df_bot_team_forecasts: pd.DataFrame, teams: list[str] -) -> pd.DataFrame: - """ - Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value - Changes the df so that the forecast is now the score for that question - """ - raise NotImplementedError( - "I accidentally implemented baseline scoring here unfortunately" - ) - score_df = df_bot_team_forecasts.copy() - team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) - for team in teams: - score_for_team = team_scores[team] - score_df[team] = score_for_team - return score_df - - def calculate_weighted_scores(df_bot_team_forecasts, teams): """ @Check: @@ -1255,3 +1237,157 @@ def parse_options_array(options_str): # Simple fallback: just split by comma and strip quotes return [p.strip().strip("\"'") for p in cleaned.split(",")] + + +def calculate_peer_score_numeric(row, bot_col, pro_col='pro_median'): + """Calculate peer score for numeric questions""" + try: + # Check if bot didn't provide a forecast + if pd.isna(row[bot_col]): + return np.nan + + resolution_value = row['resolution'] + + # Get the CDF values + bot_cdf = row[bot_col] + pro_median_cdf = row[pro_col] + + # Handle special cases + if resolution_value == 'below_lower_bound': + # Use first point in CDF + if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: + bot_prob = bot_cdf[0] + else: + return np.nan + + if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: + pro_median_prob = pro_median_cdf[0] + else: + return np.nan + + elif resolution_value == 'above_upper_bound': + # Use (1 - last point in CDF) + if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: + bot_prob = 1 - bot_cdf[-1] + else: + return np.nan + + if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: + pro_median_prob = 1 - pro_median_cdf[-1] + else: + return np.nan + + else: + # Convert to float if it's a numeric resolution + try: + resolution_float = float(resolution_value) + + # Convert CDF to PMF + if isinstance(bot_cdf, (list, np.ndarray)) and isinstance(pro_median_cdf, (list, np.ndarray)): + # Convert CDFs to PMFs + bot_pmf = np.diff(np.concatenate([[0], bot_cdf])) + pro_pmf = np.diff(np.concatenate([[0], pro_median_cdf])) + + # Use nominal_location_to_cdf_location to find the appropriate bucket + cdf_location = nominal_location_to_cdf_location(resolution_float, row) + + # Find the appropriate bucket index + bucket_index = min(int(cdf_location * (len(bot_pmf) - 1)), len(bot_pmf) - 1) + + # Get probabilities + bot_prob = bot_pmf[bucket_index] + pro_median_prob = pro_pmf[bucket_index] + else: + return np.nan + except: + return np.nan + + # Ensure non-zero probabilities + bot_prob = max(bot_prob, 1e-10) + pro_median_prob = max(pro_median_prob, 1e-10) + + # Calculate peer score and divide by 2 for continuous questions + return np.log(bot_prob / pro_median_prob) / 2 + + except Exception as e: + # Print the specific error for debugging + return np.nan + +def calculate_peer_score_binary(row, bot_col, pro_col='pro_median'): + """Calculate peer score for binary questions""" + if row['resolution'] == 'yes': + return np.log(row[bot_col] / row[pro_col]) + else: # resolution is 'no' + return np.log((1 - row[bot_col]) / (1 - row[pro_col])) + +def parse_cdf_string(cdf_string): + """Parse CDF string into numpy array""" + return np.array([float(x) for x in cdf_string.strip('[]').split(',')]) + +def calculate_peer_score_multiple_choice(row, bot_col, pro_col='pro_median'): + """Calculate peer score for multiple choice questions""" + # Check if bot didn't provide a forecast (NaN) + if pd.isna(row[bot_col]): + return np.nan + + # Get the resolution value and options + resolution_value = row['resolution'] + options = row['options_parsed'] if 'options_parsed' in row else row['options'] + + # Find the index of the resolution in options array + resolution_str = str(resolution_value) + + try: + resolution_index = options.index(resolution_str) + + # Get the forecasts + bot_pmf_raw = row[bot_col] + pro_pmf_raw = row[pro_col] + + # Parse string representations of arrays if needed + if isinstance(bot_pmf_raw, str): + bot_pmf = [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + else: + bot_pmf = bot_pmf_raw + + if isinstance(pro_pmf_raw, str): + pro_pmf = [float(x) for x in pro_pmf_raw.strip('[]').split(',')] + else: + pro_pmf = pro_pmf_raw + + # Get the probabilities at the correct index + bot_prob = bot_pmf[resolution_index] + pro_prob = pro_pmf[resolution_index] + + # Calculate peer score + return np.log(bot_prob / pro_prob) + except Exception as e: + # If any error occurs, return NaN + return np.nan + +def calculate_peer_score(row, bot_col, pro_col='pro_median'): + """Calculate peer score based on question type""" + if row['type'] == 'binary': + return calculate_peer_score_binary(row, bot_col, pro_col) + elif row['type'] == 'multiple_choice': + return calculate_peer_score_multiple_choice(row, bot_col, pro_col) + elif row['type'] == 'numeric': + return calculate_peer_score_numeric(row, bot_col, pro_col) + else: + # Unknown question type; return NaN + return np.nan + +def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): + """Calculate peer scores for all bots""" + # Create a new DataFrame to store peer scores + df_peer = df.copy() + + # Calculate peer score for each bot + for bot in all_bots: + df_peer[bot] = 100 * df.apply(lambda row: calculate_peer_score(row, bot, pro_col), axis=1) + + # Calculate peer score for bot_team_median + df_peer["bot_team_median"] = 100 * df.apply( + lambda row: calculate_peer_score(row, 'bot_median', pro_col), axis=1) + + return df_peer diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 5811dc4..265e32d 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -metac-o1,21.0,21.0,21.0,21.0,21.0 -metac-perplexity,20.3,20.3,20.3,20.3,20.3 -bot_median,17.9,17.9,17.9,17.9,17.9 +metac-perplexity,20.6,20.6,20.6,20.6,20.6 +metac-o1,20.2,20.2,20.2,20.2,20.2 acm_bot,17.7,17.7,17.7,17.7,17.7 +bot_median,17.4,17.4,17.4,17.4,17.4 manticAI,14.5,14.5,14.5,14.5,14.5 twsummerbot,14.3,14.3,14.3,14.3,14.3 jkraybill_bot,14.3,14.3,14.3,14.3,14.3 -metac-claude-3-5-sonnet-20240620,12.0,12.0,12.0,12.0,12.0 -GreeneiBot2,11.7,11.7,11.7,11.7,11.7 -metac-claude-3-5-sonnet-latest,11.5,11.5,11.5,11.5,11.5 +metac-claude-3-5-sonnet-20240620,13.0,13.0,13.0,13.0,13.0 +metac-claude-3-5-sonnet-latest,12.4,12.4,12.4,12.4,12.4 +metac-deepseek-r1,12.3,12.3,12.3,12.3,12.3 +metac-Llama-3.1,12.2,12.2,12.2,12.2,12.2 +GreeneiBot2,11.8,11.8,11.8,11.8,11.8 NextWorldLab,11.1,11.1,11.1,11.1,11.1 -metac-grok-2-1212,11.0,11.0,11.0,11.0,11.0 -metac-gpt-4o,10.5,10.5,10.5,10.5,10.5 -metac-Llama-3.1,10.2,10.2,10.2,10.2,10.2 Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 SynapseSeer,10.2,10.2,10.2,10.2,10.2 -metac-o1-preview,10.1,10.1,10.1,10.1,10.1 +metac-grok-2-1212,9.8,9.8,9.8,9.8,9.8 mmBot,9.7,9.7,9.7,9.7,9.7 -metac-exa,9.7,9.7,9.7,9.7,9.7 +metac-Gemini-Exp-1206,9.6,9.6,9.6,9.6,9.6 annabot,9.0,9.0,9.0,9.0,9.0 -metac-deepseek-r1,8.4,8.4,8.4,8.4,8.4 +metac-exa,8.8,8.8,8.8,8.8,8.8 VeritasAI,8.4,8.4,8.4,8.4,8.4 laylaps,7.6,7.6,7.6,7.6,7.6 -cookics_bot_TEST,6.4,6.4,6.4,6.4,6.4 -metac-Gemini-Exp-1206,5.8,5.8,5.8,5.8,5.8 +metac-o1-preview,6.7,6.7,6.7,6.7,6.7 +cookics_bot_TEST,6.3,6.3,6.3,6.3,6.3 MWG,5.5,5.5,5.5,5.5,5.5 ajf-bot,5.1,5.1,5.1,5.1,5.1 pgodzinai,3.5,3.5,3.5,3.5,3.5 KevinTestBot,3.3,3.3,3.3,3.3,3.3 +metac-gpt-4o,3.0,3.0,3.0,3.0,3.0 InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 Bot_Pepa,2.6,2.6,2.6,2.6,2.6 CumulativeBot,2.5,2.5,2.5,2.5,2.5 @@ -37,9 +37,9 @@ jonahsingerbot,2.2,2.2,2.2,2.2,2.2 bean_bot,2.1,2.1,2.1,2.1,2.1 X_bot,1.9,1.9,1.9,1.9,1.9 CatrachoCaster,1.8,1.8,1.8,1.8,1.8 +RPM_bot,0.8,0.8,0.8,0.8,0.8 4Shadower,0.6,0.6,0.6,0.6,0.6 krm-bot,0.6,0.6,0.6,0.6,0.6 -RPM_bot,0.6,0.6,0.6,0.6,0.6 andrewsiah,0.0,0.0,0.0,0.0,0.0 cobyj-bot,0.0,0.0,0.0,0.0,0.0 pianobot,-2.2,-2.2,-2.2,-2.2,-2.2 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index b364ee5..889922c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -metac-o1,1998.9,95.0,21.0,3.570999300115835e-15,3.663767977230083e-16,5.743007173754146e+16,1.9847501794262088,21.0,21.0,1.0,0.000000 -metac-perplexity,1927.0,95.0,20.3,0.0,0.0,inf,1.9847501794262088,20.3,20.3,1.0,0.000000 -bot_median,1698.8,95.0,17.9,0.0,0.0,inf,1.9847501794262088,17.9,17.9,1.0,0.000000 +metac-perplexity,1957.5,95.0,20.6,0.0,0.0,inf,1.9847501794262088,20.6,20.6,1.0,0.000000 +metac-o1,1921.1,95.0,20.2,0.0,0.0,inf,1.9847501794262088,20.2,20.2,1.0,0.000000 acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 +bot_median,1655.0,95.0,17.4,3.570999300115835e-15,3.663767977230083e-16,4.755070072324921e+16,1.9847501794262088,17.4,17.4,1.0,0.000000 manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -metac-claude-3-5-sonnet-20240620,1136.7,95.0,12.0,3.570999300115835e-15,3.663767977230083e-16,3.26596902511772e+16,1.9847501794262088,12.0,12.0,1.0,0.000000 -GreeneiBot2,1115.4,95.0,11.7,5.3564989501737525e-15,5.495651965845125e-16,2.1364275625153532e+16,1.9847501794262088,11.7,11.7,1.0,0.000000 -metac-claude-3-5-sonnet-latest,1091.6,95.0,11.5,5.3564989501737525e-15,5.495651965845125e-16,2.0907644050343052e+16,1.9847501794262088,11.5,11.5,1.0,0.000000 +metac-claude-3-5-sonnet-20240620,1235.2,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.097519447336572e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 +metac-claude-3-5-sonnet-latest,1180.5,95.0,12.4,0.0,0.0,inf,1.9847501794262088,12.4,12.4,1.0,0.000000 +metac-deepseek-r1,1166.0,95.0,12.3,1.7854996500579174e-15,1.8318839886150415e-16,6.700213221693384e+16,1.9847501794262088,12.3,12.3,1.0,0.000000 +metac-Llama-3.1,1154.9,95.0,12.2,3.570999300115835e-15,3.663767977230083e-16,3.3181275591894544e+16,1.9847501794262088,12.2,12.2,1.0,0.000000 +GreeneiBot2,1119.2,95.0,11.8,1.7854996500579174e-15,1.8318839886150415e-16,6.4310595726389144e+16,1.9847501794262088,11.8,11.8,1.0,0.000000 NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 -metac-grok-2-1212,1047.4,95.0,11.0,0.0,0.0,inf,1.9847501794262088,11.0,11.0,1.0,0.000000 -metac-gpt-4o,1002.0,95.0,10.5,3.570999300115835e-15,3.663767977230083e-16,2.87887889373382e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 -metac-Llama-3.1,973.0,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 -metac-o1-preview,962.8,95.0,10.1,1.7854996500579174e-15,1.8318839886150415e-16,5.5325101025506376e+16,1.9847501794262088,10.1,10.1,1.0,0.000000 +metac-grok-2-1212,932.3,95.0,9.8,1.7854996500579174e-15,1.8318839886150415e-16,5.357004504213439e+16,1.9847501794262088,9.8,9.8,1.0,0.000000 mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 -metac-exa,919.9,95.0,9.7,1.7854996500579174e-15,1.8318839886150415e-16,5.285938770788284e+16,1.9847501794262088,9.7,9.7,1.0,0.000000 +metac-Gemini-Exp-1206,910.2,95.0,9.6,1.7854996500579174e-15,1.8318839886150415e-16,5.230331909359555e+16,1.9847501794262088,9.6,9.6,1.0,0.000000 annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 -metac-deepseek-r1,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608683275523464e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 +metac-exa,836.7,95.0,8.8,1.7854996500579174e-15,1.8318839886150415e-16,4.808056144499867e+16,1.9847501794262088,8.8,8.8,1.0,0.000000 VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 -cookics_bot_TEST,612.4,95.0,6.4,1.7854996500579174e-15,1.8318839886150415e-16,3.5189490119492424e+16,1.9847501794262088,6.4,6.4,1.0,0.000000 -metac-Gemini-Exp-1206,548.0,95.0,5.8,0.0,0.0,inf,1.9847501794262088,5.8,5.8,1.0,0.000000 +metac-o1-preview,640.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.357383207755715e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 +cookics_bot_TEST,596.4,95.0,6.3,0.0,0.0,inf,1.9847501794262088,6.3,6.3,1.0,0.000000 MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 +metac-gpt-4o,280.3,95.0,3.0,8.927498250289587e-16,9.159419943075207e-17,3.221540864953186e+16,1.9847501794262088,3.0,3.0,1.0,0.000000 InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 @@ -37,9 +37,9 @@ jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.8945 bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 +RPM_bot,71.4,95.0,0.8,1.1159372812861984e-16,1.144927492884401e-17,6.560692777870449e+16,1.9847501794262088,0.8,0.8,1.0,0.000000 4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 -RPM_bot,52.6,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,4.834419627569585e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA cobyj-bot,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA pianobot,-206.5,95.0,-2.2,4.463749125144793e-16,4.579709971537604e-17,-4.745304957283875e+16,1.9847501794262088,-2.2,-2.2,0.0,0.000000 From 9e9c79433b79e391148422fd9d864c06c620a9a8 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 07:27:27 -0600 Subject: [PATCH 10/26] Refactored spot peer scoring functions --- AI_BENCHMARKING_ANALYSIS.ipynb | 3015 +++++++++-------- functions.py | 187 +- .../bootstrapped_h2h_bot_vs_pros.csv | 26 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 28 +- refactored_notebook/scoring.py | 193 +- tests/test_scoring.py | 2 +- 6 files changed, 1715 insertions(+), 1736 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 510d463..fe42ead 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 1, "metadata": { "id": "ISzIoto4hnoG" }, @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -358,7 +358,7 @@ " dtype='object')" ] }, - "execution_count": 90, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ "dtype: object" ] }, - "execution_count": 92, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 97, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -704,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -747,7 +747,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 99, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -759,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -793,6 +793,15 @@ " \n", " \n", " \n", + " 15\n", + " bot_median\n", + " 9.993738\n", + " 3777.832847\n", + " 409\n", + " 7.260052\n", + " 1.390626\n", + " \n", + " \n", " 12\n", " metac-o1\n", " 9.674740\n", @@ -811,15 +820,6 @@ " 2.298000\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.215149\n", - " 3105.490478\n", - " 409\n", - " 5.145245\n", - " 1.561660\n", - " \n", - " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -843,16 +843,16 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", + "15 bot_median 9.993738 3777.832847 409 7.260052 \n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "15 bot_median 8.215149 3105.490478 409 5.145245 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", + "15 1.390626 \n", "12 1.738353 \n", "4 2.298000 \n", - "15 1.561660 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -968,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 20, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1017,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1441,7 +1441,7 @@ " np.int64(35705)}" ] }, - "execution_count": 102, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1462,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 22, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1501,18 +1501,18 @@ " \n", " \n", " 1\n", - " metac-o1\n", - " 8861.959039\n", + " bot_median\n", + " 9389.288325\n", " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 8849.559824\n", + " metac-o1\n", + " 8861.959039\n", " \n", " \n", " 3\n", - " bot_median\n", - " 8671.898307\n", + " metac-o1-preview\n", + " 8849.559824\n", " \n", " \n", " 4\n", @@ -1531,9 +1531,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 metac-o1 8861.959039\n", - "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8671.898307\n", + "1 bot_median 9389.288325\n", + "2 metac-o1 8861.959039\n", + "3 metac-o1-preview 8849.559824\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1639,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1658,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 24, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1697,13 +1697,13 @@ " \n", " \n", " 1\n", - " metac-o1\n", - " 3864.168122\n", + " bot_median\n", + " 4077.448023\n", " \n", " \n", " 2\n", - " bot_median\n", - " 3347.538115\n", + " metac-o1\n", + " 3864.168122\n", " \n", " \n", " 3\n", @@ -1937,8 +1937,8 @@ "text/plain": [ " bot Peer Score\n", "Rank \n", - "1 metac-o1 3864.168122\n", - "2 bot_median 3347.538115\n", + "1 bot_median 4077.448023\n", + "2 metac-o1 3864.168122\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -1986,7 +1986,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 105, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2028,7 +2028,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2047,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -2056,7 +2056,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2077,7 +2077,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2256,7 +2256,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 109, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2267,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 29, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2331,8 +2331,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.02,0.7,0.2,0.07,0.01]\n", " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", @@ -2355,7 +2355,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2380,8 +2380,8 @@ " NaN\n", " ...\n", " 0.1\n", + " 0.05\n", " 0.1\n", - " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2405,7 +2405,7 @@ " ...\n", " [0.3,0.55,0.15]\n", " [0.2,0.6,0.2]\n", - " [0.15,0.55,0.3]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2427,7 +2427,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", @@ -2466,24 +2466,24 @@ "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", "\n", " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", - "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "0 NaN ... [0.45,0.3,0.15,0.05,0.05] \n", + "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", "2 NaN ... 0.1 \n", "3 [0.16,0.47,0.37] ... [0.3,0.55,0.15] \n", - "4 NaN ... [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.02,0.7,0.2,0.07,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", + "2 0.05 \n", "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.15 NaN \n", - "3 [0.15,0.55,0.3] NaN \n", + "2 0.1 NaN \n", + "3 [0.1,0.6,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2595,8 +2595,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.65\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2619,8 +2619,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2643,9 +2643,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", - " 0.25\n", + " 0.3\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -2667,9 +2667,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.03\n", + " 0.1\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2693,17 +2693,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.7 \n", - "98 None 0.03 0.3 NaN NaN ... 0.05 \n", + "95 None 0.05 0.95 NaN NaN ... 0.65 \n", + "96 None 0.97 0.85 NaN NaN ... 0.85 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.1 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.25 NaN 0.85 0.85 NaN \n", - "98 0.05 0.03 NaN 0.15 0.05 NaN \n", + "95 0.15 NaN NaN 0.15 NaN NaN \n", + "96 0.9 NaN NaN 0.9 NaN NaN \n", + "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "98 0.05 0.1 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -2771,7 +2771,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2793,7 +2793,7 @@ " dtype='object')" ] }, - "execution_count": 111, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2804,7 +2804,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2814,7 +2814,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 112, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2829,7 +2829,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2841,7 +2841,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2850,7 +2850,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2911,8 +2911,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.02,0.7,0.2,0.07,0.01]\n", " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", @@ -2935,9 +2935,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 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" NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3007,8 +3007,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 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0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", @@ -3052,26 +3052,26 @@ "3 NaN NaN [0.16,0.47,0.37] ... \n", "4 NaN NaN NaN ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 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0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...] \n", + " metac-o1 \\\n", + "0 [0.45,0.3,0.15,0.05,0.05] \n", + "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", + "2 0.1 \n", + "3 [0.3,0.55,0.15] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", + "0 [0.02,0.7,0.2,0.07,0.01] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.1 \n", + "2 0.05 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.15 \n", - "3 [0.15,0.55,0.3] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.1 \n", + "3 [0.1,0.6,0.3] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3203,8 +3203,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.65\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3227,8 +3227,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3251,9 +3251,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", - " 0.25\n", + " 0.3\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -3275,9 +3275,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.03\n", + " 0.1\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3301,17 +3301,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.7 \n", - "98 None 0.03 0.3 NaN NaN ... 0.05 \n", + "95 None 0.05 0.95 NaN NaN ... 0.65 \n", + "96 None 0.97 0.85 NaN NaN ... 0.85 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.1 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.25 NaN 0.85 0.85 NaN \n", - "98 0.05 0.03 NaN 0.15 0.05 NaN \n", + "95 0.15 NaN NaN 0.15 NaN NaN \n", + "96 0.9 NaN NaN 0.9 NaN NaN \n", + "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "98 0.05 0.1 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -3371,9 +3371,27 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'calculate_peer_score' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_peer_score\u001b[49m(row, bot, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'calculate_peer_score' is not defined" + ] + } + ], "source": [ "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." @@ -3381,7 +3399,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -3442,9 +3460,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3466,9 +3484,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3490,9 +3508,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3514,9 +3532,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3538,9 +3556,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3584,11 +3602,11 @@ "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "3 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "6 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "9 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "13 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "0 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "3 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "6 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "9 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "13 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3661,9 +3679,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3685,9 +3703,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3709,9 +3727,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3733,9 +3751,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3757,9 +3775,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3803,11 +3821,11 @@ "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "82 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "83 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "91 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "92 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "81 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "82 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "83 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "91 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "92 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3880,9 +3898,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3904,9 +3922,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3928,9 +3946,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3952,9 +3970,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3976,9 +3994,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4001,18 +4019,18 @@ "16 33876 33751 no 1.0 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "2 None 0.013 0.643473 2.597381 1.762901 ... 20.222117 \n", - "5 None 0.45 0.643473 2.597381 1.762901 ... 20.222117 \n", - "8 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", - "12 None 0.9 0.643473 2.597381 1.762901 ... 20.222117 \n", - "16 None 0.058 0.643473 2.597381 1.762901 ... 20.222117 \n", + "2 None 0.013 0.643473 2.597381 1.762901 ... 16.605891 \n", + "5 None 0.45 0.643473 2.597381 1.762901 ... 16.605891 \n", + "8 None 0.95 0.643473 2.597381 1.762901 ... 16.605891 \n", + "12 None 0.9 0.643473 2.597381 1.762901 ... 16.605891 \n", + "16 None 0.058 0.643473 2.597381 1.762901 ... 16.605891 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "5 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "8 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "12 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "16 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "2 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "5 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "8 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "12 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "16 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "2 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4085,9 +4103,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4109,9 +4127,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4133,9 +4151,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4157,9 +4175,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4181,9 +4199,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4206,18 +4224,18 @@ "98 35387 35367 no 0.85 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", - "95 None 0.05 0.643473 2.597381 1.762901 ... 20.222117 \n", - "96 None 0.97 0.643473 2.597381 1.762901 ... 20.222117 \n", - "97 None 0.666 0.643473 2.597381 1.762901 ... 20.222117 \n", - "98 None 0.03 0.643473 2.597381 1.762901 ... 20.222117 \n", + "94 None 0.95 0.643473 2.597381 1.762901 ... 16.605891 \n", + "95 None 0.05 0.643473 2.597381 1.762901 ... 16.605891 \n", + "96 None 0.97 0.643473 2.597381 1.762901 ... 16.605891 \n", + "97 None 0.666 0.643473 2.597381 1.762901 ... 16.605891 \n", + "98 None 0.03 0.643473 2.597381 1.762901 ... 16.605891 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "95 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "96 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "97 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "98 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "94 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "95 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "96 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "97 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "98 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4241,7 +4259,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 197, "metadata": {}, "outputs": [ { @@ -4282,13 +4300,13 @@ " \n", " \n", " 2\n", - " bot_median\n", - " 3347.538115\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 3\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3060.137114\n", " \n", " \n", " 4\n", @@ -4518,8 +4536,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3347.538115\n", - "3 metac-o1-preview 3162.155445\n", + "2 metac-o1-preview 3162.155445\n", + "3 bot_median 3060.137114\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -4566,7 +4584,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 118, + "execution_count": 197, "metadata": {}, "output_type": "execute_result" } @@ -4577,7 +4595,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -4586,13 +4604,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", - "mean metac-o1 forecast on questions that resolved no: 26.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 71.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" ] }, { "data": { - "image/png": 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", + "image/png": 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67733vHqbKoZm1abTTz9d7dq107///W+deuqp+uKLL3TnnXdWOi82NlaXXnqpLr30Uu3bt0/Dhw/Xvffeq8mTJysqKsrv+1b8+6xatcqr92Hfvn1at25dpfd38+bNlXqDVq9eLcmsXFgdZWVlkqQ9e/ZUiulQK1euVHx8vOf+h66yWFP+vh9V6dSpk5YtW6YBAwYcMb5OnTrJ7XZrxYoV6tmzZ5XnfPXVV9qxY4feeustnX766Z72itU0D9WjRw/16NFDd911l77//nv169dPc+bM0T333CPpyO9Xu3bt9Ne//lV//etflZ+fr169eunee++lkAJwWMyRAoAaGDJkiMrLy/XEE094tc+ePVs2m83vJOzMM8/UzJkz9cQTT3iWxa7KJZdcosWLF2vBggWVju3atcuTnA8ZMkRlZWVeS5aXl5fr8ccfP2osFT1MB/coOZ1OzZ071+fvx1d2u10XXXSR3n//fb3wwgsqKyvzGtYnmRXiDhYREaG0tDRZlqX9+/dX674DBw5URESEHnvsMa/v89lnn5XT6dR5553ndX5ZWZmefvppz/N9+/bp6aefVuvWrdW7d+9qxfD+++9LOrAhbrt27dSzZ0/NmzfPa8nx5cuX69NPP9WQIUM8bRUFVVXL2VeHv+9HVS655BL98ccf+te//lXp2N69e1VUVCRJGjZsmOx2u2bMmFGpd7Pi3lX9DO7bt09PPvmk1/kul8vzM1+hR48estvtXkuux8bGVnqvysvLK833S0hIUGJiYpXLtQNABXqkAKAGhg4dqjPPPFN33nmnfv/9d6Wnp+vTTz/Vu+++q5tuusmzvLKv7Ha77rrrrqOed+utt+q9997T+eef71livKioSL/88oveeOMN/f7774qPj9fQoUPVr18/3XHHHfr999+Vlpamt956q1LiWJVzzjlHERERGjp0qK677jrt2bNH//rXv5SQkKAtW7b49X354tJLL9Xjjz+uadOmqUePHp55ZgfH07ZtW/Xr109t2rRRbm6unnjiCZ133nmV5qj5qnXr1po8ebKmT5+uc889VxdccIFWrVqlJ598Un369Km0cW1iYqLuv/9+/f777+rSpYtnPtszzzzjtSz44fzxxx968cUXJZmCYNmyZXr66acVHx/vNazvwQcf1ODBg9W3b19dddVVnuXPHQ6H15DPiuLtzjvv1MiRI9WkSRMNHTq02vOn/H0/qjJ69Gi99tpr+stf/qIvv/xS/fr1U3l5uVauXKnXXntNCxYsUGZmplJSUnTnnXdq5syZOu200zR8+HBFRkZq6dKlSkxM1KxZs3TKKaeoRYsWysrK0g033CCbzaYXXnih0nDRL774QuPHj9fFF1+sLl26qKysTC+88ILCwsI0YsQIr/fr888/1yOPPKLExER17NhRXbt2VVJSki666CKlp6eradOm+vzzz7V06VLPIhwAUKV6Wy8QAIJAxfLnS5cuPeJ5By9Tfqjdu3dbN998s5WYmGg1adLE6ty5s/Xggw96LeFcnetWqGr584r7Tp482UpJSbEiIiKs+Ph465RTTrEeeughr6W4d+zYYY0ePdqKi4uzHA6HNXr0aM9y0Udb/vy9996zTjjhBCsqKsrq0KGDdf/991vPPfdcpSW327dvb5133nmVYu/fv79nae2jcbvdVnJycpXLyVuWZT399NPW6aefbrVq1cqKjIy0OnXqZN16662W0+n06fqWVXn58wpPPPGE1a1bN6tJkyZWmzZtrOuvv97auXNnpe+le/fu1o8//mj17dvXioqKstq3b2898cQTPt370OXP7Xa7lZCQYF122WVVLpP/+eefW/369bOio6OtuLg4a+jQodaKFSsqnTdz5kzrmGOOsex2+1GXQvfl582y/Hs/qrJv3z7r/vvvt7p3725FRkZaLVq0sHr37m1Nnz690r/Xc889Z2VkZHjO69+/v/XZZ595ji9atMg6+eSTrejoaCsxMdG67bbbPEvmV/w7/vbbb9af//xnq1OnTlZUVJTVsmVL68wzz7Q+//xzr3utXLnSOv30063o6GhLkpWVlWWVlpZat956q5Wenm41a9bMio2NtdLT060nn3zyqO8TgMbNZlk+zAIGAKCRO+OMM1RQUKDly5fXdygAgCDAHCkAAAAA8BOFFAAAAAD4iUIKAAAAAPxUr4XUN998o6FDhyoxMVE2m03vvPOO13HLsjR16lS1a9dO0dHRGjhwoNasWeN1TmFhoUaNGqW4uDg1b95cV111lWcvDgAAastXX33F/CgAgEe9FlJFRUVKT0/XP//5zyqPP/DAA3rsscc0Z84cLVmyRLGxsRo0aJBKSko854waNUq//vqrPvvsM33wwQf65ptvdO2119bVtwAAAACgEQqaVftsNpvefvttDRs2TJLpjUpMTNSkSZN0yy23SDIbQbZp00bPP/+8Ro4cqdzcXKWlpWnp0qXKzMyUJH3yyScaMmSINm3apMTExPr6dgAAAAA0YEG7Ie+6deu0detWDRw40NPmcDh00kknafHixRo5cqQWL16s5s2be4ooyezKbrfbtWTJEv3pT3+q8tqlpaVeu5W73W4VFhaqVatWstlsgfumAAAAAAQ1y7K0e/duJSYmym4//AC+oC2ktm7dKklq06aNV3ubNm08x7Zu3aqEhASv4+Hh4WrZsqXnnKrMmjVL06dPr+WIAQAAADQUGzduVFJS0mGPB20hFUiTJ0/WxIkTPc+dTqeOPfZYrV+/XnFxcXUay9q10u23Sy1aSM2aVT6+e7e0c6d0//1Sp051GlqNud1uFRQUKD4+/ojVPAAAAGoPOVjNuFwutW/fXs2qSs4PErSFVNu2bSVJ27ZtU7t27Tzt27ZtU8+ePT3n5Ofne72urKxMhYWFntdXJTIyUpGRkZXamzdvXueFlNst7d8vORxSWFjl43FxUn6+Oa958zoNrcbcbrf27dun5s2b80sMAABQR8jBaqbiPTvalJ+gfWc7duyotm3bauHChZ42l8ulJUuWqG/fvpKkvn37ateuXcrOzvac88UXX8jtduukk06q85irw+GQoqKkoqKqjxcXm+MOR93GBQAAAODw6rVHas+ePcrLy/M8X7dunX7++We1bNlSxx57rG666Sbdc8896ty5szp27KgpU6YoMTHRs7Jfamqqzj33XF1zzTWaM2eO9u/fr/Hjx2vkyJEhs2JfSoqUmiplZ5vHgwtfy5I2bZIyM815jZHbLeXlSU6nKSZTUqRQ+GAlVOMGAACNE7mL/+q1kPrxxx915plnep5XzFvKysrS888/r9tuu01FRUW69tprtWvXLp166qn65JNPFBUV5XnNSy+9pPHjx2vAgAGy2+0aMWKEHnvssTr/XqrLbpeysqT166XcXCkpSYqJMT1RmzZJ8fHSmDGN8wc5J0eaN8+8LyUlpmcuNdW8XxkZ9R3d4YVq3AAAoHEid6meoNlHqj65XC45HA45nc46nyNVoaof4LQ0U0SF6g+w2+1Wfn6+EhIS/B6fm5MjzZghFRSY4jI21gx/rCgup04NzvclVOMGAAANhz85GLlLZb7WBkG72ERjk5EhpafTpSqZruV588wv9MHDHePizPPcXGn+fPN+BdP7E6pxAwCAxoncpWZ4S4KI3S516SL16WMeG+sPbF7egWGOhy6WYrOZ9hUrzHnBJFTjBgAAjRO5S8000lQdwczpNMMbY2OrPh4dLRUWSosXS6tXm09TgsHR4o6JMcedzrqNCwAAoCrkLjVDIYWgc6Ql4QsKpEWLpHXrpCeekCZMkCZONON76xtL2QMAgFBC7lIzFFIIOhVLwm/aZJaAr1BQYJaJ37JFatVKOuEEqWVL0zZjRv0XU4eLWzqwlH1aWuNdyh4AAAQXcpeaoZBC0KlYEj4+3ozbdbmk/ful5cvNkL6WLaXjj5fCww9MhiwoMJMh63OYX1Vxl5WZx9zcxr2UPQAACD7kLjXD24KglJFhltvs3dsUT7/8Iu3YIbVrZ9ri4w+cG0yTIQ+NOy/PPGZmNs7lQwEAQHAjd6k+lj9H0Dp4SfjFi82cqBNOMD1Rh4qJkTZvDo7JkCxlDwAAQgm5S/VQSCGoVSwJL0kvv2wmPVa1L1qwTYY8OG4AAIBgR+7iP+pMhAQmQwIAACCYUEghJDAZEgAAAMGEoX0hwO0OzjGrZWXSwoXS1q1S27bSgAFVz1+qLRWTIefNM8XT5s1mOF9mpimimAwJAAAagmDN/XwRyrH7i0IqyOXkHCgcSkpM4ZCaanpn6rNweOUV6YEHpI0bzdLkTZpIycnSbbdJl10WuPsyGRIAADRkwZr7+SKUY68OCqkglpNjNpotKDDLe8fGmp2ns7Ol9evrb0nKV16RJk2S9uyRWrSQIiOl0lJp7VrTLgW2mGIyJAAAaIiCNffzRSjHXl18jh+k3G5T0RcUmEo+Lk4KC6v/DWjLykxP1J49UmKiWXY8LMw8Jiaa9gcfNOcBAADAN8Ga+/kilGOvCQqpIJWXZ7pFk5LMhrMHq88NaBcuNMP5WrSoOq4WLaQNG8x5AAAA8E2w5n6+COXYa4JCKkg5nWZsaWxs1cdjYszxut6AdutWMycqMrLq45GR5vjWrXUbFwAAQCgL1tzPF6Ece01QSAUph8NM0Csqqvp4fW1A27atWViitLTq46Wl5njbtnUbFwAAQCgL1tzPF6Ece01QSAWpYN2AdsAAszrfzp1Vx7Vzp3TsseY8AAAA+CZYcz9fhHLsNUEhFaSCdQPa8HCzxHnTpmYfp+JiqbzcPG7ebNpvvTWw+0kBAAA0NMGa+/kilGOvCZtlHVo3Nj4ul0sOh0NOp1NxcXH1HY6XqtbjT0ur/w1oq9pH6thjTRFVsfS52+1Wfn6+EhISZG9ovzkAAAABUBu5X33lYMGat/rL19qAQkrBXUhJwbtDdFmZWZ1v61YzJ2rAAO+eKAopAAAA/9U096vPHCxY81Z/+FobMAArBATrBrTh4dKgQfUdBQAAQMMSrLmfL0I5dn+FWH0IAAAAAPWPQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+Cq/vANA4ud1SXp7kdEoOh5SSItkp6wEAQGMQwETI7ZbWrJG2bpV+/llq0cJ8kWvVPgop1LmcHGnePCk3VyopkaKipNRUKStLysio7+gAAAACKICJUMWl//MfKS5O+uknqUkT6dhjpZNOIteqbRRSqFM5OdKMGVJBgZSUJMXGSkVFUna2tH69NHUqv+AAAKCBCmAiVHHp33+XduyQunUzl9+7V9q40dRs5Fq1iw4+1Bm323xKUlBgPniJi5PCwsxjaqppnz/fnAcAANCgBDARqrj09u1SWZn5io2VYmLMsL7ycmn/fnOcXKv2UEihzuTlmV7spCTJZvM+ZrOZ9hUrzHkAAAANSgAToYpLOxzSrl2miDr4FrGxZjpW8+bkWrWJQgp1xuk03cqxsVUfj4kxx53Ouo0LAAAg4AKYCFVcOjzc9EaFHzJ5p6I9LIxcqzZRSKHOOBxmPmVRUdXHi4vNcYejbuMCAAAIuAAmQhWXriiiysq8j1e0l5eTa9UmCinUmZQUMwR40ybJsryPWZZpT0sz5wEAADQoAUyEKi5dMXyvqMj7FkVFB4b9kWvVHgop1Bm73Sy7GR9vxvG6XOYTEpfLPI+Pl8aMYY8DAADQAAUwEaq4dOvWpucpPNwUT8XF0s6dZkhfkybmOLlW7bFZ1qElcePjcrnkcDjkdDoVFxdX3+E0GG63W/n5+UpISJD9oN/YqrZPSEszv9gsxwkAABq0ACZCB/aRcisuLl8//ZSgiAi7kpOlk08m1/KVr7UBhZQopALlcIWUORawDb0BAACCWwATIbdbWrPGra1b87V3b4JatLCrRQtyLX/4WhuwIS/qhd0udelS31EAAADUgwAmQna71Lmzqc8SEiieAom3FgAAAAD8RCEFAAAAAH6ikAIAAAAAP1FIAQAAAICfKKQAAAAAwE8UUgAAAADgJwopAAAAAPAThRQAAAAA+IlCCgAAAAD8RCEFAAAAAH6ikAIAAAAAP4XXdwBouNxu6Y8/pA0bpObNpZQUyU7pDgAAGjO3W8rLk5xOyeEIaIJUh7dqlCikEBA5OdL8+VJRkbR6tRQZKaWmSllZUkZGfUcHAABQD3JypHnzpNxcqaREiooKWIK0dq10//3SihUBv1WjRU2KWpeTI82YIWVnS82aSZ07Sy1bmuczZpjjAAAAjcrBCVLLlgFNkH7+WXr11Tq5VaNGIYVa5XabD1oKCsynHjExUliYFBdnnhcUmJ4qt7u+IwUAAKgjhyZIcXEBS5DcbumFFySXK+C3avQopFCr8vJMb3VSkmSzeR+z2Uz7ihXmPAAAgEahDhOkilvFx5OLBRqFFGqV02nG4cbGVn08JsYcdzrrNi4AAIB6U4cJUsWtoqICfqtGj0IKtcrhML+4RUVVHy8uNscdjrqNCwAAoN7UYYJUcauSkoDfqtGjkEKtSkkx4283bZIsy/uYZZn2tDRzHgAAQKNQhwlSxa0KCsjFAo1CCrXKbjfLasbHm/G5xcVSWZmZ8FgxXnfMGPYwAAAAjcihCZLLFbAEyW6XRo82i0sE+FaNns2yDq1VGx+XyyWHwyGn06m4uLj6DqdBMPtIuVVUlK/VqxMUGWlXWpr5xWXvAgAA0ChVtY9UABIkt9utxYvz9cYbCVqxwh7IWzVIvtYGFFKikAqUsjK3li3LV3l5gpo3t7ObNgAAgNttlsxzOs1EpQAkSG63W/n5+YqPT9Bvv9kDeasGydfaILwOY0IjY7dLxxwjJSTwSwsAACDJJEVdujS0WzVKpLcAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/BTUhVR5ebmmTJmijh07Kjo6Wp06ddLMmTNlWZbnHMuyNHXqVLVr107R0dEaOHCg1qxZU49RQ5LcbmnNmgNfbnd9RwQAAFBDbre0erW0dKl5rE6CUxvXqP1L1cl1G5rw+g7gSO6//3499dRTmjdvnrp3764ff/xRV155pRwOh2644QZJ0gMPPKDHHntM8+bNU8eOHTVlyhQNGjRIK1asUFRUVD1/B41TTo40b560cqXUrp20ZYvUrZuUlSVlZNR3dAAAANVQkeDk5kolJVJUlJSa6l+CUxvX8OFS6enV+P5qP8QGz2Yd3L0TZM4//3y1adNGzz77rKdtxIgRio6O1osvvijLspSYmKhJkybplltukSQ5nU61adNGzz//vEaOHOnTfVwulxwOh5xOp+Li4gLyvTQWOTnSjBlSQYGUnOxWx475WrcuQRs32hUfL02dyi8hAAAIMQcnOElJUmysVFQkbdoknxOc2riGj5eaMsWtxMR8JSQkyG73fQBaLYYY0nytDYJ6aN8pp5yihQsXavXq1ZKkZcuW6bvvvtPgwYMlSevWrdPWrVs1cOBAz2scDodOOukkLV68uF5ibszcbvMJRkGB+eQiLk6y281jaqppnz+f7mEAABBCqkpwwsL8S3Bq4xp+XOrFF/3Pt2oxxEYjqIf23XHHHXK5XOrWrZvCwsJUXl6ue++9V6NGjZIkbd26VZLUpk0br9e1adPGc6wqpaWlKi0t9Tx3uVySJLfbLTc/HdW2Zo0ZzpecbAoom80tyZLN5pbdbtpzc815nTvXd7QAAAA+ODTBOZjN5luCUxvX8OtSbm3ebCkhwfe8thZDDHm+1gNBXUi99tpreumll/Tyyy+re/fu+vnnn3XTTTcpMTFRWVlZ1b7urFmzNH369Ert27dvV0lJSU1CbtTy882cqMTEA4VUXJxTNpsly7KrZUspMtKc53DUd7QAAAA+ODTBOZQvCU5tXMOPS0VFueV0OpWfb/k8tK8WQwx5u3fv9um8oC6kbr31Vt1xxx2euU49evTQ+vXrNWvWLGVlZalt27aSpG3btqldu3ae123btk09e/Y87HUnT56siRMnep67XC4lJyerdevWzJGqAafTLCxRWmq6gW02tyzLpoKC1rIsu1wuqbBQSkgwXwAAAEHv0ATnUL4kOLVxDT8utXOnWw6HTQkJrX0upGoxxJDn64J1QV1IFRcXV/rHDwsL83S3dezYUW3bttXChQs9hZPL5dKSJUt0/fXXH/a6kZGRioyMrNRut9v9mpAHb507m9X5srPNWFrzVtpkWXa53XZt3ChlZprzeJsBAEBIODTBsdkOHLMs+ZTg1MY1/LhUnz5SYqLNr9y2FkMMeb6+Z0FdSA0dOlT33nuvjj32WHXv3l05OTl65JFH9Oc//1mSZLPZdNNNN+mee+5R586dPcufJyYmatiwYfUbfCNkt5ulMdevN2Nok5NNN7DLZX754uOlMWMa/i8fAABoQA5NcJKSpJgYqbj4wHJ2R0twauMaflzqiiv8z7dqMcRGI6iXP9+9e7emTJmit99+W/n5+UpMTNRll12mqVOnKiIiQpLZkHfatGl65plntGvXLp166ql68skn1aVLF5/vw/LntevAPlJutWuXry1bEpSaateYMY1jyUwAANAAVbXBUlqa/EpwauMaPlwqPd2t/Hz/lz+v5RBDlq+1QVAXUnWFQqr2ud3SmjUHfok7d7bzCQYAAAhtbreUl2cmFDkcUkqK/100tXGNo1zK7a5+IVXLIYYkX2uDoB7ah9Blt5sxtA6HmZDYmH75AABAA2W3S36MegrYNWr/UnVy3YaG9BYAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJDXkR2hr71tsAAACoFxRSCF05OdK8eVJurlRSIkVFSampUlaWlJFR39EBAACgAaOQQmjKyZFmzJAKCqSkJCk2VioqkrKzpfXrpalTKaYAAAAQMIyBQuhxu01PVEGB6YGKi5PCwsxjaqppnz/fnAcAABBi3G5p9Wpp6VLzSEoTnOiRQujJyzPD+ZKSJJvN+5jNZtpXrDDndelSPzECAABUAzMXQgeFFEKP02n+ssTGVn08JkbavNmcBwAAECKYuRBaGNqH0ONwmI9nioqqPl5cbI47HHUbFwAAQDUxcyH0UEgh9KSkmL8omzZJluV9zLJMe1qaOQ8AACAE+DNzAcGBQgqhx243A4Xj481fHJdLKiszj7m5pn3MGPaTAgAAIcOXmQslJcxcCCZkmghNGRlmoHDv3lJhofl4prBQysxkADEAAAg5zFwIPSw2gdCVkSGlp5siyuk0f1lSUuiJAgAAIadi5kJ2tnk8eHhfxcyFzExmLgQTCimENrudJc4BAEDIq5i5sH79gblSMTGmJ2rTJmYuBCP+KQAAAIAgwMyF0EKPFAAAABAkmLkQOiikAAAAgCDCzIXQQG0LAAAAAH6ikAIAAAAAP1FIAQAAAICfKKQAAAAAwE8UUgAAAADgJwopAAAAAPAThRQAAAAA+IlCCgAAAAD8RCEFAAAAAH6ikAIAAAAAP1FIAQAAAICfwus7ADQgbreUlyc5nZLDIR13XH1HBAAAgMM4NHVLSZHsdLP4jEIKtSMnR5o3T8rNlUpKpKgoKS1NuugiKSGhvqMDAADAQapK3VJTpawsKSOjvqMLDRRSqLmcHGnGDKmgQEpKkmJjpaIiKTtbKiuToqOlXr3qO0oAAADoyKnb+vXS1KkUU76g8w4143abjzMKCszHGHFxUliYeUxNlVwu6cUXzXkAAACoV0dL3QoKpPnzSd18QSGFmsnLM33CSUmSzeZ9zGaT4uOlFSvMeQAAAKhXR0vdkpJI3XxFIYWacTrNwNrY2KqPR0aa405n3cYFAACASo6WusXEkLr5ikIKNeNwmNmJRUVVHy8tNccdjrqNCwAAAJUcLXUrLiZ18xWFFGomJcUMqN20SbIs72OWZQbapqWZ8wAAAFCvjpa6bdpE6uYrCinUjN1u1smMjzcDbl0us1Kfy2Wex8VJV1zBpgQAAABB4GipW3y8NGYMqZsveItQcxkZZp3M3r2lwkIzO7GwUMrMlEaOlHr2rO8IAQAA8D9HSt1Y+tx37COF2pGRIaWne2+PfdxxZmgfAAAAgkpVqVtKCj1R/qCQQu2x26UuXQ48ZwMCAACAoHVo6gb/UHMCAAAAgJ8opAAAAADATxRSAAAAAOAn5kghtLjdzIoEAABAvaOQQujIyZHmzTObHJSUmG23U1PNZgis0wkAAIA6RCGF0JCTI82YYZZTT0qSYmOloiIpO1tav55NDwAAAFCnGBOF4Od2m56oggLTAxUXJ4WFmcfUVNM+fz7LrQMAAKDOUEgh+OXlmeF8SUmSzeZ9zGYz7StWmPMAAACAOkAhheDndJo5UbGxVR+PiTHHnc66jQsAAACNFoUUgp/DYRaWKCqq+nhxsTnucNRtXAAAAGi0KKQQ/FJSzFyoTZsky/I+ZlmmPS3NnAcAAADUAVbtQ+2oan+n2mK3myXO168/MFcqJsb0RG3aJMXHS2PGsJ8UAAAA6gyFFGrucPs7jRkjJSbWzj0yMswS5xX32bzZ3Ccz09yHpc8BAABQhyikUDNH2t9pwwZp0iQpIaF27pWRIaWnV+75oicKAAAAdYxCCtV36P5OFUuTV+zvtHKl9OWX0kkn1V6xY7dLXbrUzrUAAACAauKjfFSfL/s7bdggrV1bP/EBAAAAAUIhherzZX+n/fvZ3wkAAAANDoUUqs+X/Z2aNGF/JwAAADQ4FFKoPl/2dzr2WKlTp/qJDwAAAAgQCilUX8X+TvHxZq6UyyWVlZnH3FzTfuaZrKoHAACABocMFzVTsb9T795SYaFZgKKw0OzvdNdd9EYBAACgQWL5c9Tc4fZ3kqT8/PqNDQAAAAgACinUjqr2d3K76ycWAAAAIMAY2gcAAAAAfqKQAgAAAAA/UUgBAAAAgJ8opAAAAADATxRSAAAAAOAnCikAAAAA8BOFFAAAAAD4iUIKAAAAAPxEIQUAAAAAfqKQAgAAAAA/UUgBAAAAgJ/C6zsABBm3W8rLk5xOyeGQUlIkO/U2AABAvXC75V6dp43LnXLKoajjU5TSxU56FgRq9E9QWlpaW3Ec1h9//KErrrhCrVq1UnR0tHr06KEff/zRc9yyLE2dOlXt2rVTdHS0Bg4cqDVr1gQ8rgYpJ0eaOFGaMEG65RbzOHGiaQcAAEDdyslR/hUTtXrQBDmvuUUl10zQ8kET9fAVOaRnQcCvQurjjz9WVlaWjjvuODVp0kQxMTGKi4tT//79de+992rz5s21GtzOnTvVr18/NWnSRB9//LFWrFihhx9+WC1atPCc88ADD+ixxx7TnDlztGTJEsXGxmrQoEEqKSmp1VgavJwcacYMKTtbatlS6tzZPGZnm3Z+WwEAAOpOTo52TZyhggXZ2ljcUttbdpbVoqVSi7N10oIZemEixVR9s1mWZR3tpLffflu33367du/erSFDhujEE09UYmKioqOjVVhYqOXLl+vbb7/V4sWLNXbsWM2cOVOtW7eucXB33HGHFi1apG+//bbK45ZlKTExUZMmTdItt9wiSXI6nWrTpo2ef/55jRw50qf7uFwuORwOOZ1OxcXF1TjukON2m56n7GwpNVWy2Q4csywpN1fKzJQeftivYX5ut1v5+flKSEiQnf5nAAAA37jdsm6eqN/eyNby8lS1aOGdm7XdlaucsEwtufhhPfRI5WF+5GA142tt4NMcqQceeECzZ8/W4MGDq/zHuOSSSySZYXiPP/64XnzxRd18883VDP2A9957T4MGDdLFF1+sr7/+Wsccc4z++te/6pprrpEkrVu3Tlu3btXAgQM9r3E4HDrppJO0ePHiwxZSpaWlXsMSXS6XJPND53a7axx3yFmzRlq5UkpOrlwo2WymPTfXnNe5s8+Xdbvdsiyrcb6nAAAA1bVmjYp+WqnflayYOLssr/TMpp3NktW1OFefZq/RmjWdK6Vn5GA14+v75lMhtXjxYp8udswxx+jvf/+7T+f64rffftNTTz2liRMn6m9/+5uWLl2qG264QREREcrKytLWrVslSW3atPF6XZs2bTzHqjJr1ixNnz69Uvv27dsb55DA/HypXTspMbHqHqeWLaXISHOew+HzZd1ut5xOpyzL4tMQAAAAX+XnqzixnYqjExUda9d+m/fhMndLhRVHKrFlvvLzHZXSM3Kwmtm9e7dP59V41b6ioiKVl5cHZEic2+1WZmam7rvvPklSRkaGli9frjlz5igrK6va1508ebImTpzoee5yuZScnKzWrVs3zqF9Tqe0ZYtUWipV9f27XFJhoZSQYL585Ha7ZbPZ1Lp1a36JAQAAfOV0qmjzFsX8Vip3bJwimngfjtrnUnhxoTYfl6CEhIRK6Rk5WM1ERUX5dF61C6kVK1ZozJgx+umnn2Sz2ZSWlqa5c+cqMzOzupespF27dkpLS/NqS01N1ZtvvilJatu2rSRp27Ztateuneecbdu2qWfPnoe9bmRkpCIjIyu12+32xvnD1rmz1K3b4edIbdxo5kh17uz3Uug2m63xvq8AAADV0bmzmvbqpg6/ZWu5K1WRh8yRarF7o3LCMuXo3VmdO1e9FDo5WPX5+p5V+5297rrrNH78eO3Zs0c7duzQ8OHDa9RLVJV+/fpp1apVXm2rV69W+/btJUkdO3ZU27ZttXDhQs9xl8ulJUuWqG/fvrUaS4Nmt0tZWVJ8vJkL5XJJZWXmMTfXtI8Zw35SAAAAdcFul21sllp1iVfn/bkqLXCprLRMEaUutd6Rqy3747WkyxiNzmI/qfrk81t/4YUX6o8//vA83759uy644ALFxMSoefPmGjJkiLZt21arwd1888364YcfdN999ykvL08vv/yynnnmGY0bN06SqbRvuukm3XPPPXrvvff0yy+/aMyYMUpMTNSwYcNqNZYGLyNDmjpV6t3bDOPLyzOPmZmmPSOjviMEAABoPDIy1PyRqYof1FvJMYWK35kn+85C5cZkaum5UzX6kQzSs3rm89C+K664QmeddZbGjRunCRMmaPz48erevbv69++v/fv364svvtCkSZNqNbg+ffro7bff1uTJkzVjxgx17NhRjz76qEaNGuU557bbblNRUZGuvfZa7dq1S6eeeqo++eQTn8c24iAZGVJ6uiminE6zsERKCj1RAAAA9SEjQwkvpit+dZ42LndKcuj441M0rAs9UcHAp32kKjidTt1+++3KycnRnDlzFB4erq+++krl5eXq16+f+vTpE8hYA6bR7yMVIOxhAAAAUPfIwWqmVveRquBwODRnzhx99913ysrK0tlnn62ZM2cqJiamxgEDAAAAQKjwq0QtLCxUdna2evTooezsbMXFxSkjI0MfffRRoOIDAAAAgKDjcyH18ssvKykpSeedd57at2+vjz/+WNOmTdO7776rBx54QJdcckmtLzYBAAAAAMHI50Jq8uTJeu6557R161YtXLhQU6ZMkSR169ZNX331lc4++2yWHAcAAADQKPhcSO3Zs0ddu3aVJHXq1EnFxcVex6+55hr98MMPtRsdAAAAAAQhnxebyMrK0nnnnaczzjhDP/74o0aPHl3pnISEhFoNDgAAAACCkc+F1COPPKIzzzxTK1eu1NixY3XOOecEMi4AAAAACFp+LX8+dOhQDR06NFCxAAAAAEBI8GmO1KuvvurzBTdu3KhFixZVOyAAAAAACHY+FVJPPfWUUlNT9cADDyg3N7fScafTqY8++kiXX365evXqpR07dtR6oAAAAAAQLHwa2vf111/rvffe0+OPP67JkycrNjZWbdq0UVRUlHbu3KmtW7cqPj5eY8eO1fLly9WmTZtAxw0AAAAA9cbnOVIXXHCBLrjgAhUUFOi7777T+vXrtXfvXsXHxysjI0MZGRmy231eTR21wO2W8vIkp1NyOKSUFIl/AgAAAASrhpS/+rXYhCTFx8dr2LBhAQgF/sjJkebNk3JzpZISKSpKSk2VsrKkjIz6jg4AAADw1tDyV78LKdS/nBxpxgypoEBKSpJiY6WiIik7W1q/Xpo6NTR/GAEAANAwNcT8NUQ70hovt9tU8gUFpoKPi5PCwsxjaqppnz/fnAcAAADUt4aav1JIhZi8PNMdmpQk2Wzex2w2075ihTkPAAAAqG8NNX+lkAoxTqcZUxobW/XxmBhz3Oms27gAAACAqjTU/NXvQurLL78MRBzwkcNhJuYVFVV9vLjYHHc46jYuAAAAoCoNNX/1u5A699xz1alTJ91zzz3auHFjIGLCEaSkmLGkmzZJluV9zLJMe1qaOQ8AAACobw01f/W7kPrjjz80fvx4vfHGGzruuOM0aNAgvfbaa9q3b18g4sMh7HazRGR8vBlr6nJJZWXmMTfXtI8ZE7rr8QMAAKBhaaj5q82yDq0LfffTTz9p7ty5euWVVyRJl19+ua666iqlp6fXWoB1weVyyeFwyOl0Ki4urr7D8UlV6/CnpZkfwmBZOtLtdis/P18JCQls1gwAAFBHgjUHC4X8VfK9NqhRISVJmzdv1jPPPKO///3vCg8PV0lJifr27as5c+aoe/fuNbl0nQnFQkoK/p2hg/WXGAAAoCEL5hws2PNXyffaoFph79+/X2+88YaGDBmi9u3ba8GCBXriiSe0bds25eXlqX379rr44ourHTx8Y7dLXbpIffqYx2D7IQQAAAAO1pDy13B/XzBhwgS98sorsixLo0eP1gMPPKDjjz/eczw2NlYPPfSQEhMTazVQAAAAAAgWfhdSK1as0OOPP67hw4crMjKyynPi4+NZJh0AAABAg+V3Z9q0adN08cUXVyqiysrK9M0330iSwsPD1b9//9qJEAAAAACCjN+F1JlnnqnCwsJK7U6nU2eeeWatBAUAAAAAwczvQsqyLNlstkrtO3bsUGxsbK0EBQAAAADBzOc5UsOHD5ck2Ww2jR071mtoX3l5uf773//qlFNOqf0IAQAAACDI+FxIORwOSaZHqlmzZoqOjvYci4iI0Mknn6xrrrmm9iMEAAAAgCDjcyE1d+5cSVKHDh10yy23MIwPAAAAQKPl9/Ln06ZNC0QcAAAAABAyfCqkevXqpYULF6pFixbKyMiocrGJCj/99FOtBQcAAAAAwcinQurCCy/0LC4xbNiwQMYDAAAAAEHPZlmWVd9B1DeXyyWHwyGn06m4uLj6DqfBcLvdys/PV0JCgux2u+R2S3l5ktMpNWtmTtq9W3I4pJQUye73avwAAACN28H51f9yKrfknYPBL77WBn7PkQKqJSdHmjdPys2VCgrMlyTFx5uv1FQpK0vKyKjfOAEAAELFwflVSYkUFWVyqjFjpMTE+o6uwfOpkGrRosUR50UdrLCwsEYBoQH6+Wdp5kxTPMXGSjt2SMXF5lhBgdS6tZSdLa1fL02devRiqopPXujNAgAAjUpOjjRjhsmlkpJMjlVUZHKqDRukSZOkhIT6jrJB86mQevTRRwMcBhost1t64QXzS96tm/TDD1JpqemFsixp1y5p82bppJOklSul+fOl9PTDF0aH++SF3iwAANBYuN0mHyooMHlQRYdHXJx5vnKl9OWXJr/iw+aA8amQysrKCnQcaKi2bDFFT1KSmQ+1a5f5xEQyv/SxsdLOnZLLZc5ZscL0NnXpUvlaR/rkxdfeLAAAgFCXl3cgvzp01JjNZto3bJDWrpW6dq2fGBsBnwopl8vlmWjlcrmOeC6LNcBLcbHpOYqNlQoLpbKyA4WUJIWHm3P27ZNatjS9U05n5esc7ZOX3Nyj92YBAAA0BE7ngfyqKjEx0v79VedUqDU+z5HasmWLEhIS1Lx58yrnS1mWJZvNpvLy8loPEiEsJsYMvysqkiIiTOFUViY1aWKOl5WZtogIU1BFRZl5T4fy5ZOXI/VmAQAANBQOx4H8qqpOjOJiqWnTqnMq1BqfCqkvvvhCLVu2lCR9+eWXAQ0IDUy7dqbH6McfzRyp5s1Nr1Lz5maOVFGRmQgZF2fG82ZmmsUjDuXLJy+H680CAABoSFJSTH6Vne09Ukcy+dWmTdI550idOtVfjI2AT4VU//79q/xv4Kjsdmn0aOn3302hdMwxZj5UxfLnMTFmec6VK80CFGPGVD00z5dPXg7XmwUAANCQ2O1moa316w+M2ImJMfnQpk1mReQzz2S6Q4BVax+pnTt36tlnn1Vubq4kKS0tTVdeeaWn1wrw0rOnWQiiYrW9+HjvfaQsy/REjRlz+MUifPnk5XC9WQAAAA1NRoZ3frV5s/lQOTPTfIjNPlIBZ7Msy/LnBd98842GDh0qh8OhzMxMSVJ2drZ27dql999/X6effnpAAg0kX3cvhn/cbrf3rtoH7//UrJk5afdu3/eCOnTVvoM/eYmPZ9U+AADQ+FSxv6Zb8s7B4BdfawO/C6kePXqob9++euqppxQWFiZJKi8v11//+ld9//33+uWXX2oWeT2gkAqMSoVUbahqH6m0tCP3ZgEAADQiAcnBGhFfawO/h/bl5eXpjTfe8BRRkhQWFqaJEydq/vz51YsW8FVGhlni/JBPXhgDDAAAgLrkdyHVq1cv5ebmqushm3vl5uYqPT291gIDDstuZ4lzAAAA1CufCqn//ve/nv++4YYbdOONNyovL08nn3yyJOmHH37QP//5T/39738PTJQAAAAAEER8miNlt9tls9l0tFNDdUNe5kgFBuNzAQAA6h45WM3U6hypdevW1VpgAAAAABDqfCqk2rdvH+g4AAAAACBkVGtDXklasWKFNmzYoH379nm1X3DBBTUOCgAAAACCmd+F1G+//aY//elP+uWXX7zmTdlsNkkKyTlSAAAAAOAPv2ef3XjjjerYsaPy8/MVExOjX3/9Vd98840yMzP11VdfBSBEAAAAAAgufvdILV68WF988YXi4+Nlt9tlt9t16qmnatasWbrhhhuUk5MTiDgBAAAAIGj43SNVXl6uZs2aSZLi4+O1efNmSWZBilWrVtVudAAAAAAQhPzukTr++OO1bNkydezYUSeddJIeeOABRURE6JlnntFxxx0XiBjRALjdUl6e5HRKDoeUkiKxrQEAAABCld+F1F133aWioiJJ0owZM3T++efrtNNOU6tWrfTvf/+71gNE6Pv5Z2n+fCk3VyopkaKipNRUKStLysio7+gAAAAA//ldSA0aNMjz3ykpKVq5cqUKCwvVokULz8p9QIW1a6WHH5a2b5eSkqTYWKmoSMrOltavl6ZOpZgCAABA6KnR4KqNGzdq48aNatmyJUUUKnG7pS++kAoKTA9UXJwUFmYeU1NN+/z55jwAAAAglPhdSJWVlWnKlClyOBzq0KGDOnToIIfDobvuukv79+8PRIwIUWvXShs3mp6oQ+tsm820r1hh5k4BAAAAocTvoX0TJkzQW2+9pQceeEB9+/aVZJZEv/vuu7Vjxw499dRTtR4kQpPTKe3fb4bzVSUmRtq82ZwHAAAAhBK/C6mXX35Zr776qgYPHuxpO+GEE5ScnKzLLruMQgoeDofUpIm0Z4/0vxXzvRQXm4UnHI66jw0AAACoCb+H9kVGRqpDhw6V2jt27KiIiIjaiAkNRKdOUnKytGmTZFnexyzLtKelmaXQAQAAgFDidyE1fvx4zZw5U6WlpZ620tJS3XvvvRo/fnytBofQZrdLZ50lxcebpc9dLqmszDzm5pr2MWPYTwoAAAChx6ehfcOHD/d6/vnnnyspKUnp6emSpGXLlmnfvn0aMGBA7UeIkNapk3TXXQf2kdq82Qzny8w0RRRLnwMAACAU+VRIOQ6ZxDJixAiv58nJybUXERqcnj3NV16eWVjC4TDD+eiJAgAAQKjyqZCaO3duoONAA2e3S1261HcUAAAAQO3we9W+Ctu3b9eqVaskSV27dlXr1q1rLSgAAAAACGZ+D64qKirSn//8Z7Vr106nn366Tj/9dCUmJuqqq65ScXFxIGIEAAAAgKDidyE1ceJEff3113r//fe1a9cu7dq1S++++66+/vprTZo0KRAxAgAAAEBQ8Xto35tvvqk33nhDZ5xxhqdtyJAhio6O1iWXXMKGvAAAAAAaPL97pIqLi9WmTZtK7QkJCQztAwAAANAo+F1I9e3bV9OmTVNJSYmnbe/evZo+fbr69u1bq8EBAAAAQDDye2jfo48+qnPPPbfShrxRUVFasGBBrQcIAAAAAMHG70KqR48eWrNmjV566SWtXLlSknTZZZdp1KhRio6OrvUAAQAAACDY+FVI7d+/X926ddMHH3yga665JlAxAQAAAEBQ82uOVJMmTbzmRgEAAABAY+T3YhPjxo3T/fffr7KyskDEAwAAAABBz+85UkuXLtXChQv16aefqkePHoqNjfU6/tZbb9VacAAAAAACx+2W8vIkp1NyOKSUFMl+uK4Wv05u+PwupJo3b64RI0YEIpaj+vvf/67Jkyfrxhtv1KOPPipJKikp0aRJk/Tqq6+qtLRUgwYN0pNPPlnlXlcAAAAAjJwcad48KTdXKimRoqKk1FQpK0vKyKjJyY2D34XU3LlzAxHHUS1dulRPP/20TjjhBK/2m2++WR9++KFef/11ORwOjR8/XsOHD9eiRYvqJU4AAAAg2OXkSDNmSAUFUlKSFBsrFRVJ2dnS+vXS1KkH1Ud+ndx4+NwX53a7df/996tfv37q06eP7rjjDu3duzeQsXns2bNHo0aN0r/+9S+1aNHC0+50OvXss8/qkUce0VlnnaXevXtr7ty5+v777/XDDz/USWwAAABAKHG7TedSQYHpVIqLk8LCzGNqqmmfP9+c59/JjYvPPVL33nuv7r77bg0cOFDR0dH6xz/+ofz8fD333HOBjE+SWeDivPPO08CBA3XPPfd42rOzs7V//34NHDjQ09atWzcde+yxWrx4sU4++eQqr1daWqrS0lLPc5fLJckUi+5G+EMQKG63W5Zl8Z4CAADUoaPlYGvWSCtXSsnJlac42WymPTfXnNdZ/pzcOUDfUd3yNXf1uZCaP3++nnzySV133XWSpM8//1znnXee/u///k/2AE4ye/XVV/XTTz9p6dKllY5t3bpVERERat68uVd7mzZttHXr1sNec9asWZo+fXql9u3bt7O8ey1yu91yOp2yLCugPyMAAAA44Gg5WH6+1K6dlJhY9VoRLVtKkZHmPIf8OdkRgO+m7u3evdun83wupDZs2KAhQ4Z4ng8cOFA2m02bN29WUlKS/xH6YOPGjbrxxhv12WefKSoqqtauO3nyZE2cONHz3OVyKTk5Wa1bt1ZcXFyt3aexc7vdstlsat26NYUUAABAHTlaDuZ0Slu2SKWlZoTeoVwuqbBQSkiQEuTPyQkB+G7qnq91h8+FVFlZWaWLNmnSRPv37/cvMj9kZ2crPz9fvXr18rSVl5frm2++0RNPPKEFCxZo37592rVrl1ev1LZt29S2bdvDXjcyMlKRkZGV2u12Owl/LbPZbLyvAAAAdexIOVjnzlK3bmatiNRUM0KvgmVJGzdKmZnmPLv8Oblh5Hu+5q0+F1KWZWns2LFeBUhJSYn+8pe/eO0lVZv7SA0YMEC//PKLV9uVV16pbt266fbbb1dycrKaNGmihQsXepZkX7VqlTZs2KC+ffvWWhwAAABAQ2G3m1XL168305uSkqSYGKm4WNq0SYqPl8aMqaiL/Dq5UfG5kMrKyqrUdsUVV9RqMIdq1qyZjj/+eK+22NhYtWrVytN+1VVXaeLEiWrZsqXi4uI0YcIE9e3b97ALTQAAAACNXUaGWbW8YmuozZvN1lCZmaYu8lrN3K+TGw+fC6n62j/qaGbPni273a4RI0Z4bcgLAAAA4PAyMqT0dCkvz8ybcjiklJTDdC75dXLjYLMsy6rvIOqby+WSw+GQ0+lksYla5Ha7lZ+fr4SEBOZIAQAA1BFysJrxtTbgnQUAAAAAP1FIAQAAAICfKKQAAAAAwE8UUgAAAADgJwopAAAAAPAThRQAAAAA+IlCCgAAAAD8RCEFAAAAAH6ikAIAAAAAP1FIAQAAAICfKKQAAAAAwE/h9R0AGi+3W8rLk5xOyeGQUlIkO6U9AABoRGotHzr4QnFxUrNmdXDTxo1CCvUiJ0eaN0/KzZVKSqSoKCk1VcrKkjIy6js6AACAwKu1fOjQC0VHS/36SeedJ/XqFaCbgtITdS4nR5oxQ8rOllq2lDp3No/Z2aY9J6e+IwQAAAisWsuHDnehvDzpnnu8L0QSVqsopFCn3G7zIUhBgfnwIy5OCgszj6mppn3+fHMeAABAQ1Rr+dCRLpSc7H0hkrBaRyGFOpWXZ3qSk5Ikm837mM1m2lesMOcBAAA0RLWWD/lzIZKwWkchhTrldJrhuLGxVR+PiTHHnc66jQsAAKCu1Fo+5M+FSMJqHYUU6pTDYeY0FhVVfby42Bx3OOo2LgAAgLpSa/mQPxciCat1FFKoUykpZhjupk2SZXkfsyzTnpZmzgMAAGiIai0f8udCJGG1jkIKdcpuN6trxsebYboul1RWZh5zc037mDH/28rA7ZZWr5aWLjWPTH4EAAANgF/5UHUvtHGj94Vq7aaoYLOsQ0vSxsflcsnhcMjpdCouLq6+w2kw3G638vPzlZCQIPshv5RVbWGQlmZ+fzMyDnMCexwAAIAG5Kj5UDUv5I6OVv6ppyphyBDZfdlHqlo3bbh8rQ0opEQhFShHKqTM8cNsql2xx0FBgVlBJjbWjOfdtMl8WjJ1Kr/oAACgQThsPlSDC7nj4pTfrJkS2ratMgervZs2TL7WBuF1GBPgxW6XunQ5pPHQPQ4qlues2OMgN9fscZCezi88AAAIeVXmQzW9kNst5efXwU0bNzJRBBf2OAAAAEAIoJBCcGGPAwAAAIQACikEF/Y4AAAAQAigkEJwYY8DAAAAhAAKKQQX9jgAAABACCAbRfDJyDBLnPfuLRUWmoUlCgulzEyWPgcAAEBQYPlzBKeMDLPEOXscAAAAIAhRSCF4sccBAAAAghQf7wMAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4tNoFa43ZUX2AMAAGiQ3G5p9Wpp+XLz/PjjzQJZdbW6cFWJly/3PlzCdqRr+Xqv6sYUwiikUGM5OdK8eWa/3JISKSpKSk01++YmJtZ3dAAAALUoJ0d68EFp0SLJ5TJtcXFSv37SrbcGfr/LwyVeWVlHvndVr2vVyhzbsaPqa/l6r+rGFOIopFAjOTnSjBlSQYGUlCTFxkpFRVJ2trRhgzRpkpSQUN9RAgAA1IKcHGniROm//zW9LS1bSpYl7d4tLVggbdkiPfJI4IqHIyVe69dLU6dWfe+qXrdpk4lZkvr0kTp39r7WJZdIr7129HtVN6YGoGH3tyGg3G7z4UNBgfnQIS5OCgszj6mppv3LL815AAAAIc3tlp5/3gzpa9JEio+XIiKkyEjTs9OkiTk2b15gkh9fEq/58yvfu6rX2e3SH3+YmJs0kTZvNm0V19q+XXrgAfN4pHuVlVUvpgaCQgrVlpdnenCTkiSbzfuYzWbaN2yQ1q6tn/gAAABqTV6e6WVxu6WmTb2P2WymJ8btln780ZwbiPsfLfFasaLyvat6ncsl7dplvo/YWGnnTjO3qeJazZtLGzeauU5HutfChdWLqYGgkEK1OZ1mGGxsbNXHY2Kk/fsP/F4CAACELKfTDFmzLCm8itkx4eHmWHFxYJIfXxKvkpLK967qdfv2md6k8HDzVVZm2iqEhZkkrqrv8+B7bd1avZgaCAopVJvDYeYSFhVVfby42PQWOxz/a6hY4WbpUvPYQLt5AQBAA+RwmILBZjOFx6HKysyxmJiDkp9avv/REq+oqMr3rup1EREHCqiKgioi4sDx8nKTxFX1fR58r7ZtqxdTA0EhhWpLSTHDXzdtMh/AHMyyTPuxx0qdOunA5MwJE6RbbjGPEyeadgAAgGCXkiL17m3mEu3Z433MskwxYbdLmZmB2QfGl8QrLa3yvat6XVycGb63Z4+Ju0WLA8WOZZlhf8nJpifpSPcaMKB6MTUQFFKoNrvdrGoZH2+Gx7pc5oMLl8s8j4+XzjxTsv/3Z7OaS3a2Wd2mc2fzmJ1t2immAABAsLPbpbFjzX5R+/ebhRRKS83Xjh2mrUsXkxwFYv8kXxKvMWMq37uq15WXS8ccY2Lev9/sV1NefuBarVtLt91mHo90r/Dw6sXUQNgs69DysfFxuVxyOBxyOp2Ki4ur73BCTlVbB6SlSaNHu5XYdqsSHnxQ9h9/NJ9YHDwR0bLMizIzpYcfbrC/ZAAAoAE53D5Sp55qRt3Uxz5SaWmmYPnfvd1ut/Lz85WQkCB7RX5V1evi400+dvA+Ugdfy4d7+RpTKPG1NqCQEoVUbah6o2y38nNylHDnnbK3aGH+yBzK5ZIKC6XHHzef4gAAAAS7innfy5eb58cfb/KYuvpQuKrE66B7V1lIHe510hGvdbR7+X1eCPC1NmBDXtQKu71yHeR2y0wyPNpqLps3N9jVXAAAQANkt0vdupmv+rp/dT6APtzrjnQtX+9V3ZhCWGiWiQgdMTGNejUXAAAANEwUUgisdu0a9WouAAAAaJgopBBYdrs0enSjXc0FAAAADRPZKwKvZ09p6lSz90JhoZmIWFhoVuubOjUkV3MBAABA48ZiE6gbGRlSenqDWc0FAAAAjRuFFOpOI1zNBQAAAA0T3QEAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+YtW+xsLtDs2lx0M1bgAAADRoFFKNQU6ONG+elJsrlZRIUVFSaqqUlRXcm+GGatwAAABo8CikGrqcHGnGDKmgQEpKkmJjpaIiKTtbWr9emjo1OIuSUI0bAAAAjQJjpBoyt9v06BQUmJ6cuDgpLMw8pqaa9vnzzXnBJFTjBgAAQKNBIdWQ5eWZYXFJSZLN5n3MZjPtK1aY84JJqMYNAACARoNCqiFzOs3cotjYqo/HxJjjTmfdxnU0oRo3AAAAGg0KqYbM4TALNBQVVX28uNgcdzjqNq6jCdW4AQAA0GhQSDVkKSlmTtGmTZJleR+zLNOelmbOCyahGjcAAAAaDQqphsxuN0uFx8ebOUcul1RWZh5zc037mDHBty9TqMYNAACARoNMtKHLyDBLhffuLRUWmgUaCgulzMzgXkI8VOMGAABAo8A+Uo1BRoaUnm6KEafTzC1KSQn+Hp1QjRsAAAANHoVUY2G3S1261HcU/gvVuAEAANCg8dE+AAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+CupCatasWerTp4+aNWumhIQEDRs2TKtWrfI6p6SkROPGjVOrVq3UtGlTjRgxQtu2bauniBs4t1tavVpautQ8ut31HREAAEDoIadqEMLrO4Aj+frrrzVu3Dj16dNHZWVl+tvf/qZzzjlHK1asUGxsrCTp5ptv1ocffqjXX39dDodD48eP1/Dhw7Vo0aJ6jr6BycmR5s2TcnOlkhIpKkpKTZWysqSMjPqODgAAIDSQUzUYNsuyrPoOwlfbt29XQkKCvv76a51++ulyOp1q3bq1Xn75ZV100UWSpJUrVyo1NVWLFy/WySef7NN1XS6XHA6HnE6n4uLiAvkthKacHGnGDKmgQEpKkmJjpaIiadMmKT5emjq1yl98t9ut/Px8JSQkyG4P6s5PAACAwKtmTuUvcrCa8bU2CKl31ul0SpJatmwpScrOztb+/fs1cOBAzzndunXTscceq8WLF9dLjA2O220+NSkoMJ+WxMVJYWHmMTXVtM+fT5c0AADAkZBTNThBPbTvYG63WzfddJP69eun448/XpK0detWRUREqHnz5l7ntmnTRlu3bj3stUpLS1VaWup57nK5PPdw88Prbc0aaeVKKTlZOvQTDZvNtOfmmvM6d/Y67Ha7ZVkW7ykAAEANcip/kYPVjK/vW8gUUuPGjdPy5cv13Xff1fhas2bN0vTp0yu1b9++XSUlJTW+foOSny+1ayclJlb+pZekli2lyEhznsPhdcjtdsvpdMqyLLqVAQBA41aDnMpf5GA1s3v3bp/OC4lCavz48frggw/0zTffKCkpydPetm1b7du3T7t27fLqldq2bZvatm172OtNnjxZEydO9Dx3uVxKTk5W69atmSN1KKdT2rJFKi01Xc+HcrmkwkIpIcF8HcTtdstms6l169b8EgMAgMatBjmVv8jBaiYqKsqn84K6kLIsSxMmTNDbb7+tr776Sh07dvQ63rt3bzVp0kQLFy7UiBEjJEmrVq3Shg0b1Ldv38NeNzIyUpGRkZXa7XY7P2yH6txZ6tZNys4243dttgPHLEvauFHKzDTnVfHe2Ww23lcAAIAa5lT+IgerPl/fs6AupMaNG6eXX35Z7777rpo1a+aZ9+RwOBQdHS2Hw6GrrrpKEydOVMuWLRUXF6cJEyaob9++Pq/Yh6Ow281ynOvXm3G7SUlSTIxUXHxghZkxY2rlFx4AAKDBIqdqcIJ6+XPbwZX6QebOnauxY8dKMhvyTpo0Sa+88opKS0s1aNAgPfnkk0cc2ncolj/3QVV7HqSlmV/4wyzTydKbAAAAh6hGTuUvcrCa8bU2COpCqq5QSPnI7Zby8swYX4dDSkk54qcm/BIDAABUwc+cyv/Lk4PVhK+1QVAP7UOQsdulLl3qOwoAAIDQRk7VIFCiAgAAAICfKKQAAAAAwE8M7QtlAR5fCwAAAKBqFFKhqqoVX1JTzbKatbTiCwAAAICqUUiFopwcacYMqaDA7EEQGysVFZkN3tavl6ZOpZgCAAAAAohxYKHG7TY9UQUFpgcqLk4KCzOPqammff58cx4AAACAgKCQCjV5eQd2wz50w2KbzbSvWGHOAwAAABAQFFKhxuk0c6JiY6s+HhNjjjuddRsXAAAA0IhQSIUah8MsLFFUVPXx4mJz3OGo27gAAACARoRCKtSkpJi5UJs2SZblfcyyTHtamjkPAAAAQEBQSIUau90scR4fb+ZKuVxSWZl5zM017WPGsJ8UAAAAEEAsfx6KMjLMEucV+0ht3myG82VmmiKKpc8BAABCn9ttFhBzOs20jZQU/z4sP9Lra3rt6sZcViYtXCht3Sq1bSsNGCCFh2ZJEppRwxRL6emB+QUAAABA/crJOfCheUmJ+dA8NdWMTPLlQ/O1a6X77zerOR/6eqlm165uzK+8Ij3wgLRxo7R/v9SkiZScLN12m3TZZdW/bz2hkApldrvUpUt9RwEAAIDalJMjzZhh9gdNSjKrNRcVSdnZ0vr1ZmTSkQqen3+WXn1VWrZMOuYY79cvW2bOKSur3rWrG/OJJ0qPPy7t2SO1aCFFRkqlpabgmzTJXCPEiim6L0KU2y2tXi0tXWoe2X8XAACgAXC7Ta9OQYHpzYmLk8LCzGNqqmmfP//wyZ/bLb3wgpk/f+jru3UziePq1ea//b12dWPOz5ceecQUUYmJZruesDDzmJho2h980BR3IYQeqRBU055eAAAABKm8PJPkJSVJNpv3MZvNtK9YYc6ramRSxeu7dzc9Qgev8uxyHSiSdu/23i7Hl2tXN+YmTcxUlPj4qo+3aCFt2GDmTg0a5Pt96xk9UiGmotc0O1tq2VLq3Nk8Zmeb9pyc+o4QAAAA1eZ0mk/KY2OrPh4TY447nUd+fVRU5WP79pnCyrLMf/t77erG7HabryZNqj4eGWnmTG3d6t996xmFVAipaU8vAAAAgpzDYYqgoqKqjxcXm+MH9yZV9fqSksrHIiJMD5DNZv7b32tXN2a73Xzt31/18dJSU2S1bevffesZhVQI8aenFwAAACEoJcV8Qr5pk/ewPMk837RJSksz5x3p9QUFlV8fF3egqGnWzP9rVzfm/ftNsbVnT9XHd+6Ujj3WLIUeQiikQkhNe3oBAAAQ5Ox2M/E9Pt58gu5ymUUYXC7zPD7e7Bt6uC1v7HZp9GhTNB36+pUrzdynLl3Mf/t77erGnJAgTZwoNW1q9j8tLpbKy83j5s2m/dZbQ24/qdCKtpE7uNc0Lq7y8er2xgIAACCIZGSYZcgrVhfbvNkkeZmZptA52upiPXtKI0eawmTFisqvl6p/7ZrE3KHDgX2kdu0yw/lSUkwRFWJLn0uSzbIO7V9rfFwulxwOh5xOp+KqqlCChNttivnsbNN7evDwPssyP7OZmdLDDwfHvrxut1v5+flKSEiQPRgCAgAACCVut5mz4XSaT8pTUnxK8jw5WHy87L/9VvXrq3ntGsdcVmZW59u61cyJGjAg6HqifK0NgitqHPGHr6LXdP36A3OlYmJMT9SmTdXvjQUAAEAQstv9W4bcn9fX9NrVuadkiqYQWuL8SCikgokPG0TVtKcXAAAAQM1RSAWLig2iCgpMV1NsrJkMlZ1tuqCmTvUqptLTA9MbCwAAAODoKKSCwaEbRFVMfqrYICo312wQlZ7uNcwvEL2xAAAAAI6OPoxgwAZRAAAAQEihkAoGbBAFAAAAhBQKqWBw8AZRVWGDKAAAACCoUEgFg5QUMxdq0yazIdTBLMu0p6WZ8wAAAADUOwqpYFCxQVR8vJkr5XKZzcpcLvOcDaIAAACAoMKqfcGCDaIA1AO32619+/bVdxgIUk2aNFFYWFh9hwEAQYlCKpiwQRSAOrRv3z6tW7dObre7vkNBEGvevLnatm0r26GrygJAI0chFWzYIApAHbAsS1u2bFFYWJiSk5Nl5wMbHMKyLBUXFys/P1+S1K5du3qOCACCC4UUgpvbTQ8dEABlZWUqLi5WYmKiYmJi6jscBKno6GhJUn5+vhISEhjmB9QHf3KhinN37ZLCwsw8+9rMmwKQl4VyqkchheCVk3NgzlhJiZkzlppqFuZgzhhQI+Xl5ZKkiIiIeo4Ewa6i0N6/fz+FFFDX/MmFDj63tNSMcIqNrb259gHIy0I91aOQQnDKyZFmzJAKCqSkJPOHoKhIys6W1q83C3OEwm8YEOSY94Kj4WcEqCf+5EKHntu0qdSsmbR0qfT77zXPmwKQlzWEVC9EOs7QqLjd5uOJggLzsURcnOmejoszzwsKpPnzzXkAAAANjT+50OHOjYmpnbwpAHlZQ0n1KKQQfPLyTB9vUpJ06CehNptpX7HCnAcAdWzs2LEaNmxYwO9js9n0zjvvBPw+AIKQP7lQoPOmAFy/oaR6FFIIPk6nGSgbG1v18ZgYc9zprNu4ANS7sWPHymazyWazqUmTJurYsaNuu+02lZSU1HdodcayLA0cOFCDBg2qdOzJJ59U8+bNtWnTpnqIDECt8ScXCnTeFIDrN5RUj0IKwcfhMLMNi4qqPl5cbI47HHUbF4BK3G5p9WozDH/16roZhnHuuedqy5Yt+u233zR79mw9/fTTmjZtWuBvHCRsNpvmzp2rJUuW6Omnn/a0r1u3Trfddpsef/xxJSUl1WOEAGrMn1wo0HlTAK7fUFI9CikEn5QUM0B20ybJsryPWZZpT0sz5wGoNzk50sSJ0oQJ0i23mMeJE017IEVGRqpt27ZKTk7WsGHDNHDgQH322Wee4263W7NmzVLHjh0VHR2t9PR0vfHGG57jO3fu1KhRo9S6dWtFR0erc+fOmjt3ruf4L7/8orPOOkvR0dFq1aqVrr32Wu3Zs6fKWJ555hklJiZW2tT4wgsv1J///GfP83fffVe9evVSVFSUjjvuOE2fPl1lZWWe42vWrNHpp5+uqKgopaWleX0/VUlOTtY//vEP3XLLLVq3bp0sy9JVV12lc845R6NHj9by5cs1ePBgNW3aVG3atNHo0aNVUFDgef0bb7yhHj16eL7HgQMHquhwGQ2AuudPLhTovCkA128oqR6FFIKP3W7WvYyPNwNoXS6prMw85uaa9jFjQmeTAaABqlhtKTtbatlS6tzZPGZnm/ZAF1MVli9fru+//95rGfdZs2Zp/vz5mjNnjn799VfdfPPNuuKKK/T1119LkqZMmaIVK1bo448/Vm5urp566inFx8dLkoqKijRo0CC1aNFCS5cu1euvv67PP/9c48ePr/L+F198sXbs2KEvv/zS01ZYWKhPPvlEo0aNkiR9++23GjNmjG688UatWLFCTz/9tJ5//nnde++9kkzhN3z4cEVERGjJkiWaM2eObr/99qN+71lZWRowYID+/Oc/64knntDy5cv19NNPa9euXTrrrLOUkZGhH3/8UZ988om2bdumSy65RJK0ZcsWXXbZZfrzn/+s3NxcffXVVxo+fLisQ7MZAPXHn1zocOcWF9dO3hSAvKyhpHo2i7+ccrlccjgccjqdiouLq99ggnFXsmrG5Ha7PZs42qvzPVS1uUBaWu3thwA0YiUlJVq3bp06duyoqKgov17rdpuep+xs84niwROFLcv8ymZmSg8/XPt/vsaOHasXX3xRUVFRKisrU2lpqex2u1577TWNGDFCpaWlatmypT7//HP17dvX87qrr75axcXFevnll3XBBRcoPj5ezz33XKXr/+tf/9Ltt9+ujRs3KvZ/g/c/+ugjDR06VJs3b1abNm00duxY7dq1y7MQxLBhw9SqVSs9++yzkkwv1fTp07Vx40bZ7XYNHDhQAwYM0OTJkz33efHFF3Xbbbdp8+bN+vTTT3Xeeedp/fr1SkxMlCR98sknGjx4sN5+++0jLmyRn5+v7t27q7CwUG+++aaGDRume+65R99++60WLFjgOW/Tpk1KTk7WqlWrtGfPHvXu3Vu///672rdvf9T3vCY/KwBqyJ9c6KBz3aWlyu/SRQlNm8o+enTg9pGqYV4WrKmer7UB+0gFk2Dclaw+Y8rIkNLTg6+wBBo5f1Zb6tKl9u9/5pln6qmnnlJRUZFmz56t8PBwjRgx4n+x5am4uFhnn32212v27dunjP/9zbr++us1YsQI/fTTTzrnnHM0bNgwnXLKKZKk3Nxcpaene4ooSerXr5/cbrdWrVqlNm3aVIpn1KhRuuaaa/Tkk08qMjJSL730kkaOHOn5AGnZsmVatGiRpwdKMhsil5SUqLi4WLm5uUpOTvYUUZK8isAjSUhI0HXXXad33nnHU3AtW7ZMX375pZo2bVrp/LVr1+qcc87RgAED1KNHDw0aNEjnnHOOLrroIrVo0cKnewKoQ/7kQgefu2uXWU88PV0Kr6V0PwB5WainehRSwSIYdyULhpjs9sBkYgCqzZfVljZvDtxqS7GxsUr538D55557Tunp6Xr22Wd11VVXeeYyffjhhzrmmGO8XhcZGSlJGjx4sNavX6+PPvpIn332mQYMGKBx48bpoYceqlY8Q4cOlWVZ+vDDD9WnTx99++23mj17tuf4nj17NH36dA0fPrzSa2ujhyc8PFzhByVKe/bs0dChQ3X//fdXOrddu3YKCwvTZ599pu+//16ffvqpHn/8cd15551asmSJOnbsWON4ANQyf3KhinPdbik/v/YrkgDkZaGc6oVIvdfABeOuZMEYE4CgEEyrLdntdv3tb3/TXXfdpb179yotLU2RkZHasGGDUlJSvL6Sk5M9r2vdurWysrL04osv6tFHH9UzzzwjSUpNTdWyZcu8Fl5YtGiR7Ha7unbtWmUMUVFRGj58uF566SW98sor6tq1q3r16uU53qtXL61atapSPCkpKbLb7UpNTdXGjRu1ZcsWz2t++OGHar8nvXr10q+//qoOHTpUul9FT5vNZlO/fv00ffp05eTkKCIiQm+//Xa17wkAjRGFVDAIxl3JgjEmAEEh2FZbuvjiixUWFqZ//vOfatasmW655RbdfPPNmjdvntauXauffvpJjz/+uObNmydJmjp1qt59913l5eXp119/1QcffKDU1FRJZpheVFSUsrKytHz5cn355ZeaMGGCRo8eXeWwvgqjRo3Shx9+qOeee86zyESFqVOnav78+Zo+fbp+/fVX5ebm6tVXX9Vdd90lSRo4cKC6dOmirKwsLVu2TN9++63uvPPOar8f48aNU2FhoS677DItXbpUa9eu1YIFC3TllVeqvLxcS5Ys0X333acff/xRGzZs0FtvvaXt27d73gMAgG8opIJBMO5KFowxAQgKwbbaUnh4uMaPH68HHnhARUVFmjlzpqZMmaJZs2YpNTVV5557rj788EPPsLWIiAhNnjxZJ5xwgk4//XSFhYXp1VdflSTFxMRowYIFKiwsVJ8+fXTRRRdpwIABeuKJJ44Yw1lnnaWWLVtq1apVuvzyy72ODRo0SB988IE+/fRT9enTRyeffLJmz57tWejBbrfr7bff1t69e3XiiSfq6quv9ppP5a/ExEQtWrRI5eXlOuecc9SjRw/ddNNNat68uex2u+Li4vTNN99oyJAh6tKli+666y49/PDDGjx4cLXvCQCNEav2KQhW7Vu92mzA0rKlGTpXOUCpsFB6/PG6G0RaCzHVeNU+AAFTGyuxBetqS6hdrNoHhB5ysJph1b5QUjFO5nBrCW/aZNYSrstdyYIxJgBBJdRXWwIAoCYopIJBxTiZ9esPzEuKiTEztjdtqp9dyYIxJgBBJ5RXWwIAoCbIgoNFRoZZTrx3bzNkLi/PPGZm1s/S58EaEwAAABAE6JEKJsE4TiYYY6pNbnfD/d4AAAAQMBRSwSYYx8kEY0xH40uBVNVM+dRUM6SR3jYAAAAcAYUUGh5fCqScHGnGDLOxcFKSWea9qMgsrrF+PUMXAQAAcESMYULDUlEgZWebpds7dzaP2dmmPSfH9FbNm2eKqNRUs7x7WJh5TE017fPnm/MAAACAKtAjhbpTMdxu505p1y6peXOpRYvam5d0aIFUsWR7RYGUm2sKpOuuO7AS4cHLukvmeVKStGKFiTXUhjQCAID65Xab/TiXLzfPjz/e5BMH5zo1naPty+vdbmnNGrP3J/PAA4JCCnWjYrjdkiXSxo1myF1kpHTssdJJJ9XOvKS8PN8KpOXLzf1jY6u+TkyMtHmz+eMEAADgq5wc6cEHpUWLTAEjmQ90+/WTbr3V5Do1naPty+t//ln68EMTx969zAMPEAopBN7PP0szZ0q//y5t3y6VlZkiprT0QFFVG/OSnE7fCiTJ/EEpKjJ/3A5VXGyOOxzVjwVAgzV27Fjt2rVL77zzTkDvY7PZ9Pbbb2vYsGEBvQ+AWpKTI02cKP33v6bnp2VLybKk3bulBQukLVuka6+VXnut+nO0fZnjLUn33CM1a2ZiiIlhHniA0L+HwHK7pRdeOFBAlZWZIX0xMWZYX3m5tH+/OV7TeUkOx4ECqSoVBdLxx5tPZTZtMn/gDmZZpj0tzXSBAwgqY8eOlc1mk81mU5MmTdSxY0fddtttKikpqe/Q6lzFe/H3v//dq/2dd96R7dBeeQCB5XZLzz9vhvQ1aSLFx0sREWb0TatWpm3VKumBB0zOU5052r7M8Z43z8RRUCAlJzMPPMAopBBYW7aYrmeHw8yLio31HnYXG2t6kpo3PzAvqbpSUnwrkLp0MV3b8fEmNpfLFHgul3keHy+NGcM4YsAXFXMBli41j3XwP+dzzz1XW7Zs0W+//abZs2fr6aef1rRp0wJ+32AUFRWl+++/Xzt37qzvUIDGLS/P9Pi43VLTpt7HbDaT7+zfb0bnNG9+9Dnah7vH0aYw/PijicOXeeCoMTJFBFZxsRluFx5uipXwQ0aTVrSHhZnzajIvyW73vUDKyDBd2717S4WF5g9KYaGUmVm7Xd71kGQCdaZiGMuECdItt5jHiRNNewBFRkaqbdu2Sk5O1rBhwzRw4EB99tlnnuNut1uzZs1Sx44dFR0drfT0dL3xxhue4zt37tSoUaPUunVrRUdHq3Pnzpo7d67n+C+//KKzzjpL0dHRatWqla699lrt2bOnylieeeYZJSYmyn3I7/aFF16oP//5z57n7777rnr16qWoqCgdd9xxmj59usrKyjzH16xZo9NPP11RUVFKS0vz+n6OZODAgWrbtq1mzZp1xPPefPNNde/eXZGRkerQoYMefvhhn64PwEdOpxkRY1mVcx3JtLndppgKC6v6GjExR86FfJnCUFxs4jjSOTXNt+DBHCkEVkyMGU5XUUSVlZmu7goV7eXltTMvqaJAqpiEuXmzuW5mpimiDi6QMjKk9PSarZpzJGz4i4YsSPZiW758ub7//nu1b9/e0zZr1iy9+OKLmjNnjjp37qxvvvlGV1xxhVq3bq3+/ftrypQpWrFihT7++GPFx8crLy9Pe/fulSQVFRVp0KBB6tu3r5YuXar8/HxdffXVGj9+vJ5//vlK97/44os1YcIEffnllxowYIAkqbCwUJ988ok++ugjSdK3336rMWPG6LHHHtNpp52mtWvX6tprr5UkTZs2TW63W8OHD1ebNm20ZMkSOZ1O3XTTTT59/2FhYbrvvvt0+eWX64YbblBSUlKlc7Kzs3XJJZfo7rvv1qWXXqrvv/9ef/3rX9WqVSuNHTvWj3cbwGE5HAdG3ZSVmaF8BysrM/lFkyYm56nK0eZoHzyF4XBzvGNizH8fbZoD88BrBYUUAqtdO1M8LF1qurK3bzd/RCq6m4uKzNjhXbukPn1qZ16SPwWS3R6YJc6DJMkEAsLXrQbS0wMyRPaDDz5Q06ZNVVZWptLSUtntdj3xxBOSpNLSUt133336/PPP1bdvX0nScccdp++++05PP/20+vfvrw0bNigjI0OZmZmSpA4dOniu/fLLL6ukpETz589X7P8+0X3iiSc0dOhQ3X///WrTpo1XLC1atNDgwYP18ssvewqpN954Q/Hx8TrzzDMlSdOnT9cdd9yhrKwsTzwzZ87UbbfdpmnTpunzzz/XypUrtWDBAiUmJkqS7rvvPg0ePNin9+NPf/qTevbsqWnTpunZZ5+tdPyRRx7RgAEDNGXKFElSly5dtGLFCj344IMUUkBtSUkxo1zWrZP27DHzwCtYlskBmjQxOcGuXVJiovfQu4opCJmZh8+FKqYwZGd7/+099PWWZfKQ447zfr0v94BfGNqHwLLbpdGjpdatTc9TeLj5A1JcbPaTCgszf1hat67deUkVBVKfPpX3bgg0NvxFQ+frVgMBGoN/5pln6ueff9aSJUuUlZWlK6+8UiNGjPhfaHkqLi7W2WefraZNm3q+5s+fr7Vr10qSrr/+er366qvq2bOnbrvtNn3//feea+fm5io9Pd1TRElSv3795Ha7tWrVqirjGTVqlN58802VlpZKkl566SWNHDlS9v/93Vm2bJlmzJjhFc8111yjLVu2qLi4WLm5uUpOTvYUUZI8RaCv7r//fs2bN0+5ubmVjuXm5qpfv35ebf369dOaNWtUfrhPxgH4x26Xxo41Ocf+/eb/9aWl5mvHDtPWtat0220m56nOHG1fpjBkZZk44uPNysjMAw8oeqRQc0fbFK5nzwPD7Sr2kSoqMl3LycnSySd7D7s7+HrNmpm23btDZzM5f5JMNvxFKPJ1q4EAjcGPjY1Vyv8+TX3uueeUnp6uZ599VldddZVnLtOHH36oY445xut1kZGRkqTBgwdr/fr1+uijj/TZZ59pwIABGjdunB566KFqxTN06FBZlqUPP/xQffr00bfffqvZs2d7ju/Zs0fTp0/X8OHDK702KiqqWvc81Omnn65BgwZp8uTJ9DIB9SUjQ3rkkQP7SFUsAhMXJ516qplLmpEhdet2+CkI6elmTvXhcipfpzDcdVflfaSqmuaAGqGQQs0caR5QevqB8w4ebrdzp+mVat7cdH0f/Efi4OsVFJgvyXyCEh8fGnOM6jnJBALOl3H6dTQG3263629/+5smTpyoyy+/XGlpaYqMjNSGDRvUv3//w76udevWysrKUlZWlk477TTdeuuteuihh5Samqrnn39eRUVFnl6pRYsWyW63q2vXrlVeKyoqSsOHD9dLL72kvLw8de3aVb169fIc79Wrl1atWuUp/g6VmpqqjRs3asuWLWrXrp0k6YcffvD7vfj73/+unj17VoozNTVVixYt8mpbtGiRunTporDDTXoHUD0ZGdKLL5piaPly03b88d6jYw43BWHZMrNgz9HmVvsyhaFnT6ltW+mSS0xvVKh8GB1iKKRQfUebBzRlihkDXOFo85EOvl5srHksLjbHduwwXeGhMMcoiJJMICB8HadfR2PwL774Yt1666365z//qVtuuUW33HKLbr75Zrndbp166qlyOp1atGiR4uLilJWVpalTp6p3797q3r27SktL9cEHHyg1NVWSGaY3bdo0ZWVl6e6779b27ds1YcIEjR49utL8qIONGjVK559/vn799VddccUVXsemTp2q888/X8cee6wuuugi2e12LVu2TMuXL9c999yjgQMHqkuXLsrKytKDDz4ol8ulO++80+/3oUePHho1apQee+wxr/ZJkyapT58+mjlzpi699FItXrxYTzzxhJ588km/7wHAB3a76XXq1u3I5xycE/k7t9qXOd52u9S5M8VTAPHOonp8mQf04ou+zwM6+Hrdukl//GHGFbdqZXqiSktNW7duwT/HyNf9rJjoiVDlz1YDdSA8PFzjx4/XAw88oKKiIs2cOVNTpkzRrFmzlJqaqnPPPVcffvihOnbsKEmKiIjQ5MmTdcIJJ+j0009XWFiYXn31VUlSTEyMFixYoMLCQvXp00cXXXSRBgwY4FnM4nDOOusstWzZUqtWrdLll1/udWzQoEH64IMP9Omnn6pPnz46+eSTNXv2bM9Kg3a7XW+//bb27t2rE088UVdffbXuvffear0XM2bMqLQUe69evfTaa6/p1Vdf1fHHH6+pU6dqxowZDAEEggVzq0OWzbIOzfQaH5fLJYfDIafTqbiqehBQ2erVZs+Yli2r7nVxueTeuVP5996rhIwMz6Rrn67ndkvff2+WSa9YKn3/flNM9etnPv0uLJQefzx45xgd+slSxd4OmzaZJDOYe9TQKJSUlGjdunXq2LFj9efpVDW0Ny2NMfgNTK38rAA4PB9yKn/zHrfbrfz8fCUkJBw9B0MlvtYGDO1rTA63KMTRFouoypHmAVmW+XR6+3azg7cvSyAffL0dO8zrK/ZCkMxqf0VF0r59Zl5VsM8x8mc/KyBUBXovNgAIdb7kWMytDlkUUo3F4RaFOOkks5Kev5vGHm4eUEGB+WSloMAUPe+9J3333dGLh4OvFxFRefPeio17IyJCZ44RSSYag0DtxQYAoe5IC3IdnBMxtzpkkdE1BhXDzLKzTbdx587m8euvpUmTzOPB7dnZ5vycnMNfs6p5QAUF0k8/mZ6osjLpmGPMHCd/rxcXZ1b0Kyo6cO2iItPWrFlozTGqz/2sAABA/Thc7lVVTsTc6pBFVtfQHW4CY7NmZt7Rnj2m6GnWzL+JjYdONnc6pZUrzfUkqWlTUzjExlbves2bm7lQ+fmmMIuIMIXZypVsJgcAAIKXv4tHBNkCPvAd/yIN3eE2h3W5TPHTooXZ0+ngcbeHbhp7OBXzgHr3Np+WbN5sht8lJEi9eplffH+vd8klpvcpN9dswrt3r1lkIibGfCqTmclCDUAtYr0hHA0/I4CfDpd7SYfPiQ7OqQoLzbHCQvKeIMccqYbu4AmMlmUKqH37TJFS0RO1e7dpO1hMjFlu/L//PfL8nop5QG+9Jd13n9SpkynODv3D4ctEyZwc6bXXTKwnnWSKsv37pW3bTJzXXy9dcAGfyAC1oGIj1n379ik6Orqeo0EwK/7ffn5NmjSp50iAEFHdxSOYWx1yKKQauooJjJs2mcJo1y5TQFmW6fmRDizicLA//jAbwD36qOmOPtIiFHa7dMIJZsPc8PDKRZR09ImSB3eDp6V5XyMpyXyy8/XXppACUGPh4eGKiYnR9u3b1aRJE5bHRSWWZam4uFj5+flq3ry5p/gGcBQ1WTyCBXxCCoVUQ5eSYhZ8WLBAatLEzF2KjTU9PS6XKVzat/f+Zd6+XVq61PySJyWZ1xxpd+2K+6SmmnNSU70LoYqJkpmZh58o6Ws3+OrV5o8Mn9QANWKz2dSuXTutW7dO69evr+9wEMSaN2+utm3b1ncYQOioaU6EkEEh1dhY1oEVYWJiTDFV8RUbawqmJUvM8ZNOOlBgVUyQzM01EyQP3RuqYqLk+vUHCqLYWPOpiy8TJX3pBl+9Wpo+3YwZ9mepdgBVioiIUOfOnbXv0KG9wP80adKEnijAX1XlRDExJifatInFIxoQCqmGLi/PbHDbp8+BoX3FxWYIXmKi6T7+4w/zix0WJpWXm2N9+pihegc7dILkoV3Ph25Cu2WLOSczUxo9unr7UlX44w9p40YTQ+fOB4q+I/WSATgqu92uqKio+g4DABqWQ3OizZtNnpOZefS9NREyGkwh9c9//lMPPvigtm7dqvT0dD3++OM68cQT6zus+lfR09O5s3Tsseb5vn1mTpTDcaBwuuEGM8Rv/XrpscdMwVSVoy0acfBEyV27THGWnm7ucSRH6gZ3u6VffjFDEzMyDnyCc7ReMgAAgPrC4hENXoMopP79739r4sSJmjNnjk466SQ9+uijGjRokFatWqWEhIT6Dq9+HdrT07y59/HiYik62iwW0aWLOT86uma7a1dMlHS7zT5QvvzBOFI3+Jo1Zk5Xr16Vr3W0XjIAAID6wuIRDVqDKIkfeeQRXXPNNbryyiuVlpamOXPmKCYmRs8991x9h1b//N0tuz531z7cHgqdO0vJyUfuJSspOfLS6gAAAEAtCvkeqX379ik7O1uTJ0/2tNntdg0cOFCLFy+u8jWlpaUqLS31PHf+LwHftWuX3BW7TDckw4dLa9dKy5dLxxxjepz27jXzjlq1kv70J7PYRHXPPwy32y2Xy6WIiAjfl1bu2NEUU+vWHegGd7ulyZPN82bNKr9m924z7M9uN8MJAQAAGrFq5WDwcP0vzz3ahuQhX0gVFBSovLxcbdq08Wpv06aNVq5cWeVrZs2apenTp1dqb9++fUBiDHoffBDY8+tKZmZ9RwAAAIAGYvfu3XIcYTpLyBdS1TF58mRNnDjR89ztdquwsFCtWrWSrarNZFEtLpdLycnJ2rhxo+Kqmm8FAACAWkcOVjOWZWn37t1KTEw84nkhX0jFx8crLCxM27Zt82rftm3bYTcQjIyMVGRkpFdb80MXYUCtiYuL45cYAACgjpGDVd+ReqIqhPygyYiICPXu3VsLFy70tLndbi1cuFB9+/atx8gAAAAANFQh3yMlSRMnTlRWVpYyMzN14okn6tFHH1VRUZGuvPLK+g4NAAAAQAPUIAqpSy+9VNu3b9fUqVO1detW9ezZU5988kmlBShQtyIjIzVt2rRKwygBAAAQOORgdcNmHW1dPwAAAACAl5CfIwUAAAAAdY1CCgAAAAD8RCEFAAAAAH6ikEJQ69Chgx599FHPc5vNpnfeeafe4gEAAAAkCimEmC1btmjw4MH1HQYAAECdO+OMM3TTTTfVdxiSpJKSEo0dO1Y9evRQeHi4hg0bVt8h1TkKqUZm37599R1CjbRt25alPAEAAOpZeXm5oqOjdcMNN2jgwIH1HU69oJAKcWeccYbGjx+v8ePHy+FwKD4+XlOmTFHFqvYdOnTQzJkzNWbMGMXFxenaa6+VJL355pvq3r27IiMj1aFDBz388MM+37NDhw665557NGbMGDVt2lTt27fXe++9p+3bt+vCCy9U06ZNdcIJJ+jHH3/0et13332n0047TdHR0UpOTtYNN9ygoqIiz/H8/HwNHTpU0dHR6tixo1566aVK9z50aN/tt9+uLl26KCYmRscdd5ymTJmi/fv3e47ffffd6tmzp1544QV16NBBDodDI0eO1O7du33+fgEAAPx1xhlnaMKECbrpppvUokULtWnTRv/6179UVFSkK6+8Us2aNVNKSoo+/vhjz2uWL1+uwYMHq2nTpmrTpo1Gjx6tgoICSdLYsWP19ddf6x//+IdsNptsNpt+//13lZeX66qrrlLHjh0VHR2trl276h//+EeleJ577jlP7teuXTuNHz/+iPHv3LlTY8aMUYsWLRQTE6PBgwdrzZo1nuOxsbF66qmndM0116ht27a19K6FFgqpBmDevHkKDw/Xf/7zH/3jH//QI488ov/7v//zHH/ooYeUnp6unJwcTZkyRdnZ2brkkks0cuRI/fLLL7r77rs1ZcoUPf/88z7fc/bs2erXr59ycnJ03nnnafTo0RozZoyuuOIK/fTTT+rUqZPGjBnjKejWrl2rc889VyNGjNB///tf/fvf/9Z3333n9Us8duxYbdy4UV9++aXeeOMNPfnkk8rPzz9iHM2aNdPzzz+vFStW6B//+If+9a9/afbs2V7nrF27Vu+8844++OADffDBB/r666/197//3efvFQAAoDrmzZun+Ph4/ec//9GECRN0/fXX6+KLL9Ypp5yin376Seecc45Gjx6t4uJi7dq1S2eddZYyMjL0448/6pNPPtG2bdt0ySWXSJL+8Y9/qG/fvrrmmmu0ZcsWbdmyRcnJyXK73UpKStLrr7+uFStWaOrUqfrb3/6m1157zRPHU089pXHjxunaa6/VL7/8ovfee08pKSlHjH3s2LH68ccf9d5772nx4sWyLEtDhgzx+sC60bMQ0vr372+lpqZabrfb03b77bdbqamplmVZVvv27a1hw4Z5vebyyy+3zj77bK+2W2+91UpLS/Ppnu3bt7euuOIKz/MtW7ZYkqwpU6Z42hYvXmxJsrZs2WJZlmVdddVV1rXXXut1nW+//day2+3W3r17rVWrVlmSrP/85z+e47m5uZYka/bs2Z42Sdbbb7992NgefPBBq3fv3p7n06ZNs2JiYiyXy+X1vZ500kk+fa8AAADV0b9/f+vUU0/1PC8rK7NiY2Ot0aNHe9oqcqjFixdbM2fOtM455xyva2zcuNGSZK1atcpzzRtvvPGo9x43bpw1YsQIz/PExETrzjvv9Dn21atXW5KsRYsWedoKCgqs6Oho67XXXqt0flZWlnXhhRf6fP2Ggh6pBuDkk0+WzWbzPO/bt6/WrFmj8vJySVJmZqbX+bm5uerXr59XW79+/bxeczQnnHCC57/btGkjSerRo0eltooepWXLlun5559X06ZNPV+DBg2S2+3WunXrlJubq/DwcPXu3dtzjW7duql58+ZHjOPf//63+vXrp7Zt26pp06a66667tGHDBq9zOnTooGbNmnmet2vX7qg9XQAAADV1cL4UFhamVq1aHTZfWrZsmb788kuvXKlbt26SzOiaI/nnP/+p3r17q3Xr1mratKmeeeYZTz6Un5+vzZs3a8CAAVW+9i9/+YvXPSV58rKTTjrJc16rVq3UtWtX5ebmVuOdaJjC6zsABF5sbGytX7NJkyae/64o4qpqc7vdkqQ9e/bouuuu0w033FDpWscee6xWr17tdwyLFy/WqFGjNH36dA0aNEgOh0OvvvpqpfleB8dVEVtFXAAAAIFSVQ5yuHxpz549Gjp0qO6///5K12nXrt1h7/Hqq6/qlltu0cMPP6y+ffuqWbNmevDBB7VkyRJJUnR09BFjnDFjhm655RafvyccQCHVAFT8olT44Ycf1LlzZ4WFhVV5fmpqqhYtWuTVtmjRInXp0uWwr6mpXr16acWKFYcdj9utWzeVlZUpOztbffr0kSStWrVKu3btOuw1v//+e7Vv31533nmnp239+vW1GjcAAEBd6NWrl95880116NBB4eFVp+gRERGVRg8tWrRIp5xyiv7617962g7uwWrWrJk6dOighQsX6swzz6x0zYSEBCUkJHi1paamqqysTEuWLNEpp5wiSdqxY4dWrVqltLS0an+PDQ1D+xqADRs2aOLEiVq1apVeeeUVPf7447rxxhsPe/6kSZO0cOFCzZw5U6tXr9a8efP0xBNPBPTTiNtvv13ff/+9xo8fr59//llr1qzRu+++61lsomvXrjr33HN13XXXacmSJcrOztbVV199xE9ROnfurA0bNujVV1/V2rVr9dhjj+ntt98O2PcAAAAQKOPGjVNhYaEuu+wyLV26VGvXrtWCBQt05ZVXeoqnDh06aMmSJfr9999VUFAgt9utzp0768cff9SCBQu0evVqTZkyRUuXLvW69t13362HH35Yjz32mNasWaOffvpJjz/++GFj6dy5sy688EJdc801+u6777Rs2TJdccUVOuaYY3ThhRd6zluxYoV+/vlnFRYWyul06ueff9bPP/8ckPcnGFFINQBjxozR3r17deKJJ2rcuHG68cYbPcucV6VXr1567bXX9Oqrr+r444/X1KlTNWPGDI0dOzZgMZ5wwgn6+uuvtXr1ap122mnKyMjQ1KlTlZiY6Dln7ty5SkxMVP/+/TV8+HBde+21lT4hOdgFF1ygm2++WePHj1fPnj31/fffa8qUKQH7HgAAAAIlMTFRixYtUnl5uc455xz16NFDN910k5o3by673aTst9xyi8LCwpSWlqbWrVtrw4YNuu666zR8+HBdeumlOumkk7Rjxw6v3ilJysrK0qOPPqonn3xS3bt31/nnn++1lHlV5s6dq969e+v8889X3759ZVmWPvroI6+hiUOGDFFGRobef/99ffXVV8rIyFBGRkbtvzlBymZZ/1ufGiHpjDPOUM+ePfXoo4/WdygAAABAo0GPFAAAAAD4iUIKXr799luvJTAP/QIAAADA0D4cYu/evfrjjz8Oe/xou2ADAAAAjQGFFAAAAAD4iaF9AAAAAOAnCikAAAAA8BOFFAAAAAD4iUIKAAAAAPxEIQUAAAAAfqKQAgAAAAA/UUgBAAAAgJ8opAAAAADAT/8POLdlKL9IlCYAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -4661,7 +4679,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 199, "metadata": {}, "outputs": [ { @@ -4718,7 +4736,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -4731,7 +4749,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 201, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4794,7 +4812,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2500.508853\n", + " 2273.115089\n", " 97\n", " 93.10\n", " \n", @@ -5151,7 +5169,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2500.508853 97 \n", + "3 4 bot_median 2273.115089 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5246,7 +5264,7 @@ "46 52.10 " ] }, - "execution_count": 122, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } @@ -5315,7 +5333,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -5397,17 +5415,17 @@ " \n", " \n", " bot_median\n", - " 2500.5\n", + " 2273.1\n", " 93.1\n", - " 26.9\n", - " 62.117260\n", - " 6.437800\n", - " 4.171971\n", + " 24.4\n", + " 58.936587\n", + " 6.108156\n", + " 3.997253\n", " 1.985277\n", - " 39.6\n", - " 14.1\n", - " 0.999966\n", - " 0.000068\n", + " 36.5\n", + " 12.3\n", + " 0.999935\n", + " 0.000129\n", " \n", " \n", " acm_bot\n", @@ -6020,7 +6038,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2500.5 93.1 26.9 62.117260 \n", + "bot_median 2273.1 93.1 24.4 58.936587 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6069,7 +6087,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.437800 4.171971 1.985277 39.6 \n", + "bot_median 6.108156 3.997253 1.985277 36.5 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6118,7 +6136,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.1 0.999966 0.000068 \n", + "bot_median 12.3 0.999935 0.000129 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6164,7 +6182,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 123, + "execution_count": 202, "metadata": {}, "output_type": "execute_result" } @@ -6180,7 +6198,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 203, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6256,29 +6274,15 @@ " \n", " \n", " metac-perplexity\n", - " 1957.5\n", - " 95.0\n", - " 20.6\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", - " 1.98475\n", - " 20.6\n", - " 20.6\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " metac-o1\n", - " 1921.1\n", + " 1719.7\n", " 95.0\n", - " 20.2\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", + " 18.1\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.940951e+16\n", " 1.98475\n", - " 20.2\n", - " 20.2\n", + " 18.1\n", + " 18.1\n", " 1.0\n", " 0.000000\n", " \n", @@ -6298,15 +6302,43 @@ " \n", " \n", " bot_median\n", - " 1655.0\n", + " 1610.4\n", " 95.0\n", - " 17.4\n", + " 17.0\n", " 3.570999e-15\n", " 3.663768e-16\n", - " 4.755070e+16\n", + " 4.626691e+16\n", " 1.98475\n", - " 17.4\n", - " 17.4\n", + " 17.0\n", + " 17.0\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", + " metac-o1\n", + " 1577.6\n", + " 95.0\n", + " 16.6\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.532462e+16\n", + " 1.98475\n", + " 16.6\n", + " 16.6\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " 1405.9\n", + " 95.0\n", + " 14.8\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.039354e+16\n", + " 1.98475\n", + " 14.8\n", + " 14.8\n", " 1.0\n", " 0.000000\n", " \n", @@ -6353,13 +6385,13 @@ " 0.000000\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " 1235.2\n", + " metac-exa\n", + " 1233.6\n", " 95.0\n", " 13.0\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 7.097519e+16\n", + " 7.088710e+16\n", " 1.98475\n", " 13.0\n", " 13.0\n", @@ -6367,72 +6399,44 @@ " 0.000000\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " 1180.5\n", + " GreeneiBot2\n", + " 1163.2\n", " 95.0\n", - " 12.4\n", + " 12.2\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 12.4\n", - " 12.4\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " metac-deepseek-r1\n", - " 1166.0\n", - " 95.0\n", - " 12.3\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 6.700213e+16\n", - " 1.98475\n", - " 12.3\n", - " 12.3\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " 1154.9\n", - " 95.0\n", - " 12.2\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 3.318128e+16\n", - " 1.98475\n", " 12.2\n", " 12.2\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " GreeneiBot2\n", - " 1119.2\n", + " NextWorldLab\n", + " 1050.3\n", " 95.0\n", - " 11.8\n", + " 11.1\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 6.431060e+16\n", + " 6.035038e+16\n", " 1.98475\n", - " 11.8\n", - " 11.8\n", + " 11.1\n", + " 11.1\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " NextWorldLab\n", - " 1050.3\n", + " metac-Llama-3.1\n", + " 997.0\n", " 95.0\n", - " 11.1\n", + " 10.5\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 6.035038e+16\n", + " 5.728816e+16\n", " 1.98475\n", - " 11.1\n", - " 11.1\n", + " 10.5\n", + " 10.5\n", " 1.0\n", " 0.000000\n", " \n", @@ -6465,16 +6469,16 @@ " 0.000000\n", " \n", " \n", - " metac-grok-2-1212\n", - " 932.3\n", + " metac-claude-3-5-sonnet-latest\n", + " 949.9\n", " 95.0\n", - " 9.8\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 5.357005e+16\n", + " 10.0\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " inf\n", " 1.98475\n", - " 9.8\n", - " 9.8\n", + " 10.0\n", + " 10.0\n", " 1.0\n", " 0.000000\n", " \n", @@ -6493,20 +6497,6 @@ " 0.000000\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 910.2\n", - " 95.0\n", - " 9.6\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 5.230332e+16\n", - " 1.98475\n", - " 9.6\n", - " 9.6\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " annabot\n", " 854.4\n", " 95.0\n", @@ -6521,20 +6511,6 @@ " 0.000000\n", " \n", " \n", - " metac-exa\n", - " 836.7\n", - " 95.0\n", - " 8.8\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 4.808056e+16\n", - " 1.98475\n", - " 8.8\n", - " 8.8\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " VeritasAI\n", " 802.0\n", " 95.0\n", @@ -6549,6 +6525,20 @@ " 0.000000\n", " \n", " \n", + " metac-grok-2-1212\n", + " 775.1\n", + " 95.0\n", + " 8.2\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " inf\n", + " 1.98475\n", + " 8.2\n", + " 8.2\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " laylaps\n", " 723.4\n", " 95.0\n", @@ -6563,13 +6553,27 @@ " 0.000000\n", " \n", " \n", + " metac-Gemini-Exp-1206\n", + " 701.9\n", + " 95.0\n", + " 7.4\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 8.065986e+16\n", + " 1.98475\n", + " 7.4\n", + " 7.4\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " metac-o1-preview\n", - " 640.2\n", + " 633.2\n", " 95.0\n", " 6.7\n", " 8.927498e-16\n", " 9.159420e-17\n", - " 7.357383e+16\n", + " 7.277309e+16\n", " 1.98475\n", " 6.7\n", " 6.7\n", @@ -6591,6 +6595,20 @@ " 0.000000\n", " \n", " \n", + " metac-deepseek-r1\n", + " 545.5\n", + " 95.0\n", + " 5.7\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 6.268723e+16\n", + " 1.98475\n", + " 5.7\n", + " 5.7\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " MWG\n", " 520.8\n", " 95.0\n", @@ -6619,6 +6637,20 @@ " 0.000000\n", " \n", " \n", + " metac-gpt-4o\n", + " 451.6\n", + " 95.0\n", + " 4.8\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 5.190358e+16\n", + " 1.98475\n", + " 4.8\n", + " 4.8\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " pgodzinai\n", " 336.0\n", " 95.0\n", @@ -6647,20 +6679,6 @@ " 0.000000\n", " \n", " \n", - " metac-gpt-4o\n", - " 280.3\n", - " 95.0\n", - " 3.0\n", - " 8.927498e-16\n", - " 9.159420e-17\n", - " 3.221541e+16\n", - " 1.98475\n", - " 3.0\n", - " 3.0\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " InstitutPelFutur\n", " 256.0\n", " 95.0\n", @@ -6788,15 +6806,15 @@ " \n", " \n", " RPM_bot\n", - " 71.4\n", + " 118.6\n", " 95.0\n", - " 0.8\n", - " 1.115937e-16\n", - " 1.144927e-17\n", - " 6.560693e+16\n", + " 1.2\n", + " 4.463749e-16\n", + " 4.579710e-17\n", + " 2.726486e+16\n", " 1.98475\n", - " 0.8\n", - " 0.8\n", + " 1.2\n", + " 1.2\n", " 1.0\n", " 0.000000\n", " \n", @@ -6904,35 +6922,35 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev \\\n", - "metac-perplexity 1957.5 95.0 20.6 0.000000e+00 \n", - "metac-o1 1921.1 95.0 20.2 0.000000e+00 \n", + "metac-perplexity 1719.7 95.0 18.1 3.570999e-15 \n", "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", - "bot_median 1655.0 95.0 17.4 3.570999e-15 \n", + "bot_median 1610.4 95.0 17.0 3.570999e-15 \n", + "metac-o1 1577.6 95.0 16.6 3.570999e-15 \n", + "metac-claude-3-5-sonnet-20240620 1405.9 95.0 14.8 3.570999e-15 \n", "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", - "metac-claude-3-5-sonnet-20240620 1235.2 95.0 13.0 1.785500e-15 \n", - "metac-claude-3-5-sonnet-latest 1180.5 95.0 12.4 0.000000e+00 \n", - "metac-deepseek-r1 1166.0 95.0 12.3 1.785500e-15 \n", - "metac-Llama-3.1 1154.9 95.0 12.2 3.570999e-15 \n", - "GreeneiBot2 1119.2 95.0 11.8 1.785500e-15 \n", + "metac-exa 1233.6 95.0 13.0 1.785500e-15 \n", + "GreeneiBot2 1163.2 95.0 12.2 0.000000e+00 \n", "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", + "metac-Llama-3.1 997.0 95.0 10.5 1.785500e-15 \n", "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", - "metac-grok-2-1212 932.3 95.0 9.8 1.785500e-15 \n", + "metac-claude-3-5-sonnet-latest 949.9 95.0 10.0 0.000000e+00 \n", "mmBot 924.8 95.0 9.7 0.000000e+00 \n", - "metac-Gemini-Exp-1206 910.2 95.0 9.6 1.785500e-15 \n", "annabot 854.4 95.0 9.0 1.785500e-15 \n", - "metac-exa 836.7 95.0 8.8 1.785500e-15 \n", "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", + "metac-grok-2-1212 775.1 95.0 8.2 0.000000e+00 \n", "laylaps 723.4 95.0 7.6 8.927498e-16 \n", - "metac-o1-preview 640.2 95.0 6.7 8.927498e-16 \n", + "metac-Gemini-Exp-1206 701.9 95.0 7.4 8.927498e-16 \n", + "metac-o1-preview 633.2 95.0 6.7 8.927498e-16 \n", "cookics_bot_TEST 596.4 95.0 6.3 0.000000e+00 \n", + "metac-deepseek-r1 545.5 95.0 5.7 8.927498e-16 \n", "MWG 520.8 95.0 5.5 8.927498e-16 \n", "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", + "metac-gpt-4o 451.6 95.0 4.8 8.927498e-16 \n", "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", - "metac-gpt-4o 280.3 95.0 3.0 8.927498e-16 \n", "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", @@ -6942,7 +6960,7 @@ "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", "X_bot 181.4 95.0 1.9 0.000000e+00 \n", "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", - "RPM_bot 71.4 95.0 0.8 1.115937e-16 \n", + "RPM_bot 118.6 95.0 1.2 4.463749e-16 \n", "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", @@ -6952,35 +6970,35 @@ "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", "\n", " std_err t_stat t_crit \\\n", - "metac-perplexity 0.000000e+00 inf 1.98475 \n", - "metac-o1 0.000000e+00 inf 1.98475 \n", + "metac-perplexity 3.663768e-16 4.940951e+16 1.98475 \n", "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", - "bot_median 3.663768e-16 4.755070e+16 1.98475 \n", + "bot_median 3.663768e-16 4.626691e+16 1.98475 \n", + "metac-o1 3.663768e-16 4.532462e+16 1.98475 \n", + "metac-claude-3-5-sonnet-20240620 3.663768e-16 4.039354e+16 1.98475 \n", "manticAI 0.000000e+00 inf 1.98475 \n", "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", - "metac-claude-3-5-sonnet-20240620 1.831884e-16 7.097519e+16 1.98475 \n", - "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", - "metac-deepseek-r1 1.831884e-16 6.700213e+16 1.98475 \n", - "metac-Llama-3.1 3.663768e-16 3.318128e+16 1.98475 \n", - "GreeneiBot2 1.831884e-16 6.431060e+16 1.98475 \n", + "metac-exa 1.831884e-16 7.088710e+16 1.98475 \n", + "GreeneiBot2 0.000000e+00 inf 1.98475 \n", "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", + "metac-Llama-3.1 1.831884e-16 5.728816e+16 1.98475 \n", "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", - "metac-grok-2-1212 1.831884e-16 5.357005e+16 1.98475 \n", + "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", "mmBot 0.000000e+00 inf 1.98475 \n", - "metac-Gemini-Exp-1206 1.831884e-16 5.230332e+16 1.98475 \n", "annabot 1.831884e-16 4.909363e+16 1.98475 \n", - "metac-exa 1.831884e-16 4.808056e+16 1.98475 \n", "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", + "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", - "metac-o1-preview 9.159420e-17 7.357383e+16 1.98475 \n", + "metac-Gemini-Exp-1206 9.159420e-17 8.065986e+16 1.98475 \n", + "metac-o1-preview 9.159420e-17 7.277309e+16 1.98475 \n", "cookics_bot_TEST 0.000000e+00 inf 1.98475 \n", + "metac-deepseek-r1 9.159420e-17 6.268723e+16 1.98475 \n", "MWG 9.159420e-17 5.985647e+16 1.98475 \n", "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", + "metac-gpt-4o 9.159420e-17 5.190358e+16 1.98475 \n", "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", - "metac-gpt-4o 9.159420e-17 3.221541e+16 1.98475 \n", "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", "Bot_Pepa 0.000000e+00 inf 1.98475 \n", "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", @@ -6990,7 +7008,7 @@ "bean_bot 0.000000e+00 inf 1.98475 \n", "X_bot 0.000000e+00 inf 1.98475 \n", "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", - "RPM_bot 1.144927e-17 6.560693e+16 1.98475 \n", + "RPM_bot 4.579710e-17 2.726486e+16 1.98475 \n", "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", "andrewsiah 0.000000e+00 NaN 1.98475 \n", @@ -7000,35 +7018,35 @@ "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "metac-perplexity 20.6 20.6 1.0 0.000000 \n", - "metac-o1 20.2 20.2 1.0 0.000000 \n", + "metac-perplexity 18.1 18.1 1.0 0.000000 \n", "acm_bot 17.7 17.7 1.0 0.000000 \n", - "bot_median 17.4 17.4 1.0 0.000000 \n", + "bot_median 17.0 17.0 1.0 0.000000 \n", + "metac-o1 16.6 16.6 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-20240620 14.8 14.8 1.0 0.000000 \n", "manticAI 14.5 14.5 1.0 0.000000 \n", "twsummerbot 14.3 14.3 1.0 0.000000 \n", "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-20240620 13.0 13.0 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-latest 12.4 12.4 1.0 0.000000 \n", - "metac-deepseek-r1 12.3 12.3 1.0 0.000000 \n", - "metac-Llama-3.1 12.2 12.2 1.0 0.000000 \n", - "GreeneiBot2 11.8 11.8 1.0 0.000000 \n", + "metac-exa 13.0 13.0 1.0 0.000000 \n", + "GreeneiBot2 12.2 12.2 1.0 0.000000 \n", "NextWorldLab 11.1 11.1 1.0 0.000000 \n", + "metac-Llama-3.1 10.5 10.5 1.0 0.000000 \n", "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", "SynapseSeer 10.2 10.2 1.0 0.000000 \n", - "metac-grok-2-1212 9.8 9.8 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-latest 10.0 10.0 1.0 0.000000 \n", "mmBot 9.7 9.7 1.0 0.000000 \n", - "metac-Gemini-Exp-1206 9.6 9.6 1.0 0.000000 \n", "annabot 9.0 9.0 1.0 0.000000 \n", - "metac-exa 8.8 8.8 1.0 0.000000 \n", "VeritasAI 8.4 8.4 1.0 0.000000 \n", + "metac-grok-2-1212 8.2 8.2 1.0 0.000000 \n", "laylaps 7.6 7.6 1.0 0.000000 \n", + "metac-Gemini-Exp-1206 7.4 7.4 1.0 0.000000 \n", "metac-o1-preview 6.7 6.7 1.0 0.000000 \n", "cookics_bot_TEST 6.3 6.3 1.0 0.000000 \n", + "metac-deepseek-r1 5.7 5.7 1.0 0.000000 \n", "MWG 5.5 5.5 1.0 0.000000 \n", "ajf-bot 5.1 5.1 1.0 0.000000 \n", + "metac-gpt-4o 4.8 4.8 1.0 0.000000 \n", "pgodzinai 3.5 3.5 1.0 0.000000 \n", "KevinTestBot 3.3 3.3 1.0 0.000000 \n", - "metac-gpt-4o 3.0 3.0 1.0 0.000000 \n", "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", "CumulativeBot 2.5 2.5 1.0 0.000000 \n", @@ -7038,7 +7056,7 @@ "bean_bot 2.1 2.1 1.0 0.000000 \n", "X_bot 1.9 1.9 1.0 0.000000 \n", "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", - "RPM_bot 0.8 0.8 1.0 0.000000 \n", + "RPM_bot 1.2 1.2 1.0 0.000000 \n", "4Shadower 0.6 0.6 1.0 0.000000 \n", "krm-bot 0.6 0.6 1.0 0.000000 \n", "andrewsiah 0.0 0.0 NaN NA \n", @@ -7048,7 +7066,7 @@ "minefrac1 -3.0 -3.0 0.0 0.000000 " ] }, - "execution_count": 124, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -7074,7 +7092,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -7084,7 +7102,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 205, "metadata": { "cellView": "form", "colab": { @@ -7998,7 +8016,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 126, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -8037,7 +8055,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ @@ -8047,7 +8065,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -8252,7 +8270,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 128, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -8263,7 +8281,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 208, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8325,7 +8343,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -8747,7 +8765,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 210, "metadata": { "cellView": "form", "colab": { @@ -8797,363 +8815,363 @@ " \n", " \n", " metac-o1\n", - " 6.2\n", + " 6.1\n", " 7.4\n", " 9.7\n", - " 11.8\n", - " 13.2\n", + " 12.0\n", + " 13.1\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", - " 5.4\n", - " 8.4\n", - " 11.4\n", + " 3.5\n", + " 5.0\n", + " 8.2\n", + " 11.1\n", " 12.8\n", " \n", " \n", " manticAI\n", - " 0.1\n", - " 2.0\n", + " 0.3\n", + " 2.1\n", " 5.4\n", " 8.6\n", - " 10.2\n", + " 10.4\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.5\n", - " 2.0\n", + " 0.7\n", + " 2.2\n", " 5.0\n", - " 7.9\n", - " 9.5\n", + " 7.8\n", + " 9.2\n", " \n", " \n", " acm_bot\n", - " 0.1\n", - " 1.8\n", - " 4.5\n", - " 7.5\n", - " 8.8\n", + " -0.1\n", + " 1.4\n", + " 4.6\n", + " 7.6\n", + " 9.2\n", " \n", " \n", " metac-perplexity\n", - " -2.2\n", - " 0.2\n", + " -1.4\n", + " 0.5\n", " 4.1\n", - " 7.8\n", + " 7.9\n", " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -0.8\n", + " -1.1\n", " 0.7\n", - " 4.0\n", + " 3.9\n", " 7.2\n", - " 8.7\n", + " 8.8\n", " \n", " \n", " twsummerbot\n", - " -0.1\n", + " 0.1\n", " 1.5\n", " 3.9\n", - " 6.3\n", - " 7.7\n", + " 6.1\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 1.0\n", + " 0.1\n", + " 1.1\n", " 3.0\n", - " 4.9\n", - " 5.8\n", + " 5.1\n", + " 6.1\n", " \n", " \n", " pgodzinai\n", - " -3.5\n", - " -1.1\n", - " 2.8\n", - " 6.8\n", - " 8.9\n", + " -4.2\n", + " -1.3\n", + " 2.9\n", + " 7.0\n", + " 9.0\n", + " \n", + " \n", + " CumulativeBot\n", + " -0.2\n", + " 0.9\n", + " 2.6\n", + " 4.4\n", + " 5.2\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.4\n", - " 0.0\n", - " 2.7\n", + " -1.2\n", + " 0.1\n", + " 2.6\n", " 5.1\n", - " 6.2\n", + " 6.1\n", " \n", " \n", " SynapseSeer\n", - " 0.3\n", - " 1.0\n", - " 2.6\n", + " 0.1\n", + " 0.9\n", + " 2.4\n", " 4.0\n", - " 5.0\n", + " 4.7\n", " \n", " \n", - " CumulativeBot\n", - " -0.3\n", - " 0.7\n", - " 2.5\n", - " 4.4\n", - " 5.4\n", + " metac-exa\n", + " -5.1\n", + " -2.5\n", + " 1.8\n", + " 5.7\n", + " 7.9\n", " \n", " \n", " jkraybill_bot\n", - " -3.7\n", - " -1.8\n", - " 1.8\n", + " -3.6\n", + " -1.5\n", + " 1.7\n", " 4.9\n", " 6.4\n", " \n", " \n", - " metac-exa\n", - " -5.0\n", - " -2.4\n", - " 1.5\n", - " 5.4\n", - " 7.4\n", - " \n", - " \n", " metac-deepseek-r1\n", - " -1.7\n", - " -0.6\n", - " 1.4\n", + " -2.1\n", + " -0.8\n", + " 1.2\n", " 3.4\n", - " 4.5\n", + " 4.4\n", " \n", " \n", " MWG\n", " -1.6\n", " -0.8\n", - " 0.7\n", - " 2.1\n", + " 0.6\n", + " 2.2\n", " 2.8\n", " \n", " \n", " pianobot\n", - " -1.3\n", - " -0.8\n", + " -1.1\n", + " -0.7\n", " 0.0\n", " 0.7\n", - " 1.1\n", - " \n", - " \n", - " cobyj-bot\n", - " -1.4\n", - " -0.9\n", - " -0.0\n", - " 0.9\n", - " 1.4\n", + " 1.0\n", " \n", " \n", " andrewsiah\n", " -0.9\n", - " -0.6\n", + " -0.5\n", " -0.0\n", - " 0.5\n", + " 0.6\n", " 0.9\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", - " annabot\n", - " -3.2\n", - " -2.3\n", - " -0.4\n", - " 1.3\n", - " 2.0\n", + " cobyj-bot\n", + " -1.5\n", + " -0.9\n", + " -0.1\n", + " 0.8\n", + " 1.4\n", " \n", " \n", - " bean_bot\n", - " -3.2\n", - " -2.3\n", + " KevinTestBot\n", + " -3.9\n", + " -2.8\n", " -0.4\n", + " 1.4\n", + " 2.4\n", + " \n", + " \n", + " annabot\n", + " -3.4\n", + " -2.5\n", + " -0.5\n", " 1.2\n", - " 1.9\n", + " 2.1\n", " \n", " \n", - " KevinTestBot\n", - " -3.9\n", - " -2.7\n", - " -0.6\n", - " 1.3\n", - " 2.4\n", + " bean_bot\n", + " -3.4\n", + " -2.4\n", + " -0.5\n", + " 1.1\n", + " 2.0\n", " \n", " \n", " CatrachoCaster\n", - " -2.3\n", - " -1.8\n", - " -0.8\n", + " -2.2\n", + " -1.7\n", + " -0.7\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", " -3.0\n", - " -2.1\n", + " -2.2\n", " -0.8\n", " 0.4\n", - " 1.1\n", + " 1.0\n", " \n", " \n", " krm-bot\n", " -3.7\n", - " -2.8\n", + " -2.7\n", " -1.0\n", - " 0.6\n", - " 1.7\n", + " 0.7\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.1\n", + " -4.5\n", " -3.2\n", " -1.1\n", - " 1.1\n", - " 2.3\n", + " 1.0\n", + " 1.9\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.6\n", - " -4.7\n", - " -1.4\n", - " 1.8\n", - " 3.5\n", + " -6.2\n", + " -4.9\n", + " -1.3\n", + " 2.0\n", + " 3.6\n", " \n", " \n", " mmBot\n", - " -7.2\n", - " -5.5\n", + " -7.4\n", + " -5.3\n", " -1.5\n", " 2.2\n", " 4.0\n", " \n", " \n", " 4Shadower\n", - " -4.7\n", - " -3.8\n", - " -1.7\n", - " 0.2\n", - " 1.3\n", + " -4.6\n", + " -3.7\n", + " -1.6\n", + " 0.4\n", + " 1.2\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -4.5\n", - " -1.8\n", - " 0.9\n", - " 2.4\n", + " RPM_bot\n", + " -4.9\n", + " -3.7\n", + " -1.9\n", + " -0.6\n", + " -0.0\n", " \n", " \n", " swingswish\n", - " -5.4\n", + " -5.3\n", " -4.0\n", " -1.9\n", - " -0.2\n", - " 0.6\n", + " -0.1\n", + " 0.8\n", " \n", " \n", - " RPM_bot\n", - " -4.8\n", - " -3.8\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.2\n", + " -4.9\n", " -2.1\n", - " -0.7\n", - " -0.1\n", + " 0.8\n", + " 2.2\n", " \n", " \n", " InstitutPelFutur\n", - " -9.0\n", - " -6.4\n", - " -2.5\n", - " 1.6\n", + " -9.1\n", + " -6.5\n", + " -2.4\n", + " 1.9\n", " 3.6\n", " \n", " \n", " wunderplumb\n", - " -6.4\n", - " -4.9\n", - " -2.7\n", + " -5.9\n", + " -4.8\n", + " -2.5\n", " -0.2\n", - " 0.8\n", + " 0.9\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.3\n", - " -2.7\n", + " -6.9\n", + " -5.2\n", + " -2.8\n", " 0.0\n", - " 1.7\n", + " 1.5\n", " \n", " \n", " NextWorldLab\n", - " -8.3\n", - " -6.6\n", - " -3.6\n", - " -0.7\n", - " 1.2\n", + " -8.6\n", + " -6.9\n", + " -3.7\n", + " -0.5\n", + " 1.1\n", " \n", " \n", " Bot_Pepa\n", - " -7.2\n", - " -5.9\n", - " -4.0\n", - " -2.0\n", - " -1.3\n", + " -7.0\n", + " -6.0\n", + " -3.9\n", + " -1.9\n", + " -0.9\n", " \n", " \n", " laylaps\n", - " -10.3\n", - " -8.0\n", - " -4.0\n", + " -9.6\n", + " -7.6\n", + " -3.9\n", " -0.2\n", - " 2.1\n", + " 1.7\n", " \n", " \n", " VeritasAI\n", - " -7.7\n", - " -6.6\n", - " -4.2\n", - " -1.9\n", - " -0.6\n", + " -7.9\n", + " -6.7\n", + " -4.3\n", + " -2.0\n", + " -0.7\n", " \n", " \n", " minefrac1\n", - " -7.8\n", - " -6.7\n", - " -4.8\n", - " -2.8\n", - " -1.6\n", + " -7.9\n", + " -6.9\n", + " -4.7\n", + " -2.6\n", + " -1.4\n", " \n", " \n", " Grizeu_Bot\n", " -9.2\n", - " -7.7\n", + " -7.6\n", " -4.9\n", - " -2.4\n", + " -2.3\n", " -1.1\n", " \n", " \n", " metac-gpt-4o\n", - " -10.5\n", - " -8.9\n", + " -10.2\n", + " -8.8\n", " -5.8\n", - " -2.8\n", - " -1.3\n", + " -3.1\n", + " -1.5\n", " \n", " \n", " ajf-bot\n", - " -15.6\n", - " -12.8\n", + " -15.2\n", + " -12.9\n", " -8.4\n", - " -4.0\n", - " -1.9\n", + " -4.5\n", + " -2.3\n", " \n", " \n", "\n", @@ -9161,54 +9179,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.2 7.4 9.7 11.8 13.2\n", - "metac-o1-preview 3.9 5.4 8.4 11.4 12.8\n", - "manticAI 0.1 2.0 5.4 8.6 10.2\n", - "metac-Gemini-Exp-1206 0.5 2.0 5.0 7.9 9.5\n", - "acm_bot 0.1 1.8 4.5 7.5 8.8\n", - "metac-perplexity -2.2 0.2 4.1 7.8 9.5\n", - "GreeneiBot2 -0.8 0.7 4.0 7.2 8.7\n", - "twsummerbot -0.1 1.5 3.9 6.3 7.7\n", - "cookics_bot_TEST 0.0 1.0 3.0 4.9 5.8\n", - "pgodzinai -3.5 -1.1 2.8 6.8 8.9\n", - "metac-claude-3-5-sonnet-latest -1.4 0.0 2.7 5.1 6.2\n", - "SynapseSeer 0.3 1.0 2.6 4.0 5.0\n", - "CumulativeBot -0.3 0.7 2.5 4.4 5.4\n", - "jkraybill_bot -3.7 -1.8 1.8 4.9 6.4\n", - "metac-exa -5.0 -2.4 1.5 5.4 7.4\n", - "metac-deepseek-r1 -1.7 -0.6 1.4 3.4 4.5\n", - "MWG -1.6 -0.8 0.7 2.1 2.8\n", - "pianobot -1.3 -0.8 0.0 0.7 1.1\n", - "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", - "andrewsiah -0.9 -0.6 -0.0 0.5 0.9\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "annabot -3.2 -2.3 -0.4 1.3 2.0\n", - "bean_bot -3.2 -2.3 -0.4 1.2 1.9\n", - "KevinTestBot -3.9 -2.7 -0.6 1.3 2.4\n", - "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.1 -0.8 0.4 1.1\n", - "krm-bot -3.7 -2.8 -1.0 0.6 1.7\n", - "ProfessorSP -4.1 -3.2 -1.1 1.1 2.3\n", - "metac-grok-2-1212 -6.6 -4.7 -1.4 1.8 3.5\n", - "mmBot -7.2 -5.5 -1.5 2.2 4.0\n", - "4Shadower -4.7 -3.8 -1.7 0.2 1.3\n", - "metac-claude-3-5-sonnet-20240620 -6.5 -4.5 -1.8 0.9 2.4\n", - "swingswish -5.4 -4.0 -1.9 -0.2 0.6\n", - "RPM_bot -4.8 -3.8 -2.1 -0.7 -0.1\n", - "InstitutPelFutur -9.0 -6.4 -2.5 1.6 3.6\n", - "wunderplumb -6.4 -4.9 -2.7 -0.2 0.8\n", - "metac-Llama-3.1 -6.7 -5.3 -2.7 0.0 1.7\n", - "NextWorldLab -8.3 -6.6 -3.6 -0.7 1.2\n", - "Bot_Pepa -7.2 -5.9 -4.0 -2.0 -1.3\n", - "laylaps -10.3 -8.0 -4.0 -0.2 2.1\n", - "VeritasAI -7.7 -6.6 -4.2 -1.9 -0.6\n", - "minefrac1 -7.8 -6.7 -4.8 -2.8 -1.6\n", - "Grizeu_Bot -9.2 -7.7 -4.9 -2.4 -1.1\n", - "metac-gpt-4o -10.5 -8.9 -5.8 -2.8 -1.3\n", - "ajf-bot -15.6 -12.8 -8.4 -4.0 -1.9" + "metac-o1 6.1 7.4 9.7 12.0 13.1\n", + "metac-o1-preview 3.5 5.0 8.2 11.1 12.8\n", + "manticAI 0.3 2.1 5.4 8.6 10.4\n", + "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", + "acm_bot -0.1 1.4 4.6 7.6 9.2\n", + "metac-perplexity -1.4 0.5 4.1 7.9 9.5\n", + "GreeneiBot2 -1.1 0.7 3.9 7.2 8.8\n", + "twsummerbot 0.1 1.5 3.9 6.1 7.4\n", + "cookics_bot_TEST 0.1 1.1 3.0 5.1 6.1\n", + "pgodzinai -4.2 -1.3 2.9 7.0 9.0\n", + "CumulativeBot -0.2 0.9 2.6 4.4 5.2\n", + "metac-claude-3-5-sonnet-latest -1.2 0.1 2.6 5.1 6.1\n", + "SynapseSeer 0.1 0.9 2.4 4.0 4.7\n", + "metac-exa -5.1 -2.5 1.8 5.7 7.9\n", + "jkraybill_bot -3.6 -1.5 1.7 4.9 6.4\n", + "metac-deepseek-r1 -2.1 -0.8 1.2 3.4 4.4\n", + "MWG -1.6 -0.8 0.6 2.2 2.8\n", + "pianobot -1.1 -0.7 0.0 0.7 1.0\n", + "andrewsiah -0.9 -0.5 -0.0 0.6 0.9\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", + "cobyj-bot -1.5 -0.9 -0.1 0.8 1.4\n", + "KevinTestBot -3.9 -2.8 -0.4 1.4 2.4\n", + "annabot -3.4 -2.5 -0.5 1.2 2.1\n", + "bean_bot -3.4 -2.4 -0.5 1.1 2.0\n", + "CatrachoCaster -2.2 -1.7 -0.7 0.2 0.7\n", + "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", + "krm-bot -3.7 -2.7 -1.0 0.7 1.5\n", + "ProfessorSP -4.5 -3.2 -1.1 1.0 1.9\n", + "metac-grok-2-1212 -6.2 -4.9 -1.3 2.0 3.6\n", + "mmBot -7.4 -5.3 -1.5 2.2 4.0\n", + "4Shadower -4.6 -3.7 -1.6 0.4 1.2\n", + "RPM_bot -4.9 -3.7 -1.9 -0.6 -0.0\n", + "swingswish -5.3 -4.0 -1.9 -0.1 0.8\n", + "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.1 0.8 2.2\n", + "InstitutPelFutur -9.1 -6.5 -2.4 1.9 3.6\n", + "wunderplumb -5.9 -4.8 -2.5 -0.2 0.9\n", + "metac-Llama-3.1 -6.9 -5.2 -2.8 0.0 1.5\n", + "NextWorldLab -8.6 -6.9 -3.7 -0.5 1.1\n", + "Bot_Pepa -7.0 -6.0 -3.9 -1.9 -0.9\n", + "laylaps -9.6 -7.6 -3.9 -0.2 1.7\n", + "VeritasAI -7.9 -6.7 -4.3 -2.0 -0.7\n", + "minefrac1 -7.9 -6.9 -4.7 -2.6 -1.4\n", + "Grizeu_Bot -9.2 -7.6 -4.9 -2.3 -1.1\n", + "metac-gpt-4o -10.2 -8.8 -5.8 -3.1 -1.5\n", + "ajf-bot -15.2 -12.9 -8.4 -4.5 -2.3" ] }, - "execution_count": 131, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } @@ -9231,7 +9249,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 211, "metadata": { "cellView": "form", "colab": { @@ -9285,19 +9303,11 @@ " \n", " \n", " metac-perplexity\n", - " 20.6\n", - " 20.6\n", - " 20.6\n", - " 20.6\n", - " 20.6\n", - " \n", - " \n", - " metac-o1\n", - " 20.2\n", - " 20.2\n", - " 20.2\n", - " 20.2\n", - " 20.2\n", + " 18.1\n", + " 18.1\n", + " 18.1\n", + " 18.1\n", + " 18.1\n", " \n", " \n", " acm_bot\n", @@ -9309,11 +9319,27 @@ " \n", " \n", " bot_median\n", - " 17.4\n", - " 17.4\n", - " 17.4\n", - " 17.4\n", - " 17.4\n", + " 17.0\n", + " 17.0\n", + " 17.0\n", + " 17.0\n", + " 17.0\n", + " \n", + " \n", + " metac-o1\n", + " 16.6\n", + " 16.6\n", + " 16.6\n", + " 16.6\n", + " 16.6\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " 14.8\n", + " 14.8\n", + " 14.8\n", + " 14.8\n", + " 14.8\n", " \n", " \n", " manticAI\n", @@ -9335,36 +9361,20 @@ " jkraybill_bot\n", " 14.3\n", " 14.3\n", - " 14.3\n", - " 14.3\n", - " 14.3\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-20240620\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-latest\n", - " 12.4\n", - " 12.4\n", - " 12.4\n", - " 12.4\n", - " 12.4\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", " \n", " \n", - " metac-deepseek-r1\n", - " 12.3\n", - " 12.3\n", - " 12.3\n", - " 12.3\n", - " 12.3\n", + " metac-exa\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", " \n", " \n", - " metac-Llama-3.1\n", + " GreeneiBot2\n", " 12.2\n", " 12.2\n", " 12.2\n", @@ -9372,14 +9382,6 @@ " 12.2\n", " \n", " \n", - " GreeneiBot2\n", - " 11.8\n", - " 11.8\n", - " 11.8\n", - " 11.8\n", - " 11.8\n", - " \n", - " \n", " NextWorldLab\n", " 11.1\n", " 11.1\n", @@ -9388,6 +9390,14 @@ " 11.1\n", " \n", " \n", + " metac-Llama-3.1\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " \n", + " \n", " Grizeu_Bot\n", " 10.2\n", " 10.2\n", @@ -9404,12 +9414,12 @@ " 10.2\n", " \n", " \n", - " metac-grok-2-1212\n", - " 9.8\n", - " 9.8\n", - " 9.8\n", - " 9.8\n", - " 9.8\n", + " metac-claude-3-5-sonnet-latest\n", + " 10.0\n", + " 10.0\n", + " 10.0\n", + " 10.0\n", + " 10.0\n", " \n", " \n", " mmBot\n", @@ -9420,14 +9430,6 @@ " 9.7\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 9.6\n", - " 9.6\n", - " 9.6\n", - " 9.6\n", - " 9.6\n", - " \n", - " \n", " annabot\n", " 9.0\n", " 9.0\n", @@ -9436,14 +9438,6 @@ " 9.0\n", " \n", " \n", - " metac-exa\n", - " 8.8\n", - " 8.8\n", - " 8.8\n", - " 8.8\n", - " 8.8\n", - " \n", - " \n", " VeritasAI\n", " 8.4\n", " 8.4\n", @@ -9452,6 +9446,14 @@ " 8.4\n", " \n", " \n", + " metac-grok-2-1212\n", + " 8.2\n", + " 8.2\n", + " 8.2\n", + " 8.2\n", + " 8.2\n", + " \n", + " \n", " laylaps\n", " 7.6\n", " 7.6\n", @@ -9460,6 +9462,14 @@ " 7.6\n", " \n", " \n", + " metac-Gemini-Exp-1206\n", + " 7.4\n", + " 7.4\n", + " 7.4\n", + " 7.4\n", + " 7.4\n", + " \n", + " \n", " metac-o1-preview\n", " 6.7\n", " 6.7\n", @@ -9476,6 +9486,14 @@ " 6.3\n", " \n", " \n", + " metac-deepseek-r1\n", + " 5.7\n", + " 5.7\n", + " 5.7\n", + " 5.7\n", + " 5.7\n", + " \n", + " \n", " MWG\n", " 5.5\n", " 5.5\n", @@ -9492,6 +9510,14 @@ " 5.1\n", " \n", " \n", + " metac-gpt-4o\n", + " 4.8\n", + " 4.8\n", + " 4.8\n", + " 4.8\n", + " 4.8\n", + " \n", + " \n", " pgodzinai\n", " 3.5\n", " 3.5\n", @@ -9508,14 +9534,6 @@ " 3.3\n", " \n", " \n", - " metac-gpt-4o\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " \n", - " \n", " InstitutPelFutur\n", " 2.7\n", " 2.7\n", @@ -9589,11 +9607,11 @@ " \n", " \n", " RPM_bot\n", - " 0.8\n", - " 0.8\n", - " 0.8\n", - " 0.8\n", - " 0.8\n", + " 1.2\n", + " 1.2\n", + " 1.2\n", + " 1.2\n", + " 1.2\n", " \n", " \n", " 4Shadower\n", @@ -9657,35 +9675,35 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-perplexity 20.6 20.6 20.6 20.6 20.6\n", - "metac-o1 20.2 20.2 20.2 20.2 20.2\n", + "metac-perplexity 18.1 18.1 18.1 18.1 18.1\n", "acm_bot 17.7 17.7 17.7 17.7 17.7\n", - "bot_median 17.4 17.4 17.4 17.4 17.4\n", + "bot_median 17.0 17.0 17.0 17.0 17.0\n", + "metac-o1 16.6 16.6 16.6 16.6 16.6\n", + "metac-claude-3-5-sonnet-20240620 14.8 14.8 14.8 14.8 14.8\n", "manticAI 14.5 14.5 14.5 14.5 14.5\n", "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", - "metac-claude-3-5-sonnet-20240620 13.0 13.0 13.0 13.0 13.0\n", - "metac-claude-3-5-sonnet-latest 12.4 12.4 12.4 12.4 12.4\n", - "metac-deepseek-r1 12.3 12.3 12.3 12.3 12.3\n", - "metac-Llama-3.1 12.2 12.2 12.2 12.2 12.2\n", - "GreeneiBot2 11.8 11.8 11.8 11.8 11.8\n", + "metac-exa 13.0 13.0 13.0 13.0 13.0\n", + "GreeneiBot2 12.2 12.2 12.2 12.2 12.2\n", "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", + "metac-Llama-3.1 10.5 10.5 10.5 10.5 10.5\n", "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", - "metac-grok-2-1212 9.8 9.8 9.8 9.8 9.8\n", + "metac-claude-3-5-sonnet-latest 10.0 10.0 10.0 10.0 10.0\n", "mmBot 9.7 9.7 9.7 9.7 9.7\n", - "metac-Gemini-Exp-1206 9.6 9.6 9.6 9.6 9.6\n", "annabot 9.0 9.0 9.0 9.0 9.0\n", - "metac-exa 8.8 8.8 8.8 8.8 8.8\n", "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", + "metac-grok-2-1212 8.2 8.2 8.2 8.2 8.2\n", "laylaps 7.6 7.6 7.6 7.6 7.6\n", + "metac-Gemini-Exp-1206 7.4 7.4 7.4 7.4 7.4\n", "metac-o1-preview 6.7 6.7 6.7 6.7 6.7\n", "cookics_bot_TEST 6.3 6.3 6.3 6.3 6.3\n", + "metac-deepseek-r1 5.7 5.7 5.7 5.7 5.7\n", "MWG 5.5 5.5 5.5 5.5 5.5\n", "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", + "metac-gpt-4o 4.8 4.8 4.8 4.8 4.8\n", "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", - "metac-gpt-4o 3.0 3.0 3.0 3.0 3.0\n", "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", @@ -9695,7 +9713,7 @@ "bean_bot 2.1 2.1 2.1 2.1 2.1\n", "X_bot 1.9 1.9 1.9 1.9 1.9\n", "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", - "RPM_bot 0.8 0.8 0.8 0.8 0.8\n", + "RPM_bot 1.2 1.2 1.2 1.2 1.2\n", "4Shadower 0.6 0.6 0.6 0.6 0.6\n", "krm-bot 0.6 0.6 0.6 0.6 0.6\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", @@ -9705,7 +9723,7 @@ "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" ] }, - "execution_count": 132, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -9726,7 +9744,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 212, "metadata": {}, "outputs": [], "source": [ @@ -9736,7 +9754,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -9796,7 +9814,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 214, "metadata": { "cellView": "form", "colab": { @@ -10285,7 +10303,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 135, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -10306,7 +10324,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 215, "metadata": {}, "outputs": [], "source": [ @@ -10315,7 +10333,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 216, "metadata": {}, "outputs": [ { @@ -10354,7 +10372,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 217, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10364,506 +10382,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 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Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.01]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.97]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 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Collected 2 forecasts: [0.8, 0.6]\n", + " >>> Collected 2 forecasts: [0.7, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.25]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.8]\n", - " >>> Collected 2 forecasts: [0.05, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.25]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.15, 0.25]\n", - " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.4]\n", - " >>> Collected 2 forecasts: [0.4, 0.35]\n", + " >>> Collected 2 forecasts: [0.2, 0.35]\n", " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.98, 0.96]\n", - " >>> Collected 2 forecasts: [0.4, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.25]\n", - " >>> Collected 2 forecasts: [0.3, 0.6]\n", - " >>> Collected 2 forecasts: [0.01, 0.02]\n", - " >>> Collected 2 forecasts: [0.7, 0.7]\n", + " >>> Collected 2 forecasts: [0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.5]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.95, 0.95]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.02, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.25, 0.35]\n", + " >>> Collected 2 forecasts: [0.3, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.98, 0.98]\n", + " >>> Collected 2 forecasts: [0.4, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.55]\n", + " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", " >>> Collected 2 forecasts: 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>>> Collected 2 forecasts: [0.8, 0.9]\n", - " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.85, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.03]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.95]\n", + " >>> Collected 2 forecasts: [0.9, 0.3]\n", " >>> Collected 2 forecasts: [0.95, 0.8]\n", - " >>> Collected 2 forecasts: [0.85, 0.7]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.6, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.3, nan]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", + " >>> Collected 3 forecasts: [0.8, 0.7, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.35, nan]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.25, 0.25]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.3, 0.108]\n", - " >>> Collected 3 forecasts: [0.05, 0.25, 0.16]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.25, 0.15]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.35, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.5, 0.108]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.16]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.95]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.4, 0.35, 0.35]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", - " >>> Collected 3 forecasts: [0.3, 0.25, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.3, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", + " >>> Collected 3 forecasts: [0.02, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", + " >>> Collected 3 forecasts: [0.25, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.98, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.35, 0.3, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.3, 0.55, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.875]\n", " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", - " >>> Collected 3 forecasts: [0.95, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", " >>> Collected 3 forecasts: [0.95, 0.15, 0.4166666666666666]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.75, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.4, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.2, 0.35, 0.16]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.086]\n", + " >>> Collected 3 forecasts: [0.3, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", - " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.85, 0.3, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.03, 0.02]\n", + " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", " >>> Collected 3 forecasts: [0.95, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.3, nan, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.8, 0.7, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.35, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.25, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.05, 0.3, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.05, 0.25, 0.16, 0.652]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.05, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.95, 0.052]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.4, 0.35, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.3, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.98, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.3, 0.55, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.875, 0.92]\n", " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", " >>> Collected 4 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.75, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.35, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.85, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.03, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.3, nan, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.35, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.25, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.3, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.25, 0.16, 0.652, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.3, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999]\n", " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", " >>> Collected 5 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.2, 0.35, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.3, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.85, 0.3, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.03, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.6, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.3, nan, nan, nan, 0.65]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.35, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", " >>> Collected 6 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2]\n", - " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35]\n", - " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", " >>> Collected 7 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2]\n", + " >>> Collected 7 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", - " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", " >>> Collected 8 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85]\n", + " >>> Collected 8 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", " >>> Collected 9 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1]\n", + " >>> Collected 9 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", - " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85, 0.546]\n", + " >>> Collected 9 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95]\n", + " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", + " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35, 0.289]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", " >>> Collected 10 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10896,7 +10914,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -10906,7 +10924,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -10944,9 +10962,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " 0.012671\n", - " 0.032463\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " 0.014505\n", + " 0.097463\n", " \n", " \n", " 1\n", @@ -10954,26 +10972,26 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.038231012375000005, 0...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.037750000000000006, 0.038250620225000004, 0...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", " \n", " \n", " 2\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", " 0.085\n", - " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.2,0.6,0.2]\n", - " 0.55\n", - " 0.5125\n", + " [0.15,0.65,0.2]\n", + " 0.56\n", + " 0.56\n", " \n", " \n", " 4\n", @@ -10981,8 +10999,8 @@ " NaN\n", " 119.2\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0, 0.0022111217800000003, 0.00442770048, 0....\n", - " [0.0, 0.002199820885714286, 0.0044035395571428...\n", + " [0.0, 0.00207778844, 0.00416103382, 0.00624884...\n", + " [0.0, 0.002104582785714286, 0.0042130633714285...\n", " \n", " \n", " ...\n", @@ -10998,16 +11016,16 @@ " binary\n", " NaN\n", " yes\n", - " 0.95\n", - " 0.95\n", - " 0.92\n", + " 0.9\n", + " 0.905\n", + " 0.9025\n", " \n", " \n", " 351\n", " binary\n", " NaN\n", " no\n", - " 0.85\n", + " 0.9\n", " 0.3\n", " 0.1835\n", " \n", @@ -11018,7 +11036,7 @@ " yes\n", " 0.95\n", " 0.8\n", - " 0.775\n", + " 0.8\n", " \n", " \n", " 361\n", @@ -11026,8 +11044,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.71\n", - " 0.704\n", + " 0.8\n", + " 0.755\n", " \n", " \n", " 364\n", @@ -11058,48 +11076,48 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", - "342 0.95 \n", - "351 0.85 \n", + "342 0.9 \n", + "351 0.9 \n", "355 0.95 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.038231012375000005, 0... \n", - "2 0.085 \n", - "3 0.55 \n", - "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.063 \n", + "3 0.56 \n", + "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", ".. ... \n", - "342 0.95 \n", + "342 0.905 \n", "351 0.3 \n", "355 0.8 \n", - "361 0.71 \n", + "361 0.8 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.032463 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.085 \n", + "3 0.56 \n", + "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", ".. ... \n", - "342 0.92 \n", + "342 0.9025 \n", "351 0.1835 \n", - "355 0.775 \n", - "361 0.704 \n", + "355 0.8 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 140, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -11110,7 +11128,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 220, "metadata": {}, "outputs": [ { @@ -11130,7 +11148,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 221, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11168,52 +11186,52 @@ " \n", " 0\n", " 1\n", - " 16.52\n", + " 18.17\n", " \n", " \n", " 1\n", " 2\n", - " 26.94\n", + " 24.94\n", " \n", " \n", " 2\n", " 3\n", - " 28.15\n", + " 26.48\n", " \n", " \n", " 3\n", " 4\n", - " 27.95\n", + " 26.48\n", " \n", " \n", " 4\n", " 5\n", - " 28.09\n", + " 26.77\n", " \n", " \n", " 5\n", " 6\n", - " 28.10\n", + " 26.92\n", " \n", " \n", " 6\n", " 7\n", - " 26.82\n", + " 25.83\n", " \n", " \n", " 7\n", " 8\n", - " 27.00\n", + " 26.50\n", " \n", " \n", " 8\n", " 9\n", - " 26.79\n", + " 25.22\n", " \n", " \n", " 9\n", " 10\n", - " 26.71\n", + " 25.45\n", " \n", " \n", "\n", @@ -11221,19 +11239,19 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 16.52\n", - "1 2 26.94\n", - "2 3 28.15\n", - "3 4 27.95\n", - "4 5 28.09\n", - "5 6 28.10\n", - "6 7 26.82\n", - "7 8 27.00\n", - "8 9 26.79\n", - "9 10 26.71" + "0 1 18.17\n", + "1 2 24.94\n", + "2 3 26.48\n", + "3 4 26.48\n", + "4 5 26.77\n", + "5 6 26.92\n", + "6 7 25.83\n", + "7 8 26.50\n", + "8 9 25.22\n", + "9 10 25.45" ] }, - "execution_count": 142, + "execution_count": 221, "metadata": {}, "output_type": "execute_result" } @@ -11264,16 +11282,21 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 222, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai']" + "['metac-o1-preview',\n", + " 'metac-o1',\n", + " 'pgodzinai',\n", + " 'GreeneiBot2',\n", + " 'manticAI',\n", + " 'acm_bot']" ] }, - "execution_count": 143, + "execution_count": 222, "metadata": {}, "output_type": "execute_result" } @@ -11287,7 +11310,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -11296,7 +11319,7 @@ "(424, 47)" ] }, - "execution_count": 144, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } @@ -11307,7 +11330,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 224, "metadata": {}, "outputs": [], "source": [ @@ -11325,7 +11348,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 225, "metadata": {}, "outputs": [ { @@ -11382,18 +11405,18 @@ " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.4,0.3,0.2,0.05,0.05]\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", " ...\n", - " 0.010417\n", - " 0.205208\n", + " 0.014083\n", + " 0.207042\n", " 0.014926\n", - " 0.012671\n", - " 0.012671\n", + " 0.014505\n", + " 0.014505\n", " 0.014926\n", - " 0.032463\n", - " 0.032463\n", + " 0.097463\n", + " 0.097463\n", " 0.014926\n", " 0.014926\n", " \n", @@ -11407,19 +11430,19 @@ " 60.0\n", " 100.0\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", - " [0.03366666666666667, 0.034105259000000006, 0....\n", - " [0.037750000000000006, 0.038231012375000005, 0...\n", - " [0.037750000000000006, 0.038231012375000005, 0...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", - " [0.041833333333333333, 0.042417897133333334, 0...\n", - " [0.041833333333333333, 0.042417897133333334, 0...\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.03366666666666667, 0.0341314028, 0.03460208...\n", + " [0.037750000000000006, 0.038250620225000004, 0...\n", + " [0.037750000000000006, 0.038250620225000004, 0...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.041833333333333333, 0.042403191266666675, 0...\n", + " [0.041833333333333333, 0.042403191266666675, 0...\n", " \n", " \n", " 2\n", @@ -11430,20 +11453,20 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.1\n", + " 0.05\n", " 0.1\n", " 0.07\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.075\n", + " 0.07\n", + " 0.063\n", + " 0.063\n", + " 0.07\n", " 0.085\n", " 0.085\n", " 0.1\n", " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", " \n", " \n", " 3\n", @@ -11454,19 +11477,19 @@ " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", - " [0.2,0.6,0.2]\n", - " [0.3,0.55,0.15]\n", + " [0.15,0.65,0.2]\n", + " [0.29,0.56,0.14999999999999997]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", " ...\n", - " 0.6\n", - " 0.575\n", - " 0.55\n", - " 0.575\n", - " 0.55\n", - " 0.53125\n", - " 0.5125\n", - " 0.5125\n", - " 0.53125\n", + " 0.65\n", + " 0.605\n", + " 0.56\n", + " 0.59\n", + " 0.56\n", + " 0.53625\n", + " 0.56\n", + " 0.56\n", + " 0.53625\n", " 0.5125\n", " \n", " \n", @@ -11479,19 +11502,19 @@ " 0.0\n", " 400.0\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0033333333,0.0066666667,0.01,0.01333333...\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " ...\n", " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", - " [0.0, 0.0024830850250000002, 0.004970265075000...\n", - " [0.0, 0.0022111217800000003, 0.00442770048, 0....\n", - " [0.0, 0.0021497910333333338, 0.004304129483333...\n", - " [0.0, 0.002199820885714286, 0.0044035395571428...\n", - " [0.0, 0.002199820885714286, 0.0044035395571428...\n", - " [0.0, 0.0023415099375000002, 0.00468643045, 0....\n", - " [0.0, 0.002227114055555556, 0.0044572597222222...\n", + " [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0...\n", + " [0.0, 0.0024824972, 0.004970454466666667, 0.00...\n", + " [0.0, 0.00231641835, 0.00463693175, 0.00696020...\n", + " [0.0, 0.00207778844, 0.00416103382, 0.00624884...\n", + " [0.0, 0.002038679916666667, 0.0040819072666666...\n", + " [0.0, 0.002104582785714286, 0.0042130633714285...\n", + " [0.0, 0.002104582785714286, 0.0042130633714285...\n", + " [0.0, 0.0023970654875000003, 0.0047975415625, ...\n", + " [0.0, 0.002276496766666667, 0.0045560251555555...\n", " \n", " \n", "\n", @@ -11514,18 +11537,18 @@ "4 NaN 0.0 400.0 \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "0 [0.4,0.3,0.2,0.05,0.05] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.3,0.55,0.15] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "3 [0.29,0.56,0.14999999999999997] \n", + "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", "\n", " pgodzinai ... \\\n", "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", @@ -11535,79 +11558,79 @@ "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 0.010417 \n", + "0 0.014083 \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.1 \n", - "3 0.6 \n", + "2 0.05 \n", + "3 0.65 \n", "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 0.205208 \n", - "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", - "2 0.1 \n", - "3 0.575 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 0.207042 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.075 \n", + "3 0.605 \n", + "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", "\n", " median_forecast_3_bots \\\n", "0 0.014926 \n", - "1 [0.03366666666666667, 0.034105259000000006, 0.... \n", - "2 0.1 \n", - "3 0.55 \n", - "4 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", + "1 [0.03366666666666667, 0.0341314028, 0.03460208... \n", + "2 0.07 \n", + "3 0.56 \n", + "4 [0.0, 0.0024824972, 0.004970454466666667, 0.00... \n", "\n", " median_forecast_4_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.038231012375000005, 0... \n", - "2 0.085 \n", - "3 0.575 \n", - "4 [0.0, 0.0024830850250000002, 0.004970265075000... \n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.063 \n", + "3 0.59 \n", + "4 [0.0, 0.00231641835, 0.00463693175, 0.00696020... \n", "\n", " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.038231012375000005, 0... \n", - "2 0.085 \n", - "3 0.55 \n", - "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.063 \n", + "3 0.56 \n", + "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", "\n", " median_forecast_6_bots \\\n", "0 0.014926 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.53125 \n", - "4 [0.0, 0.0021497910333333338, 0.004304129483333... \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.07 \n", + "3 0.53625 \n", + "4 [0.0, 0.002038679916666667, 0.0040819072666666... \n", "\n", " median_forecast_7_bots \\\n", - "0 0.032463 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.085 \n", + "3 0.56 \n", + "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", "\n", " median_forecast_8_bots \\\n", - "0 0.032463 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.085 \n", + "3 0.56 \n", + "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", "\n", " median_forecast_9_bots \\\n", "0 0.014926 \n", - "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "1 [0.041833333333333333, 0.042403191266666675, 0... \n", "2 0.1 \n", - "3 0.53125 \n", - "4 [0.0, 0.0023415099375000002, 0.00468643045, 0.... \n", + "3 0.53625 \n", + "4 [0.0, 0.0023970654875000003, 0.0047975415625, ... \n", "\n", " median_forecast_10_bots \n", "0 0.014926 \n", - "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "1 [0.041833333333333333, 0.042403191266666675, 0... \n", "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.002227114055555556, 0.0044572597222222... \n", + "4 [0.0, 0.002276496766666667, 0.0045560251555555... \n", "\n", "[5 rows x 27 columns]" ] }, - "execution_count": 146, + "execution_count": 225, "metadata": {}, "output_type": "execute_result" } @@ -11618,7 +11641,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 226, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11666,14 +11689,14 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 227, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -13.5599\n" + "Weighted Total Score: -15.6339\n" ] } ], @@ -11683,7 +11706,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 228, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11695,7 +11718,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -11707,7 +11730,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -13.46\n" + "The average of 'head_to_head' is: -15.85\n" ] } ], @@ -11717,7 +11740,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 229, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11763,17 +11786,17 @@ " \n", " \n", " head_to_head\n", - " -1288.2\n", + " -1485.2\n", " 93.1\n", - " -13.8\n", - " 86.437183\n", - " 8.958303\n", - " -1.544559\n", + " -16.0\n", + " 84.368029\n", + " 8.743857\n", + " -1.824475\n", " 1.985277\n", - " 3.9\n", - " -31.6\n", - " 0.062941\n", - " 0.125882\n", + " 1.4\n", + " -33.3\n", + " 0.035661\n", + " 0.071323\n", " \n", " \n", "\n", @@ -11781,13 +11804,13 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1288.2 93.1 -13.8 86.437183 8.958303 -1.544559 \n", + "head_to_head -1485.2 93.1 -16.0 84.368029 8.743857 -1.824475 \n", "\n", " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 3.9 -31.6 0.062941 0.125882 " + "head_to_head 1.985277 1.4 -33.3 0.035661 0.071323 " ] }, - "execution_count": 150, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -11800,7 +11823,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 230, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11848,26 +11871,26 @@ " \n", " 279\n", " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.05\n", + " 0.058\n", " [0.02,0.01,0.015,0.015,0.05,0.89]\n", " Not in top 50\n", - " -287.9\n", - " \n", - " \n", - " 335\n", - " How many cubic meters of water produced and su...\n", - " [0.167, 0.17296050626666667, 0.179050010833333...\n", - " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", - " 130027.0\n", - " -187.3\n", + " -273.1\n", " \n", " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " 0.15\n", + " 0.125\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -170.0\n", + " -188.2\n", + " \n", + " \n", + " 47\n", + " What will be Donald Trump's net worth, accordi...\n", + " 0.17\n", + " [0.6,0.2,0.1,0.075,0.025]\n", + " 0-$6 billion, inclusive\n", + " -126.1\n", " \n", " \n", " 71\n", @@ -11878,48 +11901,34 @@ " -123.5\n", " \n", " \n", - " 87\n", - " How many movies will be new on Netflix's globa...\n", - " 0.28\n", - " [0.01,0.064,0.926]\n", - " 2 or more\n", - " -119.6\n", + " 247\n", + " Will the 500th richest person on Bloomberg's B...\n", + " 0.8\n", + " 0.333\n", + " no\n", + " -120.4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " title \\\n", - "279 What will Kalshi's rank in the iPhone Top Free... \n", - "335 How many cubic meters of water produced and su... \n", - "121 How many movies will be new on Netflix's top 1... \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", - "87 How many movies will be new on Netflix's globa... \n", - "\n", - " bot_team_median \\\n", - "279 0.05 \n", - "335 [0.167, 0.17296050626666667, 0.179050010833333... \n", - "121 0.15 \n", - "71 0.16 \n", - "87 0.28 \n", - "\n", - " pro_median resolution \\\n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", - "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... 130027.0 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more \n", - "71 0.55 yes \n", - "87 [0.01,0.064,0.926] 2 or more \n", - "\n", - " head_to_head \n", - "279 -287.9 \n", - "335 -187.3 \n", - "121 -170.0 \n", - "71 -123.5 \n", - "87 -119.6 " + " title bot_team_median \\\n", + "279 What will Kalshi's rank in the iPhone Top Free... 0.058 \n", + "121 How many movies will be new on Netflix's top 1... 0.125 \n", + "47 What will be Donald Trump's net worth, accordi... 0.17 \n", + "71 Will OpenAI, Anthropic, or Perplexity run an a... 0.16 \n", + "247 Will the 500th richest person on Bloomberg's B... 0.8 \n", + "\n", + " pro_median resolution head_to_head \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -273.1 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more -188.2 \n", + "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -126.1 \n", + "71 0.55 yes -123.5 \n", + "247 0.333 no -120.4 " ] }, - "execution_count": 151, + "execution_count": 230, "metadata": {}, "output_type": "execute_result" } @@ -11941,7 +11950,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -11984,10 +11993,10 @@ " \n", " 85\n", " Will Elon Musk attend the Super Bowl in 2025?\n", - " 0.125\n", + " 0.1685\n", " 0.755\n", " no\n", - " 127.3\n", + " 122.2\n", " \n", " \n", " 0\n", @@ -12000,10 +12009,10 @@ " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.025806875566666665, 0.0571614027666666...\n", + " [0.0, 0.016687996933333334, 0.0361674514166666...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 531.1\n", + " 542.5\n", " \n", " \n", " 211\n", @@ -12016,7 +12025,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.95\n", + " 0.941\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12034,11 +12043,11 @@ "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "85 0.125 \n", + "85 0.1685 \n", "0 0.014926 \n", - "189 [0.0, 0.025806875566666665, 0.0571614027666666... \n", + "189 [0.0, 0.016687996933333334, 0.0361674514166666... \n", "211 0.99 \n", - "214 0.95 \n", + "214 0.941 \n", "\n", " pro_median resolution \\\n", "85 0.755 no \n", @@ -12048,14 +12057,14 @@ "214 0.95 annulled \n", "\n", " head_to_head \n", - "85 127.3 \n", + "85 122.2 \n", "0 270.3 \n", - "189 531.1 \n", + "189 542.5 \n", "211 NaN \n", "214 NaN " ] }, - "execution_count": 152, + "execution_count": 231, "metadata": {}, "output_type": "execute_result" } @@ -12068,7 +12077,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 232, "metadata": {}, "outputs": [ { @@ -12092,7 +12101,7 @@ "dtype: object" ] }, - "execution_count": 153, + "execution_count": 232, "metadata": {}, "output_type": "execute_result" } @@ -12106,7 +12115,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 233, "metadata": {}, "outputs": [ { @@ -12179,10 +12188,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.034105259000000006, 0....\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -79.442225\n", - " -79.442225\n", + " -75.535832\n", + " -75.535832\n", " \n", " \n", " 2\n", @@ -12197,10 +12206,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.07\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -5.948545\n", + " -5.948545\n", " \n", " \n", " 3\n", @@ -12215,10 +12224,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.55\n", + " 0.53625\n", " [0.16,0.44,0.4]\n", - " 22.314355\n", - " 22.314355\n", + " 19.782574\n", + " 19.782574\n", " \n", " \n", " 4\n", @@ -12233,10 +12242,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", + " [0.0, 0.002038679916666667, 0.0040819072666666...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 25.971582\n", - " 25.971582\n", + " 12.716305\n", + " 12.716305\n", " \n", " \n", "\n", @@ -12266,27 +12275,27 @@ "\n", " question_weight bot_team_median \\\n", "0 1.0 0.014926 \n", - "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", - "2 1.0 0.1 \n", - "3 1.0 0.55 \n", - "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", + "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 1.0 0.07 \n", + "3 1.0 0.53625 \n", + "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", "\n", " pro_median head_to_head \\\n", "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", - "2 0.013 -9.227528 \n", - "3 [0.16,0.44,0.4] 22.314355 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", + "2 0.013 -5.948545 \n", + "3 [0.16,0.44,0.4] 19.782574 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", "\n", " weighted_score \n", "0 270.308741 \n", - "1 -79.442225 \n", - "2 -9.227528 \n", - "3 22.314355 \n", - "4 25.971582 " + "1 -75.535832 \n", + "2 -5.948545 \n", + "3 19.782574 \n", + "4 12.716305 " ] }, - "execution_count": 154, + "execution_count": 233, "metadata": {}, "output_type": "execute_result" } @@ -12297,7 +12306,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -12309,7 +12318,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 235, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12321,7 +12330,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -12365,7 +12374,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 236, "metadata": {}, "outputs": [], "source": [ @@ -12375,7 +12384,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 237, "metadata": {}, "outputs": [ { @@ -12428,7 +12437,7 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.07\n", " 0.013\n", " \n", " \n", @@ -12444,7 +12453,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.62\n", + " 0.55\n", " 0.45\n", " \n", " \n", @@ -12460,7 +12469,7 @@ " NaN\n", " 31294\n", " 1.0\n", - " 0.85\n", + " 0.82\n", " 0.95\n", " \n", " \n", @@ -12492,7 +12501,7 @@ " NaN\n", " 31338\n", " 1.0\n", - " 0.85\n", + " 0.825\n", " 0.9\n", " \n", " \n", @@ -12515,11 +12524,11 @@ "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.1 \n", - "5 NaN NaN 31282 1.0 0.62 \n", - "8 NaN NaN 31294 1.0 0.85 \n", + "2 NaN NaN 31270 1.0 0.07 \n", + "5 NaN NaN 31282 1.0 0.55 \n", + "8 NaN NaN 31294 1.0 0.82 \n", "10 NaN NaN 1.0 NaN \n", - "13 NaN NaN 31338 1.0 0.85 \n", + "13 NaN NaN 31338 1.0 0.825 \n", "\n", " pro_median \n", "2 0.013 \n", @@ -12529,7 +12538,7 @@ "13 0.9 " ] }, - "execution_count": 158, + "execution_count": 237, "metadata": {}, "output_type": "execute_result" } @@ -12540,7 +12549,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 238, "metadata": {}, "outputs": [ { @@ -12591,7 +12600,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 239, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12670,10 +12679,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.034105259000000006, 0....\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -79.442225\n", - " -79.442225\n", + " -75.535832\n", + " -75.535832\n", " \n", " \n", " 2\n", @@ -12688,10 +12697,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.07\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -5.948545\n", + " -5.948545\n", " \n", " \n", " 3\n", @@ -12706,10 +12715,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.55\n", + " 0.53625\n", " [0.16,0.44,0.4]\n", - " 22.314355\n", - " 22.314355\n", + " 19.782574\n", + " 19.782574\n", " \n", " \n", " 4\n", @@ -12724,10 +12733,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", + " [0.0, 0.002038679916666667, 0.0040819072666666...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 25.971582\n", - " 25.971582\n", + " 12.716305\n", + " 12.716305\n", " \n", " \n", "\n", @@ -12757,24 +12766,24 @@ "\n", " question_weight bot_team_median \\\n", "0 1.0 0.014926 \n", - "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", - "2 1.0 0.1 \n", - "3 1.0 0.55 \n", - "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", + "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 1.0 0.07 \n", + "3 1.0 0.53625 \n", + "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", "\n", " pro_median head_to_head \\\n", "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", - "2 0.013 -9.227528 \n", - "3 [0.16,0.44,0.4] 22.314355 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", + "2 0.013 -5.948545 \n", + "3 [0.16,0.44,0.4] 19.782574 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", "\n", " weighted_score \n", "0 270.308741 \n", - "1 -79.442225 \n", - "2 -9.227528 \n", - "3 22.314355 \n", - "4 25.971582 " + "1 -75.535832 \n", + "2 -5.948545 \n", + "3 19.782574 \n", + "4 12.716305 " ] }, "metadata": {}, @@ -12832,10 +12841,10 @@ " NaN\n", " 35380\n", " 1.00\n", + " 0.92\n", " 0.95\n", - " 0.95\n", - " 0.000000\n", - " 0.000000\n", + " -3.208831\n", + " -3.208831\n", " \n", " \n", " 351\n", @@ -12850,10 +12859,10 @@ " NaN\n", " 35381\n", " 1.00\n", - " 0.575\n", + " 0.1775\n", " 0.05\n", - " -80.437282\n", - " -80.437282\n", + " -14.411350\n", + " -14.411350\n", " \n", " \n", " 355\n", @@ -12868,10 +12877,10 @@ " NaN\n", " 35385\n", " 1.00\n", - " 0.875\n", + " 0.8\n", " 0.97\n", - " -10.307219\n", - " -10.307219\n", + " -19.268434\n", + " -19.268434\n", " \n", " \n", " 361\n", @@ -12886,10 +12895,10 @@ " NaN\n", " 35386\n", " 0.85\n", - " 0.85\n", + " 0.755\n", " 0.666\n", - " -80.050570\n", - " -68.042984\n", + " -30.988278\n", + " -26.340037\n", " \n", " \n", " 364\n", @@ -12929,17 +12938,17 @@ "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "342 NaN NaN 35380 1.00 0.95 \n", - "351 NaN NaN 35381 1.00 0.575 \n", - "355 NaN NaN 35385 1.00 0.875 \n", - "361 NaN NaN 35386 0.85 0.85 \n", + "342 NaN NaN 35380 1.00 0.92 \n", + "351 NaN NaN 35381 1.00 0.1775 \n", + "355 NaN NaN 35385 1.00 0.8 \n", + "361 NaN NaN 35386 0.85 0.755 \n", "364 NaN NaN 35387 0.85 0.05 \n", "\n", " pro_median head_to_head weighted_score \n", - "342 0.95 0.000000 0.000000 \n", - "351 0.05 -80.437282 -80.437282 \n", - "355 0.97 -10.307219 -10.307219 \n", - "361 0.666 -80.050570 -68.042984 \n", + "342 0.95 -3.208831 -3.208831 \n", + "351 0.05 -14.411350 -14.411350 \n", + "355 0.97 -19.268434 -19.268434 \n", + "361 0.666 -30.988278 -26.340037 \n", "364 0.03 -2.083409 -1.770897 " ] }, @@ -12953,7 +12962,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[160], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[239], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", diff --git a/functions.py b/functions.py index 2035207..38a3fb1 100644 --- a/functions.py +++ b/functions.py @@ -11,7 +11,7 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import calculate_spot_baseline_score +from refactored_notebook.scoring import calculate_spot_baseline_score, nominal_location_to_cdf_location, calculate_spot_peer_score def extract_forecast(df): @@ -1023,7 +1023,7 @@ def scaled_location_to_unscaled_location(scaled_location, question_row): return (scaled_location - range_min) / (range_max - range_min) -def nominal_location_to_cdf_location(nominal_location, question_data): +def nominal_location_to_cdf_location_via_question_dict(nominal_location, question_data): """ Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to metaculus's "internal representation" range [0, 1] incorporating question scaling @@ -1035,28 +1035,13 @@ def nominal_location_to_cdf_location(nominal_location, question_data): Returns: float: CDF location. """ - if question_data["type"] == "date": - scaled_location = datetime.fromisoformat(nominal_location).timestamp() - else: - scaled_location = float(nominal_location) + # Unscale the value to put it into the range [0,1] range_min = question_data["range_min"] range_max = question_data["range_max"] zero_point = question_data["zero_point"] - if ~np.isnan(zero_point) and (zero_point is not None): - # logarithmically scaled question - deriv_ratio = (range_max - zero_point) / (range_min - zero_point) - unscaled_location = ( - np.log( - (scaled_location - range_min) * (deriv_ratio - 1) - + (range_max - range_min) - ) - - np.log(range_max - range_min) - ) / np.log(deriv_ratio) - else: - # linearly scaled question - unscaled_location = (scaled_location - range_min) / (range_max - range_min) - return unscaled_location + + return nominal_location_to_cdf_location(nominal_location, range_min, range_max, zero_point) def get_cdf_at(cdf, unscaled_location): @@ -1103,8 +1088,8 @@ def cdf_between(row, cdf, lower_bound, upper_bound): Returns: float: Probability between the bounds. """ - a = get_cdf_at(cdf, nominal_location_to_cdf_location(lower_bound, row)) - b = get_cdf_at(cdf, nominal_location_to_cdf_location(upper_bound, row)) + a = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(lower_bound, row)) + b = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(upper_bound, row)) return b - a @@ -1190,7 +1175,7 @@ def compute_bucket_forecast_value(row): # Compute forecast_value using the extracted string_location forecast_value = get_cdf_at( - row["cdf"], nominal_location_to_cdf_location(string_location, row) + row["cdf"], nominal_location_to_cdf_location_via_question_dict(string_location, row) ) # Apply logic based on comparison_type @@ -1239,143 +1224,25 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_peer_score_numeric(row, bot_col, pro_col='pro_median'): - """Calculate peer score for numeric questions""" - try: - # Check if bot didn't provide a forecast - if pd.isna(row[bot_col]): - return np.nan - - resolution_value = row['resolution'] - - # Get the CDF values - bot_cdf = row[bot_col] - pro_median_cdf = row[pro_col] - - # Handle special cases - if resolution_value == 'below_lower_bound': - # Use first point in CDF - if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: - bot_prob = bot_cdf[0] - else: - return np.nan - - if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: - pro_median_prob = pro_median_cdf[0] - else: - return np.nan - - elif resolution_value == 'above_upper_bound': - # Use (1 - last point in CDF) - if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: - bot_prob = 1 - bot_cdf[-1] - else: - return np.nan - - if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: - pro_median_prob = 1 - pro_median_cdf[-1] - else: - return np.nan - else: - # Convert to float if it's a numeric resolution - try: - resolution_float = float(resolution_value) - - # Convert CDF to PMF - if isinstance(bot_cdf, (list, np.ndarray)) and isinstance(pro_median_cdf, (list, np.ndarray)): - # Convert CDFs to PMFs - bot_pmf = np.diff(np.concatenate([[0], bot_cdf])) - pro_pmf = np.diff(np.concatenate([[0], pro_median_cdf])) - - # Use nominal_location_to_cdf_location to find the appropriate bucket - cdf_location = nominal_location_to_cdf_location(resolution_float, row) - - # Find the appropriate bucket index - bucket_index = min(int(cdf_location * (len(bot_pmf) - 1)), len(bot_pmf) - 1) - - # Get probabilities - bot_prob = bot_pmf[bucket_index] - pro_median_prob = pro_pmf[bucket_index] - else: - return np.nan - except: - return np.nan - - # Ensure non-zero probabilities - bot_prob = max(bot_prob, 1e-10) - pro_median_prob = max(pro_median_prob, 1e-10) - - # Calculate peer score and divide by 2 for continuous questions - return np.log(bot_prob / pro_median_prob) / 2 - - except Exception as e: - # Print the specific error for debugging - return np.nan - -def calculate_peer_score_binary(row, bot_col, pro_col='pro_median'): - """Calculate peer score for binary questions""" - if row['resolution'] == 'yes': - return np.log(row[bot_col] / row[pro_col]) - else: # resolution is 'no' - return np.log((1 - row[bot_col]) / (1 - row[pro_col])) - -def parse_cdf_string(cdf_string): - """Parse CDF string into numpy array""" - return np.array([float(x) for x in cdf_string.strip('[]').split(',')]) - -def calculate_peer_score_multiple_choice(row, bot_col, pro_col='pro_median'): - """Calculate peer score for multiple choice questions""" - # Check if bot didn't provide a forecast (NaN) - if pd.isna(row[bot_col]): - return np.nan - - # Get the resolution value and options - resolution_value = row['resolution'] +def calculate_weighted_h2h_score_between_two_forecast_columns(row, col_a, col_b): + forecast_a = row[col_a] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + forecast_b = row[col_b] + resolution = row['resolution'] options = row['options_parsed'] if 'options_parsed' in row else row['options'] - - # Find the index of the resolution in options array - resolution_str = str(resolution_value) - - try: - resolution_index = options.index(resolution_str) - - # Get the forecasts - bot_pmf_raw = row[bot_col] - pro_pmf_raw = row[pro_col] - - # Parse string representations of arrays if needed - if isinstance(bot_pmf_raw, str): - bot_pmf = [float(x) for x in bot_pmf_raw.strip('[]').split(',')] - else: - bot_pmf = bot_pmf_raw - - if isinstance(pro_pmf_raw, str): - pro_pmf = [float(x) for x in pro_pmf_raw.strip('[]').split(',')] - else: - pro_pmf = pro_pmf_raw - - # Get the probabilities at the correct index - bot_prob = bot_pmf[resolution_index] - pro_prob = pro_pmf[resolution_index] - - # Calculate peer score - return np.log(bot_prob / pro_prob) - except Exception as e: - # If any error occurs, return NaN - return np.nan - -def calculate_peer_score(row, bot_col, pro_col='pro_median'): - """Calculate peer score based on question type""" - if row['type'] == 'binary': - return calculate_peer_score_binary(row, bot_col, pro_col) - elif row['type'] == 'multiple_choice': - return calculate_peer_score_multiple_choice(row, bot_col, pro_col) - elif row['type'] == 'numeric': - return calculate_peer_score_numeric(row, bot_col, pro_col) - else: - # Unknown question type; return NaN - return np.nan + range_min = row['range_min'] + range_max = row['range_max'] + question_weight = row['question_weight'] + score = calculate_spot_peer_score( + forecast=forecast_a, + forecast_for_other_users=[forecast_b], + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, + question_weight=question_weight + ) + return score def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): """Calculate peer scores for all bots""" @@ -1384,10 +1251,10 @@ def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): # Calculate peer score for each bot for bot in all_bots: - df_peer[bot] = 100 * df.apply(lambda row: calculate_peer_score(row, bot, pro_col), axis=1) + df_peer[bot] = 100 * df.apply(lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, bot, pro_col), axis=1) # Calculate peer score for bot_team_median df_peer["bot_team_median"] = 100 * df.apply( - lambda row: calculate_peer_score(row, 'bot_median', pro_col), axis=1) + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, 'bot_median', pro_col), axis=1) return df_peer diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 265e32d..17f548c 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -metac-perplexity,20.6,20.6,20.6,20.6,20.6 -metac-o1,20.2,20.2,20.2,20.2,20.2 +metac-perplexity,18.1,18.1,18.1,18.1,18.1 acm_bot,17.7,17.7,17.7,17.7,17.7 -bot_median,17.4,17.4,17.4,17.4,17.4 +bot_median,17.0,17.0,17.0,17.0,17.0 +metac-o1,16.6,16.6,16.6,16.6,16.6 +metac-claude-3-5-sonnet-20240620,14.8,14.8,14.8,14.8,14.8 manticAI,14.5,14.5,14.5,14.5,14.5 twsummerbot,14.3,14.3,14.3,14.3,14.3 jkraybill_bot,14.3,14.3,14.3,14.3,14.3 -metac-claude-3-5-sonnet-20240620,13.0,13.0,13.0,13.0,13.0 -metac-claude-3-5-sonnet-latest,12.4,12.4,12.4,12.4,12.4 -metac-deepseek-r1,12.3,12.3,12.3,12.3,12.3 -metac-Llama-3.1,12.2,12.2,12.2,12.2,12.2 -GreeneiBot2,11.8,11.8,11.8,11.8,11.8 +metac-exa,13.0,13.0,13.0,13.0,13.0 +GreeneiBot2,12.2,12.2,12.2,12.2,12.2 NextWorldLab,11.1,11.1,11.1,11.1,11.1 +metac-Llama-3.1,10.5,10.5,10.5,10.5,10.5 Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 SynapseSeer,10.2,10.2,10.2,10.2,10.2 -metac-grok-2-1212,9.8,9.8,9.8,9.8,9.8 +metac-claude-3-5-sonnet-latest,10.0,10.0,10.0,10.0,10.0 mmBot,9.7,9.7,9.7,9.7,9.7 -metac-Gemini-Exp-1206,9.6,9.6,9.6,9.6,9.6 annabot,9.0,9.0,9.0,9.0,9.0 -metac-exa,8.8,8.8,8.8,8.8,8.8 VeritasAI,8.4,8.4,8.4,8.4,8.4 +metac-grok-2-1212,8.2,8.2,8.2,8.2,8.2 laylaps,7.6,7.6,7.6,7.6,7.6 +metac-Gemini-Exp-1206,7.4,7.4,7.4,7.4,7.4 metac-o1-preview,6.7,6.7,6.7,6.7,6.7 cookics_bot_TEST,6.3,6.3,6.3,6.3,6.3 +metac-deepseek-r1,5.7,5.7,5.7,5.7,5.7 MWG,5.5,5.5,5.5,5.5,5.5 ajf-bot,5.1,5.1,5.1,5.1,5.1 +metac-gpt-4o,4.8,4.8,4.8,4.8,4.8 pgodzinai,3.5,3.5,3.5,3.5,3.5 KevinTestBot,3.3,3.3,3.3,3.3,3.3 -metac-gpt-4o,3.0,3.0,3.0,3.0,3.0 InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 Bot_Pepa,2.6,2.6,2.6,2.6,2.6 CumulativeBot,2.5,2.5,2.5,2.5,2.5 @@ -37,7 +37,7 @@ jonahsingerbot,2.2,2.2,2.2,2.2,2.2 bean_bot,2.1,2.1,2.1,2.1,2.1 X_bot,1.9,1.9,1.9,1.9,1.9 CatrachoCaster,1.8,1.8,1.8,1.8,1.8 -RPM_bot,0.8,0.8,0.8,0.8,0.8 +RPM_bot,1.2,1.2,1.2,1.2,1.2 4Shadower,0.6,0.6,0.6,0.6,0.6 krm-bot,0.6,0.6,0.6,0.6,0.6 andrewsiah,0.0,0.0,0.0,0.0,0.0 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 889922c..5e73739 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -metac-perplexity,1957.5,95.0,20.6,0.0,0.0,inf,1.9847501794262088,20.6,20.6,1.0,0.000000 -metac-o1,1921.1,95.0,20.2,0.0,0.0,inf,1.9847501794262088,20.2,20.2,1.0,0.000000 +metac-perplexity,1719.7,95.0,18.1,3.570999300115835e-15,3.663767977230083e-16,4.940951081399963e+16,1.9847501794262088,18.1,18.1,1.0,0.000000 acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 -bot_median,1655.0,95.0,17.4,3.570999300115835e-15,3.663767977230083e-16,4.755070072324921e+16,1.9847501794262088,17.4,17.4,1.0,0.000000 +bot_median,1610.4,95.0,17.0,3.570999300115835e-15,3.663767977230083e-16,4.626691199221798e+16,1.9847501794262088,17.0,17.0,1.0,0.000000 +metac-o1,1577.6,95.0,16.6,3.570999300115835e-15,3.663767977230083e-16,4.532462410721762e+16,1.9847501794262088,16.6,16.6,1.0,0.000000 +metac-claude-3-5-sonnet-20240620,1405.9,95.0,14.8,3.570999300115835e-15,3.663767977230083e-16,4.039353684227144e+16,1.9847501794262088,14.8,14.8,1.0,0.000000 manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -metac-claude-3-5-sonnet-20240620,1235.2,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.097519447336572e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 -metac-claude-3-5-sonnet-latest,1180.5,95.0,12.4,0.0,0.0,inf,1.9847501794262088,12.4,12.4,1.0,0.000000 -metac-deepseek-r1,1166.0,95.0,12.3,1.7854996500579174e-15,1.8318839886150415e-16,6.700213221693384e+16,1.9847501794262088,12.3,12.3,1.0,0.000000 -metac-Llama-3.1,1154.9,95.0,12.2,3.570999300115835e-15,3.663767977230083e-16,3.3181275591894544e+16,1.9847501794262088,12.2,12.2,1.0,0.000000 -GreeneiBot2,1119.2,95.0,11.8,1.7854996500579174e-15,1.8318839886150415e-16,6.4310595726389144e+16,1.9847501794262088,11.8,11.8,1.0,0.000000 +metac-exa,1233.6,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.088709959185136e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 +GreeneiBot2,1163.2,95.0,12.2,0.0,0.0,inf,1.9847501794262088,12.2,12.2,1.0,0.000000 NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 +metac-Llama-3.1,997.0,95.0,10.5,1.7854996500579174e-15,1.8318839886150415e-16,5.728815548098371e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 -metac-grok-2-1212,932.3,95.0,9.8,1.7854996500579174e-15,1.8318839886150415e-16,5.357004504213439e+16,1.9847501794262088,9.8,9.8,1.0,0.000000 +metac-claude-3-5-sonnet-latest,949.9,95.0,10.0,0.0,0.0,inf,1.9847501794262088,10.0,10.0,1.0,0.000000 mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 -metac-Gemini-Exp-1206,910.2,95.0,9.6,1.7854996500579174e-15,1.8318839886150415e-16,5.230331909359555e+16,1.9847501794262088,9.6,9.6,1.0,0.000000 annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 -metac-exa,836.7,95.0,8.8,1.7854996500579174e-15,1.8318839886150415e-16,4.808056144499867e+16,1.9847501794262088,8.8,8.8,1.0,0.000000 VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 +metac-grok-2-1212,775.1,95.0,8.2,0.0,0.0,inf,1.9847501794262088,8.2,8.2,1.0,0.000000 laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 -metac-o1-preview,640.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.357383207755715e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 +metac-Gemini-Exp-1206,701.9,95.0,7.4,8.927498250289587e-16,9.159419943075207e-17,8.065986188688938e+16,1.9847501794262088,7.4,7.4,1.0,0.000000 +metac-o1-preview,633.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.277309325504542e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 cookics_bot_TEST,596.4,95.0,6.3,0.0,0.0,inf,1.9847501794262088,6.3,6.3,1.0,0.000000 +metac-deepseek-r1,545.5,95.0,5.7,8.927498250289587e-16,9.159419943075207e-17,6.2687228856570984e+16,1.9847501794262088,5.7,5.7,1.0,0.000000 MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 +metac-gpt-4o,451.6,95.0,4.8,8.927498250289587e-16,9.159419943075207e-17,5.190357943531163e+16,1.9847501794262088,4.8,4.8,1.0,0.000000 pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 -metac-gpt-4o,280.3,95.0,3.0,8.927498250289587e-16,9.159419943075207e-17,3.221540864953186e+16,1.9847501794262088,3.0,3.0,1.0,0.000000 InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 @@ -37,7 +37,7 @@ jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.8945 bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 -RPM_bot,71.4,95.0,0.8,1.1159372812861984e-16,1.144927492884401e-17,6.560692777870449e+16,1.9847501794262088,0.8,0.8,1.0,0.000000 +RPM_bot,118.6,95.0,1.2,4.463749125144793e-16,4.579709971537604e-17,2.7264857831745884e+16,1.9847501794262088,1.2,1.2,1.0,0.000000 4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 080aea4..e48eae4 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,4 +1,6 @@ +from datetime import datetime import numpy as np +from scipy.stats.mstats import gmean from refactored_notebook.data_models import ForecastType, ResolutionType @@ -12,7 +14,57 @@ def calculate_spot_peer_score( range_max: float | None = None, question_weight: float = 1.0, ) -> float: - raise NotImplementedError("Not implemented") + forecast_for_resolution, _ = _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + other_user_forecasts, _ = zip( + [ + _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + for forecast in forecast_for_other_users + ] + ) + geometric_mean = gmean(other_user_forecasts) + peer_score = np.log(forecast_for_resolution / geometric_mean) + if isinstance( + resolution, float + ): # @Check: This doesn't account for resolution being 'above_upper_bound' or 'below_lower_bound' + peer_score /= 2 + return peer_score * question_weight + + +def nominal_location_to_cdf_location( + nominal_location: float, + range_min: float, + range_max: float, + zero_point: float | None = None, +) -> float: + """ + Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to + metaculus's "internal representation" range [0, 1] incorporating question scaling + """ + assert isinstance(zero_point, float | None) + + # TODO: Make sure to use datetime.fromisoformat(nominal_location).timestamp() if you start using date questions + scaled_location = float(nominal_location) + + # Unscale the value to put it into the range [0,1] + if zero_point is not None: + # logarithmically scaled question + deriv_ratio = (range_max - zero_point) / (range_min - zero_point) + unscaled_location = ( + np.log( + (scaled_location - range_min) * (deriv_ratio - 1) + + (range_max - range_min) + ) + - np.log(range_max - range_min) + ) / np.log(deriv_ratio) + else: + # linearly scaled question + unscaled_location = (scaled_location - range_min) / (range_max - range_min) + assert 0 <= unscaled_location <= 1 + return unscaled_location def calculate_spot_baseline_score( @@ -58,10 +110,19 @@ def _determine_probability_for_resolution_and_baseline( range_min: float | None = None, range_max: float | None = None, ) -> tuple[float, float]: + """ + Returns a 0 to 1 probability for the resolution + Also returns the baseline probability used in baseline scoring + """ + is_numeric = ( + isinstance(resolution, float) + or isinstance(resolution, int) + or resolution == "above_upper_bound" + or resolution == "below_lower_bound" + ) is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) if forecast is None or resolution is None: raise NotImplementedError( @@ -76,62 +137,104 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError("Forecast contains probabilities outside of 0 to 1 range") if is_binary: - if len(forecast) != 1 and len(forecast) != 2: - raise ValueError( - "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" - ) - - forecast_val = float(forecast[0]) - baseline_prob = 0.5 - if resolution: - prob_for_resolution = forecast_val - else: - prob_for_resolution = 1 - forecast_val + prob_for_resolution, baseline_prob = _binary_resolution_baseline_prob( + forecast, resolution + ) elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") - - if len(forecast) != len(options): - raise ValueError("Forecast and options have different lengths") - - pmf = [float(p) for p in forecast] - options = [str(opt) for opt in options] - resolution_idx = options.index(str(resolution)) - prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) + prob_for_resolution, baseline_prob = _multiple_choice_resolution_baseline_prob( + forecast, resolution, options + ) elif is_numeric: if range_min is None or range_max is None: raise ValueError( "Range min and range max are required for numeric questions" ) - if len(forecast) != 201: - raise ValueError("CDF should have 201 bins") - previous_prob = 0 - for current_prob in forecast: - if current_prob < previous_prob: - raise ValueError("CDF should be in increasing order") - previous_prob = current_prob - - cdf = [float(p) for p in forecast] - pmf = [cdf[0]] + [ - cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) - ] # @Check: is this a correct conversion? - pmf.append(1 - cdf[-1]) + prob_for_resolution, baseline_prob = _numeric_resolution_baseline_prob( + forecast, resolution, range_min, range_max + ) + else: + raise ValueError("Unknown question type") - resolution = float(resolution) + assert 0 < prob_for_resolution <= 1 + assert 0 < baseline_prob <= 1 + return prob_for_resolution, baseline_prob - bin_edges = np.linspace(range_min, range_max, 200) - resolution_idx = np.searchsorted(bin_edges, resolution, side="right") - if resolution_idx >= len(pmf): - raise ValueError("Resolution is out of bounds") +def _binary_resolution_baseline_prob(forecast: list[float], resolution: bool): + if len(forecast) != 1 and len(forecast) != 2: + raise ValueError( + "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" + ) + + forecast_val = float(forecast[0]) + baseline_prob = 0.5 + if resolution: + prob_for_resolution = forecast_val + else: + prob_for_resolution = 1 - forecast_val + return prob_for_resolution, baseline_prob + - prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len( - pmf - ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins +def _multiple_choice_resolution_baseline_prob( + forecast: list[float], resolution: str, options: list[str] +): + if options is None: + raise ValueError("Options are required for multiple choice questions") + if len(forecast) != len(options): + raise ValueError("Forecast and options have different lengths") + + pmf = [float(p) for p in forecast] + options = [str(opt) for opt in options] + resolution_idx = options.index(str(resolution)) + prob_for_resolution = pmf[resolution_idx] + baseline_prob = 1 / len(pmf) + return prob_for_resolution, baseline_prob + + +def _numeric_resolution_baseline_prob( + forecast: list[float], resolution: float | str, range_min: float, range_max: float +): + if len(forecast) != 201: + raise ValueError("CDF should have 201 bins") + + previous_prob = 0 + for current_prob in forecast: + if current_prob < previous_prob: + raise ValueError("CDF should be in increasing order") + previous_prob = current_prob + + cdf = [float(p) for p in forecast] + assert len(cdf) == 201 + pmf = [cdf[0]] + [ + cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) + ] # @Check: is this a correct conversion? + pmf.append(1 - cdf[-1]) + # pmf = np.diff(np.concatenate([[0], cdf])) + assert len(pmf) == 200 + + if resolution == "below_lower_bound": + prob_for_resolution = cdf[0] + elif resolution == "above_upper_bound": + prob_for_resolution = 1 - cdf[-1] # Grab probability of 201st bin else: - raise ValueError("Unknown question type") + resolution = float(resolution) + # bin_edges = np.linspace(range_min, range_max, 200) + # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") + + cdf_location = nominal_location_to_cdf_location( + resolution, range_min, range_max + ) + resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) + if resolution_bin_idx >= len(pmf): + raise ValueError("Resolution is out of bounds") + + prob_for_resolution = pmf[resolution_bin_idx] + baseline_prob = 1 / len( + pmf + ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + # @Check: Should this be either 1, 0.9, or 0.95? return prob_for_resolution, baseline_prob diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 7080b1f..c98409c 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -156,7 +156,7 @@ def test_peer_score_zero_when_all_same( ), # Numeric ( - [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], + [[0.1] * 100 + [0.9] * 101, [0.2] * 100 + [0.8] * 101, [0.5] * 201], 0.5, None, 0.0, From 33e65c9116d5b27e4b267400f62b4c1eb54ec3c7 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 09:39:06 -0600 Subject: [PATCH 11/26] Got all binary scoring tests passing --- refactored_notebook/scoring.py | 80 ++-- tests/test_scoring.py | 650 +++++++++++++++++++++++---------- 2 files changed, 503 insertions(+), 227 deletions(-) diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index e48eae4..dbe06af 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -17,14 +17,16 @@ def calculate_spot_peer_score( forecast_for_resolution, _ = _determine_probability_for_resolution_and_baseline( forecast, resolution, options, range_min, range_max ) - other_user_forecasts, _ = zip( - [ - _determine_probability_for_resolution_and_baseline( - forecast, resolution, options, range_min, range_max - ) - for forecast in forecast_for_other_users - ] - ) + other_user_forecasts_and_baseline_prob = [ + _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + for forecast in forecast_for_other_users + ] + other_user_forecasts = [ + forecast for forecast, _ in other_user_forecasts_and_baseline_prob + ] + geometric_mean = gmean(other_user_forecasts) peer_score = np.log(forecast_for_resolution / geometric_mean) if isinstance( @@ -43,6 +45,8 @@ def nominal_location_to_cdf_location( """ Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to metaculus's "internal representation" range [0, 1] incorporating question scaling + 0.8 would incidate the nomial locatoin is at cdf index 201 * 0.8 + Values higher/lower than 0 and 1 are resolutions that are above/below the upper/lower bound """ assert isinstance(zero_point, float | None) @@ -63,7 +67,6 @@ def nominal_location_to_cdf_location( else: # linearly scaled question unscaled_location = (scaled_location - range_min) / (range_max - range_min) - assert 0 <= unscaled_location <= 1 return unscaled_location @@ -115,11 +118,12 @@ def _determine_probability_for_resolution_and_baseline( Also returns the baseline probability used in baseline scoring """ + if resolution == "above_upper_bound" or resolution == "below_lower_bound": + raise ValueError("'above_upper_bound' or 'below_lower_bound' format not supported") + is_numeric = ( isinstance(resolution, float) or isinstance(resolution, int) - or resolution == "above_upper_bound" - or resolution == "below_lower_bound" ) is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) @@ -133,17 +137,16 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError("Forecast is empty") if not is_numeric and any(p <= 0 or p >= 1 for p in forecast): - # @Check: Is it valid to have a numeric forecast with 0 probability for a number? raise ValueError("Forecast contains probabilities outside of 0 to 1 range") if is_binary: - prob_for_resolution, baseline_prob = _binary_resolution_baseline_prob( + prob_for_resolution, baseline_prob = _binary_resolution_and_baseline_prob( forecast, resolution ) elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") - prob_for_resolution, baseline_prob = _multiple_choice_resolution_baseline_prob( + prob_for_resolution, baseline_prob = _multiple_choice_resolution_and_baseline_prob( forecast, resolution, options ) elif is_numeric: @@ -151,18 +154,18 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError( "Range min and range max are required for numeric questions" ) - prob_for_resolution, baseline_prob = _numeric_resolution_baseline_prob( + prob_for_resolution, baseline_prob = _numeric_resolution_and_baseline_prob( forecast, resolution, range_min, range_max ) else: raise ValueError("Unknown question type") - assert 0 < prob_for_resolution <= 1 - assert 0 < baseline_prob <= 1 + assert 0 <= prob_for_resolution <= 1, f"Probability for resolution is {prob_for_resolution} which is not between 0 and 1" + assert 0 <= baseline_prob <= 1, f"Baseline probability is {baseline_prob} which is not between 0 and 1" return prob_for_resolution, baseline_prob -def _binary_resolution_baseline_prob(forecast: list[float], resolution: bool): +def _binary_resolution_and_baseline_prob(forecast: list[float], resolution: bool): if len(forecast) != 1 and len(forecast) != 2: raise ValueError( "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" @@ -177,7 +180,7 @@ def _binary_resolution_baseline_prob(forecast: list[float], resolution: bool): return prob_for_resolution, baseline_prob -def _multiple_choice_resolution_baseline_prob( +def _multiple_choice_resolution_and_baseline_prob( forecast: list[float], resolution: str, options: list[str] ): if options is None: @@ -194,7 +197,7 @@ def _multiple_choice_resolution_baseline_prob( return prob_for_resolution, baseline_prob -def _numeric_resolution_baseline_prob( +def _numeric_resolution_and_baseline_prob( forecast: list[float], resolution: float | str, range_min: float, range_max: float ): if len(forecast) != 201: @@ -207,31 +210,28 @@ def _numeric_resolution_baseline_prob( previous_prob = current_prob cdf = [float(p) for p in forecast] - assert len(cdf) == 201 - pmf = [cdf[0]] + [ + assert len(cdf) == 201, f"There should be 201 bins, but there are {len(cdf)}" + lower_bound_prob = cdf[0] + upper_bound_prob = 1 - cdf[-1] + pmf = [lower_bound_prob] + [ cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) - ] # @Check: is this a correct conversion? - pmf.append(1 - cdf[-1]) + ] + [upper_bound_prob] # @Check: is this a correct conversion? # pmf = np.diff(np.concatenate([[0], cdf])) - assert len(pmf) == 200 + assert len(pmf) == 202, f"There should be 202 bins, but there are {len(pmf)}" - if resolution == "below_lower_bound": - prob_for_resolution = cdf[0] - elif resolution == "above_upper_bound": - prob_for_resolution = 1 - cdf[-1] # Grab probability of 201st bin - else: - resolution = float(resolution) - # bin_edges = np.linspace(range_min, range_max, 200) - # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") - cdf_location = nominal_location_to_cdf_location( - resolution, range_min, range_max - ) - resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) - if resolution_bin_idx >= len(pmf): - raise ValueError("Resolution is out of bounds") + resolution = float(resolution) + # bin_edges = np.linspace(range_min, range_max, 200) + # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") + cdf_location = nominal_location_to_cdf_location( + resolution, range_min, range_max + ) + resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) + + if resolution_bin_idx >= len(pmf): + raise ValueError("Resolution is out of bounds") - prob_for_resolution = pmf[resolution_bin_idx] + prob_for_resolution = pmf[resolution_bin_idx] baseline_prob = 1 / len( pmf diff --git a/tests/test_scoring.py b/tests/test_scoring.py index c98409c..cec5050 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -7,6 +7,8 @@ calculate_spot_peer_score, ) +from dataclasses import dataclass + # TODO: # For each of Multiple Choice, Binary, and Numeric questions # - Test spot peer score @@ -20,11 +22,11 @@ ################################### HELPER FUNCTIONS ################################### -def generate_uniform_cdf(num_points: int) -> list[float]: +def generate_uniform_cdf(num_points: int = 201) -> list[float]: return [(i + 1) / num_points for i in range(num_points)] -def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[float]: +def generate_perfect_cdf(correct_index: int) -> list[float]: assert correct_index >= 0 and correct_index <= 201 length_of_cdf = 201 perfect_forecast = 0.99999 @@ -35,12 +37,343 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ else: cdf.append(perfect_forecast) - if inverse_cdf: - cdf = [1 - c for c in cdf] - return cdf +@dataclass +class Percentile: + value: float + probability_below: float + + +def generate_cdf( + percentiles: list[Percentile], + lower_bound: float, + upper_bound: float, + open_lower_bound: bool, + open_upper_bound: bool, + zero_point: float | None = None, +) -> list[float]: + # Copied from another notebook -> definitely could be cleaned up + + percentile_values: dict[float, float] = { + percentile.probability_below * 100: percentile.value + for percentile in percentiles + } + + percentile_max = max(float(key) for key in percentile_values.keys()) + percentile_min = min(float(key) for key in percentile_values.keys()) + range_min = lower_bound + range_max = upper_bound + range_size = abs(range_max - range_min) + buffer = 1 if range_size > 100 else 0.01 * range_size + + # Adjust any values that are exactly at the bounds + for percentile, value in list(percentile_values.items()): + if not open_lower_bound and value <= range_min + buffer: + percentile_values[percentile] = range_min + buffer + if not open_upper_bound and value >= range_max - buffer: + percentile_values[percentile] = range_max - buffer + + # Set cdf values outside range + if open_upper_bound: + if range_max > percentile_values[percentile_max]: + percentile_values[int(100 - (0.5 * (100 - percentile_max)))] = range_max + else: + percentile_values[100] = range_max + + # Set cdf values outside range + if open_lower_bound: + if range_min < percentile_values[percentile_min]: + percentile_values[int(0.5 * percentile_min)] = range_min + else: + percentile_values[0] = range_min + + sorted_percentile_values = dict(sorted(percentile_values.items())) + + # Normalize percentile keys + normalized_percentile_values = {} + for key, value in sorted_percentile_values.items(): + percentile = float(key) / 100 + normalized_percentile_values[percentile] = value + + value_percentiles = { + value: key for key, value in normalized_percentile_values.items() + } + + # function for log scaled questions + def generate_cdf_locations( + range_min: float, range_max: float, zero_point: float | None + ) -> list[float]: + if zero_point is None: + scale = lambda x: range_min + (range_max - range_min) * x + else: + deriv_ratio = (range_max - zero_point) / (range_min - zero_point) + scale = lambda x: range_min + (range_max - range_min) * ( + deriv_ratio**x - 1 + ) / (deriv_ratio - 1) + return [scale(x) for x in np.linspace(0, 1, 201)] + + cdf_xaxis = generate_cdf_locations(range_min, range_max, zero_point) + + def linear_interpolation( + x_values: list[float], xy_pairs: dict[float, float] + ) -> list[float]: + # Sort the xy_pairs by x-values + sorted_pairs = sorted(xy_pairs.items()) + + # Extract sorted x and y values + known_x = [pair[0] for pair in sorted_pairs] + known_y = [pair[1] for pair in sorted_pairs] + + # Initialize the result list + y_values = [] + + for x in x_values: + # Check if x is exactly in the known x values + if x in known_x: + y_values.append(known_y[known_x.index(x)]) + else: + # Find the indices of the two nearest known x-values + i = 0 + while i < len(known_x) and known_x[i] < x: + i += 1 + # If x is outside the range of known x-values, use the nearest endpoint + if i == 0: + y_values.append(known_y[0]) + elif i == len(known_x): + y_values.append(known_y[-1]) + else: + # Perform linear interpolation + x0, x1 = known_x[i - 1], known_x[i] + y0, y1 = known_y[i - 1], known_y[i] + + # Linear interpolation formula + y = y0 + (x - x0) * (y1 - y0) / (x1 - x0) + y_values.append(y) + + return y_values + + continuous_cdf = linear_interpolation(cdf_xaxis, value_percentiles) + + percentiles = [ + Percentile(value=value, probability_below=percentile) + for value, percentile in zip(cdf_xaxis, continuous_cdf) + ] + assert len(percentiles) == 201 + + # Validate minimum spacing between consecutive values + for i in range(len(percentiles) - 1): + assert ( + abs(percentiles[i + 1].probability_below - percentiles[i].probability_below) + >= 5e-05 + ), ( + f"Percentiles at indices {i} and {i+1} are too close: " + f"{percentiles[i].probability_below} and {percentiles[i+1].probability_below} " + f"at values {percentiles[i].value} and {percentiles[i+1].value}. " + "It is possible that your prediction is mostly or completely out of the upper/lower bound range " + "Thus making this cdf mostly meaningless." + ) + + return [percentile.probability_below for percentile in percentiles] + + +################################### BASELINE SCORES ################################### + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight,expected", + [ + # Binary: uniform forecast, should be 0 + ([0.5], True, None, None, None, 1.0, 0.0), + ([0.5], False, None, None, None, 1.0, 0.0), + ([0.5, 0.5], False, None, None, None, 1.0, 0.0), + # Multiple Choice: uniform forecast, should be 0 + ([1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None, 1.0, 0.0), + ([0.25, 0.25, 0.25, 0.25], "B", ["A", "B", "C", "D"], None, None, 1.0, 0.0), + # Numeric: uniform CDF, should be 0 + (generate_uniform_cdf(), 0.5, None, 0.0, 1.0, 1.0, 0.0), + ], +) +def test_baseline_score_is_0_with_uniform_prediction( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, + expected: float, +): + score = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score - expected) == pytest.approx(0) + + +def test_binary_baseline_score_when_perfect_forecast(): + score = calculate_spot_baseline_score( + forecast=[0.99999999], + resolution=True, + ) + assert score == pytest.approx(100) + +@pytest.mark.parametrize( + "forecast,resolution,expected", + [ + ([0.001], True, -896.57), # Completely incorrect + ([0.999], True, 99.86), # Completely correct + ([0.001], False, 99.86), # Completely correct + ([0.4], True, -32.19), # Examples found here: https://www.metaculus.com/help/scores-faq/#:~:text=details%20for%20nerds-,Do%20all%20my%20predictions%20on%20a%20question%20count%20toward%20my%20score%3F,-Yes.%20Metaculus%20uses + ([0.7], True, 48.542), + ([0.4, 0.6], True, -32.19), + ], +) +def test_binary_baseline_examples(forecast: list[float], resolution: bool, expected: float): + score = calculate_spot_baseline_score( + forecast=forecast, + resolution=resolution, + ) + assert score == pytest.approx(expected, abs=1e-1) + + +def test_numeric_baseline_when_perfect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=generate_perfect_cdf(correct_index), + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(183) + + +def test_numeric_baseline_if_completly_incorrect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=[0.0] * 200 + [1.0], # all probability assigned to upper bound + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(-230) + + +def test_multiple_choice_perfect_forecast(): + forecast_for_answer_a = 0.999 + num_other_forecasts = 7 + other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts + score = calculate_spot_baseline_score( + forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, + resolution="A", + options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], + ) + assert score == pytest.approx(99.87) + + +def test_multiple_choice_if_completly_incorrect_forecast(): + forecast_for_answer_a = 0.001 + other_forecasts = (1 - forecast_for_answer_a) / 2 + score = calculate_spot_baseline_score( + forecast=[forecast_for_answer_a, other_forecasts, other_forecasts], + resolution="A", + options=["A", "B", "C"], + ) + assert score == pytest.approx(-232) + + +@pytest.mark.parametrize( + "forecast_closer,forecast_further,resolution,options,range_min,range_max", + [ + # Binary: closer to True + ([0.8], [0.2], True, None, None, None), + # Binary: closer to False + ([0.2], [0.8], False, None, None, None), + # Multiple Choice: closer to "A" + ([0.7, 0.2, 0.1], [0.1, 0.2, 0.7], "A", ["A", "B", "C"], None, None), + # Numeric: CDF with more mass near 0.5 vs near 0.0 + ( + generate_cdf( + [ + Percentile(value=40, probability_below=0.1), + Percentile(value=60, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + generate_cdf( + [ + Percentile(value=30, probability_below=0.1), + Percentile(value=49, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + 50, + None, + -1, + 96, + ), + ], +) +def test_baseline_score_better_when_closer( + forecast_closer: list[float], + forecast_further: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + score_closer = calculate_spot_baseline_score( + forecast_closer, resolution, options, range_min, range_max, 1.0 + ) + score_further = calculate_spot_baseline_score( + forecast_further, resolution, options, range_min, range_max, 1.0 + ) + assert score_closer > score_further + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight", + [ + # Binary + ([0.8], True, None, None, None, 2.0), + # Multiple Choice + ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), + # Numeric + ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), + ], +) +def test_baseline_score_weighted( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, +): + score_unweighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 + + ################################### PEER SCORES ################################### @@ -58,7 +391,7 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ # Multiple Choice: forecast closer to resolution gets better score ( [ - [0.9, 0.1, 0.0], + [0.9, 0.09, 0.01], [0.7, 0.2, 0.1], [0.5, 0.3, 0.2], [0.3, 0.4, 0.3], @@ -72,16 +405,80 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ # Numeric: forecast CDFs with more mass near resolution get better score ( [ - [0.1] * 100 + [0.9] * 101, # most mass above 0.5 - [0.2] * 100 + [0.8] * 101, - [0.5] * 201, - [0.8] * 100 + [0.2] * 101, - [0.9] * 100 + [0.1] * 101, # most mass below 0.5 + generate_cdf( # Best CDF + [ + Percentile(value=40, probability_below=0.1), + Percentile(value=60, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + generate_cdf( + [ + Percentile(value=20, probability_below=0.1), + Percentile(value=50, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + generate_cdf( # worst CDF + [ + Percentile(value=10, probability_below=0.1), + Percentile(value=20, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), ], - 0.5, + 49, None, - 0.0, - 1.0, + -1, + 96, # Not even range + ), + # Numeric: forecast CDFs with more mass near upper bound get better score + ( + [ + generate_cdf( # Best CDF + [ + Percentile(value=100, probability_below=0.1), + Percentile(value=120, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=True, + ), + generate_cdf( + [ + Percentile(value=100, probability_below=0.1), + Percentile(value=120, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=True, + ), + generate_cdf( # worst CDF + [ + Percentile(value=100, probability_below=0.1), + Percentile(value=120, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=False, # No upper bound = no probability mass at upper bound + ), + ], + 120, + None, + 0, + 100, ), ], ) @@ -105,15 +502,19 @@ def test_better_forecast_means_better_peer_score( for idx, forecast in enumerate(forecasts) ] sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) - assert sorted_indices == list(range(len(scores))), "Scores should be ordered as expected (descending)" + assert len(scores) == len(set(scores)), "Scores should all be different" + assert sorted_indices == list( + range(len(scores)) + ), "Scores should be ordered as expected (descending)" @pytest.mark.parametrize( "question_type,forecast,resolution,options,range_min,range_max", [ ("binary", [0.5], True, None, None, None), - ("mc", [1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None), - ("numeric", [0.5] * 201, 0.5, None, 0.0, 1.0), + ("mc", [0.25, 0.25, 0.25, 0.25], "A", ["A", "B", "C", "D"], None, None), + ("numeric", generate_perfect_cdf(100), 100, None, 0, 100), + ("numeric", generate_uniform_cdf(), 50, None, 0, 100), ], ) def test_peer_score_zero_when_all_same( @@ -156,11 +557,42 @@ def test_peer_score_zero_when_all_same( ), # Numeric ( - [[0.1] * 100 + [0.9] * 101, [0.2] * 100 + [0.8] * 101, [0.5] * 201], - 0.5, + [ + generate_cdf( + [ + Percentile(value=30, probability_below=0.1), + Percentile(value=60, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=True, + open_upper_bound=False, + ), + generate_cdf( + [ + Percentile(value=20, probability_below=0.4), + Percentile(value=80, probability_below=0.6), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=True, + open_upper_bound=True, + ), + generate_cdf( + [ + Percentile(value=10, probability_below=0.1), + Percentile(value=70, probability_below=0.3), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + ], + 50, None, - 0.0, - 1.0, + -1, + 96, ), ], ) @@ -179,7 +611,6 @@ def test_peer_score_average_zero( options, range_min, range_max, - 1.0, ) for idx, forecast in enumerate(forecasts) ] @@ -202,12 +633,16 @@ def test_peer_score_average_zero( ), # Numeric ( - [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], - 0.5, + [ + generate_uniform_cdf(), + generate_perfect_cdf(100), + generate_perfect_cdf(101), + ], + 50, None, - 0.0, - 1.0, - 3.0, + 0, + 100, + 0.8, ), ], ) @@ -229,169 +664,10 @@ def test_peer_score_weighted( ) assert score_weighted == pytest.approx(score_unweighted * weight) + # TODO: Test the below # Best score for MC and binary is 996 # Worst score for MC and binary is -996 # Best score for numeric is 408 # Worst score for numeric is -408 # @Check: Can we even validate this (won't we need infinite other forecasters to get max score?) - -################################### BASELINE SCORES ################################### - - -@pytest.mark.parametrize( - "forecast,resolution,options,range_min,range_max,question_weight,expected", - [ - # Binary: uniform forecast, should be 0 - ([0.5], True, None, None, None, 1.0, 0.0), - ([0.5], False, None, None, None, 1.0, 0.0), - ([0.5, 0.5], False, None, None, None, 1.0, 0.0), - # Multiple Choice: uniform forecast, should be 0 - ([1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None, 1.0, 0.0), - ([0.25, 0.25, 0.25, 0.25], "B", ["A", "B", "C", "D"], None, None, 1.0, 0.0), - # Numeric: uniform CDF, should be 0 - (generate_uniform_cdf(201), 0.5, None, 0.0, 1.0, 1.0, 0.0), - ], -) -def test_baseline_score_is_0_with_uniform_prediction( - forecast: list[float], - resolution: bool | str | None, - options: list[str] | None, - range_min: float | None, - range_max: float | None, - question_weight: float, - expected: float, -): - score = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, question_weight - ) - assert abs(score - expected) == pytest.approx(0) - - -def test_binary_baseline_score_when_perfect_forecast(): - score = calculate_spot_baseline_score( - forecast=[0.99999999], - resolution=True, - ) - assert score == pytest.approx(100) - - -def test_binary_baseline_if_completly_incorrect_forecast(): - score = calculate_spot_baseline_score( - forecast=[0.0000001], - resolution=True, - ) - assert score == pytest.approx(-897) - - -def test_numeric_baseline_when_perfect_forecast(): - correct_index = 30 - length_of_cdf = 201 - index_to_answer_ratio = 3 - correct_answer = correct_index * index_to_answer_ratio - range_max = length_of_cdf * index_to_answer_ratio - - score = calculate_spot_baseline_score( - forecast=generate_perfect_cdf(correct_index), - resolution=correct_answer, - range_min=0, - range_max=range_max, - ) - assert score == pytest.approx(183) - - -def test_numeric_baseline_if_completly_incorrect_forecast(): - correct_index = 30 - length_of_cdf = 201 - index_to_answer_ratio = 3 - correct_answer = correct_index * index_to_answer_ratio - range_max = length_of_cdf * index_to_answer_ratio - - score = calculate_spot_baseline_score( - forecast=generate_perfect_cdf(correct_index), - resolution=correct_answer, - range_min=0, - range_max=range_max, - ) - assert score == pytest.approx(-230) - - -def test_multiple_choice_perfect_forecast(): - forecast_for_answer_a = 0.999999999 - num_other_forecasts = 7 - other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts - score = calculate_spot_baseline_score( - forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, - resolution="A", - options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], - ) - assert score == pytest.approx(100) - - -def test_multiple_choice_if_completly_incorrect_forecast(): - forecast_for_answer_c = 0.999999999 - other_forecasts = (1 - forecast_for_answer_c) / 2 - score = calculate_spot_baseline_score( - forecast=[other_forecasts, other_forecasts, forecast_for_answer_c], - resolution="C", - options=["A", "B", "C"], - ) - assert score == pytest.approx(-232) - - -@pytest.mark.parametrize( - "forecast_closer,forecast_further,resolution,options,range_min,range_max", - [ - # Binary: closer to True - ([0.8], [0.2], True, None, None, None), - # Binary: closer to False - ([0.2], [0.8], False, None, None, None), - # Multiple Choice: closer to "A" - ([0.7, 0.2, 0.1], [0.1, 0.2, 0.7], "A", ["A", "B", "C"], None, None), - # Numeric: CDF with more mass near 0.5 vs near 0.0 - ([0.1] * 52 + [0.9] * 149, [0.9] * 52 + [0.1] * 149, 0.5, None, 0.0, 1.0), - ], -) -def test_baseline_score_better_when_closer( - forecast_closer: list[float], - forecast_further: list[float], - resolution: bool | str | None, - options: list[str] | None, - range_min: float | None, - range_max: float | None, -): - score_closer = calculate_spot_baseline_score( - forecast_closer, resolution, options, range_min, range_max, 1.0 - ) - score_further = calculate_spot_baseline_score( - forecast_further, resolution, options, range_min, range_max, 1.0 - ) - assert score_closer > score_further - - -@pytest.mark.parametrize( - "forecast,resolution,options,range_min,range_max,question_weight", - [ - # Binary - ([0.8], True, None, None, None, 2.0), - # Multiple Choice - ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), - # Numeric - ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), - ], -) -def test_baseline_score_weighted( - forecast: list[float], - resolution: bool | str | None, - options: list[str] | None, - range_min: float | None, - range_max: float | None, - question_weight: float, -): - score_unweighted = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, 1.0 - ) - score_weighted = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, question_weight - ) - assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 From 12e767a5559331a68df75d0b04b829ac3379106a Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 11:29:21 -0600 Subject: [PATCH 12/26] Got MC scoring tests passing --- refactored_notebook/scoring.py | 147 ++++++++++++++++++++------------- tests/test_scoring.py | 95 +++++++++------------ 2 files changed, 129 insertions(+), 113 deletions(-) diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index dbe06af..ba126f9 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -14,24 +14,19 @@ def calculate_spot_peer_score( range_max: float | None = None, question_weight: float = 1.0, ) -> float: - forecast_for_resolution, _ = _determine_probability_for_resolution_and_baseline( + forecast_for_resolution = _determine_probability_for_resolution( forecast, resolution, options, range_min, range_max ) - other_user_forecasts_and_baseline_prob = [ - _determine_probability_for_resolution_and_baseline( + other_user_forecasts = [ + _determine_probability_for_resolution( forecast, resolution, options, range_min, range_max ) for forecast in forecast_for_other_users ] - other_user_forecasts = [ - forecast for forecast, _ in other_user_forecasts_and_baseline_prob - ] geometric_mean = gmean(other_user_forecasts) peer_score = np.log(forecast_for_resolution / geometric_mean) - if isinstance( - resolution, float - ): # @Check: This doesn't account for resolution being 'above_upper_bound' or 'below_lower_bound' + if isinstance(resolution, float): # @Check: shouldn't other q types get a divsor? peer_score /= 2 return peer_score * question_weight @@ -83,48 +78,76 @@ def calculate_spot_baseline_score( Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ - prob_for_resolution, baseline_prob = ( - _determine_probability_for_resolution_and_baseline( - forecast, resolution, options, range_min, range_max - ) + prob_for_resolution = _determine_probability_for_resolution( + forecast, resolution, options, range_min, range_max ) - + baseline_prob = _determine_baseline(resolution, options) + divisor = _determine_divisor_for_baseline_score(resolution, options) if prob_for_resolution <= 0 or baseline_prob <= 0: raise ValueError( "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" ) - baseline_score = ( - np.log2(prob_for_resolution / baseline_prob) * 100 - ) # @Check: check correctness (also shouldn't this be natural log?) + # if resolution_bucket in [0, len(pmf) - 1]: + # baseline = 0.05 + # else: + # open_bound_count = bool(question.open_upper_bound) + bool( + # question.open_lower_bound + # ) + # baseline = (1 - 0.05 * open_bounds_count) / (len(pmf) - 2) + # forecast_score = 100 * np.log(pmf[resolution_bucket] / baseline) / 2 - if isinstance(resolution, float): - baseline_score /= 2 # Numeric scores are halved + baseline_score = np.log(prob_for_resolution / baseline_prob) / divisor * 100 weighted_score = baseline_score * question_weight return weighted_score -def _determine_probability_for_resolution_and_baseline( +def _determine_baseline( + resolution: ResolutionType, options: list[str] | None = None +) -> float: + is_binary = isinstance(resolution, bool) + is_multiple_choice = isinstance(resolution, str) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) + + if is_binary: + baseline_prob = 0.5 + elif is_multiple_choice: + if options is None: + raise ValueError("Options are required for multiple choice questions") + baseline_prob = 1 / len(options) + elif is_numeric: + baseline_prob = ( + 1 / 202 + ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + # @Check: Should this be either 1, 0.9, or 0.95 based on whether open or closed bounds + else: + raise ValueError("Unknown question type") + assert ( + 0 <= baseline_prob <= 1 + ), f"Baseline probability is {baseline_prob} which is not between 0 and 1" + return baseline_prob + + +def _determine_probability_for_resolution( forecast: ForecastType, resolution: ResolutionType, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, -) -> tuple[float, float]: +) -> float: """ Returns a 0 to 1 probability for the resolution Also returns the baseline probability used in baseline scoring """ if resolution == "above_upper_bound" or resolution == "below_lower_bound": - raise ValueError("'above_upper_bound' or 'below_lower_bound' format not supported") + raise ValueError( + "'above_upper_bound' or 'below_lower_bound' format not supported" + ) - is_numeric = ( - isinstance(resolution, float) - or isinstance(resolution, int) - ) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) @@ -140,13 +163,11 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError("Forecast contains probabilities outside of 0 to 1 range") if is_binary: - prob_for_resolution, baseline_prob = _binary_resolution_and_baseline_prob( - forecast, resolution - ) + prob_for_resolution = _binary_resolution_prob(forecast, resolution) elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") - prob_for_resolution, baseline_prob = _multiple_choice_resolution_and_baseline_prob( + prob_for_resolution = _multiple_choice_resolution_prob( forecast, resolution, options ) elif is_numeric: @@ -154,38 +175,35 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError( "Range min and range max are required for numeric questions" ) - prob_for_resolution, baseline_prob = _numeric_resolution_and_baseline_prob( + prob_for_resolution = _numeric_resolution_prob( forecast, resolution, range_min, range_max ) else: raise ValueError("Unknown question type") - assert 0 <= prob_for_resolution <= 1, f"Probability for resolution is {prob_for_resolution} which is not between 0 and 1" - assert 0 <= baseline_prob <= 1, f"Baseline probability is {baseline_prob} which is not between 0 and 1" - return prob_for_resolution, baseline_prob + assert ( + 0 <= prob_for_resolution <= 1 + ), f"Probability for resolution is {prob_for_resolution} which is not between 0 and 1" + return prob_for_resolution -def _binary_resolution_and_baseline_prob(forecast: list[float], resolution: bool): +def _binary_resolution_prob(forecast: list[float], resolution: bool) -> float: if len(forecast) != 1 and len(forecast) != 2: raise ValueError( "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" ) forecast_val = float(forecast[0]) - baseline_prob = 0.5 if resolution: prob_for_resolution = forecast_val else: prob_for_resolution = 1 - forecast_val - return prob_for_resolution, baseline_prob + return prob_for_resolution -def _multiple_choice_resolution_and_baseline_prob( +def _multiple_choice_resolution_prob( forecast: list[float], resolution: str, options: list[str] -): - if options is None: - raise ValueError("Options are required for multiple choice questions") - +) -> float: if len(forecast) != len(options): raise ValueError("Forecast and options have different lengths") @@ -193,13 +211,12 @@ def _multiple_choice_resolution_and_baseline_prob( options = [str(opt) for opt in options] resolution_idx = options.index(str(resolution)) prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) - return prob_for_resolution, baseline_prob + return prob_for_resolution -def _numeric_resolution_and_baseline_prob( +def _numeric_resolution_prob( forecast: list[float], resolution: float | str, range_min: float, range_max: float -): +) -> float: if len(forecast) != 201: raise ValueError("CDF should have 201 bins") @@ -213,19 +230,18 @@ def _numeric_resolution_and_baseline_prob( assert len(cdf) == 201, f"There should be 201 bins, but there are {len(cdf)}" lower_bound_prob = cdf[0] upper_bound_prob = 1 - cdf[-1] - pmf = [lower_bound_prob] + [ - cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) - ] + [upper_bound_prob] # @Check: is this a correct conversion? + pmf = ( + [lower_bound_prob] + + [cdf[i] - cdf[i - 1] for i in range(1, len(cdf))] + + [upper_bound_prob] + ) # @Check: is this a correct conversion? # pmf = np.diff(np.concatenate([[0], cdf])) assert len(pmf) == 202, f"There should be 202 bins, but there are {len(pmf)}" - resolution = float(resolution) # bin_edges = np.linspace(range_min, range_max, 200) # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") - cdf_location = nominal_location_to_cdf_location( - resolution, range_min, range_max - ) + cdf_location = nominal_location_to_cdf_location(resolution, range_min, range_max) resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) if resolution_bin_idx >= len(pmf): @@ -233,8 +249,23 @@ def _numeric_resolution_and_baseline_prob( prob_for_resolution = pmf[resolution_bin_idx] - baseline_prob = 1 / len( - pmf - ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins - # @Check: Should this be either 1, 0.9, or 0.95? - return prob_for_resolution, baseline_prob + return prob_for_resolution + + +def _determine_divisor_for_baseline_score( + resolution: ResolutionType, options: list[str] | None = None +) -> float: + is_binary = isinstance(resolution, bool) + is_multiple_choice = isinstance(resolution, str) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) + + if is_binary: + return np.log(2) + elif is_multiple_choice: + if options is None: + raise ValueError("Options are required for multiple choice questions") + return np.log(len(options)) + elif is_numeric: + return 2 + else: + raise ValueError("Unknown question type") diff --git a/tests/test_scoring.py b/tests/test_scoring.py index cec5050..57fc7f1 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -1,13 +1,11 @@ +from dataclasses import dataclass + import numpy as np import pytest from refactored_notebook.data_models import ForecastType -from refactored_notebook.scoring import ( - calculate_spot_baseline_score, - calculate_spot_peer_score, -) - -from dataclasses import dataclass +from refactored_notebook.scoring import (calculate_spot_baseline_score, + calculate_spot_peer_score) # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -25,18 +23,13 @@ def generate_uniform_cdf(num_points: int = 201) -> list[float]: return [(i + 1) / num_points for i in range(num_points)] - -def generate_perfect_cdf(correct_index: int) -> list[float]: - assert correct_index >= 0 and correct_index <= 201 - length_of_cdf = 201 - perfect_forecast = 0.99999 +def generate_cdf_with_forecast_at_index(index: int, forecast: float) -> list[float]: cdf = [] - for i in range(length_of_cdf): - if i < correct_index: - cdf.append(1 - perfect_forecast) + for i in range(201): + if i < index: + cdf.append(0.0) else: - cdf.append(perfect_forecast) - + cdf.append(forecast) return cdf @@ -163,17 +156,17 @@ def linear_interpolation( assert len(percentiles) == 201 # Validate minimum spacing between consecutive values - for i in range(len(percentiles) - 1): - assert ( - abs(percentiles[i + 1].probability_below - percentiles[i].probability_below) - >= 5e-05 - ), ( - f"Percentiles at indices {i} and {i+1} are too close: " - f"{percentiles[i].probability_below} and {percentiles[i+1].probability_below} " - f"at values {percentiles[i].value} and {percentiles[i+1].value}. " - "It is possible that your prediction is mostly or completely out of the upper/lower bound range " - "Thus making this cdf mostly meaningless." - ) + # for i in range(len(percentiles) - 1): + # assert ( + # abs(percentiles[i + 1].probability_below - percentiles[i].probability_below) + # >= 5e-05 + # ), ( + # f"Percentiles at indices {i} and {i+1} are too close: " + # f"{percentiles[i].probability_below} and {percentiles[i+1].probability_below} " + # f"at values {percentiles[i].value} and {percentiles[i+1].value}. " + # "It is possible that your prediction is mostly or completely out of the upper/lower bound range " + # "Thus making this cdf mostly meaningless." + # ) return [percentile.probability_below for percentile in percentiles] @@ -210,13 +203,6 @@ def test_baseline_score_is_0_with_uniform_prediction( assert abs(score - expected) == pytest.approx(0) -def test_binary_baseline_score_when_perfect_forecast(): - score = calculate_spot_baseline_score( - forecast=[0.99999999], - resolution=True, - ) - assert score == pytest.approx(100) - @pytest.mark.parametrize( "forecast,resolution,expected", [ @@ -237,14 +223,16 @@ def test_binary_baseline_examples(forecast: list[float], resolution: bool, expec def test_numeric_baseline_when_perfect_forecast(): - correct_index = 30 + correct_index = 31 length_of_cdf = 201 index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.59) + # As of May 3, 2025, 0.59 is max difference between 2 points on a cdf score = calculate_spot_baseline_score( - forecast=generate_perfect_cdf(correct_index), + forecast=forecast, resolution=correct_answer, range_min=0, range_max=range_max, @@ -253,14 +241,15 @@ def test_numeric_baseline_when_perfect_forecast(): def test_numeric_baseline_if_completly_incorrect_forecast(): - correct_index = 30 + correct_index = 31 length_of_cdf = 201 index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.001) score = calculate_spot_baseline_score( - forecast=[0.0] * 200 + [1.0], # all probability assigned to upper bound + forecast=forecast, resolution=correct_answer, range_min=0, range_max=range_max, @@ -268,27 +257,23 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): assert score == pytest.approx(-230) -def test_multiple_choice_perfect_forecast(): - forecast_for_answer_a = 0.999 - num_other_forecasts = 7 +@pytest.mark.parametrize( + "forecast_for_answer_a,num_total_forecasts,expected", + [ + (0.999, 8, 99.95), + (0.001, 8, -232.19), + ], +) +def test_multiple_choice_examples(forecast_for_answer_a: float, num_total_forecasts: int, expected: float): + num_other_forecasts = num_total_forecasts - 1 other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts score = calculate_spot_baseline_score( forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, resolution="A", options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], ) - assert score == pytest.approx(99.87) - + assert score == pytest.approx(expected, abs=1e-2) -def test_multiple_choice_if_completly_incorrect_forecast(): - forecast_for_answer_a = 0.001 - other_forecasts = (1 - forecast_for_answer_a) / 2 - score = calculate_spot_baseline_score( - forecast=[forecast_for_answer_a, other_forecasts, other_forecasts], - resolution="A", - options=["A", "B", "C"], - ) - assert score == pytest.approx(-232) @pytest.mark.parametrize( @@ -513,7 +498,7 @@ def test_better_forecast_means_better_peer_score( [ ("binary", [0.5], True, None, None, None), ("mc", [0.25, 0.25, 0.25, 0.25], "A", ["A", "B", "C", "D"], None, None), - ("numeric", generate_perfect_cdf(100), 100, None, 0, 100), + ("numeric", generate_cdf_with_forecast_at_index(100, 0.999), 100, None, 0, 100), ("numeric", generate_uniform_cdf(), 50, None, 0, 100), ], ) @@ -635,8 +620,8 @@ def test_peer_score_average_zero( ( [ generate_uniform_cdf(), - generate_perfect_cdf(100), - generate_perfect_cdf(101), + generate_cdf_with_forecast_at_index(100, 0.999), + generate_cdf_with_forecast_at_index(101, 0.999), ], 50, None, From 02d3635bc362748d03a194f17f03b218604c4c8e Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 6 May 2025 12:51:21 -0600 Subject: [PATCH 13/26] Got all scoring tests passing except numeric baseline max/min --- AI_BENCHMARKING_ANALYSIS.ipynb | 9952 +++----------------------------- functions.py | 80 +- refactored_notebook/scoring.py | 61 +- tests/test_scoring.py | 94 +- 4 files changed, 856 insertions(+), 9331 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index fe42ead..f98ede4 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -54,7 +54,16 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1495376/643149966.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" + ] + } + ], "source": [ "# @title Create df_bot_resolved_questions, df_pro_resolved_questions, df_pro_bot_resolved_questions, df_bot_question_weights\n", "\n", @@ -551,6 +560,8 @@ " options\n", " range_min\n", " range_max\n", + " open_lower_bound\n", + " open_upper_bound\n", " post_id\n", " forecast\n", " is_median\n", @@ -572,6 +583,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.568,0.366,0.041,0.024]\n", " False\n", @@ -591,6 +604,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.62,0.35,0.019,0.01]\n", " True\n", @@ -610,6 +625,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.005,0.7,0.25,0.04,0.005]\n", " False\n", @@ -629,6 +646,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", @@ -648,6 +667,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", @@ -678,19 +699,26 @@ "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", - " type options range_min range_max post_id \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "\n", - " forecast is_median \n", - "0 [0.001,0.568,0.366,0.041,0.024] False \n", - "1 [0.001,0.62,0.35,0.019,0.01] True \n", - "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "5 [0.001,0.49,0.365,0.1,0.044] False \n", - "6 [0.001,0.56,0.36,0.059,0.02] False " + " type options range_min range_max \\\n", + "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "\n", + " open_lower_bound open_upper_bound post_id forecast \\\n", + "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", + "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", + "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", + "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", + "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "\n", + " is_median \n", + "0 False \n", + "1 True \n", + "2 False \n", + "5 False \n", + "6 False " ] }, "execution_count": 16, @@ -793,15 +821,6 @@ " \n", " \n", " \n", - " 15\n", - " bot_median\n", - " 9.993738\n", - " 3777.832847\n", - " 409\n", - " 7.260052\n", - " 1.390626\n", - " \n", - " \n", " 12\n", " metac-o1\n", " 9.674740\n", @@ -811,6 +830,15 @@ " 1.738353\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 9.550728\n", + " 3610.366154\n", + " 409\n", + " 6.843423\n", + " 1.377206\n", + " \n", + " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -843,15 +871,15 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "15 bot_median 9.993738 3777.832847 409 7.260052 \n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "15 bot_median 9.550728 3610.366154 409 6.843423 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", - "15 1.390626 \n", "12 1.738353 \n", + "15 1.377206 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1502,7 +1530,7 @@ " \n", " 1\n", " bot_median\n", - " 9389.288325\n", + " 9303.299412\n", " \n", " \n", " 2\n", @@ -1531,7 +1559,7 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 bot_median 9389.288325\n", + "1 bot_median 9303.299412\n", "2 metac-o1 8861.959039\n", "3 metac-o1-preview 8849.559824\n", "4 acm_bot 7605.922314\n", @@ -1697,13 +1725,13 @@ " \n", " \n", " 1\n", - " bot_median\n", - " 4077.448023\n", + " metac-o1\n", + " 3864.168122\n", " \n", " \n", " 2\n", - " metac-o1\n", - " 3864.168122\n", + " bot_median\n", + " 3821.107768\n", " \n", " \n", " 3\n", @@ -1937,8 +1965,8 @@ "text/plain": [ " bot Peer Score\n", "Rank \n", - "1 bot_median 4077.448023\n", - "2 metac-o1 3864.168122\n", + "1 metac-o1 3864.168122\n", + "2 bot_median 3821.107768\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2114,6 +2142,8 @@ " options\n", " range_min\n", " range_max\n", + " open_lower_bound\n", + " open_upper_bound\n", " post_id\n", " forecast\n", " is_median\n", @@ -2135,6 +2165,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.568,0.366,0.041,0.024]\n", " False\n", @@ -2154,6 +2186,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.62,0.35,0.019,0.01]\n", " True\n", @@ -2173,6 +2207,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.005,0.7,0.25,0.04,0.005]\n", " False\n", @@ -2192,6 +2228,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", @@ -2211,6 +2249,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", @@ -2241,19 +2281,26 @@ "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", - " type options range_min range_max post_id \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "\n", - " forecast is_median \n", - "0 [0.001,0.568,0.366,0.041,0.024] False \n", - "1 [0.001,0.62,0.35,0.019,0.01] True \n", - "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "5 [0.001,0.49,0.365,0.1,0.044] False \n", - "6 [0.001,0.56,0.36,0.059,0.02] False " + " type options range_min range_max \\\n", + "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "\n", + " open_lower_bound open_upper_bound post_id forecast \\\n", + "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", + "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", + "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", + "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", + "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "\n", + " is_median \n", + "0 False \n", + "1 True \n", + "2 False \n", + "5 False \n", + "6 False " ] }, "execution_count": 28, @@ -2331,9 +2378,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.35000000000000003,0.30000000000000004,0.250...\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.02,0.7,0.2,0.06,0.02]\n", + " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2355,8 +2402,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", @@ -2379,9 +2426,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.15\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2403,9 +2450,9 @@ " NaN\n", " [0.16,0.47,0.37]\n", " ...\n", - " [0.3,0.55,0.15]\n", + " [0.29,0.56,0.14999999999999997]\n", " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2429,7 +2476,7 @@ " ...\n", " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", @@ -2466,25 +2513,25 @@ "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", "\n", " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.45,0.3,0.15,0.05,0.05] \n", - "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", - "2 NaN ... 0.1 \n", - "3 [0.16,0.47,0.37] ... [0.3,0.55,0.15] \n", + "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", + "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "2 NaN ... 0.15 \n", + "3 [0.16,0.47,0.37] ... [0.29,0.56,0.14999999999999997] \n", "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", + "0 [0.02,0.7,0.2,0.06,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", + "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", - "3 [0.1,0.6,0.3] NaN \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", + "2 0.15 NaN \n", + "3 [0.15,0.6,0.25] NaN \n", + "4 [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,... NaN \n", "\n", " mmBot \\\n", "0 [0.009900990099009901,0.39603960396039606,0.44... \n", @@ -2595,8 +2642,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.65\n", - " 0.15\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2619,8 +2666,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.85\n", - " 0.9\n", + " 0.8\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2643,7 +2690,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2667,9 +2714,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.07\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2693,17 +2740,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.65 \n", - "96 None 0.97 0.85 NaN NaN ... 0.85 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", - "98 None 0.03 0.3 NaN NaN ... 0.1 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.85 \n", + "98 None 0.03 0.3 NaN NaN ... 0.07 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.15 NaN NaN 0.15 NaN NaN \n", - "96 0.9 NaN NaN 0.9 NaN NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.05 0.1 NaN 0.15 0.05 NaN \n", + "98 0.1 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -2911,9 +2958,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.02,0.7,0.2,0.06,0.02]\n", + " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2935,9 +2982,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 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0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.1 \n", - "3 [0.1,0.6,0.3] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...] \n", + "2 0.15 \n", + "3 [0.29,0.56,0.14999999999999997] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.02,0.7,0.2,0.06,0.02] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.1 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.15 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3203,8 +3250,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.65\n", - " 0.15\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3227,8 +3274,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.85\n", - " 0.9\n", + " 0.8\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3251,7 +3298,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3275,9 +3322,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.07\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3301,17 +3348,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.65 \n", - "96 None 0.97 0.85 NaN NaN ... 0.85 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", - "98 None 0.03 0.3 NaN NaN ... 0.1 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.85 \n", + "98 None 0.03 0.3 NaN NaN ... 0.07 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.15 NaN NaN 0.15 NaN NaN \n", - "96 0.9 NaN NaN 0.9 NaN NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.05 0.1 NaN 0.15 0.05 NaN \n", + "98 0.1 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -3375,20 +3422,32 @@ "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'calculate_peer_score' is not defined", + "ename": "KeyError", + "evalue": "'Range_min'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Range_min'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1275\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1276\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1279\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1280\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m 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ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_peer_score\u001b[49m(row, bot, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'calculate_peer_score' is not defined" + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1276\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1276\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1279\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1280\u001b[0m )\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1253\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1251\u001b[0m resolution \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1252\u001b[0m options \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m row \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m-> 1253\u001b[0m range_min \u001b[38;5;241m=\u001b[39m \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRange_min\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 1254\u001b[0m range_max \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRange_max\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1255\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestion_weight\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'Range_min'" ] } ], @@ -3399,858 +3458,9 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
0312683126201.0multiple_choice[0, 1, 2-3, 4-6, >6][0.001,0.62,0.35,0.019,0.01]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
331280312745-91.0multiple_choice[0-4, 5-9, >9][0.16,0.44,0.4]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
63129231286Jeff Bezos1.0multiple_choice[Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else][0.2,0.025,0.225,0.08,0.445,0.025]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
9313213137001.0multiple_choice[0, 1, 2, Greater than 2][0.336,0.364,0.2,0.1]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
133136831366≥0% and <5%1.0multiple_choice[Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%][0.05,0.45,0.45,0.05]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
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5 rows × 53 columns

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pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
813516935119Not in top 501.0multiple_choice[0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50][0.02,0.01,0.015,0.015,0.05,0.89]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
8235170351213 or more1.0multiple_choice[0, 1, 2, 3 or more][0.01,0.18,0.54,0.27]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
833517135123≥7.5 and ≤8.51.0multiple_choice[<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5][0.02,0.3,0.3,0.3,0.08]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
913537735334Jimmy Patronis1.0multiple_choice[Jimmy Patronis, Gay Valimont, Someone else][0.997,0.001,0.002]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
92353783533631-491.0multiple_choice[0-24, 25-30, 31-49, 50-70, >70][0.001,0.359,0.55,0.08,0.01]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
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botPeer Score
Rank
1metac-o13864.168122
2metac-o1-preview3162.155445
3bot_median3060.137114
4manticAI2142.538438
5metac-Gemini-Exp-12062072.216227
6acm_bot1876.466009
7twsummerbot1763.532046
8metac-perplexity1697.555196
9GreeneiBot21603.998618
10cookics_bot_TEST1140.390796
11metac-claude-3-5-sonnet-latest1134.209821
12SynapseSeer1066.533051
13CumulativeBot1030.716475
14pgodzinai926.081448
15jkraybill_bot627.932509
16metac-deepseek-r1614.572462
17question_weight378.020000
18metac-exa265.384263
19MWG215.551323
20annabot21.125670
21andrewsiah-4.170684
22cobyj-bot-15.593332
23X_bot-16.052813
24pianobot-20.745921
25CatrachoCaster-214.389722
26KevinTestBot-244.046973
27jonahsingerbot-318.088290
28krm-bot-387.131345
29ProfessorSP-406.072162
30mmBot-453.312468
31metac-grok-2-1212-492.938695
32bean_bot-494.373003
334Shadower-586.017986
34metac-claude-3-5-sonnet-20240620-647.579684
35swingswish-763.021897
36RPM_bot-905.938514
37metac-Llama-3.1-1029.014161
38InstitutPelFutur-1087.748963
39wunderplumb-1189.786803
40VeritasAI-1521.091541
41NextWorldLab-1565.096041
42Bot_Pepa-1589.575284
43laylaps-1665.296188
44minefrac1-1850.747385
45Grizeu_Bot-1898.666894
46metac-gpt-4o-2618.918368
47ajf-bot-3239.712801
\n", - "
" - ], - "text/plain": [ - " bot Peer Score\n", - "Rank \n", - "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 3060.137114\n", - "4 manticAI 2142.538438\n", - "5 metac-Gemini-Exp-1206 2072.216227\n", - "6 acm_bot 1876.466009\n", - "7 twsummerbot 1763.532046\n", - "8 metac-perplexity 1697.555196\n", - "9 GreeneiBot2 1603.998618\n", - "10 cookics_bot_TEST 1140.390796\n", - "11 metac-claude-3-5-sonnet-latest 1134.209821\n", - "12 SynapseSeer 1066.533051\n", - "13 CumulativeBot 1030.716475\n", - "14 pgodzinai 926.081448\n", - "15 jkraybill_bot 627.932509\n", - "16 metac-deepseek-r1 614.572462\n", - "17 question_weight 378.020000\n", - "18 metac-exa 265.384263\n", - "19 MWG 215.551323\n", - "20 annabot 21.125670\n", - "21 andrewsiah -4.170684\n", - "22 cobyj-bot -15.593332\n", - "23 X_bot -16.052813\n", - "24 pianobot -20.745921\n", - "25 CatrachoCaster -214.389722\n", - "26 KevinTestBot -244.046973\n", - "27 jonahsingerbot -318.088290\n", - "28 krm-bot -387.131345\n", - "29 ProfessorSP -406.072162\n", - "30 mmBot -453.312468\n", - "31 metac-grok-2-1212 -492.938695\n", - "32 bean_bot -494.373003\n", - "33 4Shadower -586.017986\n", - "34 metac-claude-3-5-sonnet-20240620 -647.579684\n", - "35 swingswish -763.021897\n", - "36 RPM_bot -905.938514\n", - "37 metac-Llama-3.1 -1029.014161\n", - "38 InstitutPelFutur -1087.748963\n", - "39 wunderplumb -1189.786803\n", - "40 VeritasAI -1521.091541\n", - "41 NextWorldLab -1565.096041\n", - "42 Bot_Pepa -1589.575284\n", - "43 laylaps -1665.296188\n", - "44 minefrac1 -1850.747385\n", - "45 Grizeu_Bot -1898.666894\n", - "46 metac-gpt-4o -2618.918368\n", - "47 ajf-bot -3239.712801" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean pro median forecast on questions that resolved yes: 74.0%\n", - "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 71.0%\n", - "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Average pro median forecast on questions that resolved yes/no vs top bot\n", - "\n", - "top_bot = leaderboard['bot'][1]\n", - "\n", - "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", - "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", - "\n", - "# Calculate the average pro median forecast for questions that resolved yes\n", - "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", - "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", - "\n", - "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", - "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", - "\n", - "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", - "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", - "\n", - "# Plot the data\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Set up the figure\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Create x-coordinates with jitter for each group separately\n", - "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", - "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", - "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", - "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", - "\n", - "# Plot points for \"yes\" resolution\n", - "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", - " color='blue', alpha=0.6, label='Resolved Yes')\n", - "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", - " color='blue', alpha=0.6)\n", - "\n", - "# Plot points for \"no\" resolution\n", - "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", - " color='red', alpha=0.6, label='Resolved No')\n", - "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", - " color='red', alpha=0.6)\n", - "\n", - "# Customize the plot\n", - "plt.xticks([0, 1], ['pro_median', top_bot])\n", - "plt.ylabel('Probability (%)')\n", - "plt.title('Pro Median vs Top Bot Forecasts')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set y-axis limits from 0 to 100\n", - "plt.ylim(0, 100)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_739597/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" - ] - } - ], - "source": [ - "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", - "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", - "\n", - "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", - "\n", - "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", - "\n", - "# First pivot to long format - each row will be a question-forecaster pair\n", - "df_long = df_bot_vs_pro_peer.melt(\n", - " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", - " var_name='forecaster',\n", - " value_name='score'\n", - ")\n", - "\n", - "# Drop any rows where score is NaN\n", - "df_long = df_long.dropna(subset=['score'])\n", - "\n", - "# Cast question_weight as numeric\n", - "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", - "\n", - "# Group first, then do the multiplication and sum\n", - "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", - "\n", - "# Calculate number of questions answered by each bot\n", - "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", - "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", - "\n", - "# Create a new DataFrame with the results\n", - "results = pd.DataFrame({\n", - " 'Peer_vs_Pro': total_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "weighted_results = pd.DataFrame({\n", - " 'W_Peer_vs_Pro': weighted_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", - "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [], - "source": [ - "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", - "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", - "\n", - "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", - "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": { - "cellView": "form", - "id": "tXKRpXAVHMRt" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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RankForecasterWeighted_BaselineCountWeighted Count
01pro_median4238.5616079793.10
12metac-o13010.3537889692.10
23metac-perplexity2774.0803319490.10
34bot_median2273.1150899793.10
45acm_bot2239.0586758581.25
56metac-claude-3-5-sonnet-202406202018.1102119591.50
67manticAI1865.1262607470.45
78metac-exa1826.2756819490.10
89twsummerbot1819.0641416259.40
910metac-claude-3-5-sonnet-latest1740.3151889692.10
1011metac-Llama-3.11701.1824039490.10
1112jkraybill_bot1616.0557094745.05
1213metac-Gemini-Exp-12061595.6826128177.50
1314NextWorldLab1583.0262268581.25
1415metac-o1-preview1527.6571419692.10
1516metac-deepseek-r11518.3086255552.10
1617laylaps1500.5678746865.10
1718mmBot1482.7264459793.10
1819Grizeu_Bot1399.4777185552.35
1920metac-grok-2-12121167.8671619692.10
2021VeritasAI1136.6824928278.10
2122metac-gpt-4o1045.1336789692.10
2223SynapseSeer1039.4846352826.15
2324annabot1031.9739303129.30
2425GreeneiBot2932.8835806259.35
2526MWG741.4247473028.60
2627InstitutPelFutur722.6870159591.10
2728cookics_bot_TEST714.1983722927.40
2829Bot_Pepa660.8016994745.05
2930ajf-bot484.4450303735.25
3031swingswish429.96611287.70
3132KevinTestBot331.09944498.40
3233X_bot274.53936577.00
3334CumulativeBot253.8397011110.25
3435CatrachoCaster247.2667172119.70
3536jonahsingerbot224.15439254.70
36374Shadower210.5486171514.00
3738bean_bot210.54275254.70
3839pgodzinai177.1341048177.40
3940wunderplumb112.1502452725.55
4041krm-bot65.989405109.50
4142andrewsiah0.00000000.00
4243cobyj-bot0.00000000.00
4344RPM_bot-8.69053388.00
4445ProfessorSP-217.1062982018.60
4546pianobot-217.32120454.70
4647minefrac1-299.5665065552.10
\n", - "
" - ], - "text/plain": [ - " Rank Forecaster Weighted_Baseline Count \\\n", - "0 1 pro_median 4238.561607 97 \n", - "1 2 metac-o1 3010.353788 96 \n", - "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2273.115089 97 \n", - "4 5 acm_bot 2239.058675 85 \n", - "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", - "6 7 manticAI 1865.126260 74 \n", - "7 8 metac-exa 1826.275681 94 \n", - "8 9 twsummerbot 1819.064141 62 \n", - "9 10 metac-claude-3-5-sonnet-latest 1740.315188 96 \n", - "10 11 metac-Llama-3.1 1701.182403 94 \n", - "11 12 jkraybill_bot 1616.055709 47 \n", - "12 13 metac-Gemini-Exp-1206 1595.682612 81 \n", - "13 14 NextWorldLab 1583.026226 85 \n", - "14 15 metac-o1-preview 1527.657141 96 \n", - "15 16 metac-deepseek-r1 1518.308625 55 \n", - "16 17 laylaps 1500.567874 68 \n", - "17 18 mmBot 1482.726445 97 \n", - "18 19 Grizeu_Bot 1399.477718 55 \n", - "19 20 metac-grok-2-1212 1167.867161 96 \n", - "20 21 VeritasAI 1136.682492 82 \n", - "21 22 metac-gpt-4o 1045.133678 96 \n", - "22 23 SynapseSeer 1039.484635 28 \n", - "23 24 annabot 1031.973930 31 \n", - "24 25 GreeneiBot2 932.883580 62 \n", - "25 26 MWG 741.424747 30 \n", - "26 27 InstitutPelFutur 722.687015 95 \n", - "27 28 cookics_bot_TEST 714.198372 29 \n", - "28 29 Bot_Pepa 660.801699 47 \n", - "29 30 ajf-bot 484.445030 37 \n", - "30 31 swingswish 429.966112 8 \n", - "31 32 KevinTestBot 331.099444 9 \n", - "32 33 X_bot 274.539365 7 \n", - "33 34 CumulativeBot 253.839701 11 \n", - "34 35 CatrachoCaster 247.266717 21 \n", - "35 36 jonahsingerbot 224.154392 5 \n", - "36 37 4Shadower 210.548617 15 \n", - "37 38 bean_bot 210.542752 5 \n", - "38 39 pgodzinai 177.134104 81 \n", - "39 40 wunderplumb 112.150245 27 \n", - "40 41 krm-bot 65.989405 10 \n", - "41 42 andrewsiah 0.000000 0 \n", - "42 43 cobyj-bot 0.000000 0 \n", - "43 44 RPM_bot -8.690533 8 \n", - "44 45 ProfessorSP -217.106298 20 \n", - "45 46 pianobot -217.321204 5 \n", - "46 47 minefrac1 -299.566506 55 \n", - "\n", - " Weighted Count \n", - "0 93.10 \n", - "1 92.10 \n", - "2 90.10 \n", - "3 93.10 \n", - "4 81.25 \n", - "5 91.50 \n", - "6 70.45 \n", - "7 90.10 \n", - "8 59.40 \n", - "9 92.10 \n", - "10 90.10 \n", - "11 45.05 \n", - "12 77.50 \n", - "13 81.25 \n", - "14 92.10 \n", - "15 52.10 \n", - "16 65.10 \n", - "17 93.10 \n", - "18 52.35 \n", - "19 92.10 \n", - "20 78.10 \n", - "21 92.10 \n", - "22 26.15 \n", - "23 29.30 \n", - "24 59.35 \n", - "25 28.60 \n", - "26 91.10 \n", - "27 27.40 \n", - "28 45.05 \n", - "29 35.25 \n", - "30 7.70 \n", - "31 8.40 \n", - "32 7.00 \n", - "33 10.25 \n", - "34 19.70 \n", - "35 4.70 \n", - "36 14.00 \n", - "37 4.70 \n", - "38 77.40 \n", - "39 25.55 \n", - "40 9.50 \n", - "41 0.00 \n", - "42 0.00 \n", - "43 8.00 \n", - "44 18.60 \n", - "45 4.70 \n", - "46 52.10 " - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_bot_baseline_wide,\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_baseline_weights,\n", - " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", - " on='pro_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", - "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", - "\n", - "# Create a list of columns to keep\n", - "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", - "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", - "\n", - "# Calculate the sum for each forecaster\n", - "forecaster_scores = df_filtered.sum()\n", - "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "question_counts = df_filtered.notna().sum()\n", - "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_scores.index,\n", - " 'Baseline': forecaster_scores.values,\n", - " 'Count': question_counts.values\n", - "})\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "weighted_leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_weighted_scores.index,\n", - " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", - " 'Count': question_counts.values,\n", - " 'Weighted Count': question_weighted_counts.values\n", - "})\n", - "\n", - "# Sort the leaderboard by score in descending order\n", - "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", - "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", - "\n", - "# Add a 'Rank' column\n", - "leaderboard['Rank'] = leaderboard.index + 1\n", - "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", - "\n", - "# Reorder columns to have Rank first\n", - "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", - "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", - "\n", - "#leaderboard\n", - "weighted_leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
pro_median4238.693.145.562.2291686.4493987.0591051.98527758.332.71.0000000.000000
metac-o13010.492.132.757.7568596.0182995.4310541.98555044.620.71.0000000.000000
metac-perplexity2774.190.130.867.2103837.0806644.3483081.98611444.916.70.9999820.000036
bot_median2273.193.124.458.9365876.1081563.9972531.98527736.512.30.9999350.000129
acm_bot2239.181.227.655.5540546.1631694.4713431.98898539.815.30.9999870.000025
metac-claude-3-5-sonnet-202406202018.191.522.164.2193076.7135943.2852521.98578835.48.70.9992750.001450
manticAI1865.170.426.566.3530597.9053383.3489361.99348842.210.70.9993430.001314
metac-exa1826.390.120.382.2195858.6618942.3400691.98611437.53.10.9892430.021514
twsummerbot1819.159.430.654.7477997.1035174.3111002.00016344.816.40.9999680.000063
metac-claude-3-5-sonnet-latest1740.392.118.971.5459837.4551342.5346201.98555033.74.10.9935180.012963
metac-Llama-3.11701.290.118.962.1549296.5480682.8834531.98611431.95.90.9975350.004930
jkraybill_bot1616.145.035.959.7568388.9030794.0292232.01341253.817.90.9998910.000218
metac-Gemini-Exp-12061595.777.520.667.0999817.6220462.7013031.99042635.85.40.9957490.008502
NextWorldLab1583.081.219.566.4117477.3677222.6444271.98898534.14.80.9950800.009840
metac-o1-preview1527.792.116.687.1115689.0770771.8273441.98555034.6-1.40.9645390.070922
metac-deepseek-r11518.352.129.162.7649708.6955783.3513822.00537946.611.70.9992410.001519
laylaps1500.665.123.174.4573659.2282042.4977991.99634141.54.60.9924630.015074
mmBot1482.793.115.979.9905028.2901731.9210901.98527732.4-0.50.9710930.057813
Grizeu_Bot1399.552.426.760.8869058.4152223.1767552.00555543.69.90.9987400.002521
metac-grok-2-12121167.992.112.779.3224498.2654461.5341491.98555029.1-3.70.9357710.128459
VeritasAI1136.778.114.661.1249136.9166012.1042411.99009528.30.80.9806920.038617
metac-gpt-4o1045.192.111.367.7641657.0610661.6070961.98555025.4-2.70.9442530.111494
SynapseSeer1039.526.239.862.84354812.2892353.2346072.05307665.014.50.9983020.003397
annabot1032.029.335.257.68962410.6577103.3047392.04418357.013.40.9987070.002586
GreeneiBot2932.959.415.773.8321869.5837481.6401042.00014134.9-3.50.9468180.106364
MWG741.428.625.978.73589114.7227771.7608052.04656156.1-4.20.9553250.089349
InstitutPelFutur722.791.17.9100.84063310.5651670.7508541.98582928.9-13.00.7726510.454697
cookics_bot_TEST714.227.426.163.25665212.0845622.1569372.04954150.81.30.9798560.040287
Bot_Pepa660.845.014.769.73878710.3902741.4117232.01341235.6-6.30.9174720.165057
ajf-bot484.435.213.786.56822814.5807200.9425542.02873043.3-15.80.8237450.352510
swingswish430.07.755.852.06574018.7631902.9760272.367123100.311.40.9891420.021716
KevinTestBot331.18.439.476.25685526.3111141.4980972.311496100.2-21.40.9122520.175497
X_bot274.57.039.231.69380111.9791313.2740202.44691268.59.90.9915260.016949
CumulativeBot253.810.224.878.92471924.6519411.0045802.23184879.8-30.30.8296730.340653
CatrachoCaster247.319.712.675.37158416.9814400.7391372.08877748.0-22.90.7655000.469001
jonahsingerbot224.24.747.764.22018229.6225611.6100032.784843130.2-34.80.9057990.188401
bean_bot210.54.744.876.35643935.2205991.2718792.784843142.9-53.30.8612620.277476
4Shadower210.514.015.0116.14611231.0413540.4844892.14723981.7-51.60.6819500.636100
pgodzinai177.177.42.3103.63911911.7802150.1942711.99045325.7-21.20.5767600.846479
wunderplumb112.225.64.4102.06900020.1928870.2173762.05660345.9-37.10.5851440.829712
krm-bot66.09.56.968.18212422.1212020.3140092.26470957.0-43.20.6194580.761083
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
RPM_bot-8.78.0-1.189.62555931.687420-0.0342822.36462473.8-76.00.4868050.973609
ProfessorSP-217.118.6-11.780.59407218.687303-0.6246162.09524327.5-50.80.2701180.540237
pianobot-217.34.7-46.2124.35072857.358714-0.8061302.798986114.3-206.80.2343880.468776
minefrac1-299.652.1-5.770.5819809.778562-0.5880042.00564913.9-25.40.2795600.559119
\n", - "
" - ], - "text/plain": [ - " W_score W_count W_ave W_stdev \\\n", - "pro_median 4238.6 93.1 45.5 62.229168 \n", - "metac-o1 3010.4 92.1 32.7 57.756859 \n", - "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2273.1 93.1 24.4 58.936587 \n", - "acm_bot 2239.1 81.2 27.6 55.554054 \n", - "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", - "manticAI 1865.1 70.4 26.5 66.353059 \n", - "metac-exa 1826.3 90.1 20.3 82.219585 \n", - "twsummerbot 1819.1 59.4 30.6 54.747799 \n", - "metac-claude-3-5-sonnet-latest 1740.3 92.1 18.9 71.545983 \n", - "metac-Llama-3.1 1701.2 90.1 18.9 62.154929 \n", - "jkraybill_bot 1616.1 45.0 35.9 59.756838 \n", - "metac-Gemini-Exp-1206 1595.7 77.5 20.6 67.099981 \n", - "NextWorldLab 1583.0 81.2 19.5 66.411747 \n", - "metac-o1-preview 1527.7 92.1 16.6 87.111568 \n", - "metac-deepseek-r1 1518.3 52.1 29.1 62.764970 \n", - "laylaps 1500.6 65.1 23.1 74.457365 \n", - "mmBot 1482.7 93.1 15.9 79.990502 \n", - "Grizeu_Bot 1399.5 52.4 26.7 60.886905 \n", - "metac-grok-2-1212 1167.9 92.1 12.7 79.322449 \n", - "VeritasAI 1136.7 78.1 14.6 61.124913 \n", - "metac-gpt-4o 1045.1 92.1 11.3 67.764165 \n", - "SynapseSeer 1039.5 26.2 39.8 62.843548 \n", - "annabot 1032.0 29.3 35.2 57.689624 \n", - "GreeneiBot2 932.9 59.4 15.7 73.832186 \n", - "MWG 741.4 28.6 25.9 78.735891 \n", - "InstitutPelFutur 722.7 91.1 7.9 100.840633 \n", - "cookics_bot_TEST 714.2 27.4 26.1 63.256652 \n", - "Bot_Pepa 660.8 45.0 14.7 69.738787 \n", - "ajf-bot 484.4 35.2 13.7 86.568228 \n", - "swingswish 430.0 7.7 55.8 52.065740 \n", - "KevinTestBot 331.1 8.4 39.4 76.256855 \n", - "X_bot 274.5 7.0 39.2 31.693801 \n", - "CumulativeBot 253.8 10.2 24.8 78.924719 \n", - "CatrachoCaster 247.3 19.7 12.6 75.371584 \n", - "jonahsingerbot 224.2 4.7 47.7 64.220182 \n", - "bean_bot 210.5 4.7 44.8 76.356439 \n", - "4Shadower 210.5 14.0 15.0 116.146112 \n", - "pgodzinai 177.1 77.4 2.3 103.639119 \n", - "wunderplumb 112.2 25.6 4.4 102.069000 \n", - "krm-bot 66.0 9.5 6.9 68.182124 \n", - "andrewsiah 0.0 0.0 NaN NaN \n", - "cobyj-bot 0.0 0.0 NaN NaN \n", - "RPM_bot -8.7 8.0 -1.1 89.625559 \n", - "ProfessorSP -217.1 18.6 -11.7 80.594072 \n", - "pianobot -217.3 4.7 -46.2 124.350728 \n", - "minefrac1 -299.6 52.1 -5.7 70.581980 \n", - "\n", - " std_err t_stat t_crit upper_bound \\\n", - "pro_median 6.449398 7.059105 1.985277 58.3 \n", - "metac-o1 6.018299 5.431054 1.985550 44.6 \n", - "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.108156 3.997253 1.985277 36.5 \n", - "acm_bot 6.163169 4.471343 1.988985 39.8 \n", - "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", - "manticAI 7.905338 3.348936 1.993488 42.2 \n", - "metac-exa 8.661894 2.340069 1.986114 37.5 \n", - "twsummerbot 7.103517 4.311100 2.000163 44.8 \n", - "metac-claude-3-5-sonnet-latest 7.455134 2.534620 1.985550 33.7 \n", - "metac-Llama-3.1 6.548068 2.883453 1.986114 31.9 \n", - "jkraybill_bot 8.903079 4.029223 2.013412 53.8 \n", - "metac-Gemini-Exp-1206 7.622046 2.701303 1.990426 35.8 \n", - "NextWorldLab 7.367722 2.644427 1.988985 34.1 \n", - "metac-o1-preview 9.077077 1.827344 1.985550 34.6 \n", - "metac-deepseek-r1 8.695578 3.351382 2.005379 46.6 \n", - "laylaps 9.228204 2.497799 1.996341 41.5 \n", - "mmBot 8.290173 1.921090 1.985277 32.4 \n", - "Grizeu_Bot 8.415222 3.176755 2.005555 43.6 \n", - "metac-grok-2-1212 8.265446 1.534149 1.985550 29.1 \n", - "VeritasAI 6.916601 2.104241 1.990095 28.3 \n", - "metac-gpt-4o 7.061066 1.607096 1.985550 25.4 \n", - "SynapseSeer 12.289235 3.234607 2.053076 65.0 \n", - "annabot 10.657710 3.304739 2.044183 57.0 \n", - "GreeneiBot2 9.583748 1.640104 2.000141 34.9 \n", - "MWG 14.722777 1.760805 2.046561 56.1 \n", - "InstitutPelFutur 10.565167 0.750854 1.985829 28.9 \n", - "cookics_bot_TEST 12.084562 2.156937 2.049541 50.8 \n", - "Bot_Pepa 10.390274 1.411723 2.013412 35.6 \n", - "ajf-bot 14.580720 0.942554 2.028730 43.3 \n", - "swingswish 18.763190 2.976027 2.367123 100.3 \n", - "KevinTestBot 26.311114 1.498097 2.311496 100.2 \n", - "X_bot 11.979131 3.274020 2.446912 68.5 \n", - "CumulativeBot 24.651941 1.004580 2.231848 79.8 \n", - "CatrachoCaster 16.981440 0.739137 2.088777 48.0 \n", - "jonahsingerbot 29.622561 1.610003 2.784843 130.2 \n", - "bean_bot 35.220599 1.271879 2.784843 142.9 \n", - "4Shadower 31.041354 0.484489 2.147239 81.7 \n", - "pgodzinai 11.780215 0.194271 1.990453 25.7 \n", - "wunderplumb 20.192887 0.217376 2.056603 45.9 \n", - "krm-bot 22.121202 0.314009 2.264709 57.0 \n", - "andrewsiah NaN NaN NaN NaN \n", - "cobyj-bot NaN NaN NaN NaN \n", - "RPM_bot 31.687420 -0.034282 2.364624 73.8 \n", - "ProfessorSP 18.687303 -0.624616 2.095243 27.5 \n", - "pianobot 57.358714 -0.806130 2.798986 114.3 \n", - "minefrac1 9.778562 -0.588004 2.005649 13.9 \n", - "\n", - " lower_bound cdf p_value \n", - "pro_median 32.7 1.000000 0.000000 \n", - "metac-o1 20.7 1.000000 0.000000 \n", - "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 12.3 0.999935 0.000129 \n", - "acm_bot 15.3 0.999987 0.000025 \n", - "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", - "manticAI 10.7 0.999343 0.001314 \n", - "metac-exa 3.1 0.989243 0.021514 \n", - "twsummerbot 16.4 0.999968 0.000063 \n", - "metac-claude-3-5-sonnet-latest 4.1 0.993518 0.012963 \n", - "metac-Llama-3.1 5.9 0.997535 0.004930 \n", - "jkraybill_bot 17.9 0.999891 0.000218 \n", - "metac-Gemini-Exp-1206 5.4 0.995749 0.008502 \n", - "NextWorldLab 4.8 0.995080 0.009840 \n", - "metac-o1-preview -1.4 0.964539 0.070922 \n", - "metac-deepseek-r1 11.7 0.999241 0.001519 \n", - "laylaps 4.6 0.992463 0.015074 \n", - "mmBot -0.5 0.971093 0.057813 \n", - "Grizeu_Bot 9.9 0.998740 0.002521 \n", - "metac-grok-2-1212 -3.7 0.935771 0.128459 \n", - "VeritasAI 0.8 0.980692 0.038617 \n", - "metac-gpt-4o -2.7 0.944253 0.111494 \n", - "SynapseSeer 14.5 0.998302 0.003397 \n", - "annabot 13.4 0.998707 0.002586 \n", - "GreeneiBot2 -3.5 0.946818 0.106364 \n", - "MWG -4.2 0.955325 0.089349 \n", - "InstitutPelFutur -13.0 0.772651 0.454697 \n", - "cookics_bot_TEST 1.3 0.979856 0.040287 \n", - "Bot_Pepa -6.3 0.917472 0.165057 \n", - "ajf-bot -15.8 0.823745 0.352510 \n", - "swingswish 11.4 0.989142 0.021716 \n", - "KevinTestBot -21.4 0.912252 0.175497 \n", - "X_bot 9.9 0.991526 0.016949 \n", - "CumulativeBot -30.3 0.829673 0.340653 \n", - "CatrachoCaster -22.9 0.765500 0.469001 \n", - "jonahsingerbot -34.8 0.905799 0.188401 \n", - "bean_bot -53.3 0.861262 0.277476 \n", - "4Shadower -51.6 0.681950 0.636100 \n", - "pgodzinai -21.2 0.576760 0.846479 \n", - "wunderplumb -37.1 0.585144 0.829712 \n", - "krm-bot -43.2 0.619458 0.761083 \n", - "andrewsiah NaN NaN NA \n", - "cobyj-bot NaN NaN NA \n", - "RPM_bot -76.0 0.486805 0.973609 \n", - "ProfessorSP -50.8 0.270118 0.540237 \n", - "pianobot -206.8 0.234388 0.468776 \n", - "minefrac1 -25.4 0.279560 0.559119 " - ] - }, - "execution_count": 202, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make me a list that's pro_median and all the bot forecasters\n", - "forecasters = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", - "\n", - "hey = calculate_t_test(df_pro_bot_baseline_weights, forecasters)\n", - "\n", - "hey" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "aGNedTHmU-Bm", - "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n" - ] - }, - { - "data": { - "text/html": [ - "
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W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
metac-perplexity1719.795.018.13.570999e-153.663768e-164.940951e+161.9847518.118.11.00.000000
acm_bot1680.695.017.73.570999e-153.663768e-164.828449e+161.9847517.717.71.00.000000
bot_median1610.495.017.03.570999e-153.663768e-164.626691e+161.9847517.017.01.00.000000
metac-o11577.695.016.63.570999e-153.663768e-164.532462e+161.9847516.616.61.00.000000
metac-claude-3-5-sonnet-202406201405.995.014.83.570999e-153.663768e-164.039354e+161.9847514.814.81.00.000000
manticAI1378.295.014.50.000000e+000.000000e+00inf1.9847514.514.51.00.000000
twsummerbot1355.495.014.31.785500e-151.831884e-167.788325e+161.9847514.314.31.00.000000
jkraybill_bot1354.595.014.31.785500e-151.831884e-167.783286e+161.9847514.314.31.00.000000
metac-exa1233.695.013.01.785500e-151.831884e-167.088710e+161.9847513.013.01.00.000000
GreeneiBot21163.295.012.20.000000e+000.000000e+00inf1.9847512.212.21.00.000000
NextWorldLab1050.395.011.11.785500e-151.831884e-166.035038e+161.9847511.111.11.00.000000
metac-Llama-3.1997.095.010.51.785500e-151.831884e-165.728816e+161.9847510.510.51.00.000000
Grizeu_Bot966.495.010.20.000000e+000.000000e+00inf1.9847510.210.21.00.000000
SynapseSeer964.795.010.21.785500e-151.831884e-165.543440e+161.9847510.210.21.00.000000
metac-claude-3-5-sonnet-latest949.995.010.00.000000e+000.000000e+00inf1.9847510.010.01.00.000000
mmBot924.895.09.70.000000e+000.000000e+00inf1.984759.79.71.00.000000
annabot854.495.09.01.785500e-151.831884e-164.909363e+161.984759.09.01.00.000000
VeritasAI802.095.08.41.785500e-151.831884e-164.608352e+161.984758.48.41.00.000000
metac-grok-2-1212775.195.08.20.000000e+000.000000e+00inf1.984758.28.21.00.000000
laylaps723.495.07.68.927498e-169.159420e-178.313180e+161.984757.67.61.00.000000
metac-Gemini-Exp-1206701.995.07.48.927498e-169.159420e-178.065986e+161.984757.47.41.00.000000
metac-o1-preview633.295.06.78.927498e-169.159420e-177.277309e+161.984756.76.71.00.000000
cookics_bot_TEST596.495.06.30.000000e+000.000000e+00inf1.984756.36.31.00.000000
metac-deepseek-r1545.595.05.78.927498e-169.159420e-176.268723e+161.984755.75.71.00.000000
MWG520.895.05.58.927498e-169.159420e-175.985647e+161.984755.55.51.00.000000
ajf-bot481.295.05.11.785500e-151.831884e-162.764898e+161.984755.15.11.00.000000
metac-gpt-4o451.695.04.88.927498e-169.159420e-175.190358e+161.984754.84.81.00.000000
pgodzinai336.095.03.58.927498e-169.159420e-173.861639e+161.984753.53.51.00.000000
KevinTestBot314.595.03.38.927498e-169.159420e-173.614852e+161.984753.33.31.00.000000
InstitutPelFutur256.095.02.78.927498e-169.159420e-172.941623e+161.984752.72.71.00.000000
Bot_Pepa246.895.02.60.000000e+000.000000e+00inf1.984752.62.61.00.000000
CumulativeBot241.195.02.54.463749e-164.579710e-175.542703e+161.984752.52.51.00.000000
swingswish229.195.02.44.463749e-164.579710e-175.265549e+161.984752.42.41.00.000000
wunderplumb225.495.02.44.463749e-164.579710e-175.180942e+161.984752.42.41.00.000000
jonahsingerbot212.995.02.24.463749e-164.579710e-174.894511e+161.984752.22.21.00.000000
bean_bot200.095.02.10.000000e+000.000000e+00inf1.984752.12.11.00.000000
X_bot181.495.01.90.000000e+000.000000e+00inf1.984751.91.91.00.000000
CatrachoCaster167.595.01.84.463749e-164.579710e-173.849373e+161.984751.81.81.00.000000
RPM_bot118.695.01.24.463749e-164.579710e-172.726486e+161.984751.21.21.00.000000
4Shadower61.195.00.62.231875e-162.289855e-172.810106e+161.984750.60.61.00.000000
krm-bot60.895.00.61.115937e-161.144927e-175.586129e+161.984750.60.61.00.000000
andrewsiah0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
cobyj-bot0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
pianobot-206.595.0-2.24.463749e-164.579710e-17-4.745305e+161.98475-2.2-2.20.00.000000
ProfessorSP-280.495.0-3.08.927498e-169.159420e-17-3.222942e+161.98475-3.0-3.00.00.000000
minefrac1-283.995.0-3.04.463749e-164.579710e-17-6.524424e+161.98475-3.0-3.00.00.000000
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" - ], - "text/plain": [ - " W_score W_count W_ave W_stdev \\\n", - "metac-perplexity 1719.7 95.0 18.1 3.570999e-15 \n", - "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", - "bot_median 1610.4 95.0 17.0 3.570999e-15 \n", - "metac-o1 1577.6 95.0 16.6 3.570999e-15 \n", - "metac-claude-3-5-sonnet-20240620 1405.9 95.0 14.8 3.570999e-15 \n", - "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", - "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", - "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", - "metac-exa 1233.6 95.0 13.0 1.785500e-15 \n", - "GreeneiBot2 1163.2 95.0 12.2 0.000000e+00 \n", - "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", - "metac-Llama-3.1 997.0 95.0 10.5 1.785500e-15 \n", - "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", - "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", - "metac-claude-3-5-sonnet-latest 949.9 95.0 10.0 0.000000e+00 \n", - "mmBot 924.8 95.0 9.7 0.000000e+00 \n", - "annabot 854.4 95.0 9.0 1.785500e-15 \n", - "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", - "metac-grok-2-1212 775.1 95.0 8.2 0.000000e+00 \n", - "laylaps 723.4 95.0 7.6 8.927498e-16 \n", - "metac-Gemini-Exp-1206 701.9 95.0 7.4 8.927498e-16 \n", - "metac-o1-preview 633.2 95.0 6.7 8.927498e-16 \n", - "cookics_bot_TEST 596.4 95.0 6.3 0.000000e+00 \n", - "metac-deepseek-r1 545.5 95.0 5.7 8.927498e-16 \n", - "MWG 520.8 95.0 5.5 8.927498e-16 \n", - "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", - "metac-gpt-4o 451.6 95.0 4.8 8.927498e-16 \n", - "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", - "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", - "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", - "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", - "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", - "swingswish 229.1 95.0 2.4 4.463749e-16 \n", - "wunderplumb 225.4 95.0 2.4 4.463749e-16 \n", - "jonahsingerbot 212.9 95.0 2.2 4.463749e-16 \n", - "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", - "X_bot 181.4 95.0 1.9 0.000000e+00 \n", - "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", - "RPM_bot 118.6 95.0 1.2 4.463749e-16 \n", - "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", - "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", - "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", - "cobyj-bot 0.0 95.0 0.0 0.000000e+00 \n", - "pianobot -206.5 95.0 -2.2 4.463749e-16 \n", - "ProfessorSP -280.4 95.0 -3.0 8.927498e-16 \n", - "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", - "\n", - " std_err t_stat t_crit \\\n", - "metac-perplexity 3.663768e-16 4.940951e+16 1.98475 \n", - "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", - "bot_median 3.663768e-16 4.626691e+16 1.98475 \n", - "metac-o1 3.663768e-16 4.532462e+16 1.98475 \n", - "metac-claude-3-5-sonnet-20240620 3.663768e-16 4.039354e+16 1.98475 \n", - "manticAI 0.000000e+00 inf 1.98475 \n", - "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", - "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", - "metac-exa 1.831884e-16 7.088710e+16 1.98475 \n", - "GreeneiBot2 0.000000e+00 inf 1.98475 \n", - "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", - "metac-Llama-3.1 1.831884e-16 5.728816e+16 1.98475 \n", - "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", - "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", - "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", - "mmBot 0.000000e+00 inf 1.98475 \n", - "annabot 1.831884e-16 4.909363e+16 1.98475 \n", - "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", - "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", - "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", - "metac-Gemini-Exp-1206 9.159420e-17 8.065986e+16 1.98475 \n", - "metac-o1-preview 9.159420e-17 7.277309e+16 1.98475 \n", - "cookics_bot_TEST 0.000000e+00 inf 1.98475 \n", - "metac-deepseek-r1 9.159420e-17 6.268723e+16 1.98475 \n", - "MWG 9.159420e-17 5.985647e+16 1.98475 \n", - "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", - "metac-gpt-4o 9.159420e-17 5.190358e+16 1.98475 \n", - "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", - "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", - "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", - "Bot_Pepa 0.000000e+00 inf 1.98475 \n", - "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", - "swingswish 4.579710e-17 5.265549e+16 1.98475 \n", - "wunderplumb 4.579710e-17 5.180942e+16 1.98475 \n", - "jonahsingerbot 4.579710e-17 4.894511e+16 1.98475 \n", - "bean_bot 0.000000e+00 inf 1.98475 \n", - "X_bot 0.000000e+00 inf 1.98475 \n", - "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", - "RPM_bot 4.579710e-17 2.726486e+16 1.98475 \n", - "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", - "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", - "andrewsiah 0.000000e+00 NaN 1.98475 \n", - "cobyj-bot 0.000000e+00 NaN 1.98475 \n", - "pianobot 4.579710e-17 -4.745305e+16 1.98475 \n", - "ProfessorSP 9.159420e-17 -3.222942e+16 1.98475 \n", - "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", - "\n", - " upper_bound lower_bound cdf p_value \n", - "metac-perplexity 18.1 18.1 1.0 0.000000 \n", - "acm_bot 17.7 17.7 1.0 0.000000 \n", - "bot_median 17.0 17.0 1.0 0.000000 \n", - "metac-o1 16.6 16.6 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-20240620 14.8 14.8 1.0 0.000000 \n", - "manticAI 14.5 14.5 1.0 0.000000 \n", - "twsummerbot 14.3 14.3 1.0 0.000000 \n", - "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", - "metac-exa 13.0 13.0 1.0 0.000000 \n", - "GreeneiBot2 12.2 12.2 1.0 0.000000 \n", - "NextWorldLab 11.1 11.1 1.0 0.000000 \n", - "metac-Llama-3.1 10.5 10.5 1.0 0.000000 \n", - "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", - "SynapseSeer 10.2 10.2 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-latest 10.0 10.0 1.0 0.000000 \n", - "mmBot 9.7 9.7 1.0 0.000000 \n", - "annabot 9.0 9.0 1.0 0.000000 \n", - "VeritasAI 8.4 8.4 1.0 0.000000 \n", - "metac-grok-2-1212 8.2 8.2 1.0 0.000000 \n", - "laylaps 7.6 7.6 1.0 0.000000 \n", - "metac-Gemini-Exp-1206 7.4 7.4 1.0 0.000000 \n", - "metac-o1-preview 6.7 6.7 1.0 0.000000 \n", - "cookics_bot_TEST 6.3 6.3 1.0 0.000000 \n", - "metac-deepseek-r1 5.7 5.7 1.0 0.000000 \n", - "MWG 5.5 5.5 1.0 0.000000 \n", - "ajf-bot 5.1 5.1 1.0 0.000000 \n", - "metac-gpt-4o 4.8 4.8 1.0 0.000000 \n", - "pgodzinai 3.5 3.5 1.0 0.000000 \n", - "KevinTestBot 3.3 3.3 1.0 0.000000 \n", - "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", - "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", - "CumulativeBot 2.5 2.5 1.0 0.000000 \n", - "swingswish 2.4 2.4 1.0 0.000000 \n", - "wunderplumb 2.4 2.4 1.0 0.000000 \n", - "jonahsingerbot 2.2 2.2 1.0 0.000000 \n", - "bean_bot 2.1 2.1 1.0 0.000000 \n", - "X_bot 1.9 1.9 1.0 0.000000 \n", - "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", - "RPM_bot 1.2 1.2 1.0 0.000000 \n", - "4Shadower 0.6 0.6 1.0 0.000000 \n", - "krm-bot 0.6 0.6 1.0 0.000000 \n", - "andrewsiah 0.0 0.0 NaN NA \n", - "cobyj-bot 0.0 0.0 NaN NA \n", - "pianobot -2.2 -2.2 0.0 0.000000 \n", - "ProfessorSP -3.0 -3.0 0.0 0.000000 \n", - "minefrac1 -3.0 -3.0 0.0 0.000000 " - ] - }, - "execution_count": 203, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# @title Weighted head-to-head, T test\n", - "\n", - "\"\"\"\n", - "df_W_leaderboard: A leaderboard based on df_bot_vs_pro_peer with question\n", - "weighting and the calculations for doing a weighted T test\n", - "\"\"\"\n", - "\n", - "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", - "\n", - "# Cast weights as numeric\n", - "df_bot_vs_pro_peer['question_weight'] = pd.to_numeric(df_bot_vs_pro_peer['question_weight'], errors='coerce')\n", - "\n", - "# Calculate weighted statistics for each bot\n", - "df_W_leaderboard = calculate_t_test(df_bot_vs_pro_peer, all_bots)\n", - "\n", - "df_W_leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": 204, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Write to csv\n", - "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv', index=True)" + "leaderboard" ] }, { "cell_type": "code", - "execution_count": 205, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3d_ZdL0A0qTz", - "outputId": "e30ee8fb-0faf-45ae-974e-d4af282e0252" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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RankBotW_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
01metac-o13631.1375.39.735.0711401.8102945.3442931.96598513.26.11.0000000.000000
12metac-o1-preview3121.4368.78.545.9615892.3935733.5368201.96609313.23.80.9997720.000457
23metac-Gemini-Exp-12061880.5347.15.444.8958442.4097192.2481331.96645810.20.70.9874020.025197
34SynapseSeer966.5152.06.435.6992152.8951132.1955681.97487912.10.60.9851760.029648
45manticAI2055.2315.76.555.6900633.1344982.0771541.96718712.70.30.9807010.038598
56twsummerbot1450.0241.36.045.0911402.9027092.0701531.96931311.70.30.9802470.039507
67acm_bot1738.4344.85.045.8463322.4691432.0421541.9665219.90.20.9790510.041899
78cookics_bot_TEST1143.8162.67.046.7964543.6698871.9168291.97413814.3-0.20.9714880.057024
89CumulativeBot991.4104.59.552.1803255.1044461.8585841.98213619.6-0.60.9670360.065928
910metac-claude-3-5-sonnet-latest951.3370.32.638.2630661.9883421.2919541.9660636.5-1.30.9014100.197181
1011GreeneiBot21494.7264.15.759.7283543.6750521.5398111.96859612.9-1.60.9375960.124808
1112metac-perplexity1558.4354.44.459.5883783.1652091.3891811.96637110.6-1.80.9171740.165652
1213metac-deepseek-r1516.8277.91.937.3532102.2407800.8299751.9681656.3-2.60.7963660.407268
1314pgodzinai1106.7325.43.466.6861593.6966950.9199541.96694910.7-3.90.8208600.358280
1415metac-exa599.9365.31.663.4593893.3201610.4946111.9661428.2-4.90.6894130.621173
1516MWG253.8113.42.240.6740843.8190370.5859361.9804689.8-5.30.7204540.559093
1617jkraybill_bot625.4207.43.068.5607804.7604770.6333891.97101512.4-6.40.7364100.527181
1718metac-claude-3-5-sonnet-20240620-759.5373.7-2.044.0904802.280718-0.8910111.9660142.5-6.50.1867490.373498
1819metac-grok-2-1212-550.1373.3-1.550.1642462.596293-0.5675531.9660163.6-6.60.2853400.570681
1920metac-Llama-3.1-980.9370.6-2.641.8100632.171783-1.2186111.9660621.6-6.90.1118850.223769
2021mmBot-587.4373.0-1.658.2984393.018498-0.5216711.9660174.4-7.50.3011050.602210
2122VeritasAI-1602.2330.0-4.938.7547802.133316-2.2757101.966760-0.7-9.10.0117530.023506
2223InstitutPelFutur-877.8356.0-2.564.6034773.423881-0.7201271.9663054.3-9.20.2359600.471921
2324NextWorldLab-1377.9337.6-4.151.4333882.799472-1.4581571.9666641.4-9.60.0728650.145730
2425metac-gpt-4o-2235.4373.3-6.045.4016702.349802-2.5482091.966016-1.4-10.60.0056140.011229
2526CatrachoCaster-289.481.6-3.531.9567253.538536-1.0026081.9883423.5-10.60.1595260.319052
2627laylaps-1489.1322.1-4.663.9802383.564926-1.2968551.9670502.4-11.60.0978060.195612
2728ProfessorSP-426.8128.6-3.355.1654604.863650-0.6821421.9781236.3-12.90.2481930.496385
2829krm-bot-354.7104.0-3.449.8754924.890694-0.6973341.9823276.3-13.10.2435820.487165
2930wunderplumb-986.1174.0-5.752.9658934.015334-1.4114341.9731952.3-13.60.0799560.159913
3031andrewsiah2.625.10.135.8050927.1467390.0146792.06034114.8-14.60.5057960.988409
3132annabot-190.683.8-2.359.1122286.458906-0.3522221.98640810.6-15.10.3627840.725567
3233Bot_Pepa-1490.1169.4-8.844.2657023.400530-2.5860051.973733-2.1-15.50.0052780.010555
33344Shadower-646.3115.5-5.653.8678675.012320-1.1163051.9797854.3-15.50.1333140.266629
3435minefrac1-1757.1188.2-9.344.1258493.216071-2.9021901.972106-3.0-15.70.0020750.004150
3536KevinTestBot-220.489.5-2.567.6508777.150920-0.3443101.98550511.7-16.70.3657150.731430
3637jonahsingerbot-333.464.8-5.148.0155485.964779-0.8626001.9952736.8-17.00.1957940.391588
3738bean_bot-208.867.8-3.159.9556627.281408-0.4229401.99377111.4-17.60.3368490.673697
3839Grizeu_Bot-1882.6193.2-9.756.7042374.079442-2.3885211.971774-1.7-17.80.0089420.017884
3940cobyj-bot-12.131.5-0.448.0409918.559663-0.0450462.03985017.1-17.80.4821820.964365
4041X_bot-16.17.0-2.323.9086329.036614-0.2537742.44691219.8-24.40.4040710.808142
4142ajf-bot-3208.3229.2-14.083.2955695.502524-2.5444141.969928-3.2-24.80.0058030.011607
4243pianobot-12.719.6-0.752.32348711.833775-0.0550422.09382324.1-25.40.4783470.956694
4344swingswish-777.064.8-12.073.0478929.074447-1.3214361.9952736.1-30.10.0955380.191075
4445RPM_bot-815.623.8-34.391.54540218.784720-1.8281002.0615084.4-73.10.0403390.080679
\n", - "
" - ], - "text/plain": [ - " Rank Bot W_score W_count W_ave \\\n", - "0 1 metac-o1 3631.1 375.3 9.7 \n", - "1 2 metac-o1-preview 3121.4 368.7 8.5 \n", - "2 3 metac-Gemini-Exp-1206 1880.5 347.1 5.4 \n", - "3 4 SynapseSeer 966.5 152.0 6.4 \n", - "4 5 manticAI 2055.2 315.7 6.5 \n", - "5 6 twsummerbot 1450.0 241.3 6.0 \n", - "6 7 acm_bot 1738.4 344.8 5.0 \n", - "7 8 cookics_bot_TEST 1143.8 162.6 7.0 \n", - "8 9 CumulativeBot 991.4 104.5 9.5 \n", - "9 10 metac-claude-3-5-sonnet-latest 951.3 370.3 2.6 \n", - "10 11 GreeneiBot2 1494.7 264.1 5.7 \n", - "11 12 metac-perplexity 1558.4 354.4 4.4 \n", - "12 13 metac-deepseek-r1 516.8 277.9 1.9 \n", - "13 14 pgodzinai 1106.7 325.4 3.4 \n", - "14 15 metac-exa 599.9 365.3 1.6 \n", - "15 16 MWG 253.8 113.4 2.2 \n", - "16 17 jkraybill_bot 625.4 207.4 3.0 \n", - "17 18 metac-claude-3-5-sonnet-20240620 -759.5 373.7 -2.0 \n", - "18 19 metac-grok-2-1212 -550.1 373.3 -1.5 \n", - "19 20 metac-Llama-3.1 -980.9 370.6 -2.6 \n", - "20 21 mmBot -587.4 373.0 -1.6 \n", - "21 22 VeritasAI -1602.2 330.0 -4.9 \n", - "22 23 InstitutPelFutur -877.8 356.0 -2.5 \n", - "23 24 NextWorldLab -1377.9 337.6 -4.1 \n", - "24 25 metac-gpt-4o -2235.4 373.3 -6.0 \n", - "25 26 CatrachoCaster -289.4 81.6 -3.5 \n", - "26 27 laylaps -1489.1 322.1 -4.6 \n", - "27 28 ProfessorSP -426.8 128.6 -3.3 \n", - "28 29 krm-bot -354.7 104.0 -3.4 \n", - "29 30 wunderplumb -986.1 174.0 -5.7 \n", - "30 31 andrewsiah 2.6 25.1 0.1 \n", - "31 32 annabot -190.6 83.8 -2.3 \n", - "32 33 Bot_Pepa -1490.1 169.4 -8.8 \n", - "33 34 4Shadower -646.3 115.5 -5.6 \n", - "34 35 minefrac1 -1757.1 188.2 -9.3 \n", - "35 36 KevinTestBot -220.4 89.5 -2.5 \n", - "36 37 jonahsingerbot -333.4 64.8 -5.1 \n", - "37 38 bean_bot -208.8 67.8 -3.1 \n", - "38 39 Grizeu_Bot -1882.6 193.2 -9.7 \n", - "39 40 cobyj-bot -12.1 31.5 -0.4 \n", - "40 41 X_bot -16.1 7.0 -2.3 \n", - "41 42 ajf-bot -3208.3 229.2 -14.0 \n", - "42 43 pianobot -12.7 19.6 -0.7 \n", - "43 44 swingswish -777.0 64.8 -12.0 \n", - "44 45 RPM_bot -815.6 23.8 -34.3 \n", - "\n", - " W_stdev std_err t_stat t_crit upper_bound lower_bound \\\n", - "0 35.071140 1.810294 5.344293 1.965985 13.2 6.1 \n", - "1 45.961589 2.393573 3.536820 1.966093 13.2 3.8 \n", - "2 44.895844 2.409719 2.248133 1.966458 10.2 0.7 \n", - "3 35.699215 2.895113 2.195568 1.974879 12.1 0.6 \n", - "4 55.690063 3.134498 2.077154 1.967187 12.7 0.3 \n", - "5 45.091140 2.902709 2.070153 1.969313 11.7 0.3 \n", - "6 45.846332 2.469143 2.042154 1.966521 9.9 0.2 \n", - "7 46.796454 3.669887 1.916829 1.974138 14.3 -0.2 \n", - "8 52.180325 5.104446 1.858584 1.982136 19.6 -0.6 \n", - "9 38.263066 1.988342 1.291954 1.966063 6.5 -1.3 \n", - "10 59.728354 3.675052 1.539811 1.968596 12.9 -1.6 \n", - "11 59.588378 3.165209 1.389181 1.966371 10.6 -1.8 \n", - "12 37.353210 2.240780 0.829975 1.968165 6.3 -2.6 \n", - "13 66.686159 3.696695 0.919954 1.966949 10.7 -3.9 \n", - "14 63.459389 3.320161 0.494611 1.966142 8.2 -4.9 \n", - "15 40.674084 3.819037 0.585936 1.980468 9.8 -5.3 \n", - "16 68.560780 4.760477 0.633389 1.971015 12.4 -6.4 \n", - "17 44.090480 2.280718 -0.891011 1.966014 2.5 -6.5 \n", - "18 50.164246 2.596293 -0.567553 1.966016 3.6 -6.6 \n", - "19 41.810063 2.171783 -1.218611 1.966062 1.6 -6.9 \n", - "20 58.298439 3.018498 -0.521671 1.966017 4.4 -7.5 \n", - "21 38.754780 2.133316 -2.275710 1.966760 -0.7 -9.1 \n", - "22 64.603477 3.423881 -0.720127 1.966305 4.3 -9.2 \n", - "23 51.433388 2.799472 -1.458157 1.966664 1.4 -9.6 \n", - "24 45.401670 2.349802 -2.548209 1.966016 -1.4 -10.6 \n", - "25 31.956725 3.538536 -1.002608 1.988342 3.5 -10.6 \n", - "26 63.980238 3.564926 -1.296855 1.967050 2.4 -11.6 \n", - "27 55.165460 4.863650 -0.682142 1.978123 6.3 -12.9 \n", - "28 49.875492 4.890694 -0.697334 1.982327 6.3 -13.1 \n", - "29 52.965893 4.015334 -1.411434 1.973195 2.3 -13.6 \n", - "30 35.805092 7.146739 0.014679 2.060341 14.8 -14.6 \n", - "31 59.112228 6.458906 -0.352222 1.986408 10.6 -15.1 \n", - "32 44.265702 3.400530 -2.586005 1.973733 -2.1 -15.5 \n", - "33 53.867867 5.012320 -1.116305 1.979785 4.3 -15.5 \n", - "34 44.125849 3.216071 -2.902190 1.972106 -3.0 -15.7 \n", - "35 67.650877 7.150920 -0.344310 1.985505 11.7 -16.7 \n", - "36 48.015548 5.964779 -0.862600 1.995273 6.8 -17.0 \n", - "37 59.955662 7.281408 -0.422940 1.993771 11.4 -17.6 \n", - "38 56.704237 4.079442 -2.388521 1.971774 -1.7 -17.8 \n", - "39 48.040991 8.559663 -0.045046 2.039850 17.1 -17.8 \n", - "40 23.908632 9.036614 -0.253774 2.446912 19.8 -24.4 \n", - "41 83.295569 5.502524 -2.544414 1.969928 -3.2 -24.8 \n", - "42 52.323487 11.833775 -0.055042 2.093823 24.1 -25.4 \n", - "43 73.047892 9.074447 -1.321436 1.995273 6.1 -30.1 \n", - "44 91.545402 18.784720 -1.828100 2.061508 4.4 -73.1 \n", - "\n", - " cdf p_value \n", - "0 1.000000 0.000000 \n", - "1 0.999772 0.000457 \n", - "2 0.987402 0.025197 \n", - "3 0.985176 0.029648 \n", - "4 0.980701 0.038598 \n", - "5 0.980247 0.039507 \n", - "6 0.979051 0.041899 \n", - "7 0.971488 0.057024 \n", - "8 0.967036 0.065928 \n", - "9 0.901410 0.197181 \n", - "10 0.937596 0.124808 \n", - "11 0.917174 0.165652 \n", - "12 0.796366 0.407268 \n", - "13 0.820860 0.358280 \n", - "14 0.689413 0.621173 \n", - "15 0.720454 0.559093 \n", - "16 0.736410 0.527181 \n", - "17 0.186749 0.373498 \n", - "18 0.285340 0.570681 \n", - "19 0.111885 0.223769 \n", - "20 0.301105 0.602210 \n", - "21 0.011753 0.023506 \n", - "22 0.235960 0.471921 \n", - "23 0.072865 0.145730 \n", - "24 0.005614 0.011229 \n", - "25 0.159526 0.319052 \n", - "26 0.097806 0.195612 \n", - "27 0.248193 0.496385 \n", - "28 0.243582 0.487165 \n", - "29 0.079956 0.159913 \n", - "30 0.505796 0.988409 \n", - "31 0.362784 0.725567 \n", - "32 0.005278 0.010555 \n", - "33 0.133314 0.266629 \n", - "34 0.002075 0.004150 \n", - "35 0.365715 0.731430 \n", - "36 0.195794 0.391588 \n", - "37 0.336849 0.673697 \n", - "38 0.008942 0.017884 \n", - "39 0.482182 0.964365 \n", - "40 0.404071 0.808142 \n", - "41 0.005803 0.011607 \n", - "42 0.478347 0.956694 \n", - "43 0.095538 0.191075 \n", - "44 0.040339 0.080679 " - ] - }, - "execution_count": 205, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# @title Weighted Bot Peer, T test (to compare bots against each other, use ALL QUESTIONS)\n", + "# Average pro median forecast on questions that resolved yes/no vs top bot\n", + "\n", + "top_bot = leaderboard['bot'][1]\n", + "\n", + "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", + "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", "\n", - "df_W_bot_peer_leaderboard = pd.DataFrame()\n", + "# Calculate the average pro median forecast for questions that resolved yes\n", + "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", + "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", "\n", - "df3 = pd.DataFrame()\n", + "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", + "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", "\n", - "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", + "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", + "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", "\n", - "# OMIT bot_median column for this bit\n", - "df_bot_peer_wide_b = df_bot_peer_wide.drop('bot_median', axis=1)\n", - "df_bot_peer = df_bot_peer[df_bot_peer['forecaster'] != 'bot_median']\n", + "# Plot the data\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", - "bots_for_peer = np.array(list(set(df_bot_peer['forecaster'])))\n", + "# Set up the figure\n", + "plt.figure(figsize=(10, 6))\n", "\n", - "df_W_leaderboard = calculate_t_test(df_bot_peer_wide_b, bots_for_peer)\n", + "# Create x-coordinates with jitter for each group separately\n", + "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", + "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", + "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", + "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", "\n", - "df_W_leaderboard_print = df_W_leaderboard.sort_values(by='lower_bound', ascending=False)\n", - "df_W_leaderboard_print['Rank'] = range(1, len(df_W_leaderboard_print) + 1)\n", + "# Plot points for \"yes\" resolution\n", + "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", + " color='blue', alpha=0.6, label='Resolved Yes')\n", + "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", + " color='blue', alpha=0.6)\n", "\n", - "# Make index into a column - Bot\n", - "df_W_leaderboard_print = df_W_leaderboard_print.reset_index()\n", - "df_W_leaderboard_print = df_W_leaderboard_print.rename(columns={'index': 'Bot'})\n", - "#df_W_leaderboard_print = df_W_leaderboard_print[['Rank', 'Bot', 'W_ave', 'W_count', 'lower_bound', 'upper_bound']]\n", - "# Make rank the first column; leave rest the same\n", - "cols = df_W_leaderboard_print.columns.tolist()\n", - "cols = ['Rank'] + cols[:-1]\n", - "df_W_leaderboard_print = df_W_leaderboard_print[cols]\n", + "# Plot points for \"no\" resolution\n", + "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", + " color='red', alpha=0.6, label='Resolved No')\n", + "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", + " color='red', alpha=0.6)\n", "\n", - "df_W_leaderboard_print" + "# Customize the plot\n", + "plt.xticks([0, 1], ['pro_median', top_bot])\n", + "plt.ylabel('Probability (%)')\n", + "plt.title('Pro Median vs Top Bot Forecasts')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set y-axis limits from 0 to 100\n", + "plt.ylim(0, 100)\n", + "\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Write to csv\n", - "df_W_leaderboard_print.to_csv('notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nz28kRQp1ES5cuBAlpMcmZ86cRpI5e/asNW3jxo1GkilZsqSRZEaOHPnYZaIL3RFiCjURIj4nI37Q+O+//6x5d+7cMfXr1zeSzKuvvhrn5/Xwdp80dBvz+KAY27IRnnS/PGbMGOsHsof7Y1hYmHnjjTes1yw+nz8R++OhQ4fGeZnoLFu2zJw7dy7K9K1btxpfX1/j7u5u/v3330jznjR0x6e/R/TdMmXKmODg4Cjtd+zYEek7gzEx98+Efn/Jly+fOXTokDXv/v37plu3bkaSqVevXrSvAVI2QjcQi4idcVz/xTV0Fy9e3EgyV69ejVMdjwtFp06dMi4uLsZms0X5YmyMMf/++6/x8vIyksyWLVus6Z988omRZCpWrBjtet97773Hhu5PPvkkTs/hUStXrjSSTFBQUKTpD39oLV26NMpyo0aNssJqdB+Ib731lpFkhgwZEudaIn4Z79OnT7TzmzRpEm0gSIzQ/ejzN+bBWQsRr0Hfvn2jzN+5c6f1C/zDvvvuO+sLfHRu3rxpsmXLZtzc3CL1vYQ8D2Nifi9Hjx5tpAdHcR4ODQnxpO/VoxI7dHt7e0cKCxEWL15s/Tj18NHuiO27u7ubY8eORVkuPDzcCjd//vlntNv+/PPPowTo4OBg4+PjY1xcXKJ86b13757JmjWrkRTlR4nogsOT1hDdUeuQkBDj6elpqlevbvr27WskRTraE9PR8djcu3fP9O7d2zpr6OF/WbJkMW+++WaU18AYY50V8WiYjEn37t2N9ODIVnR69uxpJJm6detGmh4RuosUKRJt/9+/f7+x2WwmZ86c0QYEY4xp1KiRkWQWL15sTfPx8THp06ePU+2PE3HUevLkyda0jz/+2EgyCxcuNG5ubqZBgwbWvJiOjidG6LbZbGbPnj1R5m/fvt1IMgUKFIjXc3vc5/fDn8uODN1Pul8uVKiQkWS+++67KMvcuXPH+Pn5xXu/7e3tbSSZ77//Ptr5s2bNinS0PuLfpUuX4ryN/v37G0lm7NixkaY/aeiOT3+fM2eOkWTefvvtONf7uP4Znbh8f1m0aFGU5f777z8jPTjafe/evThvDykDN1ID4iC2IUekB9fcXrhwIc7rCwgI0P79+9WhQwd9+OGHeuGFF+Tm9uR/jps2bZLdblfZsmVVsmTJKPNz5cql+vXra+HChVq/fr0qVaokSda1aB06dIh2vR06dNCXX34Z67Zbt24d6/zQ0FCtWrVKO3bs0MWLFxUaGipjjG7evClJOnToULTLubm5qV69elGmR9y0LG/evHruuedinH/u3LlY64pw//59687jMd3htnv37lqyZInWr18fp3XGR6NGjaJMy5QpkzJnzqwrV65EOz+m57h06VJJUtu2baPdVtq0aVWuXDktW7ZMO3bsiPb1jU1838sVK1ZIevD6ubq6xmtb0XH2exWbevXqyc/PL8r0Jk2aWO/lrl27rL+9CGXKlFGBAgWiLLd7926dO3dOBQsW1PPPPx/tNiOufdy6das1LV26dGrdurWmTp2qqVOnqn///ta8pUuX6tKlSwoICNCzzz772Of0pDUUKFBA+fPn14kTJ3Ts2DEVLFhQmzdvVmhoqOrWravy5cvriy++0Jo1a1ShQgUZY7Ru3TrZbDbVrl37sXVFcHd311dffaV+/fppwYIF2rx5s3bt2qVDhw7p8uXLGjt2rGbOnKlVq1ZZ9Z8/f1579uyRi4uLunfvHqftRFznG1ufGzNmjDZv3qzw8PAofb1FixbR9v9ly5bJGKOGDRsqXbp00a67Ro0aWrZsmbZu3WoNexYQEKANGzaoU6dO6tWrl8qUKROna3OjU6dOHU2bNk1r1qyx7lGxZs0a+fj4qEGDBipfvrw2b96se/fuycPDw7pe1xHj2+fNm1elSpWKMr1YsWKSpLNnzz7RemP6/H5aN357kv3y2bNndfToUUlSx44doyzj5eWlNm3a6JtvvknUWnfs2KEpU6ZEmT548GBlyZIl0rQrV65o6dKl2rdvn65du2bdY+HIkSOSYv5sj6/49PeyZcvK1dVVkyZNUpEiRaz7UDyphHx/adCgQZTpfn5+ypgxo65du6YrV65E+5mBlIvQDcTB44YcqVGjRrxC9/Dhw/X3339r+fLlWr58uby9vVW2bFnVqFFDHTp0sL5kxFXEl5H8+fPH2CbiDp0Pf3H5999/JSnGsVPjMqZqbG22b9+utm3bWnf5jk5wcHC003PkyBHtDxFp06aVFPPdZiO+vMZ2I6mHXblyxWob0+sX3WuXWGJ6HmnTptWVK1einR/xHENDQyNNj7gB1UsvvaSXXnop1u1eunQpXnU+yXsZcQOluH65/eyzz3Tw4MEo07/88ktlyZLlqb9XCxYs0IIFC6JMf/nll1WlSpVI02L72/P399eVK1esv7dH50Un4r2MGEc3No++l926ddPUqVM1efLkSKH7p59+kqQ4j1eekBrq1KmjCRMmaM2aNSpYsKAV1urWrasSJUrI09NTa9as0UcffaTdu3frypUrKlOmjDJnzhyn2h7m5+en119/3RoC7sKFC5oxY4aGDBmiq1evqlOnTvrnn38k/d+IAzly5FD69OnjtP7H7V8j+tzdu3d15cqVKDd2e9x7PHHiRE2cODHWGh5+fceNG6cmTZpo2rRpmjZtmtKlS6fy5curVq1aeumll+J1J+6I8Lx27VpJD/6Gd+zYobp168rDw0N16tTRtm3btG3bNlWvXt3hoTs6vr6+kqLu7+IqLkOGOdKT7Jcj9hVZsmSxPvMeFds+JyZZsmTRmTNnYtz/f/nll5F+aHdzc1N4eHiUdhMmTNA777wT601LY/psj6/49PeCBQvqq6++Ut++fdWzZ0/17NlT+fLlU8WKFdWkSRMFBQXJw8MjTttN6PeXmIbP8/X11bVr1+L8HQUpB6EbcAI/Pz/9+eef2rhxo9asWaMtW7bo999/15YtWzRs2DANHz5c/fr1e2r1xPSFOi534vb29o52ekhIiFq0aKELFy6oa9eueuONN1SoUCH5+vrK1dVVhw8f1jPPPBPlzrgRHnfk5kmP7CQ1ifk8I+6m2qBBg8eOTRzdEEsxSeh7GVcrVqywzr54WHRHWZ6GPXv2RHvUp0aNGlFCd1xE9/rE9PcT8V76+fmpfv36sa730demWrVqKliwoA4fPqytW7eqUqVKunjxopYtWyYvLy+1a9cuTvUmpIaI0L169Wq99tprWrNmjTJmzKhy5crJxcXFGts+JCQk0YNc9uzZ9c4778jf31+tWrXS/v37deTIEacN7fe497h06dLRHuF9WIUKFaz/LlasmA4dOqRVq1Zp3bp12rp1qzZv3qx169bpk08+0cSJE6M9OhqdnDlzqlixYjpw4ID27dun48eP6/79+6pbt66kB+/J0KFDtXr1alWuXFkbN26Um5tbtHeYTqiUsk9/lKP2y0+ibNmyOnPmTIJGedi5c6dee+01ubq6asSIEWratKny5s0rHx8f2Ww2jR8/Xq+99lq8Pw9iGgEjvv39rbfeUps2bbRo0SL99ttv+u233zRr1izNmjVLgwYN0ubNmx979NvR31+QOhG6ASeJGBoj4svL3bt3NXnyZL355pv68MMP1bp16ziPH5krVy5JinWonYh5EW0j/vvQoUNRhsqIENP0uNi0aZMuXLigsmXLatKkSVHmR5yC5myZM2eWp6enQkNDdfz48WhPz4/utUuK8uTJo4MHD6p79+6PPe0/Pp70vcybN68OHDiggwcPxilQxTRcVYSn/V4NHjw4xuFtHnXixIkY50X8HeXOnTvO286TJ4+kB8/50aGHHidiSKOPP/5YP/30kypVqqTp06fr/v37atOmjTJkyODwGmrXri2bzab169fr4sWL2rNnj1q2bGl9Ga1Tp47Wr1+vTZs2Oezo6cOXT1y+fFmFCxe2jor9999/unHjRpyOdufKlUvHjh3T8ePHo72kJaLPRQw7FlcRr2/lypU1ZsyYOC8nPTgC2ahRI+vyk+DgYI0aNUpDhgzRa6+9ppYtWypNmjRxWledOnV04MABrVmzxnouEe9FxYoVlSZNGq1Zs0aNGjVScHCwKlasaB19xuM9yX45Yv91+fJl3bp1K9qj3U/y+dysWTMtXLhQK1eu1OXLl5/ox8y5c+fKGKO33npL77//fpT5MX0eRBxhjjg1+1HRDdMXIb79PXv27HrllVf0yiuvSHownGK3bt20bds2ffDBB9H+mPqw5PL9BckLP8UASYSXl5def/11lSxZUna7XX///bc1L+LD6v79+9EuW61aNbm4uGjPnj3666+/osz/77//rOtra9asGWk5SZo5c2a0650xY8aTPRnJGgszplMGp0+f/sTrTkxubm7WUcuYgkXEh+7Dr11S1LBhQ0nSnDlz4rXc4/rXk76XEde0TZo0KdpTFOMrKb9Xq1at0sWLF6NMX7Zsma5cuaJ06dLFeF10dMqXL68sWbJo//791qnR8dGlSxe5uLhozpw5CgkJifep5QmtIXPmzCpdurSuXr2qL774QsYY6+ip9H+hbsmSJfrtt9/k6empqlWrxnn9cTmK9vBpoREhxs/PT6VKlZLdbo/2y3R0In4YfVyfq1q1arzuzRHx97po0aIEn2rq6+urwYMHK0OGDAoJCdHhw4fjvGzEe7F69WqtWbNGfn5+KlGihKQH181Xq1ZNf/75p3755ZdI7ePqcfuXpCwutT+uzZPsl3Pnzm3d6yG6z+HQ0FDNnTs3zuuL0LFjR+XLl093797Vm2+++URnJ0V8HkR3VP7u3buaN29etMtF/A3GNOZ6xLXvcRHf/l60aFHr7ME9e/Y8dv3J5fsLkhdCN+AEX375ZbTXCR08eND6BfXhD7SII2T79++Pdn158+ZVUFCQjDF67bXXdOXKFWve7du39eqrr+ru3buqVKlSpBs5de/eXT4+Pvrtt980duzYSOvcsmWLxo0b98TPMeK69LVr10ape/z48Zo9e/YTrzuxvfvuu5Kk7777zrq2McLkyZO1aNEiubu7q1evXs4oL85effVV5cuXT3PnzlW/fv2iPaJw/vx5TZgwIdK0x/WvJ30vX375ZeXOnVu7d+/WK6+8EuX6v+DgYOsoZ1wl1ffqzp07euONN3Tnzh1r2rlz56x6X3/9dXl5ecV5fe7u7ho0aJCMMWrZsqV+++23KG3Cw8O1bt06bd++Pcq83Llzq27dugoODtaHH36offv2KW/evKpVq9ZTqyEinEUcxX04dJcrV04ZMmTQxIkTdefOHVWqVCnG07Cjc+PGDZUtW1bTpk3TrVu3osw/fvy4unXrJkmqVKlSpC/PgwYNkiR99NFH0QaE/fv3RwoGvXr1kpubmxYsWBDly/aqVav0ww8/SJLee++9ONcvPbiJXmBgoM6cOaNWrVpFe+Ty9u3b+vnnn617hoSEhGjUqFHRXpO7efNmXb9+Xa6urvE6q6JGjRpyc3PTunXrdODAgSihuk6dOgoPD9d3331nPY6PiFqe5McjZ4tL7Y9r86T75d69e0t6cMbNw/e6CA8P13vvvRfnm4U+zMPDQ3PnzpWXl5fmzJmjli1bWjdse9TWrVujDeURnwdTpkyJ9Fzu3r2rHj16xHjWT61ateTi4qKVK1dGuozIGKNvvvkm2r/F+Pb3devWadmyZdZN3R7expIlSyTF7RT+5PT9BcnI071ZOpC8OGqc7vTp0xtJpmjRoqZly5bmxRdfNDVq1DBubm5GejCm8cNCQ0OtoXvKlCljOnXqZLp3724+//xzq83ly5etsSPTp09vWrRoYVq3bm0NEZQ/f/5oh8SYNm2acXFxMdKDcVnbt29vqlevblxcXKwhw9zd3aMspxiGEntY8+bNjSTj4eFh6tWrZ9q1a2eKFi1qbDab+eijj6IdYia2oWdie00jPOnwVwMGDLCGralSpYp58cUXTdmyZY0k4+rqaiZOnJho2zLm8cPMPG4Yk5he/3379hl/f38jyWTIkMFUq1bNvPjii6ZFixamePHixmazmezZs0daJi7960neS2OM2bVrlzW0TYYMGUzjxo1N27ZtTaVKlYy3t3eM72NsnuS9MsaYChUqWP8KFChgDeX18PQlS5bEq5aIYXA6depkMmXKZPz8/ExQUJBp2rSpNc59xYoVI43fbUzc+07E8FqSzLPPPmuaN29u2rVrZ2rUqGEyZMgQ45BCxjwY/idiWUlm4MCBMW4ntv74pDVEDKsTsf95VMuWLa35n376aayvw6OuXbtmLevp6WkCAgJMUFCQad26talQoYK1T8uXL1+kMcQjfPrpp8Zms1n74bZt25pmzZpZwzk+us//4YcfrHWWLVvWvPjii6Zy5crWOgYPHhxlGxFDhsX2+REcHGwNg+fh4WHKly9v2rRpY4KCgkz58uWtMcAPHDgQ6Xm7uLiYUqVKmdatW5v27dubihUrWrXE9j7HpGLFitbrOWXKlEjz/vrrL2temjRpoh3qKLb9dsR402nTpjWtWrUy3bt3N927dzcHDx40xjx+n25M3D5vHpUY43SfP3/e+juuXLmy6dKli+nevbuZNGmS1WbJkiXW+9ekSRPTrVs3071790jDcz7Jfjk8PNw0bdrUWnf9+vVNu3btTP78+Y2Xl5c1VveTfP7s2LHDGv/aZrOZ5557zrRs2dJ07NjRNG3aNNJwa02bNjU3b960lr127Zo1P3PmzKZFixYmMDDQZMuWzaRLl8706tUrxroi5rm6upoaNWqYVq1amYIFCxp3d3fzwQcfROkH8e3vEWPb+/r6mho1apgXX3zRtGzZ0qo3ffr0Zvfu3ZFqiumz1hHfX2LbHlI+QjcQC0eF7unTp5uuXbua5557zmTKlMl4enqafPnymYYNG5r58+cbu90eZRt79+41zZo1M1mzZrW+/D263tu3b5vhw4eb0qVLGx8fH+Pl5WWKFStmPvzww1jHBN+wYYOpW7eu8fX1NT4+PqZs2bJm4sSJ5vTp00aSyZEjR5Rl4vIl6N69e+aLL74wJUqUMD4+PiZTpkymXr16ZtWqVTF+ODkrdBtjzPLly02jRo1M5syZjZubmxWgfv/990TflqNCtzEPvsh//vnnpmLFiiZDhgzG3d3d5MiRw5QvX9707dvXbN26Ncoyj+tfT/JeRrh06ZIZMGCAKVGihEmTJo3x9vY2BQoUMG3btjUrVqyI7WWKUXzfK2P+7zWL7V9cvqA/7OGxZ48fP27at29vsmfPbjw8PEyhQoXMwIEDze3bt6MsF5++s2XLFtOhQweTL18+4+npadKlS2eKFCliWrRoYX788ccY/7bv3r1rMmXKZH2xPn78eIzbeFx/fJIaIsbmlqIfN33s2LHW6x7b+xYdu91ufv/9dzNs2DBTr149U7hwYZMuXTrj7u5usmXLZmrWrGlGjRplbt26FeM6tm3bZtq3b29y5cpl3N3dTaZMmUypUqXM+++/b06dOhWl/fbt203r1q2Nn5+fcXNzM5kzZzaNGzc2q1atinb9cQndxjwIVzNmzDCNGjUy2bNnN+7u7iZz5szmueeeM127djXz58+3gm5YWJj5/vvvTfv27U3RokVN+vTpjbe3tylYsKAJDAw0a9eujfuL+JCIsbklmbNnz0aaZ7fbTbZs2Ywk07Bhw2iXj20fEB4eboYPH26effZZ4+XlZW0noq8l5dBtjDGbNm0yderUMRkzZrT2jY/+3U6YMMGULVvW+Pj4xLgveZL9clhYmBk5cqQpXry48fT0NJkzZzbNmzc3e/bsSdDnjzEP9ulTpkwxrVq1Mnnz5jXe3t7Gw8PDZMuWzVSrVs3079/f7N27N9plL126ZHr06GEKFixoPD09Tc6cOU3Hjh3NkSNHYq3LbrebkSNHmmLFihkPDw+TKVMm07RpU7Nz585o+0F8+/vRo0fN4MGDTe3atU3evHmNl5eXyZgxoylZsqT54IMPzJkzZ6LUFNNnrSO+v8S2PaR8NmMSeLtZACnW1KlT1blzZzVt2lSLFi1ydjlAkjN48GANGTJEgwYNivNN1wAAQOrCNd1AKnf69GmdP38+yvQtW7ZY1yjG5+ZLAAAAAP4PQ4YBqdy6devUvXt3lSpVSnnz5pWrq6uOHTtm3QW9a9euatmypZOrBAAAAJInQjeQyr3wwgvq2rWrNm/erA0bNuj27dvKkCGD6tSpo27duql9+/bOLhEAAABItrimGwAAAAAAB+GabgAAAAAAHITQDQAAAACAg3BNdyKy2+06d+6c0qVLJ5vN5uxyAAAAAAAOYozRzZs3lTNnTrm4xHw8m9CdiM6dO6c8efI4uwwAAAAAwFNy5swZ5c6dO8b5hO5ElC5dOkkPXnRfX18nVwPEj91u16VLl5Q1a9ZYf6kDkhv6NlIi+jVSKvo2kpPg4GDlyZPHyoExIXQnoohTyn19fQndSHbsdrvu3r0rX19fPuSQotC3kRLRr5FS0beRHD3u0mJ6MgAAAAAADkLoBgAAAADAQQjdAAAAAAA4CKEbAAAAAAAHIXQDAAAAAOAghG4AAAAAAByE0A0AAAAAgIMQugEAAAAAcBBCNwAAAAAADkLoBgAAAADAQQjdAAAAAAA4CKEbAAAAAAAHIXQDAAAAAOAgbs4uAM5x9+5d/ffff7p7966MMc4uJ1Wx2Wzy8vJSjhw55OXl5exyAAAAADgQoTuVOXnypLZu3apjx44pPDzc2eWkaq6uripUqJAqVaqkfPnyObscAAAAAA5A6E5Fjhw5olmzZilbtmyqW7euChUqJB8fH7m4cJXB02S32xUSEqKjR49qz549mjZtmtq1a6dChQo5uzQAAAAAiYzQnUrcuHFDs2bNUuHChRUUFCRXV1dnl5Sq+fj4KEuWLCpXrpzmzJmjWbNmqVevXkqXLp2zSwMAAACQiDjEmUrs379fNptNLVu2JHAnIW5ubmrZsqWMMdq/f7+zywEAAACQyAjdqcSBAwdUsGBBeXp6OrsUPMLb21sFChTQgQMHnF0KAAAAgERG6E4lrl27phw5cji7DMQgR44cunbtmrPLAAAAAJDICN2pRFhYmDw8PJxdBmLg4eGhe/fuObsMAAAAAImMG6kBAACkIHOP3XDo+oMKpnfo+gEgpeFINwAAAAAADkLoBgAAAADAQQjdSLImT54sm80W67/atWvHeX12u11jxoxR2bJl5ePjI19fX1WrVk2LFi2K0vbu3bvq06ePqlWrppw5c8rLy0t+fn6qXLmyfvrpJ4WFhSXmUwUAAACQQnFNN5Ks0qVLa9CgQdHO++WXX/TPP/+ofv36cVqXMUZt2rTRvHnzVLBgQXXv3l2hoaFauHChmjdvrm+//VY9e/a02t+6dUvfffedAgIC1LhxY2XNmlXXrl3T8uXL1a1bN82aNUvLly+Xiwu/WwEAAACIGaEbSVbp0qVVunTpKNPv3bunMWPGyM3NTZ07d47TuubNm6d58+apcuXKWr16tby9vSVJw4YNU7ly5fTee++pSZMm8vf3lyRlypRJN27ciHLH9/v376tu3bpatWqVli9frsaNGyfoOQIAAABI2ThMh0iGDh0qm82mlStXRpm3ePFi2Ww2jRw50gmV/Z8FCxboypUratKkibJnzx6nZRYuXChJ+vDDD63ALUlZsmTRO++8o9DQUP3000/WdBcXl2iHWHNzc1PLli0lSUePHk3I0wAAAACQChC6Ecnu3bslSWXLlo0yb9euXTHOe5p+/PFHSdLLL78c52XOnz8vScqfP3+UeRHT1q1b99j12O12rVixQpL03HPPxXn7AAAAAFInTi9HJLt371bu3LmVNWvWKPMiQnd0p3xHGD16tK5fvx7n7bVo0SLW9T3q1KlTWrt2rXLnzq0GDRrEebksWbJIkk6cOKFixYpFmnfixAlJ0uHDh6Msd+/ePQ0bNkzGGF25ckVr167VwYMH1bVr13jdxA0AAABA6kTohuXatWs6efKkmjVrFu38Xbt2yd/fXxkzZoxxHaNHj9apU6fivE1/f/94he6ffvpJdrtdXbp0kaura5yXa9iwoWbNmqXPPvtMtWrVkpeXlyTpypUrGj16tCRF+2PBvXv3NGTIEOuxzWbTe++9p+HDh8d52wAAAABSL0I3LHv27JEU/enjly5d0r///mtdzxyTkydPOqCyB+x2u3766SfZbDZ169YtXsu++OKLmjx5stavX68SJUqoQYMGCgsL04IFC6zrwqO7E3natGlljJHdbte5c+e0ePFiffjhh9q2bZuWLVsmX1/fRHluAAAAAFImrumGJeJ67jJlykSZF3FqeXTznpY1a9bo9OnTqlWrVrTXZsfGzc1Ny5cv1+DBg+Xi4qLx48fr119/VfPmzfXLL79IkrJlyxbj8i4uLsqdO7feeOMNjR8/Xlu2bNGnn36aoOcDAAAAIOXjSDcssd1EbevWrZIeH7odeU33k9xA7WGenp4aNGhQlLG/N2zYIEkqV65cnNZTr169SMsBAAAAQEwI3bDs3r1bNptNOXPmjDTdbrdr3rx5kuIWuh1xTfeVK1e0cOFCZcqU6bGnuMfXzz//LElq165dnNqfO3dOkuTu7p6odQAAAABIeQjdkCTduXNHBw8elDFG27ZtU+XKlSVJxhgNGjRI//zzjzJmzKhcuXLFuh5HXdM9bdo03bt3Tx07dpSnp2esbY8dO6awsDAVLFgwUjAODg6Ocg32L7/8okmTJql8+fJq1aqVNX3//v3y9/eXj49PpPYhISHq06ePJKlRo0YJfVoAAAAAUjhCNyRJe/fuVXh4uLJly6aGDRsqMDBQ3t7e2rp1q4KDg2Wz2RQcHKzu3btr3Lhxjw2+iW3ixImS4nZqee3atXXq1CmdOHFC/v7+1vQKFSooT548KlasmLy8vPTHH39ow4YNKlCggObOnRvpbuhz5szRqFGjVKVKFfn7+8vX11dnz57V8uXLdeXKFVWtWlXvvPNOoj9PAAAAACkLoRuS/u967s8//1zbtm3TzJkzJUl169bVV199pX79+mnRokW6c+fOUw/cf/zxh/bt26eAgACVKFHiidfTtm1b/frrr9q+fbvCwsKUP39+DRgwQH379o1yBLxJkyY6d+6ctm7dqm3btunWrVtKnz69SpYsqXbt2qlbt25yc+PPBwAAAEDsSA2Q9H+hu0KFCurcubO+//77SPNnzJjhjLIkSQEBATLGxLl9TKe4Dx48WIMHD47TOsqVKxfnG6sBAAAAQEwYMgySHoRuHx8fFSlSxNmlAAAAAECKQeiGwsPDtXfvXpUoUUIuLnQJAAAAAEgsJCzo4MGDunPnTpzHywYAAAAAxA3XdEPPPvtsvK6ZBgAAAADEDUe6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdCNZGTFihGw2m2w2m7Zv3x5ju/nz56tu3brKnDmzvLy8lD9/frVv315nzpyJ03bu3r2rPn36qFq1asqZM6e8vLzk5+enypUr66efflJYWFiUZYwx+vXXX1WzZk3lyJFDPj4+euaZZ/Taa6/p+PHjT/ycAQAAACRfDBmGZGPfvn0aNGiQ0qRJo9u3b0fbxhij119/XePHj1fBggXVrl07pUuXTufOndPGjRt16tQp5cmT57HbunXrlr777jsFBASocePGypo1q65du6bly5erW7dumjVrlpYvXy4Xl//73eq9997TqFGjlCNHDrVo0UK+vr7666+/NGHCBM2cOVNbt27Vc889l2ivBwAAAICkL8ke6R47dqz8/f3l5eWlChUq6I8//oi1/dy5c1W0aFF5eXmpRIkSWrZsmTUvLCxM/fr1U4kSJZQmTRrlzJlTnTp10rlz5yKt4+rVq+rQoYN8fX2VIUMGde/eXbdu3XLI80P8hIWFqXPnzipdurRatmwZY7tvvvlG48ePV48ePXTo0CGNHTtWn332maZOnapTp07phRdeiNP2MmXKpBs3bmjjxo2aMGGChg0bpu+++05Hjx5VjRo1tGrVKi1fvtxqf/78eY0ePVr58uXTgQMH9N1332nEiBFasWKFRo4cqZs3b2rUqFEJfh0AAAAAJC9JMnTPnj1bffr00aBBg7Rr1y6VKlVK9evX18WLF6Ntv3XrVrVv317du3fX7t271aJFC7Vo0UL79u2TJIWEhGjXrl36+OOPtWvXLv366686dOiQmjVrFmk9HTp00D///KPVq1dryZIl2rRpk1599VWHP9+k5vLly3r//fdVvHhx+fj4WKdzP/yvSpUqT7WmTz/9VP/8848mTZokV1fXaNvcuXNHQ4YMUYECBfT1119H287NLW4nd7i4uMjDwyPa5SNC/9GjR63pJ0+elN1uV+XKlZU+ffpIyzRp0kSSdOnSpThtGwAAAEDKkSRPLx81apReeeUVde3aVZL0/fffa+nSpZo0aZI++OCDKO2//vprNWjQQH379pUkDR06VKtXr9aYMWP0/fffK3369Fq9enWkZcaMGaOAgACdPn1aefPm1YEDB7RixQrt2LFD5cqVkyR9++23atSokb788kvlzJnTwc86aTh16pSqVq2qM2fOqGrVqmrevLkuXbqkmTNnKiQkROnTp1eWLFlUq1atp1bTrl279Omnn+qTTz5R8eLFY2y3atUqXbt2TV27dlV4eLgWLVqkw4cPK0OGDKpTp44KFSqU4FrsdrtWrFghSZFOFS9cuLA8PDy0ZcsWBQcHy9fX15q3ZMkSSVLt2rUTvH0AAAAAyUuSC9337t3Tzp071b9/f2uai4uL6tSpo23btkW7zLZt29SnT59I0+rXr68FCxbEuJ0bN27IZrMpQ4YM1joyZMhgBW5JqlOnjlxcXPT7779He0pzaGioQkNDrcfBwcGSHgQzu93+2Of6NBljrH8xsdvtatOmjc6cOaNvv/1Wb775pjWvRYsWatq0qYoVK6atW7da63zU6NGjdf369TjX1aJFC5UuXTrG+aGhoerUqZNKly6tvn37Rtrmo8/nzz//lPSgv5QsWVKHDx+25rm4uKh379768ssv41yb9KA/Dhs2TMYYXblyRevWrdPBgwfVpUsX1apVy9p+pkyZNHz4cL333nsqWrSomjVrJl9fX/39999at26d3njjDb355psxvv4Rz8WZ/cZutzu9BsAR6NtIiWLt18axfZ2/JTgS+2wkJ3Htp0kudF++fFnh4eHKnj17pOnZs2fXwYMHo13m/Pnz0bY/f/58tO3v3r2rfv36qX379tYRyfPnzytbtmyR2rm5uSlTpkwxrmf48OEaMmRIlOmXLl3S3bt3o3+CThISEqI7d+7EeAMySVq8eLH++OMPNWvWTF26dInUtmrVqvL19dWff/6pW7duyWazRbuO0aNH6/Tp03GuK0eOHCpcuHCM8z/++GMdOXJEmzdvtl7TiDuHP/p8Iq7R/+qrr1S6dGlt2LBBzzzzjP766y+9/fbbGjVqlPLkyaOXX345zvXdunVLn3zyifXYZrPp7bff1pAhQ6K8lq+++qqyZMminj176ocffrCmV6xYUS1btozyI83DIp5LTJdQPA12u103btyQMSbSDeKA5I6+jZQotn5tuxnzZ31iuHgx+s8yIDGwz0ZycvPmzTi1S3Kh29HCwsLUpk0bGWP03XffJWhd/fv3j3SEPTg4WHny5FHWrFkjnV6cFPj4+Mjb21tp0qSJsc38+fMlSX369Im2nbe3t27fvi0fH58Yd4InT55MlHqlB2cffPPNNxo0aJDKly9vTXd3d7fqebjOiJo8PDy0cOFC65KAevXq6ZdfflHp0qU1ZswY9erVK841pEmTxjpz4dy5c1q8eLE++ugj7dy5U0uXLo30Pn/yySf69NNPNWTIEHXs2FEZMmTQnj171KdPHzVq1Ei//PJLlPsIRIh4Lo/+8PM02e122Ww2Zc2alQ85pCj0baREsfVrc+uGQ7edLVv6xzcCnhD7bCQnXl5ecWqX5EJ3lixZ5OrqqgsXLkSafuHCBfn5+UW7jJ+fX5zaRwTuU6dOad26dZECk5+fX5SjjPfv39fVq1dj3K6np6c8PT2jTHdxcUlyO4mHb4IWk99++01p0qRRlSpVorS7c+eOrl69qvz588d4I7PEdP/+fXXp0kUlS5ZU//79o6370ecTcalAuXLllCtXrkhtS5QooQIFCujo0aO6ceOG1TauXF1dlSdPHvXo0UNZs2ZVmzZtNGzYMI0YMUKStGbNGg0ePFjvvPNOpEsjqlatqsWLF6tAgQJ677331Lx582jXH/FcnN1vImpwdh1AYqNvIyWKsV/bHNvP+TuCo7HPRnIR1z6a5EK3h4eHnn/+ea1du1YtWrSQ9OAXr7Vr16pnz57RLlOxYkWtXbtWvXv3tqatXr1aFStWtB5HBO4jR45o/fr1ypw5c5R1XL9+XTt37tTzzz8vSVq3bp3sdrsqVKiQuE8yCQoNDdV///0XY6hetWqVwsLCHnsDtcS6pvvWrVs6cuSIJEV7F3FJ1vs7f/58tWjRQs8884wkxRioI6bfuXMn3qH7YfXq1ZMkbdiwwZoWMXxYzZo1o7T38/NT0aJFtXv3bt26dUtp06Z94m0DAAAASF6SXOiWHpze3LlzZ5UrV04BAQEaPXq0bt++bd3NvFOnTsqVK5eGDx8uSerVq5eqV6+ukSNHqnHjxpo1a5b+/PNPjR8/XtKDwN26dWvt2rVLS5YsUXh4uHWddqZMmeTh4aFixYqpQYMGeuWVV/T9998rLCxMPXv2VLt27VLNnculB6fI2+32SL/aGGOsMaYfdz306NGjderUqThvz9/fP9rQ7enpqe7du0e7zKZNm3TkyBE1a9ZMWbNmlb+/v6T/C7wHDhyIskxYWJiOHj2qNGnSKGvWrHGuLzoR145HnOYuPbjhmhTzsGCXLl2Si4tLpGUAAAAApHxJMnS3bdtWly5d0sCBA3X+/HmVLl1aK1assG6Wdvr06UihsFKlSpoxY4YGDBigDz/8UIULF9aCBQusIZ3Onj2rRYsWSVKUgLd+/XrVqFFDkvTzzz+rZ8+eql27tlxcXBQYGKhvvvnG8U84CfD09FSxYsV04MABrVy5Ug0bNrTmffzxx9q0aZM6deoU6drq6CTWNd3e3t768ccfo53XpUsXHTlyRP3799cLL7xgTS9YsKDq1aunVatW6ccff4z0A8Fnn32m69evq2PHjlHG6j527JjCwsJUsGBBKxTv379f/v7+8vHxidQ2JCTEuo6/UaNG1vTKlStrzJgxGjVqlAIDAyON1f3999/r33//VeXKlaO9HAEAAABAypUkQ7ck9ezZM8bTyR8+rTdCUFCQgoKCom3v7+8f61BZETJlyqQZM2bEq86U5KOPPlLHjh0VGBioDh06KHPmzFqzZo127typevXq6fvvv3d2iY81btw4VapUSa+88ooWLFhgnda9bt065cuXT1988UWUZWrXrq1Tp07pxIkT1lHzOXPmaNSoUapSpYr8/f3l6+urs2fPavny5bpy5YqqVq2qd955x1pHUFCQvvvuO23atElFihRRs2bNlCFDBu3atUvr1q2Tt7e3dbYAAAAAgNQjyYZuPH0dOnSQi4uLRo4cqZ9//tka7/rHH39U165dk8XNLAoWLKg///xTAwcO1IoVK7Rq1Sr5+fnpzTff1MCBA+N8d/AmTZro3Llz2rp1q7Zt26Zbt24pffr0KlmypNq1a6du3bpFOmLu6uqqVatW6auvvtKcOXM0Y8YM3bt3T9mzZ1fHjh314YcfqlixYo562gAAAACSKJuJyyFgxElwcLDSp0+vGzduJLkhwz777DNVq1ZNlSpVcnYpiMZvv/2mLVu2qF+/fk6rwW636+LFi8qWLVuy+IEFiCv6NlKi2Pr13GOOHTIsqCBDhsFx2GcjOYlr/qMnAwAAAADgIIRuAAAAAAAchNANAAAAAICDELpTCRcXF4WHhzu7DMQgPDxcrq6uzi4DAAAAQCIjdKcSPj4+Cg4OdnYZiEFwcHCUMcEBAAAAJH+E7lSiQIECOnToUJzGK8fTZbfbdfjwYRUoUMDZpQAAAABIZITuVKJ48eIKDg7Wvn37nF0KHrF3717dvHlTzz77rLNLAQAAAJDI3JxdAJ6OfPny6bnnntP8+fN1584dlSxZUl5eXs4uK1W7e/eu/vrrL61cuVIlS5ZU7ty5nV0SAAAAgERG6E4lbDabWrVqJVdXVy1btkwrV65U7ty55ePjIxcXTnh4mux2u0JCQvTvv/8qPDxcZcqUUdOmTWWz2ZxdGgAAAIBERuhORVxcXNSyZUvVqlVLBw4c0JkzZ3T37l2u837KbDab0qZNq7p166p48eLy9fV1dkkAAAAAHITQnQqlT59eL7zwgl544QVnlwIAAAAAKRrnFQMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBkmToHjt2rPz9/eXl5aUKFSrojz/+iLX93LlzVbRoUXl5ealEiRJatmxZpPm//vqr6tWrp8yZM8tms2nPnj1R1lGjRg3ZbLZI/15//fXEfFoAAAAAgFQmyYXu2bNnq0+fPho0aJB27dqlUqVKqX79+rp48WK07bdu3ar27dure/fu2r17t1q0aKEWLVpo3759Vpvbt2+rSpUqGjFiRKzbfuWVV/Tff/9Z/z7//PNEfW4AAAAAgNQlyYXuUaNG6ZVXXlHXrl1VvHhxff/99/Lx8dGkSZOibf/111+rQYMG6tu3r4oVK6ahQ4eqbNmyGjNmjNXmpZde0sCBA1WnTp1Yt+3j4yM/Pz/rn6+vb6I+NwAAAABA6uLm7AIedu/ePe3cuVP9+/e3prm4uKhOnTratm1btMts27ZNffr0iTStfv36WrBgQby3//PPP2v69Ony8/NT06ZN9fHHH8vHxyfG9qGhoQoNDbUeBwcHS5Lsdrvsdnu8tw84k91ulzGGvosUh76NlCjWfm0c29f5W4Ijsc9GchLXfpqkQvfly5cVHh6u7NmzR5qePXt2HTx4MNplzp8/H2378+fPx2vbL774ovLly6ecOXPq77//Vr9+/XTo0CH9+uuvMS4zfPhwDRkyJMr0S5cu6e7du/HaPuBsdrtdN27ckDFGLi5J7iQY4InRt5ESxdavbTdvO3TbFy+GPr4R8ITYZyM5uXnzZpzaJanQ7Uyvvvqq9d8lSpRQjhw5VLt2bR07dkwFCxaMdpn+/ftHOsoeHBysPHnyKGvWrJyajmTHbrfLZrMpa9asfMghRaFvIyWKrV+bWzccuu1s2dI7dP1I3dhnIznx8vKKU7skFbqzZMkiV1dXXbhwIdL0CxcuyM/PL9pl/Pz84tU+ripUqCBJOnr0aIyh29PTU56enlGmu7i4sJNAsmSz2ei/SJHo20iJYuzXNsf2c/6O4Gjss5FcxLWPJqme7OHhoeeff15r1661ptntdq1du1YVK1aMdpmKFStGai9Jq1evjrF9XEUMK5YjR44ErQcAAAAAkHolqSPdktSnTx917txZ5cqVU0BAgEaPHq3bt2+ra9eukqROnTopV65cGj58uCSpV69eql69ukaOHKnGjRtr1qxZ+vPPPzV+/HhrnVevXtXp06d17tw5SdKhQ4ckybpL+bFjxzRjxgw1atRImTNn1t9//6133nlH1apVU8mSJZ/yKwAAAAAASCmSXOhu27atLl26pIEDB+r8+fMqXbq0VqxYYd0s7fTp05EO41eqVEkzZszQgAED9OGHH6pw4cJasGCBnnvuOavNokWLrNAuSe3atZMkDRo0SIMHD5aHh4fWrFljBfw8efIoMDBQAwYMeErPGgAAAACQEtmMMcbZRaQUwcHBSp8+vW7cuMGN1JDs2O12Xbx4UdmyZeMaKqQo9G2kRLH167nHHHsjtaCC3EgNjsM+G8lJXPMfPRkAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAdJUOg+d+5cYtUBAAAAAECKk6DQ7e/vr+bNm2vJkiWy2+2JVRMAAAAAAClCgkL3Cy+8oMWLF6t58+bKmzevBg4cqJMnTyZSaQAAAAAAJG8JCt2bNm3SwYMH1adPH92/f1//+9//VKhQITVo0EDz5s3T/fv3E6tOAAAAAACSnQTfSK1IkSL64osv9O+//2ru3LmqW7eu1qxZozZt2ihXrlzq16+fDh8+nBi1AgAAAACQrCTa3cvd3NwUGBio5cuX6+TJkxo0aJBcXFz05ZdfqlixYqpZs6bmzJkjY0xibRIAAAAAgCQt0YcMs9vt2rlzp3bs2KFLly7JGKM8efJoy5Ytat++vUqVKqUjR44k9mYBAAAAAEhyEi10Hz9+XB9++KHy5MmjVq1aadWqVQoMDNTatWt18uRJnT59Wu+9954OHjyoN954I7E2CwAAAABAkuWWkIXDwsI0b948TZgwQRs3bpTdblf+/Pk1bNgwde3aVdmyZbPa+vn5acSIEQoODtbUqVMTXDgAAAAAAEldgkJ3zpw5dfXqVbm6uqp58+Z67bXXVK9evViXyZcvn+7cuZOQzQIAAAAAkCwkKHT7+PioV69e6t69u3LkyBGnZXr06KH27dsnZLMAAAAAACQLCQrdJ0+elM1mi9cyvr6+8vX1TchmAQAAAABIFhJ0I7WCBQvq22+/jbXN2LFjVaBAgYRsBgAAAACAZClBofvkyZO6du1arG2uX7+uU6dOJWQzAAAAAAAkS4k+Tvejbty4IU9PT0dvBgAAAACAJCfe13Rv2rQp0uOTJ09GmSZJ4eHhOnPmjH7++WcVKVLkySsEAAAAACCZinforlGjhnXzNJvNpilTpmjKlCnRtjXGyGaz6bPPPktYlQAAAAAAJEPxDt0DBw6UzWaTMUaffPKJqlevrho1akRp5+rqqkyZMqlmzZoqVqxYYtQKAAAAAECyEu/QPXjwYOu/N27cqK5du6pTp06JWRMAAAAAAClCgsbpXr9+fWLVAQAAAABAiuPwu5cDAAAAAJBaxetId4ECBWSz2bRmzRrlz59fBQoUiNNyNptNx44de6ICAQAAAABIruIVuu12u3Xn8ugex8QYE//KAAAAAABI5uIVuk+ePBnrYwAAAAAA8H+4phsAAAAAAAdJ0N3LYxIcHKzff/9dXl5eqlKlSpxOQQcAAAAAIKVJ0JHuCRMmqHr16rp27Zo17a+//lLRokXVoEED1ahRQ1WrVlVISEiCCwUAAAAAILlJUOieNm2aQkNDlTFjRmvau+++q4sXL6pr165q1KiRtm3bpu+++y7BhQIAAAAAkNwkKHQfPnxYpUqVsh5fuXJF69ev18svv6wff/xRixcvVvny5fXzzz8nuFAAAAAAAJKbBIXu69evK2vWrNbjzZs3S5JatWplTatSpQp3OQcAAAAApEoJCt2ZM2fWf//9Zz1eu3atXF1dVblyZWuaMUZhYWEJ2QwAAAAAAMlSgkJ3yZIltXDhQu3bt09Hjx7VjBkzVLlyZaVJk8Zqc/LkSeXIkSPBhQIAAAAAkNwkKHS///77unbtmkqVKqVnnnlG169fV58+faz5drtdv/32m55//vkEFwoAAAAAQHKToHG6a9asqUWLFumnn36SJLVr105Nmza15m/ZskU5c+aMdI03AAAAAACpRYJCtyQ1btxYjRs3jnZe1apVtXv37oRuAgAAAEnE3GM3HL6NoILpHb4NAHhaEnR6OQAAAAAAiFmCj3RL0h9//KEdO3bo+vXrCg8PjzLfZrPp448/ToxNAQAAAACQbCQodF+9elUtWrTQli1bZIyJsR2hGwAAAACQGiUodPfp00e//fabatSooc6dOyt37txyc0uUg+cAAAAAACR7CUrIS5YsUUBAgNauXSubzZZYNQEAAAAAkCIk6EZqd+7cUbVq1QjcAAAAAABEI0Ghu3Tp0jp58mQilQIAAAAAQMqSoNA9aNAgLVq0SNu3b0+segAAAAAASDESdE33+fPn1bhxY1WvXl0dOnRQ2bJl5evrG23bTp06JWRTAAAAAAAkOwkK3V26dJHNZpMxRpMnT9bkyZOjXN9tjJHNZiN0AwAAAABSnQSF7p9++imx6gAAAAAAIMVJUOju3LlzYtUBAAAAAECKk6AbqQEAAAAAgJglSuieP3++2rRpo5IlS6pQoULW9IMHD+rzzz/X2bNnE2MzAAAAAAAkKwk6vdxut6t9+/b65ZdfJEne3t66c+eONT9jxoz66KOPFB4erv79+yesUgAAAAAAkpkEHen+6quvNHfuXL322mu6du2a3nvvvUjzs2fPrqpVq2rp0qUJKhIAAAAAgOQoQaF78uTJKl++vMaNGydfX98ow4VJUqFChXTixImEbAYAAAAAgGQpQaH76NGjqlq1aqxtMmfOrCtXriRkMwAAAAAAJEsJCt3e3t66ceNGrG1OnTqlDBkyJGQzAAAAAAAkSwkK3WXKlNHKlSt19+7daOdfvXpVK1as0AsvvJCQzQAAAAAAkCwlKHS//fbb+vfffxUYGKh///030rxjx46pZcuWunHjht5+++0EFQkAAAAAQHKUoCHDmjdvrn79+mnEiBHKly+f0qRJI0nKli2brly5ImOMPv74Y9WqVStRigUAAAAAIDlJ0JFuSRo+fLhWrlypJk2ayMfHR66urrLb7WrQoIGWL1+uIUOGJEadAAAAAAAkOwkO3ZJUt25dLVy4UOfPn9e9e/d0+fJlLV26VPXr13+i9Y0dO1b+/v7y8vJShQoV9Mcff8Tafu7cuSpatKi8vLxUokQJLVu2LNL8X3/9VfXq1VPmzJlls9m0Z8+eKOu4e/eu3nzzTWXOnFlp06ZVYGCgLly48ET1AwAAAAAgJTB0nz17VmPHjlWXLl3UpEkTNWnSRN26ddN3332n//7774nWOXv2bPXp00eDBg3Srl27VKpUKdWvX18XL16Mtv3WrVvVvn17de/eXbt371aLFi3UokUL7du3z2pz+/ZtValSRSNGjIhxu++8844WL16suXPnauPGjTp37pxatWr1RM8BAAAAAABJshljzJMsOGjQIH3++ee6d++eHl2FzWaTp6en+vfvr48//jhe661QoYLKly+vMWPGSJLsdrvy5Mmjt956Sx988EGU9m3bttXt27e1ZMkSa9oLL7yg0qVL6/vvv4/U9uTJk8qfP792796t0qVLW9Nv3LihrFmzasaMGWrdurUk6eDBgypWrJi2bdsW57uvBwcHK3369Lpx44Z8fX3j9bwBZ7Pb7bp48aKyZcsmF5dEOQkGSBLo20iJYuvXc4/FPpxrchBUML2zS4CTsM9GchLX/PdEN1L76KOPNHz4cHl6eqpjx46qUaOGcubMKUk6d+6c1q9fr7lz52rw4MEKDw/X4MGD47Tee/fuaefOnerfv781zcXFRXXq1NG2bduiXWbbtm3q06dPpGn169fXggUL4vx8du7cqbCwMNWpU8eaVrRoUeXNmzfW0B0aGqrQ0FDrcXBwsKQHOwu73R7n7QNJgd1ulzGGvosUh76NlCjWfm2Sf1/n7zX1Yp+N5CSu/TTeofv48eP6/PPPlT9/fi1fvlxFihSJ0qZr164aMGCA6tevr2HDhqlz587Knz//Y9d9+fJlhYeHK3v27JGmZ8+eXQcPHox2mfPnz0fb/vz583F+TufPn5eHh4cyZMgQr/UMHz482hvFXbp0Kcaxy4Gkym6368aNGzLG8MsyUhT6NlKi2Pq17eZtJ1WVeC5eDH18I6RI7LORnNy8eTNO7eIduqdMmSK73a5p06ZFG7gjFClSRNOnT1fVqlU1depUDRo0KL6bSvL69+8f6Sh7cHCw8uTJo6xZs3J6OZIdu90um82mrFmz8iGHFIW+jZQotn5tbiX/08uzZeP08tSKfTaSEy8vrzi1i3fo3rJli5577jlVqlTpsW0rV66sEiVKaPPmzXFad5YsWeTq6hrlruEXLlyQn59ftMv4+fnFq31M67h3756uX78e6Wj349bj6ekpT0/PKNNdXFzYSSBZstls9F+kSPRtpEQx9mtb8u/n/K2mbuyzkVzEtY/GuycfOHBAAQEBcW4fEBAQ46nhj/Lw8NDzzz+vtWvXWtPsdrvWrl2rihUrRrtMxYoVI7WXpNWrV8fYPjrPP/+83N3dI63n0KFDOn36dLzWAwAAAADAw+J9pPv69evKli1bnNtny5ZN169fj3P7Pn36qHPnzipXrpwCAgI0evRo3b59W127dpUkderUSbly5dLw4cMlSb169VL16tU1cuRINW7cWLNmzdKff/6p8ePHW+u8evWqTp8+rXPnzkl6EKilB0e4/fz8lD59enXv3l19+vRRpkyZ5Ovrq7feeksVK1aM853LAQAAAAB4VLxD9507d6I9pTomHh4eunPnTpzbt23bVpcuXdLAgQN1/vx5lS5dWitWrLBulnb69OlIh/ErVaqkGTNmaMCAAfrwww9VuHBhLViwQM8995zVZtGiRVZol6R27dpJejDsWcSd1b/66iu5uLgoMDBQoaGhql+/vsaNGxfnugEAAAAAeNQTDRnmaD179lTPnj2jnbdhw4Yo04KCghQUFBTj+rp06aIuXbrEuk0vLy+NHTtWY8eOjU+pAAAAAADE6IlC9/Tp07V9+/Y4tT169OiTbAIAAAAAgGTviUL30aNH4xWmbTbbk2wGAAAAAIBkLd6h+8SJE46oAwAAAACAFCfeoTtfvnyOqAMAAAAAgBSHEecBAAAAAHAQQjcAAAAAAA5C6AYAAAAAwEEI3QAAAAAAOAihGwAAAAAAByF0AwAAAADgIIRuAAAAAAAchNANAAAAAICDELoBAAAAAHAQQjcAAAAAAA5C6AYAAAAAwEEI3QAAAAAAOAihGwAAAAAAByF0AwAAAADgIIRuAAAAAAAchNANAAAAAICDELoBAAAAAHAQQjcAAAAAAA5C6AYAAEiF7OHhCr0TInt4uLNLAYAUzc3ZBQAAACDx3LpxXQd3bNX+P7bo8O4duh18XWH3QhV2757u3wvVvdBQ3b8XqvD79yVJ7p5eyvtMcfkXK/HgX/ESyvfMs/JKk9bJzwQAUgZCNwAAQDJ28eJFbdq0SZs2bdLGjRu1d+9eGWPivHxY6F0d+3uXjv29y5pms9nkl6+A8hUroUIly6pS41bKkjO3I8oHgBSP0A0AAJDMXL16Vd98840WLFigAwcOxNrWO006uXl4yMPTS24eHnL38JS7p+eD//fw0NWL53X+5LFIQd0Yo/9OHtN/J49p+/IF+vmLwSpTva7qtOuiMtXrytWNr5AAEFfsMQEAAJ6SucduJGj5i2dOaunk77Ru7nSFhtyOMt/m4iL/YiVUrHwlFa9QWUWfryjfTJkfu967t2/p9KH9Onlgr/Xv1MF/FBZ6V5Jk7HbtWr9Su9avVGa/XKoZ1FG1gl7i6DcAxAGhGwAAIIk7+tdOLfrxW/2+cpGM3W5Nt9lsKlTyeRWrUFnFy1dS0XIvyCdd+niv3ytNWhUpG6AiZQOsaeH37+vc8SP6feUirZ0zTVf++1eSdOX8Wf3y7QjNG/vFg6PfbTurbM36cnF1TfgTBYAUiNANAACQBNntdu1at0KLJ36rAzu2RZrn4eWtms0C1fjV3vLzL+SQ7bu6uSlPkWLKU6SYWvV4T3s2rdGaWZO1c/1KGbs90tFv/2Il1H3Il3qmbAWH1AIAyRmhGwAAIIk58c9f+v7Dt3Xin78iTU+fOasadHpV9dp3la+bZNJleir1uLi6qmzN+ipbs76u/HdW6+f9rLWzp1pHv08e2KuP29RXjcAX1eH9IUqfOetTqQsAkgPG6QYAAEgi7oXe1YwvP1H/VrUiBe5cBYvotU+/0dhNexX4Zl+ly/h0wnZ0MufIpdY939fYDX/pgwmz5V+shDVvw7wZ6lXnea2YOt4akgwAUjtCNwAAQBJwcOd2vd+0qhZ8P0r28HBJUp4ixdVv/CyNXL5dtdt2koenl5Or/D8RR7+Hz1+vbgM/l086X0lSyM1gTfrkfX3QsoYO7tzu3CIBIAkgdAMAADjRnVs3NWlIXw1q11Dnjh+RJLm6u6tN7w81YsEGPV+rgVxcku5XNlc3NzXo9Kq+XrNTNQJftKafOrBPA9s20Nj339D1yxedWCEAOFfS3YMDAACkcHs2rdW7jSpqxbQJ1jjZhUuV0+cLN6l1z/fl5uHh5ArjLn3mrOoxYpyGzlkZ6ZTzjb/OVN/GlfXP9s1OrA4AnIfQDQAA8JTdDbmtcf16aFi3QF0+9+BmZJ7ePur80TANnbNSeYoUc3KFT+6ZshX02YIN6jboC6XxfTB82Y0rlzS0cwst+vFb68cFAEgtCN0AAABP0eVz/2pQu4baMG+GNa1Eper6culWNe7aI0WMd+3i6qoGL72i0av/VKmqtSVJ9vBwTf/sY331VhfduXXTyRUCwNND6AYAAHhKDu/648Gdyff/LUnyTpNOrw/7RgOmLFD2vP7OLc4B0mfOqv4/zlFgz77WtO0rFurDwNo6e+ywEysDgKeH0A0AAPAUTJs2TYM7NNGN/39Tsex5/PXpL6tVq00n2Ww2J1fnOC6urmrb+yO9/8NM6w7nZ48dVv9WtbR9xUInVwcAjkfoBgAAcKDw8HB98MEH6tSpk+6H3ZMkPVuhiob9uk65Cxd1cnVPT7naDfXZ/A3K+0xxSdLd27c0qmdnTR8xkDG9AaRohG4AAAAHCQ4OVosWLTRixAhrWt0Xu+mjyfOVLmMmJ1bmHH7+BfS/uatVpVmQNW3RhG/0addWCrl5w4mVAYDjELoBAAAc4Pjx46pUqZKWLFkiSXJ1dVX3wV/qlU9Gyc3d3cnVOY+XTxq9NXK8ug4cIVc3N0nSvm2bNKRjMwVfuezk6gAg8RG6AQAAEtnvv/+ugIAA/fPPP5KkDBkyaMWKFarf8WUnV5Y02Gw2Nez0mgb9vETpMmaWJJ345y8NerGxrp4/5+TqACBxEboBAAAS0datW1W3bl1duXJFkvTMM8/ojz/+UJ06dZxcWdJT9PkXNGTmMmXKnlOSdPbYIQ1s31DHjx93cmUAkHgI3QAAAIlk8+bNql+/vm7efDAOdc2aNbV9+3YVLlzYyZUlXbkLPaNPZi1T9jz+kqSLZ06patWq2r9/v3MLA4BEQugGAABIBBs2bFCDBg1069YtSVLdunW1ZMkSZciQwbmFJQPZ8vhryKzlyl3owd3cz507p+rVq2vXrl1OrgwAEo7QDQAAkEBr165Vo0aNFBISIklq0KCBFi5cKB8fHydXlnxkyp5Dg2csVYHnSkuSLl++rJo1a2rLli3OLQwAEojQDQAAkACrVq1SkyZNdOfOHUlS48aNNX/+fHl7ezu5suTHN1NmDZy2UFWqVJH0YMi1evXqafXq1U6uDACeHKEbAADgCS1fvlzNmjXT3bt3JUnNmjXTvHnz5OXl5eTKki+fdOm1cuVK1atXT5IUEhKiJk2aaNmyZU6uDACeDKEbAADgCSxZskQtWrRQaGioJKlly5aaO3euPD09nVxZ8ufj46NFixapVatWkqR79+6pdevWnGoOIFkidAMAAMTTkiVL1KpVK927d0+S1Lp1a82ePVseHh5Orizl8PT01OzZsxUUFCRJunPnjpo0aaK///7byZUBQPwQugEAAOJhy5YtCgoKUlhYmCSpXbt2mjlzptzd3Z1cWcrj5uam6dOnW6eaX79+XfXr19exY8ecXBkAxB2hGwAAII7279+vpk2bWtdwt23bVtOmTZObm5uTK0u5PDw8NG/ePFWoUEGSdP78edWrV0///fefkysDgLghdAMAAMTB2bNn1aBBA127dk2SVKdOHU2dOpXA/RSkTZtWS5cuVfHixSVJx48fV/369a33AgCSMkI3AADAY1y/fl0NGjTQmTNnJEllypTRr7/+yjXcT1HmzJm1atUq5cuXT5K0d+9eNW3a1BobHQCSKkI3AABALO7evasWLVpo3759kqT8+fNr2bJlSpcunZMrS31y5cqlVatWKWvWrJKiXl8PAEkRoRsAACAG4eHheumll7Rx40ZJUpYsWbRy5Ur5+fk5ubLUq0iRIlqxYoX1o8eyZcvUpUsX2e12J1cGANEjdAMAAETDGKPevXvrl19+kfRg7OilS5eqcOHCTq4MZcuW1eLFi60x0WfMmKH33nvPyVUBQPQI3QAAANEYMWKExowZI0lydXXVL7/8ooCAACdXhQjVq1fXnDlz5OrqKkn66quvNH78eCdXBQBREboBAAAeMXXqVPXv3996/OOPP6phw4ZOrAjRadasmcaNG2c9fvPNN7V+/XonVgQAURG6AQAAHrJ582a9/PLL1uNhw4apS5cuzisIsXr11VfVu3dvSdL9+/cVGBioI0eOOLcoAHgIoRsAAOD/O3XqlAIDA627Yffo0UMffPCBk6vC43z55ZfWmQjXrl1TkyZNGMMbQJJB6AYAAJB0+/ZtNW/eXJcuXZIk1alTR19//bVsNpuTK8PjuLq6atasWSpevLgk6fDhw2rTpg1DiQFIEgjdAAAg1bPb7ercubP++usvSVKhQoU0e/Zsubm5ObkyxJWvr6+WLFmiLFmySJLWrFljnXYOAM5E6AYAAKne//73P82bN0+SlC5dOi1atEiZMmVyclWIr/z582v+/Plyd3eXJI0bN05jx451clUAUjtCNwAASNV+/fVXDRo0SJJks9k0c+ZMFStWzMlV4UlVqVJFEyZMsB736tVLq1atcmJFAFI7QjcAAEi1/vrrL7300kvW488++0yNGzd2YkVIDJ07d1a/fv0kSeHh4WrTpo0OHjzo5KoApFaEbgAAkCpdunRJzZs3V0hIiCSpY8eO6tu3r5OrQmIZNmyYmjdvLkm6ceOGmjZtquvXrzu3KACpEqEbAACkOvfu3VPr1q116tQpSVJAQIAmTJjAncpTEBcXF02fPl2lSpWSJB09elSdOnWS3W53cmUAUhtCNwAASFWMMXrrrbe0adMmSVKOHDk0f/58eXl5ObkyJLa0adNqwYIF1k3xFi9erOHDhzu5KgCpDaEbAACkKhMnTtT48eMlSZ6enlqwYIFy5szp5KrgKP7+/po5c6Z1FsPHH3+slStXOrkqAKkJoRsAAKQaO3fuVM+ePa3HEyZMUEBAgBMrwtNQr149DR06VNKDMx1efPFFnTx50rlFAUg1CN0AACBVuHr1qlq3bq3Q0FBJUs+ePSPduRwpW//+/dW0aVNJD/pCYGCg7ty54+SqAKQGhG4AAJDi2e12vfTSS9bRzQoVKmjkyJHOLQpPlYuLi6ZOnapChQpJknbt2qU333xTxhgnVwYgpSN0AwCAFG/YsGFatmyZJClLliyaO3euPDw8nFwVnrYMGTLo119/lY+PjyTpp59+0oQJE5xcFYCUjtANAABStNWrV2vgwIGSJJvNppkzZypPnjxOrgrOUqJECf3444/W47feekt//PGHEysCkNK5ObsAAAAARzl9+rTat29vnUI8dOhQ1alTx8lV4XHmHrvh0PW7BTTS22+/rW+++Ub37t1TYGCgdu7cqWzZsjl0uwBSJ450AwCAFCk0NFRBQUG6cuWKJKlx48bq37+/k6tCUvHll1+qcuXKkqR///1X7dq10/37951cFYCUiNANAABSpHfffdc6bdjf31/Tpk2TiwtfffCAu7u75s6dKz8/P0nS+vXrNWTIECdXBSAl4pMHAACkOD///LPGjh0rSfL09NS8efOUMWNGJ1eFpCZHjhyaM2eOXF1dJUmffvqpVq1a5eSqAKQ0hG4AAJCi/PPPP3r11Vetx2PHjlXZsmWdWBGSsqpVq+rTTz+VJBlj1LFjR507d87JVQFISQjdAAAgxbh9+7aCgoIUEhIiSerWrZu6d+/u5KqQ1PXt21eNGjWSJF26dInruwEkKkI3AABIMXr27KkDBw5IkkqWLKkxY8Y4uSIkBy4uLpoyZYpy584tSdq8ebMGDRrk5KoApBSEbgAAkCJMmTJFkydPliSlSZNGc+bMkbe3t3OLQrKRJUsWzZ4927q+e9iwYVqxYoWTqwKQEhC6AQBAsrd//3716NHDevzDDz/omWeecWJFSI4qVaqk4cOHW487duyof//914kVAUgJCN0AACBZCwkJUZs2bazruF9++WV16NDByVUhuXr33XfVpEkTSdKVK1e4vhtAghG6AQBAsvb222/rn3/+kSQ999xz+vrrr51cEZKziOu78+bNK0nasmWLBgwY4OSqACRnhG4AAJBsTZ8+XRMnTpQk+fj4aM6cOfLx8XFyVUjuMmXKpNmzZ8vNzU2SNGLECC1dutTJVQFIrgjdAAAgWTp48KBef/116/F3332nYsWKObEipCQvvPCCRowYYT3u1KmTzpw548SKACRXhG4AAJDs3LlzR23atNHt27clSV27dlWnTp2cXBVSmnfeeUfNmjWTJF29elXt27fn+m4A8UboBgAAyU7v3r21d+9eSVLx4sX17bffOrkipEQ2m02TJ09Wvnz5JD24vnvw4MHOLQpAskPoBgAAycrMmTM1fvx4SZK3t7fmzJmjNGnSOLkqpFQZM2bUzJkzI43fvWbNGidXBSA5IXQDAIBk4+jRo3r11Vetx2PHjtWzzz7rxIqQGlSsWFGffvqpJMkYo44dO+rChQtOrgpAckHoBgAAyUJoaKjatm2rW7duSZJeeukldenSxblFIdXo27ev6tevL0m6cOGCOnXqJLvd7uSqACQHhG4AAJAsfPDBB9q1a5ckqUiRIho3bpxsNpuTq0Jq4eLioqlTp8rPz0+StGrVKn3++edOrgpAckDoBgAASd7ixYs1evRoSZKnp6dmz56ttGnTOrcopDrZsmXT9OnTrR97BgwYoC1btji5KgBJHaEbAAAkaWfOnIl0GvnIkSNVunRpp9WD1K127dr66KOPJEnh4eFq3769rl696uSqACRlhG4AAJBk3b9/Xy+++KIValq2bKkePXo4uSqkdoMGDVLVqlUlPfhRqHv37jLGOLkqAEkVoRsAACRZQ4YM0W+//SZJyps3ryZOnMh13HA6Nzc3zZgxQ5kyZZIkLViwQGPGjHFyVQCSKkI3AABIktatW2cN0+Tq6qqZM2cqY8aMTq4KeCB37tyaPHmy9fi9996zbvQHAA9LsqF77Nix8vf3l5eXlypUqKA//vgj1vZz585V0aJF5eXlpRIlSmjZsmWR5htjNHDgQOXIkUPe3t6qU6eOjhw5EqmNv7+/bDZbpH+fffZZoj83AAAQu4sXL6pDhw7WKbv/+9//VKlSJSdXhZRk7rEbCf53t3g1Ne764HKHe/fuqXGrIE3961/NPXbDyc8OQFKSJEP37Nmz1adPHw0aNEi7du1SqVKlVL9+fV28eDHa9lu3blX79u3VvXt37d69Wy1atFCLFi20b98+q83nn3+ub775Rt9//71+//13pUmTRvXr19fdu3cjreuTTz7Rf//9Z/176623HPpcAQBAZHa7XZ06ddL58+clSfXq1dP777/v5KqA6HXoO1gFS5SRJJ0/dVwTBvbh+m4AkSTJ0D1q1Ci98sor6tq1q4oXL67vv/9ePj4+mjRpUrTtv/76azVo0EB9+/ZVsWLFNHToUJUtW9a6tsYYo9GjR2vAgAFq3ry5SpYsqalTp+rcuXNasGBBpHWlS5dOfn5+1r80adI4+ukCAICHfPnll1q5cqUkyc/PT1OnTpWLS5L8ygLIzcNDvUZPkneadJKk3xbN1fpfpju5KgBJSZL7BLt375527typOnXqWNNcXFxUp04dbdu2Ldpltm3bFqm9JNWvX99qf+LECZ0/fz5Sm/Tp06tChQpR1vnZZ58pc+bMKlOmjL744gvdv38/sZ4aAAB4jG3btlnDMdlsNk2fPl3Zs2d3clVA7Pzy5ddrw76xHk8a8r7279/vxIoAJCVuzi7gUZcvX1Z4eHiUD9js2bPr4MGD0S5z/vz5aNtHnJYW8f+xtZGkt99+W2XLllWmTJm0detW9e/fX//9959GjRoV7XZDQ0MVGhpqPQ4ODpb04LQ4u90el6cLJBl2u13GGPouUhz6dvJx7do1tWvXzvrBu3///qpZs2bKeu9MIj0XY/7vn1LQ65OMVWrUXPu2dtaa2VN07+4dtWnTRtu3b5ePj4+zS0tW2GcjOYlrP01yoduZ+vTpY/13yZIl5eHhoddee03Dhw+Xp6dnlPbDhw/XkCFDoky/dOlSlGvFgaTObrfrxo0bMsZwGidSFPp28mCMUbdu3XT69GlJUkBAgN54440Y7+eSXNlu3k6kNRnZ7tyUbNL//x8kAV169dWhndt05uhh/fPPP3r99df15ZdfOrusZIV9NpKTmzdvxqldkgvdWbJkkaurqy5cuBBp+oULF+Tn5xftMn5+frG2j/j/CxcuKEeOHJHalC5dOsZaKlSooPv37+vkyZN65plnoszv379/pKAeHBysPHnyKGvWrPL19Y39iQJJjN1ul81mU9asWfmQQ4pC304exowZoxUrVkiSMmfOrLlz5ypnzpxOrirxmVuJdFdrYyQjmbSZJMYtTzI80knvfDtF/VvVVuidEP38889q1KiR2rVr5+zSkg322UhOvLy84tQuyYVuDw8PPf/881q7dq1atGgh6cEf39q1a9WzZ89ol6lYsaLWrl2r3r17W9NWr16tihUrSpLy588vPz8/rV271grZwcHB+v333/XGG2/EWMuePXvk4uKibNmyRTvf09Mz2iPgLi4u7CSQLNlsNvovUiT6dtK2c+dO9e3b13o8ZcoU5c2b14kVOZAtsfqg/UHYttkScZ1IDLkLF1O3QZ/ruw8efG99/fXXFRAQoEKFCjm5suSDfTaSi7j20SQXuqUHp3l37txZ5cqVU0BAgEaPHq3bt2+ra9eukqROnTopV65cGj58uCSpV69eql69ukaOHKnGjRtr1qxZ+vPPPzV+/HhJD/5we/furf/9738qXLiw8ufPr48//lg5c+a0gv22bdv0+++/q2bNmkqXLp22bdumd955Rx07dlTGjBmd8joAAJDSBQcHq23btrp3754k6d1331Xjxo2dXBWQMDUCO+jmvu2aPn26bt68qXbt2mnLli3RHqwBkPIlydDdtm1bXbp0SQMHDtT58+dVunRprVixwroR2unTpyP9qlCpUiXNmDFDAwYM0IcffqjChQtrwYIFeu6556w277//vm7fvq1XX31V169fV5UqVbRixQrrlABPT0/NmjVLgwcPVmhoqPLnz6933nkn0unjAAAg8Rhj9Oqrr+rYsWOSHlzHPWzYMCdXBSSczWbTuHHj9Pvvv+vIkSPauXOnPvjgA3311VfOLg2AE9iMMcbZRaQUwcHBSp8+vW7cuME13Uh27Ha7Ll68qGzZsnE6F1IU+nbSNWHCBL366quSHgzluXv3buXPn9/JVTnW3GOJdU23XbabV2XSZeL08iQqqGB67dmzRy+88II12s3ChQvVrFkzJ1eWtLHPRnIS1/xHTwYAAE/d3r179fbbb1uPJ06cmOIDN1Kf0qVLa+TIkdbjLl266NSpU06sCIAzELoBAMBTdfv2bbVp08YaXvPNN99UYGCgk6sCHKNHjx5q1aqVpAdj0T98DwMAqQOhGwAAPFVvvvmmDh48KOnBkUDGMUZKZrPZIp3J8fvvv+uDDz5wclUAniZCNwAAeGp++uknTZkyRZKUNm1azZ49O87jnALJVYYMGTR37lx5eHhIkr766ivNnz/fyVUBeFoI3QAA4Kn4+++/1aNHD+vxDz/8oCJFijixIuDpef755zVq1CjrcdeuXXX8+HEnVgTgaSF0AwAAhwsODlZQUJB1Hfdrr72mF1980clVAU9Xjx49FBQUJEm6ceOG2rRpY93ZHEDKRegGAAAOFTEe9+HDhyVJZcqU0ejRo51bFOAENptNP/74owoVKiRJ2rlzp9577z0nVwXA0QjdAADAocaNG6fZs2dLknx9fTV37lyu40aqFfE34OnpKUkaM2aM5s6d6+SqADgSoRsAADjMjh079M4771iPf/rpJxUsWNCJFQHOV7p0aX399dfW4+7du+vo0aNOrAiAIxG6AQCAQ1y7dk1BQUEKCwuTJL3zzjvWeMVAavfqq6+qffv2kqSbN29GuucBgJSF0A0AABKd3W5X586dderUKUlSxYoVNWLECCdXBSQdNpst0h389+zZE+msEAApB6EbAAAkupEjR2rx4sWSpMyZM2v27Nlyd3d3clVA0pIuXbpI9zj4/vvvNW3aNCdXBSCxEboBAECi2rx5s/r37289nj59uvLkyePEioCkq2TJkho7dqz1+LXXXtNff/3lxIoAJDZCNwAASDQXL15Uu3btFB4eLkn66KOP1KBBAydXBSRt3bp108svvyxJunPnjlq1aqVr1645uSoAiYXQDQAAEkVYWJjatGmjc+fOSZJq1qypIUOGOLkqIHn49ttv9fzzz0uSjh8/rk6dOslutzu5KgCJgdANAAASRb9+/bRx40ZJUo4cOTRjxgy5uro6uSogefDy8tK8efOUKVMmSdKSJUs0bNgwJ1cFIDEQugEAQILNmDFDX331lSTJ3d1d8+bNk5+fn5OrApKXfPnyaebMmbLZbJKkgQMHauXKlU6uCkBCEboBAECC/P3339b1qJL0zTffqGLFik6sCEi+6tWrp6FDh0qSjDF68cUXdfLkSecWBSBBCN0AAOCJXb16VS1bttSdO3ckSV27dtVrr73m5KqA5K1///5q2rSppAd/Y4GBgbp7966TqwLwpAjdAADgiYSHh6tDhw46fvy4JKlcuXIaN26cdWosgCfj4uKiqVOnqmDBgpKkXbt2qWfPnk6uCsCTInQDAIAnMnjwYK1YsUKSlCVLFs2bN09eXl5OrgpIGTJkyKBff/1V3t7ekqSJEyfqxx9/dHJVAJ4EoRsAAMTbwoUL9b///U/Sg6Nys2fPVt68eZ1cFZCylCxZUhMmTLAev/nmm/r999+dWBGAJ0HoBgAA8XLo0CG99NJL1uMRI0aoVq1aTqwISLk6dOigt956S5J07949tWzZUmfPnnVyVQDig9ANAADi7ObNm2rZsqVu3rwpSWrTpo3effddJ1cFpGxffvmlqlWrJkn677//1KJFC+vmhQCSPkI3AACIE7vdrpdeekkHDhyQJD333HOaOHEiN04DHMzDw0O//PKL/P39JUl//vmnunfvLmOMcwsDECeEbgAAECcffvihFi5cKElKnz69fv31V6VNm9bJVQGpQ9asWbVw4UKlSZNGkjRz5kx99tlnTq4KQFy4ObsAAACQ9E2ZMkUjRoyQJLm6umru3LkqXLiwk6tKXHOP3XB2CUCsSpYsqWnTpqlVq1aSpI8++kjPPvusmjVr5uTKAMSGI90AACBWv/32m1555RXr8ddff626des6sSIg9WrZsqWGDh0qSTLGqEOHDtq3b5+TqwIQG0I3AACI0cmTJ9WyZUuFhYVJknr06KE333zTyVUBqdtHH32ktm3bSpJu3bqlZs2a6cqVK06uCkBMCN0AACBawcHBatq0qS5fvixJqlOnjkaPHu3cogDIZrNp0qRJKlu2rCTpxIkTat26tfXjGICkhWu6AQBAFOHh4XrxxRet01aLFCmiOXPmyN3d3Wk1cc018H98fHy0YMEClS9fXhcuXNCGDRvUu3dvjR071tmlAXgER7oBAEAUH3zwgZYuXSpJypgxo5YsWaKMGTM6uSoAD8uTJ4/mz58vDw8PSdK4ceM0ZswYJ1cF4FGEbgAAEMmkSZP05ZdfSpLc3Nz0yy+/pLg7lQMpRcWKFTV+/Hjrca9evayh/QAkDYRuAABg2bRpk15//XXr8ZgxY1SrVi0nVgTgcTp37qwPPvhAkmS329W+fXv9/vvvTq4KQARCNwAAkCTt379fzZs3t27G9Pbbb+u1115zclUA4uLTTz/Viy++KEm6c+eOmjZtqmPHjjm5KgASoRsAAEj6999/1aBBA12/fl2SVL9+fY0cOdK5RQGIMxcXF02aNEk1atSQJF26dEkNGza0Rh8A4DyEbgAAUrnr16+rYcOGOnPmjCTp+eef19y5c+XmxiAnQHLi6emp+fPn69lnn5UkHTlyRM2aNdOdO3ecXBmQuhG6AQBIxe7evavmzZtbQ4MVKFBAS5cuVbp06ZxcGYAnkSFDBi1btkw5c+aUJG3btk0dOnRQeHi4kysDUi9CNwAAqVR4eLheeuklbdq0SZKUNWtWrVy5UtmzZ3dyZQASIm/evFq6dKnSpk0rSZo/f7769OkjY4yTKwNSJ0I3AACpkDFGvXv31i+//CJJ8vHx0dKlS1WoUCEnVwYgMZQuXVrz5s2zLhP55ptv9NVXXzm5KiB1InQDAJAKjRgxQmPGjJH0YCzuefPmqXz58k6uCkBiqlevniZMmGA9fvfddzVnzhwnVgSkToRuAABSmSlTpqh///7W4x9//FENGjRwYkUAHKVLly4aPHiw9bhjx45atmyZ8woCUiFCNwAAqciKFSvUvXt36/Hw4cPVuXNnJ1YEwNEGDhyol19+WZIUFhamwMBArV+/3slVAakHoRsAgFRi06ZNCgwMtO5i3LNnT/Xr18/JVQFwNJvNpu+//15t27aV9GDUgqZNm2rbtm1OrgxIHQjdAACkAtu2bVPjxo0VEhIiSWrdurVGjx4tm83m5MoAPA2urq6aNm2amjZtKkm6ffu2GjZsqD179ji3MCAVIHQDAJDC7dixQw0aNNCtW7ckSY0aNdL06dPl6urq5MoAPE3u7u6aM2eOateuLUm6ceOG6tWrpwMHDji5MiBlI3QDAJCC7d69W/Xq1VNwcLAkqW7dupo3b548PT2dXBkAZ/Dy8tKCBQtUqVIlSdKlS5dUp04dHT9+3MmVASkXoRsAgBRq7969qlu3rq5fvy5JqlGjhhYsWCAvLy/nFgbAqdKmTaulS5eqbNmykqRz586pdu3a+vfff51cGZAyEboBAEiBDhw4oNq1a+vKlSuSpMqVK2vx4sXy8fFxcmUAkoIMGTJo5cqVKl68uCTp5MmTql27ti5cuODkyoCUh9ANAEAKc/jwYdWqVUuXLl2SJFWoUEHLli1T2rRpnVwZgKQkS5YsWr16tQoWLCjpwb6jTp06BG8gkRG6AQBIQY4fP65atWrp/PnzkqSyZctqxYoV8vX1dXJlAJKinDlzau3atcqdO7ckad++fapevTqnmgOJiNANAEAKcezYMdWqVUtnz56VJJUsWVKrVq1ShgwZnFsYgCQtX758WrdunfLkySNJOnTokKpVq6YTJ044uTIgZSB0AwCQAuzdu1dVqlTRqVOnJEnFixfXmjVrlDlzZidXBiA5KFy4sDZv3qwCBQpIkk6cOKGqVavq0KFDTq4MSP4I3QAAJHPbt29X9erVrVPKn332Wa1du1ZZs2Z1cmUAkpN8+fJp8+bNKlasmCTp7Nmzqlatmv7++28nVwYkb4RuAACSsdWrV6t27dq6du2aJCkgIEAbN26Un5+fkysDkBzlzJlTGzduVKlSpSRJFy9eVI0aNbRjxw4nVwYkX4RuAACSqXnz5qlx48YKCQmRJNWuXVtr167llHIACZI1a1atX79eFSpUkCRdu3ZNtWvX1m+//ebkyoDkidANAEAyNGnSJLVp00ZhYWGSpJYtW2rp0qUMCwYgUWTMmFGrV69WtWrVJEk3b95U/fr1tWbNGidXBiQ/hG4AAJKZUaNGqXv37rLb7ZKkLl26aM6cOfL09HRyZQBSknTp0mn58uWqV6+eJCkkJESNGzfWrFmznFwZkLwQugEASCaMMRowYIDeffdda1rv3r01ceJEubm5ObEyACmVj4+PFi1apObNm0uS7t27p/bt2+vTTz+VMcbJ1QHJA5/QAAAkA6GhoXr99dc1efJka9rQoUP10UcfyWazOa8wANGae+yGs0tIFEEF08vT01Nz585Vjx499OOPP0qSBgwYoKNHj+qHH36Qh4eHk6sEkjaOdAMAkMRdunRJderUiRS4v/32Ww0YMIDADeCpcHd31/jx4zV8+HBr2uTJk9WwYUNdv37deYUByQChGwCAJGzfvn0KCAiw7hrs7e2tOXPmqGfPnk6uDEBqY7PZ9MEHH2j27NnWPSTWrVunSpUq6cSJE06uDki6CN0AACRRS5YsUcWKFXXy5ElJD8bP3bx5s4KCgpxbGIBUrU2bNlq/fr2yZMkiSTpw4IAqVKig33//3cmVAUkT13QDAOBkj177aYzRkoljNH3EQOtGRQVLlFHf72foeIYcOh7Pa0WDCqZPtFpjklKuXwUQNxUrVtT27dvVuHFjHTp0SJcuXVKNGjU0ffp0BQYGOrs8IEnhSDcAAEnI/Xv39F3/npr22cdW4K7YqKUGz1iqTNlzOLk6APg/BQsW1NatW1W9enVJ0t27d9W6dWsNGjRI4eHhTq4OSDoI3QAAJBHBVy5raOfm2vDLz9a01m/1U++vJ8nT28eJlQFA9DJlyqRVq1bppZdesqZ98sknatCggS5evOjEyoCkg9ANAEASsP/339S3WVUd2LFNkuTu6aXeX09Sm179uUM5gCTNw8NDU6ZM0WeffSYXlwfxYs2aNSpTpoy2bNni5OoA5yN0AwDgROHh4Zr7zWca8lIzXbvwnyQpYzY/DZm5TJUat3JydQAQNzabTf369dPatWuVPXt2SdK5c+dUvXp1jRw50rpcBkiNCN0AADjJuXPnVLduXc395jMZu12S9FzFavpswQYVKlnWydUBQPzVqFFDu3fvtq7zDg8P13vvvafAwEDG80aqRegGAMAJVqxYodKlS2v9+vWSJJuLi9q+85EGTJ6vjNn8nFwdADy5HDlyaM2aNerfv781bf78+SpXrpz27NnjvMIAJyF0AwDwFIWFhen9999Xw4YNdenSJUlSpuw5NfjnJQp8s69cXF2dXCEAJJybm5uGDRumxYsXK2PGjJKkY8eO6YUXXtCYMWNk//9n9wCpAaEbAICn5OTJk6pWrZq++OILa1qTJk30+eLNKla+khMrAwDHaNKkiXbt2qXy5ctLkkJDQ/XWW2+pdu3aOnHihJOrA54OQjcAAA5mt9s1ZswYPffcc9q+fbskyd3dXaNGjdKiRYvkmymzkysEAMfx9/fX5s2b9dZbb1nTNmzYoBIlSui7/9fevUdFVS1+AP8OAgMOLwF5+UAUEV+BohAkXjUEH7eyuohZ+bimmI9UME1Le1moJZWvQNdNq5tdIdc1TCO5YyUlgQqKEuCTi6IDIsLA8Bhgzu8PLufnCCoqw8Dw/ax11szZZ8+ZfWCvge+cc/b+/HOe9SaDx9BNRESkQ9nZ2QgMDMTixYuhUqkAAH379sXvv/+OZcuWcTowIuoUpFIpNm/eDLlcDldXVwCASqXCggULMH78eOTl5em3gUQ6xNBNRESkA2q1GuvWrYO3tzeOHTsmloeHh2tdaklE1JmMGzcOZ86cQXh4uFh25MgRDB06FLGxsZxajAwSQzcREVErO378OEaMGIE1a9ZArVYDANzd3fHLL78gJiYG1tbWem4hEZH+WFpaIiYmBocPH0avXr0AABUVFZg/fz5CQkJw5coVPbeQqHUxdBMREbWSyspKLF++HI8//jjOnDkDAOjSpQtWrlyJzMxMcd5aIiICxo8fj7Nnz+KVV14Ry+RyOUaPHo13330XlZWVemwdUeth6CYiInpEgiAgLi4OgwcPxqZNm8RBgby9vZGWlob169fD3Nxcz60kImp/rKyssHPnTiQmJqJnz54AgOrqarz33nvw9PREXFwcLzmnDo+hm4iI6BGkpKQgICAAYWFh4kBAUqkUUVFRSEtLw/Dhw/XbQCKiDiAkJARnz57FkiVLYGxsDAC4cuUKwsLCMGbMGJw6dUq/DSR6BAzdRERED+HSpUuYOnUqAgICxGnAACAoKAinT5/GG2+8ARMTEz22kIioY7G2tkZ0dDTkcjmCg4PF8qNHj8LHxwfz58/HjRs39NhCoofD0E1ERPQAbt26heXLl2PgwIGIj48XywcNGoRDhw7h8OHDGDBggB5bSETUsXl4eODQoUNISEhAv379AAAajQaxsbHw8PBAdHQ0qqqq9NxKopZj6CYiImqB6upqfPbZZ3B3d8emTZvEUckdHBwQExOD06dPY+LEiZx3m4ioFUgkEjz11FPIysrChg0bYGFhAQAoLS1FZGQk+vbti08++YSDrVGHwNBNRER0D0qlEhs3bkSfPn2wdOlSlJSUAADMzMywevVqnD9/HuHh4eI9iERE1HqkUilWrFiBc+fOYdasWWK5QqFAREQE+vbti+joaIZvatckAocDbDVKpRLW1tYoKyuDlZWVvptD9EA0Gg2Kiorg4OAAIyN+H0eG42H7dnFxMTZv3owtW7agtLRUa9tLL72EDz74AL17926VNsZfLGuV/VAnImggKS+BYGkLSPiZTboT2s+6Td/vfp/ZmZmZeO+997Bv3z6tcgcHB6xYsQLz58+HTCZrq+ZSJ9fS/MdPaSIiotsUFBQgIiICrq6ueP/998XALZFIEBoaivT0dHz99detFriJiKjlHnvsMXz33Xc4ffo0/va3v4nlRUVFWL58Odzc3LBx40bcunVLj60k0sbQTUREBODs2bOYN29ek/sEjY2NMXv2bGRnZyMuLg7Dhg3Tc0uJiOixxx5DfHw8MjMzERoaKo6ncePGDaxcuRI9evTAvHnzcPr0aT23lIihm4iIOrGqqip89dVXeOKJJzB06FDs3LlTHCDNzMwMixcvxsWLF/HFF19wRHIionZo6NChiIuLw5kzZxAWFiaG76qqKuzcuRPe3t4IDAzE3r17UVtbq+fWUmfFe7pbEe/ppo6M93SToWqub2dnZyM2NhZfffVVk0sQrayssHDhQixZsgSOjo6835raJ97TTYbqEfv2tUvnkfjPnfh137eoUpVrbXN2dkZ4eDjmzZsHZ2fn1moxdWItzX8M3a2IoZs6MoZuMlSNfdvS0hL79+9HbGwskpOTm9QbPHgwwsPD8fLLL8PGxkYsZ+imdomhmwxVK/XtqopyHN2/F4lf70TBxVytbcbGxggJCcH06dPxzDPPcOA1emgM3XrA0E0dGUM3GaLq6mocOnQI//znP3H48GGoVCqt7VKpFFOnTkV4eDgCAgKanWOboZvaJYZuMlSt3LcFQYB9fjq2bt2K77//HhqNRmt7165dMWXKFEyfPh3BwcEwMTF55PekzoOhWw8YuqkjY+gmQ1FTU4OffvoJcXFxSEhIQHl5eZM6np6eCA8Px4wZM2Bra3vP/TF0U7vE0E2GSgd9u3Has/z8fPHWoqtXrzapZ2dnh9DQULz44osICAjg/0N0XwzdesDQTR0ZQ3f70hZBT9dzr7ZlWK0sL8PZlKNISzqI40mHUFWhbFJHZm2DkeMnY8xz0zFwZPNntYk6DIZuMlQ6DN2NNBoNkpOTsWfPHsTHxzc7vZiDgwMmTpyISZMmITg4WOu2I6JGDN16wNBNHRlDd/vC0H1vmvp6XMo6jdPJcpxOPoJzGWnQ1Nc3qSezssbIoEkIGBuEIeMmw1hqprM2EbUphm4yVG0Qum+nVquRmJiIPXv2ICEhAVVVVU3qdOnSBaNGjcLkyZMxadIkDBo0iF/cEgCGbr1g6KaOjKG7fWHoburm9QKcOfYLTiUfwZnff0b5rZJm63W1tMLI8ZPhP+lZPBYwBsYmxgwnZHgYuslQtXHovl15eTm+//57xMfHQy6XNxkHpJGrqyvGjx+P0aNHIzAwEK6urgzhnRRDtx4wdFNHxtDdvnT20F1XW4v/5pxFbnoqzqWnITc9DTevN73/rpGzmzu8AsfBe3QQhvr/BSZS6f9vZDghQ8R+TYZKj6H7djU1Nfj1119x8OBBHDx4EBcvXrxr3Z49eyIwMBCBgYEYPXo0Bg4cyP+lOgmGbj1g6KaOjKG7felMoVsQBNy8fhV5f57BuVMncC49FRcy06GubnqJXyNzCysMDRgNr8An4RU4Dg49Xe/xBgwnZIDYr8lQtZPQfadz587h4MGDOHToEH799VfU1tbeta6trS38/f0xfPhwDBs2DMOHD0fv3r15NtwAtTT/Gbdhm4iIqJNTV1fhyvkc/DfnbMOSfRb/zc2Cqqz0nq+TmneFu5cPPEc8Dq9R4+DuNQLGnNaFiIjaiIeHBzw8PLBs2TKoVCqkpqYiOTkZycnJSElJQWVlpVi3pKREPEPeyNbWFsOGDRNDuLe3N9zd3R9oijJdfyGv6y/jOzOGbiIialWCIEChUODP1HRcz7uE6/+9iOuXL+DapQu4dvk8hDvmSG1O95694THMFwOG+cJjuC9cPYegizH/ZBERkf7JZDKMGzcO48aNAwDU1tYiIyNDDOG//fYbbt68qfWakpISyOVyyOVysczY2Bj9+vXDgAED4OnpCU9PT/H5/aazpI6F/8EQEdEDEQQBt27dQn5+PvLz83HlyhXk5+cjLy8P58+fx/nz51FRUdHi/XVzdEYfzyFw9RyCvkO84THcF7aOzjo8AiIiotZjYmICX19f+Pr6IjIyEoIgIC8vDxkZGUhPTxeXwsJCrdfV1dUhNzcXubm5SEhI0Npmb28PNzc39OnTB66urujTpw+umNqje8/e6O7SC+YWlm15iPSI2m3o3rZtGz766CMoFAp4eXlhy5Yt8PX1vWv9+Ph4rFmzBnl5eejfvz82bNiASZMmidsFQcDbb7+NnTt3orS0FE888QQ+//xz9O/fX6xTUlKCxYsX48CBAzAyMsLzzz+Pzz77DBYWFjo9ViKi9kCj0aCkpAQKhQKFhYVNloKCAjFg334ZXUsZm5iip/sAuA4cAtcBg+E6cChcPYfAytZOB0dDRESkHxKJBG5ubnBzc8Nzzz0nll+/fh0ZGRk4efIksrKykJOTg9zcXFRXVzfZR3FxMYqLi3H8+PFm30NmbQNbRxd06+4IGwdH2HR3bHje+OjgBGs7e3S1tOa95O1AuxxIbe/evZgxYwZiYmLg5+eHTz/9FPHx8cjNzYWDg0OT+seOHcPo0aMRFRWFv/71r9izZw82bNiA9PR0DBkyBACwYcMGREVF4csvv4SbmxvWrFmDM2fO4M8//4SZWcO8rRMnTsT169cRGxuL2tpazJ49GyNHjsSePXta1G4OpPbwDOEelbYY+EqnBA0CLWo4kFoLtOffdZ1ajcqKclRVlKOqQokqVQWqKspRWVEOVVkpKspuoaL0lvioUpaJ68qS4mbnun4QRl26wKGnK5z79INTn75w7uMO5z794NynL+xdesGoS5dWOtIHwAGnyBCxX5Oh6mR9W6PR4Ob1qyi4eB7XLp1DwaXzuHbpPK5dvoDSIgUeNaoZdekCC+tusOxmCwsbW1jadINFN1tYdbODzMoGXS2txGXSAGdYW1uLi6WlJYx5a9c9dejRy/38/DBy5Ehs3boVQENn7NWrFxYvXow33nijSf2wsDCoVCr88MMPYtnjjz8Ob29vxMTEQBAEuLi4IDIyEsuXLwcAlJWVwdHREbt378a0adOQnZ2NQYMG4fjx4xgxYgQAIDExEZMmTcLVq1fh4uJy33YzdD88hu52gKG7xe78XWs0Gmjq6lBfX4f6ujpo6usbntfW/q+sHvV1tairVaOu9n+P6obnjeW1ajVqa6qhrqlBbU21uF5bUwN1TXXDUlWJmqoq1FRVoqaqEurqKlRXVkJdXYnqykpUVZSjrlat02OXmneFnXMP2Dv3hL1Lz4bnLj1h9791h56u7W+As072Dxx1EuzXZKjYt0V1ajWKrxeg+Fo+iq7mo7jgCooKGh5vXLuCW4UK3f/dl0ohk8lgYWEBmUymtVhYWMDc3BxmZmYwMzMTn99eJpVKIZVKYWpqqrU0lpmYmIiLsbFxs89NTEzQRR9f2rdAhx29XK1W4+TJk1i1apVYZmRkhKCgIKSkpDT7mpSUFERERGiVhYSEYP/+/QCAy5cvQ6FQICgoSNxubW0NPz8/pKSkYNq0aUhJSYGNjY0YuAEgKCgIRkZGSE1NxbPPPtuKR6kfCxYswI0bN/TdjGZdrbj7tAutIc5C9yFA18dwp9b4vkzAbfsQBOw21kAqlYqXId35Ho+yfr/n93q8fWmu7G6LRqNpst5Ydvtjc0t9fb3W89sXdV19Q7jW1D/ymeH2wExmAZmVDaxs7WBj7wBr++6wtmt4bFhveN6tuxMsbLrxMjUiIqI2YGxqCidXNzi5ujW7XRAEqJRlKC1S4NaNQpTeKGx4/N96eclNlJeWoKK0BOWlt1Ctavl4K41qampQU1ODkpKSRz2chxYaGoq4uDi9vX9raHehu7i4GPX19XB0dNQqd3R0RE5OTrOvUSgUzdZXKBTi9saye9W589J1Y2Nj2NrainXu1NgJG5WVNZz9Ki0thaYFo/O2tR9++AFXrlzRdzOIqLVIJDAz7wpTM3NIzbvCrKsM5hYWMJdZwkxmAXOZDGYyS5jLLGAmk0FmZQ2ZlQ1k1jawsLKBzMYGMkvrBzozXVXeEa/oECApV0JAFwD8woAMBfs1GSr27QdhJAFsHZ1g6+h037q1ajUqym413G72v1vMqlQNt6BVlZejh7EaSqUSSqUS5eXlUCqVqKysRGVlJVQqFSorKx9ooNTWotFoUFpa2ubv2xJKpRLA/U+GtbvQ3ZFERUXh3XffbVLu6uqqh9YQUacjCKiuVKG6UqXvlhARERHpxL59+7Bv3z59N+OeysvLYW1999tZ213otre3R5cuXZoMqV9YWAgnp+a/wXFycrpn/cbHwsJCODs7a9Xx9vYW6xQVFWnto66uDiUlJXd931WrVmld1t448q+dnR0vv6QOR6lUolevXrhy5QrHJCCDwr5Nhoj9mgwV+zZ1JIIgoLy8/L7jf7W70G1qagofHx/I5XJMmTIFQEOYlcvlWLRoUbOv8ff3h1wux9KlS8WypKQk+Pv7AwDc3Nzg5OQEuVwuhmylUonU1FS8+uqr4j5KS0tx8uRJ+Pj4AACOHDkCjUYDPz+/Zt+3cWCA29nY2DzkkRO1D1ZWVvwjRwaJfZsMEfs1GSr2beoo7nWGu1G7C90AEBERgZkzZ2LEiBHw9fXFp59+CpVKhdmzZwMAZsyYgR49eiAqKgoAsGTJEvzlL3/Bpk2bMHnyZPzrX//CiRMnsGPHDgANc+UtXboU69atQ//+/cUpw1xcXMRgP3DgQEyYMAFz585FTEwMamtrsWjRIkybNq1FI5cTERERERER3aldhu6wsDDcuHEDa9euhUKhgLe3NxITE8WB0PLz87WmNAoICMCePXvw1ltvYfXq1ejfvz/2798vztENACtWrIBKpcK8efNQWlqKUaNGITExUZyjGwC++eYbLFq0CE8++SSMjIzw/PPPY/PmzW134ERERERERGRQ2uU83UTU9mpqahAVFYVVq1Y1uW2CqCNj3yZDxH5Nhop9mwwRQzcRERERERGRjhjdvwoRERERERERPQyGbiIiIiIiIiIdYegmIiIiIiIi0hGGbqJOJi8vD3PmzIGbmxvMzc3Rr18/vP3221Cr1Vr1MjMzERgYCDMzM/Tq1QsbN25ssq/4+Hh4enrCzMwMQ4cOxaFDh9rqMIia+OCDDxAQEICuXbvCxsam2Tr5+fmYPHkyunbtCgcHB7z++uuoq6vTqvPLL79g+PDhkEqlcHd3x+7du3XfeKIHtG3bNvTp0wdmZmbw8/NDWlqavptEdFdHjx7FU089BRcXF0gkEuzfv19ruyAIWLt2LZydnWFubo6goCCcP39eq05JSQlefPFFWFlZwcbGBnPmzEFFRUUbHgXRw2PoJupkcnJyoNFoEBsbi6ysLHzyySeIiYnB6tWrxTpKpRLBwcFwdXXFyZMn8dFHH+Gdd97Bjh07xDrHjh3DCy+8gDlz5iAjIwNTpkzBlClTcPbsWX0cFhHUajVCQ0Px6quvNru9vr4ekydPhlqtxrFjx/Dll19i9+7dWLt2rVjn8uXLmDx5MsaOHYtTp05h6dKleOWVV/DTTz+11WEQ3dfevXsRERGBt99+G+np6fDy8kJISAiKior03TSiZqlUKnh5eWHbtm3Nbt+4cSM2b96MmJgYpKamQiaTISQkBNXV1WKdF198EVlZWUhKSsIPP/yAo0ePYt68eW11CESPRiCiTm/jxo2Cm5ubuL59+3ahW7duQk1NjVi2cuVKYcCAAeL61KlThcmTJ2vtx8/PTwgPD9d9g4nuYdeuXYK1tXWT8kOHDglGRkaCQqEQyz7//HPByspK7OsrVqwQBg8erPW6sLAwISQkRKdtJnoQvr6+wsKFC8X1+vp6wcXFRYiKitJjq4haBoDw73//W1zXaDSCk5OT8NFHH4llpaWlglQqFb799ltBEAThzz//FAAIx48fF+v8+OOPgkQiEQoKCtqs7UQPi2e6iQhlZWWwtbUV11NSUjB69GiYmpqKZSEhIcjNzcWtW7fEOkFBQVr7CQkJQUpKSts0mugBpaSkYOjQoXB0dBTLQkJCoFQqkZWVJdZhv6b2TK1W4+TJk1r91MjICEFBQeyn1CFdvnwZCoVCq09bW1vDz89P7NMpKSmwsbHBiBEjxDpBQUEwMjJCampqm7eZ6EExdBN1chcuXMCWLVsQHh4ulikUCq1gAkBcVygU96zTuJ2ovXmUfq1UKlFVVdU2DSW6h+LiYtTX1/PzlwxGY7+9V59WKBRwcHDQ2m5sbAxbW1v2e+oQGLqJDMQbb7wBiURyzyUnJ0frNQUFBZgwYQJCQ0Mxd+5cPbWc6O4epl8TERERtSfG+m4AEbWOyMhIzJo16551+vbtKz6/du0axo4di4CAAK0B0gDAyckJhYWFWmWN605OTves07idqDU8aL++FycnpyYjPLe0X1tZWcHc3LyFrSbSHXt7e3Tp0oWfv2QwGvttYWEhnJ2dxfLCwkJ4e3uLde4cKLCurg4lJSXs99QhMHQTGYju3buje/fuLapbUFCAsWPHwsfHB7t27YKRkfZFL/7+/njzzTdRW1sLExMTAEBSUhIGDBiAbt26iXXkcjmWLl0qvi4pKQn+/v6tc0BEeLB+fT/+/v744IMPUFRUJF6mmJSUBCsrKwwaNEisc+fUd+zX1J6YmprCx8cHcrkcU6ZMAQBoNBrI5XIsWrRIv40jeghubm5wcnKCXC4XQ7ZSqURqaqo4G4W/vz9KS0tx8uRJ+Pj4AACOHDkCjUYDPz8/fTWdqMV4eTlRJ1NQUIAxY8agd+/e+Pjjj3Hjxg0oFAqte6KmT58OU1NTzJkzB1lZWdi7dy8+++wzREREiHWWLFmCxMREbNq0CTk5OXjnnXdw4sQJ/tNHepOfn49Tp04hPz8f9fX1OHXqFE6dOiXO4xocHIxBgwbh5ZdfxunTp/HTTz/hrbfewsKFCyGVSgEA8+fPx6VLl7BixQrk5ORg+/btiIuLw7Jly/R5aERaIiIisHPnTnz55ZfIzs7Gq6++CpVKhdmzZ+u7aUTNqqioED+TgYbB0xo/ryUSCZYuXYp169YhISEBZ86cwYwZM+Di4iJ+sTRw4EBMmDABc+fORVpaGn7//XcsWrQI06ZNg4uLi/4OjKil9D18OhG1rV27dgkAml1ud/r0aWHUqFGCVCoVevToIaxfv77JvuLi4gQPDw/B1NRUGDx4sHDw4MG2OgyiJmbOnNlsv/7555/FOnl5ecLEiRMFc3Nzwd7eXoiMjBRqa2u19vPzzz8L3t7egqmpqdC3b19h165dbXsgRC2wZcsWoXfv3oKpqang6+sr/PHHH/puEtFd/fzzz81+Ps+cOVMQhIZpw9asWSM4OjoKUqlUePLJJ4Xc3Fytfdy8eVN44YUXBAsLC8HKykqYPXu2UF5eroejIXpwEkEQhLaP+kRERERERESGj5eXExEREREREekIQzcRERERERGRjjB0ExEREREREekIQzcRERERERGRjjB0ExEREREREekIQzcRERERERGRjjB0ExEREREREekIQzcRERERERGRjjB0ExEREREREekIQzcREZGBU6lU+PDDDzF8+HBYWFhAKpWiZ8+eCAwMxKpVq3Dx4kV9N5GIiMhgGeu7AURERKQ75eXlGDVqFDIzM+Hu7o6XXnoJdnZ2KC4uRlpaGtavX49+/fqhX79++m4qERGRQWLoJiIiMmCffvopMjMz8corr2DHjh2QSCRa2y9fvoyamho9tY6IiMjw8fJyIiIiA5aSkgIAWLhwYZPADQBubm7w9PTUKisqKkJkZCQGDBgAc3Nz2Nraws/PDx9//HGT1x84cABjx46FtbU1zM3N4eXlhejoaNTV1WnVy8vLg0QiwaxZs5CdnY1nn30WdnZ2kEgkyMvLE+t9//33ePLJJ9GtWzeYmZlhyJAh+Pjjj1FfX98KPw0iIqK2x9BNRERkwOzs7AAA586da1H93NxceHt7Izo6Gg4ODnjttdcwffp0dO3aFR9++KFW3ejoaDz99NPIzMzE9OnTsXDhQlRVVSEyMhKhoaEQBKHJ/i9cuIDHH38cN27cwKxZszBz5kyYmpoCAFatWoUpU6YgNzcXzz33HBYsWABzc3O8/vrrmDZt2iP+JIiIiPRDIjT3F5GIiIgMQkJCAp555hlYWlpi3rx5CA4Oho+PjxjG7zRy5EicOHECO3bswNy5c7W2Xb16FT179gQAXLx4EZ6enrC1tcWJEyfQq1cvAEBNTQ2CgoLw22+/4auvvsLLL78MoOFMt5ubGwBg7dq1ePfdd7X2nZSUhODgYISEhGDfvn2QyWQAAEEQsGDBAsTExOC7777D888/33o/HCIiojbAM91EREQG7Omnn8amTZsgCAI2bdqEkJAQ2Nvbw93dHYsWLcL58+fFumlpaThx4gRGjx7dJHADEAM3AOzZswd1dXWIjIwUAzcASKVSbNiwAQCwe/fuJvtwcnLCm2++2aR869atAIAdO3aIgRsAJBIJ1q9fD4lEgm+//fbBfwBERER6xoHUiIiIDFxERATmzp2LxMREHDt2DCdOnEBqaiq2bduGf/zjH9i7dy+efvpppKWlAQCCg4Pvu8+MjAwAwJgxY5ps8/f3h5mZGU6dOtVkm5eXl3g5+e3++OMPyGQyfPHFF82+n7m5OXJycu7bLiIiovaGoZuIiKgTsLS0RGhoKEJDQwEAZWVlWL16NbZv3445c+agoKAAZWVlAIAePXrcd39KpRIA4Ojo2GSbRCKBo6MjCgoKmmxrrj4AlJSUoK6ursll57dTqVT3bRcREVF7w8vLiYiIOiFra2ts3boVrq6uKC4uxpkzZ2BjYwMAzYblO1lZWQEACgsLm2wTBAGFhYVinds1N4J64/7s7OwgCMJdl8uXLz/AERIREbUPDN1ERESdlEQi0bp/2tfXFwBw+PDh+7522LBhAIBffvmlybbU1FRUV1fD29u7xW3x8/PDzZs3te4xJyIiMgQM3URERAYsNjYWx48fb3bb/v37kZ2dDRsbGwwZMgQjR47EyJEjcfToUezcubNJ/dvPgE+fPh3GxsaIjo7GtWvXxHK1Wo2VK1cCAGbNmtXidr722msAgL///e+4efNmk+0KhQLZ2dkt3h8REVF7wXu6iYiIDNiPP/6I+fPnw93dHU888QRcXFygUqmQkZGB5ORkGBkZYfv27ZBKpQCAb775BmPGjMG8efPw9ddfw9/fH9XV1cjKykJGRoYYiPv164cNGzYgMjISjz32GKZOnQqZTIYDBw4gNzcXzzzzDF566aUWt3PChAlYs2YN3n//fbi7u2PChAlwdXXFzZs3ceHCBSQnJ2PdunUYOHCgTn5OREREusJ5uomIiAxYbm4uEhISkJSUhAsXLuD69esAGgZLGzVqFBYvXgwfHx+t1xQWFiIqKgoHDhzA1atXYWFhgf79+yMsLAzLli3TqpuQkIDo6Gikp6dDrVbDw8MDs2bNwmuvvQZj4///br9xnu6ZM2c2O5VYo//85z/YvHkz/vjjD5SWlsLOzg5ubm6YNGkSZs6cqTU9GRERUUfA0E1ERERERESkI7ynm4iIiIiIiEhHGLqJiIiIiIiIdIShm4iIiIiIiEhHGLqJiIiIiIiIdIShm4iIiIiIiEhHGLqJiIiIiIiIdIShm4iIiIiIiEhHGLqJiIiIiIiIdIShm4iIiIiIiEhHGLqJiIiIiIiIdIShm4iIiIiIiEhHGLqJiIiIiIiIdIShm4iIiIiIiEhH/g/J231pc8qYnwAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Histogram of bot\n", - "\n", - "if 'mf-bot-1' in df_bot_peer_wide.columns:\n", - " name = 'mf-bot-1'\n", - "else:\n", - " name = 'metac-o1-preview'\n", - "\n", - "scores = df_bot_peer_wide[name].dropna()\n", + "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", + "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", "\n", - "# Create the histogram\n", - "plt.figure(figsize=(10, 6))\n", - "n, bins, patches = plt.hist(scores, bins=30, density=True, alpha=0.7, color='skyblue')\n", + "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", "\n", - "# Fit a normal distribution to the data\n", - "mu, std = norm.fit(scores)\n", + "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", "\n", - "# Plot the PDF of the fitted normal distribution\n", - "xmin, xmax = plt.xlim()\n", - "x = np.linspace(xmin, xmax, 100)\n", - "p = norm.pdf(x, mu, std)\n", - "plt.plot(x, p, 'k', linewidth=2)\n", + "# First pivot to long format - each row will be a question-forecaster pair\n", + "df_long = df_bot_vs_pro_peer.melt(\n", + " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", + " var_name='forecaster',\n", + " value_name='score'\n", + ")\n", "\n", - "# Customize the plot\n", - "plt.title(f\"Histogram of {name} Scores with Fitted Gaussian\", fontsize=16)\n", - "plt.xlabel(\"Score\", fontsize=14)\n", - "plt.ylabel(\"Density\", fontsize=14)\n", + "# Drop any rows where score is NaN\n", + "df_long = df_long.dropna(subset=['score'])\n", "\n", - "# Add text box with distribution parameters\n", - "textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", - "props = dict(boxstyle='round', facecolor='white', alpha=0.5)\n", - "plt.text(0.05, 0.95, textstr, transform=plt.gca().transAxes, fontsize=14,\n", - " verticalalignment='top', bbox=props)\n", + "# Cast question_weight as numeric\n", + "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", "\n", - "plt.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2.5% CI10% CIMedian90% CI97.5% CI
metac-o16.17.49.712.013.1
metac-o1-preview3.55.08.211.112.8
manticAI0.32.15.48.610.4
metac-Gemini-Exp-12060.72.25.07.89.2
acm_bot-0.11.44.67.69.2
metac-perplexity-1.40.54.17.99.5
GreeneiBot2-1.10.73.97.28.8
twsummerbot0.11.53.96.17.4
cookics_bot_TEST0.11.13.05.16.1
pgodzinai-4.2-1.32.97.09.0
CumulativeBot-0.20.92.64.45.2
metac-claude-3-5-sonnet-latest-1.20.12.65.16.1
SynapseSeer0.10.92.44.04.7
metac-exa-5.1-2.51.85.77.9
jkraybill_bot-3.6-1.51.74.96.4
metac-deepseek-r1-2.1-0.81.23.44.4
MWG-1.6-0.80.62.22.8
pianobot-1.1-0.70.00.71.0
andrewsiah-0.9-0.5-0.00.60.9
X_bot-0.4-0.2-0.00.10.2
cobyj-bot-1.5-0.9-0.10.81.4
KevinTestBot-3.9-2.8-0.41.42.4
annabot-3.4-2.5-0.51.22.1
bean_bot-3.4-2.4-0.51.12.0
CatrachoCaster-2.2-1.7-0.70.20.7
jonahsingerbot-3.0-2.2-0.80.41.0
krm-bot-3.7-2.7-1.00.71.5
ProfessorSP-4.5-3.2-1.11.01.9
metac-grok-2-1212-6.2-4.9-1.32.03.6
mmBot-7.4-5.3-1.52.24.0
4Shadower-4.6-3.7-1.60.41.2
RPM_bot-4.9-3.7-1.9-0.6-0.0
swingswish-5.3-4.0-1.9-0.10.8
metac-claude-3-5-sonnet-20240620-6.2-4.9-2.10.82.2
InstitutPelFutur-9.1-6.5-2.41.93.6
wunderplumb-5.9-4.8-2.5-0.20.9
metac-Llama-3.1-6.9-5.2-2.80.01.5
NextWorldLab-8.6-6.9-3.7-0.51.1
Bot_Pepa-7.0-6.0-3.9-1.9-0.9
laylaps-9.6-7.6-3.9-0.21.7
VeritasAI-7.9-6.7-4.3-2.0-0.7
minefrac1-7.9-6.9-4.7-2.6-1.4
Grizeu_Bot-9.2-7.6-4.9-2.3-1.1
metac-gpt-4o-10.2-8.8-5.8-3.1-1.5
ajf-bot-15.2-12.9-8.4-4.5-2.3
\n", - "
" - ], - "text/plain": [ - " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.1 7.4 9.7 12.0 13.1\n", - "metac-o1-preview 3.5 5.0 8.2 11.1 12.8\n", - "manticAI 0.3 2.1 5.4 8.6 10.4\n", - "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", - "acm_bot -0.1 1.4 4.6 7.6 9.2\n", - "metac-perplexity -1.4 0.5 4.1 7.9 9.5\n", - "GreeneiBot2 -1.1 0.7 3.9 7.2 8.8\n", - "twsummerbot 0.1 1.5 3.9 6.1 7.4\n", - "cookics_bot_TEST 0.1 1.1 3.0 5.1 6.1\n", - "pgodzinai -4.2 -1.3 2.9 7.0 9.0\n", - "CumulativeBot -0.2 0.9 2.6 4.4 5.2\n", - "metac-claude-3-5-sonnet-latest -1.2 0.1 2.6 5.1 6.1\n", - "SynapseSeer 0.1 0.9 2.4 4.0 4.7\n", - "metac-exa -5.1 -2.5 1.8 5.7 7.9\n", - "jkraybill_bot -3.6 -1.5 1.7 4.9 6.4\n", - "metac-deepseek-r1 -2.1 -0.8 1.2 3.4 4.4\n", - "MWG -1.6 -0.8 0.6 2.2 2.8\n", - "pianobot -1.1 -0.7 0.0 0.7 1.0\n", - "andrewsiah -0.9 -0.5 -0.0 0.6 0.9\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "cobyj-bot -1.5 -0.9 -0.1 0.8 1.4\n", - "KevinTestBot -3.9 -2.8 -0.4 1.4 2.4\n", - "annabot -3.4 -2.5 -0.5 1.2 2.1\n", - "bean_bot -3.4 -2.4 -0.5 1.1 2.0\n", - "CatrachoCaster -2.2 -1.7 -0.7 0.2 0.7\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", - "krm-bot -3.7 -2.7 -1.0 0.7 1.5\n", - "ProfessorSP -4.5 -3.2 -1.1 1.0 1.9\n", - "metac-grok-2-1212 -6.2 -4.9 -1.3 2.0 3.6\n", - "mmBot -7.4 -5.3 -1.5 2.2 4.0\n", - "4Shadower -4.6 -3.7 -1.6 0.4 1.2\n", - "RPM_bot -4.9 -3.7 -1.9 -0.6 -0.0\n", - "swingswish -5.3 -4.0 -1.9 -0.1 0.8\n", - "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.1 0.8 2.2\n", - "InstitutPelFutur -9.1 -6.5 -2.4 1.9 3.6\n", - "wunderplumb -5.9 -4.8 -2.5 -0.2 0.9\n", - "metac-Llama-3.1 -6.9 -5.2 -2.8 0.0 1.5\n", - "NextWorldLab -8.6 -6.9 -3.7 -0.5 1.1\n", - "Bot_Pepa -7.0 -6.0 -3.9 -1.9 -0.9\n", - "laylaps -9.6 -7.6 -3.9 -0.2 1.7\n", - "VeritasAI -7.9 -6.7 -4.3 -2.0 -0.7\n", - "minefrac1 -7.9 -6.9 -4.7 -2.6 -1.4\n", - "Grizeu_Bot -9.2 -7.6 -4.9 -2.3 -1.1\n", - "metac-gpt-4o -10.2 -8.8 -5.8 -3.1 -1.5\n", - "ajf-bot -15.2 -12.9 -8.4 -4.5 -2.3" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_resolved_questions,\n", + " df_bot_baseline_wide,\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_baseline_weights,\n", + " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", + " on='pro_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", + "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", + "\n", + "# Create a list of columns to keep\n", + "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", + "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", + "\n", + "# Calculate the sum for each forecaster\n", + "forecaster_scores = df_filtered.sum()\n", + "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "question_counts = df_filtered.notna().sum()\n", + "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_scores.index,\n", + " 'Baseline': forecaster_scores.values,\n", + " 'Count': question_counts.values\n", + "})\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "weighted_leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_weighted_scores.index,\n", + " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", + " 'Count': question_counts.values,\n", + " 'Weighted Count': question_weighted_counts.values\n", + "})\n", + "\n", + "# Sort the leaderboard by score in descending order\n", + "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", + "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", + "\n", + "# Add a 'Rank' column\n", + "leaderboard['Rank'] = leaderboard.index + 1\n", + "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", + "\n", + "# Reorder columns to have Rank first\n", + "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", + "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", + "\n", + "#leaderboard\n", + "weighted_leaderboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# Drop 'bot_median' from all_bots list\n", - "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", - "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "# make me a list that's pro_median and all the bot forecasters\n", + "forecasters = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", "\n", - "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", - "ITER = 1000\n", + "hey = calculate_t_test(df_pro_bot_baseline_weights, forecasters)\n", "\n", - "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", - "average_df = result_df / NUM\n", + "hey" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aGNedTHmU-Bm", + "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" + }, + "outputs": [], + "source": [ + "# @title Weighted head-to-head, T test\n", "\n", - "print(f'BOT LEADERBOARD\\n\\n')\n", - "df_rounded = average_df.round(1)\n", - "df_rounded" + "\"\"\"\n", + "df_W_leaderboard: A leaderboard based on df_bot_vs_pro_peer with question\n", + "weighting and the calculations for doing a weighted T test\n", + "\"\"\"\n", + "\n", + "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", + "\n", + "# Cast weights as numeric\n", + "df_bot_vs_pro_peer['question_weight'] = pd.to_numeric(df_bot_vs_pro_peer['question_weight'], errors='coerce')\n", + "\n", + "# Calculate weighted statistics for each bot\n", + "df_W_leaderboard = calculate_t_test(df_bot_vs_pro_peer, all_bots)\n", + "\n", + "df_W_leaderboard" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "# Write to csv\n", + "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv', index=True)" ] }, { "cell_type": "code", - "execution_count": 211, + "execution_count": null, "metadata": { "cellView": "form", "colab": { - "base_uri": "https://localhost:8080/", - "height": 125 + "base_uri": "https://localhost:8080/" }, - "id": "MXAev2sNXdbZ", - "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" + "id": "3d_ZdL0A0qTz", + "outputId": "e30ee8fb-0faf-45ae-974e-d4af282e0252" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "HEAD-TO-HEAD LEADERBOARD\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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2.5% CI10% CIMedian90% CI97.5% CI
metac-perplexity18.118.118.118.118.1
acm_bot17.717.717.717.717.7
bot_median17.017.017.017.017.0
metac-o116.616.616.616.616.6
metac-claude-3-5-sonnet-2024062014.814.814.814.814.8
manticAI14.514.514.514.514.5
twsummerbot14.314.314.314.314.3
jkraybill_bot14.314.314.314.314.3
metac-exa13.013.013.013.013.0
GreeneiBot212.212.212.212.212.2
NextWorldLab11.111.111.111.111.1
metac-Llama-3.110.510.510.510.510.5
Grizeu_Bot10.210.210.210.210.2
SynapseSeer10.210.210.210.210.2
metac-claude-3-5-sonnet-latest10.010.010.010.010.0
mmBot9.79.79.79.79.7
annabot9.09.09.09.09.0
VeritasAI8.48.48.48.48.4
metac-grok-2-12128.28.28.28.28.2
laylaps7.67.67.67.67.6
metac-Gemini-Exp-12067.47.47.47.47.4
metac-o1-preview6.76.76.76.76.7
cookics_bot_TEST6.36.36.36.36.3
metac-deepseek-r15.75.75.75.75.7
MWG5.55.55.55.55.5
ajf-bot5.15.15.15.15.1
metac-gpt-4o4.84.84.84.84.8
pgodzinai3.53.53.53.53.5
KevinTestBot3.33.33.33.33.3
InstitutPelFutur2.72.72.72.72.7
Bot_Pepa2.62.62.62.62.6
CumulativeBot2.52.52.52.52.5
swingswish2.42.42.42.42.4
wunderplumb2.42.42.42.42.4
jonahsingerbot2.22.22.22.22.2
bean_bot2.12.12.12.12.1
X_bot1.91.91.91.91.9
CatrachoCaster1.81.81.81.81.8
RPM_bot1.21.21.21.21.2
4Shadower0.60.60.60.60.6
krm-bot0.60.60.60.60.6
andrewsiah0.00.00.00.00.0
cobyj-bot0.00.00.00.00.0
pianobot-2.2-2.2-2.2-2.2-2.2
ProfessorSP-3.0-3.0-3.0-3.0-3.0
minefrac1-3.0-3.0-3.0-3.0-3.0
\n", - "
" - ], - "text/plain": [ - " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-perplexity 18.1 18.1 18.1 18.1 18.1\n", - "acm_bot 17.7 17.7 17.7 17.7 17.7\n", - "bot_median 17.0 17.0 17.0 17.0 17.0\n", - "metac-o1 16.6 16.6 16.6 16.6 16.6\n", - "metac-claude-3-5-sonnet-20240620 14.8 14.8 14.8 14.8 14.8\n", - "manticAI 14.5 14.5 14.5 14.5 14.5\n", - "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", - "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", - "metac-exa 13.0 13.0 13.0 13.0 13.0\n", - "GreeneiBot2 12.2 12.2 12.2 12.2 12.2\n", - "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", - "metac-Llama-3.1 10.5 10.5 10.5 10.5 10.5\n", - "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", - "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", - "metac-claude-3-5-sonnet-latest 10.0 10.0 10.0 10.0 10.0\n", - "mmBot 9.7 9.7 9.7 9.7 9.7\n", - "annabot 9.0 9.0 9.0 9.0 9.0\n", - "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", - "metac-grok-2-1212 8.2 8.2 8.2 8.2 8.2\n", - "laylaps 7.6 7.6 7.6 7.6 7.6\n", - "metac-Gemini-Exp-1206 7.4 7.4 7.4 7.4 7.4\n", - "metac-o1-preview 6.7 6.7 6.7 6.7 6.7\n", - "cookics_bot_TEST 6.3 6.3 6.3 6.3 6.3\n", - "metac-deepseek-r1 5.7 5.7 5.7 5.7 5.7\n", - "MWG 5.5 5.5 5.5 5.5 5.5\n", - "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", - "metac-gpt-4o 4.8 4.8 4.8 4.8 4.8\n", - "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", - "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", - "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", - "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", - "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", - "swingswish 2.4 2.4 2.4 2.4 2.4\n", - "wunderplumb 2.4 2.4 2.4 2.4 2.4\n", - "jonahsingerbot 2.2 2.2 2.2 2.2 2.2\n", - "bean_bot 2.1 2.1 2.1 2.1 2.1\n", - "X_bot 1.9 1.9 1.9 1.9 1.9\n", - "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", - "RPM_bot 1.2 1.2 1.2 1.2 1.2\n", - "4Shadower 0.6 0.6 0.6 0.6 0.6\n", - "krm-bot 0.6 0.6 0.6 0.6 0.6\n", - "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", - "pianobot -2.2 -2.2 -2.2 -2.2 -2.2\n", - "ProfessorSP -3.0 -3.0 -3.0 -3.0 -3.0\n", - "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", - "ITER = 1000\n", + "# @title Weighted Bot Peer, T test (to compare bots against each other, use ALL QUESTIONS)\n", "\n", - "result_df = weighted_bootstrap_analysis(df_bot_vs_pro_peer, all_bots, NUM, ITER)\n", - "average_df = result_df / NUM\n", + "df_W_bot_peer_leaderboard = pd.DataFrame()\n", "\n", - "print(f'\\n\\n\\nHEAD-TO-HEAD LEADERBOARD\\n\\n')\n", - "#df_rounded = result_df.round(0).astype(int)\n", - "df_rounded = average_df.round(1)\n", + "df3 = pd.DataFrame()\n", + "\n", + "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", + "\n", + "# OMIT bot_median column for this bit\n", + "df_bot_peer_wide_b = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "df_bot_peer = df_bot_peer[df_bot_peer['forecaster'] != 'bot_median']\n", + "\n", + "bots_for_peer = np.array(list(set(df_bot_peer['forecaster'])))\n", + "\n", + "df_W_leaderboard = calculate_t_test(df_bot_peer_wide_b, bots_for_peer)\n", + "\n", + "df_W_leaderboard_print = df_W_leaderboard.sort_values(by='lower_bound', ascending=False)\n", + "df_W_leaderboard_print['Rank'] = range(1, len(df_W_leaderboard_print) + 1)\n", + "\n", + "# Make index into a column - Bot\n", + "df_W_leaderboard_print = df_W_leaderboard_print.reset_index()\n", + "df_W_leaderboard_print = df_W_leaderboard_print.rename(columns={'index': 'Bot'})\n", + "#df_W_leaderboard_print = df_W_leaderboard_print[['Rank', 'Bot', 'W_ave', 'W_count', 'lower_bound', 'upper_bound']]\n", + "# Make rank the first column; leave rest the same\n", + "cols = df_W_leaderboard_print.columns.tolist()\n", + "cols = ['Rank'] + cols[:-1]\n", + "df_W_leaderboard_print = df_W_leaderboard_print[cols]\n", "\n", - "df_rounded" + "df_W_leaderboard_print" ] }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ - "# Write df_rounded (bootstrapping h2h) to csv\n", - "df_rounded.to_csv('notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv')" + "# Write to csv\n", + "df_W_leaderboard_print.to_csv('notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv', index=False)" ] }, { "cell_type": "code", - "execution_count": 213, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Weighted score for annabot: -190.5513637093994\n", - "Total score for annabot: 21.125669919166132\n", - "\n" - ] + "outputs": [], + "source": [ + "df_bot_peer_wide.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "88QO8eyW6T_T", + "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" + }, + "outputs": [], "source": [ - "# @title Check specific bot records\n", - "\n", - "bot_name = 'annabot'\n", - "\n", - "df_bot = df_bot_peer_wide[['bot_question_id', 'question_weight', bot_name]]\n", - "df_bot = df_bot.dropna()\n", - "df_bot = df_bot.reset_index(drop=True)\n", - "\n", - "df_bot['weighted_score'] = df_bot[bot_name] * df_bot['question_weight']\n", + "# @title Histogram of bot\n", "\n", - "weighted_score = df_bot['weighted_score'].sum()\n", + "if 'mf-bot-1' in df_bot_peer_wide.columns:\n", + " name = 'mf-bot-1'\n", + "else:\n", + " name = 'metac-o1-preview'\n", "\n", - "print(f\"Weighted score for {bot_name}: {weighted_score}\")\n", + "scores = df_bot_peer_wide[name].dropna()\n", "\n", - "total_score = df_bot[bot_name].sum()\n", + "# Create the histogram\n", + "plt.figure(figsize=(10, 6))\n", + "n, bins, patches = plt.hist(scores, bins=30, density=True, alpha=0.7, color='skyblue')\n", "\n", - "print(f\"Total score for {bot_name}: {total_score}\\n\")\n", + "# Fit a normal distribution to the data\n", + "mu, std = norm.fit(scores)\n", "\n", - "# Create the histogram\n", - "plt.figure(figsize=(10, 6)) # Set the figure size (optional)\n", - "plt.hist(df_bot[bot_name], bins=10, edgecolor='black')\n", + "# Plot the PDF of the fitted normal distribution\n", + "xmin, xmax = plt.xlim()\n", + "x = np.linspace(xmin, xmax, 100)\n", + "p = norm.pdf(x, mu, std)\n", + "plt.plot(x, p, 'k', linewidth=2)\n", "\n", "# Customize the plot\n", - "plt.title(f'Histogram of Scores for {bot_name}')\n", - "plt.xlabel('Score')\n", - "plt.ylabel('Frequency')\n", + "plt.title(f\"Histogram of {name} Scores with Fitted Gaussian\", fontsize=16)\n", + "plt.xlabel(\"Score\", fontsize=14)\n", + "plt.ylabel(\"Density\", fontsize=14)\n", "\n", - "# Add grid lines (optional)\n", - "plt.grid(axis='y', alpha=0.75)\n", + "# Add text box with distribution parameters\n", + "textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", + "props = dict(boxstyle='round', facecolor='white', alpha=0.5)\n", + "plt.text(0.05, 0.95, textstr, transform=plt.gca().transAxes, fontsize=14,\n", + " verticalalignment='top', bbox=props)\n", "\n", - "# Show the plot\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 214, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_bot_peer_wide.shape\n", + "\n", + "display_head_and_tail(df_bot_peer_wide)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, - "id": "I7W8JXutv2ks", - "outputId": "5e7053d3-2124-42b7-bd53-48a40a53caf2" + "id": "oxVJxrCpuXV_", + "outputId": "3df39cbc-b594-40e1-d08f-1b0e9736d6ec" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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W_aveW_countlower_boundupper_boundp_value
metac-o1-preview12.2276.67.117.30.000004
metac-o18.4283.24.012.70.000179
pgodzinai8.7248.01.116.30.025267
GreeneiBot29.2204.81.117.30.026930
manticAI7.7245.20.514.90.035671
acm_bot5.4263.5-0.211.00.058135
metac-Gemini-Exp-12065.3269.6-0.310.80.062806
SynapseSeer6.0125.9-0.512.50.068737
metac-claude-3-5-sonnet-latest3.6278.2-0.98.20.116899
twsummerbot4.9181.9-1.811.60.152393
cookics_bot_TEST5.8135.2-1.813.40.132509
CumulativeBot8.094.2-3.018.90.153662
metac-deepseek-r10.8225.8-4.25.80.763142
MWG3.684.8-4.311.50.365354
metac-perplexity2.8264.3-4.810.30.470416
metac-grok-2-12120.1281.2-5.76.00.961620
metac-exa1.7275.2-5.89.20.654608
mmBot-0.5279.9-7.56.50.887163
InstitutPelFutur-0.1264.9-8.18.00.988352
metac-Llama-3.1-3.7280.5-8.30.90.117806
metac-claude-3-5-sonnet-20240620-3.3282.2-8.52.00.224671
VeritasAI-4.5251.9-9.40.40.072948
jkraybill_bot1.4162.4-9.712.40.808839
CatrachoCaster-2.761.9-10.65.20.493061
metac-gpt-4o-5.2281.2-10.60.10.054453
NextWorldLab-4.6256.3-10.91.80.156859
wunderplumb-5.4148.4-13.52.60.184061
4Shadower-3.7101.5-13.96.40.463979
minefrac1-7.3136.2-14.4-0.20.043444
andrewsiah0.125.1-14.614.80.988409
krm-bot-4.494.5-14.75.90.399741
ProfessorSP-4.0110.0-14.86.80.464316
laylaps-7.2257.0-15.40.90.082564
pianobot6.814.8-16.229.80.535822
cobyj-bot-0.431.5-17.817.10.964365
KevinTestBot-2.781.1-17.912.60.730388
jonahsingerbot-6.560.1-19.26.20.309592
bean_bot-4.163.1-19.611.40.600896
Bot_Pepa-12.3124.4-20.6-4.10.003751
annabot-6.354.5-23.711.10.470037
Grizeu_Bot-16.5140.9-25.8-7.10.000639
ajf-bot-16.0193.9-28.2-3.70.011119
swingswish-16.757.1-36.93.50.103364
RPM_bot-44.015.8-101.413.40.126191
\n", - "
" - ], - "text/plain": [ - " W_ave W_count lower_bound upper_bound \\\n", - "metac-o1-preview 12.2 276.6 7.1 17.3 \n", - "metac-o1 8.4 283.2 4.0 12.7 \n", - "pgodzinai 8.7 248.0 1.1 16.3 \n", - "GreeneiBot2 9.2 204.8 1.1 17.3 \n", - "manticAI 7.7 245.2 0.5 14.9 \n", - "acm_bot 5.4 263.5 -0.2 11.0 \n", - "metac-Gemini-Exp-1206 5.3 269.6 -0.3 10.8 \n", - "SynapseSeer 6.0 125.9 -0.5 12.5 \n", - "metac-claude-3-5-sonnet-latest 3.6 278.2 -0.9 8.2 \n", - "twsummerbot 4.9 181.9 -1.8 11.6 \n", - "cookics_bot_TEST 5.8 135.2 -1.8 13.4 \n", - "CumulativeBot 8.0 94.2 -3.0 18.9 \n", - "metac-deepseek-r1 0.8 225.8 -4.2 5.8 \n", - "MWG 3.6 84.8 -4.3 11.5 \n", - "metac-perplexity 2.8 264.3 -4.8 10.3 \n", - "metac-grok-2-1212 0.1 281.2 -5.7 6.0 \n", - "metac-exa 1.7 275.2 -5.8 9.2 \n", - "mmBot -0.5 279.9 -7.5 6.5 \n", - "InstitutPelFutur -0.1 264.9 -8.1 8.0 \n", - "metac-Llama-3.1 -3.7 280.5 -8.3 0.9 \n", - "metac-claude-3-5-sonnet-20240620 -3.3 282.2 -8.5 2.0 \n", - "VeritasAI -4.5 251.9 -9.4 0.4 \n", - "jkraybill_bot 1.4 162.4 -9.7 12.4 \n", - "CatrachoCaster -2.7 61.9 -10.6 5.2 \n", - "metac-gpt-4o -5.2 281.2 -10.6 0.1 \n", - "NextWorldLab -4.6 256.3 -10.9 1.8 \n", - "wunderplumb -5.4 148.4 -13.5 2.6 \n", - "4Shadower -3.7 101.5 -13.9 6.4 \n", - "minefrac1 -7.3 136.2 -14.4 -0.2 \n", - "andrewsiah 0.1 25.1 -14.6 14.8 \n", - "krm-bot -4.4 94.5 -14.7 5.9 \n", - "ProfessorSP -4.0 110.0 -14.8 6.8 \n", - "laylaps -7.2 257.0 -15.4 0.9 \n", - "pianobot 6.8 14.8 -16.2 29.8 \n", - "cobyj-bot -0.4 31.5 -17.8 17.1 \n", - "KevinTestBot -2.7 81.1 -17.9 12.6 \n", - "jonahsingerbot -6.5 60.1 -19.2 6.2 \n", - "bean_bot -4.1 63.1 -19.6 11.4 \n", - "Bot_Pepa -12.3 124.4 -20.6 -4.1 \n", - "annabot -6.3 54.5 -23.7 11.1 \n", - "Grizeu_Bot -16.5 140.9 -25.8 -7.1 \n", - "ajf-bot -16.0 193.9 -28.2 -3.7 \n", - "swingswish -16.7 57.1 -36.9 3.5 \n", - "RPM_bot -44.0 15.8 -101.4 13.4 \n", - "\n", - " p_value \n", - "metac-o1-preview 0.000004 \n", - "metac-o1 0.000179 \n", - "pgodzinai 0.025267 \n", - "GreeneiBot2 0.026930 \n", - "manticAI 0.035671 \n", - "acm_bot 0.058135 \n", - "metac-Gemini-Exp-1206 0.062806 \n", - "SynapseSeer 0.068737 \n", - "metac-claude-3-5-sonnet-latest 0.116899 \n", - "twsummerbot 0.152393 \n", - "cookics_bot_TEST 0.132509 \n", - "CumulativeBot 0.153662 \n", - "metac-deepseek-r1 0.763142 \n", - "MWG 0.365354 \n", - "metac-perplexity 0.470416 \n", - "metac-grok-2-1212 0.961620 \n", - "metac-exa 0.654608 \n", - "mmBot 0.887163 \n", - "InstitutPelFutur 0.988352 \n", - "metac-Llama-3.1 0.117806 \n", - "metac-claude-3-5-sonnet-20240620 0.224671 \n", - "VeritasAI 0.072948 \n", - "jkraybill_bot 0.808839 \n", - "CatrachoCaster 0.493061 \n", - "metac-gpt-4o 0.054453 \n", - "NextWorldLab 0.156859 \n", - "wunderplumb 0.184061 \n", - "4Shadower 0.463979 \n", - "minefrac1 0.043444 \n", - "andrewsiah 0.988409 \n", - "krm-bot 0.399741 \n", - "ProfessorSP 0.464316 \n", - "laylaps 0.082564 \n", - "pianobot 0.535822 \n", - "cobyj-bot 0.964365 \n", - "KevinTestBot 0.730388 \n", - "jonahsingerbot 0.309592 \n", - "bean_bot 0.600896 \n", - "Bot_Pepa 0.003751 \n", - "annabot 0.470037 \n", - "Grizeu_Bot 0.000639 \n", - "ajf-bot 0.011119 \n", - "swingswish 0.103364 \n", - "RPM_bot 0.126191 " - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "# Drop 'bot_median' from all_bots list\n", + "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", + "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "\n", + "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", + "ITER = 1000\n", + "\n", + "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", + "average_df = result_df / NUM\n", + "\n", + "print(f'BOT LEADERBOARD\\n\\n')\n", + "df_rounded = average_df.round(1)\n", + "df_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + }, + "id": "MXAev2sNXdbZ", + "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" + }, + "outputs": [], + "source": [ + "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", + "ITER = 1000\n", + "\n", + "result_df = weighted_bootstrap_analysis(df_bot_vs_pro_peer, all_bots, NUM, ITER)\n", + "average_df = result_df / NUM\n", + "\n", + "print(f'\\n\\n\\nHEAD-TO-HEAD LEADERBOARD\\n\\n')\n", + "#df_rounded = result_df.round(0).astype(int)\n", + "df_rounded = average_df.round(1)\n", + "\n", + "df_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "# Write df_rounded (bootstrapping h2h) to csv\n", + "df_rounded.to_csv('notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @title Check specific bot records\n", + "\n", + "bot_name = 'annabot'\n", + "\n", + "df_bot = df_bot_peer_wide[['bot_question_id', 'question_weight', bot_name]]\n", + "df_bot = df_bot.dropna()\n", + "df_bot = df_bot.reset_index(drop=True)\n", + "\n", + "df_bot['weighted_score'] = df_bot[bot_name] * df_bot['question_weight']\n", + "\n", + "weighted_score = df_bot['weighted_score'].sum()\n", + "\n", + "print(f\"Weighted score for {bot_name}: {weighted_score}\")\n", + "\n", + "total_score = df_bot[bot_name].sum()\n", + "\n", + "print(f\"Total score for {bot_name}: {total_score}\\n\")\n", + "\n", + "# Create the histogram\n", + "plt.figure(figsize=(10, 6)) # Set the figure size (optional)\n", + "plt.hist(df_bot[bot_name], bins=10, edgecolor='black')\n", + "\n", + "# Customize the plot\n", + "plt.title(f'Histogram of Scores for {bot_name}')\n", + "plt.xlabel('Score')\n", + "plt.ylabel('Frequency')\n", + "\n", + "# Add grid lines (optional)\n", + "plt.grid(axis='y', alpha=0.75)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I7W8JXutv2ks", + "outputId": "5e7053d3-2124-42b7-bd53-48a40a53caf2" + }, + "outputs": [], "source": [ "# @title Weighted Bot Only Peer, T test\n", "\n", @@ -10333,27 +3991,9 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 10 bots:\n", - "1. metac-o1-preview\n", - "2. metac-o1\n", - "3. pgodzinai\n", - "4. GreeneiBot2\n", - "5. manticAI\n", - "6. acm_bot\n", - "7. metac-Gemini-Exp-1206\n", - "8. SynapseSeer\n", - "9. metac-claude-3-5-sonnet-latest\n", - "10. twsummerbot\n" - ] - } - ], + "outputs": [], "source": [ "# Sort the DataFrame by the lower_bound column in descending order\n", "sorted_df = df_W_bot_only_peer_leaderboard.sort_values(by='lower_bound', ascending=False)\n", @@ -10372,519 +4012,12 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": null, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.02]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.98]\n", - " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.05]\n", - 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" >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - 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" >>> Collected 6 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", - " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", - " >>> Collected 7 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95]\n", - " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", - " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35, 0.289]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" - ] - } - ], + "outputs": [], "source": [ "# @title Calculate df_bot_team_forecasts\n", "\n", @@ -10924,221 +4057,18 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Bot_Team_SizeWeighted_Baseline_Score_for_Bot_Team_Median
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" - ], - "text/plain": [ - " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 18.17\n", - "1 2 24.94\n", - "2 3 26.48\n", - "3 4 26.48\n", - "4 5 26.77\n", - "5 6 26.92\n", - "6 7 25.83\n", - "7 8 26.50\n", - "8 9 25.22\n", - "9 10 25.45" - ] - }, - "execution_count": 221, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# @title Calculate the baseline scores for each team size\n", "\n", @@ -11281,360 +4112,49 @@ ] }, { - "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['metac-o1-preview',\n", - " 'metac-o1',\n", - " 'pgodzinai',\n", - " 'GreeneiBot2',\n", - " 'manticAI',\n", - " 'acm_bot']" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Index of top bot team from weighted_scores_print?\n", - "winning_bot_team_size = weighted_scores_print.sort_values(by='Weighted_Baseline_Score_for_Bot_Team_Median', ascending=False).head(1)['Bot_Team_Size'].values[0]\n", - "top_bot_team = top_10_bots[:winning_bot_team_size]\n", - "top_bot_team" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(424, 47)" - ] - }, - "execution_count": 223, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_bot_forecasts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge bot_team_forecasts with df_top_bot_forecasts, just get type and options columns from bot_team_forecasts, merge on bot_question_id\n", - "df_bot_forecasts = pd.merge(\n", - " df_bot_forecasts,\n", - " df_bot_team_forecasts[['bot_question_id', 'type', 'options', 'resolution']],\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# And make bot_question_id, type and options the first columns\n", - "df_bot_forecasts = df_bot_forecasts[['bot_question_id', 'type', 'options', 'resolution'] + [col for col in df_bot_forecasts.columns if col not in ['bot_question_id', 'type', 'options']]]" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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5 rows × 27 columns

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" - ], - "text/plain": [ - " bot_question_id question_weight resolution type \\\n", - "0 31262 1.0 0 multiple_choice \n", - "1 31263 1.0 86.82 numeric \n", - "2 31264 1.0 no binary \n", - "3 31274 1.0 5-9 multiple_choice \n", - "4 31275 1.0 119.2 numeric \n", - "\n", - " options range_min range_max \\\n", - "0 [0, 1, 2-3, 4-6, >6] NaN NaN \n", - "1 NaN 60.0 100.0 \n", - "2 NaN NaN NaN \n", - "3 [0-4, 5-9, >9] NaN NaN \n", - "4 NaN 0.0 400.0 \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", - "\n", - " metac-o1 \\\n", - "0 [0.4,0.3,0.2,0.05,0.05] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.29,0.56,0.14999999999999997] \n", - "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", - "\n", - " pgodzinai ... \\\n", - "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", - "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... ... \n", - "2 0.07 ... \n", - "3 [0.27499999999999997,0.5125,0.21249999999999997] ... \n", - "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", - "\n", - " median_forecast_1_bots \\\n", - "0 0.014083 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", - "\n", - " median_forecast_2_bots \\\n", - "0 0.207042 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.075 \n", - "3 0.605 \n", - "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", - "\n", - " median_forecast_3_bots \\\n", - "0 0.014926 \n", - "1 [0.03366666666666667, 0.0341314028, 0.03460208... \n", - "2 0.07 \n", - "3 0.56 \n", - "4 [0.0, 0.0024824972, 0.004970454466666667, 0.00... \n", - "\n", - " median_forecast_4_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.063 \n", - "3 0.59 \n", - "4 [0.0, 0.00231641835, 0.00463693175, 0.00696020... \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.063 \n", - "3 0.56 \n", - "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", - "\n", - " median_forecast_6_bots \\\n", - "0 0.014926 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.07 \n", - "3 0.53625 \n", - "4 [0.0, 0.002038679916666667, 0.0040819072666666... \n", - "\n", - " median_forecast_7_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.085 \n", - "3 0.56 \n", - "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", - "\n", - " median_forecast_8_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.085 \n", - "3 0.56 \n", - "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", - "\n", - " median_forecast_9_bots \\\n", - "0 0.014926 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", - "2 0.1 \n", - "3 0.53625 \n", - "4 [0.0, 0.0023970654875000003, 0.0047975415625, ... \n", - "\n", - " median_forecast_10_bots \n", - "0 0.014926 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002276496766666667, 0.0045560251555555... \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Index of top bot team from weighted_scores_print?\n", + "winning_bot_team_size = weighted_scores_print.sort_values(by='Weighted_Baseline_Score_for_Bot_Team_Median', ascending=False).head(1)['Bot_Team_Size'].values[0]\n", + "top_bot_team = top_10_bots[:winning_bot_team_size]\n", + "top_bot_team" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_bot_forecasts.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge bot_team_forecasts with df_top_bot_forecasts, just get type and options columns from bot_team_forecasts, merge on bot_question_id\n", + "df_bot_forecasts = pd.merge(\n", + " df_bot_forecasts,\n", + " df_bot_team_forecasts[['bot_question_id', 'type', 'options', 'resolution']],\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# And make bot_question_id, type and options the first columns\n", + "df_bot_forecasts = df_bot_forecasts[['bot_question_id', 'type', 'options', 'resolution'] + [col for col in df_bot_forecasts.columns if col not in ['bot_question_id', 'type', 'options']]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df_bot_team_forecasts.head()" ] @@ -11689,24 +4209,16 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Weighted Total Score: -15.6339\n" - ] - } - ], + "outputs": [], "source": [ "weighted_total_score = get_weighted_score(df_top_bot_pro_forecasts)" ] }, { "cell_type": "code", - "execution_count": 228, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11715,32 +4227,14 @@ "id": "JlU9zyqn26Rl", "outputId": "ac54d636-670b-4a8f-aea9-402679efacf9" }, - "outputs": [ - { - "data": { - "image/png": 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kfv36Wft4enrqu+++0/r16/XVV18pOjpay5Yt02233aa1a9de8d9UtisF8n+6/Mr2tbr77rut84etXbtWs2fP1syZM7VixQp17dq1wPsFYF+ELwCl2qVLlyRJZ8+elZQz4MCZM2dsrn79/vvv1u3Z/jm4RUFlD9RRvXr1q/6C9vfffys2NlZPP/20pk6dam3PvnJW3FSrVk0VK1ZUZmbmv/7i+cknn+jGG2/UihUrbM7rPyf2rV27tmJjY3X27FmbX6j37t1r0y8iIkIxMTF5ftaV/rvVrl1bv/zyi7KysmxCwD//2z/22GM2g7RUqlTpqsd2JY48P9cq+4rx8ePHJeV8z+7atUt169bN8z3Z52vv3r25ruru3bs3zxEV/6lOnTr65ptv1LZt23wFk9atW6t169Z6/vnntWTJEg0cOFBLly61uf32n8qXL68ePXpo+fLlevnll7Vs2TK1b99eNWrUsOnn5OSkzp07q3Pnznr55Zf1wgsv6IknntD69euLLJBefg7/6ffff1fVqlVthuP39/fXgw8+qAcffFBJSUm65ZZb9PzzzxO+gBKEZ74AlFoZGRlau3at3NzcrLeWdevWTZmZmXr99ddt+r7yyiuyWCw2v8SUL18+30N4X01ERIS8vLz0wgsv5Pm8yokTJyTlXLH65xWcy2+PKk6cnZ3Vp08fffrpp9q1a1eu7dnHld1Xsj22TZs2KS4uzuY93bp106VLlzR//nxrW2ZmpubNm2fTz9/fX+Hh4TZLtvLly+d5i2a3bt2UkJCgZcuWWdsuXbqkefPmqUKFCurQoYMkqVGjRjb7DQkJydf5+CdHnp8r+f777/P8Hsx+tij79rc77rhDFStW1IwZM3ThwgWbvtk1tmjRQtWrV9eCBQt08eJF6/Y1a9Zoz5496t69+7/Wc/fddyszM1PPPvtsrm2XLl2y/vv7+++/c/27yB4B8vLPvpKoqCgdO3ZM77zzjnbu3KmoqCib7cnJybnecy37Lyh/f38FBwfr3XfftflZs2vXLq1du1bdunWTZP43/uf3dPXq1VWjRo0irQ9A4ePKF4BSY82aNdarGElJSVqyZIn27dunSZMmWZ+r6tmzpzp16qQnnnhCBw8eVLNmzbR27Vp9/vnnGjdunM1w8iEhIfrmm2/08ssvq0aNGgoKClJoaOg11+Xl5aX58+dr0KBBuuWWW9S/f39Vq1ZN8fHx+uqrr9S2bVu9/vrr8vLy0q233qpZs2YpIyNDNWvW1Nq1a61zBRVHL774otavX6/Q0FDdf//9atSokZKTk7Vt2zZ988031l9qe/TooRUrVqh3797q3r27Dhw4oAULFqhRo0bWq5KS+d+nbdu2mjRpkg4ePKhGjRppxYoVeYapKwkJCdGyZcs0fvx4tWzZUhUqVFDPnj01cuRIvfXWWxo6dKi2bt2qwMBAffLJJ9q4caPmzp2b6znA0nh+Zs6cqa1bt+quu+6y3vK4bds2vffee6pcubJ16HsvLy+98soruu+++9SyZUvdc889qlSpknbu3Klz587p3Xfflaurq2bOnKlhw4apQ4cOGjBggHWo+cDAQD3yyCP/Wk+HDh30wAMPaMaMGdqxY4fuuOMOubq6at++fVq+fLleffVV9e3bV++++67efPNN9e7dW3Xq1NGZM2f09ttvy8vLyxpQriZ7brGJEydaQ/HlnnnmGX333Xfq3r27ateuraSkJL355pu64YYb1K5du3yd24KaPXu2unbtqrCwMI0YMcI61Ly3t7emT58uyZzj64YbblDfvn3VrFkzVahQQd988402b95cpHP4ASgCjhhiEQAKU15DzXt4eBjBwcHG/PnzrUNjZztz5ozxyCOPGDVq1DBcXV2NevXqGbNnz87V7/fffzduvfVW69Dc+R12/p9DzWdbv369ERERYXh7exseHh5GnTp1jKFDhxpbtmyx9jly5IjRu3dvw8fHx/D29jb69etnHDt2zJBkTJs2zdove6j5fw6NPWTIEKN8+fK5aurQoYPRuHHjf629du3aRvfu3fPctn79+lxDqRuGYSQmJhoPPfSQERAQYLi6uhp+fn5G586djYULF1r7ZGVlGS+88IJRu3Ztw93d3WjevLmxatUqY8iQIbmGhT916pQxaNAgw8vLy/D29jYGDRpkbN++Pd9DzZ89e9a45557DB8fH0OSzf4TExONYcOGGVWrVjXc3NyMpk2b5muf2Ur6+dm4caPx0EMPGU2aNDG8vb0NV1dXo1atWsbQoUON/fv35+r/xRdfGG3atDE8PT0NLy8vo1WrVsZHH31k02fZsmVG8+bNDXd3d6Ny5crGwIEDjSNHjtj0udL3ZbaFCxcaISEhhqenp1GxYkWjadOmxmOPPWYcO3bMMAzD2LZtmzFgwACjVq1ahru7u1G9enWjR48eNv92/s3AgQMNSUZ4eHiubbGxsUavXr2MGjVqGG5ubkaNGjWMAQMGGH/88ce/7vdq3xPZsoeanz17dp7bv/nmG6Nt27bW89yzZ09j9+7d1u0XL140Hn30UaNZs2ZGxYoVjfLlyxvNmjUz3nzzzX+tD0DxYjGMQn4CGwAAAACQC898AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgEmWCygrK0vHjh1TxYoVZbFYHF0OAAAAAAcxDENnzpxRjRo15OR05etbhK8COnbsmAICAhxdBgAAAIBi4vDhw7rhhhuuuJ3wVUAVK1aUZJ5gLy8vB1cDAAAAwFFSU1MVEBBgzQhXQvgqoOxbDb28vAhfAAAAAP71cSQG3AAAAAAAOyB8AQAAAIAdEL4AAAAAwA545gsAAAAlVmZmpjIyMhxdBko5Z2dnubi4XPcUUw4PX2+88YZmz56thIQENWvWTPPmzVOrVq2u2H/58uV66qmndPDgQdWrV08zZ85Ut27drNtXrFihBQsWaOvWrUpOTtb27dsVHByc574Mw1C3bt0UHR2tzz77TJGRkYV8dAAAACgqZ8+e1ZEjR2QYhqNLQRlQrlw5+fv7y83NrcD7cGj4WrZsmcaPH68FCxYoNDRUc+fOVUREhPbu3avq1avn6v/jjz9qwIABmjFjhnr06KElS5YoMjJS27ZtU5MmTSRJaWlpateune6++27df//9V/38uXPnMkEyAABACZSZmakjR46oXLlyqlatGr/TocgYhqH09HSdOHFCBw4cUL169a46kfLVWAwH/qkgNDRULVu21Ouvvy5JysrKUkBAgMaMGaNJkybl6h8VFaW0tDStWrXK2ta6dWsFBwdrwYIFNn0PHjyooKCgK1752rFjh3r06KEtW7bI39//mq98paamytvbWykpKQw1DwAAYGcXLlzQgQMHFBgYKE9PT0eXgzLg3LlzOnTokIKCguTh4WGzLb/ZwGEDbqSnp2vr1q0KDw/PKcbJSeHh4YqLi8vzPXFxcTb9JSkiIuKK/a/k3Llzuueee/TGG2/Iz88vX++5ePGiUlNTbRYAAAA4Fle8YC8Fvdpls49CqKNATp48qczMTPn6+tq0+/r6KiEhIc/3JCQkXFP/K3nkkUfUpk0b9erVK9/vmTFjhry9va1LQEDANX0mAAAAgLKtzA01/8UXX2jdunWaO3fuNb1v8uTJSklJsS6HDx8umgIBAAAAlEoOC19Vq1aVs7OzEhMTbdoTExOveCugn5/fNfXPy7p167R//375+PjIxcVFLi7mmCN9+vRRx44dr/g+d3d3eXl52SwAAABAYenYsaPGjRtnt89bvHixfHx87PZ5Rcne566gHBa+3NzcFBISotjYWGtbVlaWYmNjFRYWlud7wsLCbPpLUkxMzBX752XSpEn65ZdftGPHDusiSa+88ooWLVp07QcCAAAA5NPQoUNlsVhyLX/++adWrFihZ5991to3MDAw191ajghM69evV48ePVStWjV5eHioTp06ioqK0nfffWfXOq7mn+euuHLoUPPjx4/XkCFD1KJFC7Vq1Upz585VWlqahg0bJkkaPHiwatasqRkzZkiSxo4dqw4dOmjOnDnq3r27li5dqi1btmjhwoXWfSYnJys+Pl7Hjh2TJO3du1eSedXs8uWfatWqpaCgoKI+ZAAAAJRxXbp0yfVH/2rVqsnZ2dlBFV3Zm2++qdGjR2vQoEFatmyZ6tSpo5SUFK1fv16PPPKItm7d6ugSJUmVK1d2dAn54tBnvqKiovTSSy9p6tSpCg4O1o4dOxQdHW0dVCM+Pl7Hjx+39m/Tpo2WLFmihQsXqlmzZvrkk0+0cuVK6xxfkvlMV/PmzdW9e3dJUv/+/dW8efNcQ9EDAAAAjuDu7p7rwoCzs7PNrXMdO3bUoUOH9Mgjj1ivjm3YsEHDhg1TSkqKtW369OmSzJG5J06cqJo1a6p8+fIKDQ3Vhg0bbD538eLFqlWrlsqVK6fevXvr1KlTV60zPj5e48aN07hx4/Tuu+/qtttuU+3atXXzzTdr7Nix2rJli7XvqVOnNGDAANWsWVPlypVT06ZN9dFHH9nsL68recHBwdZjMAxD06dPV61ateTu7q4aNWro4YcftvZ98803Va9ePXl4eMjX11d9+/a1bvvnbYfvv/++WrRooYoVK8rPz0/33HOPkpKSrNs3bNggi8Wi2NhYtWjRQuXKlVObNm2sF26KikOvfEnS6NGjNXr06Dy3/fMbRpL69eunfv36XXF/Q4cO1dChQ6+pBmZFBwAAKAVatJCucRTsQuHnJ10WRArDihUr1KxZM40cOVL333+/JPPqzty5czV16lRrSKhQoYIk83fq3bt3a+nSpapRo4Y+++wzdenSRb/++qvq1aunTZs2acSIEZoxY4YiIyMVHR2tadOmXbWGTz/9VBkZGXrsscfy3H75MP8XLlxQSEiIHn/8cXl5eemrr77SoEGDVKdOHbVq1Spfx/zpp5/qlVde0dKlS9W4cWMlJCRo586dkqQtW7bo4Ycf1vvvv682bdooOTlZ33///RX3lZGRoWeffVb169dXUlKSxo8fr6FDh2r16tU2/Z544gnNmTNH1apV06hRozR8+HBt3LgxX/UWhMPDFwAAAFAoEhKko0cdXcW/WrVqlTU0SVLXrl21fPlymz6VK1eWs7Oz9cpNNm9vb1ksFpu2+Ph4LVq0SPHx8apRo4YkaeLEiYqOjtaiRYv0wgsv6NVXX1WXLl2sQeqmm27Sjz/+qOjo6CvW+ccff8jLy8vmsz799FMNGTLEuh4XF6emTZuqZs2amjhxorV9zJgx+vrrr/Xxxx/nO3zFx8fLz89P4eHhcnV1Va1atazvjY+PV/ny5dWjRw9VrFhRtWvXVvPmza+4r+HDh1tf33jjjXrttdfUsmVLnT171ubcP//88+rQoYMkc2yI7t2768KFC7kmUS4shC8AAACUDtcwArYjP7dTp06aP3++db18+fLX9fG//vqrMjMzddNNN9m0X7x4UVWqVJEk7dmzR71797bZHhYWdtXwJeWexDoiIkI7duzQ0aNH1bFjR2VmZkqSMjMz9cILL+jjjz/W0aNHlZ6erosXL6pcuXL5Po5+/fpp7ty5uvHGG9WlSxd169ZNPXv2lIuLi26//XbVrl3buq1Lly7q3bv3Ffe/detWTZ8+XTt37tTff/+trKwsSWaIa9SokbXfzTffbH3t7+8vSUpKSlKtWrXyXfe1IHwBAACgdCjkW/+KSvny5VW3bt1C29/Zs2fl7OysrVu35hq04/KrPNeqXr16SklJUUJCgvXqV4UKFVS3bl3rdE3ZZs+erVdffVVz585V06ZNVb58eY0bN07p6enWPk5OTrke98nIyLC+DggI0N69e/XNN98oJiZGDz74oGbPnq1vv/1WFStW1LZt27RhwwatXbtWU6dO1fTp07V58+Zcoz+mpaUpIiJCERER+vDDD1WtWjXFx8crIiLCph5JcnV1tb7ODprZQa0olLlJlgEAAICSwM3NzXpl6WptzZs3V2ZmppKSklS3bl2bJTs0NWzYUJs2bbJ5308//XTVz+/bt69cXV01c+bMf61148aN6tWrl+699141a9ZMN954o/744w+bPtWqVbMZTC81NVUHDhyw6ePp6amePXvqtdde04YNGxQXF6dff/1VkuTi4qLw8HDNmjVLv/zyiw4ePKh169blquX333/XqVOn9OKLL6p9+/Zq0KCBzWAbjsSVLwC4zPL9KUW6/351vIt0/wCA0iMwMFDfffed+vfvL3d3d1WtWlWBgYE6e/asYmNj1axZM5UrV0433XSTBg4cqMGDB2vOnDlq3ry5Tpw4odjYWN18883q3r27Hn74YbVt21YvvfSSevXqpa+//vpfbzmsVauW5syZo7Fjxyo5OVlDhw5VUFCQkpOT9cEHH0iS9UpbvXr19Mknn+jHH39UpUqV9PLLLysxMdHmFr/bbrtNixcvVs+ePeXj46OpU6faXKlbvHixMjMzFRoaqnLlyumDDz6Qp6enateurVWrVumvv/7SrbfeqkqVKmn16tXKyspS/fr186zbzc1N8+bN06hRo7Rr165iMwcYV74AAACAYuiZZ57RwYMHVadOHVWrVk2SOfXSqFGjFBUVpWrVqmnWrFmSpEWLFmnw4MGaMGGC6tevr8jISG3evNn67FLr1q319ttv69VXX1WzZs20du1aPfnkk/9aw5gxY7R27VqdOHFCffv2Vb169dStWzcdOHBA0dHRatq0qSTpySef1C233KKIiAh17NhRfn5+ioyMtNnX5MmT1aFDB/Xo0UPdu3dXZGSk6tSpY93u4+Ojt99+W23bttXNN9+sb775Rl9++aWqVKkiHx8frVixQrfddpsaNmyoBQsW6KOPPlLjxo1z1VytWjUtXrxYy5cvV6NGjfTiiy/qpZdeKtB/g8JmMRhnvUBSU1Pl7e2tlJQUeXl5ObocAIWEK18AUDJcuHBBBw4cUFBQUJGNTAdc7mrfc/nNBlz5AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAACUWIwdB3spjO81whcAAABKnOz5odLT0x1cCcqKc+fOSZJcXV0LvA8mWQYAAECJ4+LionLlyunEiRNydXWVkxPXFFA0DMPQuXPnlJSUJB8fH5uJoa8V4QsAAAAljsVikb+/vw4cOKBDhw45uhyUAT4+PvLz87uufRC+AAAAUCK5ubmpXr163HqIIufq6npdV7yyEb4AAABQYjk5OcnDw8PRZQD5ws2xAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOzA4eHrjTfeUGBgoDw8PBQaGqqff/75qv2XL1+uBg0ayMPDQ02bNtXq1atttq9YsUJ33HGHqlSpIovFoh07dthsT05O1pgxY1S/fn15enqqVq1aevjhh5WSklLYhwYAAAAAVg4NX8uWLdP48eM1bdo0bdu2Tc2aNVNERISSkpLy7P/jjz9qwIABGjFihLZv367IyEhFRkZq165d1j5paWlq166dZs6cmec+jh07pmPHjumll17Srl27tHjxYkVHR2vEiBFFcowAAAAAIEkWwzAMR314aGioWrZsqddff12SlJWVpYCAAI0ZM0aTJk3K1T8qKkppaWlatWqVta1169YKDg7WggULbPoePHhQQUFB2r59u4KDg69ax/Lly3XvvfcqLS1NLi4u+ao9NTVV3t7eSklJkZeXV77eA6D4W76/aK+C96vjXaT7BwAA9pffbOCwK1/p6enaunWrwsPDc4pxclJ4eLji4uLyfE9cXJxNf0mKiIi4Yv/8yj5JVwteFy9eVGpqqs0CAAAAAPnlsPB18uRJZWZmytfX16bd19dXCQkJeb4nISHhmvrnt45nn31WI0eOvGq/GTNmyNvb27oEBAQU+DMBAAAAlD0OH3DDkVJTU9W9e3c1atRI06dPv2rfyZMnKyUlxbocPnzYPkUCAAAAKBXy94BTEahataqcnZ2VmJho056YmCg/P7883+Pn53dN/a/mzJkz6tKliypWrKjPPvtMrq6uV+3v7u4ud3f3a/4cAAAAAJAceOXLzc1NISEhio2NtbZlZWUpNjZWYWFheb4nLCzMpr8kxcTEXLH/laSmpuqOO+6Qm5ubvvjiC3l4eFz7AQAAAADANXDYlS9JGj9+vIYMGaIWLVqoVatWmjt3rtLS0jRs2DBJ0uDBg1WzZk3NmDFDkjR27Fh16NBBc+bMUffu3bV06VJt2bJFCxcutO4zOTlZ8fHxOnbsmCRp7969ksyrZn5+ftbgde7cOX3wwQc2g2dUq1ZNzs7O9jwFAAAAAMoIh4avqKgonThxQlOnTlVCQoKCg4MVHR1tHVQjPj5eTk45F+fatGmjJUuW6Mknn9SUKVNUr149rVy5Uk2aNLH2+eKLL6zhTZL69+8vSZo2bZqmT5+ubdu2adOmTZKkunXr2tRz4MABBQYGFtXhAgAAACjDHDrPV0nGPF9A6cQ8XwAA4FoV+3m+AAAAAKAsIXwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYgcPD1xtvvKHAwEB5eHgoNDRUP//881X7L1++XA0aNJCHh4eaNm2q1atX22xfsWKF7rjjDlWpUkUWi0U7duzItY8LFy7ooYceUpUqVVShQgX16dNHiYmJhXlYAAAAAGDDoeFr2bJlGj9+vKZNm6Zt27apWbNmioiIUFJSUp79f/zxRw0YMEAjRozQ9u3bFRkZqcjISO3atcvaJy0tTe3atdPMmTOv+LmPPPKIvvzySy1fvlzffvutjh07prvuuqvQjw8AAAAAslkMwzAc9eGhoaFq2bKlXn/9dUlSVlaWAgICNGbMGE2aNClX/6ioKKWlpWnVqlXWttatWys4OFgLFiyw6Xvw4EEFBQVp+/btCg4OtranpKSoWrVqWrJkifr27StJ+v3339WwYUPFxcWpdevWedZ68eJFXbx40bqempqqgIAApaSkyMvLq8DnAEDxsnx/SpHuv18d7yLdPwAAsL/U1FR5e3v/azZw2JWv9PR0bd26VeHh4TnFODkpPDxccXFxeb4nLi7Opr8kRUREXLF/XrZu3aqMjAyb/TRo0EC1atW66n5mzJghb29v6xIQEJDvzwQAAAAAh4WvkydPKjMzU76+vjbtvr6+SkhIyPM9CQkJ19T/Svtwc3OTj4/PNe1n8uTJSklJsS6HDx/O92cCAAAAgIujCygp3N3d5e7u7ugyAAAAAJRQDrvyVbVqVTk7O+caZTAxMVF+fn55vsfPz++a+l9pH+np6Tp9+vR17QcAAAAAroXDwpebm5tCQkIUGxtrbcvKylJsbKzCwsLyfE9YWJhNf0mKiYm5Yv+8hISEyNXV1WY/e/fuVXx8/DXtBwAAAACuhUNvOxw/fryGDBmiFi1aqFWrVpo7d67S0tI0bNgwSdLgwYNVs2ZNzZgxQ5I0duxYdejQQXPmzFH37t21dOlSbdmyRQsXLrTuMzk5WfHx8Tp27JgkM1hJ5hUvPz8/eXt7a8SIERo/frwqV64sLy8vjRkzRmFhYVcc6RAAAAAArpdDw1dUVJROnDihqVOnKiEhQcHBwYqOjrYOqhEfHy8np5yLc23atNGSJUv05JNPasqUKapXr55WrlypJk2aWPt88cUX1vAmSf3795ckTZs2TdOnT5ckvfLKK3JyclKfPn108eJFRURE6M0337TDEQMAAAAoqxw6z1dJlt+x/AGULMzzBQAArlWxn+cLAAAAAMoSwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAO3B4+HrjjTcUGBgoDw8PhYaG6ueff75q/+XLl6tBgwby8PBQ06ZNtXr1apvthmFo6tSp8vf3l6enp8LDw7Vv3z6bPn/88Yd69eqlqlWrysvLS+3atdP69esL/dgAAAAAIJtDw9eyZcs0fvx4TZs2Tdu2bVOzZs0UERGhpKSkPPv/+OOPGjBggEaMGKHt27crMjJSkZGR2rVrl7XPrFmz9Nprr2nBggXatGmTypcvr4iICF24cMHap0ePHrp06ZLWrVunrVu3qlmzZurRo4cSEhKK/JgBAAAAlE0WwzAMR314aGioWrZsqddff12SlJWVpYCAAI0ZM0aTJk3K1T8qKkppaWlatWqVta1169YKDg7WggULZBiGatSooQkTJmjixImSpJSUFPn6+mrx4sXq37+/Tp48qWrVqum7775T+/btJUlnzpyRl5eXYmJiFB4enq/aU1NT5e3trZSUFHl5eV3vqQBQTCzfn1Kk++9Xx7tI9w8AAOwvv9nAYVe+0tPTtXXrVpuw4+TkpPDwcMXFxeX5nri4uFzhKCIiwtr/wIEDSkhIsOnj7e2t0NBQa58qVaqofv36eu+995SWlqZLly7prbfeUvXq1RUSEnLFei9evKjU1FSbBQAAAADyy2Hh6+TJk8rMzJSvr69Nu6+v7xVv/0tISLhq/+yvV+tjsVj0zTffaPv27apYsaI8PDz08ssvKzo6WpUqVbpivTNmzJC3t7d1CQgIuLYDBgAAAFCmOXzADXszDEMPPfSQqlevru+//14///yzIiMj1bNnTx0/fvyK75s8ebJSUlKsy+HDh+1YNQAAAICSzmHhq2rVqnJ2dlZiYqJNe2Jiovz8/PJ8j5+f31X7Z3+9Wp9169Zp1apVWrp0qdq2batbbrlFb775pjw9PfXuu+9esV53d3d5eXnZLAAAAACQXw4LX25ubgoJCVFsbKy1LSsrS7GxsQoLC8vzPWFhYTb9JSkmJsbaPygoSH5+fjZ9UlNTtWnTJmufc+fOSTKfL7uck5OTsrKyrv/AAAAAACAPBQpff/31V6F8+Pjx4/X222/r3Xff1Z49e/Sf//xHaWlpGjZsmCRp8ODBmjx5srX/2LFjFR0drTlz5uj333/X9OnTtWXLFo0ePVqS+TzXuHHj9Nxzz+mLL77Qr7/+qsGDB6tGjRqKjIyUZAa4SpUqaciQIdq5c6f++OMPPfroozpw4IC6d+9eKMcFAAAAAP/kUpA31a1bVx06dNCIESPUt29feXh4FOjDo6KidOLECU2dOlUJCQkKDg5WdHS0dcCM+Ph4mytUbdq00ZIlS/Tkk09qypQpqlevnlauXKkmTZpY+zz22GNKS0vTyJEjdfr0abVr107R0dHWGqtWraro6Gg98cQTuu2225SRkaHGjRvr888/V7NmzQp0HAAAAADwbwo0z9eOHTu0aNEiffTRR0pPT1dUVJRGjBihVq1aFUWNxRLzfAGlE/N8AQCAa1Wk83wFBwfr1Vdf1bFjx/S///1Px48fV7t27dSkSRO9/PLLOnHiRIELBwAAAIDS6LoG3HBxcdFdd92l5cuXa+bMmfrzzz81ceJEBQQEaPDgwVcduh0AAAAAypLrCl9btmzRgw8+KH9/f7388suaOHGi9u/fr5iYGB07dky9evUqrDoBAAAAoEQr0IAbL7/8shYtWqS9e/eqW7dueu+999StWzfr4BhBQUFavHixAgMDC7NWAAAAACixChS+5s+fr+HDh2vo0KHy9/fPs0/16tX13//+97qKAwAAAIDSokDhKyYmRrVq1co1UbFhGDp8+LBq1aolNzc3DRkypFCKBAAAAICSrkDPfNWpU0cnT57M1Z6cnKygoKDrLgoAAAAASpsCha8rTQ129uzZAk+4DAAAAACl2TXddjh+/HhJksVi0dSpU1WuXDnrtszMTG3atEnBwcGFWiAAAAAAlAbXFL62b98uybzy9euvv8rNzc26zc3NTc2aNdPEiRMLt0IAAAAAKAWuKXytX79ekjRs2DC9+uqr8vLyKpKiAAAAAKC0KdBoh4sWLSrsOgAAAACgVMt3+Lrrrru0ePFieXl56a677rpq3xUrVlx3YQAAAABQmuQ7fHl7e8tisVhfAwAAAADyL9/h6/JbDbntEAAAAACuTYHm+Tp//rzOnTtnXT906JDmzp2rtWvXFlphAAAAAFCaFCh89erVS++9954k6fTp02rVqpXmzJmjXr16af78+YVaIAAAAACUBgUKX9u2bVP79u0lSZ988on8/Px06NAhvffee3rttdcKtUAAAAAAKA0KFL7OnTunihUrSpLWrl2ru+66S05OTmrdurUOHTpUqAUCAAAAQGlQoHm+6tatq5UrV6p37976+uuv9cgjj0iSkpKSmHgZAK5i+f6UItt3vzqMRAsAQHFWoCtfU6dO1cSJExUYGKjQ0FCFhYVJMq+CNW/evFALBAAAAIDSoEBXvvr27at27drp+PHjatasmbW9c+fO6t27d6EVBwAAAAClRYHClyT5+fnJz8/Ppq1Vq1bXXRAAAAAAlEYFCl9paWl68cUXFRsbq6SkJGVlZdls/+uvvwqlOAAAAAAoLQoUvu677z59++23GjRokPz9/WWxWAq7LgAAAAAoVQoUvtasWaOvvvpKbdu2Lex6AAAAAKBUKtBoh5UqVVLlypULuxYAAAAAKLUKFL6effZZTZ06VefOnSvsegAAAACgVCrQbYdz5szR/v375evrq8DAQLm6utps37ZtW6EUBwAAAAClRYHCV2RkZCGXAQAAAAClW4HC17Rp0wq7DgAAAAAo1Qr0zJcknT59Wu+8844mT56s5ORkSebthkePHi204gAAAACgtCjQla9ffvlF4eHh8vb21sGDB3X//fercuXKWrFiheLj4/Xee+8Vdp0AAAAAUKIV6MrX+PHjNXToUO3bt08eHh7W9m7duum7774rtOIAAAAAoLQoUPjavHmzHnjggVztNWvWVEJCwnUXBQAAAAClTYHCl7u7u1JTU3O1//HHH6pWrdp1FwUAAAAApU2Bwtedd96pZ555RhkZGZIki8Wi+Ph4Pf744+rTp0+hFggAAAAApUGBwtecOXN09uxZVatWTefPn1eHDh1Ut25dVaxYUc8//3xh1wgAAAAAJV6BRjv09vZWTEyMNm7cqJ07d+rs2bO65ZZbFB4eXtj1AQAAAECpcM3hKysrS4sXL9aKFSt08OBBWSwWBQUFyc/PT4ZhyGKxFEWdAAAAAFCiXdNth4Zh6M4779R9992no0ePqmnTpmrcuLEOHTqkoUOHqnfv3kVVJwAAAACUaNd05Wvx4sX67rvvFBsbq06dOtlsW7dunSIjI/Xee+9p8ODBhVokAAAAAJR013Tl66OPPtKUKVNyBS9Juu222zRp0iR9+OGHhVYcAAAAAJQW1xS+fvnlF3Xp0uWK27t27aqdO3ded1EAAAAAUNpcU/hKTk6Wr6/vFbf7+vrq77//vu6iAAAAAKC0uabwlZmZKReXKz8m5uzsrEuXLl13UQAAAABQ2lzTgBuGYWjo0KFyd3fPc/vFixcLpSgAAAAAKG2uKXwNGTLkX/sw0iEAAAAA5HZN4WvRokVFVQcAAAAAlGrX9MwXAAAAAKBgCF8AAAAAYAeELwAAAACwA8IXAAAAANiBw8PXG2+8ocDAQHl4eCg0NFQ///zzVfsvX75cDRo0kIeHh5o2barVq1fbbDcMQ1OnTpW/v788PT0VHh6uffv25drPV199pdDQUHl6eqpSpUqKjIwszMMCAAAAABsODV/Lli3T+PHjNW3aNG3btk3NmjVTRESEkpKS8uz/448/asCAARoxYoS2b9+uyMhIRUZGateuXdY+s2bN0muvvaYFCxZo06ZNKl++vCIiInThwgVrn08//VSDBg3SsGHDtHPnTm3cuFH33HNPkR8vAAAAgLLLYhiG4agPDw0NVcuWLfX6669LkrKyshQQEKAxY8Zo0qRJufpHRUUpLS1Nq1atsra1bt1awcHBWrBggQzDUI0aNTRhwgRNnDhRkpSSkiJfX18tXrxY/fv316VLlxQYGKinn35aI0aMKHDtqamp8vb2VkpKiry8vAq8HwDFy/L9KY4uocD61fF2dAkAAJRJ+c0GDrvylZ6erq1btyo8PDynGCcnhYeHKy4uLs/3xMXF2fSXpIiICGv/AwcOKCEhwaaPt7e3QkNDrX22bdumo0ePysnJSc2bN5e/v7+6du1qc/UsLxcvXlRqaqrNAgAAAAD55bDwdfLkSWVmZsrX19em3dfXVwkJCXm+JyEh4ar9s79erc9ff/0lSZo+fbqefPJJrVq1SpUqVVLHjh2VnJx8xXpnzJghb29v6xIQEHANRwsAAACgrHP4gBv2lpWVJUl64okn1KdPH4WEhGjRokWyWCxavnz5Fd83efJkpaSkWJfDhw/bq2QAAAAApYDDwlfVqlXl7OysxMREm/bExET5+fnl+R4/P7+r9s/+erU+/v7+kqRGjRpZt7u7u+vGG29UfHz8Fet1d3eXl5eXzQIAAAAA+eWw8OXm5qaQkBDFxsZa27KyshQbG6uwsLA83xMWFmbTX5JiYmKs/YOCguTn52fTJzU1VZs2bbL2CQkJkbu7u/bu3Wvtk5GRoYMHD6p27dqFdnwAAAAAcDkXR374+PHjNWTIELVo0UKtWrXS3LlzlZaWpmHDhkmSBg8erJo1a2rGjBmSpLFjx6pDhw6aM2eOunfvrqVLl2rLli1auHChJMlisWjcuHF67rnnVK9ePQUFBempp55SjRo1rPN4eXl5adSoUZo2bZoCAgJUu3ZtzZ49W5LUr18/+58EAAAAAGWCQ8NXVFSUTpw4oalTpyohIUHBwcGKjo62DpgRHx8vJ6eci3Nt2rTRkiVL9OSTT2rKlCmqV6+eVq5cqSZNmlj7PPbYY0pLS9PIkSN1+vRptWvXTtHR0fLw8LD2mT17tlxcXDRo0CCdP39eoaGhWrdunSpVqmS/gwcAAABQpjh0nq+SjHm+gNKJeb4AAMC1KvbzfAEAAABAWUL4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOzAxdEFAMC1Wr4/xdElAAAAXDOufAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwAxdHFwAAJYlz2lmVP3JIhourMip6Kd3LW1nuHpLF4ujSAABAMUf4AoA8eCQlqOL+vfLav8/8+tc+Vdy/T+USjubqm+XqqowKXtYwlnpTQyW0u02JbTspvUpVB1QPAACKI8IXAPw/95NJqvXlJ6r92VJV2v1Lvt/nlJEh979Pyf3vU5Kkyrt2KHDFR5Kkvxs3M4NYu046GdJahptbkdQOAACKP8IXgDLN6eIF1YiNVu3PPpLfd9/IKTPzin0v+lTSmTr1dSbwRlmysuSamiLXM6nmcjZVbv+/bsnKsr6n0m87Vem3nWr41iu6VK68jne8Q78/ME6nGzezx+EBAIBihPAFoEzy+mO36r7/tgK+WiG31JRc25NvvkUnQ1ortU59nalTT6k33qT0ylX+9dkup4sXVGXbJvl9v16+38eq0p5frdtczqUpYPVnClj9mY53uF17HpygUyGtC/3YAABA8WQxDMNwdBElUWpqqry9vZWSkiIvLy9HlwOUKcv35w5L+eV+6oQav/K8bvz4PZsrVJJ0zq+mDkVG6VBklM7UrX+9ZZqfdzJJvhs3yPeHdfLfEGO9NTFbUqu22vPgRCW17Xjdg3b0q+N9Xe8HAAAFk99sQPgqIMIX4DgFCV9OFy+q7ntvqdEbL8n1bKq1/ZJnOR2J6KlDd92jpNB2krNzYZb6jxouKGj5B6q/cK7KHztis+1UsxDtHv2YEjpFFHj/hC8AAByD8FXECF+A41xT+DIM1YhZpWYznlKFwwetzRnlK2rPf8brz0H3K7N8hcIv8iosGRmq9cXHarjgFVU88KfNtsPdemvb9JfMWxyvEeELAADHyG82YJJlAKWW9++71OHenmr74CBr8DIsFv0VNURrYrdq76hH7B68JMlwddWhPgMVHb1Jca8u0ukGja3bAlZ/poiurVVj7Zd2rwsAABQtwheA0scwVOe9herc+zZV3/SDtTkptJ1iPv9WW59/VRerVndggf/P2VlHuvdWzJc/6KdX3tFFn0qSJI9TJ9T2wUFqNX6kXE//7eAiAQBAYSF8AShVXM6kqPXDw3TLM4/JOSNdknS2VpA2zv9A337wpVIa3ezgCvNgsehwz776es1POtq5q7W59hcfK6Jra/mvi3ZgcQAAoLAQvgCUGj6/7dTtvToqYM1Ka9ve4Q/p6zU/6djtPa57NMGidrGar35csESbZi9Qupf5/JbniUS1G9lfLR5/UM5pZx1cIQAAuB6ELwAln2HoxiX/02397lCF+AOSpHQvb22c/6F+mfK8stzdHVzgNbBYFN+7v75eHafjHW63Ngd9ukSd+neV5/GjDiwOAABcD8IXgBLN5ewZhY6/XyFTx8s5/aIkc4LkmM+/1bHbuzu4uoK74FdDP7zzsTbPmKeM8hUlSZX2/KrOfcPls2uHY4sDAAAFQvgCUGKVO3JInft0Vq0vP7G27Rs8Uus/WqNzAYGOK6ywWCw62G+QYj+J0dmA2pIkz8Tj6jSgG6MhAgBQAhG+AJRIXn/s1m1RXeS1/w9JUkYFL/04713tmDqrZN1mmA9n6jVQ7CexOnlLqCTJ5fw5tX1wkOq/NVdiqkYAAEoMwheAEqfKtk3qNKCbPBOPS5JSb6ynmJUbdLRrLwdXVnTSq1TVt+9/rkN33m1tu3n2dLWYPEaW9HQHVgYAAPKL8AWgZFmzRrcOjpRbymlJUnLT5lr/0RqlBd7o2LrsIMvdQz/PeUu7xk2xtgV98oFuHdpbrv9/PgAAQPFF+AJQcnz4oXTnnXK5cF6SlNimg759/wulV6nq4MLsyGLRntGPKe7V/ynTzby9svrPG9Vh0J1ScrKDiwMAAFdD+AJQMrz2mnTvvdKlS5Kkw1166Ye3P9alChUdXJhjHOl+lzZ8uEoXqlSTJFXa/YsUHk4AAwCgGCN8ASjeDEN66ilp7Fhr0/57huunV/9X6gbWuFbJzVuaAaxqdbNh+3YCGAAAxRjhC0DxNnWq9NxzOetPPaVtT8+RnJ0dV1MxcqZufW34cJXOV/M1G7Zvlzp3lk6dcmxhAAAgF8IXgOJr1izb4PXqq9Izz0gWi+NqKobO1LlJ337wpeTnZzbs2GFeASOAAQBQrBC+ABRPb74pPf54zvq8edLDDzuunmLuTJ2bpA0bJH9/s2HHDvMK2MmTjiwLAABchvAFoPh5/33poYdy1l94QRo92nH1lBT160vr1+cEsJ07CWAAABQjxSJ8vfHGGwoMDJSHh4dCQ0P1888/X7X/8uXL1aBBA3l4eKhp06ZavXq1zXbDMDR16lT5+/vL09NT4eHh2rdvX577unjxooKDg2WxWLRjx47COiQABbVihTR0aM765MnmgvypX9+8Alajhrn+yy9St25SWppDywIAAMUgfC1btkzjx4/XtGnTtG3bNjVr1kwRERFKSkrKs/+PP/6oAQMGaMSIEdq+fbsiIyMVGRmpXbt2WfvMmjVLr732mhYsWKBNmzapfPnyioiI0IULF3Lt77HHHlON7F9SADjW119L/ftLWVnm+ujR0vPPO7amkuimm2wD2ObN0t13SxkZDi0LAICyzmIYhuHIAkJDQ9WyZUu9/vrrkqSsrCwFBARozJgxmjRpUq7+UVFRSktL06pVq6xtrVu3VnBwsBYsWCDDMFSjRg1NmDBBEydOlCSlpKTI19dXixcvVv/+/a3vW7NmjcaPH69PP/1UjRs31vbt2xUcHJyvulNTU+Xt7a2UlBR5eXldxxkAIEn6/nspIkI6b06grCFDpP/9T3LK/Tei5ftT7FxcydCvjrdtw65dUrt2Usr/n6/hw6V33mHAEgAACll+s4FDr3ylp6dr69atCg8Pt7Y5OTkpPDxccXFxeb4nLi7Opr8kRUREWPsfOHBACQkJNn28vb0VGhpqs8/ExETdf//9ev/991WuXLl/rfXixYtKTU21WQAUkh07pO7dc4JXnz5mSMgjeOEaNGkiff655OZmrv/vf+bQ/QAAwCEc+pvNyZMnlZmZKV9fX5t2X19fJSQk5PmehISEq/bP/nq1PoZhaOjQoRo1apRatGiRr1pnzJghb29v6xIQEJCv9wH4F0eOmMHrzBlzvUsXackSycXFsXWVFh06SB98kHO167nnpAULHFsTAABlVJn8s/K8efN05swZTb6Gh/gnT56slJQU63L48OEirBAoI86ckXr2lI4dM9fDwqRPP825UoPC0a+fNHduzvpDD0krVzqqGgAAyiyHhq+qVavK2dlZiYmJNu2JiYnyy54s9B/8/Pyu2j/769X6rFu3TnFxcXJ3d5eLi4vq1q0rSWrRooWGDBmS5+e6u7vLy8vLZgFwHS5dMgfXyB5l9MYbzVvk8nEbMArg4Yelxx4zX2dlSQMGSBs3OrYmAADKGIeGLzc3N4WEhCg2NtbalpWVpdjYWIWFheX5nrCwMJv+khQTE2PtHxQUJD8/P5s+qamp2rRpk7XPa6+9pp07d2rHjh3asWOHdaj6ZcuW6XlGVgPs45FHpOxpInx8pK++kqpVc2hJpd6MGdLAgebrCxfMq4579ji2JgAAyhCHP1Qxfvx4DRkyRC1atFCrVq00d+5cpaWladiwYZKkwYMHq2bNmpoxY4YkaezYserQoYPmzJmj7t27a+nSpdqyZYsWLlwoSbJYLBo3bpyee+451atXT0FBQXrqqadUo0YNRUZGSpJq1aplU0OFChUkSXXq1NENN9xgpyMHyrDXXpP+f4RTubiYc3s1aODYmsoCJydz0I3EROmbb6S//zaft/v5Z6lqVUdXBwBAqefw8BUVFaUTJ05o6tSpSkhIUHBwsKKjo60DZsTHx8vpshHP2rRpoyVLlujJJ5/UlClTVK9ePa1cuVJNmjSx9nnssceUlpamkSNH6vTp02rXrp2io6Pl4eFh9+MD8A9ffCGNG5ez/vbbUqdODiunzHFzM5+r69DBvOXzwAHzmbC1ayVXV0dXBwBAqebweb5KKub5Agpg61bp1lulc+fM9SeflJ599pp3wzxfecs1z9fVHD4stWxpXgWTpFGjpPnzi6YwAABKuRIxzxeAMuTIEfMZo+zgNWCA9Mwzjq2pLAsIMEc8zB5ZcsEC6c03HVoSAAClHeELQNE7f16KjJSOHzfX27Y1nz3KnnsKjtG6tXnbZ7aHH5bWrXNcPQAAlHKELwBFyzDMW9q2bjXXg4Kkzz6TeAazeBg8WJo40XydmWk+/7V/v2NrAgCglCJ8ASha8+ZJ771nvi5XzpzLiyHli5cXX5S6djVfJyebt4empjq2JgAASiHCF4Cis369NH58zvrixVLTpg4rB1fg7Cx99FHOcP979kj33GNeCQMAAIWG8AWgaBw6JN19d84v8JMmmbe0oXjy9janAahUyVz/6ivpiSccWxMAAKUM4QtA4Tt3TurdWzp50lzv0kV67jnH1oR/V6+e9PHH5pUwSZo505wTDAAAFArCF4DCZRjSyJHS9u3mep060pIlOb/Qo3gLD5fmzMlZHzpU+v13h5UDAEBpQvgCULheeUX68EPzdfny5lxS2beyoWR4+GHzmS9JOntWuusu6cwZx9YEAEApQPgCUHhiY6VHH81Zf+89qUkTx9WDgrFYpIULc/7b7dkjDR9uXtUEAAAFRvgCUDiOHJH695eyssz1J54wr5igZCpfXlqxQvLyMtc/+UR6+WXH1gQAQAlH+AJw/dLTzZENswfY6NpVevppx9aE61evnvT++znrjz8ubdjgsHIAACjpCF8Art+jj0pxcebr2rWlDz5ggI3S4s47c4acz8yUoqLMq5wAAOCaEb4AXJ9ly6TXXjNfu7mZt6dVruzYmlC4nn5auuMO83VSkjlfW3q6Y2sCAKAEInwBKLjff5fuuy9n/dVXpRYtHFcPioazszldQO3a5vpPP0mPPOLYmgAAKIEIXwAK5uxZqU8f86skDRokPfCAY2tC0alSxZxw2d3dXH/zTTOQAQCAfCN8Abh22RMp795trjdpIi1YYA5RjtIrJER6442c9ZEjzWHoAQBAvhC+AFy7N9+UPvrIfF2xonlFpFw5x9YE+xg+XBoyxHydlib17Wt+BQAA/4rwBeDabNpk+7zPokXSTTc5rh7Yl8Vihu/sCZh375b+8x8mYAYAIB8IXwDy79Qpcz6vjAxzffx487kvlC3lypmjWlaoYK6//770zjuOrQkAgBKA8AUgf7KypMGDpfh4c71tW+nFFx1bExynfn3bwDVmjLR9u+PqAQCgBCB8AcifWbOk1avN11WrmvN7ubo6tiY4VlSU9NBD5uuLF835v1JSHFsTAADFGOELwL/79lvpiSfM1xaL9OGHUs2ajq0JxcOcOVLLlubr/fulYcN4/gsAgCsgfAG4usREacAA87ZDSXrqKemOOxxbE4oPd3fp44+lSpXM9c8+k+bOdWhJAAAUVy6OLgBAMZaZKQ0cKB0/bq537ixNnfqvb1u+n1vPypTAQOm996SePc31xx6TWreWwsIcWhYAAMUNV74AXNmzz0qxseZrf3/zdkNnZ8fWhOKpRw/p8cfN15cumaNinjzp2JoAAChmCF8A8hYTIz3zjPnayUlaulTy9XVsTSjenntOat/efH3kiDk6ZvbtqgAAgPAFIA9Hj5q3G2YPnPD889Kttzq2JhR/Li5mSK9WzVxfs0aaOdOxNQEAUIwQvgDYysiQ+veXTpww17t1M5/hAfKjRg1pyRJzVExJevJJc7RMAABA+ALwD088If3wg/m6Vi1zIAUnflTgGoSHS9Omma+zsswwn5jo2JoAACgG+I0KQI4vvpBmzzZfu7qaEylXqeLYmlAyPfmkGcIkKSFBuucec/RMAADKMMIXANOBA9KQITnrs2ebw4UDBeHsbI6O6e9vrq9blzOACwAAZRThC4B08aI5NPjp0+Z6nz7Sww87tCSUAtWrmwNwZE9P8Oyz0tq1jq0JAAAHInwBkMaPl7ZsMV/XrSv99785AyYA1+PWW83RMiVz9MyBA81h6AEAKIMIX0BZt3Sp9Oab5mt3d2n5csnb27E1oXR59FGpe3fz9cmT5lXWjAzH1gQAgAMQvoCybO9e6f77c9Zff10KDnZYOSilnJzMUTNr1zbX4+KYvgAAUCYRvoCy6tw5qW9f6exZc33wYGnECMfWhNKrcmXzqqqbm7k+d665DgBAGUL4Asoiw5AefFDatctcb9zYvPWQ57xQlFq2NENXtuHDzauvAACUEYQvoCx6+23p3XfN1+XLm1cgypd3bE0oG0aNMgfdkMyrrn37Smlpjq0JAAA7IXwBZc3mzdKYMTnrb78tNWzouHpQtlgs0ltvSY0ameu7dkn/+Y95NRYAgFKO8AWUJSdPmlca0tPN9YcflgYMcGxNKHvKl5c+/VSqUMFcf/99848AAACUcoQvoKzIzDRv94qPN9fbtJFmz3ZsTSi7GjSQ3nknZ33MGGnrVsfVAwCAHRC+gLLi6aeltWvN19WrSx9/nDPyHOAIUVE5t8Cmp5tXZZOTHVsTAABFiPAFlAWrVknPPmu+dnaWli2TatZ0bE2AJL30ktS6tfn64EHz6mxmpkNLAgCgqBC+gNLur7+kQYNy1mfMkDp2dFg5gA03N/MqbNWq5np0tDRtmmNrAgCgiBC+gNLs/HmpTx/p9Glz/a67pIkTHVoSkEtAgBnAnJ3N9eeflz77zLE1AQBQBAhfQGmVPZHyjh3m+k03SYsWMZEyiqdOnaRZs3LWBw+W9uxxXD0AABQBwhdQWr3xhrR4sfm6XDlpxQrJy8uhJQFX9cgjUv/+5uuzZ6XevaXUVMfWBABAISJ8AaXRhg3SuHE56//9r9S4saOqAfLHYjGHn7/5ZnN9717zClhWlmPrAgCgkBC+gNLm0CGpX7+cEeMefTTnagJQ3JUvb16l9fEx1z//XHrhBYeWBABAYSF8AaVJWpoUGSmdPGmud+lijm4IlCR16kgffZTzfOLUqdKaNY6tCQCAQkD4AkoLw5BGjMgZYKNuXWnJkpwR5ICSpEuXnLnpDEO65x7pzz8dWxMAANeJ8AWUFjNnmpMnS1KFCubtWpUqObYm4HpMnmxeyZXM6RJ69syZNgEAgBKoWISvN954Q4GBgfLw8FBoaKh+/vnnq/Zfvny5GjRoIA8PDzVt2lSrV6+22W4YhqZOnSp/f395enoqPDxc+/bts24/ePCgRowYoaCgIHl6eqpOnTqaNm2a0tPTi+T4gCK3erU0ZUrO+gcfSI0aOa4eoDA4OUnvvpvzvfz77+bzi5cuObYuAAAKyOHha9myZRo/frymTZumbdu2qVmzZoqIiFBSUlKe/X/88UcNGDBAI0aM0Pbt2xUZGanIyEjt2rXL2mfWrFl67bXXtGDBAm3atEnly5dXRESELly4IEn6/ffflZWVpbfeeku//fabXnnlFS1YsEBTLv/lFSgp/vjDvCXLMMz1p5+WevVybE1AYfHykr74Qqpc2Vz/+msmCgcAlFgWw8j+jc0xQkND1bJlS73++uuSpKysLAUEBGjMmDGaNGlSrv5RUVFKS0vTqlWrrG2tW7dWcHCwFixYIMMwVKNGDU2YMEET//9/0CkpKfL19dXixYvV/wqjvs2ePVvz58/XX3/9la+6U1NT5e3trZSUFHkxdxIc5fRpKSzMvCIgmfMiffKJecXAgZbvT3Ho55dV/ep4O7qEovPtt1J4eM5Vr4ULpfvvd2xNAAD8v/xmA4f+hpaenq6tW7cqPDzc2ubk5KTw8HDFxcXl+Z64uDib/pIUERFh7X/gwAElJCTY9PH29lZoaOgV9ymZAa1y9l9W83Dx4kWlpqbaLIBDZWSYQ8pnB6/Gjc1btBwcvIAi0aGDNH9+zvqDD5rz2QEAUII49Le0kydPKjMzU76+vjbtvr6+SkhIyPM9CQkJV+2f/fVa9vnnn39q3rx5euCBB65Y64wZM+Tt7W1dAgICrn5wQFEyDOmhh6RvvjHXq1QxB9ioWNGxdQFF6b77ciYPv3RJ6tNH2r/foSUBAHAtyvyfyI8ePaouXbqoX79+uv8qt7BMnjxZKSkp1uXw4cN2rBL4h5dekt5+23zt5iatXGnOjQSUdrNnm8PQS1JysjkCYgq3uQIASgaHhq+qVavK2dlZiYmJNu2JiYny8/PL8z1+fn5X7Z/9NT/7PHbsmDp16qQ2bdpo4cKFV63V3d1dXl5eNgvgECtWSI8/nrO+aJHUrp3j6gHsycVFWrpUatjQXN+zhxEQAQAlhkPDl5ubm0JCQhQbG2tty8rKUmxsrMLCwvJ8T1hYmE1/SYqJibH2DwoKkp+fn02f1NRUbdq0yWafR48eVceOHRUSEqJFixbJiedkUBJs3izde6/tyIb33OPYmgB78/aWvvwyZwTE6GjzdkTHjh8FAMC/cnF0AePHj9eQIUPUokULtWrVSnPnzlVaWpqGDRsmSRo8eLBq1qypGTNmSJLGjh2rDh06aM6cOerevbuWLl2qLVu2WK9cWSwWjRs3Ts8995zq1aunoKAgPfXUU6pRo4Yi/3+yzuzgVbt2bb300ks6ceKEtZ4rXXEDHO7QIfMWq/PnzfVBg6Snnirw7hiRECVanTrSp59Kt99uXvV64w2pVi3pscccXRkAAFfk8PAVFRWlEydOaOrUqUpISFBwcLCio6OtA2bEx8fbXJVq06aNlixZoieffFJTpkxRvXr1tHLlSjVp0sTa57HHHlNaWppGjhyp06dPq127doqOjpaHh4ck80rZn3/+qT///FM33HCDTT0OHnkfyFtKitSjh5R9O+2tt5rPfFksjq0LcKSOHaV33pGGDjXXH39cqllTGjjQkVUBAHBFDp/nq6Rini/YzaVLZvD6+mtzvV49KS7OHOHwOnDlq/Qp1fN8Xc3zz0tPPmm+dnWV1qyROnd2bE0AgDKlRMzzBeBfZGWZw2tnB6/KlaWvvrru4AWUKlOmSKNGma8zMszJxnfudGxNAADkgfAFFFeGYT6/8u675nr2kPL16jm0LKDYsVik11+X7rzTXD9zRura1XxOEgCAYoTwBRRXs2ZJc+aYr52czOG127d3bE1AceXsLH30kdS6tbl+/LgZwJKTHVsXAACXIXwBxdE770iTJuWsv/WWeSsVgCsrV84cgj776vCePVKvXtKFC46tCwCA/0f4AoqbFSukBx7IWZ8xw3zuC8C/q1rVnPerenVz/YcfzEmYMzIcWxcAACJ8AcXL+vXSgAHmQBuSNH68OXw2gPy78UZp9WqpfHlz/fPPpcGDpcxMx9YFACjzCF9AcbF1q3mLVHq6uT54sDR7NnN5AQUREmKGLnd3c33pUvMKcvYfNgAAcADCF1Ac/P67OTjAmTPmeo8e5nNfTvwTBQqsc2fp00/Nub8kafFiacwYcyRRAAAcgN/sAEf7/XepUyfpxAlzvV076eOPc35hBFBw3buboyBm/yHjzTelRx8lgAEAHMLF0QUAZVp28EpIMNeDg83R2jw9tXx/ikNLQ8lT1N8z/ep4F+n+i0yfPuZ8eYMHm6FrzhzzebCnn3Z0ZQCAMoYrX4Cj/DN4NW8uxcZKPj4OLQsole6915yyIdszz0gvvui4egAAZRLhC3CEvILXN99IlSs7ti6gNLv/funVV3PWJ0+WXnnFcfUAAMocwhdgbwQvwHEeftj2itf48dLzz/MMGADALghfgD0RvADHe/xx2+e9nnzSvApGAAMAFDHCF2Avu3cTvIDiYupUcx69bDNnSqNHMw8YAKBIEb4Ae4iLk9q3J3gBxcnEidKCBTkTmb/5pjRsmHTpkmPrAgCUWoQvoKh99ZU52Wtysrl+yy0EL6C4eOAB6b33JGdnc/2996SoKOniRcfWBQAolQhfQFFavFjq1Us6f95c79xZ2rCB4AUUJ/feKy1fLrm5mesrVkiRkdK5cw4tCwBQ+hC+gKJgGOYzJMOGSZmZZltUlHkVrGJFx9YGILfeva0TnEuSoqOlO+6QTp50bF0AgFKF8AUUtqws6ZFHpEmTctrGjJGWLJHc3R1XF4Cru+MOae1aycvLXN+4UWrd2hylFACAQkD4AgpTerp5C9PlE7m+8IK57sQ/N6DYa9dOWr9e8vc31/fvl8LCpHXrHFsXAKBU4LdBoLAkJUm33y599JG57uws/fe/5vxB2aOpASj+brlF2rRJatbMXD99WoqIkP73P4eWBQAo+QhfQGHYtk1q0UL67jtz3cND+uwzafhwx9YFoGACAqTvv5e6dzfXL12SRowwbydmLjAAQAERvoDrtWSJ1LatdPiwuV6jhjmiYc+eDi0LwHWqWFH6/HNp7NictpkzpbvvZiREAECBEL6AgsrMlB59VBo4ULpwwWxr3VraskUKDXVsbQAKh7OzNHeu9PrrOc9tfvqpOWn6gQMOLQ0AUPIQvoCC+PtvqVs36aWXctpGjDCveGU/qA+g9HjoIWnVqpypIrZtM58N+/xzx9YFAChRCF/Atdq1S2rZ0hySWpJcXMy/ir/9NkPJA6VZ167m8PN165rrp0+bkzFPmCBlZDiyMgBACUH4AvLLMMyQ1bKlOfy0JFWtKn3zjflXcUY0BEq/pk2lrVulfv1y2l5+Wbr1Vik+3nF1AQBKBMIXkB9JSVKPHuZkydnPdwUHm893dejg0NIA2JmXl7RsmTRvnuTqarb99JPUvLm0erVjawMAFGuEL+DfrFlj/rX78l+qxoyRfvxRql3bcXUBcByLRRo92vw5EBhotiUnm0PTP/64dPGiQ8sDABRPhC/gSs6flx5+2BxYIynJbKteXfrqK+m11yRPT8fWB8DxWrQwB9/o1SunbdYsKSRE2rzZcXUBAIolwheQl19+kVq1Mm8rytatm/Trr+ZXAMhWqZI5qfqcOeYAPJL022/m1BOTJuXcqgwAKPMIX8Dl0tLMW4ZCQsxRDSXJw8McaGPVKvPKFwD8k8UijR9vDsZxyy1mW1aWOSlz8+ZSXJxj6wMAFAsWwzAMRxdREqWmpsrb21spKSny8vJydDkoDKtWmc9wHDqU09a0qfTRR1LjxnYvZ/n+FLt/JuAo/ep4O7qEwpORIc2eLT39tJSebrZlh7Nnn+WWZQAohfKbDbjyBRw5IvXpI/XsmRO83Nyk6dPNZzYcELwAlGCurtKUKeazYC1bmm2GYd6WePPN0pdfmusAgDKH8IWy69Il6dVXpYYNpRUrcto7dzaf7Zo2jUmTARRc48bmaIgzZ+b8LPnzT+nOO6WICPO5MABAmUL4QtljGOaw8SEh0rhx0tmzZnv16tIHH0gxMdJNNzm0RAClhIuL9Nhj0o4dUvv2Oe0xMeZVsIcekk6edFh5AAD7InyhbImLkzp2NOfi+eWXnPYHHpB+/10aONB8NgMAClODBtK330off5wzP2BWlvTmm1K9etLcueazYgCAUo3whbLht9+kyEipTRvpu+9y2lu0MG8LWrDAHC4aAIqKxSL162f+oeeFF6Ty5c3206elRx6RmjQxr75fuuTQMgEARYfwhdLt0CFp6FBz1MLPP89pv+kmafly6eefpbAwh5UHoAzy8JAmT5b27ZOGDcu52v7HH9KgQeZVsv/9jythAFAKEb5QOm3fLt17r1S3rvTuuzkji9WoIS1caF4J69uXWwwBOI6/vxmyNm+WOnTIad+/XxoxwrwdccEC6eJFx9UIAChUhC+UHoYhrVljjlZ4yy3Shx/m3L7j42OOOPbnn9L995sPwQNAcRASIm3YYC6dO+e0Hzok/ec/0o03miOznjnjqAoBAIWE8IWS7+JFadEi89bCbt2kdetytlWpIk2dKv31lzniGJObAiiuOnSQvvnGfA61a9ec9mPHzJFZa9Qww9jlgwUBAEoUwhdKrl27pAkTpFq1pOHDbefMqVtXmj9fio+Xnn6awTQAlBxhYeZ0GJs3mwMFZTt71rwNsVkzqV07ackSbkkEgBKG8IWS5fRpM1S1amVe6Xr5ZSkpKWd727bSZ5+Zo4mNGiWVK+ewUgHgurRoYf48++UXczqM7NERJWnjRnNqjIAAadIkafdux9UJAMg3i2Fkj0SAa5Gamipvb2+lpKTIy8vL0eWUbhkZ0vr10uLF5i8iFy7YbndzM/86PG5cqRq5cPn+FEeXANhNvzreji6h+EtNld5/3/wD1OVX+rM1aSL17y9FRZlX/wEAdpPfbED4KqDiFr6K8hd1h/xSlJYmRUdLK1dKq1aZV7z+qXlzc5jme+4xn+3KQ1EHmKI8N4QvlCVF/XOmVP2MNAzp++/NEPbpp3kPSd+ihRnE7r7bvDpWCpXkn+9AWVJW/q3mNxsw5BuKjxMnzKD12WdSTEzuK1ySGbIGDjRDV3Cw3UsEAIezWKRbbzWXxETp44+lpUvNgTqybdliLhMnmn+o6tLFXMLCJFdXx9UOAGUc4QuOc+aM9MMPUmysOULhjh0583FdzstL6t5d6tNH6tFDcne3e6kAUCz5+kpjxpjLoUM5QWzbtpw+27eby4wZ5s/T8HBzNMWIiFJ7VQwAiivCF+znzBlz9K71682w9fPPOfNw/ZO/v9Srl/ksV6dO5nNdAIArq11bevRRc9m3T1q2zLyT4PIglpoqrVhhLpJUp445UFH20rCh5MRYXABQVAhfKBoXL5ojdG3ebIaszZulPXvyvrKVLThYuuMOqXdvczRDfgEAgIKpV0968klzSUyU1q41J6Ffu1Y6dSqn3/795vLee+a6j4/Upo25tGxpDmvv6+uQQwCA0ojwhetjGNKRI+bIW7t3m19//VXauVNKT7/6e+vXlzp3lm67zZxctGpV+9QMAGWJr680aJC5ZGZKW7eaQeybb8w/jF0+V9jp0+YcY6tX57T5+ZkhLDg452u9epILv0IAwLXiJyf+nWFIf/8t/fVXzvLnnzmBKzX13/fh4iLdfLP5l9S2bc3AVbNm0dcOAMjh7GzeWdCqlTRtmhm8tm0z5w3LXk6csH1PQoK5fP11TpuLi3TjjdJNN5lB7PKvNWty5wIAXAHhC3I+lybPpAR5JhyTR1KCPBOPyzPxuModP6ryhw9KRw9JKdc4TGj9+ub/3Fu2NJfgYMnDoyjKBwAUlLu7OQJiWJg5MqJhmH9ci4sz72DYscP8evmtipL5vO4ff5jLP7m6mgN51KqVs2Sv16xpXomrUsUMggBQxhSL8PXGG29o9uzZSkhIULNmzTRv3jy1atXqiv2XL1+up556SgcPHlS9evU0c+ZMdevWzbrdMAxNmzZNb7/9tk6fPq22bdtq/vz5qlevnrVPcnKyxowZoy+//FJOTk7q06ePXn31VVWoUKFIj7VIGYZc0s7KNTVFrmdS5JaaItczqXJL+Vvup07K7e9kuSeflPvfp+SefMr8ejJJbmfyceXqSmrXlho3tl0aNJBK8nkEgLLKYjGvYF32/0sZhnT0qBnCspe9e81BPc6dy72PjIycuySuxMlJqlbNDGLZS/XqUuXK5lKpUs7r7PWKFbnVEUCJ5/CfYsuWLdP48eO1YMEChYaGau7cuYqIiNDevXtVvXr1XP1//PFHDRgwQDNmzFCPHj20ZMkSRUZGatu2bWrSpIkkadasWXrttdf07rvvKigoSE899ZQiIiK0e/duefz/1ZeBAwfq+PHjiomJUUZGhoYNG6aRI0dqyZIldj3+6/b++9K0aeqV/Ldcz6TKkpVVqLvPcnaWU+3a5u0lly9BQYQsACgLLBbphhvMpXv3nPasLOnYMTOEZV8F++MPc8j7+Pir3zGRlWUOBJKYeG21eHqaIczLS+Fu5ZRRoaIula+gTM9yulSunDI9yumSp6e57llOmR6eynR3V5abuzLdPZTl7q5MN/ecr66uynJ1k+Hqar52MdezXF2kc65m2HNx4TZKAIXGYhhXG36u6IWGhqply5Z6/fXXJUlZWVkKCAjQmDFjNGnSpFz9o6KilJaWplWrVlnbWrdureDgYC1YsECGYahGjRqaMGGCJk6cKElKSUmRr6+vFi9erP79+2vPnj1q1KiRNm/erBYtWkiSoqOj1a1bNx05ckQ1atT417rzO4t1kXvnHen++wv01owKXrpQpaouVPfTeb8aOl/dT+d9/XXet4bO+/7/a/8b1Ld+lUIu2n5K8qzqRV07UJwU5b8lqWj/PRV17SVWaqp0+LAZxA4fNkPZ8eM5oSt7ychwdKX/zmLJCWIuLuYtk1dbnJyuvFgsuV9bLPlfsuv55/rlX/Nqu9q2Kx1zYW/Lz3aUOofPFu2/8YDq3tK77xbpZ+RHfrOBQ698paena+vWrZo8ebK1zcnJSeHh4YqLi8vzPXFxcRo/frxNW0REhFauXClJOnDggBISEhQeHm7d7u3trdDQUMXFxal///6Ki4uTj4+PNXhJUnh4uJycnLRp0yb17t071+devHhRFy8bESrl//+il5qfwSaKkqenVLmyzpT3Mv8CWNFLGRW9lF7RS5cq/P9rL2+lV66idJ/KulipsvnVp7IMV9d/3//5c0pNzUe/Yurc9dxSmQ+pqUX3P5Girh0oTory35JUtP+eirr2Ei0g4OoTORuGOcLiiRPSyZPm67//Npd/vj5zxlzOnpXOnFFWaqqcCvluj6vWmZFRMoIiUMwU9Z+nUr288jf4W1HX8f81/Nt1LYeGr5MnTyozM1O+/5hDxNfXV7///nue70lISMizf0JCgnV7dtvV+vzzlkYXFxdVrlzZ2uefZsyYoaeffjpXe8DV/qdiT8nJRbbroUW255JvqKMLAEqJoY4u4DoMdXQBAFCWpaZK3sXnDoQzZ87I+yr1OPyZr5Ji8uTJNlfcsrKylJycrCpVqsiSj0voqampCggI0OHDhx17m2Ipw3ktOpzbosF5LRqc16LDuS0anNeiwXktOpzbqzMMQ2fOnPnXx5ccGr6qVq0qZ2dnJf7jgdvExET5+fnl+R4/P7+r9s/+mpiYKH9/f5s+wcHB1j5JSUk2+7h06ZKSk5Ov+Lnu7u5yd3e3afPx8bn6AebBy8uLb9giwHktOpzbosF5LRqc16LDuS0anNeiwXktOpzbK7vaFa9sDh2+x83NTSEhIYqNjbW2ZWVlKTY2VmFhYXm+JywszKa/JMXExFj7BwUFyc/Pz6ZPamqqNm3aZO0TFham06dPa+vWrdY+69atU1ZWlkJDQwvt+AAAAAAgm8NvOxw/fryGDBmiFi1aqFWrVpo7d67S0tI0bNgwSdLgwYNVs2ZNzZgxQ5I0duxYdejQQXPmzFH37t21dOlSbdmyRQsXLpQkWSwWjRs3Ts8995zq1atnHWq+Ro0aioyMlCQ1bNhQXbp00f33368FCxYoIyNDo0ePVv/+/fM10iEAAAAAXCuHh6+oqCidOHFCU6dOVUJCgoKDgxUdHW0dMCM+Pl5Ol82v0aZNGy1ZskRPPvmkpkyZonr16mnlypXWOb4k6bHHHlNaWppGjhyp06dPq127doqOjrbO8SVJH374oUaPHq3OnTtbJ1l+7bXXiuw43d3dNW3atFy3LuL6cF6LDue2aHBeiwbntehwbosG57VocF6LDue2cDh8ni8AAAAAKAuYsh0AAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+Ctmdd96pWrVqycPDQ/7+/ho0aJCOHTtm0+eXX35R+/bt5eHhoYCAAM2aNSvXfpYvX64GDRrIw8NDTZs21erVq+11CMXSwYMHNWLECAUFBcnT01N16tTRtGnTlJ6ebtOPc3vtnn/+ebVp00blypW74sTh8fHx6t69u8qVK6fq1avr0Ucf1aVLl2z6bNiwQbfccovc3d1Vt25dLV68uOiLL2HeeOMNBQYGysPDQ6Ghofr5558dXVKx991336lnz56qUaOGLBaLVq5cabPdMAxNnTpV/v7+8vT0VHh4uPbt22fTJzk5WQMHDpSXl5d8fHw0YsQInT171o5HUfzMmDFDLVu2VMWKFVW9enVFRkZq7969Nn0uXLighx56SFWqVFGFChXUp08fJSYm2vTJz8+GsmT+/Pm6+eabrZPQhoWFac2aNdbtnNPC8eKLL1qnFsrGuS2Y6dOny2Kx2CwNGjSwbue8FgEDherll1824uLijIMHDxobN240wsLCjLCwMOv2lJQUw9fX1xg4cKCxa9cu46OPPjI8PT2Nt956y9pn48aNhrOzszFr1ixj9+7dxpNPPmm4uroav/76qyMOqVhYs2aNMXToUOPrr7829u/fb3z++edG9erVjQkTJlj7cG4LZurUqcbLL79sjB8/3vD29s61/dKlS0aTJk2M8PBwY/v27cbq1auNqlWrGpMnT7b2+euvv4xy5coZ48ePN3bv3m3MmzfPcHZ2NqKjo+14JMXb0qVLDTc3N+N///uf8dtvvxn333+/4ePjYyQmJjq6tGJt9erVxhNPPGGsWLHCkGR89tlnNttffPFFw9vb21i5cqWxc+dO48477zSCgoKM8+fPW/t06dLFaNasmfHTTz8Z33//vVG3bl1jwIABdj6S4iUiIsJYtGiRsWvXLmPHjh1Gt27djFq1ahlnz5619hk1apQREBBgxMbGGlu2bDFat25ttGnTxro9Pz8bypovvvjC+Oqrr4w//vjD2Lt3rzFlyhTD1dXV2LVrl2EYnNPC8PPPPxuBgYHGzTffbIwdO9bazrktmGnTphmNGzc2jh8/bl1OnDhh3c55LXyEryL2+eefGxaLxUhPTzcMwzDefPNNo1KlSsbFixetfR5//HGjfv361vW7777b6N69u81+QkNDjQceeMA+RZcQs2bNMoKCgqzrnNvrs2jRojzD1+rVqw0nJycjISHB2jZ//nzDy8vLeq4fe+wxo3Hjxjbvi4qKMiIiIoq05pKkVatWxkMPPWRdz8zMNGrUqGHMmDHDgVWVLP8MX1lZWYafn58xe/Zsa9vp06cNd3d346OPPjIMwzB2795tSDI2b95s7bNmzRrDYrEYR48etVvtxV1SUpIhyfj2228NwzDPo6urq7F8+XJrnz179hiSjLi4OMMw8vezAYZRqVIl45133uGcFoIzZ84Y9erVM2JiYowOHTpYwxfntuCmTZtmNGvWLM9tnNeiwW2HRSg5OVkffvih2rRpI1dXV0lSXFycbr31Vrm5uVn7RUREaO/evfr777+tfcLDw232FRERobi4OPsVXwKkpKSocuXK1nXObdGIi4tT06ZNrROfS+Y5S01N1W+//Wbtw3m9svT0dG3dutXmHDk5OSk8PJxzdB0OHDighIQEm/Pq7e2t0NBQ63mNi4uTj4+PWrRoYe0THh4uJycnbdq0ye41F1cpKSmSZP2ZunXrVmVkZNic2wYNGqhWrVo25/bffjaUZZmZmVq6dKnS0tIUFhbGOS0EDz30kLp3757r/zec2+uzb98+1ahRQzfeeKMGDhyo+Ph4SZzXokL4KgKPP/64ypcvrypVqig+Pl6ff/65dVtCQoLNN6gk63pCQsJV+2Rvh/Tnn39q3rx5euCBB6xtnNuicT3nNTU1VefPn7dPocXYyZMnlZmZyfdeIcs+d1c7rwkJCapevbrNdhcXF1WuXJlz//+ysrI0btw4tW3bVk2aNJFknjc3N7dcz4H+89z+28+GsujXX39VhQoV5O7urlGjRumzzz5To0aNOKfXaenSpdq2bZtmzJiRaxvntuBCQ0O1ePFiRUdHa/78+Tpw4IDat2+vM2fOcF6LCOErHyZNmpTrYcR/Lr///ru1/6OPPqrt27dr7dq1cnZ21uDBg2UYhgOPoPi61nMrSUePHlWXLl3Ur18/3X///Q6qvHgryHkFUDY99NBD2rVrl5YuXeroUkqF+vXra8eOHdq0aZP+85//aMiQIdq9e7ejyyrRDh8+rLFjx+rDDz+Uh4eHo8spVbp27ap+/frp5ptvVkREhFavXq3Tp0/r448/dnRppZaLowsoCSZMmKChQ4detc+NN95ofV21alVVrVpVN910kxo2bKiAgAD99NNPCgsLk5+fX65RYrLX/fz8rF/z6pO9vTS51nN77NgxderUSW3atNHChQtt+nFuc1zreb0aPz+/XKPy5fe8enl5ydPTM59Vl15Vq1aVs7Nzmfjes6fsc5eYmCh/f39re2JiooKDg619kpKSbN536dIlJScnc+4ljR49WqtWrdJ3332nG264wdru5+en9PR0nT592uav3pd/z+bnZ0NZ5Obmprp160qSQkJCtHnzZr366quKiorinBbQ1q1blZSUpFtuucXalpmZqe+++06vv/66vv76a85tIfHx8dFNN92kP//8U7fffjvntQhw5SsfqlWrpgYNGlx1ufw5o8tlZWVJki5evChJCgsL03fffaeMjAxrn5iYGNWvX1+VKlWy9omNjbXZT0xMjMLCwori8BzqWs7t0aNH1bFjR4WEhGjRokVycrL99uXc5rie79l/CgsL06+//mrzC2xMTIy8vLzUqFEja5+ycF4Lys3NTSEhITbnKCsrS7GxsZyj6xAUFCQ/Pz+b85qamqpNmzZZz2tYWJhOnz6trVu3WvusW7dOWVlZCg0NtXvNxYVhGBo9erQ+++wzrVu3TkFBQTbbQ0JC5OrqanNu9+7dq/j4eJtz+28/G2D+W7948SLn9Dp07txZv/76q3bs2GFdWrRooYEDB1pfc24Lx9mzZ7V//375+/vzPVtUHD3iR2ny008/GfPmzTO2b99uHDx40IiNjTXatGlj1KlTx7hw4YJhGObIMb6+vsagQYOMXbt2GUuXLjXKlSuXazh0FxcX46WXXjL27NljTJs2rcwPh37kyBGjbt26RufOnY0jR47YDImajXNbMIcOHTK2b99uPP3000aFChWM7du3G9u3bzfOnDljGEbOMLJ33HGHsWPHDiM6OtqoVq1ankPNP/roo8aePXuMN954g6Hm/2Hp0qWGu7u7sXjxYmP37t3GyJEjDR8fH5sRopDbmTNnrN+TkoyXX37Z2L59u3Ho0CHDMMyh5n18fIzPP//c+OWXX4xevXrlOdR88+bNjU2bNhk//PCDUa9evTI/1Px//vMfw9vb29iwYYPNz9Nz585Z+4waNcqoVauWsW7dOmPLli25pk7Jz8+GsmbSpEnGt99+axw4cMD45ZdfjEmTJhkWi8VYu3atYRic08J0+WiHhsG5LagJEyYYGzZsMA4cOGBs3LjRCA8PN6pWrWokJSUZhsF5LQqEr0L0yy+/GJ06dTIqV65suLu7G4GBgcaoUaOMI0eO2PTbuXOn0a5dO8Pd3d2oWbOm8eKLL+ba18cff2zcdNNNhpubm9G4cWPjq6++stdhFEuLFi0yJOW5XI5ze+2GDBmS53ldv369tc/BgweNrl27Gp6enkbVqlWNCRMmGBkZGTb7Wb9+vREcHGy4ubkZN954o7Fo0SL7HkgJMG/ePKNWrVqGm5ub0apVK+Onn35ydEnF3vr16/P8/hwyZIhhGOZw80899ZTh6+truLu7G507dzb27t1rs49Tp04ZAwYMMCpUqGB4eXkZw4YNs/5xoay60s/Ty//dnj9/3njwwQeNSpUqGeXKlTN69+5t8wcvw8jfz4ayZPjw4Ubt2rUNNzc3o1q1akbnzp2twcswOKeF6Z/hi3NbMFFRUYa/v7/h5uZm1KxZ04iKijL+/PNP63bOa+GzGAYjQQAAAABAUeOZLwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwBAibFhwwZZLBadPn3a0aXYTWBgoObOnevoMgAAhYDwBQDIt6FDhyoyMjJXe3EKRdOnT1dwcHCh7Otqx1VcQtG5c+c0efJk1alTRx4eHqpWrZo6dOigzz//3NGlAQD+wcXRBQAAgIIbNWqUNm3apHnz5qlRo0Y6deqUfvzxR506darIPjM9PV1ubm5Ftn8AKK248gUAKBI//PCD2rdvL09PTwUEBOjhhx9WWlqadfv777+vFi1aqGLFivLz89M999yjpKQkm32sXr1aN910kzw9PdWpUycdPHjwqp+5ePFiPf3009q5c6csFossFosWL14sSYqPj1evXr1UoUIFeXl56e6771ZiYmKhHe/p06d13333qVq1avLy8tJtt92mnTt3Wrfv379fvXr1kq+vrypUqKCWLVvqm2++sdlHUlKSevbsKU9PTwUFBenDDz/818/94osvNGXKFHXr1k2BgYEKCQnRmDFjNHz4cGufixcv6vHHH1dAQIDc3d1Vt25d/fe//7Vu//bbb9WqVSu5u7vL399fkyZN0qVLl6zbO3bsqNGjR2vcuHGqWrWqIiIiJEm7du1S165dVaFCBfn6+mrQoEE6efJkgc8hAJR2hC8AQKHbv3+/unTpoj59+uiXX37RsmXL9MMPP2j06NHWPhkZGXr22We1c+dOrVy5UgcPHtTQoUOt2w8fPqy77rpLPXv21I4dO3Tfffdp0qRJV/3cqKgoTZgwQY0bN9bx48d1/PhxRUVFKSsrS7169VJycrK+/fZbxcTE6K+//lJUVFShHXO/fv2UlJSkNWvWaOvWrbrlllvUuXNnJScnS5LOnj2rbt26KTY2Vtu3b1eXLl3Us2dPxcfHW/cxdOhQHT58WOvXr9cnn3yiN998M1cg/Sc/Pz+tXr1aZ86cuWKfwYMH66OPPtJrr72mPXv26K233lKFChUkSUePHlW3bt3UsmVL7dy5U/Pnz9d///tfPffcczb7ePfdd+Xm5qaNGzdqwYIFOn36tG677TY1b95cW7ZsUXR0tBITE3X33XcX9BQCQOlnAACQT0OGDDGcnZ2N8uXL2yweHh6GJOPvv/82DMMwRowYYYwcOdLmvd9//73h5ORknD9/Ps99b9682ZBknDlzxjAMw5g8ebLRqFEjmz6PP/64zefkZdq0aUazZs1s2tauXWs4Ozsb8fHx1rbffvvNkGT8/PPPV9zX+vXrDUm5jrd8+fKGxWIxXnnlFeuxeXl5GRcuXLB5f506dYy33nrrivtv3LixMW/ePMMwDGPv3r256tmzZ48hyfo5efn222+NG264wXB1dTVatGhhjBs3zvjhhx+s27P3GxMTk+f7p0yZYtSvX9/Iysqytr3xxhtGhQoVjMzMTMMwDKNDhw5G8+bNbd737LPPGnfccYdN2+HDhw1Jxt69e69YLwCUZVz5AgBck06dOmnHjh02yzvvvGPTZ+fOnVq8eLEqVKhgXSIiIpSVlaUDBw5IkrZu3aqePXuqVq1aqlixojp06CBJ1itBe/bsUWhoqM1+w8LCbNYv3/+oUaOuWPOePXsUEBCggIAAa1ujRo3k4+OjPXv2SJIaN25s3VfXrl1t3v/999/nOuYaNWrYHO/Zs2dVpUoVm5oOHDig/fv3SzKvfE2cOFENGzaUj4+PKlSooD179tgcr4uLi0JCQqz7bdCggXx8fK54XJJ066236q+//lJsbKz69u2r3377Te3bt9ezzz4rSdqxY4ecnZ2t5zevcxMWFiaLxWJta9u2rc6ePasjR45Y2y6vK/uY169fb3O8DRo0kCTrMQMAbDHgBgDgmpQvX15169a1abv8l3TJDBoPPPCAHn744Vzvr1WrltLS0hQREaGIiAh9+OGHqlatmuLj4xUREaH09PR817Jjxw7ray8vr2s7kH9YvXq1MjIyJEmenp4224KCgnKFIBeXnP+Fnj17Vv7+/tqwYUOu/Wa/b+LEiYqJidFLL72kunXrytPTU3379r2m470SV1dXtW/fXu3bt9fjjz+u5557Ts8884wef/zxXMdSUOXLl7dZP3v2rHr27KmZM2fm6uvv718onwkApQ3hCwBQ6G655Rbt3r07V0jL9uuvv+rUqVN68cUXrVejtmzZYtOnYcOG+uKLL2zafvrpJ5v1vPbv5uamzMzMXPs6fPiwDh8+bP283bt36/Tp02rUqJEkqXbt2tdwhLZuueUWJSQkyMXFRYGBgXn22bhxo4YOHarevXtLMsPL5QOINGjQQJcuXdLWrVvVsmVLSdLevXsLNHx/o0aNdOnSJV24cEFNmzZVVlaWvv32W4WHh+fq27BhQ3366acyDMN69Wvjxo2qWLGibrjhhqse86effqrAwECbIAoAuDJuOwQAFLrHH39cP/74o0aPHq0dO3Zo3759+vzzz60DbtSqVUtubm6aN2+e/vrrL33xxRfW2+SyjRo1Svv27dOjjz6qvXv3asmSJdaRC68mMDBQBw4c0I4dO3Ty5EldvHhR4eHhatq0qQYOHKht27bp559/1uDBg9WhQwe1aNHiuo83PDxcYWFhioyM1Nq1a3Xw4EH9+OOPeuKJJ6yhsl69elqxYoV27NihnTt36p577lFWVpZ1H/Xr11eXLl30wAMPaNOmTdq6davuu+++f71y1bFjR7311lvaunWrDh48qNWrV2vKlCnq1KmTvLy8FBgYqCFDhmj48OFauXKlDhw4oA0bNujjjz+WJD344IM6fPiwxowZo99//12ff/65pk2bpvHjx8vJ6cq/Jjz00ENKTk7WgAEDtHnzZu3fv19ff/21hg0bliv8AgBMhC8AQKG7+eab9e233+qPP/5Q+/bt1bx5c02dOtX6nFS1atW0ePFiLV++XI0aNdKLL76ol156yWYftWrV0qeffqqVK1eqWbNmWrBggV544YV//ew+ffqoS5cu/9fOHaMmFoVhGP7uDrRQQbsUwRRBSGmhBIK3spEUaUUUCQguwDXYWopVSJnGHdi4iyxEnC4wjdVwB2aeZwHn/0/5nuLk+fk5jUYjHx8fKYoiX19fqdfrGQwGeXl5yd3dXT4/P//IfYuiyPF4zGAwyHQ6zf39fd7e3vL9/Z1Wq5Uk2W63qdfr6ff7GY/HKcsyT09Pv52z3+/TbrczHA4zmUyyWCzSbDZvzi7LMofDIaPRKA8PD1mtVinL8ieukmS32+X19TXv7+/pdruZz+c/3/53Op0cj8ecz+f0er0sl8vMZrNsNpubc9vtdk6nUy6XS0ajUR4fH7Ner1Or1W5GG8D/rLher9e/vQQAAMC/ztMUAABABcQXAABABcQXAABABcQXAABABcQXAABABcQXAABABcQXAABABcQXAABABcQXAABABcQXAABABcQXAABABX4B0Ak5jAR9VeYAAAAASUVORK5CYII=", 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W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
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titlebot_team_medianpro_medianresolutionhead_to_head
279What will Kalshi's rank in the iPhone Top Free...0.058[0.02,0.01,0.015,0.015,0.05,0.89]Not in top 50-273.1
121How many movies will be new on Netflix's top 1...0.125[0.005,0.017,0.157,0.821]3 or more-188.2
47What will be Donald Trump's net worth, accordi...0.17[0.6,0.2,0.1,0.075,0.025]0-$6 billion, inclusive-126.1
71Will OpenAI, Anthropic, or Perplexity run an a...0.160.55yes-123.5
247Will the 500th richest person on Bloomberg's B...0.80.333no-120.4
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" - ], - "text/plain": [ - " title bot_team_median \\\n", - "279 What will Kalshi's rank in the iPhone Top Free... 0.058 \n", - "121 How many movies will be new on Netflix's top 1... 0.125 \n", - "47 What will be Donald Trump's net worth, accordi... 0.17 \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... 0.16 \n", - "247 Will the 500th richest person on Bloomberg's B... 0.8 \n", - "\n", - " pro_median resolution head_to_head \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -273.1 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -188.2 \n", - "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -126.1 \n", - "71 0.55 yes -123.5 \n", - "247 0.333 no -120.4 " - ] - }, - "execution_count": 230, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pd.set_option('display.max_colwidth', 50)\n", "\n", @@ -11952,160 +4279,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Bottom 5:\n" - ] - }, - { - "data": { - "text/html": [ - "
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titlebot_team_medianpro_medianresolutionhead_to_head
85Will Elon Musk attend the Super Bowl in 2025?0.16850.755no122.2
0For Q1 2025, how many banks will be listed on ...0.014926[0.001,0.62,0.35,0.019,0.01]0270.3
189What will the highest rank of metac-GPT4o or m...[0.0, 0.016687996933333334, 0.0361674514166666...[0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...34.0542.5
211Will Nikola Corporation file for bankruptcy be...0.990.999annulledNaN
214Will the state of Rhode Island have any recrea...0.9410.95annulledNaN
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" - ], - "text/plain": [ - " title \\\n", - "85 Will Elon Musk attend the Super Bowl in 2025? \n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "189 What will the highest rank of metac-GPT4o or m... \n", - "211 Will Nikola Corporation file for bankruptcy be... \n", - "214 Will the state of Rhode Island have any recrea... \n", - "\n", - " bot_team_median \\\n", - "85 0.1685 \n", - "0 0.014926 \n", - "189 [0.0, 0.016687996933333334, 0.0361674514166666... \n", - "211 0.99 \n", - "214 0.941 \n", - "\n", - " pro_median resolution \\\n", - "85 0.755 no \n", - "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", - "211 0.999 annulled \n", - "214 0.95 annulled \n", - "\n", - " head_to_head \n", - "85 122.2 \n", - "0 270.3 \n", - "189 542.5 \n", - "211 NaN \n", - "214 NaN " - ] - }, - "execution_count": 231, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"\\nBottom 5:\")\n", "\n", - "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bot_question_id Int64\n", - "title object\n", - "resolution float64\n", - "scheduled_close_time datetime64[ns]\n", - "actual_close_time datetime64[ns]\n", - "type object\n", - "options object\n", - "range_min float64\n", - "range_max float64\n", - "pro_question_id Int64\n", - "question_weight float64\n", - "bot_team_median object\n", - "pro_median object\n", - "head_to_head float64\n", - "weighted_score float64\n", - "dtype: object" - ] - }, - "execution_count": 232, - "metadata": {}, - "output_type": "execute_result" - } - ], + "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", @@ -12115,191 +4300,9 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaN312681.00.014926[0.001,0.62,0.35,0.019,0.01]270.308741270.308741
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0312691.0[0.0402, 0.040750496180000005, 0.04130456232, ...[0.0013749738,0.0014499743,0.001526641,0.00160...-75.535832-75.535832
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaN312701.00.070.013-5.948545-5.948545
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaN312801.00.53625[0.16,0.44,0.4]19.78257419.782574
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0312811.0[0.0, 0.002038679916666667, 0.0040819072666666...[0.0,0.0005044914,0.0010323506,0.0015847475,0....12.71630512.716305
\n", - "
" - ], - "text/plain": [ - " bot_question_id title \\\n", - "0 31262 For Q1 2025, how many banks will be listed on ... \n", - "1 31263 What percentage of the vote will Alexander Luk... \n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "3 31274 How many arms sales globally will the US State... \n", - "4 31275 How much will it rain in Brasília, Brazil in F... \n", - "\n", - " resolution scheduled_close_time actual_close_time type \\\n", - "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", - "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", - "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", - "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", - "\n", - " options range_min range_max pro_question_id \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", - "1 NaN 60.0 100.0 31269 \n", - "2 NaN NaN NaN 31270 \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", - "4 NaN 0.0 400.0 31281 \n", - "\n", - " question_weight bot_team_median \\\n", - "0 1.0 0.014926 \n", - "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 1.0 0.07 \n", - "3 1.0 0.53625 \n", - "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", - "\n", - " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", - "2 0.013 -5.948545 \n", - "3 [0.16,0.44,0.4] 19.782574 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", - "\n", - " weighted_score \n", - "0 270.308741 \n", - "1 -75.535832 \n", - "2 -5.948545 \n", - "3 19.782574 \n", - "4 12.716305 " - ] - }, - "execution_count": 233, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_top_bot_pro_forecasts.head()" ] @@ -12318,7 +4321,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12327,25 +4330,7 @@ "id": "BjNQ4IND6Ct7", "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, - "outputs": [ - { - "data": { - "image/png": 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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_median
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaN312701.00.070.013
531276Will the USDA-posted recall by Pork Dynasty In...1.02025-01-21 11:42:002025-01-21 11:42:00binaryNaNNaNNaN312821.00.550.45
831288Will Eric Adams be Mayor of New York City on t...1.02025-01-22 20:19:002025-01-22 20:19:00binaryNaNNaNNaN312941.00.820.95
1031318Will the S&P 500 index go up in January 2025?1.02025-01-23 23:23:002025-01-23 23:23:00binaryNaNNaNNaN<NA>1.0NaNNaN
1331334At the end of March 2025, will Wikipedia still...1.02025-01-24 14:23:002025-01-24 14:23:00binaryNaNNaNNaN313381.00.8250.9
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" - ], - "text/plain": [ - " bot_question_id title \\\n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", - "8 31288 Will Eric Adams be Mayor of New York City on t... \n", - "10 31318 Will the S&P 500 index go up in January 2025? \n", - "13 31334 At the end of March 2025, will Wikipedia still... \n", - "\n", - " resolution scheduled_close_time actual_close_time type options \\\n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", - "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", - "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", - "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", - "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", - "\n", - " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.07 \n", - "5 NaN NaN 31282 1.0 0.55 \n", - "8 NaN NaN 31294 1.0 0.82 \n", - "10 NaN NaN 1.0 NaN \n", - "13 NaN NaN 31338 1.0 0.825 \n", - "\n", - " pro_median \n", - "2 0.013 \n", - "5 0.45 \n", - "8 0.95 \n", - "10 NaN \n", - "13 0.9 " - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], + "plt.show()\n", + "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "# Map resolution to 0 and 1\n", + "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df_top_bot_pro_forecasts_all_binary.head()" ] }, { "cell_type": "code", - "execution_count": 238, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaN312681.00.014926[0.001,0.62,0.35,0.019,0.01]270.308741270.308741
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0312691.0[0.0402, 0.040750496180000005, 0.04130456232, ...[0.0013749738,0.0014499743,0.001526641,0.00160...-75.535832-75.535832
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaN312701.00.070.013-5.948545-5.948545
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaN312801.00.53625[0.16,0.44,0.4]19.78257419.782574
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0312811.0[0.0, 0.002038679916666667, 0.0040819072666666...[0.0,0.0005044914,0.0010323506,0.0015847475,0....12.71630512.716305
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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
34235345Will the US Citizenship and Immigration Servic...1.02025-03-12 22:00:002025-03-12 22:00:00binaryNaNNaNNaN353801.000.920.95-3.208831-3.208831
35135354Will the United States impose any new tariffs ...0.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaN353811.000.17750.05-14.411350-14.411350
35535358Will ChatGPT rank in the top 10 global website...1.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaN353851.000.80.97-19.268434-19.268434
36135364Will Doge's Agency Efficiency Leaderboard have...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN353860.850.7550.666-30.988278-26.340037
36435367Will the Project 2025 Tracker spreadsheet mark...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN353870.850.050.03-2.083409-1.770897
\n", - "
" - ], - "text/plain": [ - " bot_question_id title \\\n", - "342 35345 Will the US Citizenship and Immigration Servic... \n", - "351 35354 Will the United States impose any new tariffs ... \n", - "355 35358 Will ChatGPT rank in the top 10 global website... \n", - "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", - "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", - "\n", - " resolution scheduled_close_time actual_close_time type options \\\n", - "342 1.0 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", - "351 0.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", - "355 1.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", - "361 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", - "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", - "\n", - " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "342 NaN NaN 35380 1.00 0.92 \n", - "351 NaN NaN 35381 1.00 0.1775 \n", - "355 NaN NaN 35385 1.00 0.8 \n", - "361 NaN NaN 35386 0.85 0.755 \n", - "364 NaN NaN 35387 0.85 0.05 \n", - "\n", - " pro_median head_to_head weighted_score \n", - "342 0.95 -3.208831 -3.208831 \n", - "351 0.05 -14.411350 -14.411350 \n", - "355 0.97 -19.268434 -19.268434 \n", - "361 0.666 -30.988278 -26.340037 \n", - "364 0.03 -2.083409 -1.770897 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[239], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" - ] - } - ], + "outputs": [], "source": [ "# Calculate confidence scores for bot_team_median and pro_median\n", "display_head_and_tail(df_top_bot_pro_forecasts)\n", diff --git a/functions.py b/functions.py index 38a3fb1..dbb8331 100644 --- a/functions.py +++ b/functions.py @@ -11,7 +11,11 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import calculate_spot_baseline_score, nominal_location_to_cdf_location, calculate_spot_peer_score +from refactored_notebook.scoring import ( + calculate_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location, +) def extract_forecast(df): @@ -428,19 +432,27 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - q_type = row["type"] - resolution = row["resolution"] - options = row.get("options") - range_min = row.get("range_min") - range_max = row.get("range_max") - question_weight = row["question_weight"] + resolution = row["Resolution"] + options = row["Options"] + range_min = row["Range_min"] + range_max = row["Range_max"] + question_weight = row["Question_weight"] + open_upper_bound = row["Open_upper_bound"] + open_lower_bound = row["Open_lower_bound"] for team in teams: forecast = row[team] try: - weighted_score = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, question_weight + weighted_score = calculate_baseline_score( + forecast, + resolution, + options, + range_min, + range_max, + question_weight, + open_upper_bound=open_upper_bound, + open_lower_bound=open_lower_bound, ) team_scores[team] += weighted_score @@ -1041,7 +1053,9 @@ def nominal_location_to_cdf_location_via_question_dict(nominal_location, questio range_max = question_data["range_max"] zero_point = question_data["zero_point"] - return nominal_location_to_cdf_location(nominal_location, range_min, range_max, zero_point) + return nominal_location_to_cdf_location( + nominal_location, range_min, range_max, zero_point + ) def get_cdf_at(cdf, unscaled_location): @@ -1088,8 +1102,12 @@ def cdf_between(row, cdf, lower_bound, upper_bound): Returns: float: Probability between the bounds. """ - a = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(lower_bound, row)) - b = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(upper_bound, row)) + a = get_cdf_at( + cdf, nominal_location_to_cdf_location_via_question_dict(lower_bound, row) + ) + b = get_cdf_at( + cdf, nominal_location_to_cdf_location_via_question_dict(upper_bound, row) + ) return b - a @@ -1175,7 +1193,8 @@ def compute_bucket_forecast_value(row): # Compute forecast_value using the extracted string_location forecast_value = get_cdf_at( - row["cdf"], nominal_location_to_cdf_location_via_question_dict(string_location, row) + row["cdf"], + nominal_location_to_cdf_location_via_question_dict(string_location, row), ) # Apply logic based on comparison_type @@ -1224,37 +1243,48 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] - def calculate_weighted_h2h_score_between_two_forecast_columns(row, col_a, col_b): - forecast_a = row[col_a] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + forecast_a = row[ + col_a + ] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] forecast_b = row[col_b] - resolution = row['resolution'] - options = row['options_parsed'] if 'options_parsed' in row else row['options'] - range_min = row['range_min'] - range_max = row['range_max'] - question_weight = row['question_weight'] - score = calculate_spot_peer_score( + resolution = row["resolution"] + options = row["options_parsed"] if "options_parsed" in row else row["options"] + range_min = row["range_min"] + range_max = row["range_max"] + question_weight = row["question_weight"] + score = calculate_peer_score( forecast=forecast_a, forecast_for_other_users=[forecast_b], resolution=resolution, options=options, range_min=range_min, range_max=range_max, - question_weight=question_weight + question_weight=question_weight, ) return score -def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): + +def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): """Calculate peer scores for all bots""" # Create a new DataFrame to store peer scores df_peer = df.copy() # Calculate peer score for each bot for bot in all_bots: - df_peer[bot] = 100 * df.apply(lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, bot, pro_col), axis=1) + df_peer[bot] = 100 * df.apply( + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( + row, bot, pro_col + ), + axis=1, + ) # Calculate peer score for bot_team_median df_peer["bot_team_median"] = 100 * df.apply( - lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, 'bot_median', pro_col), axis=1) + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( + row, "bot_median", pro_col + ), + axis=1, + ) return df_peer diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index ba126f9..1cd27a6 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,11 +1,12 @@ from datetime import datetime + import numpy as np from scipy.stats.mstats import gmean from refactored_notebook.data_models import ForecastType, ResolutionType -def calculate_spot_peer_score( +def calculate_peer_score( forecast: ForecastType, forecast_for_other_users: list[ForecastType], resolution: ResolutionType, @@ -65,38 +66,32 @@ def nominal_location_to_cdf_location( return unscaled_location -def calculate_spot_baseline_score( +def calculate_baseline_score( forecast: ForecastType, resolution: ResolutionType, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, + open_upper_bound: bool = False, + open_lower_bound: bool = False, ) -> float: """ Question type can be infered from resolution type Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ - prob_for_resolution = _determine_probability_for_resolution( forecast, resolution, options, range_min, range_max ) - baseline_prob = _determine_baseline(resolution, options) + baseline_prob = _determine_baseline( + resolution, options, range_min, range_max, open_upper_bound, open_lower_bound + ) divisor = _determine_divisor_for_baseline_score(resolution, options) if prob_for_resolution <= 0 or baseline_prob <= 0: raise ValueError( "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" ) - # if resolution_bucket in [0, len(pmf) - 1]: - # baseline = 0.05 - # else: - # open_bound_count = bool(question.open_upper_bound) + bool( - # question.open_lower_bound - # ) - # baseline = (1 - 0.05 * open_bounds_count) / (len(pmf) - 2) - # forecast_score = 100 * np.log(pmf[resolution_bucket] / baseline) / 2 - baseline_score = np.log(prob_for_resolution / baseline_prob) / divisor * 100 weighted_score = baseline_score * question_weight @@ -105,7 +100,12 @@ def calculate_spot_baseline_score( def _determine_baseline( - resolution: ResolutionType, options: list[str] | None = None + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, + open_upper_bound: bool | None = None, + open_lower_bound: bool | None = None, ) -> float: is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) @@ -118,9 +118,34 @@ def _determine_baseline( raise ValueError("Options are required for multiple choice questions") baseline_prob = 1 / len(options) elif is_numeric: - baseline_prob = ( - 1 / 202 - ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + if open_upper_bound is None or open_lower_bound is None: + raise ValueError("Open upper bound and lower bound are required for numeric questions") + # @Check: Which version is correct? + + # Version 1: + resolved_outside_bounds = False + assert range_min is not None and range_max is not None and resolution is not None + if resolution > range_max or resolution < range_min: + resolved_outside_bounds = True + if resolved_outside_bounds: + baseline_prob = 0.05 + else: + open_bound_count = bool(open_upper_bound) + bool(open_lower_bound) + baseline_prob = (1 - 0.05 * open_bound_count) / 200 # PMF has 202 bins, 2 of which represent the bounds. So 200 is the internal bins + + # Version 2: + # open_bound_count = bool(open_upper_bound) + bool(open_lower_bound) + # if open_bound_count == 0: + # baseline_prob = 1 + # elif open_bound_count == 1: + # baseline_prob = 0.95 + # else: + # baseline_prob = 0.9 + + # Version 3: + # baseline_prob = ( + # 1 / 202 + # ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins # @Check: Should this be either 1, 0.9, or 0.95 based on whether open or closed bounds else: raise ValueError("Unknown question type") @@ -145,7 +170,7 @@ def _determine_probability_for_resolution( if resolution == "above_upper_bound" or resolution == "below_lower_bound": raise ValueError( "'above_upper_bound' or 'below_lower_bound' format not supported" - ) + ) # This is an old resolution type in Q4 2024 is_numeric = isinstance(resolution, float) or isinstance(resolution, int) is_binary = isinstance(resolution, bool) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 57fc7f1..3008279 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -4,8 +4,7 @@ import pytest from refactored_notebook.data_models import ForecastType -from refactored_notebook.scoring import (calculate_spot_baseline_score, - calculate_spot_peer_score) +from refactored_notebook.scoring import calculate_baseline_score, calculate_peer_score # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -20,8 +19,10 @@ ################################### HELPER FUNCTIONS ################################### -def generate_uniform_cdf(num_points: int = 201) -> list[float]: - return [(i + 1) / num_points for i in range(num_points)] +def generate_uniform_cdf() -> list[float]: + num_points = 200 # cdf has 201 points, but first point is 0% if we assume closed bounds + return [0] + [(i + 1) / num_points for i in range(num_points)] + def generate_cdf_with_forecast_at_index(index: int, forecast: float) -> list[float]: cdf = [] @@ -197,7 +198,7 @@ def test_baseline_score_is_0_with_uniform_prediction( question_weight: float, expected: float, ): - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast, resolution, options, range_min, range_max, question_weight ) assert abs(score - expected) == pytest.approx(0) @@ -206,16 +207,22 @@ def test_baseline_score_is_0_with_uniform_prediction( @pytest.mark.parametrize( "forecast,resolution,expected", [ - ([0.001], True, -896.57), # Completely incorrect - ([0.999], True, 99.86), # Completely correct - ([0.001], False, 99.86), # Completely correct - ([0.4], True, -32.19), # Examples found here: https://www.metaculus.com/help/scores-faq/#:~:text=details%20for%20nerds-,Do%20all%20my%20predictions%20on%20a%20question%20count%20toward%20my%20score%3F,-Yes.%20Metaculus%20uses + ([0.001], True, -896.57), # Completely incorrect + ([0.999], True, 99.86), # Completely correct + ([0.001], False, 99.86), # Completely correct + ( + [0.4], + True, + -32.19, + ), # Examples found here: https://www.metaculus.com/help/scores-faq/#:~:text=details%20for%20nerds-,Do%20all%20my%20predictions%20on%20a%20question%20count%20toward%20my%20score%3F,-Yes.%20Metaculus%20uses ([0.7], True, 48.542), ([0.4, 0.6], True, -32.19), ], ) -def test_binary_baseline_examples(forecast: list[float], resolution: bool, expected: float): - score = calculate_spot_baseline_score( +def test_binary_baseline_examples( + forecast: list[float], resolution: bool, expected: float +): + score = calculate_baseline_score( forecast=forecast, resolution=resolution, ) @@ -228,14 +235,16 @@ def test_numeric_baseline_when_perfect_forecast(): index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio - forecast = generate_cdf_with_forecast_at_index(correct_index, 0.59) + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.999) # As of May 3, 2025, 0.59 is max difference between 2 points on a cdf - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast=forecast, resolution=correct_answer, range_min=0, range_max=range_max, + open_upper_bound=False, + open_lower_bound=False, ) assert score == pytest.approx(183) @@ -248,7 +257,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): range_max = length_of_cdf * index_to_answer_ratio forecast = generate_cdf_with_forecast_at_index(correct_index, 0.001) - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast=forecast, resolution=correct_answer, range_min=0, @@ -264,10 +273,12 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): (0.001, 8, -232.19), ], ) -def test_multiple_choice_examples(forecast_for_answer_a: float, num_total_forecasts: int, expected: float): +def test_multiple_choice_examples( + forecast_for_answer_a: float, num_total_forecasts: int, expected: float +): num_other_forecasts = num_total_forecasts - 1 other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, resolution="A", options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], @@ -275,7 +286,6 @@ def test_multiple_choice_examples(forecast_for_answer_a: float, num_total_foreca assert score == pytest.approx(expected, abs=1e-2) - @pytest.mark.parametrize( "forecast_closer,forecast_further,resolution,options,range_min,range_max", [ @@ -322,10 +332,10 @@ def test_baseline_score_better_when_closer( range_min: float | None, range_max: float | None, ): - score_closer = calculate_spot_baseline_score( + score_closer = calculate_baseline_score( forecast_closer, resolution, options, range_min, range_max, 1.0 ) - score_further = calculate_spot_baseline_score( + score_further = calculate_baseline_score( forecast_further, resolution, options, range_min, range_max, 1.0 ) assert score_closer > score_further @@ -339,7 +349,23 @@ def test_baseline_score_better_when_closer( # Multiple Choice ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), # Numeric - ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), + ( + generate_cdf( + [ + Percentile(value=0.1, probability_below=0.1), + Percentile(value=0.9, probability_below=0.9), + ], + lower_bound=0.0, + upper_bound=1.0, + open_lower_bound=False, + open_upper_bound=False, + ), + 0.5, + None, + 0.0, + 1.0, + 3.0, + ), ], ) def test_baseline_score_weighted( @@ -350,10 +376,10 @@ def test_baseline_score_weighted( range_max: float | None, question_weight: float, ): - score_unweighted = calculate_spot_baseline_score( + score_unweighted = calculate_baseline_score( forecast, resolution, options, range_min, range_max, 1.0 ) - score_weighted = calculate_spot_baseline_score( + score_weighted = calculate_baseline_score( forecast, resolution, options, range_min, range_max, question_weight ) assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 @@ -431,8 +457,8 @@ def test_baseline_score_weighted( [ generate_cdf( # Best CDF [ - Percentile(value=100, probability_below=0.1), - Percentile(value=120, probability_below=0.9), + Percentile(value=110, probability_below=0.1), + Percentile(value=130, probability_below=0.9), ], lower_bound=0, upper_bound=100, @@ -441,8 +467,8 @@ def test_baseline_score_weighted( ), generate_cdf( [ - Percentile(value=100, probability_below=0.1), - Percentile(value=120, probability_below=0.9), + Percentile(value=90, probability_below=0.1), + Percentile(value=140, probability_below=0.9), ], lower_bound=0, upper_bound=100, @@ -451,13 +477,13 @@ def test_baseline_score_weighted( ), generate_cdf( # worst CDF [ - Percentile(value=100, probability_below=0.1), - Percentile(value=120, probability_below=0.9), + Percentile(value=30, probability_below=0.1), + Percentile(value=110, probability_below=0.9), ], lower_bound=0, upper_bound=100, open_lower_bound=False, - open_upper_bound=False, # No upper bound = no probability mass at upper bound + open_upper_bound=True, # No upper bound = no probability mass at upper bound ), ], 120, @@ -475,7 +501,7 @@ def test_better_forecast_means_better_peer_score( range_max: float | None, ): scores = [ - calculate_spot_peer_score( + calculate_peer_score( forecast, [f for i, f in enumerate(forecasts) if i != idx], resolution, @@ -512,7 +538,7 @@ def test_peer_score_zero_when_all_same( ): forecasts = [forecast for _ in range(5)] scores = [ - calculate_spot_peer_score( + calculate_peer_score( f, [f2 for i2, f2 in enumerate(forecasts) if i2 != i], resolution, @@ -589,7 +615,7 @@ def test_peer_score_average_zero( range_max: float | None, ): scores = [ - calculate_spot_peer_score( + calculate_peer_score( forecast, [f for i, f in enumerate(forecasts) if i != idx], resolution, @@ -641,10 +667,10 @@ def test_peer_score_weighted( ): for idx, forecast in enumerate(forecasts): other_forecasts = [f for i, f in enumerate(forecasts) if i != idx] - score_unweighted = calculate_spot_peer_score( + score_unweighted = calculate_peer_score( forecast, other_forecasts, resolution, options, range_min, range_max, 1.0 ) - score_weighted = calculate_spot_peer_score( + score_weighted = calculate_peer_score( forecast, other_forecasts, resolution, options, range_min, range_max, weight ) assert score_weighted == pytest.approx(score_unweighted * weight) From cf44360f30076cae6af586e2445f33e5e1527a62 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 04:51:40 -0600 Subject: [PATCH 14/26] Fixed some dataframe row to scoring parameter conversion --- AI_BENCHMARKING_ANALYSIS.ipynb | 707 +++++++++++++-------------------- functions.py | 49 ++- refactored_notebook/scoring.py | 2 +- tests/test_scoring.py | 4 +- 4 files changed, 324 insertions(+), 438 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index f98ede4..a114c83 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -59,7 +59,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1495376/643149966.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1914202/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] } @@ -96,7 +96,7 @@ "df_pro_forecasts = pd.read_csv('https://data.heroku.com/dataclips/roxytxphqvznkgbygmfgzymjtfxx.csv')\n", "df_pro_questions = df_pro_forecasts.rename(columns={'question_id': 'pro_question_id', 'question_title': 'title'})\n", "\n", - "if False: # Temporary\n", + "if False: # Temporary - Only keep Binary\n", " df_bot_questions = df_bot_questions[df_bot_questions['resolution'].isin(['yes', 'no'])]\n", " df_bot_forecasts = df_bot_forecasts[df_bot_forecasts['resolution'].isin(['yes', 'no'])]\n", " df_bot_scores = df_bot_scores[df_bot_scores['resolution'].isin(['yes', 'no'])]\n", @@ -104,8 +104,8 @@ " df_pro_forecasts = df_pro_forecasts[df_pro_forecasts['resolution'].isin(['yes', 'no'])]\n", " df_pro_scores = df_pro_scores[df_pro_scores['resolution'].isin(['yes', 'no'])]\n", "\n", - "df_pro_resolved_questions = df_pro_questions[['pro_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max']]\n", - "df_bot_resolved_questions = df_bot_questions[['bot_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max']]\n", + "df_pro_resolved_questions = df_pro_questions[['pro_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max', 'open_upper_bound', 'open_lower_bound']]\n", + "df_bot_resolved_questions = df_bot_questions[['bot_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max', 'open_upper_bound', 'open_lower_bound']]\n", "\n", "df_pro_bot_resolved_questions = pd.merge(\n", " df_bot_resolved_questions,\n", @@ -198,147 +198,6 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [ - "# @title Relationships between Bot Questions, create df_bot_question_related_weights (FOR Q3 ONLY)\n", - "if 25871 in df_pro_bot_resolved_questions['bot_question_id'].values:\n", - " \"\"\"\n", - " Relationships between questions are entered as tuples. These relationships\n", - " will be used to perform logical consistency checks.\n", - "\n", - " Weights are assigned to questions based on relationships. This is a way to\n", - " deal with correlations between questions.\n", - " \"\"\"\n", - "\n", - " # Scope sensitity list of tuples where the first entry should equal the sum of the others\n", - " bot_scope_questions = [\n", - " (26019, 26017, 26018), # Starship launches\n", - " (26098, 26096, 26097), # SENSEX\n", - " (26159, 26158, 26157), # Geomagnetic storm July 28\n", - " (26194, 26195, 26196), # measles cases\n", - " (26006, 26005, 26004), # Trump lead over Biden\n", - " (26642, 26643, 26644), # spanish wikipedia\n", - " (26700, 26701, 26702), # market cap cryptocurrencies\n", - " (27261, 27262, 27263), # Geomagnetic storm Sept 11\n", - " ]\n", - "\n", - " # Sum of each tuple should logically equal 1\n", - " bot_sum_to_1_questions = [\n", - " (25952, 25953, 25954), # French PM party July 30\n", - " (25957, 25958, 25959), # Tour de France winner\n", - " (26570, 26571, 26572, 26573), # Warhammer\n", - " (26574, 26575, 26576, 26577), # H5 cases in US\n", - " (26671, 26670, 26669), # DOES NOT SUM TO EXACTLY 1 PM France Aug 31\n", - " (27748, 27747, 27746, 27749), # Speed Chess\n", - " (27488, 27489, 27490, 27491, 27492, 27493), # August CPI\n", - " (27932, 27933, 27934, 27935), # Chinese youth unemployment\n", - " (27484, 27485, 27486, 27487), # Fed rate cut Sept meeting\n", - " (28045, 28044, 28043, 28042), # Afd vote share\n", - " (28038, 28039, 28040, 28041), # Major Atlantic hurricanes\n", - " (26776, 26777, 26778, 26779), # Seattle-Tacoma-Bellevu Air Quality\n", - " ]\n", - "\n", - " # parent, child, if_yes, if_no\n", - " bot_conditional_pair = [\n", - " (26917, 26918, 26919, 26920) # israel lebanon conflict\n", - " ]\n", - "\n", - " # CDFs - Logically the probability of each successive question must not decrease\n", - " bot_increasing_questions = [\n", - " (26981, 26982, 26983, 26984, 26985, 26986), # aircraft ADIZ\n", - " (26977, 26978, 26979, 26980), # hurricane energy\n", - " (27548, 27547, 27546, 27545), # mpox CDC risk level\n", - " (28306, 28305, 28304, 28303, 28302), # Gas prices in US Sept 30\n", - " ]\n", - "\n", - " bot_repeated_questions = [\n", - " (26646, 26021), # mens 100m dash record\n", - " (26555, 27021), # USA gold silver\n", - " (26210, 26917), # israel invade lebanon\n", - " (26781, 26304), # ruto\n", - " (26100, 27136), # rfk drop out\n", - " (25956, 27158), # democrat brokered convention\n", - " (26102, 27022), # astronauts NOT EXACT REPEAT\n", - " (26022, 27085), # arrest warrants NOT EXACT REPEAT\n", - " (26235, 27281), # Buffett Indicator\n", - " (26390, 27789), # Bubble Magnificent 7\n", - " (26024, 27161), # QB Bo Nix starting for Broncos\n", - " (26302, 27282), # riots\n", - " (25955, 27157), # armed forces death US, China, Japan\n", - " (26958, 27640), # Youtube banned in Russia\n", - " (25936, 27141), # Crimean bridge attack\n", - " ]\n", - "\n", - " bot_similar_questions = [\n", - " (26915, 26916), # harris favorability\n", - " (26913, 26914), # trump favorability\n", - " (26193, 27733), # debate on Sept 10\n", - " (27886, 27968), # Taylor Swift awards\n", - " (27723, 27637), # Best Rock VMAs\n", - " (27583, 27582, 27584, 27602, 27603, 27604), # mpox Zambia, US, Angola, Russia, Japan, Mexico\n", - " (26306, 26838), # Richest people 250th > $10.2, 500th > 6.2\n", - " (27887, 27969), # Emmys Outstanding Limited or Anthology Series\n", - " (28206, 28207, 28208, 28209, 28210), # LMSYS leaderboard\n", - " (28154, 28336), # Nigeria Edo gubernatorial election\n", - " (26407, 27897), # Second Russian mobilization wave\n", - " (27539, 26215), # Nuclear weapons used\n", - " (27606, 27607, 27608, 27609, 27610), # Ukranian forces capture\n", - " (26387, 27788), # Will Tesla increase deliveries in Q3 2024\n", - " (26821, 26959), # VP debate\n", - " (26212, 26213, 26214), # number of dairy cow herds with H5N1\n", - " (26639, 26640, 26641) # Presidential debate 0, 1, or 2+\n", - " ]\n", - "\n", - " ####### CREATE QUESTION WEIGHTS #########\n", - "\n", - " # Combine both lists of tuples\n", - " all_questions = bot_scope_questions + bot_sum_to_1_questions + bot_increasing_questions + bot_similar_questions + bot_conditional_pair\n", - "\n", - " # Create an empty list to store the data\n", - " data = []\n", - "\n", - " # Process each tuple\n", - " for tuple_questions in all_questions:\n", - " # Calculate the weight for each question in the tuple\n", - " weight = np.log2(1 + len(tuple_questions))/(1 + len(tuple_questions))\n", - "\n", - " # Add each question and its weight to the data list\n", - " for question_id in tuple_questions:\n", - " data.append({'bot_question_id': question_id, 'question_weight': weight})\n", - "\n", - " # Process each tuple\n", - " for tuple_questions in bot_repeated_questions:\n", - " # 1st iteration has weight 1, 2nd has weight 1/2, 3rd weight 1/3....\n", - " count = 1\n", - "\n", - " # Add each question and its weight to the data list\n", - " for question_id in tuple_questions:\n", - " data.append({'bot_question_id': question_id, 'question_weight': 1/count})\n", - " count += 1\n", - "\n", - " # Create the DataFrame\n", - " df = pd.DataFrame(data)\n", - "\n", - " # Sort the DataFrame by bot_question_id for better readability\n", - " df_bot_question_related_weights = df.sort_values('bot_question_id').reset_index(drop=True)\n", - "\n", - "# if df_bot_question_related_weights is defined, replace the question weights in df_pro_bot_resolved_questions\n", - "if 'df_bot_question_related_weights' in locals():\n", - " df_pro_bot_resolved_questions = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_bot_question_related_weights,\n", - " on='bot_question_id',\n", - " how='left'\n", - " )\n", - "\n", - " df_pro_bot_resolved_questions['question_weight'] = df_pro_bot_resolved_questions['question_weight_y'].combine_first(df_pro_bot_resolved_questions['question_weight_x'])\n", - " df_pro_bot_resolved_questions.drop(['question_weight_x', 'question_weight_y'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -355,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -363,11 +222,12 @@ "text/plain": [ "Index(['bot_question_id', 'title', 'resolution', 'scheduled_close_time',\n", " 'actual_close_time', 'type', 'options', 'range_min', 'range_max',\n", - " 'pro_question_id', 'question_weight'],\n", + " 'open_upper_bound', 'open_lower_bound', 'pro_question_id',\n", + " 'question_weight'],\n", " dtype='object')" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -378,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -413,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -428,12 +288,14 @@ "options object\n", "range_min float64\n", "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", "pro_question_id Int64\n", "question_weight float64\n", "dtype: object" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -444,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -476,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -508,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -523,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -721,7 +583,7 @@ "6 False " ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -732,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -755,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -775,7 +637,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -832,11 +694,11 @@ " \n", " 15\n", " bot_median\n", - " 9.550728\n", - " 3610.366154\n", + " 8.839589\n", + " 3341.541338\n", " 409\n", - " 6.843423\n", - " 1.377206\n", + " 6.106284\n", + " 1.390432\n", " \n", " \n", " 4\n", @@ -872,14 +734,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 9.550728 3610.366154 409 6.843423 \n", + "15 bot_median 8.839589 3341.541338 409 6.106284 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.377206 \n", + "15 1.390432 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -996,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1045,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1469,7 +1331,7 @@ " np.int64(35705)}" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1490,7 +1352,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1529,20 +1391,20 @@ " \n", " \n", " 1\n", - " bot_median\n", - " 9303.299412\n", - " \n", - " \n", - " 2\n", " metac-o1\n", " 8861.959039\n", " \n", " \n", - " 3\n", + " 2\n", " metac-o1-preview\n", " 8849.559824\n", " \n", " \n", + " 3\n", + " bot_median\n", + " 8784.525527\n", + " \n", + " \n", " 4\n", " acm_bot\n", " 7605.922314\n", @@ -1559,9 +1421,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 bot_median 9303.299412\n", - "2 metac-o1 8861.959039\n", - "3 metac-o1-preview 8849.559824\n", + "1 metac-o1 8861.959039\n", + "2 metac-o1-preview 8849.559824\n", + "3 bot_median 8784.525527\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1667,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1686,7 +1548,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1731,7 +1593,7 @@ " \n", " 2\n", " bot_median\n", - " 3821.107768\n", + " 3555.373483\n", " \n", " \n", " 3\n", @@ -1966,7 +1828,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3821.107768\n", + "2 bot_median 3555.373483\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2014,7 +1876,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2056,7 +1918,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -2075,7 +1937,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2084,7 +1946,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2105,7 +1967,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2303,7 +2165,7 @@ "6 False " ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2314,7 +2176,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2347,10 +2209,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -2373,14 +2235,14 @@ " 1.0\n", " multiple_choice\n", " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.06,0.02]\n", - " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.25,0.3,0.25,0.15,0.05]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2397,14 +2259,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 60.0\n", + " 100.0\n", + " True\n", + " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2421,12 +2283,12 @@ " 1.0\n", " binary\n", " None\n", - " 0.013\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.15\n", + " 0.1\n", " 0.1\n", " 0.15\n", " NaN\n", @@ -2445,13 +2307,13 @@ " 1.0\n", " multiple_choice\n", " [\"0-4\",\"5-9\",\">9\"]\n", - " [0.16,0.44,0.4]\n", " NaN\n", " NaN\n", - " [0.16,0.47,0.37]\n", + " None\n", + " None\n", " ...\n", - " [0.29,0.56,0.14999999999999997]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -2469,14 +2331,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 400.0\n", + " False\n", + " False\n", " ...\n", " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", @@ -2487,7 +2349,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -2498,40 +2360,40 @@ "3 31280 31274 5-9 1.0 \n", "4 31281 31275 119.2 1.0 \n", "\n", - " type options \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] \n", - "1 numeric None \n", - "2 binary None \n", - "3 multiple_choice [\"0-4\",\"5-9\",\">9\"] \n", - "4 numeric None \n", + " type options range_min range_max \\\n", + "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "1 numeric None 60.0 100.0 \n", + "2 binary None NaN NaN \n", + "3 multiple_choice [\"0-4\",\"5-9\",\">9\"] NaN NaN \n", + "4 numeric None 0.0 400.0 \n", "\n", - " pro_median 4Shadower Bot_Pepa \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] NaN NaN \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... NaN NaN \n", - "2 0.013 NaN NaN \n", - "3 [0.16,0.44,0.4] NaN NaN \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", + " open_upper_bound open_lower_bound ... \\\n", + "0 False False ... \n", + "1 True True ... \n", + "2 False False ... \n", + "3 None None ... \n", + "4 False False ... \n", "\n", - " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", - "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", - "2 NaN ... 0.15 \n", - "3 [0.16,0.47,0.37] ... [0.29,0.56,0.14999999999999997] \n", - "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + " metac-o1 \\\n", + "0 [0.5,0.3,0.15,0.04,0.01] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.06,0.02] \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "0 [0.25,0.3,0.25,0.15,0.05] NaN \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", "2 0.15 NaN \n", "3 [0.15,0.6,0.25] NaN \n", - "4 [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,... NaN \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", "0 [0.009900990099009901,0.39603960396039606,0.44... \n", @@ -2554,7 +2416,7 @@ "3 [0.116,0.42,0.464] NaN \n", "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0... NaN \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -2587,10 +2449,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -2613,13 +2475,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.95\n", - " 0.9\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", " 0.95\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2637,13 +2499,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.05\n", - " 0.95\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.35\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2661,13 +2523,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.97\n", - " 0.85\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2685,12 +2547,12 @@ " 0.85\n", " binary\n", " None\n", - " 0.666\n", - " 0.8\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2709,14 +2571,14 @@ " 0.85\n", " binary\n", " None\n", - " 0.03\n", - " 0.3\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.07\n", - " 0.1\n", - " 0.03\n", + " 0.05\n", + " 0.05\n", + " 0.01\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2727,7 +2589,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -2738,28 +2600,28 @@ "97 35386 35364 no 0.85 binary \n", "98 35387 35367 no 0.85 binary \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.85 \n", - "98 None 0.03 0.3 NaN NaN ... 0.07 \n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.03 NaN 0.15 0.05 NaN \n", + " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", "\n", - " swingswish twsummerbot wunderplumb \n", - "94 0.9 0.762 0.9 \n", - "95 0.1 0.126 0.95 \n", - "96 0.85 0.828 0.85 \n", - "97 0.7 0.132 0.3 \n", - "98 0.2 0.27 0.2 \n", + " pianobot swingswish twsummerbot wunderplumb \n", + "94 NaN 0.9 0.762 0.9 \n", + "95 NaN 0.1 0.126 0.95 \n", + "96 NaN 0.85 0.828 0.85 \n", + "97 NaN 0.7 0.132 0.3 \n", + "98 NaN 0.2 0.27 0.2 \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -2797,7 +2659,11 @@ "df_bot_forecasts = df_bot_forecasts.reset_index()\n", "\n", "# One row per question, with pro_question_id and bot_question_id and resolution\n", - "df_pro_bot_resolved_questions_first = df_pro_bot_resolved_questions.groupby(['pro_question_id', 'bot_question_id']).first().reset_index()[['pro_question_id', 'bot_question_id', 'resolution', 'question_weight', 'type', 'options']]\n", + "df_pro_bot_resolved_questions_first = df_pro_bot_resolved_questions.groupby(\n", + " ['pro_question_id', 'bot_question_id']\n", + " ).first().reset_index()[\n", + " ['pro_question_id', 'bot_question_id', 'resolution', 'question_weight', 'type', 'options', 'range_min', 'range_max', 'open_upper_bound', 'open_lower_bound']\n", + " ]\n", "\n", "df2 = pd.merge(\n", " df_pro_bot_resolved_questions_first,\n", @@ -2818,14 +2684,15 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pro_question_id', 'bot_question_id', 'resolution', 'question_weight',\n", - " 'type', 'options', 'pro_median', '4Shadower', 'Bot_Pepa',\n", + " 'type', 'options', 'range_min', 'range_max', 'open_upper_bound',\n", + " 'open_lower_bound', 'pro_median', '4Shadower', 'Bot_Pepa',\n", " 'CatrachoCaster', 'CumulativeBot', 'GreeneiBot2', 'Grizeu_Bot',\n", " 'InstitutPelFutur', 'KevinTestBot', 'MWG', 'NextWorldLab',\n", " 'ProfessorSP', 'RPM_bot', 'SynapseSeer', 'VeritasAI', 'X_bot',\n", @@ -2840,7 +2707,7 @@ " dtype='object')" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2851,7 +2718,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2861,7 +2728,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2876,7 +2743,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2888,7 +2755,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2897,7 +2764,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2927,10 +2794,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -2953,14 +2820,14 @@ " 1.0\n", " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.06,0.02]\n", - " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.25,0.3,0.25,0.15,0.05]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2977,14 +2844,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0013749738, 0.0014499743, 0.001526641, 0.0016050848, 0.0016854241, 0.0017677851, 0.0018523023, 0.0019391193, 0.002028389, 0.0021202748, 0.0022149507, 0.0023126022, 0.0024134273, 0.002517637, 0.0026254563, 0.0027371251, 0.0028528992, 0.0029730514, 0.0030978724, 0.0032276722, 0.0033627814, 0.0035035523, 0.0036503604, 0.003803606, 0.0039637158, 0.0041311448, 0.0043063775, 0.0044899306, 0.0046823546, 0.0048842361, 0.0050962001, 0.0053189126, 0.0055530831, 0.0057994673, 0.0060588703, 0.0063321494, 0.0066202178, 0.0069240477, 0.0072446744, 0.0075831999, 0.0079407973, 0.0083187152, 0.0087182821, 0.0091409116, 0.0095881072, 0.0100614684, 0.0105626958, 0.0110935973, 0.0116560946, 0.0122522299, 0.0128841727, 0.0135542271, 0.0142648397, 0.0150186074, 0.0158182855, 0.0166667968, 0.0175672405, 0.0185229009, 0.0195372578, 0.0206139958, 0.0217570149, 0.0229704403, 0.0242586335, 0.0256262025, 0.027078013, 0.0286191989, 0.0302551733, 0.0319916387, 0.0338345977, 0.0357903626, 0.0378655653, 0.0400671652, 0.042402458, 0.044879082, 0.0475050233, 0.0502886206, 0.0532385667, 0.0563639085, 0.0596740451, 0.0631787221, 0.0668880234, 0.0708123591, 0.0749624495, 0.0793493045, 0.0839841985, 0.0888786389, 0.0940443298, 0.0994931287, 0.1052369965, 0.1112879404, 0.1176579487, 0.1243589183, 0.1314025737, 0.1388003774, 0.1465634324, 0.1547023763, 0.1632272673, 0.1721474631, 0.1814714929, 0.1912069234, ...]\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 60.0\n", + " 100.0\n", + " True\n", + " True\n", " ...\n", - " [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 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" metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.06,0.02] \n", - "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + " metac-o1-preview \\\n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.15 \n", - "3 [0.15,0.6,0.25] \n", - "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", + " metac-perplexity \\\n", + "0 [0.25,0.3,0.25,0.15,0.05] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...] \n", + "2 0.15 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3162,7 +3022,7 @@ "3 NaN \n", "4 NaN \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -3195,10 +3055,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -3221,13 +3081,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.95\n", - " 0.9\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", " 0.95\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3245,13 +3105,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.05\n", - " 0.95\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.35\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3269,13 +3129,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.97\n", - " 0.85\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3293,12 +3153,12 @@ " 0.85\n", " binary\n", " None\n", - " 0.666\n", - " 0.8\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3317,14 +3177,14 @@ " 0.85\n", " binary\n", " None\n", - " 0.03\n", - " 0.3\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.07\n", - " 0.1\n", - " 0.03\n", + " 0.05\n", + " 0.05\n", + " 0.01\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3335,7 +3195,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -3346,28 +3206,28 @@ "97 35386 35364 no 0.85 binary \n", "98 35387 35367 no 0.85 binary \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.85 \n", - "98 None 0.03 0.3 NaN NaN ... 0.07 \n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.03 NaN 0.15 0.05 NaN \n", + " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", "\n", - " swingswish twsummerbot wunderplumb \n", - "94 0.9 0.762 0.9 \n", - "95 0.1 0.126 0.95 \n", - "96 0.85 0.828 0.85 \n", - "97 0.7 0.132 0.3 \n", - "98 0.2 0.27 0.2 \n", + " pianobot swingswish twsummerbot wunderplumb \n", + "94 NaN 0.9 0.762 0.9 \n", + "95 NaN 0.1 0.126 0.95 \n", + "96 NaN 0.85 0.828 0.85 \n", + "97 NaN 0.7 0.132 0.3 \n", + "98 NaN 0.2 0.27 0.2 \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -3418,36 +3278,27 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'Range_min'", + "ename": "AssertionError", + "evalue": "Probability for resolution is nan which is not between 0 and 1", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Range_min'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1275\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1276\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1279\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1280\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1310\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1276\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1276\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1279\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1280\u001b[0m )\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1253\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1251\u001b[0m resolution \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1252\u001b[0m options \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m row \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m-> 1253\u001b[0m range_min \u001b[38;5;241m=\u001b[39m \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRange_min\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 1254\u001b[0m range_max \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRange_max\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1255\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestion_weight\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'Range_min'" + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1311\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1314\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1315\u001b[0m )\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1291\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m question_weight:\n\u001b[1;32m 1289\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(question_weight)\n\u001b[0;32m-> 1291\u001b[0m score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1292\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast_for_other_users\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mforecast_b\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1294\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1295\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m score\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:18\u001b[0m, in \u001b[0;36mcalculate_peer_score\u001b[0;34m(forecast, forecast_for_other_users, resolution, options, range_min, range_max, question_weight)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_peer_score\u001b[39m(\n\u001b[1;32m 10\u001b[0m forecast: ForecastType,\n\u001b[1;32m 11\u001b[0m forecast_for_other_users: \u001b[38;5;28mlist\u001b[39m[ForecastType],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m question_weight: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m 17\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m---> 18\u001b[0m forecast_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m other_user_forecasts \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 22\u001b[0m _determine_probability_for_resolution(\n\u001b[1;32m 23\u001b[0m forecast, resolution, options, range_min, range_max\n\u001b[1;32m 24\u001b[0m )\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m forecast \u001b[38;5;129;01min\u001b[39;00m forecast_for_other_users\n\u001b[1;32m 26\u001b[0m ]\n\u001b[1;32m 28\u001b[0m geometric_mean \u001b[38;5;241m=\u001b[39m gmean(other_user_forecasts)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:210\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown question type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m prob_for_resolution \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 211\u001b[0m ), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProbability for resolution is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprob_for_resolution\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m which is not between 0 and 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prob_for_resolution\n", + "\u001b[0;31mAssertionError\u001b[0m: Probability for resolution is nan which is not between 0 and 1" ] } ], diff --git a/functions.py b/functions.py index dbb8331..806cca9 100644 --- a/functions.py +++ b/functions.py @@ -1243,16 +1243,51 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_weighted_h2h_score_between_two_forecast_columns(row, col_a, col_b): +def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str): forecast_a = row[ col_a - ] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + ] + if isinstance(forecast_a, str): + forecast_a = [float(x) for x in forecast_a.strip('[]').split(',')] + forecast_b = row[col_b] - resolution = row["resolution"] + if isinstance(forecast_b, str): + forecast_b = [float(x) for x in forecast_b.strip('[]').split(',')] + options = row["options_parsed"] if "options_parsed" in row else row["options"] - range_min = row["range_min"] - range_max = row["range_max"] + resolution = row["resolution"] + question_type = row["type"] + if question_type == "binary": + if resolution == "yes": + resolution = True + elif resolution == "no": + resolution = False + + assert isinstance(forecast_a, float) + assert isinstance(forecast_b, float) + forecast_a = [forecast_a] + forecast_b = [forecast_b] + elif question_type == "multiple_choice": + resolution = resolution + elif question_type == "numeric": + if not isinstance(resolution, float): + resolution = float(resolution) + else: + raise ValueError(f"Unknown question type: {question_type}") + + + range_min = row.get("range_min") + if range_min: + range_min = float(range_min) + + range_max = row.get("range_max") + if range_max: + range_max = float(range_max) + question_weight = row["question_weight"] + if question_weight: + question_weight = float(question_weight) + score = calculate_peer_score( forecast=forecast_a, forecast_for_other_users=[forecast_b], @@ -1272,7 +1307,7 @@ def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): # Calculate peer score for each bot for bot in all_bots: - df_peer[bot] = 100 * df.apply( + df_peer[bot] = df.apply( lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( row, bot, pro_col ), @@ -1280,7 +1315,7 @@ def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): ) # Calculate peer score for bot_team_median - df_peer["bot_team_median"] = 100 * df.apply( + df_peer["bot_team_median"] = df.apply( lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( row, "bot_median", pro_col ), diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 1cd27a6..596304e 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -124,7 +124,7 @@ def _determine_baseline( # Version 1: resolved_outside_bounds = False - assert range_min is not None and range_max is not None and resolution is not None + assert range_min is not None and range_max is not None and resolution is not None, f"These need to be not None: Range min: {range_min}, range max: {range_max}, resolution: {resolution}" if resolution > range_max or resolution < range_min: resolved_outside_bounds = True if resolved_outside_bounds: diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 3008279..b42c719 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -255,7 +255,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio - forecast = generate_cdf_with_forecast_at_index(correct_index, 0.001) + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.01/200) score = calculate_baseline_score( forecast=forecast, @@ -263,7 +263,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): range_min=0, range_max=range_max, ) - assert score == pytest.approx(-230) + assert score == pytest.approx(-230.25, abs=1e-1) @pytest.mark.parametrize( From 2cf9c6fae825036c6c125bb62b227a5ab3dc1f69 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 06:30:16 -0600 Subject: [PATCH 15/26] Got calculate_all_peer_scores working --- AI_BENCHMARKING_ANALYSIS.ipynb | 7918 ++++++++++++++++- functions.py | 48 +- .../bootstrapped_h2h_bot_vs_pros.csv | 90 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 92 +- refactored_notebook/scoring.py | 172 +- 5 files changed, 7769 insertions(+), 551 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index a114c83..92e1549 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,21 +27,32 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 177, "metadata": { "id": "ISzIoto4hnoG" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# @title Import libraries\n", + "%load_ext autoreload\n", + "%autoreload 2\n", "from functions import *\n", "from IPython.display import display, clear_output\n", - "import pandas as pd" + "import pandas as pd\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -52,14 +63,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1914202/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1932996/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] } @@ -131,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -149,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -175,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 182, "metadata": {}, "outputs": [ { @@ -196,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 183, "metadata": {}, "outputs": [ { @@ -214,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 184, "metadata": {}, "outputs": [ { @@ -227,7 +238,7 @@ " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -238,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 185, "metadata": {}, "outputs": [ { @@ -273,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 186, "metadata": {}, "outputs": [ { @@ -295,7 +306,7 @@ "dtype: object" ] }, - "execution_count": 10, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" } @@ -306,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 187, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -338,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 189, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 190, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 191, "metadata": {}, "outputs": [ { @@ -583,7 +594,7 @@ "6 False " ] }, - "execution_count": 15, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -594,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ @@ -617,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -637,7 +648,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 17, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" } @@ -649,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -692,15 +703,6 @@ " 1.738353\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.839589\n", - " 3341.541338\n", - " 409\n", - " 6.106284\n", - " 1.390432\n", - " \n", - " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -710,6 +712,15 @@ " 2.298000\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 6.860987\n", + " 2593.590381\n", + " 409\n", + " 3.788648\n", + " 1.562899\n", + " \n", + " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -734,15 +745,15 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.839589 3341.541338 409 6.106284 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "15 bot_median 6.860987 2593.590381 409 3.788648 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.390432 \n", "4 2.298000 \n", + "15 1.562899 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -858,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 195, "metadata": { "id": "BmAFBHIhK77X" }, @@ -907,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -1331,7 +1342,7 @@ " np.int64(35705)}" ] }, - "execution_count": 20, + "execution_count": 196, "metadata": {}, "output_type": "execute_result" } @@ -1352,7 +1363,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 197, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1402,7 +1413,7 @@ " \n", " 3\n", " bot_median\n", - " 8784.525527\n", + " 8567.705563\n", " \n", " \n", " 4\n", @@ -1423,7 +1434,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8784.525527\n", + "3 bot_median 8567.705563\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1529,7 +1540,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -1548,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 199, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1592,13 +1603,13 @@ " \n", " \n", " 2\n", - " bot_median\n", - " 3555.373483\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 3\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 2974.983652\n", " \n", " \n", " 4\n", @@ -1828,8 +1839,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3555.373483\n", - "3 metac-o1-preview 3162.155445\n", + "2 metac-o1-preview 3162.155445\n", + "3 bot_median 2974.983652\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -1876,7 +1887,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 23, + "execution_count": 199, "metadata": {}, "output_type": "execute_result" } @@ -1918,7 +1929,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -1937,7 +1948,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -1946,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -1967,7 +1978,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -2165,7 +2176,7 @@ "6 False " ] }, - "execution_count": 27, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -2176,7 +2187,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 204, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2240,9 +2251,9 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.25,0.3,0.25,0.15,0.05]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2266,7 +2277,7 @@ " ...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2290,7 +2301,7 @@ " ...\n", " 0.1\n", " 0.1\n", - " 0.15\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2313,8 +2324,8 @@ " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", - " [0.15,0.6,0.25]\n", + " [0.7,0.25,0.05]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2375,24 +2386,24 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.5,0.3,0.15,0.04,0.01] \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", "3 [0.25,0.6,0.15] \n", "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "3 [0.7,0.25,0.05] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.25,0.3,0.25,0.15,0.05] NaN \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", - "2 0.15 NaN \n", - "3 [0.15,0.6,0.25] NaN \n", + "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "2 0.1 NaN \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2480,7 +2491,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2504,7 +2515,7 @@ " False\n", " False\n", " ...\n", - " 0.35\n", + " 0.65\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2576,9 +2587,9 @@ " False\n", " False\n", " ...\n", + " 0.02\n", " 0.05\n", - " 0.05\n", - " 0.01\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2608,11 +2619,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", + "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -2684,7 +2695,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -2707,7 +2718,7 @@ " dtype='object')" ] }, - "execution_count": 29, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -2718,7 +2729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -2728,7 +2739,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 30, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } @@ -2743,7 +2754,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 207, "metadata": {}, "outputs": [], "source": [ @@ -2755,7 +2766,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 208, "metadata": {}, "outputs": [], "source": [ @@ -2764,7 +2775,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -2825,9 +2836,9 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.25,0.3,0.25,0.15,0.05]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", + " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2851,7 +2862,7 @@ " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 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0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-perplexity \\\n", + "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...] \n", + "2 0.1 \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3086,7 +3097,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3110,7 +3121,7 @@ " False\n", " False\n", " ...\n", - " 0.35\n", + " 0.65\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3182,9 +3193,9 @@ " False\n", " False\n", " ...\n", + " 0.02\n", " 0.05\n", - " 0.05\n", - " 0.01\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3214,11 +3225,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", + "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3278,254 +3289,2868 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 210, "metadata": {}, "outputs": [ { - "ename": "AssertionError", - "evalue": "Probability for resolution is nan which is not between 0 and 1", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1310\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1311\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1314\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1315\u001b[0m )\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1291\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m question_weight:\n\u001b[1;32m 1289\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(question_weight)\n\u001b[0;32m-> 1291\u001b[0m score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1292\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast_for_other_users\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mforecast_b\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1294\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1295\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m score\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:18\u001b[0m, in \u001b[0;36mcalculate_peer_score\u001b[0;34m(forecast, forecast_for_other_users, resolution, options, range_min, range_max, question_weight)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_peer_score\u001b[39m(\n\u001b[1;32m 10\u001b[0m forecast: ForecastType,\n\u001b[1;32m 11\u001b[0m forecast_for_other_users: \u001b[38;5;28mlist\u001b[39m[ForecastType],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m question_weight: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m 17\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m---> 18\u001b[0m forecast_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m other_user_forecasts \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 22\u001b[0m _determine_probability_for_resolution(\n\u001b[1;32m 23\u001b[0m forecast, resolution, options, range_min, range_max\n\u001b[1;32m 24\u001b[0m )\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m forecast \u001b[38;5;129;01min\u001b[39;00m forecast_for_other_users\n\u001b[1;32m 26\u001b[0m ]\n\u001b[1;32m 28\u001b[0m geometric_mean \u001b[38;5;241m=\u001b[39m gmean(other_user_forecasts)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:210\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown question type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m prob_for_resolution \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 211\u001b[0m ), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProbability for resolution is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprob_for_resolution\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m which is not between 0 and 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prob_for_resolution\n", - "\u001b[0;31mAssertionError\u001b[0m: Probability for resolution is nan which is not between 0 and 1" + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" ] } ], "source": [ + "from functions import *\n", "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Average pro median forecast on questions that resolved yes/no vs top bot\n", - "\n", - "top_bot = leaderboard['bot'][1]\n", - "\n", - "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", - "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", - "\n", - "# Calculate the average pro median forecast for questions that resolved yes\n", - "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", - "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", - "\n", - "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", - "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", - "\n", - "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", - "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", - "\n", - "# Plot the data\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Set up the figure\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Create x-coordinates with jitter for each group separately\n", - "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", - "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", - "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", - "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", - "\n", - "# Plot points for \"yes\" resolution\n", - "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", - " color='blue', alpha=0.6, label='Resolved Yes')\n", - "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", - " color='blue', alpha=0.6)\n", - "\n", - "# Plot points for \"no\" resolution\n", - "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", - " color='red', alpha=0.6, label='Resolved No')\n", - "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", - " color='red', alpha=0.6)\n", - "\n", - "# Customize the plot\n", - "plt.xticks([0, 1], ['pro_median', top_bot])\n", - "plt.ylabel('Probability (%)')\n", - "plt.title('Pro Median vs Top Bot Forecasts')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set y-axis limits from 0 to 100\n", - "plt.ylim(0, 100)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", - "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", - "\n", - "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", - "\n", - "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", - "\n", - "# First pivot to long format - each row will be a question-forecaster pair\n", - "df_long = df_bot_vs_pro_peer.melt(\n", - " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", - " var_name='forecaster',\n", - " value_name='score'\n", - ")\n", - "\n", - "# Drop any rows where score is NaN\n", - "df_long = df_long.dropna(subset=['score'])\n", - "\n", - "# Cast question_weight as numeric\n", - "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", - "\n", - "# Group first, then do the multiplication and sum\n", - "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", - "\n", - "# Calculate number of questions answered by each bot\n", - "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", - "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", - "\n", - "# Create a new DataFrame with the results\n", - "results = pd.DataFrame({\n", - " 'Peer_vs_Pro': total_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "weighted_results = pd.DataFrame({\n", - " 'W_Peer_vs_Pro': weighted_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", - "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, + "execution_count": 211, "metadata": {}, - "outputs": [], - "source": [ - "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", - "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", - "\n", - "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", - "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "tXKRpXAVHMRt" - }, - "outputs": [], - "source": [ - "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_bot_baseline_wide,\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_baseline_weights,\n", - " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", - " on='pro_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", - "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", - "\n", - "# Create a list of columns to keep\n", - "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", - "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", - "\n", - "# Calculate the sum for each forecaster\n", - "forecaster_scores = df_filtered.sum()\n", - "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "question_counts = df_filtered.notna().sum()\n", - "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_scores.index,\n", - " 'Baseline': forecaster_scores.values,\n", - " 'Count': question_counts.values\n", - "})\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "weighted_leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_weighted_scores.index,\n", - " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", - " 'Count': question_counts.values,\n", - " 'Weighted Count': question_weighted_counts.values\n", - "})\n", - "\n", - "# Sort the leaderboard by score in descending order\n", - "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", - "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", - "\n", - "# Add a 'Rank' column\n", - "leaderboard['Rank'] = leaderboard.index + 1\n", - "weighted_leaderboard['Rank'] = weighted_leaderboard.index 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pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
943538035345yes1.00binaryNoneNaNNaNFalseFalse...-0.054067NaNNaN0.0000000.000000NaN-0.054067-0.220515-0.054067-0.054067
953538135354no1.00binaryNoneNaNNaNFalseFalse...-2.251292NaNNaN-0.111226NaNNaN-0.054067-0.083382-2.944439-0.111226
963538535358yes1.00binaryNoneNaNNaNFalseFalse...-0.074901NaNNaN-0.074901NaNNaN-0.132060-0.158283-0.132060-0.132060
973538635364no0.85binaryNoneNaNNaNFalseFalse...-0.6804300.628948NaN-0.680430-0.680430NaN-0.0912550.8117930.628948-0.091255
983538735367no0.85binaryNoneNaNNaNFalseFalse...-0.0177090.000000NaN-0.112251-0.017709NaN-0.163782-0.241614-0.163782-0.112251
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5 rows × 58 columns

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" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", + "\n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 -0.054067 NaN NaN 0.000000 0.000000 \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.074901 NaN NaN -0.074901 NaN \n", + "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", + "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", + "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", + "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", + "\n", + "[5 rows x 58 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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botPeer Score
Rank
1metac-o13864.168122
2metac-o1-preview3162.155445
3bot_median2974.983652
4manticAI2142.538438
5metac-Gemini-Exp-12062072.216227
6acm_bot1876.466009
7twsummerbot1763.532046
8metac-perplexity1697.555196
9GreeneiBot21603.998618
10cookics_bot_TEST1140.390796
11metac-claude-3-5-sonnet-latest1134.209821
12SynapseSeer1066.533051
13CumulativeBot1030.716475
14pgodzinai926.081448
15jkraybill_bot627.932509
16metac-deepseek-r1614.572462
17question_weight378.020000
18metac-exa265.384263
19MWG215.551323
20annabot21.125670
21andrewsiah-4.170684
22cobyj-bot-15.593332
23X_bot-16.052813
24pianobot-20.745921
25CatrachoCaster-214.389722
26KevinTestBot-244.046973
27jonahsingerbot-318.088290
28krm-bot-387.131345
29ProfessorSP-406.072162
30mmBot-453.312468
31metac-grok-2-1212-492.938695
32bean_bot-494.373003
334Shadower-586.017986
34metac-claude-3-5-sonnet-20240620-647.579684
35swingswish-763.021897
36RPM_bot-905.938514
37metac-Llama-3.1-1029.014161
38InstitutPelFutur-1087.748963
39wunderplumb-1189.786803
40VeritasAI-1521.091541
41NextWorldLab-1565.096041
42Bot_Pepa-1589.575284
43laylaps-1665.296188
44minefrac1-1850.747385
45Grizeu_Bot-1898.666894
46metac-gpt-4o-2618.918368
47ajf-bot-3239.712801
\n", + "
" + ], + "text/plain": [ + " bot Peer Score\n", + "Rank \n", + "1 metac-o1 3864.168122\n", + "2 metac-o1-preview 3162.155445\n", + "3 bot_median 2974.983652\n", + "4 manticAI 2142.538438\n", + "5 metac-Gemini-Exp-1206 2072.216227\n", + "6 acm_bot 1876.466009\n", + "7 twsummerbot 1763.532046\n", + "8 metac-perplexity 1697.555196\n", + "9 GreeneiBot2 1603.998618\n", + "10 cookics_bot_TEST 1140.390796\n", + "11 metac-claude-3-5-sonnet-latest 1134.209821\n", + "12 SynapseSeer 1066.533051\n", + "13 CumulativeBot 1030.716475\n", + "14 pgodzinai 926.081448\n", + "15 jkraybill_bot 627.932509\n", + "16 metac-deepseek-r1 614.572462\n", + "17 question_weight 378.020000\n", + "18 metac-exa 265.384263\n", + "19 MWG 215.551323\n", + "20 annabot 21.125670\n", + "21 andrewsiah -4.170684\n", + "22 cobyj-bot -15.593332\n", + "23 X_bot -16.052813\n", + "24 pianobot -20.745921\n", + "25 CatrachoCaster -214.389722\n", + "26 KevinTestBot -244.046973\n", + "27 jonahsingerbot -318.088290\n", + "28 krm-bot -387.131345\n", + "29 ProfessorSP -406.072162\n", + "30 mmBot -453.312468\n", + "31 metac-grok-2-1212 -492.938695\n", + "32 bean_bot -494.373003\n", + "33 4Shadower -586.017986\n", + "34 metac-claude-3-5-sonnet-20240620 -647.579684\n", + "35 swingswish -763.021897\n", + "36 RPM_bot -905.938514\n", + "37 metac-Llama-3.1 -1029.014161\n", + "38 InstitutPelFutur -1087.748963\n", + "39 wunderplumb -1189.786803\n", + "40 VeritasAI -1521.091541\n", + "41 NextWorldLab -1565.096041\n", + "42 Bot_Pepa -1589.575284\n", + "43 laylaps -1665.296188\n", + "44 minefrac1 -1850.747385\n", + "45 Grizeu_Bot -1898.666894\n", + "46 metac-gpt-4o -2618.918368\n", + "47 ajf-bot -3239.712801" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "leaderboard" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean pro median forecast on questions that resolved yes: 74.0%\n", + "mean pro median forecast on questions that resolved no: 22.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Average pro median forecast on questions that resolved yes/no vs top bot\n", + "\n", + "top_bot = leaderboard['bot'][1]\n", + "\n", + "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", + "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", + "\n", + "# Calculate the average pro median forecast for questions that resolved yes\n", + "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", + "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", + "\n", + "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", + "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", + "\n", + "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", + "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", + "\n", + "# Plot the data\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Set up the figure\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Create x-coordinates with jitter for each group separately\n", + "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", + "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", + "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", + "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", + "\n", + "# Plot points for \"yes\" resolution\n", + "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", + " color='blue', alpha=0.6, label='Resolved Yes')\n", + "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", + " color='blue', alpha=0.6)\n", + "\n", + "# Plot points for \"no\" resolution\n", + "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", + " color='red', alpha=0.6, label='Resolved No')\n", + "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", + " color='red', alpha=0.6)\n", + "\n", + "# Customize the plot\n", + "plt.xticks([0, 1], ['pro_median', top_bot])\n", + "plt.ylabel('Probability (%)')\n", + "plt.title('Pro Median vs Top Bot Forecasts')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set y-axis limits from 0 to 100\n", + "plt.ylim(0, 100)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1932996/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" + ] + } + ], + "source": [ + "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", + "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", + "\n", + "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", + "\n", + "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", + "\n", + "# First pivot to long format - each row will be a question-forecaster pair\n", + "df_long = df_bot_vs_pro_peer.melt(\n", + " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", + " var_name='forecaster',\n", + " value_name='score'\n", + ")\n", + "\n", + "# Drop any rows where score is NaN\n", + "df_long = df_long.dropna(subset=['score'])\n", + "\n", + "# Cast question_weight as numeric\n", + "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", + "\n", + "# Group first, then do the multiplication and sum\n", + "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", + "\n", + "# Calculate number of questions answered by each bot\n", + "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", + "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", + "\n", + "# Create a new DataFrame with the results\n", + "results = pd.DataFrame({\n", + " 'Peer_vs_Pro': total_scores,\n", + " 'Count': num_questions\n", + "})\n", + "\n", + "weighted_results = pd.DataFrame({\n", + " 'W_Peer_vs_Pro': weighted_scores,\n", + " 'Count': num_questions\n", + "})\n", + "\n", + "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", + "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", + "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", + "\n", + "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", + "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "cellView": "form", + "id": "tXKRpXAVHMRt" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RankForecasterWeighted_BaselineCountWeighted Count
01pro_median4238.5616079793.10
12metac-o13010.3537889692.10
23metac-perplexity2774.0803319490.10
34acm_bot2239.0586758581.25
45metac-claude-3-5-sonnet-202406202018.1102119591.50
56bot_median1970.6330699793.10
67manticAI1865.1262607470.45
78metac-exa1826.2756819490.10
89twsummerbot1819.0641416259.40
910metac-claude-3-5-sonnet-latest1740.3151889692.10
1011metac-Llama-3.11701.1824039490.10
1112jkraybill_bot1616.0557094745.05
1213metac-Gemini-Exp-12061595.6826128177.50
1314NextWorldLab1583.0262268581.25
1415metac-o1-preview1527.6571419692.10
1516metac-deepseek-r11518.3086255552.10
1617laylaps1500.5678746865.10
1718mmBot1482.7264459793.10
1819Grizeu_Bot1399.4777185552.35
1920metac-grok-2-12121167.8671619692.10
2021VeritasAI1136.6824928278.10
2122metac-gpt-4o1045.1336789692.10
2223SynapseSeer1039.4846352826.15
2324annabot1031.9739303129.30
2425GreeneiBot2932.8835806259.35
2526MWG741.4247473028.60
2627InstitutPelFutur722.6870159591.10
2728cookics_bot_TEST714.1983722927.40
2829Bot_Pepa660.8016994745.05
2930ajf-bot484.4450303735.25
3031swingswish429.96611287.70
3132KevinTestBot331.09944498.40
3233X_bot274.53936577.00
3334CumulativeBot253.8397011110.25
3435CatrachoCaster247.2667172119.70
3536jonahsingerbot224.15439254.70
36374Shadower210.5486171514.00
3738bean_bot210.54275254.70
3839pgodzinai177.1341048177.40
3940wunderplumb112.1502452725.55
4041krm-bot65.989405109.50
4142andrewsiah0.00000000.00
4243cobyj-bot0.00000000.00
4344RPM_bot-8.69053388.00
4445ProfessorSP-217.1062982018.60
4546pianobot-217.32120454.70
4647minefrac1-299.5665065552.10
\n", + "
" + ], + "text/plain": [ + " Rank Forecaster Weighted_Baseline Count \\\n", + "0 1 pro_median 4238.561607 97 \n", + "1 2 metac-o1 3010.353788 96 \n", + "2 3 metac-perplexity 2774.080331 94 \n", + "3 4 acm_bot 2239.058675 85 \n", + "4 5 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", + "5 6 bot_median 1970.633069 97 \n", + "6 7 manticAI 1865.126260 74 \n", + "7 8 metac-exa 1826.275681 94 \n", + "8 9 twsummerbot 1819.064141 62 \n", + "9 10 metac-claude-3-5-sonnet-latest 1740.315188 96 \n", + "10 11 metac-Llama-3.1 1701.182403 94 \n", + "11 12 jkraybill_bot 1616.055709 47 \n", + "12 13 metac-Gemini-Exp-1206 1595.682612 81 \n", + "13 14 NextWorldLab 1583.026226 85 \n", + "14 15 metac-o1-preview 1527.657141 96 \n", + "15 16 metac-deepseek-r1 1518.308625 55 \n", + "16 17 laylaps 1500.567874 68 \n", + "17 18 mmBot 1482.726445 97 \n", + "18 19 Grizeu_Bot 1399.477718 55 \n", + "19 20 metac-grok-2-1212 1167.867161 96 \n", + "20 21 VeritasAI 1136.682492 82 \n", + "21 22 metac-gpt-4o 1045.133678 96 \n", + "22 23 SynapseSeer 1039.484635 28 \n", + "23 24 annabot 1031.973930 31 \n", + "24 25 GreeneiBot2 932.883580 62 \n", + "25 26 MWG 741.424747 30 \n", + "26 27 InstitutPelFutur 722.687015 95 \n", + "27 28 cookics_bot_TEST 714.198372 29 \n", + "28 29 Bot_Pepa 660.801699 47 \n", + "29 30 ajf-bot 484.445030 37 \n", + "30 31 swingswish 429.966112 8 \n", + "31 32 KevinTestBot 331.099444 9 \n", + "32 33 X_bot 274.539365 7 \n", + "33 34 CumulativeBot 253.839701 11 \n", + "34 35 CatrachoCaster 247.266717 21 \n", + "35 36 jonahsingerbot 224.154392 5 \n", + "36 37 4Shadower 210.548617 15 \n", + "37 38 bean_bot 210.542752 5 \n", + "38 39 pgodzinai 177.134104 81 \n", + "39 40 wunderplumb 112.150245 27 \n", + "40 41 krm-bot 65.989405 10 \n", + "41 42 andrewsiah 0.000000 0 \n", + "42 43 cobyj-bot 0.000000 0 \n", + "43 44 RPM_bot -8.690533 8 \n", + "44 45 ProfessorSP -217.106298 20 \n", + "45 46 pianobot -217.321204 5 \n", + "46 47 minefrac1 -299.566506 55 \n", + "\n", + " Weighted Count \n", + "0 93.10 \n", + "1 92.10 \n", + "2 90.10 \n", + "3 81.25 \n", + "4 91.50 \n", + "5 93.10 \n", + "6 70.45 \n", + "7 90.10 \n", + "8 59.40 \n", + "9 92.10 \n", + "10 90.10 \n", + "11 45.05 \n", + "12 77.50 \n", + "13 81.25 \n", + "14 92.10 \n", + "15 52.10 \n", + "16 65.10 \n", + "17 93.10 \n", + "18 52.35 \n", + "19 92.10 \n", + "20 78.10 \n", + "21 92.10 \n", + "22 26.15 \n", + "23 29.30 \n", + "24 59.35 \n", + "25 28.60 \n", + "26 91.10 \n", + "27 27.40 \n", + "28 45.05 \n", + "29 35.25 \n", + "30 7.70 \n", + "31 8.40 \n", + "32 7.00 \n", + "33 10.25 \n", + "34 19.70 \n", + "35 4.70 \n", + "36 14.00 \n", + "37 4.70 \n", + "38 77.40 \n", + "39 25.55 \n", + "40 9.50 \n", + "41 0.00 \n", + "42 0.00 \n", + "43 8.00 \n", + "44 18.60 \n", + "45 4.70 \n", + "46 52.10 " + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_resolved_questions,\n", + " df_bot_baseline_wide,\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_baseline_weights,\n", + " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", + " on='pro_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", + "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", + "\n", + "# Create a list of columns to keep\n", + "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", + "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", + "\n", + "# Calculate the sum for each forecaster\n", + "forecaster_scores = df_filtered.sum()\n", + "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "question_counts = df_filtered.notna().sum()\n", + "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_scores.index,\n", + " 'Baseline': forecaster_scores.values,\n", + " 'Count': question_counts.values\n", + "})\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "weighted_leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_weighted_scores.index,\n", + " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", + " 'Count': question_counts.values,\n", + " 'Weighted Count': question_weighted_counts.values\n", + "})\n", + "\n", + "# Sort the leaderboard by score in descending order\n", + "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", + "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", + "\n", + "# Add a 'Rank' column\n", + "leaderboard['Rank'] = leaderboard.index + 1\n", + "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", + "\n", + "# Reorder columns to have Rank first\n", + "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", + "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", + "\n", + "#leaderboard\n", "weighted_leaderboard" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 217, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
pro_median4238.693.145.562.2291686.4493987.0591051.98527758.332.71.0000000.000000
metac-o13010.492.132.757.7568596.0182995.4310541.98555044.620.71.0000000.000000
metac-perplexity2774.190.130.867.2103837.0806644.3483081.98611444.916.70.9999820.000036
acm_bot2239.181.227.655.5540546.1631694.4713431.98898539.815.30.9999870.000025
metac-claude-3-5-sonnet-202406202018.191.522.164.2193076.7135943.2852521.98578835.48.70.9992750.001450
bot_median1970.693.121.265.5547436.7940583.1154931.98527734.77.70.9987760.002449
manticAI1865.170.426.566.3530597.9053383.3489361.99348842.210.70.9993430.001314
metac-exa1826.390.120.382.2195858.6618942.3400691.98611437.53.10.9892430.021514
twsummerbot1819.159.430.654.7477997.1035174.3111002.00016344.816.40.9999680.000063
metac-claude-3-5-sonnet-latest1740.392.118.971.5459837.4551342.5346201.98555033.74.10.9935180.012963
metac-Llama-3.11701.290.118.962.1549296.5480682.8834531.98611431.95.90.9975350.004930
jkraybill_bot1616.145.035.959.7568388.9030794.0292232.01341253.817.90.9998910.000218
metac-Gemini-Exp-12061595.777.520.667.0999817.6220462.7013031.99042635.85.40.9957490.008502
NextWorldLab1583.081.219.566.4117477.3677222.6444271.98898534.14.80.9950800.009840
metac-o1-preview1527.792.116.687.1115689.0770771.8273441.98555034.6-1.40.9645390.070922
metac-deepseek-r11518.352.129.162.7649708.6955783.3513822.00537946.611.70.9992410.001519
laylaps1500.665.123.174.4573659.2282042.4977991.99634141.54.60.9924630.015074
mmBot1482.793.115.979.9905028.2901731.9210901.98527732.4-0.50.9710930.057813
Grizeu_Bot1399.552.426.760.8869058.4152223.1767552.00555543.69.90.9987400.002521
metac-grok-2-12121167.992.112.779.3224498.2654461.5341491.98555029.1-3.70.9357710.128459
VeritasAI1136.778.114.661.1249136.9166012.1042411.99009528.30.80.9806920.038617
metac-gpt-4o1045.192.111.367.7641657.0610661.6070961.98555025.4-2.70.9442530.111494
SynapseSeer1039.526.239.862.84354812.2892353.2346072.05307665.014.50.9983020.003397
annabot1032.029.335.257.68962410.6577103.3047392.04418357.013.40.9987070.002586
GreeneiBot2932.959.415.773.8321869.5837481.6401042.00014134.9-3.50.9468180.106364
MWG741.428.625.978.73589114.7227771.7608052.04656156.1-4.20.9553250.089349
InstitutPelFutur722.791.17.9100.84063310.5651670.7508541.98582928.9-13.00.7726510.454697
cookics_bot_TEST714.227.426.163.25665212.0845622.1569372.04954150.81.30.9798560.040287
Bot_Pepa660.845.014.769.73878710.3902741.4117232.01341235.6-6.30.9174720.165057
ajf-bot484.435.213.786.56822814.5807200.9425542.02873043.3-15.80.8237450.352510
swingswish430.07.755.852.06574018.7631902.9760272.367123100.311.40.9891420.021716
KevinTestBot331.18.439.476.25685526.3111141.4980972.311496100.2-21.40.9122520.175497
X_bot274.57.039.231.69380111.9791313.2740202.44691268.59.90.9915260.016949
CumulativeBot253.810.224.878.92471924.6519411.0045802.23184879.8-30.30.8296730.340653
CatrachoCaster247.319.712.675.37158416.9814400.7391372.08877748.0-22.90.7655000.469001
jonahsingerbot224.24.747.764.22018229.6225611.6100032.784843130.2-34.80.9057990.188401
bean_bot210.54.744.876.35643935.2205991.2718792.784843142.9-53.30.8612620.277476
4Shadower210.514.015.0116.14611231.0413540.4844892.14723981.7-51.60.6819500.636100
pgodzinai177.177.42.3103.63911911.7802150.1942711.99045325.7-21.20.5767600.846479
wunderplumb112.225.64.4102.06900020.1928870.2173762.05660345.9-37.10.5851440.829712
krm-bot66.09.56.968.18212422.1212020.3140092.26470957.0-43.20.6194580.761083
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
RPM_bot-8.78.0-1.189.62555931.687420-0.0342822.36462473.8-76.00.4868050.973609
ProfessorSP-217.118.6-11.780.59407218.687303-0.6246162.09524327.5-50.80.2701180.540237
pianobot-217.34.7-46.2124.35072857.358714-0.8061302.798986114.3-206.80.2343880.468776
minefrac1-299.652.1-5.770.5819809.778562-0.5880042.00564913.9-25.40.2795600.559119
\n", + "
" + ], + "text/plain": [ + " W_score W_count W_ave W_stdev \\\n", + "pro_median 4238.6 93.1 45.5 62.229168 \n", + "metac-o1 3010.4 92.1 32.7 57.756859 \n", + "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "acm_bot 2239.1 81.2 27.6 55.554054 \n", + "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", + "bot_median 1970.6 93.1 21.2 65.554743 \n", + "manticAI 1865.1 70.4 26.5 66.353059 \n", + "metac-exa 1826.3 90.1 20.3 82.219585 \n", + "twsummerbot 1819.1 59.4 30.6 54.747799 \n", + "metac-claude-3-5-sonnet-latest 1740.3 92.1 18.9 71.545983 \n", + "metac-Llama-3.1 1701.2 90.1 18.9 62.154929 \n", + "jkraybill_bot 1616.1 45.0 35.9 59.756838 \n", + "metac-Gemini-Exp-1206 1595.7 77.5 20.6 67.099981 \n", + "NextWorldLab 1583.0 81.2 19.5 66.411747 \n", + "metac-o1-preview 1527.7 92.1 16.6 87.111568 \n", + "metac-deepseek-r1 1518.3 52.1 29.1 62.764970 \n", + "laylaps 1500.6 65.1 23.1 74.457365 \n", + "mmBot 1482.7 93.1 15.9 79.990502 \n", + "Grizeu_Bot 1399.5 52.4 26.7 60.886905 \n", + "metac-grok-2-1212 1167.9 92.1 12.7 79.322449 \n", + "VeritasAI 1136.7 78.1 14.6 61.124913 \n", + "metac-gpt-4o 1045.1 92.1 11.3 67.764165 \n", + "SynapseSeer 1039.5 26.2 39.8 62.843548 \n", + "annabot 1032.0 29.3 35.2 57.689624 \n", + "GreeneiBot2 932.9 59.4 15.7 73.832186 \n", + "MWG 741.4 28.6 25.9 78.735891 \n", + "InstitutPelFutur 722.7 91.1 7.9 100.840633 \n", + "cookics_bot_TEST 714.2 27.4 26.1 63.256652 \n", + "Bot_Pepa 660.8 45.0 14.7 69.738787 \n", + "ajf-bot 484.4 35.2 13.7 86.568228 \n", + "swingswish 430.0 7.7 55.8 52.065740 \n", + "KevinTestBot 331.1 8.4 39.4 76.256855 \n", + "X_bot 274.5 7.0 39.2 31.693801 \n", + "CumulativeBot 253.8 10.2 24.8 78.924719 \n", + "CatrachoCaster 247.3 19.7 12.6 75.371584 \n", + "jonahsingerbot 224.2 4.7 47.7 64.220182 \n", + "bean_bot 210.5 4.7 44.8 76.356439 \n", + "4Shadower 210.5 14.0 15.0 116.146112 \n", + "pgodzinai 177.1 77.4 2.3 103.639119 \n", + "wunderplumb 112.2 25.6 4.4 102.069000 \n", + "krm-bot 66.0 9.5 6.9 68.182124 \n", + "andrewsiah 0.0 0.0 NaN NaN \n", + "cobyj-bot 0.0 0.0 NaN NaN \n", + "RPM_bot -8.7 8.0 -1.1 89.625559 \n", + "ProfessorSP -217.1 18.6 -11.7 80.594072 \n", + "pianobot -217.3 4.7 -46.2 124.350728 \n", + "minefrac1 -299.6 52.1 -5.7 70.581980 \n", + "\n", + " std_err t_stat t_crit upper_bound \\\n", + "pro_median 6.449398 7.059105 1.985277 58.3 \n", + "metac-o1 6.018299 5.431054 1.985550 44.6 \n", + "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "acm_bot 6.163169 4.471343 1.988985 39.8 \n", + "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", + "bot_median 6.794058 3.115493 1.985277 34.7 \n", + "manticAI 7.905338 3.348936 1.993488 42.2 \n", + "metac-exa 8.661894 2.340069 1.986114 37.5 \n", + "twsummerbot 7.103517 4.311100 2.000163 44.8 \n", + "metac-claude-3-5-sonnet-latest 7.455134 2.534620 1.985550 33.7 \n", + "metac-Llama-3.1 6.548068 2.883453 1.986114 31.9 \n", + "jkraybill_bot 8.903079 4.029223 2.013412 53.8 \n", + "metac-Gemini-Exp-1206 7.622046 2.701303 1.990426 35.8 \n", + "NextWorldLab 7.367722 2.644427 1.988985 34.1 \n", + "metac-o1-preview 9.077077 1.827344 1.985550 34.6 \n", + "metac-deepseek-r1 8.695578 3.351382 2.005379 46.6 \n", + "laylaps 9.228204 2.497799 1.996341 41.5 \n", + "mmBot 8.290173 1.921090 1.985277 32.4 \n", + "Grizeu_Bot 8.415222 3.176755 2.005555 43.6 \n", + "metac-grok-2-1212 8.265446 1.534149 1.985550 29.1 \n", + "VeritasAI 6.916601 2.104241 1.990095 28.3 \n", + "metac-gpt-4o 7.061066 1.607096 1.985550 25.4 \n", + "SynapseSeer 12.289235 3.234607 2.053076 65.0 \n", + "annabot 10.657710 3.304739 2.044183 57.0 \n", + "GreeneiBot2 9.583748 1.640104 2.000141 34.9 \n", + "MWG 14.722777 1.760805 2.046561 56.1 \n", + "InstitutPelFutur 10.565167 0.750854 1.985829 28.9 \n", + "cookics_bot_TEST 12.084562 2.156937 2.049541 50.8 \n", + "Bot_Pepa 10.390274 1.411723 2.013412 35.6 \n", + "ajf-bot 14.580720 0.942554 2.028730 43.3 \n", + "swingswish 18.763190 2.976027 2.367123 100.3 \n", + "KevinTestBot 26.311114 1.498097 2.311496 100.2 \n", + "X_bot 11.979131 3.274020 2.446912 68.5 \n", + "CumulativeBot 24.651941 1.004580 2.231848 79.8 \n", + "CatrachoCaster 16.981440 0.739137 2.088777 48.0 \n", + "jonahsingerbot 29.622561 1.610003 2.784843 130.2 \n", + "bean_bot 35.220599 1.271879 2.784843 142.9 \n", + "4Shadower 31.041354 0.484489 2.147239 81.7 \n", + "pgodzinai 11.780215 0.194271 1.990453 25.7 \n", + "wunderplumb 20.192887 0.217376 2.056603 45.9 \n", + "krm-bot 22.121202 0.314009 2.264709 57.0 \n", + "andrewsiah NaN NaN NaN NaN \n", + "cobyj-bot NaN NaN NaN NaN \n", + "RPM_bot 31.687420 -0.034282 2.364624 73.8 \n", + "ProfessorSP 18.687303 -0.624616 2.095243 27.5 \n", + "pianobot 57.358714 -0.806130 2.798986 114.3 \n", + "minefrac1 9.778562 -0.588004 2.005649 13.9 \n", + "\n", + " lower_bound cdf p_value \n", + "pro_median 32.7 1.000000 0.000000 \n", + "metac-o1 20.7 1.000000 0.000000 \n", + "metac-perplexity 16.7 0.999982 0.000036 \n", + "acm_bot 15.3 0.999987 0.000025 \n", + "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", + "bot_median 7.7 0.998776 0.002449 \n", + "manticAI 10.7 0.999343 0.001314 \n", + "metac-exa 3.1 0.989243 0.021514 \n", + "twsummerbot 16.4 0.999968 0.000063 \n", + "metac-claude-3-5-sonnet-latest 4.1 0.993518 0.012963 \n", + "metac-Llama-3.1 5.9 0.997535 0.004930 \n", + "jkraybill_bot 17.9 0.999891 0.000218 \n", + "metac-Gemini-Exp-1206 5.4 0.995749 0.008502 \n", + "NextWorldLab 4.8 0.995080 0.009840 \n", + "metac-o1-preview -1.4 0.964539 0.070922 \n", + "metac-deepseek-r1 11.7 0.999241 0.001519 \n", + "laylaps 4.6 0.992463 0.015074 \n", + "mmBot -0.5 0.971093 0.057813 \n", + "Grizeu_Bot 9.9 0.998740 0.002521 \n", + "metac-grok-2-1212 -3.7 0.935771 0.128459 \n", + "VeritasAI 0.8 0.980692 0.038617 \n", + "metac-gpt-4o -2.7 0.944253 0.111494 \n", + "SynapseSeer 14.5 0.998302 0.003397 \n", + "annabot 13.4 0.998707 0.002586 \n", + "GreeneiBot2 -3.5 0.946818 0.106364 \n", + "MWG -4.2 0.955325 0.089349 \n", + "InstitutPelFutur -13.0 0.772651 0.454697 \n", + "cookics_bot_TEST 1.3 0.979856 0.040287 \n", + "Bot_Pepa -6.3 0.917472 0.165057 \n", + "ajf-bot -15.8 0.823745 0.352510 \n", + "swingswish 11.4 0.989142 0.021716 \n", + "KevinTestBot -21.4 0.912252 0.175497 \n", + "X_bot 9.9 0.991526 0.016949 \n", + "CumulativeBot -30.3 0.829673 0.340653 \n", + "CatrachoCaster -22.9 0.765500 0.469001 \n", + "jonahsingerbot -34.8 0.905799 0.188401 \n", + "bean_bot -53.3 0.861262 0.277476 \n", + "4Shadower -51.6 0.681950 0.636100 \n", + "pgodzinai -21.2 0.576760 0.846479 \n", + "wunderplumb -37.1 0.585144 0.829712 \n", + "krm-bot -43.2 0.619458 0.761083 \n", + "andrewsiah NaN NaN NA \n", + "cobyj-bot NaN NaN NA \n", + "RPM_bot -76.0 0.486805 0.973609 \n", + "ProfessorSP -50.8 0.270118 0.540237 \n", + "pianobot -206.8 0.234388 0.468776 \n", + "minefrac1 -25.4 0.279560 0.559119 " + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# make me a list that's pro_median and all the bot forecasters\n", "forecasters = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", @@ -3537,7 +6162,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 218, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3545,7 +6170,841 @@ "id": "aGNedTHmU-Bm", "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
bean_bot-0.64.7-0.10.0698490.032219-4.2651062.784843-0.0-0.20.0076740.015349
jonahsingerbot-0.64.7-0.10.0502720.023189-5.2736302.784843-0.1-0.20.0038390.007677
X_bot-0.77.0-0.10.3540680.133825-0.7471952.4469120.2-0.40.2415940.483189
RPM_bot-1.17.0-0.20.8245320.311644-0.5234062.4469120.6-0.90.3097260.619452
CumulativeBot-1.110.2-0.10.2577980.080522-1.3151322.2318480.1-0.30.1100660.220132
swingswish-1.27.7-0.20.1402750.050552-3.0749472.367123-0.0-0.30.0094760.018953
SynapseSeer-1.326.2-0.10.4525550.088498-0.5689102.0530760.1-0.20.2872310.574463
KevinTestBot-1.58.4-0.20.5894660.203385-0.8971162.3114960.3-0.70.1989520.397903
Grizeu_Bot-1.751.4-0.01.1733920.163747-0.2066162.0064470.3-0.40.4185710.837143
pianobot-2.74.7-0.60.9162040.422613-1.3843272.7989860.6-1.80.1219410.243882
CatrachoCaster-3.219.7-0.20.5209010.117361-1.3655322.0887770.1-0.40.0941440.188288
krm-bot-5.19.5-0.50.5115460.165967-3.2298462.264709-0.2-0.90.0055630.011127
annabot-6.229.3-0.20.5208690.096226-2.2117952.044183-0.0-0.40.0176100.035221
4Shadower-6.214.0-0.40.7673220.205075-2.1431942.1472390.0-0.90.0257970.051593
cookics_bot_TEST-6.927.4-0.30.7446990.142267-1.7648762.0495410.0-0.50.0445760.089152
jkraybill_bot-7.544.0-0.20.5128530.077272-2.1971332.014642-0.0-0.30.0167210.033441
twsummerbot-8.958.4-0.20.6597100.086327-1.7583912.0008550.0-0.30.0420060.084012
MWG-9.828.6-0.30.7052400.131872-2.5896252.046561-0.1-0.60.0075810.015163
ProfessorSP-10.018.6-0.50.9362770.217094-2.4844802.095243-0.1-1.00.0116440.023289
GreeneiBot2-10.458.4-0.20.8498830.111260-1.5979762.0008320.0-0.40.0577720.115544
acm_bot-10.580.2-0.10.9142650.102059-1.2877171.9893440.1-0.30.1007960.201592
ajf-bot-10.934.2-0.31.0855890.185496-1.7223952.0307780.1-0.70.0471450.094289
metac-o1-11.591.1-0.10.8882270.093060-1.3604681.9858290.1-0.30.0885380.177076
Bot_Pepa-11.544.0-0.30.7375370.111125-2.3431662.014642-0.0-0.50.0119050.023810
metac-perplexity-11.989.1-0.10.9936690.105270-1.2647311.9864050.1-0.30.1046520.209303
laylaps-12.964.1-0.20.6619050.082674-2.4404611.996907-0.0-0.40.0087440.017488
wunderplumb-13.625.6-0.50.9000510.178062-2.9840942.056603-0.2-0.90.0031740.006348
manticAI-14.669.4-0.20.6709460.080510-2.6133541.993968-0.0-0.40.0055070.011014
metac-deepseek-r1-14.652.1-0.30.7315250.101347-2.7666892.005379-0.1-0.50.0039320.007864
metac-Gemini-Exp-1206-15.276.5-0.20.9437970.107907-1.8467741.9908220.0-0.40.0343490.068698
NextWorldLab-16.980.2-0.20.9069640.101244-2.0783931.989344-0.0-0.40.0204550.040909
bot_median-17.392.1-0.20.9191220.095773-1.9639961.9855500.0-0.40.0262900.052579
minefrac1-19.251.1-0.40.8809900.123242-3.0436412.006545-0.1-0.60.0018590.003717
metac-claude-3-5-sonnet-20240620-19.590.5-0.21.0091380.106078-2.0310651.986072-0.0-0.40.0226080.045215
mmBot-21.992.1-0.20.7250100.075546-3.1501041.985550-0.1-0.40.0011040.002208
metac-grok-2-1212-22.991.1-0.31.0488290.109887-2.2835281.985829-0.0-0.50.0123750.024750
pgodzinai-23.976.4-0.30.9564520.109425-2.8586861.990849-0.1-0.50.0027490.005498
VeritasAI-24.377.1-0.30.6607030.075245-4.1859101.990482-0.2-0.50.0000380.000076
metac-claude-3-5-sonnet-latest-24.491.1-0.30.7843150.082173-3.2658271.985829-0.1-0.40.0007720.001544
metac-Llama-3.1-26.189.1-0.30.9987990.105813-2.7685651.986405-0.1-0.50.0034320.006863
metac-exa-26.689.1-0.30.8489740.089941-3.3240971.986405-0.1-0.50.0006470.001294
InstitutPelFutur-26.990.1-0.30.9737670.102587-2.9085241.986114-0.1-0.50.0022920.004584
metac-o1-preview-27.891.1-0.30.8774340.091930-3.3149741.985829-0.1-0.50.0006610.001322
metac-gpt-4o-30.591.1-0.30.9139400.095754-3.4928271.985829-0.1-0.50.0003710.000743
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" + ], + "text/plain": [ + " W_score W_count W_ave W_stdev std_err \\\n", + "cobyj-bot 0.0 0.0 NaN NaN NaN \n", + "andrewsiah 0.0 0.0 NaN NaN NaN \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", + "RPM_bot -1.1 7.0 -0.2 0.824532 0.311644 \n", + "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", + "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", + "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", + "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", + "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", + "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", + "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", + "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", + "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", + "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", + "cookics_bot_TEST -6.9 27.4 -0.3 0.744699 0.142267 \n", + "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", + "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", + "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", + "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", + "GreeneiBot2 -10.4 58.4 -0.2 0.849883 0.111260 \n", + "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", + "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", + "metac-o1 -11.5 91.1 -0.1 0.888227 0.093060 \n", + "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", + "metac-perplexity -11.9 89.1 -0.1 0.993669 0.105270 \n", + "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", + "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", + "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", + "metac-deepseek-r1 -14.6 52.1 -0.3 0.731525 0.101347 \n", + "metac-Gemini-Exp-1206 -15.2 76.5 -0.2 0.943797 0.107907 \n", + "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", + "bot_median -17.3 92.1 -0.2 0.919122 0.095773 \n", + "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", + "metac-claude-3-5-sonnet-20240620 -19.5 90.5 -0.2 1.009138 0.106078 \n", + "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", + "metac-grok-2-1212 -22.9 91.1 -0.3 1.048829 0.109887 \n", + "pgodzinai -23.9 76.4 -0.3 0.956452 0.109425 \n", + "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", + "metac-claude-3-5-sonnet-latest -24.4 91.1 -0.3 0.784315 0.082173 \n", + "metac-Llama-3.1 -26.1 89.1 -0.3 0.998799 0.105813 \n", + "metac-exa -26.6 89.1 -0.3 0.848974 0.089941 \n", + "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", + "metac-o1-preview -27.8 91.1 -0.3 0.877434 0.091930 \n", + "metac-gpt-4o -30.5 91.1 -0.3 0.913940 0.095754 \n", + "\n", + " t_stat t_crit upper_bound \\\n", + "cobyj-bot NaN NaN NaN \n", + "andrewsiah NaN NaN NaN \n", + "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "X_bot -0.747195 2.446912 0.2 \n", + "RPM_bot -0.523406 2.446912 0.6 \n", + "CumulativeBot -1.315132 2.231848 0.1 \n", + "swingswish -3.074947 2.367123 -0.0 \n", + "SynapseSeer -0.568910 2.053076 0.1 \n", + "KevinTestBot -0.897116 2.311496 0.3 \n", + "Grizeu_Bot -0.206616 2.006447 0.3 \n", + "pianobot -1.384327 2.798986 0.6 \n", + "CatrachoCaster -1.365532 2.088777 0.1 \n", + "krm-bot -3.229846 2.264709 -0.2 \n", + "annabot -2.211795 2.044183 -0.0 \n", + "4Shadower -2.143194 2.147239 0.0 \n", + "cookics_bot_TEST -1.764876 2.049541 0.0 \n", + "jkraybill_bot -2.197133 2.014642 -0.0 \n", + "twsummerbot -1.758391 2.000855 0.0 \n", + "MWG -2.589625 2.046561 -0.1 \n", + "ProfessorSP -2.484480 2.095243 -0.1 \n", + "GreeneiBot2 -1.597976 2.000832 0.0 \n", + "acm_bot -1.287717 1.989344 0.1 \n", + "ajf-bot -1.722395 2.030778 0.1 \n", + "metac-o1 -1.360468 1.985829 0.1 \n", + "Bot_Pepa -2.343166 2.014642 -0.0 \n", + "metac-perplexity -1.264731 1.986405 0.1 \n", + "laylaps -2.440461 1.996907 -0.0 \n", + "wunderplumb -2.984094 2.056603 -0.2 \n", + "manticAI -2.613354 1.993968 -0.0 \n", + "metac-deepseek-r1 -2.766689 2.005379 -0.1 \n", + "metac-Gemini-Exp-1206 -1.846774 1.990822 0.0 \n", + "NextWorldLab -2.078393 1.989344 -0.0 \n", + "bot_median -1.963996 1.985550 0.0 \n", + "minefrac1 -3.043641 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.031065 1.986072 -0.0 \n", + "mmBot -3.150104 1.985550 -0.1 \n", + "metac-grok-2-1212 -2.283528 1.985829 -0.0 \n", + "pgodzinai -2.858686 1.990849 -0.1 \n", + "VeritasAI -4.185910 1.990482 -0.2 \n", + "metac-claude-3-5-sonnet-latest -3.265827 1.985829 -0.1 \n", + "metac-Llama-3.1 -2.768565 1.986405 -0.1 \n", + "metac-exa -3.324097 1.986405 -0.1 \n", + "InstitutPelFutur -2.908524 1.986114 -0.1 \n", + "metac-o1-preview -3.314974 1.985829 -0.1 \n", + "metac-gpt-4o -3.492827 1.985829 -0.1 \n", + "\n", + " lower_bound cdf p_value \n", + "cobyj-bot NaN NaN NA \n", + "andrewsiah NaN NaN NA \n", + "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", + "X_bot -0.4 0.241594 0.483189 \n", + "RPM_bot -0.9 0.309726 0.619452 \n", + "CumulativeBot -0.3 0.110066 0.220132 \n", + "swingswish -0.3 0.009476 0.018953 \n", + "SynapseSeer -0.2 0.287231 0.574463 \n", + "KevinTestBot -0.7 0.198952 0.397903 \n", + "Grizeu_Bot -0.4 0.418571 0.837143 \n", + "pianobot -1.8 0.121941 0.243882 \n", + "CatrachoCaster -0.4 0.094144 0.188288 \n", + "krm-bot -0.9 0.005563 0.011127 \n", + "annabot -0.4 0.017610 0.035221 \n", + "4Shadower -0.9 0.025797 0.051593 \n", + "cookics_bot_TEST -0.5 0.044576 0.089152 \n", + "jkraybill_bot -0.3 0.016721 0.033441 \n", + "twsummerbot -0.3 0.042006 0.084012 \n", + "MWG -0.6 0.007581 0.015163 \n", + "ProfessorSP -1.0 0.011644 0.023289 \n", + "GreeneiBot2 -0.4 0.057772 0.115544 \n", + "acm_bot -0.3 0.100796 0.201592 \n", + "ajf-bot -0.7 0.047145 0.094289 \n", + "metac-o1 -0.3 0.088538 0.177076 \n", + "Bot_Pepa -0.5 0.011905 0.023810 \n", + "metac-perplexity -0.3 0.104652 0.209303 \n", + "laylaps -0.4 0.008744 0.017488 \n", + "wunderplumb -0.9 0.003174 0.006348 \n", + "manticAI -0.4 0.005507 0.011014 \n", + "metac-deepseek-r1 -0.5 0.003932 0.007864 \n", + "metac-Gemini-Exp-1206 -0.4 0.034349 0.068698 \n", + "NextWorldLab -0.4 0.020455 0.040909 \n", + "bot_median -0.4 0.026290 0.052579 \n", + "minefrac1 -0.6 0.001859 0.003717 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.022608 0.045215 \n", + "mmBot -0.4 0.001104 0.002208 \n", + "metac-grok-2-1212 -0.5 0.012375 0.024750 \n", + "pgodzinai -0.5 0.002749 0.005498 \n", + "VeritasAI -0.5 0.000038 0.000076 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.000772 0.001544 \n", + "metac-Llama-3.1 -0.5 0.003432 0.006863 \n", + "metac-exa -0.5 0.000647 0.001294 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 \n", + "metac-o1-preview -0.5 0.000661 0.001322 \n", + "metac-gpt-4o -0.5 0.000371 0.000743 " + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# @title Weighted head-to-head, T test\n", "\n", @@ -3567,7 +7026,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 219, "metadata": {}, "outputs": [], "source": [ @@ -3577,7 +7036,7 @@ }, { "cell_type": "code", - "execution_count": null, + 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RankBotW_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
01metac-o13631.1375.39.735.0711401.8102945.3442931.96598513.26.11.0000000.000000
12metac-o1-preview3121.4368.78.545.9615892.3935733.5368201.96609313.23.80.9997720.000457
23metac-Gemini-Exp-12061880.5347.15.444.8958442.4097192.2481331.96645810.20.70.9874020.025197
34SynapseSeer966.5152.06.435.6992152.8951132.1955681.97487912.10.60.9851760.029648
45manticAI2055.2315.76.555.6900633.1344982.0771541.96718712.70.30.9807010.038598
56twsummerbot1450.0241.36.045.0911402.9027092.0701531.96931311.70.30.9802470.039507
67acm_bot1738.4344.85.045.8463322.4691432.0421541.9665219.90.20.9790510.041899
78cookics_bot_TEST1143.8162.67.046.7964543.6698871.9168291.97413814.3-0.20.9714880.057024
89CumulativeBot991.4104.59.552.1803255.1044461.8585841.98213619.6-0.60.9670360.065928
910metac-claude-3-5-sonnet-latest951.3370.32.638.2630661.9883421.2919541.9660636.5-1.30.9014100.197181
1011GreeneiBot21494.7264.15.759.7283543.6750521.5398111.96859612.9-1.60.9375960.124808
1112metac-perplexity1558.4354.44.459.5883783.1652091.3891811.96637110.6-1.80.9171740.165652
1213metac-deepseek-r1516.8277.91.937.3532102.2407800.8299751.9681656.3-2.60.7963660.407268
1314pgodzinai1106.7325.43.466.6861593.6966950.9199541.96694910.7-3.90.8208600.358280
1415metac-exa599.9365.31.663.4593893.3201610.4946111.9661428.2-4.90.6894130.621173
1516MWG253.8113.42.240.6740843.8190370.5859361.9804689.8-5.30.7204540.559093
1617jkraybill_bot625.4207.43.068.5607804.7604770.6333891.97101512.4-6.40.7364100.527181
1718metac-claude-3-5-sonnet-20240620-759.5373.7-2.044.0904802.280718-0.8910111.9660142.5-6.50.1867490.373498
1819metac-grok-2-1212-550.1373.3-1.550.1642462.596293-0.5675531.9660163.6-6.60.2853400.570681
1920metac-Llama-3.1-980.9370.6-2.641.8100632.171783-1.2186111.9660621.6-6.90.1118850.223769
2021mmBot-587.4373.0-1.658.2984393.018498-0.5216711.9660174.4-7.50.3011050.602210
2122VeritasAI-1602.2330.0-4.938.7547802.133316-2.2757101.966760-0.7-9.10.0117530.023506
2223InstitutPelFutur-877.8356.0-2.564.6034773.423881-0.7201271.9663054.3-9.20.2359600.471921
2324NextWorldLab-1377.9337.6-4.151.4333882.799472-1.4581571.9666641.4-9.60.0728650.145730
2425metac-gpt-4o-2235.4373.3-6.045.4016702.349802-2.5482091.966016-1.4-10.60.0056140.011229
2526CatrachoCaster-289.481.6-3.531.9567253.538536-1.0026081.9883423.5-10.60.1595260.319052
2627laylaps-1489.1322.1-4.663.9802383.564926-1.2968551.9670502.4-11.60.0978060.195612
2728ProfessorSP-426.8128.6-3.355.1654604.863650-0.6821421.9781236.3-12.90.2481930.496385
2829krm-bot-354.7104.0-3.449.8754924.890694-0.6973341.9823276.3-13.10.2435820.487165
2930wunderplumb-986.1174.0-5.752.9658934.015334-1.4114341.9731952.3-13.60.0799560.159913
3031andrewsiah2.625.10.135.8050927.1467390.0146792.06034114.8-14.60.5057960.988409
3132annabot-190.683.8-2.359.1122286.458906-0.3522221.98640810.6-15.10.3627840.725567
3233Bot_Pepa-1490.1169.4-8.844.2657023.400530-2.5860051.973733-2.1-15.50.0052780.010555
33344Shadower-646.3115.5-5.653.8678675.012320-1.1163051.9797854.3-15.50.1333140.266629
3435minefrac1-1757.1188.2-9.344.1258493.216071-2.9021901.972106-3.0-15.70.0020750.004150
3536KevinTestBot-220.489.5-2.567.6508777.150920-0.3443101.98550511.7-16.70.3657150.731430
3637jonahsingerbot-333.464.8-5.148.0155485.964779-0.8626001.9952736.8-17.00.1957940.391588
3738bean_bot-208.867.8-3.159.9556627.281408-0.4229401.99377111.4-17.60.3368490.673697
3839Grizeu_Bot-1882.6193.2-9.756.7042374.079442-2.3885211.971774-1.7-17.80.0089420.017884
3940cobyj-bot-12.131.5-0.448.0409918.559663-0.0450462.03985017.1-17.80.4821820.964365
4041X_bot-16.17.0-2.323.9086329.036614-0.2537742.44691219.8-24.40.4040710.808142
4142ajf-bot-3208.3229.2-14.083.2955695.502524-2.5444141.969928-3.2-24.80.0058030.011607
4243pianobot-12.719.6-0.752.32348711.833775-0.0550422.09382324.1-25.40.4783470.956694
4344swingswish-777.064.8-12.073.0478929.074447-1.3214361.9952736.1-30.10.0955380.191075
4445RPM_bot-815.623.8-34.391.54540218.784720-1.8281002.0615084.4-73.10.0403390.080679
\n", + "
" + ], + "text/plain": [ + " Rank Bot W_score W_count W_ave \\\n", + "0 1 metac-o1 3631.1 375.3 9.7 \n", + "1 2 metac-o1-preview 3121.4 368.7 8.5 \n", + "2 3 metac-Gemini-Exp-1206 1880.5 347.1 5.4 \n", + "3 4 SynapseSeer 966.5 152.0 6.4 \n", + "4 5 manticAI 2055.2 315.7 6.5 \n", + "5 6 twsummerbot 1450.0 241.3 6.0 \n", + "6 7 acm_bot 1738.4 344.8 5.0 \n", + "7 8 cookics_bot_TEST 1143.8 162.6 7.0 \n", + "8 9 CumulativeBot 991.4 104.5 9.5 \n", + "9 10 metac-claude-3-5-sonnet-latest 951.3 370.3 2.6 \n", + "10 11 GreeneiBot2 1494.7 264.1 5.7 \n", + "11 12 metac-perplexity 1558.4 354.4 4.4 \n", + "12 13 metac-deepseek-r1 516.8 277.9 1.9 \n", + "13 14 pgodzinai 1106.7 325.4 3.4 \n", + "14 15 metac-exa 599.9 365.3 1.6 \n", + "15 16 MWG 253.8 113.4 2.2 \n", + "16 17 jkraybill_bot 625.4 207.4 3.0 \n", + "17 18 metac-claude-3-5-sonnet-20240620 -759.5 373.7 -2.0 \n", + "18 19 metac-grok-2-1212 -550.1 373.3 -1.5 \n", + "19 20 metac-Llama-3.1 -980.9 370.6 -2.6 \n", + "20 21 mmBot -587.4 373.0 -1.6 \n", + "21 22 VeritasAI -1602.2 330.0 -4.9 \n", + "22 23 InstitutPelFutur -877.8 356.0 -2.5 \n", + "23 24 NextWorldLab -1377.9 337.6 -4.1 \n", + "24 25 metac-gpt-4o -2235.4 373.3 -6.0 \n", + "25 26 CatrachoCaster -289.4 81.6 -3.5 \n", + "26 27 laylaps -1489.1 322.1 -4.6 \n", + "27 28 ProfessorSP -426.8 128.6 -3.3 \n", + "28 29 krm-bot -354.7 104.0 -3.4 \n", + "29 30 wunderplumb -986.1 174.0 -5.7 \n", + "30 31 andrewsiah 2.6 25.1 0.1 \n", + "31 32 annabot -190.6 83.8 -2.3 \n", + "32 33 Bot_Pepa -1490.1 169.4 -8.8 \n", + "33 34 4Shadower -646.3 115.5 -5.6 \n", + "34 35 minefrac1 -1757.1 188.2 -9.3 \n", + "35 36 KevinTestBot -220.4 89.5 -2.5 \n", + "36 37 jonahsingerbot -333.4 64.8 -5.1 \n", + "37 38 bean_bot -208.8 67.8 -3.1 \n", + "38 39 Grizeu_Bot -1882.6 193.2 -9.7 \n", + "39 40 cobyj-bot -12.1 31.5 -0.4 \n", + "40 41 X_bot -16.1 7.0 -2.3 \n", + "41 42 ajf-bot -3208.3 229.2 -14.0 \n", + "42 43 pianobot -12.7 19.6 -0.7 \n", + "43 44 swingswish -777.0 64.8 -12.0 \n", + "44 45 RPM_bot -815.6 23.8 -34.3 \n", + "\n", + " W_stdev std_err t_stat t_crit upper_bound lower_bound \\\n", + "0 35.071140 1.810294 5.344293 1.965985 13.2 6.1 \n", + "1 45.961589 2.393573 3.536820 1.966093 13.2 3.8 \n", + "2 44.895844 2.409719 2.248133 1.966458 10.2 0.7 \n", + "3 35.699215 2.895113 2.195568 1.974879 12.1 0.6 \n", + "4 55.690063 3.134498 2.077154 1.967187 12.7 0.3 \n", + "5 45.091140 2.902709 2.070153 1.969313 11.7 0.3 \n", + "6 45.846332 2.469143 2.042154 1.966521 9.9 0.2 \n", + "7 46.796454 3.669887 1.916829 1.974138 14.3 -0.2 \n", + "8 52.180325 5.104446 1.858584 1.982136 19.6 -0.6 \n", + "9 38.263066 1.988342 1.291954 1.966063 6.5 -1.3 \n", + "10 59.728354 3.675052 1.539811 1.968596 12.9 -1.6 \n", + "11 59.588378 3.165209 1.389181 1.966371 10.6 -1.8 \n", + "12 37.353210 2.240780 0.829975 1.968165 6.3 -2.6 \n", + "13 66.686159 3.696695 0.919954 1.966949 10.7 -3.9 \n", + "14 63.459389 3.320161 0.494611 1.966142 8.2 -4.9 \n", + "15 40.674084 3.819037 0.585936 1.980468 9.8 -5.3 \n", + "16 68.560780 4.760477 0.633389 1.971015 12.4 -6.4 \n", + "17 44.090480 2.280718 -0.891011 1.966014 2.5 -6.5 \n", + "18 50.164246 2.596293 -0.567553 1.966016 3.6 -6.6 \n", + "19 41.810063 2.171783 -1.218611 1.966062 1.6 -6.9 \n", + "20 58.298439 3.018498 -0.521671 1.966017 4.4 -7.5 \n", + "21 38.754780 2.133316 -2.275710 1.966760 -0.7 -9.1 \n", + "22 64.603477 3.423881 -0.720127 1.966305 4.3 -9.2 \n", + "23 51.433388 2.799472 -1.458157 1.966664 1.4 -9.6 \n", + "24 45.401670 2.349802 -2.548209 1.966016 -1.4 -10.6 \n", + "25 31.956725 3.538536 -1.002608 1.988342 3.5 -10.6 \n", + "26 63.980238 3.564926 -1.296855 1.967050 2.4 -11.6 \n", + "27 55.165460 4.863650 -0.682142 1.978123 6.3 -12.9 \n", + "28 49.875492 4.890694 -0.697334 1.982327 6.3 -13.1 \n", + "29 52.965893 4.015334 -1.411434 1.973195 2.3 -13.6 \n", + "30 35.805092 7.146739 0.014679 2.060341 14.8 -14.6 \n", + "31 59.112228 6.458906 -0.352222 1.986408 10.6 -15.1 \n", + "32 44.265702 3.400530 -2.586005 1.973733 -2.1 -15.5 \n", + "33 53.867867 5.012320 -1.116305 1.979785 4.3 -15.5 \n", + "34 44.125849 3.216071 -2.902190 1.972106 -3.0 -15.7 \n", + "35 67.650877 7.150920 -0.344310 1.985505 11.7 -16.7 \n", + "36 48.015548 5.964779 -0.862600 1.995273 6.8 -17.0 \n", + "37 59.955662 7.281408 -0.422940 1.993771 11.4 -17.6 \n", + "38 56.704237 4.079442 -2.388521 1.971774 -1.7 -17.8 \n", + "39 48.040991 8.559663 -0.045046 2.039850 17.1 -17.8 \n", + "40 23.908632 9.036614 -0.253774 2.446912 19.8 -24.4 \n", + "41 83.295569 5.502524 -2.544414 1.969928 -3.2 -24.8 \n", + "42 52.323487 11.833775 -0.055042 2.093823 24.1 -25.4 \n", + "43 73.047892 9.074447 -1.321436 1.995273 6.1 -30.1 \n", + "44 91.545402 18.784720 -1.828100 2.061508 4.4 -73.1 \n", + "\n", + " cdf p_value \n", + "0 1.000000 0.000000 \n", + "1 0.999772 0.000457 \n", + "2 0.987402 0.025197 \n", + "3 0.985176 0.029648 \n", + "4 0.980701 0.038598 \n", + "5 0.980247 0.039507 \n", + "6 0.979051 0.041899 \n", + "7 0.971488 0.057024 \n", + "8 0.967036 0.065928 \n", + "9 0.901410 0.197181 \n", + "10 0.937596 0.124808 \n", + "11 0.917174 0.165652 \n", + "12 0.796366 0.407268 \n", + "13 0.820860 0.358280 \n", + "14 0.689413 0.621173 \n", + "15 0.720454 0.559093 \n", + "16 0.736410 0.527181 \n", + "17 0.186749 0.373498 \n", + "18 0.285340 0.570681 \n", + "19 0.111885 0.223769 \n", + "20 0.301105 0.602210 \n", + "21 0.011753 0.023506 \n", + "22 0.235960 0.471921 \n", + "23 0.072865 0.145730 \n", + "24 0.005614 0.011229 \n", + "25 0.159526 0.319052 \n", + "26 0.097806 0.195612 \n", + "27 0.248193 0.496385 \n", + "28 0.243582 0.487165 \n", + "29 0.079956 0.159913 \n", + "30 0.505796 0.988409 \n", + "31 0.362784 0.725567 \n", + "32 0.005278 0.010555 \n", + "33 0.133314 0.266629 \n", + "34 0.002075 0.004150 \n", + "35 0.365715 0.731430 \n", + "36 0.195794 0.391588 \n", + "37 0.336849 0.673697 \n", + "38 0.008942 0.017884 \n", + "39 0.482182 0.964365 \n", + "40 0.404071 0.808142 \n", + "41 0.005803 0.011607 \n", + "42 0.478347 0.956694 \n", + "43 0.095538 0.191075 \n", + "44 0.040339 0.080679 " + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# @title Weighted Bot Peer, T test (to compare bots against each other, use ALL QUESTIONS)\n", "\n", @@ -3621,7 +7989,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 221, "metadata": {}, "outputs": [], "source": [ @@ -3631,16 +7999,223 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 222, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
031262NaNNaNNaNNaN-242.660874135.57527347.259183NaNNaN...-205.076095121.194882NaN-242.660874-198.879258NaNNaNNaNNaN1.0
131263NaNNaNNaNNaN-96.476789-99.090018-94.660371NaNNaN...7.9517037.951703NaN55.81904144.625993NaNNaNNaNNaN1.0
231264NaNNaNNaNNaN18.89298023.948225-86.527528NaNNaN...13.82151813.821518NaN1.30707117.305437NaNNaNNaNNaN1.0
331274NaNNaN2.076868NaN31.0945314.282464-28.806893NaN14.663415...6.44257916.621639NaN8.55905311.145899NaNNaN-9.706540NaN1.0
431275NaNNaNNaNNaN30.694891-66.461608-58.368696NaNNaN...35.698675-0.691552NaN39.41450214.411756NaNNaN-70.932651NaN1.0
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5 rows × 48 columns

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" + ], + "text/plain": [ + " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", + "0 31262 NaN NaN NaN NaN \n", + "1 31263 NaN NaN NaN NaN \n", + "2 31264 NaN NaN NaN NaN \n", + "3 31274 NaN NaN 2.076868 NaN \n", + "4 31275 NaN NaN NaN NaN \n", + "\n", + " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", + "0 -242.660874 135.575273 47.259183 NaN NaN ... \n", + "1 -96.476789 -99.090018 -94.660371 NaN NaN ... \n", + "2 18.892980 23.948225 -86.527528 NaN NaN ... \n", + "3 31.094531 4.282464 -28.806893 NaN 14.663415 ... \n", + "4 30.694891 -66.461608 -58.368696 NaN NaN ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "0 -205.076095 121.194882 NaN -242.660874 -198.879258 \n", + "1 7.951703 7.951703 NaN 55.819041 44.625993 \n", + "2 13.821518 13.821518 NaN 1.307071 17.305437 \n", + "3 6.442579 16.621639 NaN 8.559053 11.145899 \n", + "4 35.698675 -0.691552 NaN 39.414502 14.411756 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb question_weight \n", + "0 NaN NaN NaN NaN 1.0 \n", + "1 NaN NaN NaN NaN 1.0 \n", + "2 NaN NaN NaN NaN 1.0 \n", + "3 NaN NaN -9.706540 NaN 1.0 \n", + "4 NaN NaN -70.932651 NaN 1.0 \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_bot_peer_wide.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 223, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -3649,7 +8224,18 @@ "id": "88QO8eyW6T_T", "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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2.5% CI10% CIMedian90% CI97.5% CI
metac-o16.27.49.711.813.1
metac-o1-preview3.15.38.311.112.8
manticAI0.22.15.68.810.6
metac-Gemini-Exp-12060.61.95.28.19.4
acm_bot0.11.74.67.58.9
metac-perplexity-1.70.44.28.09.6
GreeneiBot2-1.20.74.07.18.9
twsummerbot0.21.43.86.17.3
cookics_bot_TEST0.11.03.05.16.1
pgodzinai-3.5-1.42.96.98.7
CumulativeBot-0.30.92.74.45.4
metac-claude-3-5-sonnet-latest-1.10.12.65.16.4
SynapseSeer0.41.12.64.04.9
jkraybill_bot-3.9-1.81.74.96.3
metac-exa-5.3-2.81.65.47.8
metac-deepseek-r1-1.7-0.81.33.54.6
MWG-1.5-0.70.72.22.8
andrewsiah-0.9-0.6-0.00.61.0
cobyj-bot-1.4-0.9-0.00.81.3
X_bot-0.4-0.3-0.00.10.2
pianobot-1.3-0.8-0.00.71.1
annabot-3.5-2.3-0.41.32.2
bean_bot-3.1-2.2-0.51.11.7
KevinTestBot-4.3-2.8-0.61.42.6
jonahsingerbot-3.0-2.2-0.80.41.0
CatrachoCaster-2.3-1.7-0.80.20.8
krm-bot-3.5-2.6-0.90.71.6
ProfessorSP-4.5-3.4-1.21.02.2
metac-grok-2-1212-6.6-4.9-1.61.73.5
4Shadower-4.8-3.6-1.70.31.2
mmBot-7.8-5.7-1.72.14.2
swingswish-5.2-4.0-1.9-0.20.6
RPM_bot-4.8-3.8-2.0-0.7-0.1
InstitutPelFutur-8.8-6.6-2.12.04.0
metac-claude-3-5-sonnet-20240620-6.8-5.0-2.10.92.2
wunderplumb-6.0-4.7-2.5-0.30.7
metac-Llama-3.1-6.7-5.4-2.70.01.5
NextWorldLab-8.9-6.9-3.6-0.50.9
laylaps-10.1-8.1-3.8-0.11.6
Bot_Pepa-7.2-6.0-3.9-2.0-0.9
VeritasAI-7.7-6.4-4.3-2.0-0.8
minefrac1-8.0-6.7-4.6-2.6-1.5
Grizeu_Bot-9.2-7.6-5.0-2.3-0.6
metac-gpt-4o-10.6-9.1-5.7-2.9-1.4
ajf-bot-14.6-12.4-8.3-4.4-2.0
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2.5% CI10% CIMedian90% CI97.5% CI
cobyj-bot0.00.00.00.00.0
andrewsiah0.00.00.00.00.0
X_bot-0.0-0.0-0.00.00.0
jonahsingerbot-0.0-0.0-0.0-0.0-0.0
bean_bot-0.0-0.0-0.0-0.0-0.0
RPM_bot-0.1-0.0-0.00.00.0
CumulativeBot-0.0-0.0-0.0-0.00.0
swingswish-0.0-0.0-0.0-0.0-0.0
KevinTestBot-0.1-0.0-0.00.00.0
SynapseSeer-0.1-0.0-0.00.00.0
Grizeu_Bot-0.2-0.1-0.00.10.2
pianobot-0.1-0.1-0.0-0.00.0
CatrachoCaster-0.1-0.1-0.0-0.00.0
krm-bot-0.1-0.1-0.1-0.0-0.0
4Shadower-0.1-0.1-0.1-0.0-0.0
annabot-0.1-0.1-0.1-0.0-0.0
cookics_bot_TEST-0.2-0.1-0.1-0.00.0
jkraybill_bot-0.2-0.1-0.1-0.0-0.0
twsummerbot-0.2-0.2-0.1-0.00.0
MWG-0.2-0.2-0.1-0.0-0.0
ProfessorSP-0.2-0.2-0.1-0.1-0.0
GreeneiBot2-0.2-0.2-0.1-0.00.0
ajf-bot-0.3-0.2-0.1-0.00.0
acm_bot-0.3-0.2-0.10.00.1
Bot_Pepa-0.2-0.2-0.1-0.1-0.0
metac-o1-0.3-0.2-0.1-0.00.1
metac-perplexity-0.3-0.2-0.10.00.1
laylaps-0.2-0.2-0.1-0.1-0.0
wunderplumb-0.3-0.2-0.1-0.1-0.0
manticAI-0.3-0.2-0.2-0.1-0.0
metac-deepseek-r1-0.3-0.2-0.2-0.1-0.0
metac-Gemini-Exp-1206-0.3-0.3-0.2-0.00.0
NextWorldLab-0.3-0.3-0.2-0.1-0.0
bot_median-0.4-0.3-0.2-0.10.0
minefrac1-0.3-0.3-0.2-0.1-0.1
metac-claude-3-5-sonnet-20240620-0.4-0.3-0.2-0.10.0
mmBot-0.4-0.3-0.2-0.1-0.1
metac-grok-2-1212-0.4-0.4-0.2-0.1-0.0
pgodzinai-0.4-0.4-0.2-0.1-0.1
VeritasAI-0.4-0.3-0.3-0.2-0.1
metac-claude-3-5-sonnet-latest-0.4-0.4-0.3-0.2-0.1
metac-Llama-3.1-0.5-0.4-0.3-0.1-0.1
metac-exa-0.5-0.4-0.3-0.2-0.1
InstitutPelFutur-0.5-0.4-0.3-0.2-0.1
metac-o1-preview-0.5-0.4-0.3-0.2-0.1
metac-gpt-4o-0.5-0.4-0.3-0.2-0.1
\n", + "
" + ], + "text/plain": [ + " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", + "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", + "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", + "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", + "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", + "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", + "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", + "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", + "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", + "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", + "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", + "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", + "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", + "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", + "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", + "ProfessorSP -0.2 -0.2 -0.1 -0.1 -0.0\n", + "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", + "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", + "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-o1 -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-perplexity -0.3 -0.2 -0.1 0.0 0.1\n", + "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", + "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", + "metac-deepseek-r1 -0.3 -0.2 -0.2 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.3 -0.2 -0.0 0.0\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", + "bot_median -0.4 -0.3 -0.2 -0.1 0.0\n", + "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", + "metac-grok-2-1212 -0.4 -0.4 -0.2 -0.1 -0.0\n", + "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-claude-3-5-sonnet-latest -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.1 -0.1\n", + "metac-exa -0.5 -0.4 -0.3 -0.2 -0.1\n", + "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", "ITER = 1000\n", @@ -3757,7 +9678,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 227, "metadata": {}, "outputs": [], "source": [ @@ -3767,9 +9688,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 228, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted score for annabot: -190.5513637093994\n", + "Total score for annabot: 21.125669919166132\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# @title Check specific bot records\n", "\n", @@ -3807,7 +9748,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 229, "metadata": { "cellView": "form", "colab": { @@ -3816,7 +9757,491 @@ "id": "I7W8JXutv2ks", "outputId": "5e7053d3-2124-42b7-bd53-48a40a53caf2" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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W_aveW_countlower_boundupper_boundp_value
metac-o1-preview12.2276.67.117.30.000004
metac-o18.4283.24.012.70.000179
pgodzinai8.7248.01.116.30.025267
GreeneiBot29.2204.81.117.30.026930
manticAI7.7245.20.514.90.035671
acm_bot5.4263.5-0.211.00.058135
metac-Gemini-Exp-12065.3269.6-0.310.80.062806
SynapseSeer6.0125.9-0.512.50.068737
metac-claude-3-5-sonnet-latest3.6278.2-0.98.20.116899
twsummerbot4.9181.9-1.811.60.152393
cookics_bot_TEST5.8135.2-1.813.40.132509
CumulativeBot8.094.2-3.018.90.153662
metac-deepseek-r10.8225.8-4.25.80.763142
MWG3.684.8-4.311.50.365354
metac-perplexity2.8264.3-4.810.30.470416
metac-grok-2-12120.1281.2-5.76.00.961620
metac-exa1.7275.2-5.89.20.654608
mmBot-0.5279.9-7.56.50.887163
InstitutPelFutur-0.1264.9-8.18.00.988352
metac-Llama-3.1-3.7280.5-8.30.90.117806
metac-claude-3-5-sonnet-20240620-3.3282.2-8.52.00.224671
VeritasAI-4.5251.9-9.40.40.072948
jkraybill_bot1.4162.4-9.712.40.808839
CatrachoCaster-2.761.9-10.65.20.493061
metac-gpt-4o-5.2281.2-10.60.10.054453
NextWorldLab-4.6256.3-10.91.80.156859
wunderplumb-5.4148.4-13.52.60.184061
4Shadower-3.7101.5-13.96.40.463979
minefrac1-7.3136.2-14.4-0.20.043444
andrewsiah0.125.1-14.614.80.988409
krm-bot-4.494.5-14.75.90.399741
ProfessorSP-4.0110.0-14.86.80.464316
laylaps-7.2257.0-15.40.90.082564
pianobot6.814.8-16.229.80.535822
cobyj-bot-0.431.5-17.817.10.964365
KevinTestBot-2.781.1-17.912.60.730388
jonahsingerbot-6.560.1-19.26.20.309592
bean_bot-4.163.1-19.611.40.600896
Bot_Pepa-12.3124.4-20.6-4.10.003751
annabot-6.354.5-23.711.10.470037
Grizeu_Bot-16.5140.9-25.8-7.10.000639
ajf-bot-16.0193.9-28.2-3.70.011119
swingswish-16.757.1-36.93.50.103364
RPM_bot-44.015.8-101.413.40.126191
\n", + "
" + ], + "text/plain": [ + " W_ave W_count lower_bound upper_bound \\\n", + "metac-o1-preview 12.2 276.6 7.1 17.3 \n", + "metac-o1 8.4 283.2 4.0 12.7 \n", + "pgodzinai 8.7 248.0 1.1 16.3 \n", + "GreeneiBot2 9.2 204.8 1.1 17.3 \n", + "manticAI 7.7 245.2 0.5 14.9 \n", + "acm_bot 5.4 263.5 -0.2 11.0 \n", + "metac-Gemini-Exp-1206 5.3 269.6 -0.3 10.8 \n", + "SynapseSeer 6.0 125.9 -0.5 12.5 \n", + "metac-claude-3-5-sonnet-latest 3.6 278.2 -0.9 8.2 \n", + "twsummerbot 4.9 181.9 -1.8 11.6 \n", + "cookics_bot_TEST 5.8 135.2 -1.8 13.4 \n", + "CumulativeBot 8.0 94.2 -3.0 18.9 \n", + "metac-deepseek-r1 0.8 225.8 -4.2 5.8 \n", + "MWG 3.6 84.8 -4.3 11.5 \n", + "metac-perplexity 2.8 264.3 -4.8 10.3 \n", + "metac-grok-2-1212 0.1 281.2 -5.7 6.0 \n", + "metac-exa 1.7 275.2 -5.8 9.2 \n", + "mmBot -0.5 279.9 -7.5 6.5 \n", + "InstitutPelFutur -0.1 264.9 -8.1 8.0 \n", + "metac-Llama-3.1 -3.7 280.5 -8.3 0.9 \n", + "metac-claude-3-5-sonnet-20240620 -3.3 282.2 -8.5 2.0 \n", + "VeritasAI -4.5 251.9 -9.4 0.4 \n", + "jkraybill_bot 1.4 162.4 -9.7 12.4 \n", + "CatrachoCaster -2.7 61.9 -10.6 5.2 \n", + "metac-gpt-4o -5.2 281.2 -10.6 0.1 \n", + "NextWorldLab -4.6 256.3 -10.9 1.8 \n", + "wunderplumb -5.4 148.4 -13.5 2.6 \n", + "4Shadower -3.7 101.5 -13.9 6.4 \n", + "minefrac1 -7.3 136.2 -14.4 -0.2 \n", + "andrewsiah 0.1 25.1 -14.6 14.8 \n", + "krm-bot -4.4 94.5 -14.7 5.9 \n", + "ProfessorSP -4.0 110.0 -14.8 6.8 \n", + "laylaps -7.2 257.0 -15.4 0.9 \n", + "pianobot 6.8 14.8 -16.2 29.8 \n", + "cobyj-bot -0.4 31.5 -17.8 17.1 \n", + "KevinTestBot -2.7 81.1 -17.9 12.6 \n", + "jonahsingerbot -6.5 60.1 -19.2 6.2 \n", + "bean_bot -4.1 63.1 -19.6 11.4 \n", + "Bot_Pepa -12.3 124.4 -20.6 -4.1 \n", + "annabot -6.3 54.5 -23.7 11.1 \n", + "Grizeu_Bot -16.5 140.9 -25.8 -7.1 \n", + "ajf-bot -16.0 193.9 -28.2 -3.7 \n", + "swingswish -16.7 57.1 -36.9 3.5 \n", + "RPM_bot -44.0 15.8 -101.4 13.4 \n", + "\n", + " p_value \n", + "metac-o1-preview 0.000004 \n", + "metac-o1 0.000179 \n", + "pgodzinai 0.025267 \n", + "GreeneiBot2 0.026930 \n", + "manticAI 0.035671 \n", + "acm_bot 0.058135 \n", + "metac-Gemini-Exp-1206 0.062806 \n", + "SynapseSeer 0.068737 \n", + "metac-claude-3-5-sonnet-latest 0.116899 \n", + "twsummerbot 0.152393 \n", + "cookics_bot_TEST 0.132509 \n", + "CumulativeBot 0.153662 \n", + "metac-deepseek-r1 0.763142 \n", + "MWG 0.365354 \n", + "metac-perplexity 0.470416 \n", + "metac-grok-2-1212 0.961620 \n", + "metac-exa 0.654608 \n", + "mmBot 0.887163 \n", + "InstitutPelFutur 0.988352 \n", + "metac-Llama-3.1 0.117806 \n", + "metac-claude-3-5-sonnet-20240620 0.224671 \n", + "VeritasAI 0.072948 \n", + "jkraybill_bot 0.808839 \n", + "CatrachoCaster 0.493061 \n", + "metac-gpt-4o 0.054453 \n", + "NextWorldLab 0.156859 \n", + "wunderplumb 0.184061 \n", + "4Shadower 0.463979 \n", + "minefrac1 0.043444 \n", + "andrewsiah 0.988409 \n", + "krm-bot 0.399741 \n", + "ProfessorSP 0.464316 \n", + "laylaps 0.082564 \n", + "pianobot 0.535822 \n", + "cobyj-bot 0.964365 \n", + "KevinTestBot 0.730388 \n", + "jonahsingerbot 0.309592 \n", + "bean_bot 0.600896 \n", + "Bot_Pepa 0.003751 \n", + "annabot 0.470037 \n", + "Grizeu_Bot 0.000639 \n", + "ajf-bot 0.011119 \n", + "swingswish 0.103364 \n", + "RPM_bot 0.126191 " + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# @title Weighted Bot Only Peer, T test\n", "\n", @@ -3833,7 +10258,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 230, "metadata": {}, "outputs": [], "source": [ @@ -3842,9 +10267,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 231, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 bots:\n", + "1. metac-o1-preview\n", + "2. metac-o1\n", + "3. pgodzinai\n", + "4. GreeneiBot2\n", + "5. manticAI\n", + "6. acm_bot\n", + "7. metac-Gemini-Exp-1206\n", + "8. SynapseSeer\n", + "9. metac-claude-3-5-sonnet-latest\n", + "10. twsummerbot\n" + ] + } + ], "source": [ "# Sort the DataFrame by the lower_bound column in descending order\n", "sorted_df = df_W_bot_only_peer_leaderboard.sort_values(by='lower_bound', ascending=False)\n", @@ -3863,18 +10306,525 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 237, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.75, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> Collected 2 forecasts: [0.85, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.2, 0.1]\n", + " >>> Collected 2 forecasts: [0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.15, 0.35]\n", + " >>> Collected 2 forecasts: [0.25, 0.25]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.15, 0.4]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.02]\n", + " >>> Collected 2 forecasts: [0.15, 0.4]\n", + " >>> Collected 2 forecasts: [0.6, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.97, 0.98]\n", + " >>> Collected 2 forecasts: [0.4, 0.3]\n", + " >>> Collected 2 forecasts: [0.4, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.45]\n", + " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.6, 0.8]\n", + " >>> Collected 2 forecasts: [0.99, 0.9]\n", + " >>> Collected 2 forecasts: [0.97, 0.98]\n", + " >>> Collected 2 forecasts: [0.99, 0.25]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.6, 0.8]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.25, 0.25]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.65]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.82]\n", + " >>> Collected 3 forecasts: [0.75, 0.85, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.1, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.35, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.25, 0.16]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", + " >>> Collected 3 forecasts: [0.15, 0.4, 0.15]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.05]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.125]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", + " >>> Collected 3 forecasts: [0.15, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.6, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", + " >>> Collected 3 forecasts: [0.4, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.35, 0.45, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.6, 0.8, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.25, 0.14]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.6, 0.8, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.25, 0.3925]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.086]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.02, 0.05]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.75, 0.85, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.1, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.15, 0.35, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.25, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.866]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999]\n", + " >>> Collected 4 forecasts: [0.6, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.35, 0.45, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.6, 0.8, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.25, 0.14, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.8, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.25, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.1, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.35, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.25, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.35, 0.45, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.6, 0.8, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.25, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.85, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88]\n", + " >>> Collected 7 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85]\n", + " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3]\n", + " >>> Collected 7 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", + " >>> Collected 8 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15, 0.289]\n", + " >>> Collected 10 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + ] + } + ], "source": [ "# @title Calculate df_bot_team_forecasts\n", "\n", "df_bot_team_forecasts = pd.merge(\n", " df_bot_forecasts,\n", - " df_pro_bot_resolved_questions[['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max']],\n", + " df_pro_bot_resolved_questions[['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max', 'open_lower_bound', 'open_upper_bound']],\n", " on='bot_question_id',\n", " how='left'\n", ")\n", @@ -3882,7 +10832,7 @@ "# KEEP ONLY ROWS WHERE PRO_QUESTION_ID IS NA\n", "df_bot_team_forecasts = df_bot_team_forecasts[~df_bot_team_forecasts['pro_question_id'].isna()]\n", "\n", - "columns_to_keep = ['bot_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max'] + top_10_bots\n", + "columns_to_keep = ['bot_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max', 'open_lower_bound', 'open_upper_bound'] + top_10_bots\n", "\n", "# Filter the DataFrame to keep only the specified columns\n", "df_bot_team_forecasts = df_bot_team_forecasts[columns_to_keep]\n", @@ -3898,7 +10848,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 238, "metadata": {}, "outputs": [], "source": [ @@ -3908,18 +10858,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 239, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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typeoptionsresolutionmetac-o1-previewmedian_forecast_5_botsmedian_forecast_8_bots
0multiple_choice[0, 1, 2-3, 4-6, >6]0[0.014083333333333333,0.6016666666666668,0.178...0.0145050.082463
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" + ], + "text/plain": [ + " type options resolution \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] 0 \n", + "1 numeric NaN 86.82 \n", + "2 binary NaN no \n", + "3 multiple_choice [0-4, 5-9, >9] 5-9 \n", + "4 numeric NaN 119.2 \n", + ".. ... ... ... \n", + "342 binary NaN yes \n", + "351 binary NaN no \n", + "355 binary NaN yes \n", + "361 binary NaN no \n", + "364 binary NaN no \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.1 \n", + "3 [0.7,0.25,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + ".. ... \n", + "342 0.9 \n", + "351 0.9 \n", + "355 0.9 \n", + "361 0.85 \n", + "364 0.05 \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.085 \n", + "3 0.5125 \n", + "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", + ".. ... \n", + "342 0.9 \n", + "351 0.65 \n", + "355 0.85 \n", + "361 0.8 \n", + "364 0.05 \n", + "\n", + " median_forecast_8_bots \n", + "0 0.082463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.1 \n", + "3 0.5 \n", + "4 [0.0, 0.002036555585714286, 0.0040770089428571... \n", + ".. ... \n", + "342 0.9025 \n", + "351 0.3585 \n", + "355 0.775 \n", + "361 0.755 \n", + "364 0.041 \n", + "\n", + "[99 rows x 6 columns]" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_bot_team_forecasts[['type', 'options', 'resolution', 'metac-o1-preview', 'median_forecast_5_bots', 'median_forecast_8_bots']]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 240, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum of weights: 95.0, Number of questions: 99\n" + ] + } + ], "source": [ "# Sanity check\n", "a = df_bot_team_forecasts['question_weight'].sum()\n", @@ -3929,7 +11082,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 241, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3937,7 +11090,22 @@ "id": "3-FedHpWV_1v", "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, - "outputs": [], + "outputs": [ + { + "ename": "NotImplementedError", + "evalue": "Havent decided how to handle null forecasts or anulled resolutions", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[241], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# @title Calculate the baseline scores for each team size\u001b[39;00m\n\u001b[1;32m 3\u001b[0m teams \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_1_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_2_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_3_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_9_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_10_bots\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m weighted_scores \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_weighted_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_bot_team_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mteams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Print nicely - round to 2 decimal places and first column should be just an integer (bot team size)\u001b[39;00m\n\u001b[1;32m 17\u001b[0m weighted_scores_print \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(weighted_scores)\u001b[38;5;241m.\u001b[39mreset_index()\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:448\u001b[0m, in \u001b[0;36mcalculate_weighted_scores\u001b[0;34m(df_bot_team_forecasts, teams)\u001b[0m\n\u001b[1;32m 445\u001b[0m forecast \u001b[38;5;241m=\u001b[39m row[team]\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 448\u001b[0m weighted_score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_baseline_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 449\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 450\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mq_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_upper_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_upper_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_lower_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_lower_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 458\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 459\u001b[0m team_scores[team] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m weighted_score\n\u001b[1;32m 461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mIndexError\u001b[39;00m):\n\u001b[1;32m 462\u001b[0m \u001b[38;5;66;03m# @Check: Does skipping introduce any problems?\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:61\u001b[0m, in \u001b[0;36mcalculate_baseline_score\u001b[0;34m(forecast, resolution, q_type, options, range_min, range_max, question_weight, open_upper_bound, open_lower_bound)\u001b[0m\n\u001b[1;32m 59\u001b[0m question_type \u001b[38;5;241m=\u001b[39m _determine_question_type(q_type, resolution)\n\u001b[1;32m 60\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(question_type, resolution, range_min, range_max)\n\u001b[0;32m---> 61\u001b[0m prob_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m baseline_prob \u001b[38;5;241m=\u001b[39m _determine_baseline(\n\u001b[1;32m 65\u001b[0m question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound\n\u001b[1;32m 66\u001b[0m )\n\u001b[1;32m 67\u001b[0m divisor \u001b[38;5;241m=\u001b[39m _determine_divisor_for_baseline_score(question_type, options)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:153\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(q_type, forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 150\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(q_type, resolution, range_min, range_max)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m forecast \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m resolution \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 154\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHavent decided how to handle null forecasts or anulled resolutions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(forecast) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mForecast is empty\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNotImplementedError\u001b[0m: Havent decided how to handle null forecasts or anulled resolutions" + ] + } + ], "source": [ "# @title Calculate the baseline scores for each team size\n", "\n", diff --git a/functions.py b/functions.py index 806cca9..8e894df 100644 --- a/functions.py +++ b/functions.py @@ -432,32 +432,34 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - resolution = row["Resolution"] - options = row["Options"] - range_min = row["Range_min"] - range_max = row["Range_max"] - question_weight = row["Question_weight"] - open_upper_bound = row["Open_upper_bound"] - open_lower_bound = row["Open_lower_bound"] + resolution = row["resolution"] + options = row["options"] + range_min = row["range_min"] + range_max = row["range_max"] + question_weight = row["question_weight"] + open_upper_bound = row["open_upper_bound"] + open_lower_bound = row["open_lower_bound"] + question_type = row["type"] for team in teams: forecast = row[team] try: weighted_score = calculate_baseline_score( - forecast, - resolution, - options, - range_min, - range_max, - question_weight, + forecast=forecast, + resolution=resolution, + q_type=question_type, + options=options, + range_min=range_min, + range_max=range_max, + question_weight=question_weight, open_upper_bound=open_upper_bound, open_lower_bound=open_lower_bound, ) team_scores[team] += weighted_score except (ValueError, TypeError, IndexError): - # @Ben: Does skipping introduce any problems? + # @Check: Does skipping introduce any problems? continue # Be robust to bad/missing data return pd.Series(team_scores) @@ -1243,19 +1245,28 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str): +def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str) -> float: + question_type = row["type"] + forecast_a = row[ col_a ] if isinstance(forecast_a, str): forecast_a = [float(x) for x in forecast_a.strip('[]').split(',')] + elif isinstance(forecast_a, float) and math.isnan(forecast_a): + return np.nan forecast_b = row[col_b] if isinstance(forecast_b, str): forecast_b = [float(x) for x in forecast_b.strip('[]').split(',')] + elif isinstance(forecast_b, float) and math.isnan(forecast_b): + return np.nan options = row["options_parsed"] if "options_parsed" in row else row["options"] resolution = row["resolution"] + if resolution == "annulled" or resolution == "ambiguous": + return np.nan + question_type = row["type"] if question_type == "binary": if resolution == "yes": @@ -1270,8 +1281,12 @@ def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, co elif question_type == "multiple_choice": resolution = resolution elif question_type == "numeric": - if not isinstance(resolution, float): + if resolution == "above_upper_bound" or resolution == "below_lower_bound": + resolution = resolution + elif not isinstance(resolution, float): resolution = float(resolution) + else: + raise ValueError(f"Unknown resolution type: {resolution}") else: raise ValueError(f"Unknown question type: {question_type}") @@ -1289,6 +1304,7 @@ def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, co question_weight = float(question_weight) score = calculate_peer_score( + q_type=question_type, forecast=forecast_a, forecast_for_other_users=[forecast_b], resolution=resolution, diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 17f548c..9c027a3 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -metac-perplexity,18.1,18.1,18.1,18.1,18.1 -acm_bot,17.7,17.7,17.7,17.7,17.7 -bot_median,17.0,17.0,17.0,17.0,17.0 -metac-o1,16.6,16.6,16.6,16.6,16.6 -metac-claude-3-5-sonnet-20240620,14.8,14.8,14.8,14.8,14.8 -manticAI,14.5,14.5,14.5,14.5,14.5 -twsummerbot,14.3,14.3,14.3,14.3,14.3 -jkraybill_bot,14.3,14.3,14.3,14.3,14.3 -metac-exa,13.0,13.0,13.0,13.0,13.0 -GreeneiBot2,12.2,12.2,12.2,12.2,12.2 -NextWorldLab,11.1,11.1,11.1,11.1,11.1 -metac-Llama-3.1,10.5,10.5,10.5,10.5,10.5 -Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 -SynapseSeer,10.2,10.2,10.2,10.2,10.2 -metac-claude-3-5-sonnet-latest,10.0,10.0,10.0,10.0,10.0 -mmBot,9.7,9.7,9.7,9.7,9.7 -annabot,9.0,9.0,9.0,9.0,9.0 -VeritasAI,8.4,8.4,8.4,8.4,8.4 -metac-grok-2-1212,8.2,8.2,8.2,8.2,8.2 -laylaps,7.6,7.6,7.6,7.6,7.6 -metac-Gemini-Exp-1206,7.4,7.4,7.4,7.4,7.4 -metac-o1-preview,6.7,6.7,6.7,6.7,6.7 -cookics_bot_TEST,6.3,6.3,6.3,6.3,6.3 -metac-deepseek-r1,5.7,5.7,5.7,5.7,5.7 -MWG,5.5,5.5,5.5,5.5,5.5 -ajf-bot,5.1,5.1,5.1,5.1,5.1 -metac-gpt-4o,4.8,4.8,4.8,4.8,4.8 -pgodzinai,3.5,3.5,3.5,3.5,3.5 -KevinTestBot,3.3,3.3,3.3,3.3,3.3 -InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 -Bot_Pepa,2.6,2.6,2.6,2.6,2.6 -CumulativeBot,2.5,2.5,2.5,2.5,2.5 -swingswish,2.4,2.4,2.4,2.4,2.4 -wunderplumb,2.4,2.4,2.4,2.4,2.4 -jonahsingerbot,2.2,2.2,2.2,2.2,2.2 -bean_bot,2.1,2.1,2.1,2.1,2.1 -X_bot,1.9,1.9,1.9,1.9,1.9 -CatrachoCaster,1.8,1.8,1.8,1.8,1.8 -RPM_bot,1.2,1.2,1.2,1.2,1.2 -4Shadower,0.6,0.6,0.6,0.6,0.6 -krm-bot,0.6,0.6,0.6,0.6,0.6 -andrewsiah,0.0,0.0,0.0,0.0,0.0 cobyj-bot,0.0,0.0,0.0,0.0,0.0 -pianobot,-2.2,-2.2,-2.2,-2.2,-2.2 -ProfessorSP,-3.0,-3.0,-3.0,-3.0,-3.0 -minefrac1,-3.0,-3.0,-3.0,-3.0,-3.0 +andrewsiah,0.0,0.0,0.0,0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 +jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 +bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 +swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 +KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 +SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 +Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 +pianobot,-0.1,-0.1,-0.0,-0.0,0.0 +CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 +krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 +4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 +annabot,-0.1,-0.1,-0.1,-0.0,-0.0 +cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 +jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 +twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 +MWG,-0.2,-0.2,-0.1,-0.0,-0.0 +ProfessorSP,-0.2,-0.2,-0.1,-0.1,-0.0 +GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 +ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +acm_bot,-0.3,-0.2,-0.1,0.0,0.1 +Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-o1,-0.3,-0.2,-0.1,-0.0,0.1 +metac-perplexity,-0.3,-0.2,-0.1,0.0,0.1 +laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 +metac-deepseek-r1,-0.3,-0.2,-0.2,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.3,-0.2,-0.0,0.0 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 +bot_median,-0.4,-0.3,-0.2,-0.1,0.0 +minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 +metac-grok-2-1212,-0.4,-0.4,-0.2,-0.1,-0.0 +pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-claude-3-5-sonnet-latest,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.5,-0.4,-0.3,-0.1,-0.1 +metac-exa,-0.5,-0.4,-0.3,-0.2,-0.1 +InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 5e73739..49d442c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -metac-perplexity,1719.7,95.0,18.1,3.570999300115835e-15,3.663767977230083e-16,4.940951081399963e+16,1.9847501794262088,18.1,18.1,1.0,0.000000 -acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 -bot_median,1610.4,95.0,17.0,3.570999300115835e-15,3.663767977230083e-16,4.626691199221798e+16,1.9847501794262088,17.0,17.0,1.0,0.000000 -metac-o1,1577.6,95.0,16.6,3.570999300115835e-15,3.663767977230083e-16,4.532462410721762e+16,1.9847501794262088,16.6,16.6,1.0,0.000000 -metac-claude-3-5-sonnet-20240620,1405.9,95.0,14.8,3.570999300115835e-15,3.663767977230083e-16,4.039353684227144e+16,1.9847501794262088,14.8,14.8,1.0,0.000000 -manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 -twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -metac-exa,1233.6,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.088709959185136e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 -GreeneiBot2,1163.2,95.0,12.2,0.0,0.0,inf,1.9847501794262088,12.2,12.2,1.0,0.000000 -NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 -metac-Llama-3.1,997.0,95.0,10.5,1.7854996500579174e-15,1.8318839886150415e-16,5.728815548098371e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 -Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 -SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 -metac-claude-3-5-sonnet-latest,949.9,95.0,10.0,0.0,0.0,inf,1.9847501794262088,10.0,10.0,1.0,0.000000 -mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 -annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 -VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 -metac-grok-2-1212,775.1,95.0,8.2,0.0,0.0,inf,1.9847501794262088,8.2,8.2,1.0,0.000000 -laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 -metac-Gemini-Exp-1206,701.9,95.0,7.4,8.927498250289587e-16,9.159419943075207e-17,8.065986188688938e+16,1.9847501794262088,7.4,7.4,1.0,0.000000 -metac-o1-preview,633.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.277309325504542e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 -cookics_bot_TEST,596.4,95.0,6.3,0.0,0.0,inf,1.9847501794262088,6.3,6.3,1.0,0.000000 -metac-deepseek-r1,545.5,95.0,5.7,8.927498250289587e-16,9.159419943075207e-17,6.2687228856570984e+16,1.9847501794262088,5.7,5.7,1.0,0.000000 -MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 -ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 -metac-gpt-4o,451.6,95.0,4.8,8.927498250289587e-16,9.159419943075207e-17,5.190357943531163e+16,1.9847501794262088,4.8,4.8,1.0,0.000000 -pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 -KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 -InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 -Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 -CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 -swingswish,229.1,95.0,2.4,4.463749125144793e-16,4.579709971537604e-17,5.265549431654757e+16,1.9847501794262088,2.4,2.4,1.0,0.000000 -wunderplumb,225.4,95.0,2.4,4.463749125144793e-16,4.579709971537604e-17,5.180942325472045e+16,1.9847501794262088,2.4,2.4,1.0,0.000000 -jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.894510648634918e+16,1.9847501794262088,2.2,2.2,1.0,0.000000 -bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 -X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 -CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 -RPM_bot,118.6,95.0,1.2,4.463749125144793e-16,4.579709971537604e-17,2.7264857831745884e+16,1.9847501794262088,1.2,1.2,1.0,0.000000 -4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 -krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 -andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA -cobyj-bot,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA -pianobot,-206.5,95.0,-2.2,4.463749125144793e-16,4.579709971537604e-17,-4.745304957283875e+16,1.9847501794262088,-2.2,-2.2,0.0,0.000000 -ProfessorSP,-280.4,95.0,-3.0,8.927498250289587e-16,9.159419943075207e-17,-3.2229421543642156e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 -minefrac1,-283.9,95.0,-3.0,4.463749125144793e-16,4.579709971537604e-17,-6.524423956604449e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 +cobyj-bot,0.0,0.0,,,,,,,,,NA +andrewsiah,0.0,0.0,,,,,,,,,NA +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 +RPM_bot,-1.1,7.0,-0.2,0.824531966811415,0.3116437903151381,-0.5234058432057136,2.4469118511449692,0.6,-0.9,0.3097258948590483,0.619452 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calculate_peer_score( forecast: ForecastType, forecast_for_other_users: list[ForecastType], resolution: ResolutionType, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, ) -> float: + question_type = _determine_question_type(q_type, resolution) + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) forecast_for_resolution = _determine_probability_for_resolution( - forecast, resolution, options, range_min, range_max + question_type, forecast, resolution, options, range_min, range_max ) other_user_forecasts = [ _determine_probability_for_resolution( - forecast, resolution, options, range_min, range_max + question_type, forecast, resolution, options, range_min, range_max ) for forecast in forecast_for_other_users ] @@ -32,43 +41,10 @@ def calculate_peer_score( return peer_score * question_weight -def nominal_location_to_cdf_location( - nominal_location: float, - range_min: float, - range_max: float, - zero_point: float | None = None, -) -> float: - """ - Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to - metaculus's "internal representation" range [0, 1] incorporating question scaling - 0.8 would incidate the nomial locatoin is at cdf index 201 * 0.8 - Values higher/lower than 0 and 1 are resolutions that are above/below the upper/lower bound - """ - assert isinstance(zero_point, float | None) - - # TODO: Make sure to use datetime.fromisoformat(nominal_location).timestamp() if you start using date questions - scaled_location = float(nominal_location) - - # Unscale the value to put it into the range [0,1] - if zero_point is not None: - # logarithmically scaled question - deriv_ratio = (range_max - zero_point) / (range_min - zero_point) - unscaled_location = ( - np.log( - (scaled_location - range_min) * (deriv_ratio - 1) - + (range_max - range_min) - ) - - np.log(range_max - range_min) - ) / np.log(deriv_ratio) - else: - # linearly scaled question - unscaled_location = (scaled_location - range_min) / (range_max - range_min) - return unscaled_location - - def calculate_baseline_score( forecast: ForecastType, resolution: ResolutionType, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, @@ -80,13 +56,15 @@ def calculate_baseline_score( Question type can be infered from resolution type Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ + question_type = _determine_question_type(q_type, resolution) + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) prob_for_resolution = _determine_probability_for_resolution( - forecast, resolution, options, range_min, range_max + question_type, forecast, resolution, options, range_min, range_max ) baseline_prob = _determine_baseline( - resolution, options, range_min, range_max, open_upper_bound, open_lower_bound + question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound ) - divisor = _determine_divisor_for_baseline_score(resolution, options) + divisor = _determine_divisor_for_baseline_score(question_type, options) if prob_for_resolution <= 0 or baseline_prob <= 0: raise ValueError( "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" @@ -100,6 +78,7 @@ def calculate_baseline_score( def _determine_baseline( + question_type: QuestionType, resolution: ResolutionType, options: list[str] | None = None, range_min: float | None = None, @@ -107,21 +86,22 @@ def _determine_baseline( open_upper_bound: bool | None = None, open_lower_bound: bool | None = None, ) -> float: - is_binary = isinstance(resolution, bool) - is_multiple_choice = isinstance(resolution, str) - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - - if is_binary: + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) + if question_type == QuestionType.BINARY: baseline_prob = 0.5 - elif is_multiple_choice: + elif question_type == QuestionType.MULTIPLE_CHOICE: if options is None: raise ValueError("Options are required for multiple choice questions") baseline_prob = 1 / len(options) - elif is_numeric: + elif question_type == QuestionType.NUMERIC: if open_upper_bound is None or open_lower_bound is None: raise ValueError("Open upper bound and lower bound are required for numeric questions") - # @Check: Which version is correct? + if range_min is None or range_max is None: + raise ValueError("Range min and range max are required for numeric questions") + if not isinstance(resolution, float): + raise ValueError("Resolution must be a float for numeric questions") + # @Check: Which version is correct? # Version 1: resolved_outside_bounds = False assert range_min is not None and range_max is not None and resolution is not None, f"These need to be not None: Range min: {range_min}, range max: {range_max}, resolution: {resolution}" @@ -156,6 +136,7 @@ def _determine_baseline( def _determine_probability_for_resolution( + q_type: QuestionType, forecast: ForecastType, resolution: ResolutionType, options: list[str] | None = None, @@ -166,15 +147,7 @@ def _determine_probability_for_resolution( Returns a 0 to 1 probability for the resolution Also returns the baseline probability used in baseline scoring """ - - if resolution == "above_upper_bound" or resolution == "below_lower_bound": - raise ValueError( - "'above_upper_bound' or 'below_lower_bound' format not supported" - ) # This is an old resolution type in Q4 2024 - - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - is_binary = isinstance(resolution, bool) - is_multiple_choice = isinstance(resolution, str) + resolution = _normalize_resolution(q_type, resolution, range_min, range_max) if forecast is None or resolution is None: raise NotImplementedError( @@ -184,18 +157,20 @@ def _determine_probability_for_resolution( if len(forecast) == 0: raise ValueError("Forecast is empty") - if not is_numeric and any(p <= 0 or p >= 1 for p in forecast): + if not q_type == QuestionType.NUMERIC and any(p <= 0 or p >= 1 for p in forecast): raise ValueError("Forecast contains probabilities outside of 0 to 1 range") - if is_binary: + if q_type == QuestionType.BINARY: + assert isinstance(resolution, bool) prob_for_resolution = _binary_resolution_prob(forecast, resolution) - elif is_multiple_choice: + elif q_type == QuestionType.MULTIPLE_CHOICE: + assert isinstance(resolution, str) if options is None: raise ValueError("Options are required for multiple choice questions") prob_for_resolution = _multiple_choice_resolution_prob( forecast, resolution, options ) - elif is_numeric: + elif q_type == QuestionType.NUMERIC: if range_min is None or range_max is None: raise ValueError( "Range min and range max are required for numeric questions" @@ -278,19 +253,78 @@ def _numeric_resolution_prob( def _determine_divisor_for_baseline_score( - resolution: ResolutionType, options: list[str] | None = None + question_type: QuestionType, options: list[str] | None = None ) -> float: - is_binary = isinstance(resolution, bool) - is_multiple_choice = isinstance(resolution, str) - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - - if is_binary: + if question_type == QuestionType.BINARY: return np.log(2) - elif is_multiple_choice: + elif question_type == QuestionType.MULTIPLE_CHOICE: if options is None: raise ValueError("Options are required for multiple choice questions") return np.log(len(options)) - elif is_numeric: + elif question_type == QuestionType.NUMERIC: return 2 else: raise ValueError("Unknown question type") + +def nominal_location_to_cdf_location( + nominal_location: float, + range_min: float, + range_max: float, + zero_point: float | None = None, +) -> float: + """ + Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to + metaculus's "internal representation" range [0, 1] incorporating question scaling + 0.8 would incidate the nomial locatoin is at cdf index 201 * 0.8 + Values higher/lower than 0 and 1 are resolutions that are above/below the upper/lower bound + """ + assert isinstance(zero_point, float | None) + + # TODO: Make sure to use datetime.fromisoformat(nominal_location).timestamp() if you start using date questions + scaled_location = float(nominal_location) + + # Unscale the value to put it into the range [0,1] + if zero_point is not None: + # logarithmically scaled question + deriv_ratio = (range_max - zero_point) / (range_min - zero_point) + unscaled_location = ( + np.log( + (scaled_location - range_min) * (deriv_ratio - 1) + + (range_max - range_min) + ) + - np.log(range_max - range_min) + ) / np.log(deriv_ratio) + else: + # linearly scaled question + unscaled_location = (scaled_location - range_min) / (range_max - range_min) + return unscaled_location + +def _normalize_resolution(question_type: QuestionType, resolution: ResolutionType, range_min: float | None, range_max: float | None) -> ResolutionType: + if resolution == "annulled" or resolution == "ambiguous": + return None + + if question_type == QuestionType.NUMERIC: + if range_min is None or range_max is None: + raise ValueError("Range min and range max are required for numeric questions") + if resolution == "above_upper_bound": + return range_max + 0.1 + elif resolution == "below_lower_bound": + return range_min - 0.1 + else: + return resolution + else: + return resolution + + +def _determine_question_type(question_type: Literal["binary", "multiple_choice", "numeric"] | None, resolution: ResolutionType) -> QuestionType: + if question_type is None: + if isinstance(resolution, bool): + return QuestionType.BINARY + elif isinstance(resolution, float) or isinstance(resolution, int) or resolution == "above_upper_bound" or resolution == "below_lower_bound": + return QuestionType.NUMERIC + elif isinstance(resolution, str): + return QuestionType.MULTIPLE_CHOICE + else: + raise ValueError(f"Cannot infer question type from resolution. Please provide a question type. Resolution: {resolution}") + else: + return QuestionType(question_type) From 9037133e2bbaefd6423500d98384acde3e7ff795 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 07:20:53 -0600 Subject: [PATCH 16/26] unified peer and head to head functions --- AI_BENCHMARKING_ANALYSIS.ipynb | 4467 +++++++++++------ functions.py | 270 +- .../bootstrapped_h2h_bot_vs_pros.csv | 34 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 38 +- refactored_notebook/scoring.py | 4 +- tests/test_scoring.py | 15 +- 6 files changed, 3175 insertions(+), 1653 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 92e1549..dc8f1ff 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 277, "metadata": { "id": "ISzIoto4hnoG" }, @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 278, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 279, "metadata": {}, "outputs": [ { @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 280, "metadata": {}, "outputs": [ { @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 281, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 282, "metadata": {}, "outputs": [ { @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 283, "metadata": {}, "outputs": [ { @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 284, "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ " dtype='object')" ] }, - "execution_count": 184, + "execution_count": 284, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 285, "metadata": {}, "outputs": [ { @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 286, "metadata": {}, "outputs": [ { @@ -306,7 +306,7 @@ "dtype: object" ] }, - "execution_count": 186, + "execution_count": 286, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 287, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 288, "metadata": {}, "outputs": [ { @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 289, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 290, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 291, "metadata": {}, "outputs": [ { @@ -445,7 +445,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -466,7 +466,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -487,7 +487,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -508,7 +508,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -529,7 +529,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -551,12 +551,19 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "5 31268 darkives \n", + "6 31268 datscilly \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -594,7 +601,7 @@ "6 False " ] }, - "execution_count": 191, + "execution_count": 291, "metadata": {}, "output_type": "execute_result" } @@ -605,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 292, "metadata": {}, "outputs": [], "source": [ @@ -628,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 293, "metadata": {}, "outputs": [ { @@ -648,7 +655,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 193, + "execution_count": 293, "metadata": {}, "output_type": "execute_result" } @@ -660,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 294, "metadata": {}, "outputs": [ { @@ -703,6 +710,15 @@ " 1.738353\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 8.520428\n", + " 3220.892206\n", + " 409\n", + " 5.620668\n", + " 1.475108\n", + " \n", + " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -712,15 +728,6 @@ " 2.298000\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 6.860987\n", - " 2593.590381\n", - " 409\n", - " 3.788648\n", - " 1.562899\n", - " \n", - " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -745,15 +752,15 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "15 bot_median 8.520428 3220.892206 409 5.620668 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "15 bot_median 6.860987 2593.590381 409 3.788648 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", + "15 1.475108 \n", "4 2.298000 \n", - "15 1.562899 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -869,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 295, "metadata": { "id": "BmAFBHIhK77X" }, @@ -918,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 296, "metadata": {}, "outputs": [ { @@ -1342,7 +1349,7 @@ " np.int64(35705)}" ] }, - "execution_count": 196, + "execution_count": 296, "metadata": {}, "output_type": "execute_result" } @@ -1363,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 297, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1413,7 +1420,7 @@ " \n", " 3\n", " bot_median\n", - " 8567.705563\n", + " 8766.210698\n", " \n", " \n", " 4\n", @@ -1434,7 +1441,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8567.705563\n", + "3 bot_median 8766.210698\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1540,7 +1547,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 298, "metadata": {}, "outputs": [ { @@ -1559,7 +1566,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 299, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1603,13 +1610,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3504.379897\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2974.983652\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -1839,8 +1846,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2974.983652\n", + "2 bot_median 3504.379897\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -1887,7 +1894,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 199, + "execution_count": 299, "metadata": {}, "output_type": "execute_result" } @@ -1929,7 +1936,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 300, "metadata": {}, "outputs": [], "source": [ @@ -1948,7 +1955,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 301, "metadata": {}, "outputs": [], "source": [ @@ -1957,7 +1964,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 302, "metadata": {}, "outputs": [ { @@ -1978,7 +1985,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 303, "metadata": {}, "outputs": [ { @@ -2027,7 +2034,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -2048,7 +2055,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -2069,7 +2076,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -2090,7 +2097,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -2111,7 +2118,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -2133,12 +2140,19 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "5 31268 darkives \n", + "6 31268 datscilly \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -2176,7 +2190,7 @@ "6 False " ] }, - "execution_count": 203, + "execution_count": 303, "metadata": {}, "output_type": "execute_result" } @@ -2187,7 +2201,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 304, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2251,12 +2265,12 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", - " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", - " [0.009900990099009901,0.39603960396039606,0.44...\n", - " 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\n", + "\n", + " pianobot swingswish \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " twsummerbot \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 [0.116,0.42,0.464] \n", + "4 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\n", + "\n", + " wunderplumb \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", "[5 rows x 57 columns]" ] @@ -2491,7 +2526,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.95\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2515,8 +2550,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2563,7 +2598,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2587,9 +2622,9 @@ " False\n", " False\n", " ...\n", - " 0.02\n", + " 0.1\n", + " 0.05\n", " 0.05\n", - " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2619,11 +2654,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.4 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", + "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -2695,7 +2730,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 305, "metadata": {}, "outputs": [ { @@ -2718,7 +2753,7 @@ " dtype='object')" ] }, - "execution_count": 205, + "execution_count": 305, "metadata": {}, "output_type": "execute_result" } @@ -2729,7 +2764,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 306, "metadata": {}, "outputs": [ { @@ -2739,7 +2774,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 206, + "execution_count": 306, "metadata": {}, "output_type": "execute_result" } @@ -2754,7 +2789,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 307, "metadata": {}, "outputs": [], "source": [ @@ -2766,7 +2801,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 308, "metadata": {}, "outputs": [], "source": [ @@ -2775,7 +2810,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 309, "metadata": {}, "outputs": [ { @@ -2836,9 +2871,9 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", - " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2860,9 +2895,9 @@ " True\n", " True\n", " ...\n", + " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 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0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 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[0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.1 \n", - "3 [0.7,0.25,0.05] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...] \n", - "2 0.1 \n", - "3 [0.15000000000000002,0.54,0.31000000000000005] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...] \n", + "2 0.15 \n", + "3 [0.15,0.45,0.4] \n", + "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3097,7 +3132,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.95\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3121,8 +3156,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3169,7 +3204,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3193,9 +3228,9 @@ " False\n", " False\n", " ...\n", - " 0.02\n", + " 0.1\n", + " 0.05\n", " 0.05\n", - " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3225,11 +3260,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.4 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", + "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3289,7 +3324,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 310, "metadata": {}, "outputs": [ { @@ -3363,7 +3398,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 311, "metadata": {}, "outputs": [ { @@ -3424,8 +3459,8 @@ " False\n", " False\n", " ...\n", - " 2.644992\n", - " 5.703782\n", + " 2.343407\n", + " 5.857933\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3448,8 +3483,8 @@ " None\n", " None\n", " ...\n", - " -0.565314\n", - " 0.204794\n", + " 0.390198\n", + " 0.022473\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -3472,7 +3507,7 @@ " False\n", " False\n", " ...\n", - " 0.247562\n", + " 0.298855\n", " 0.096331\n", " NaN\n", " -0.184571\n", @@ -3481,7 +3516,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.112526\n", + " -0.106610\n", " \n", " \n", " 9\n", @@ -3521,7 +3556,7 @@ " None\n", " ...\n", " 0.441833\n", - " 0.510826\n", + " 0.287682\n", " 0.021979\n", " 0.200671\n", " 0.253781\n", @@ -3529,7 +3564,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.325422\n", + " -0.062598\n", " \n", " \n", "\n", @@ -3566,18 +3601,18 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", - "3 -0.565314 0.204794 NaN 0.127833 0.152526 \n", - "6 0.247562 0.096331 NaN -0.184571 0.112526 \n", + "0 2.343407 5.857933 NaN 2.292635 2.703087 \n", + "3 0.390198 0.022473 NaN 0.127833 0.152526 \n", + "6 0.298855 0.096331 NaN -0.184571 0.112526 \n", "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", + "13 0.441833 0.287682 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "0 NaN NaN NaN NaN 5.010635 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN 0.112526 \n", + "6 NaN NaN NaN NaN -0.106610 \n", "9 NaN NaN -0.624154 NaN -0.693147 \n", - "13 NaN NaN NaN NaN -0.325422 \n", + "13 NaN NaN NaN NaN -0.062598 \n", "\n", "[5 rows x 58 columns]" ] @@ -3643,7 +3678,7 @@ " False\n", " False\n", " ...\n", - " -2.879198\n", + " -3.795489\n", " -1.780586\n", " -3.007032\n", " -2.879198\n", @@ -3652,7 +3687,7 @@ " NaN\n", " -2.348570\n", " -2.409195\n", - " -3.795489\n", + " -3.390024\n", " \n", " \n", " 82\n", @@ -3667,8 +3702,8 @@ " None\n", " None\n", " ...\n", - " -0.656780\n", - " -0.300105\n", + " -0.993252\n", + " -0.186776\n", " -0.523248\n", " 0.105361\n", " 0.259511\n", @@ -3676,7 +3711,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.656780\n", + " 0.276509\n", " \n", " \n", " 83\n", @@ -3691,8 +3726,8 @@ " None\n", " None\n", " ...\n", - " -1.321756\n", - " -0.265703\n", + " -0.693147\n", + " -0.182322\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -3716,7 +3751,7 @@ " False\n", " ...\n", " -0.069566\n", - " -0.048289\n", + " -0.102356\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -3739,8 +3774,8 @@ " False\n", " False\n", " ...\n", - " -1.704748\n", - " -1.704748\n", + " -0.606136\n", + " -4.007333\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -3748,7 +3783,7 @@ " -0.480973\n", " NaN\n", " -0.749237\n", - " -0.480973\n", + " -0.200671\n", " \n", " \n", "\n", @@ -3778,25 +3813,25 @@ "92 [0-24, 25-30, 31-49, 50-70, >70] NaN \n", "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", - "81 NaN False False ... -2.879198 \n", - "82 NaN None None ... -0.656780 \n", - "83 NaN None None ... -1.321756 \n", + "81 NaN False False ... -3.795489 \n", + "82 NaN None None ... -0.993252 \n", + "83 NaN None None ... -0.693147 \n", "91 NaN False False ... -0.069566 \n", - "92 NaN False False ... -1.704748 \n", + "92 NaN False False ... -0.606136 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", "81 -1.780586 -3.007032 -2.879198 -3.390024 NaN NaN \n", - "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.265703 NaN -0.182322 NaN NaN NaN \n", - "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "82 -0.186776 -0.523248 0.105361 0.259511 NaN NaN \n", + "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", + "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -4.007333 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -3.795489 \n", - "82 0.276509 -0.644609 -0.656780 \n", + "81 -2.348570 -2.409195 -3.390024 \n", + "82 0.276509 -0.644609 0.276509 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.124829 \n", - "92 NaN -0.749237 -0.480973 \n", + "92 NaN -0.749237 -0.200671 \n", "\n", "[5 rows x 58 columns]" ] @@ -3862,8 +3897,8 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", - " -0.092275\n", + " -0.038208\n", + " -0.149434\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -3871,7 +3906,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.149434\n", + " -0.179287\n", " \n", " \n", " 5\n", @@ -3886,8 +3921,8 @@ " None\n", " None\n", " ...\n", - " -0.251314\n", - " 0.441833\n", + " -0.810930\n", + " 0.200671\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -3911,7 +3946,7 @@ " False\n", " ...\n", " -0.054067\n", - " -0.054067\n", + " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -3958,8 +3993,8 @@ " False\n", " False\n", " ...\n", - " 0.008457\n", - " 0.008457\n", + " -0.045611\n", + " 0.039547\n", " NaN\n", " -0.068083\n", " NaN\n", @@ -3967,7 +4002,7 @@ " NaN\n", " -0.076070\n", " NaN\n", - " -0.076070\n", + " -0.096728\n", " \n", " \n", "\n", @@ -3990,18 +4025,18 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", - "5 -0.251314 0.441833 NaN 0.510826 0.320472 \n", - "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", + "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", + "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", "12 -0.182322 0.000000 NaN 0.054067 -0.057158 \n", - "16 0.008457 0.008457 NaN -0.068083 NaN \n", + "16 -0.045611 0.039547 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "2 NaN NaN NaN NaN -0.149434 \n", + "2 NaN NaN NaN NaN -0.179287 \n", "5 NaN NaN NaN NaN 0.287682 \n", "8 NaN NaN -0.398124 NaN -0.171850 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", - "16 NaN NaN -0.076070 NaN -0.076070 \n", + "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", "[5 rows x 58 columns]" ] @@ -4091,7 +4126,7 @@ " False\n", " False\n", " ...\n", - " -2.251292\n", + " -0.459532\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4164,7 +4199,7 @@ " False\n", " ...\n", " -0.017709\n", - " 0.000000\n", + " -0.017709\n", " NaN\n", " -0.112251\n", " -0.017709\n", @@ -4196,10 +4231,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -2.251292 NaN NaN -0.111226 NaN \n", + "95 -0.459532 NaN NaN -0.111226 NaN \n", "96 -0.074901 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -4223,7 +4258,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 312, "metadata": {}, "outputs": [ { @@ -4264,13 +4299,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3504.379897\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2974.983652\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -4500,8 +4535,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2974.983652\n", + "2 bot_median 3504.379897\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -4548,7 +4583,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 212, + "execution_count": 312, "metadata": {}, "output_type": "execute_result" } @@ -4559,7 +4594,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 313, "metadata": {}, "outputs": [ { @@ -4568,13 +4603,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", - "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" + "mean metac-o1 forecast on questions that resolved yes: 69.0%\n", + "mean metac-o1 forecast on questions that resolved no: 30.0%\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -4643,7 +4678,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 314, "metadata": {}, "outputs": [ { @@ -4700,7 +4735,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 315, "metadata": {}, "outputs": [], "source": [ @@ -4713,7 +4748,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 316, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4775,28 +4810,28 @@ " \n", " 3\n", " 4\n", + " bot_median\n", + " 2456.727963\n", + " 97\n", + " 93.10\n", + " \n", + " \n", + " 4\n", + " 5\n", " acm_bot\n", " 2239.058675\n", " 85\n", " 81.25\n", " \n", " \n", - " 4\n", - " 5\n", + " 5\n", + " 6\n", " metac-claude-3-5-sonnet-20240620\n", " 2018.110211\n", " 95\n", " 91.50\n", " \n", " \n", - " 5\n", - " 6\n", - " bot_median\n", - " 1970.633069\n", - " 97\n", - " 93.10\n", - " \n", - " \n", " 6\n", " 7\n", " manticAI\n", @@ -5133,9 +5168,9 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 acm_bot 2239.058675 85 \n", - "4 5 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", - "5 6 bot_median 1970.633069 97 \n", + "3 4 bot_median 2456.727963 97 \n", + "4 5 acm_bot 2239.058675 85 \n", + "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", "8 9 twsummerbot 1819.064141 62 \n", @@ -5182,9 +5217,9 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 81.25 \n", - "4 91.50 \n", - "5 93.10 \n", + "3 93.10 \n", + "4 81.25 \n", + "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", "8 59.40 \n", @@ -5228,7 +5263,7 @@ "46 52.10 " ] }, - "execution_count": 216, + "execution_count": 316, "metadata": {}, "output_type": "execute_result" } @@ -5297,7 +5332,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 317, "metadata": {}, "outputs": [ { @@ -5378,6 +5413,20 @@ " 0.000036\n", " \n", " \n", + " bot_median\n", + " 2456.7\n", + " 93.1\n", + " 26.4\n", + " 58.198995\n", + " 6.031713\n", + " 4.374886\n", + " 1.985277\n", + " 38.4\n", + " 14.4\n", + " 0.999984\n", + " 0.000032\n", + " \n", + " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5406,20 +5455,6 @@ " 0.001450\n", " \n", " \n", - " bot_median\n", - " 1970.6\n", - " 93.1\n", - " 21.2\n", - " 65.554743\n", - " 6.794058\n", - " 3.115493\n", - " 1.985277\n", - " 34.7\n", - " 7.7\n", - " 0.998776\n", - " 0.002449\n", - " \n", - " \n", " manticAI\n", " 1865.1\n", " 70.4\n", @@ -6002,9 +6037,9 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "bot_median 2456.7 93.1 26.4 58.198995 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", - "bot_median 1970.6 93.1 21.2 65.554743 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", "twsummerbot 1819.1 59.4 30.6 54.747799 \n", @@ -6051,9 +6086,9 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "bot_median 6.031713 4.374886 1.985277 38.4 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", - "bot_median 6.794058 3.115493 1.985277 34.7 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", "twsummerbot 7.103517 4.311100 2.000163 44.8 \n", @@ -6100,9 +6135,9 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", + "bot_median 14.4 0.999984 0.000032 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", - "bot_median 7.7 0.998776 0.002449 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", "twsummerbot 16.4 0.999968 0.000063 \n", @@ -6146,7 +6181,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 217, + "execution_count": 317, "metadata": {}, "output_type": "execute_result" } @@ -6162,7 +6197,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 318, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6235,18 +6270,18 @@ " NA\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", + " RPM_bot\n", + " -0.5\n", + " 7.0\n", " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", + " 0.840163\n", + " 0.317552\n", + " -0.229115\n", + " 2.446912\n", + " 0.7\n", + " -0.8\n", + " 0.413195\n", + " 0.826390\n", " \n", " \n", " jonahsingerbot\n", @@ -6263,6 +6298,20 @@ " 0.007677\n", " \n", " \n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", + " -0.1\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", + " \n", + " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6277,20 +6326,6 @@ " 0.483189\n", " \n", " \n", - " RPM_bot\n", - " -1.1\n", - " 7.0\n", - " -0.2\n", - " 0.824532\n", - " 0.311644\n", - " -0.523406\n", - " 2.446912\n", - " 0.6\n", - " -0.9\n", - " 0.309726\n", - " 0.619452\n", - " \n", - " \n", " CumulativeBot\n", " -1.1\n", " 10.2\n", @@ -6432,17 +6467,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.9\n", + " -6.5\n", " 27.4\n", - " -0.3\n", - " 0.744699\n", - " 0.142267\n", - " -1.764876\n", + " -0.2\n", + " 0.747831\n", + " 0.142866\n", + " -1.667933\n", " 2.049541\n", - " 0.0\n", + " 0.1\n", " -0.5\n", - " 0.044576\n", - " 0.089152\n", + " 0.053575\n", + " 0.107149\n", " \n", " \n", " jkraybill_bot\n", @@ -6501,18 +6536,18 @@ " 0.023289\n", " \n", " \n", - " GreeneiBot2\n", + " metac-o1\n", " -10.4\n", - " 58.4\n", - " -0.2\n", - " 0.849883\n", - " 0.111260\n", - " -1.597976\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.057772\n", - " 0.115544\n", + " 91.1\n", + " -0.1\n", + " 0.931550\n", + " 0.097599\n", + " -1.171004\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.122342\n", + " 0.244685\n", " \n", " \n", " acm_bot\n", @@ -6529,6 +6564,20 @@ " 0.201592\n", " \n", " \n", + " GreeneiBot2\n", + " -10.6\n", + " 58.4\n", + " -0.2\n", + " 0.849331\n", + " 0.111188\n", + " -1.638406\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.053406\n", + " 0.106813\n", + " \n", + " \n", " ajf-bot\n", " -10.9\n", " 34.2\n", @@ -6543,18 +6592,18 @@ " 0.094289\n", " \n", " \n", - " metac-o1\n", - " -11.5\n", - " 91.1\n", + " bot_median\n", + " -11.1\n", + " 92.1\n", " -0.1\n", - " 0.888227\n", - " 0.093060\n", - " -1.360468\n", - " 1.985829\n", + " 0.834391\n", + " 0.086944\n", + " -1.391942\n", + " 1.985550\n", " 0.1\n", " -0.3\n", - " 0.088538\n", - " 0.177076\n", + " 0.083665\n", + " 0.167329\n", " \n", " \n", " Bot_Pepa\n", @@ -6571,20 +6620,6 @@ " 0.023810\n", " \n", " \n", - " metac-perplexity\n", - " -11.9\n", - " 89.1\n", - " -0.1\n", - " 0.993669\n", - " 0.105270\n", - " -1.264731\n", - " 1.986405\n", - " 0.1\n", - " -0.3\n", - " 0.104652\n", - " 0.209303\n", - " \n", - " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6613,6 +6648,20 @@ " 0.006348\n", " \n", " \n", + " metac-deepseek-r1\n", + " -14.1\n", + " 52.1\n", + " -0.3\n", + " 0.817209\n", + " 0.113218\n", + " -2.393750\n", + " 2.005379\n", + " -0.0\n", + " -0.5\n", + " 0.010193\n", + " 0.020386\n", + " \n", + " \n", " manticAI\n", " -14.6\n", " 69.4\n", @@ -6627,39 +6676,39 @@ " 0.011014\n", " \n", " \n", - " metac-deepseek-r1\n", - " -14.6\n", - " 52.1\n", - " -0.3\n", - " 0.731525\n", - " 0.101347\n", - " -2.766689\n", - " 2.005379\n", - " -0.1\n", - " -0.5\n", - " 0.003932\n", - " 0.007864\n", - " \n", - " \n", " metac-Gemini-Exp-1206\n", - " -15.2\n", + " -14.6\n", " 76.5\n", " -0.2\n", - " 0.943797\n", - " 0.107907\n", - " -1.846774\n", + " 0.936930\n", + " 0.107121\n", + " -1.780658\n", " 1.990822\n", " 0.0\n", " -0.4\n", - " 0.034349\n", - " 0.068698\n", + " 0.039496\n", + " 0.078991\n", " \n", " \n", - " NextWorldLab\n", - " -16.9\n", - " 80.2\n", + " metac-perplexity\n", + " -16.1\n", + " 89.1\n", " -0.2\n", - " 0.906964\n", + " 1.069491\n", + " 0.113302\n", + " -1.599489\n", + " 1.986405\n", + " 0.0\n", + " -0.4\n", + " 0.056646\n", + " 0.113292\n", + " \n", + " \n", + " NextWorldLab\n", + " -16.9\n", + " 80.2\n", + " -0.2\n", + " 0.906964\n", " 0.101244\n", " -2.078393\n", " 1.989344\n", @@ -6669,46 +6718,60 @@ " 0.040909\n", " \n", " \n", - " bot_median\n", - " -17.3\n", - " 92.1\n", - " -0.2\n", - " 0.919122\n", - " 0.095773\n", - " -1.963996\n", - " 1.985550\n", - " 0.0\n", - " -0.4\n", - " 0.026290\n", - " 0.052579\n", - " \n", - " \n", " minefrac1\n", - " -19.2\n", + " -18.5\n", " 51.1\n", " -0.4\n", - " 0.880990\n", - " 0.123242\n", - " -3.043641\n", + " 0.878223\n", + " 0.122855\n", + " -2.945421\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.001859\n", - " 0.003717\n", + " 0.002441\n", + " 0.004882\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -19.5\n", + " -20.8\n", " 90.5\n", " -0.2\n", - " 1.009138\n", - " 0.106078\n", - " -2.031065\n", + " 0.985458\n", + " 0.103589\n", + " -2.217659\n", " 1.986072\n", " -0.0\n", " -0.4\n", - " 0.022608\n", - " 0.045215\n", + " 0.014555\n", + " 0.029110\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " -21.0\n", + " 89.1\n", + " -0.2\n", + " 1.131903\n", + " 0.119914\n", + " -1.966710\n", + " 1.986405\n", + " 0.0\n", + " -0.5\n", + " 0.026182\n", + " 0.052364\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -21.7\n", + " 91.1\n", + " -0.2\n", + " 0.867992\n", + " 0.090940\n", + " -2.614756\n", + " 1.985829\n", + " -0.1\n", + " -0.4\n", + " 0.005233\n", + " 0.010466\n", " \n", " \n", " mmBot\n", @@ -6725,32 +6788,18 @@ " 0.002208\n", " \n", " \n", - " metac-grok-2-1212\n", - " -22.9\n", - " 91.1\n", - " -0.3\n", - " 1.048829\n", - " 0.109887\n", - " -2.283528\n", - " 1.985829\n", - " -0.0\n", - " -0.5\n", - " 0.012375\n", - " 0.024750\n", - " \n", - " \n", " pgodzinai\n", - " -23.9\n", + " -23.5\n", " 76.4\n", " -0.3\n", - " 0.956452\n", - " 0.109425\n", - " -2.858686\n", + " 0.973567\n", + " 0.111383\n", + " -2.763550\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.002749\n", - " 0.005498\n", + " 0.003591\n", + " 0.007181\n", " \n", " \n", " VeritasAI\n", @@ -6767,88 +6816,74 @@ " 0.000076\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -24.4\n", - " 91.1\n", - " -0.3\n", - " 0.784315\n", - " 0.082173\n", - " -3.265827\n", - " 1.985829\n", - " -0.1\n", - " -0.4\n", - " 0.000772\n", - " 0.001544\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " -26.1\n", + " metac-exa\n", + " -24.7\n", " 89.1\n", " -0.3\n", - " 0.998799\n", - " 0.105813\n", - " -2.768565\n", + " 0.812195\n", + " 0.086044\n", + " -3.219787\n", " 1.986405\n", " -0.1\n", - " -0.5\n", - " 0.003432\n", - " 0.006863\n", + " -0.4\n", + " 0.000899\n", + " 0.001797\n", " \n", " \n", - " metac-exa\n", - " -26.6\n", - " 89.1\n", + " metac-o1-preview\n", + " -25.5\n", + " 91.1\n", " -0.3\n", - " 0.848974\n", - " 0.089941\n", - " -3.324097\n", - " 1.986405\n", + " 0.849888\n", + " 0.089044\n", + " -3.149214\n", + " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000647\n", - " 0.001294\n", + " 0.001111\n", + " 0.002221\n", " \n", " \n", " InstitutPelFutur\n", " -26.9\n", " 90.1\n", " -0.3\n", - " 0.973767\n", - " 0.102587\n", - " -2.908524\n", + " 0.973971\n", + " 0.102609\n", + " -2.904302\n", " 1.986114\n", " -0.1\n", " -0.5\n", - " 0.002292\n", - " 0.004584\n", + " 0.002320\n", + " 0.004640\n", " \n", " \n", - " metac-o1-preview\n", - " -27.8\n", + " metac-grok-2-1212\n", + " -27.9\n", " 91.1\n", " -0.3\n", - " 0.877434\n", - " 0.091930\n", - " -3.314974\n", + " 1.005409\n", + " 0.105338\n", + " -2.903858\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000661\n", - " 0.001322\n", + " 0.002318\n", + " 0.004635\n", " \n", " \n", " metac-gpt-4o\n", - " -30.5\n", + " -28.8\n", " 91.1\n", " -0.3\n", - " 0.913940\n", - " 0.095754\n", - " -3.492827\n", + " 0.819883\n", + " 0.085900\n", + " -3.676519\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000371\n", - " 0.000743\n", + " 0.000201\n", + " 0.000401\n", " \n", " \n", "\n", @@ -6858,10 +6893,10 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "RPM_bot -0.5 7.0 -0.1 0.840163 0.317552 \n", "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", - "RPM_bot -1.1 7.0 -0.2 0.824532 0.311644 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", @@ -6872,44 +6907,44 @@ "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.9 27.4 -0.3 0.744699 0.142267 \n", + "cookics_bot_TEST -6.5 27.4 -0.2 0.747831 0.142866 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", - "GreeneiBot2 -10.4 58.4 -0.2 0.849883 0.111260 \n", + "metac-o1 -10.4 91.1 -0.1 0.931550 0.097599 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", + "GreeneiBot2 -10.6 58.4 -0.2 0.849331 0.111188 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", - "metac-o1 -11.5 91.1 -0.1 0.888227 0.093060 \n", + "bot_median -11.1 92.1 -0.1 0.834391 0.086944 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-perplexity -11.9 89.1 -0.1 0.993669 0.105270 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", + "metac-deepseek-r1 -14.1 52.1 -0.3 0.817209 0.113218 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-deepseek-r1 -14.6 52.1 -0.3 0.731525 0.101347 \n", - "metac-Gemini-Exp-1206 -15.2 76.5 -0.2 0.943797 0.107907 \n", + "metac-Gemini-Exp-1206 -14.6 76.5 -0.2 0.936930 0.107121 \n", + "metac-perplexity -16.1 89.1 -0.2 1.069491 0.113302 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "bot_median -17.3 92.1 -0.2 0.919122 0.095773 \n", - "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", - "metac-claude-3-5-sonnet-20240620 -19.5 90.5 -0.2 1.009138 0.106078 \n", + "minefrac1 -18.5 51.1 -0.4 0.878223 0.122855 \n", + "metac-claude-3-5-sonnet-20240620 -20.8 90.5 -0.2 0.985458 0.103589 \n", + "metac-Llama-3.1 -21.0 89.1 -0.2 1.131903 0.119914 \n", + "metac-claude-3-5-sonnet-latest -21.7 91.1 -0.2 0.867992 0.090940 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-grok-2-1212 -22.9 91.1 -0.3 1.048829 0.109887 \n", - "pgodzinai -23.9 76.4 -0.3 0.956452 0.109425 \n", + "pgodzinai -23.5 76.4 -0.3 0.973567 0.111383 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-claude-3-5-sonnet-latest -24.4 91.1 -0.3 0.784315 0.082173 \n", - "metac-Llama-3.1 -26.1 89.1 -0.3 0.998799 0.105813 \n", - "metac-exa -26.6 89.1 -0.3 0.848974 0.089941 \n", - "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", - "metac-o1-preview -27.8 91.1 -0.3 0.877434 0.091930 \n", - "metac-gpt-4o -30.5 91.1 -0.3 0.913940 0.095754 \n", + "metac-exa -24.7 89.1 -0.3 0.812195 0.086044 \n", + "metac-o1-preview -25.5 91.1 -0.3 0.849888 0.089044 \n", + "InstitutPelFutur -26.9 90.1 -0.3 0.973971 0.102609 \n", + "metac-grok-2-1212 -27.9 91.1 -0.3 1.005409 0.105338 \n", + "metac-gpt-4o -28.8 91.1 -0.3 0.819883 0.085900 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "bean_bot -4.265106 2.784843 -0.0 \n", + "RPM_bot -0.229115 2.446912 0.7 \n", "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "bean_bot -4.265106 2.784843 -0.0 \n", "X_bot -0.747195 2.446912 0.2 \n", - "RPM_bot -0.523406 2.446912 0.6 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", @@ -6920,44 +6955,44 @@ "krm-bot -3.229846 2.264709 -0.2 \n", "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.764876 2.049541 0.0 \n", + "cookics_bot_TEST -1.667933 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", "MWG -2.589625 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", - "GreeneiBot2 -1.597976 2.000832 0.0 \n", + "metac-o1 -1.171004 1.985829 0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", + "GreeneiBot2 -1.638406 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", - "metac-o1 -1.360468 1.985829 0.1 \n", + "bot_median -1.391942 1.985550 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-perplexity -1.264731 1.986405 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", + "metac-deepseek-r1 -2.393750 2.005379 -0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-deepseek-r1 -2.766689 2.005379 -0.1 \n", - "metac-Gemini-Exp-1206 -1.846774 1.990822 0.0 \n", + "metac-Gemini-Exp-1206 -1.780658 1.990822 0.0 \n", + "metac-perplexity -1.599489 1.986405 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "bot_median -1.963996 1.985550 0.0 \n", - "minefrac1 -3.043641 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-20240620 -2.031065 1.986072 -0.0 \n", + "minefrac1 -2.945421 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.217659 1.986072 -0.0 \n", + "metac-Llama-3.1 -1.966710 1.986405 0.0 \n", + "metac-claude-3-5-sonnet-latest -2.614756 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-grok-2-1212 -2.283528 1.985829 -0.0 \n", - "pgodzinai -2.858686 1.990849 -0.1 \n", + "pgodzinai -2.763550 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-claude-3-5-sonnet-latest -3.265827 1.985829 -0.1 \n", - "metac-Llama-3.1 -2.768565 1.986405 -0.1 \n", - "metac-exa -3.324097 1.986405 -0.1 \n", - "InstitutPelFutur -2.908524 1.986114 -0.1 \n", - "metac-o1-preview -3.314974 1.985829 -0.1 \n", - "metac-gpt-4o -3.492827 1.985829 -0.1 \n", + "metac-exa -3.219787 1.986405 -0.1 \n", + "metac-o1-preview -3.149214 1.985829 -0.1 \n", + "InstitutPelFutur -2.904302 1.986114 -0.1 \n", + "metac-grok-2-1212 -2.903858 1.985829 -0.1 \n", + "metac-gpt-4o -3.676519 1.985829 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "bean_bot -0.2 0.007674 0.015349 \n", + "RPM_bot -0.8 0.413195 0.826390 \n", "jonahsingerbot -0.2 0.003839 0.007677 \n", + "bean_bot -0.2 0.007674 0.015349 \n", "X_bot -0.4 0.241594 0.483189 \n", - "RPM_bot -0.9 0.309726 0.619452 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", @@ -6968,39 +7003,39 @@ "krm-bot -0.9 0.005563 0.011127 \n", "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.044576 0.089152 \n", + "cookics_bot_TEST -0.5 0.053575 0.107149 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", "MWG -0.6 0.007581 0.015163 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", - "GreeneiBot2 -0.4 0.057772 0.115544 \n", + "metac-o1 -0.3 0.122342 0.244685 \n", "acm_bot -0.3 0.100796 0.201592 \n", + "GreeneiBot2 -0.4 0.053406 0.106813 \n", "ajf-bot -0.7 0.047145 0.094289 \n", - "metac-o1 -0.3 0.088538 0.177076 \n", + "bot_median -0.3 0.083665 0.167329 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-perplexity -0.3 0.104652 0.209303 \n", "laylaps -0.4 0.008744 0.017488 \n", "wunderplumb -0.9 0.003174 0.006348 \n", + "metac-deepseek-r1 -0.5 0.010193 0.020386 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-deepseek-r1 -0.5 0.003932 0.007864 \n", - "metac-Gemini-Exp-1206 -0.4 0.034349 0.068698 \n", + "metac-Gemini-Exp-1206 -0.4 0.039496 0.078991 \n", + "metac-perplexity -0.4 0.056646 0.113292 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "bot_median -0.4 0.026290 0.052579 \n", - "minefrac1 -0.6 0.001859 0.003717 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.022608 0.045215 \n", + "minefrac1 -0.6 0.002441 0.004882 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.014555 0.029110 \n", + "metac-Llama-3.1 -0.5 0.026182 0.052364 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.005233 0.010466 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-grok-2-1212 -0.5 0.012375 0.024750 \n", - "pgodzinai -0.5 0.002749 0.005498 \n", + "pgodzinai -0.5 0.003591 0.007181 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.000772 0.001544 \n", - "metac-Llama-3.1 -0.5 0.003432 0.006863 \n", - "metac-exa -0.5 0.000647 0.001294 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 \n", - "metac-o1-preview -0.5 0.000661 0.001322 \n", - "metac-gpt-4o -0.5 0.000371 0.000743 " + "metac-exa -0.4 0.000899 0.001797 \n", + "metac-o1-preview -0.5 0.001111 0.002221 \n", + "InstitutPelFutur -0.5 0.002320 0.004640 \n", + "metac-grok-2-1212 -0.5 0.002318 0.004635 \n", + "metac-gpt-4o -0.5 0.000201 0.000401 " ] }, - "execution_count": 218, + "execution_count": 318, "metadata": {}, "output_type": "execute_result" } @@ -7026,7 +7061,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 319, "metadata": {}, "outputs": [], "source": [ @@ -7036,7 +7071,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 320, "metadata": { "cellView": "form", "colab": { @@ -7950,7 +7985,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 220, + "execution_count": 320, "metadata": {}, "output_type": "execute_result" } @@ -7989,7 +8024,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 321, "metadata": {}, "outputs": [], "source": [ @@ -7999,7 +8034,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 322, "metadata": {}, "outputs": [ { @@ -8204,7 +8239,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 222, + "execution_count": 322, "metadata": {}, "output_type": "execute_result" } @@ -8215,7 +8250,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 323, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8277,7 +8312,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 324, "metadata": {}, "outputs": [ { @@ -8699,7 +8734,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 325, "metadata": { "cellView": "form", "colab": { @@ -8749,160 +8784,160 @@ " \n", " \n", " metac-o1\n", - " 6.2\n", - " 7.4\n", - " 9.7\n", - " 11.8\n", + " 6.1\n", + " 7.2\n", + " 9.6\n", + " 11.9\n", " 13.1\n", " \n", " \n", " metac-o1-preview\n", - " 3.1\n", + " 3.7\n", " 5.3\n", " 8.3\n", - " 11.1\n", - " 12.8\n", + " 11.3\n", + " 12.7\n", " \n", " \n", " manticAI\n", - " 0.2\n", - " 2.1\n", - " 5.6\n", - " 8.8\n", + " 0.0\n", + " 2.2\n", + " 5.7\n", + " 8.9\n", " 10.6\n", " \n", " \n", " metac-Gemini-Exp-1206\n", " 0.6\n", - " 1.9\n", - " 5.2\n", - " 8.1\n", - " 9.4\n", + " 2.2\n", + " 4.9\n", + " 7.8\n", + " 9.3\n", " \n", " \n", " acm_bot\n", " 0.1\n", " 1.7\n", - " 4.6\n", - " 7.5\n", - " 8.9\n", + " 4.7\n", + " 7.6\n", + " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -1.7\n", - " 0.4\n", + " -1.6\n", + " 0.2\n", " 4.2\n", - " 8.0\n", - " 9.6\n", + " 7.9\n", + " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -1.2\n", - " 0.7\n", + " -1.4\n", + " 0.6\n", " 4.0\n", - " 7.1\n", - " 8.9\n", + " 7.3\n", + " 9.0\n", " \n", " \n", " twsummerbot\n", - " 0.2\n", - " 1.4\n", - " 3.8\n", - " 6.1\n", - " 7.3\n", + " 0.3\n", + " 1.6\n", + " 3.7\n", + " 6.2\n", + " 7.4\n", + " \n", + " \n", + " pgodzinai\n", + " -3.8\n", + " -1.0\n", + " 3.1\n", + " 7.1\n", + " 9.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.1\n", + " -0.3\n", " 1.0\n", - " 3.0\n", - " 5.1\n", + " 3.1\n", + " 5.0\n", " 6.1\n", " \n", " \n", - " pgodzinai\n", - " -3.5\n", - " -1.4\n", - " 2.9\n", - " 6.9\n", - " 8.7\n", - " \n", - " \n", " CumulativeBot\n", - " -0.3\n", - " 0.9\n", - " 2.7\n", + " -0.2\n", + " 0.8\n", + " 2.6\n", " 4.4\n", " 5.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.1\n", - " 0.1\n", - " 2.6\n", - " 5.1\n", - " 6.4\n", - " \n", - " \n", " SynapseSeer\n", " 0.4\n", " 1.1\n", - " 2.6\n", - " 4.0\n", + " 2.5\n", + " 4.1\n", + " 4.9\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -1.4\n", + " 0.1\n", + " 2.4\n", " 4.9\n", + " 6.1\n", " \n", " \n", " jkraybill_bot\n", - " -3.9\n", - " -1.8\n", - " 1.7\n", + " -3.4\n", + " -1.7\n", + " 1.8\n", " 4.9\n", - " 6.3\n", + " 6.2\n", " \n", " \n", " metac-exa\n", - " -5.3\n", - " -2.8\n", + " -4.6\n", + " -2.3\n", " 1.6\n", - " 5.4\n", - " 7.8\n", + " 5.5\n", + " 7.7\n", " \n", " \n", " metac-deepseek-r1\n", - " -1.7\n", + " -2.0\n", " -0.8\n", " 1.3\n", - " 3.5\n", - " 4.6\n", + " 3.4\n", + " 4.4\n", " \n", " \n", " MWG\n", - " -1.5\n", - " -0.7\n", + " -1.7\n", + " -0.8\n", " 0.7\n", - " 2.2\n", - " 2.8\n", + " 2.1\n", + " 2.9\n", " \n", " \n", " andrewsiah\n", " -0.9\n", " -0.6\n", - " -0.0\n", + " 0.0\n", " 0.6\n", - " 1.0\n", + " 0.9\n", " \n", " \n", " cobyj-bot\n", " -1.4\n", " -0.9\n", " -0.0\n", - " 0.8\n", - " 1.3\n", + " 0.9\n", + " 1.4\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", @@ -8918,194 +8953,194 @@ " \n", " annabot\n", " -3.5\n", - " -2.3\n", - " -0.4\n", - " 1.3\n", - " 2.2\n", + " -2.4\n", + " -0.5\n", + " 1.1\n", + " 2.0\n", " \n", " \n", " bean_bot\n", - " -3.1\n", - " -2.2\n", + " -2.9\n", + " -2.1\n", " -0.5\n", - " 1.1\n", - " 1.7\n", + " 1.3\n", + " 2.1\n", " \n", " \n", " KevinTestBot\n", - " -4.3\n", - " -2.8\n", - " -0.6\n", - " 1.4\n", + " -4.0\n", + " -2.6\n", + " -0.5\n", + " 1.5\n", " 2.6\n", " \n", " \n", - " jonahsingerbot\n", - " -3.0\n", + " CatrachoCaster\n", " -2.2\n", + " -1.7\n", " -0.8\n", - " 0.4\n", - " 1.0\n", + " 0.2\n", + " 0.7\n", " \n", " \n", - " CatrachoCaster\n", + " jonahsingerbot\n", + " -2.8\n", " -2.3\n", - " -1.7\n", " -0.8\n", - " 0.2\n", - " 0.8\n", + " 0.5\n", + " 1.2\n", " \n", " \n", " krm-bot\n", - " -3.5\n", - " -2.6\n", - " -0.9\n", - " 0.7\n", + " -3.4\n", + " -2.5\n", + " -1.0\n", + " 0.8\n", " 1.6\n", " \n", " \n", " ProfessorSP\n", " -4.5\n", - " -3.4\n", - " -1.2\n", - " 1.0\n", - " 2.2\n", + " -3.3\n", + " -1.0\n", + " 1.0\n", + " 1.9\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.6\n", + " -6.4\n", " -4.9\n", " -1.6\n", - " 1.7\n", - " 3.5\n", + " 1.8\n", + " 3.1\n", " \n", " \n", - " 4Shadower\n", - " -4.8\n", - " -3.6\n", - " -1.7\n", - " 0.3\n", - " 1.2\n", + " mmBot\n", + " -7.3\n", + " -5.5\n", + " -1.6\n", + " 2.2\n", + " 3.9\n", " \n", " \n", - " mmBot\n", - " -7.8\n", - " -5.7\n", + " 4Shadower\n", + " -5.0\n", + " -3.8\n", " -1.7\n", - " 2.1\n", - " 4.2\n", + " 0.2\n", + " 1.2\n", " \n", " \n", " swingswish\n", - " -5.2\n", - " -4.0\n", - " -1.9\n", - " -0.2\n", - " 0.6\n", + " -5.4\n", + " -4.2\n", + " -2.0\n", + " -0.1\n", + " 0.9\n", " \n", " \n", " RPM_bot\n", - " -4.8\n", - " -3.8\n", + " -4.9\n", + " -3.9\n", " -2.0\n", " -0.7\n", " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", - " -8.8\n", - " -6.6\n", - " -2.1\n", - " 2.0\n", - " 4.0\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.5\n", + " -4.8\n", + " -2.0\n", + " 0.8\n", + " 2.7\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.8\n", - " -5.0\n", - " -2.1\n", - " 0.9\n", - " 2.2\n", + " InstitutPelFutur\n", + " -9.2\n", + " -6.7\n", + " -2.2\n", + " 1.6\n", + " 4.0\n", " \n", " \n", " wunderplumb\n", - " -6.0\n", - " -4.7\n", - " -2.5\n", - " -0.3\n", + " -6.5\n", + " -5.1\n", + " -2.6\n", + " -0.2\n", " 0.7\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.4\n", + " -6.9\n", + " -5.3\n", " -2.7\n", - " 0.0\n", - " 1.5\n", + " -0.1\n", + " 1.4\n", " \n", " \n", " NextWorldLab\n", - " -8.9\n", - " -6.9\n", + " -8.6\n", + " -6.7\n", " -3.6\n", - " -0.5\n", - " 0.9\n", + " -0.6\n", + " 1.0\n", " \n", " \n", - " laylaps\n", - " -10.1\n", - " -8.1\n", + " Bot_Pepa\n", + " -7.0\n", + " -5.9\n", " -3.8\n", - " -0.1\n", - " 1.6\n", + " -1.9\n", + " -1.0\n", " \n", " \n", - " Bot_Pepa\n", - " -7.2\n", - " -6.0\n", - " -3.9\n", - " -2.0\n", - " -0.9\n", + " laylaps\n", + " -9.7\n", + " -7.7\n", + " -4.0\n", + " -0.1\n", + " 2.2\n", " \n", " \n", " VeritasAI\n", " -7.7\n", - " -6.4\n", - " -4.3\n", - " -2.0\n", - " -0.8\n", + " -6.6\n", + " -4.2\n", + " -1.8\n", + " -0.5\n", " \n", " \n", " minefrac1\n", - " -8.0\n", - " -6.7\n", + " -7.9\n", + " -6.8\n", " -4.6\n", - " -2.6\n", - " -1.5\n", + " -2.5\n", + " -1.7\n", " \n", " \n", " Grizeu_Bot\n", - " -9.2\n", + " -9.0\n", " -7.6\n", " -5.0\n", - " -2.3\n", + " -2.2\n", " -0.6\n", " \n", " \n", " metac-gpt-4o\n", " -10.6\n", - " -9.1\n", - " -5.7\n", + " -8.9\n", + " -6.0\n", " -2.9\n", - " -1.4\n", + " -1.6\n", " \n", " \n", " ajf-bot\n", " -14.6\n", - " -12.4\n", - " -8.3\n", + " -12.6\n", + " -8.5\n", " -4.4\n", - " -2.0\n", + " -2.4\n", " \n", " \n", "\n", @@ -9113,54 +9148,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.2 7.4 9.7 11.8 13.1\n", - "metac-o1-preview 3.1 5.3 8.3 11.1 12.8\n", - "manticAI 0.2 2.1 5.6 8.8 10.6\n", - "metac-Gemini-Exp-1206 0.6 1.9 5.2 8.1 9.4\n", - "acm_bot 0.1 1.7 4.6 7.5 8.9\n", - "metac-perplexity -1.7 0.4 4.2 8.0 9.6\n", - "GreeneiBot2 -1.2 0.7 4.0 7.1 8.9\n", - "twsummerbot 0.2 1.4 3.8 6.1 7.3\n", - "cookics_bot_TEST 0.1 1.0 3.0 5.1 6.1\n", - "pgodzinai -3.5 -1.4 2.9 6.9 8.7\n", - "CumulativeBot -0.3 0.9 2.7 4.4 5.4\n", - "metac-claude-3-5-sonnet-latest -1.1 0.1 2.6 5.1 6.4\n", - "SynapseSeer 0.4 1.1 2.6 4.0 4.9\n", - "jkraybill_bot -3.9 -1.8 1.7 4.9 6.3\n", - "metac-exa -5.3 -2.8 1.6 5.4 7.8\n", - "metac-deepseek-r1 -1.7 -0.8 1.3 3.5 4.6\n", - "MWG -1.5 -0.7 0.7 2.2 2.8\n", - "andrewsiah -0.9 -0.6 -0.0 0.6 1.0\n", - "cobyj-bot -1.4 -0.9 -0.0 0.8 1.3\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "metac-o1 6.1 7.2 9.6 11.9 13.1\n", + "metac-o1-preview 3.7 5.3 8.3 11.3 12.7\n", + "manticAI 0.0 2.2 5.7 8.9 10.6\n", + "metac-Gemini-Exp-1206 0.6 2.2 4.9 7.8 9.3\n", + "acm_bot 0.1 1.7 4.7 7.6 8.8\n", + "metac-perplexity -1.6 0.2 4.2 7.9 9.5\n", + "GreeneiBot2 -1.4 0.6 4.0 7.3 9.0\n", + "twsummerbot 0.3 1.6 3.7 6.2 7.4\n", + "pgodzinai -3.8 -1.0 3.1 7.1 9.4\n", + "cookics_bot_TEST -0.3 1.0 3.1 5.0 6.1\n", + "CumulativeBot -0.2 0.8 2.6 4.4 5.4\n", + "SynapseSeer 0.4 1.1 2.5 4.1 4.9\n", + "metac-claude-3-5-sonnet-latest -1.4 0.1 2.4 4.9 6.1\n", + "jkraybill_bot -3.4 -1.7 1.8 4.9 6.2\n", + "metac-exa -4.6 -2.3 1.6 5.5 7.7\n", + "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.4\n", + "MWG -1.7 -0.8 0.7 2.1 2.9\n", + "andrewsiah -0.9 -0.6 0.0 0.6 0.9\n", + "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", - "annabot -3.5 -2.3 -0.4 1.3 2.2\n", - "bean_bot -3.1 -2.2 -0.5 1.1 1.7\n", - "KevinTestBot -4.3 -2.8 -0.6 1.4 2.6\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", - "CatrachoCaster -2.3 -1.7 -0.8 0.2 0.8\n", - "krm-bot -3.5 -2.6 -0.9 0.7 1.6\n", - "ProfessorSP -4.5 -3.4 -1.2 1.0 2.2\n", - "metac-grok-2-1212 -6.6 -4.9 -1.6 1.7 3.5\n", - "4Shadower -4.8 -3.6 -1.7 0.3 1.2\n", - "mmBot -7.8 -5.7 -1.7 2.1 4.2\n", - "swingswish -5.2 -4.0 -1.9 -0.2 0.6\n", - "RPM_bot -4.8 -3.8 -2.0 -0.7 -0.1\n", - "InstitutPelFutur -8.8 -6.6 -2.1 2.0 4.0\n", - "metac-claude-3-5-sonnet-20240620 -6.8 -5.0 -2.1 0.9 2.2\n", - "wunderplumb -6.0 -4.7 -2.5 -0.3 0.7\n", - "metac-Llama-3.1 -6.7 -5.4 -2.7 0.0 1.5\n", - "NextWorldLab -8.9 -6.9 -3.6 -0.5 0.9\n", - "laylaps -10.1 -8.1 -3.8 -0.1 1.6\n", - "Bot_Pepa -7.2 -6.0 -3.9 -2.0 -0.9\n", - "VeritasAI -7.7 -6.4 -4.3 -2.0 -0.8\n", - "minefrac1 -8.0 -6.7 -4.6 -2.6 -1.5\n", - "Grizeu_Bot -9.2 -7.6 -5.0 -2.3 -0.6\n", - "metac-gpt-4o -10.6 -9.1 -5.7 -2.9 -1.4\n", - "ajf-bot -14.6 -12.4 -8.3 -4.4 -2.0" + "annabot -3.5 -2.4 -0.5 1.1 2.0\n", + "bean_bot -2.9 -2.1 -0.5 1.3 2.1\n", + "KevinTestBot -4.0 -2.6 -0.5 1.5 2.6\n", + "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", + "jonahsingerbot -2.8 -2.3 -0.8 0.5 1.2\n", + "krm-bot -3.4 -2.5 -1.0 0.8 1.6\n", + "ProfessorSP -4.5 -3.3 -1.0 1.0 1.9\n", + "metac-grok-2-1212 -6.4 -4.9 -1.6 1.8 3.1\n", + "mmBot -7.3 -5.5 -1.6 2.2 3.9\n", + "4Shadower -5.0 -3.8 -1.7 0.2 1.2\n", + "swingswish -5.4 -4.2 -2.0 -0.1 0.9\n", + "RPM_bot -4.9 -3.9 -2.0 -0.7 -0.1\n", + "metac-claude-3-5-sonnet-20240620 -6.5 -4.8 -2.0 0.8 2.7\n", + "InstitutPelFutur -9.2 -6.7 -2.2 1.6 4.0\n", + "wunderplumb -6.5 -5.1 -2.6 -0.2 0.7\n", + "metac-Llama-3.1 -6.9 -5.3 -2.7 -0.1 1.4\n", + "NextWorldLab -8.6 -6.7 -3.6 -0.6 1.0\n", + "Bot_Pepa -7.0 -5.9 -3.8 -1.9 -1.0\n", + "laylaps -9.7 -7.7 -4.0 -0.1 2.2\n", + "VeritasAI -7.7 -6.6 -4.2 -1.8 -0.5\n", + "minefrac1 -7.9 -6.8 -4.6 -2.5 -1.7\n", + "Grizeu_Bot -9.0 -7.6 -5.0 -2.2 -0.6\n", + "metac-gpt-4o -10.6 -8.9 -6.0 -2.9 -1.6\n", + "ajf-bot -14.6 -12.6 -8.5 -4.4 -2.4" ] }, - "execution_count": 225, + "execution_count": 325, "metadata": {}, "output_type": "execute_result" } @@ -9183,7 +9218,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 326, "metadata": { "cellView": "form", "colab": { @@ -9252,6 +9287,14 @@ " 0.0\n", " \n", " \n", + " RPM_bot\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", " X_bot\n", " -0.0\n", " -0.0\n", @@ -9276,14 +9319,6 @@ " -0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", - " -0.0\n", - " -0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", " CumulativeBot\n", " -0.0\n", " -0.0\n", @@ -9392,7 +9427,7 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " -0.0\n", " \n", " \n", @@ -9405,19 +9440,19 @@ " \n", " \n", " GreeneiBot2\n", - " -0.2\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " ajf-bot\n", + " metac-o1\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", " 0.0\n", + " 0.1\n", " \n", " \n", " acm_bot\n", @@ -9428,31 +9463,23 @@ " 0.1\n", " \n", " \n", - " Bot_Pepa\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", - " -0.1\n", - " -0.0\n", - " \n", - " \n", - " metac-o1\n", + " ajf-bot\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " metac-perplexity\n", + " bot_median\n", " -0.3\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " 0.1\n", " \n", " \n", - " laylaps\n", + " Bot_Pepa\n", " -0.2\n", " -0.2\n", " -0.1\n", @@ -9465,20 +9492,28 @@ " -0.2\n", " -0.1\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " manticAI\n", - " -0.3\n", + " laylaps\n", " -0.2\n", " -0.2\n", " -0.1\n", + " -0.1\n", " -0.0\n", " \n", " \n", " metac-deepseek-r1\n", " -0.3\n", " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " \n", + " \n", + " manticAI\n", + " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.0\n", @@ -9492,16 +9527,16 @@ " 0.0\n", " \n", " \n", - " NextWorldLab\n", - " -0.3\n", + " metac-perplexity\n", + " -0.4\n", " -0.3\n", " -0.2\n", - " -0.1\n", " -0.0\n", + " 0.0\n", " \n", " \n", - " bot_median\n", - " -0.4\n", + " NextWorldLab\n", + " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -9521,23 +9556,31 @@ " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " -0.4\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", " 0.0\n", " \n", " \n", - " mmBot\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-grok-2-1212\n", - " -0.4\n", + " mmBot\n", " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", " pgodzinai\n", @@ -9551,12 +9594,12 @@ " VeritasAI\n", " -0.4\n", " -0.3\n", - " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " metac-exa\n", " -0.4\n", " -0.4\n", " -0.3\n", @@ -9564,16 +9607,8 @@ " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -0.5\n", + " metac-o1-preview\n", " -0.4\n", - " -0.3\n", - " -0.1\n", - " -0.1\n", - " \n", - " \n", - " metac-exa\n", - " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9588,7 +9623,7 @@ " -0.1\n", " \n", " \n", - " metac-o1-preview\n", + " metac-grok-2-1212\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9611,10 +9646,10 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", @@ -9628,36 +9663,36 @@ "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", + "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.1 -0.0\n", - "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", - "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", + "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", + "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-o1 -0.3 -0.2 -0.1 -0.0 0.1\n", - "metac-perplexity -0.3 -0.2 -0.1 0.0 0.1\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", + "metac-deepseek-r1 -0.3 -0.2 -0.1 -0.1 -0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-deepseek-r1 -0.3 -0.2 -0.2 -0.1 -0.0\n", "metac-Gemini-Exp-1206 -0.3 -0.3 -0.2 -0.0 0.0\n", - "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", - "bot_median -0.4 -0.3 -0.2 -0.1 0.0\n", + "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 -0.0\n", + "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 0.0\n", + "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.0\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-grok-2-1212 -0.4 -0.4 -0.2 -0.1 -0.0\n", "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", - "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", - "metac-claude-3-5-sonnet-latest -0.4 -0.4 -0.3 -0.2 -0.1\n", - "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.1 -0.1\n", - "metac-exa -0.5 -0.4 -0.3 -0.2 -0.1\n", + "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", + "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-o1-preview -0.4 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" ] }, - "execution_count": 226, + "execution_count": 326, "metadata": {}, "output_type": "execute_result" } @@ -9678,7 +9713,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 327, "metadata": {}, "outputs": [], "source": [ @@ -9688,7 +9723,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 328, "metadata": {}, "outputs": [ { @@ -9748,7 +9783,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 329, "metadata": { "cellView": "form", "colab": { @@ -10237,7 +10272,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 229, + "execution_count": 329, "metadata": {}, "output_type": "execute_result" } @@ -10258,7 +10293,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 330, "metadata": {}, "outputs": [], "source": [ @@ -10267,7 +10302,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 331, "metadata": {}, "outputs": [ { @@ -10306,7 +10341,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 332, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10316,15 +10351,15 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.8]\n", + " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.05]\n", - 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" >>> Collected 3 forecasts: [0.6, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", - " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", - " >>> Collected 3 forecasts: [0.4, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.35, 0.45, 0.17]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.6, 0.8, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", - " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.25, 0.14]\n", + " >>> Collected 3 forecasts: [0.25, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.15, 0.25, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.01, 0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.1, 0.7, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", + " >>> Collected 3 forecasts: [0.2, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.25, 0.4166666666666666]\n", " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.7, 0.6, 0.875]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.16]\n", - " >>> Collected 3 forecasts: [0.6, 0.8, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.25, 0.3925]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.086]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", + " >>> Collected 3 forecasts: [0.25, 0.5, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.02, 0.05, 0.086]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.285]\n", + " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.4, 0.35, nan]\n", " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.02, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.75, 0.85, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.6, nan, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.75, 0.7, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.35, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.1, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.15, 0.35, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.25, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.15, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.1, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, 0.15, 0.12]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.866]\n", - " >>> Collected 4 forecasts: [0.1, 0.2, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.918]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999]\n", - " >>> Collected 4 forecasts: [0.6, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.35, 0.45, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.6, 0.8, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.25, 0.14, 0.2]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.25, 0.4, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.01, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.1, 0.7, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2]\n", " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.8, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.25, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.25, 0.5, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.75, 0.35, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.1, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.35, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.25, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.1, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.35, 0.45, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336]\n", " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.6, 0.8, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.25, 0.25, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.25, 0.5, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.85, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.75, 0.35, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325]\n", " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88]\n", - " >>> Collected 7 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 7 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95]\n", + " >>> Collected 7 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85]\n", - " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3]\n", - " >>> Collected 7 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3]\n", + " >>> Collected 7 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", + " >>> Collected 7 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3]\n", + " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan]\n", + " >>> Collected 8 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765]\n", + " >>> Collected 8 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", + " >>> Collected 8 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15]\n", - " >>> Collected 9 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8]\n", + " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75, nan]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15, 0.289]\n", - " >>> Collected 10 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10848,7 +10883,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 333, "metadata": {}, "outputs": [], "source": [ @@ -10858,7 +10893,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 334, "metadata": {}, "outputs": [ { @@ -10896,9 +10931,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", - " 0.014505\n", - " 0.082463\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", + " 0.012671\n", + " 0.097463\n", " \n", " \n", " 1\n", @@ -10906,26 +10941,26 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", " \n", " \n", " 2\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.085\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", + " 0.11\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.7,0.25,0.05]\n", + " [0.15,0.65,0.2]\n", + " 0.6\n", " 0.5125\n", - " 0.5\n", " \n", " \n", " 4\n", @@ -10934,7 +10969,7 @@ " 119.2\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0, 0.0019825503600000003, 0.003970557620000...\n", - " [0.0, 0.002036555585714286, 0.0040770089428571...\n", + " [0.0, 0.0020603651142857148, 0.004124627985714...\n", " \n", " \n", " ...\n", @@ -10951,17 +10986,17 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.9\n", - " 0.9025\n", + " 0.905\n", + " 0.92\n", " \n", " \n", " 351\n", " binary\n", " NaN\n", " no\n", - " 0.9\n", - " 0.65\n", - " 0.3585\n", + " 0.4\n", + " 0.35\n", + " 0.2085\n", " \n", " \n", " 355\n", @@ -10978,8 +11013,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.8\n", - " 0.755\n", + " 0.85\n", + " 0.78\n", " \n", " \n", " 364\n", @@ -10988,7 +11023,7 @@ " no\n", " 0.05\n", " 0.05\n", - " 0.041\n", + " 0.046\n", " \n", " \n", "\n", @@ -11010,48 +11045,48 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.7,0.25,0.05] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", - "351 0.9 \n", + "351 0.4 \n", "355 0.9 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.085 \n", - "3 0.5125 \n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.063 \n", + "3 0.6 \n", "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", ".. ... \n", - "342 0.9 \n", - "351 0.65 \n", + "342 0.905 \n", + "351 0.35 \n", "355 0.85 \n", - "361 0.8 \n", + "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.082463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.1 \n", - "3 0.5 \n", - "4 [0.0, 0.002036555585714286, 0.0040770089428571... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.11 \n", + "3 0.5125 \n", + "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", ".. ... \n", - "342 0.9025 \n", - "351 0.3585 \n", + "342 0.92 \n", + "351 0.2085 \n", "355 0.775 \n", - "361 0.755 \n", - "364 0.041 \n", + "361 0.78 \n", + "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 239, + "execution_count": 334, "metadata": {}, "output_type": "execute_result" } @@ -11062,7 +11097,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 335, "metadata": {}, "outputs": [ { @@ -11082,7 +11117,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 336, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11092,18 +11127,102 @@ }, "outputs": [ { - "ename": "NotImplementedError", - "evalue": "Havent decided how to handle null forecasts or anulled resolutions", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[241], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# @title Calculate the baseline scores for each team size\u001b[39;00m\n\u001b[1;32m 3\u001b[0m teams \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_1_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_2_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_3_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_9_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_10_bots\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m weighted_scores \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_weighted_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_bot_team_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mteams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Print nicely - round to 2 decimal places and first column should be just an integer (bot team size)\u001b[39;00m\n\u001b[1;32m 17\u001b[0m weighted_scores_print \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(weighted_scores)\u001b[38;5;241m.\u001b[39mreset_index()\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:448\u001b[0m, in \u001b[0;36mcalculate_weighted_scores\u001b[0;34m(df_bot_team_forecasts, teams)\u001b[0m\n\u001b[1;32m 445\u001b[0m forecast \u001b[38;5;241m=\u001b[39m row[team]\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 448\u001b[0m weighted_score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_baseline_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 449\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 450\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mq_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_upper_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_upper_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_lower_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_lower_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 458\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 459\u001b[0m team_scores[team] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m weighted_score\n\u001b[1;32m 461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mIndexError\u001b[39;00m):\n\u001b[1;32m 462\u001b[0m \u001b[38;5;66;03m# @Check: Does skipping introduce any problems?\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:61\u001b[0m, in \u001b[0;36mcalculate_baseline_score\u001b[0;34m(forecast, resolution, q_type, options, range_min, range_max, question_weight, open_upper_bound, open_lower_bound)\u001b[0m\n\u001b[1;32m 59\u001b[0m question_type \u001b[38;5;241m=\u001b[39m _determine_question_type(q_type, resolution)\n\u001b[1;32m 60\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(question_type, resolution, range_min, range_max)\n\u001b[0;32m---> 61\u001b[0m prob_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m baseline_prob \u001b[38;5;241m=\u001b[39m _determine_baseline(\n\u001b[1;32m 65\u001b[0m question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound\n\u001b[1;32m 66\u001b[0m )\n\u001b[1;32m 67\u001b[0m divisor \u001b[38;5;241m=\u001b[39m _determine_divisor_for_baseline_score(question_type, options)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:153\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(q_type, forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 150\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(q_type, resolution, range_min, range_max)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m forecast \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m resolution \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 154\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHavent decided how to handle null forecasts or anulled resolutions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(forecast) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mForecast is empty\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mNotImplementedError\u001b[0m: Havent decided how to handle null forecasts or anulled resolutions" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", + "0 1 60.20\n", + "1 2 59.19\n", + "2 3 19.30\n", + "3 4 17.74\n", + "4 5 6.91\n", + "5 6 7.07\n", + "6 7 16.34\n", + "7 8 16.34\n", + "8 9 21.85\n", + "9 10 21.85" + ] + }, + "execution_count": 336, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -11132,9 +11251,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 337, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['metac-o1-preview']" + ] + }, + "execution_count": 337, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Index of top bot team from weighted_scores_print?\n", "winning_bot_team_size = weighted_scores_print.sort_values(by='Weighted_Baseline_Score_for_Bot_Team_Median', ascending=False).head(1)['Bot_Team_Size'].values[0]\n", @@ -11144,16 +11274,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 338, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(424, 47)" + ] + }, + "execution_count": 338, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_bot_forecasts.shape" ] }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 339, "metadata": {}, "outputs": [], "source": [ @@ -11171,106 +11312,468 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_bot_team_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Z3TTBVWoZVzU", - "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" - }, - "outputs": [], - "source": [ - "# @title Weighted team-vs-pro\n", - "\n", - "# We have our top bot team members.\n", - "# Calculate their median forecast on the pro_bot questions.\n", - "# Create df with bot_question_id, forecasts, resolution, weights\n", - "# Calculate the head-to-head score\n", - "\n", - "df_top_bot_forecasts = df_bot_team_forecasts[['bot_question_id', f'median_forecast_{len(top_bot_team)}_bots']]\n", - "df_top_bot_forecasts = df_top_bot_forecasts.rename(columns={f'median_forecast_{len(top_bot_team)}_bots': 'bot_team_median'})\n", - "\n", - "df_pro_median = df_pro_forecasts[['pro_question_id', 'pro_median']]\n", - "\n", - "df_top_bot_pro_forecasts = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_top_bot_forecasts[['bot_question_id', 'bot_team_median']],\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "df_top_bot_pro_forecasts = pd.merge(\n", - " df_top_bot_pro_forecasts,\n", - " df_pro_median,\n", - " on='pro_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# Copy with union (not just overlapping questions)\n", - "df_top_bot_pro_forecasts_all = df_top_bot_pro_forecasts.copy()\n", - "\n", - "# Filter to only those rows where pro_median is not NA\n", - "df_top_bot_pro_forecasts = df_top_bot_pro_forecasts.dropna(subset=['pro_median'])\n", - "\n", - "# Add the head_to_head column\n", - "df_top_bot_pro_forecasts['head_to_head'] = df_top_bot_pro_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'pro_median'), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 340, "metadata": {}, - "outputs": [], - "source": [ - "weighted_total_score = get_weighted_score(df_top_bot_pro_forecasts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 582 - }, - "id": "JlU9zyqn26Rl", - "outputId": "ac54d636-670b-4a8f-aea9-402679efacf9" - }, - "outputs": [], - "source": [ - "plot_head_to_head_distribution(df_top_bot_pro_forecasts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "V1qC4m2VefLe", - "outputId": "2f110b55-caf6-4ea8-9dfe-b746c3e4d892" - }, - "outputs": [], - "source": [ - "df_bot_team_h2h = calculate_t_test(df_top_bot_pro_forecasts, ['head_to_head'])\n", - "\n", - "df_bot_team_h2h" - ] - }, - { + "outputs": [ + { + "data": { + "text/html": [ + "
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0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " median_forecast_2_bots \\\n", + "0 0.205208 \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", + "2 0.1 \n", + "3 0.625 \n", + "4 [0.0, 0.00342857145, 0.00685714285, 0.01028571... \n", + "\n", + " median_forecast_3_bots \\\n", + "0 0.014926 \n", + "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", + "2 0.07 \n", + "3 0.6 \n", + "4 [0.0, 0.0023237670666666666, 0.004652994133333... \n", + "\n", + " median_forecast_4_bots \\\n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.063 \n", + "3 0.61 \n", + "4 [0.0, 0.00219737075, 0.0043988365, 0.006603060... \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.063 \n", + "3 0.6 \n", + "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", + "\n", + " median_forecast_6_bots \\\n", + "0 0.014926 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.07 \n", + "3 0.55625 \n", + "4 [0.0, 0.0019593148500000003, 0.0039231771, 0.0... \n", + "\n", + " median_forecast_7_bots \\\n", + "0 0.097463 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.11 \n", + "3 0.5125 \n", + "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + "\n", + " median_forecast_8_bots \\\n", + "0 0.097463 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.11 \n", + "3 0.5125 \n", + "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + "\n", + " median_forecast_9_bots \\\n", + "0 0.048475 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", + "2 0.15 \n", + "3 0.55625 \n", + "4 [0.0, 0.0022194861375, 0.004442382825, 0.00666... \n", + "\n", + " median_forecast_10_bots \n", + "0 0.048475 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", + "2 0.15 \n", + "3 0.5125 \n", + "4 [0.0, 0.002118648455555556, 0.0042403284999999... \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 340, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_forecasts.head()" + ] + }, + { "cell_type": "code", - "execution_count": null, + "execution_count": 341, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z3TTBVWoZVzU", + "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" + }, + "outputs": [], + "source": [ + "# @title Weighted team-vs-pro\n", + "\n", + "# We have our top bot team members.\n", + "# Calculate their median forecast on the pro_bot questions.\n", + "# Create df with bot_question_id, forecasts, resolution, weights\n", + "# Calculate the head-to-head score\n", + "\n", + "df_top_bot_forecasts = df_bot_team_forecasts[['bot_question_id', f'median_forecast_{len(top_bot_team)}_bots']]\n", + "df_top_bot_forecasts = df_top_bot_forecasts.rename(columns={f'median_forecast_{len(top_bot_team)}_bots': 'bot_team_median'})\n", + "\n", + "df_pro_median = df_pro_forecasts[['pro_question_id', 'pro_median']]\n", + "\n", + "df_top_bot_pro_forecasts = pd.merge(\n", + " df_pro_bot_resolved_questions,\n", + " df_top_bot_forecasts[['bot_question_id', 'bot_team_median']],\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "df_top_bot_pro_forecasts = pd.merge(\n", + " df_top_bot_pro_forecasts,\n", + " df_pro_median,\n", + " on='pro_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# Copy with union (not just overlapping questions)\n", + "df_top_bot_pro_forecasts_all = df_top_bot_pro_forecasts.copy()\n", + "\n", + "# Filter to only those rows where pro_median is not NA\n", + "df_top_bot_pro_forecasts = df_top_bot_pro_forecasts.dropna(subset=['pro_median'])\n", + "\n", + "# Add the head_to_head column\n", + "df_top_bot_pro_forecasts['head_to_head'] = df_top_bot_pro_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'pro_median'), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted Total Score: -30.1253\n" + ] + } + ], + "source": [ + "weighted_total_score = get_weighted_score(df_top_bot_pro_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582 + }, + "id": "JlU9zyqn26Rl", + "outputId": "ac54d636-670b-4a8f-aea9-402679efacf9" + }, + "outputs": [ + { + "data": { + "image/png": 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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse312681.00.010417[0.001,0.62,0.35,0.019,0.01]234.340709234.340709
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0TrueTrue312691.0[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.0013749738,0.0014499743,0.001526641,0.00160...-101.083204-101.083204
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.050.013-3.820805-3.820805
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaNNaNNaN312801.00.65[0.16,0.44,0.4]39.01976439.019764
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0FalseFalse312811.0[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0,0.0005044914,0.0010323506,0.0015847475,0....45.54604145.546041
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" + ], + "text/plain": [ + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max open_upper_bound \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_lower_bound pro_question_id question_weight \\\n", + "0 False 31268 1.0 \n", + "1 True 31269 1.0 \n", + "2 False 31270 1.0 \n", + "3 NaN 31280 1.0 \n", + "4 False 31281 1.0 \n", + "\n", + " bot_team_median \\\n", + "0 0.010417 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 0.65 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", + "2 0.013 -3.820805 \n", + "3 [0.16,0.44,0.4] 39.019764 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "\n", + " weighted_score \n", + "0 234.340709 \n", + "1 -101.083204 \n", + "2 -3.820805 \n", + "3 39.019764 \n", + "4 45.546041 " + ] + }, + "execution_count": 348, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_top_bot_pro_forecasts.head()" ] }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 349, "metadata": {}, "outputs": [], "source": [ @@ -11340,7 +12303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 350, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11349,7 +12312,25 @@ "id": "BjNQ4IND6Ct7", "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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CiEq79tprFaBOnDhRLe1dunRJAWrq1KkW2ysTdF/94UcppT777DMFqDlz5pi3mT6gjR07tsL9HTVqlHJwcFAFBQXF2qtI0D19+nQFqDVr1hQ7/vPPP1eAmj59unmb6QPboEGDih1v2jdv3rwy+//VV18pQN11111lHmvtHJUJug8dOmS1zYkTJ5oDqKImTJhQLDCYO3dusS8iTAwGg/L391e9evWy2B4TE6OOHz9uEeyUxvR7tPbPzs5OzZkzRyUmJlrc54UXXlCAWrx4cbH2du7cWSxwqKmg22AwKG9vb9W6dWuLgNtk/fr1ClDvvPNOmW2X9Jo0vc5MAVXRc9vb2yugWIAQGxurAHXvvfeW63EVDbqVUqpr166qQ4cO5v0dOnRQ3bp1U0opq0H3/v37i712ipo3b54C1JEjR5RS/3z5NWrUqGLHRkdHK1tb23IH3SVZsmSJAlRkZKR5m+k6aNWqlUXgVHTfuHHjytW+Kcj79ttvLbY/8cQTClDr1q0zbzMF3VFRUeVq2xrT+9mMGTPUggUL1L///W81ffp05eXlpQA1evRo87Gm3+djjz1WrJ2YmBgFqI4dOxa7Zg0Gg/lvTdEvs0pSNOhWSqnbbrtNeXt7m4PkESNGKH9/f1VQUGA16E5OTla2trYlBvlvv/22AtSGDRss+t61a9dix2ZmZpqfi/IE3SX59ttvFaBWrVplsd30JUZmZqbF9sLCQmVnZ6d69uxZrvaFEDVPppcLIWqdUoqVK1eyatUqjh49Snp6Okaj0bz/woULVT7HwIEDS9x29Vps0LLJluTgwYO89tpr7Nixg4sXLxZLKJaSklKl5Gim/lib9hwREWHuw9VM0yWLatasGaBNOaxvnJyc6NKli9V9kydP5ssvv+Szzz6jZ8+eAGRkZLBhwwa6dOliMUXzzz//BDBPD76avb09J06csNjWokWLSvXZlG0etEz9CQkJrFu3jscff5xNmzaxf/9+81Tp0n6P/fr1w8nJyervsbpFRUWRmppKSEiIRS4FE1P5t6LPUWVfk1cvobCxsSEgIICcnJxiz7npNVLZ1/f06dN59NFH2bVrFwDHjx+3uuzCxHSdJCYmWs0/YHr8J06coHPnzua109beO1q2bEnz5s3LnS8hKSmJV199lZ9++omYmBhyc3Mt9lt7Drp3746NjWWqnYq+nidPnsyiRYv47LPPzGuqjUYjX3zxBb6+vowcOdJ87F133cV3333H9ddfz6RJkxg6dCgDBw6sVEbt//73v+af3dzc6NChA3fffTcPPfRQsWOtvdeaXheDBw8uNh3dxsaGQYMGceLECQ4ePEjz5s0r1Lfp06ezfv16vv/+ewYNGsT//vc/HnnkkRKXMO3ZsweDwUB+fr7V6+bUqVOAdt3ceuut5uvGVOWgKDc3N7p3717u9dWZmZm88cYbrFu3jjNnzpCdnW2x39p1065dO9zc3Cy2mZbu1Me/A0I0VRJ0CyEqLSgoiBMnThAfH0/79u3Lfb+5c+fy7rvv0rx5c2677TaCg4PNa76ff/558vPzq9w3a+sfTdvS09PLdTzAH3/8wZAhQwC46aabaNu2LW5ubuh0OtatW8ehQ4eq3N+MjAxsbGzw9/e32i+dTmdeY16UKbFZUaa11waDoczzBgUFARAfH1/RLldKQEBAibWjb7rpJgIDA/nqq6944403sLW15ZtvviE3N5fJkydbHHv58mUAXn755Rrvc1E2NjaEhoby0EMPkZCQwMsvv8y7777LM888A2D+HVm7lnQ6HYGBgbXyXJuen2PHjnHs2LESjyv6gb6yr8mSrsHSrs3KZsG/5557eOKJJ8wJ1RwcHLj77rtLPN70PGzcuJGNGzeWeJzpeTC9L1y9Tt8kMDCwXEH35cuX6dOnD7GxsfTv359hw4bh5eWFra0tBw8e5IcffrD6fFb19QzQoUMHevXqxaZNm0hNTcXb25vIyEji4uJ48MEHLQLNO+64g3Xr1vHmm2/y4Ycf8t5776HT6YiIiGDJkiUVykmxa9cui+zlpbH2+ijttQP/fGFj7X2wLLfccguBgYGsWLGCs2fPYjQamT59eonHm66bnTt3snPnzhKPq8h1Ux4FBQWEh4ezf/9+evToweTJk/H19cXOzo7o6Gg++eSTcl83oF075b1uhBA1T4JuIUSl9e/fn8jISDZv3mwOTMuSlJTEe++9R9euXdm1axcuLi7mfRcvXrQ6MlcZiYmJxUbaEhMTAawmcSopGHz55ZfJz8/n999/LzaS8eeff5Y7s3BpPDw8MBqNJCcnF/vglpSUhFKqxA9WVdGnTx8cHBzYu3cvGRkZ5T6HaTTOWn1ba19omJT0HAPY2toyceJEli5dym+//cbw4cP57LPPsLGxYdKkSRbHmvqZkZGBu7t7ufpc3UzJt0yZmYv2KzExkZYtW1ocr5QiMTGxRn6PVzOdY/z48XzzzTdlHl9br8mq8vX1ZfTo0axZswbQEhf6+vqWeLzpebg6aVxJTO8LSUlJVveb3j/K8t///pfY2FhefPFFnn32WYt9r776Kj/88EO52qmsyZMn8+ijj/L1118ze/ZsPvvsM/P2q40ePZrRo0eTmZnJzp07zQn1RowYwYkTJ/Dy8qr2/ll7Hyj62rHm4sWLFsdVhJ2dHffeey9Llizh2LFjXHfddaUmFzOd4/HHH+eNN94os/3qum5++OEH9u/fz4wZM1i+fLnFvq+++opPPvmkXO0IIeonKRkmhKi0qVOnYmtry8cff2yesloS0zf0Z8+eRSnFsGHDLD7cA/z+++/V1jdrbZm29ejRo9ztnDlzBh8fn2IBd05ODvv37y92vCkTb0VGGEz9sTYF0bStIqNO5eXi4sJdd91Fbm6u1ay4Ren1evN0Y1MWZ2ujttam7peXKShYvXo158+fZ9u2bURERBAaGmpxnCngNU0frgupqakAFlOwS/s9/vXXX+Tl5Vn8HitzrZRHhw4d8PDwYO/eveUaVa6t12R1mD59OpmZmWRmZpY6Wgn/XCem6ehlMS1hsPaYY2JiSiwbdrUzZ84AWkB7tdp4PidOnIidnR2rV68mNzeX7777jjZt2pQ6Eu3u7s6IESP4+OOPmTp1KomJifz111813lcT0+ti+/btxco6KqXMZdAq+z44ffp08/KQsq6bPn36oNPpKnzdWCsllpWVVe4lJXV93QghapYE3UKISmvTpg1PPPEEKSkp3HzzzZw7d67YMXl5ebz55pvmtXGmEcA//vjDImCJi4vjqaeeqra+vfjiixajrunp6bz00kvodLpSa7NerWXLlqSmplpM0zUYDMyfP9/qFw0+Pj4A5f6ADpj78/zzz1tMn0xPTzePMlakzxXx8ssv4+/vz8svv8zbb79t8TsxOXz4MOHh4ea+tW/fHnd3d9avX2+eignaiM5LL71U6b707NmTjh078v333/PRRx+hlLI6Ovfggw9iZ2fHww8/bLV+b1paWrHgPzY2lhMnTlRLDe+8vDzef/99AIvSTJMmTcLOzo4333zTYu1lQUEB//rXvwAs6pd7e3uj0+kqdK2Uh52dHQ888AAxMTHMnz/fauB99OhR88hcbb0mq8NNN93EunXrWLduHTfeeGOpx1533XX07duXL7/80jw6XpTRaGTbtm3m2wMGDKBVq1b8+OOPFgGUUoqnn3663F+OmJ7Pq4OwL774gk2bNpWrjaoICAjgpptuYufOnSxdupSMjAzuueeeYsdt377d6mMyXRdllRysTi1atCAiIoJjx44Vq8f+8ccfc/z4cYYMGVLh9dwm1157LT/99BPff/99qUsSQFt2M2HCBP744w9ef/31Yl8CgPYlmum9pEWLFgwaNIjDhw/z+eefWxz3yiuvlHtddUnXzbZt2yxqqwshGiaZXi6EqJKXXnqJvLw8/vOf/9C+fXuGDBlC586dsbe359y5c/z2229cunTJHIwFBwczfvx4vv32W3r37s3QoUNJTEzkxx9/ZOjQoeZv+6uqXbt2dO7c2aJOd1xcHPPmzaN3797lbufhhx/mf//7HwMGDGDChAk4OTkRGRlJfHw84eHhxUY1+/Xrh7OzM0uXLiU1NdW8TvvqaaZFDRo0iIcffph33nnH3GellLnPc+fOtVp3tzo0a9aM//3vf4wZM4ZHHnmE//znPwwdOpTAwEAyMjLYvXs3e/bswcPDw7we1MHBgYcffphXXnmFnj17mqenbtiwgcGDB1fpdzh58mSeeuopXnvtNVxcXMy/v6I6d+7M+++/zwMPPED79u0ZOXIkrVu3JjMzk7Nnz7Jt2zamTp3Khx9+aL7PvffeW6k63b/99ht5eXmAFqRdvHiRn376ibi4OLp3786DDz5oPrZ169YsXryYxx9/nK5duzJhwgRcXV3ZsGEDUVFRjB492iL4cXNzo0+fPmzfvp3JkyfTtm1bbGxsmDx5crHp6RX1/PPPs3//ft5++202btzIoEGDCAgIID4+niNHjnDo0CF27dpFQEBArb0mq4ONjY3VkcCSfPnll0RERHDXXXexdOlSevbsibOzM7GxsezatYvk5GTz79fGxoaPP/6YkSNHMmzYMHOd7i1btpCQkEDXrl05fPhwmeecPHkyixcv5uGHH2br1q20bNmSQ4cOsXnzZsaNG8d3331X6cdfXpMnT2bTpk0sWLAAwGrQPXfuXC5cuMCAAQMICwtDp9OxY8cOdu/ezfXXX281MVhN+uCDDxgwYACzZs1iw4YNdOzYkWPHjrF+/Xr8/f354IMPqtT+iBEjyn3s+++/T1RUFE888QSfffYZ/fr1w8vLi/Pnz7N3715OnTpFQkKCeWbIe++9R//+/bn33ntZt26duU73nj17GDhwYLlGqkeNGkVYWBivvfYaR48epXPnzkRFRfHjjz8yduzYci0VEULUY3WTNF0I0djs2bNHTZ8+XbVp00Y5OzsrR0dHFRYWpiZNmlSs/nVmZqZ6/PHHVVhYmHJ0dFRt27ZVL774oiooKFCAGjx4sMXxlSkZlpubq5544gnVvHlz5eDgoNq3b6/efvvtYuVoylPy55tvvlE9e/ZULi4uys/PT02YMEGdOXPGar+UUmrjxo2qT58+ytnZuVht35Luo5RSK1asUH369FEuLi7KxcVF9enTR61YsaLYcZUp2VWW7OxstXTpUjV48GDl5+en7OzslJeXl+rXr596+eWXi9XtNRgMauHChebnt127duqtt95SZ8+eLbFkWMuWLcvsR2xsrLKxsVGAmjhxYqnH7t69W911110qJCRE2dvbKz8/P9WzZ0/15JNPquPHj1scW9k63Vf/c3V1Vd27d1cvvfRSieXHfvjhBzV48GDl7u6uHB0dVZcuXdSSJUssalabREVFqZEjRyovLy+l0+kq1EcTa3W6lVJKr9erjz76SPXv3195eHgoR0dH1aJFCzVixAj1wQcfWNTXruhr0vR8ltSfkn7X1toqydUlw0pTUp1upbR6988++6zq3LmzcnZ2Vm5ubqpt27Zq0qRJ6rvvvit2/Pbt29WgQYOUs7Oz8vHxUXfccYeKiYmx+phLev84ePCguummm5S3t7dyd3dXgwcPVr/99pvV48t6zVbkOTPJyclRHh4eClD9+vWzesxXX32lJkyYoFq3bq1cXFyUp6en6tatm1q8eHGxElQlKalOtzWm32dp13d0dLSaNm2aCg4OVnZ2dio4OFhNmzZNRUdHl6s/ShUvGVaakup0K6U9h6+99prq1auXcnV1Vc7OzqpVq1ZqzJgx6tNPPy32ej5y5IgaOXKkcnNzU+7u7urmm29WR44csfqeX1qd7vHjxyt/f3/z34CvvvqqxONLuzbK+54rhKgdOqWszJsRQgghhBBCCCFElcmabiGEEEIIIYQQooZI0C2EEEIIIYQQQtQQCbqFEEIIIYQQQogaIkG3EEIIIYQQQghRQyToFkIIIYQQQgghaogE3UIIIYQQQgghRA2RoFsIIZogpRS9evXipptuqtXzrlq1Cp1Ox6pVq2r1vPXRwoUL0el0REZG1nVXRB2YOnUqOp2O6Ojouu6KVQMHDqRv37513Q0hhGgUJOgWQogm6NNPP2X//v288MILdd0V0QCEh4ej0+ms/gsLC7N6H6PRyDvvvEOXLl1wdnbG39+fiRMncvbs2drtfB1p6F8wLVy4kN27d/PVV1/VdVeEEKLBk6BbCCGaGKPRyMKFCxk4cCDXX399XXdHNCALFiwo9u/RRx+1euzs2bOZO3cuSinmzp3LiBEj+O677+jTpw+nTp2q3Y7XQ4sWLeL48eOEhobWdVesGjp0KD179mTBggUopeq6O0LUnjvugF27tJ+NRnj4YWjdGtq0gXffLfl+mzZBz57QvTt07gyffPLPvj17oH9/6NZN279lS/n6smIFdOkCdnawdGnpx/71l9Z+u3YwZAjEx5e9Ly8PevWC9PTy9UdUml1dd0AIIUTt+umnn4iOjuaZZ56p666IBmbhwoXlOm7r1q0sX76cQYMG8euvv+Lg4ADApEmTGDlyJHPmzOGXX36pwZ7Wf8HBwQQHB9d1N0p1zz33MG/ePLZs2cLQoUPrujtC1Lzdu+HyZejXT7u9ejX8/TecPKkFpj16QEQEdOpkeT+l4J57IDISunaF6Gi49loYNw7c3GDsWFi1CoYN09oaNgyiosDZufT+9OoFX38NixaVfpzRCHffDcuWaf174w149FFYu7b0fU5OMHkyLFkCMvOtRslItxBCNDErV65Ep9Mxfvx4q/tjYmKYMWMGoaGhODg40KxZM2bMmEFsbGyxY03TjgsLC1m4cCFhYWE4OjrSrl073n///TL7kp6ejqurK52u/gBzhdFoJCwsDG9vb3Jzc8v9+Pr27Yubmxtubm707du32BTf33//HZ1Ox/Tp0622kZSUhL29Pf3797fYnpmZyYIFC+jUqRPOzs54eXkxfPhwduzYUawN03OTl5fHs88+S+vWrbG3ty8zcF2xYgWjR48mLCwMJycnfHx8GD58OFu3bi12bGRkJDqdjoULF7Jjxw7Cw8Nxd3fHy8uL8ePHc/r06dKfrBqybNkyAF588UVzwA1w8803Ex4ezv/+9z+r11NJli9fTufOnXFycqJ58+Y88cQT5OXlodPpCA8Ptzg2LCysxCnvpt/J1ZRSrFixgv79++Ph4YGLiwu9e/dmxYoVxY7Ny8tjyZIldOvWDU9PT1xdXQkLC2PChAkcOnQI0NZrT5s2DYBp06ZZTMc3KW1Nd3muYbD8/e/du5cbb7wRd3d3PD09GTt2rNW29+/fz+23306LFi1wdHTE39+fPn368PLLLxc79o477gBosFPkhaiwjz6CSZP+ub1mDcyaBba24OMDd94JX35p/b46HaSlaT9nZICvLzg6wqVLkJysBdqgjTZ7ecFPP5Xdn27doEMHsCkjZNu3TxsNj4jQbs+eDRs2aCPZpe0DuOsuLSCXGS01SoJuIYRoQpRSbN26lfbt2+Pt7V1s/8mTJ+nTpw8rVqygV69ePP744/To0YMVK1bQu3dvTp48abXdiRMnsmLFCoYPH86MGTO4fPkyDz30kDn4Komnpyd33XUXf//9N3/88Uex/b/++isxMTHcfffdOJc1IgDMnTuX6dOnEx8fz4wZM5gxYwbx8fFMmzaNRx55xHzcgAEDCAsL49tvvyXP9MGjiC+//BK9Xs/kyZPN2y5fvky/fv144YUX8Pb25v7772f8+PHs27ePiIgI1q1bZ7VP48ePZ9WqVURERPDII4/QqlWrUh/DQw89RGJiIsOGDeOxxx7j1ltvZdeuXQwbNowffvjB6n3+/PNPhg4diqenJw8//DCDBw/m+++/54Ybbii2htq01njq1Kml9sOaL774gldeeYWlS5cSGRmJ0Wi0elxkZCSurq7FvrQAGD58OADbtm0r1zlffPFFZs2aRUpKCrNmzeKOO+5gzZo15oCwqpRS3H333cyYMYPk5GQmTZrEzJkzyc7OZsaMGcyfP9/i+ClTppi3TZs2jTlz5nDDDTfw+++/s2fPHgDGjBnD6NGjARg9erTFdPyylPcaLmrPnj0MGjQIBwcHZs+eTe/evVm3bh3Dhg2zuL4PHjzIDTfcwE8//cSAAQOYN28et99+Oy4uLnz88cfF2m3WrBnNmzdn8+bN5XsyhWjoIiOhaALB2Fho2fKf22Fh2rar6XRagD5unHb8gAHa9HIHB/Dzg+BgbcQatKnmUVHaaHh1ubqf7u7g4QEXLpS+DyAoSBtxP3as+vojilNCCCGajGPHjilA3X333Vb3R0REKEB99NFHFtvfe+89BaghQ4ZYbB88eLACVN++fVV6erp5+4kTJ5SdnZ1q3769xfErV65UgFq5cqV5219//aUANXXq1GL9uf322xWgDh48WOZj27ZtmwJUhw4dVFpamnn75cuXVbt27RSgtm/fbt7+7LPPKkCtWbOmWFu9evVSDg4O6tKlS+ZtkyZNUoBatmyZxbGJiYmqefPmyt/fX+Xm5hZ7brp3727RjsmCBQsUoLZu3Wqx/ezZs8WOvXDhggoJCVFt27a12L5161YFKEB9+OGHFvs+/PBDBahbb73VYrvpdzBlypRi5ymJ6bFc/a9du3Zqz549FsdmZWUpQHXu3NlqW998840C1HPPPVfmeU+dOqXs7OxUaGioSkxMNG9PT09X7du3V4AaPHiwxX1atmypWrZsWerjKOrjjz9WgJo2bZoqKCgwb8/Pz1ejRo1SgNq7d69SSqm0tDSl0+lUr169lF6vt2hHr9er1NRU821r13pRU6ZMUYA6d+6ceVtFr+Giv/+vvvrKov3JkycrQH355ZfmbfPmzVOAWrduXbH+pKSkWO3n2LFjFWD1uhSi0XFwUCop6Z/bnTsr9ccf/9x+7z2lJk8ufr/CQqUGD1Zq2zbt9u7dSgUFKZWcrN0+eFCp4cOV6t5dqbvvVmrIEKXeeqv8/ZoyRan//Kfk/d98o9RNN1lu8/dX6syZ0veZ9Oun1E8/lb8/osJkpFsIIZqQuLg4AAIDA4vti42NZevWrXTs2JFZs2ZZ7Lv//vu59tpr2bJlC+fPny9230WLFuHh4WG+3b59e/r3709UVBSZmZml9um6666jR48erF27loyMDPP25ORk1q9fT58+fejWrVuZj+2TK0lrFi5ciKenp3m7t7e3eYSx6DRZ0yj26tWrLdo5fvw4+/btY+TIkfj4+ACQkpLCmjVrGDJkCDNnzrQ4PiAggP/7v/8jOTmZ3377rVi/nn/+eXM75WFtJDw4OJjx48dz6tQpYmJiiu1v165dsd/ZrFmzaNu2LRs3biQ5Odm8fezYsRw/fpxFZa0RLGL06NH8+OOPxMfHk5OTw99//80jjzzCmTNnuPHGGy2miqdfSchT9HdQlOk6SS9H4p4vvvgCvV7PvHnzCAgIsGjj2WefLXf/S/Puu+/i6urKe++9h729vXm7g4ODecr1l1emk+p0OpRSODk5YXPVdE9bW1u8vLyq1JeKXsMmgwYN4s4777TYZlo6YRp9L8rarBFfX1+rfTK9V5jeO4Ro1Fxc/pl2DdCiBRR9z42O1rZd7eBBbeR40CDtdp8+0KwZHDig3e7WDX7+Wbu9erV2bAnLqirl6n5mZmpr0ENCSt9nkpdX9vpyUSWSSE0IIZqQS5cuAVgNDg4ePAjA4MGDi617tbGxYdCgQZw4cYKDBw/SvHlzi/29evUq1l6zZs0ASEtLw93dvdR+zZ49m/vvv58vvviC+++/H9DKmhUUFBQLJkty4MqHm6vX+AJEXFnLZnqMoAWq1113HT///DMpKSn4+fkB/wThRaeW79mzB4PBQH5+vtU12aZs3CdOnODWW2+12HfdddeVq/8mZ8+eZdGiRWzZsoX4+Hjy8/Mt9l+4cIGWRacKAv379y8WBNrY2NC/f39OnTrFoUOHGHZlPaGnp2eJAXFJHnvsMYvbHTp0YOnSpXh4ePDiiy/yxhtv8Pbbb1eozfIwrZEeOHBgsX3WtlVUTk4OR44cISQkhMWLFxfbX1hYCGi/V9CC/ZEjR7Jp0yZ69uzJHXfcQXh4OH369LEI2CurotewSVmvP5MJEyawdOlSxo4dy5133smNN97IoEGDSs2gXvSLJyEava5dtanfpr9xd9yhrXe+4w4tUF2zBn78sfj9mjeHhAQ4flxbg336NJw5A+3ba/sTErQp5qC15+qqZREHLSN6fHzZydJK06sXFBbC1q3a2u2PPoJRo7REaaXtAzAYtL526VL584sySdAthBBNiGmEy9o6ZtMos7VRcMCcabnoaLRJ0VFuEzs77U+MwWAos1+TJk1i/vz5LF++3Bx0//e//8XNzY2JEyeWeX9Tv2xsbPD39y+2LzAwEJ1OV6zvkydPZvfu3axZs4aHHnoIpRSff/453t7e3HLLLebjLl++DMDOnTvZuXNniX3Izs62eu7yOn36NNdddx0ZGRlEREQwatQoPDw8sLGxITIykm3bthULwks7h2l7eUaVK2P27Nm8+OKLFs+JKaAv6Zym30F5An9TG0VHuU0q8ryWJDU1FaUU8fHxPP/88yUeV/T3unbtWl555RW++OILcwUADw8Ppk2bxiuvvIKLi0ul+1OZa9h0/qtZe/317duXyMhIc/9XrlwJQJ8+fVi8eLE5sC/KlMCwKo9LiAbj9tvhl1/+SXo2ebK2BrttW23d9rx5/wSn69dr/5Yvh8BA+PhjmDBBS3pmNGrBtGlU/OOP4fPPtWRlHTrA999r7YGWHf2aa6z3Z9UqePZZSE2Fdeu0zOMbNmhZ1D/8UBsxf+EF7ZyrV2tJ0vLytFHszz7T2ihtH8COHdrIfAVmZImKk6BbCCGaENOHeVMQWZTpg3tiYqLV+168eNHiuOrk7u7O3XffzUcffcTBgwfJzs7m+PHjzJw5Ezc3t3K14eHhgdFoJDk5uViQlpSUhFKqWN/vuusu5s2bx+rVq3nooYfYvn07MTExzJ49G0dHR4u2AR5//HHeeOONCj02a9myS/Kf//yH1NRUPvvsM+655x6Lfffff3+JycdK+p2Ztld0ZLu8fH190el0FkGpq6srwcHBnDt3DoPBgK2trcV9TLMC2rZtW2b7pn4nJSUVG90v6THb2NhQUFBgdd/VXwSYfq+9evVi7969ZfYHtODzpZde4qWXXuLcuXNs3bqVDz/8kLfeeovc3Fw++uijcrVjTWWu4YoaOHAgP/30E7m5ufz1119s2LCB999/n1tuuYWjR49yzVUf/k3vFda+CBCi0Zk2DW64ARYu1EajbW3hvfesH3vbbdo/k4kTtX/WLFig/bPm8GGwMtMGgKlTtX/WXPmC2qxfP60ta0rb98EH8K9/Wd8nqo2s6RZCiCakU6dO2NjYEBUVVWxf9+7dAdi+fTvqqtIhSim2b99ucVx1mz17NqCVm1q+fDlAuaeWA/To0QPQMmdfzbTt6r77+fkxYsQI/vzzT06fPm2eWn51wNunTx90Oh27du0qd38q48yZMwDmzNcmSqlSR9h37txZLJO40Wjkjz/+QKfTlWtNfGXs3r0bpVSxEl2DBw8mOzvbap9N9bkHmdY+lsLU799//73YPmvbQFv/nJSUhF6vt9ienZ1tDvhN3N3d6dChA8ePH7eYhl1erVq1Yvr06Wzbtg03NzfWr19v3mf6sqE8Mz1MKnMNV5azszPh4eEsWbKEp59+mtzcXH799ddix0VFRWFvb8+1115bLecVol5zc4P//AfOnau9c+7YoWUUrwt5eTB4MNx4Y92cvwmRoFsIIZoQLy8vunbtyt69e4sFaS1atCAiIoJjx44Vq0/88ccfc/z4cYYMGVJsPXd16dGjB3369OHzzz9n7dq1dO3atULroadMmQJoicuKTsFNT083Tx02HVOUae328uXLWbt2La1atSpW6iooKIgJEybwxx9/8Prrrxf7UgLgr7/+Iicnp9z9tcY0mnt13e9XX32Vo0ePlni/kydPFivPtmzZMk6ePMktt9xiMUqZnp7OiRMnSEhIKFefzp07Z3VmRHx8PA8++CCgLQ8o6r777gPgueeesxh1/umnn4iMjOSmm24qNnJtzaRJk7C1teXNN98kKSnJvD0jI4OXXnrJ6n369OlDYWEhn3/+uXmbUoqnnnrK6vT/uXPnkpOTw6xZs6zuP3funLnedXJystXfQ2pqKvn5+TiZ1kjyz1poa4kHS1LZa7i8du3aZXVpiWnWQNH+AxQUFHDgwAF69+4t08tF0zF0KHTuXNe9qB1OTvDAA3XdiyZBppcLIUQTM3bsWBYsWMCff/7JDTfcYLHvgw8+YMCAAcyaNYsNGzbQsWNHjh07xvr16/H39+eDDz6o0b7df//9zJgxA6jYKDdoI6cPP/ww77zzDp07d2b8+PEopfj222+Ji4tj7ty5VkdXR40ahaenJ2+++SaFhYXMnTvX6pTw999/n6ioKJ544gk+++wz+vXrh5eXF+fPn2fv3r2cOnWKhISEKgUn999/PytXrmT8+PFMmDABX19f/vzzT/bv388tt9zCxo0brd5v+PDhzJ07l02bNtGpUyeOHTvGhg0b8PPz46233rI49vvvv2fatGlMmTLFaibsq23bto0HHniAgQMH0qpVK7y9vTl37hwbN24kOzubu+++2yLpHGhJv2bOnMny5cvp2bMnt9xyCwkJCaxZswYfHx/eeeedcj0fbdq04d///jcLFiyga9euTJgwATs7O7799lu6du1qdcbGnDlzWLlyJTNnzuTXX3/F39+f33//nbS0NLp162ZOzmYye/Zs/vzzTz755BN27tzJsGHDCAkJITExkRMnTvDXX3/xxRdfEBYWRnx8PD169KBbt2507dqV0NBQLl26xA8//EBhYaFFTe9+/frh7OzM0qVLSU1NNX/xUVrW9cpew+W1ePFitm7dyqBBg2jVqhVOTk7s37+fzZs3c8011zB27FiL43///Xfy8/MZM2ZMpc8phBACqdMthBBNTXx8vLKzs1MPPPCA1f3R0dFq2rRpKjg4WNnZ2ang4GA1bdo0FR0dXexYa3WPTazVIS6rdnF2drZydHRUzs7OFjWPK2LFihWqT58+ysXFRbm4uKg+ffqoFStWlHqfmTNnmusdR0VFlXhcTk6Oeu2111SvXr2Uq6urcnZ2Vq1atVJjxoxRn376qSosLDQfW9pzo1TJdbq3bt2q+vfvr9zd3ZWXl5caOXKk2rdvn9XjTXWaFyxYoH7//Xc1ePBg5erqqjw8PNTYsWPVqVOnip23onW6Dx06pCZPnqw6duyovLy8lJ2dnfLz81M33XRTsdrQRRkMBvXWW2+pTp06KUdHR+Xr66vuvPNOdfr06XKdt6hly5apjh07KgcHB9WsWTM1f/58lZOTY7VOt1JKbdmyRfXt29d83smTJ6vExMRSfydr1qxRw4YNU97e3sre3l6Fhoaq8PBwtWTJEpV8pdZuamqqWrhwoRo0aJAKDg5WDg4OKiQkRI0YMUL9ZKXG7caNG1WfPn2Us7Oz+foysfb6MCnvNVz093+1c+fOFfs9//zzz+ree+9V7du3V+7u7srNzU117NhRPf300+bHWNTUqVOVg4ODSipat1gIIUSF6ZSyMkdOCCFEozZ58mQ2btxITExMmeW8atPevXvp06cPkydP5tNPP63r7tR7kZGRREREsGDBAqulzBo7nU7H4MGDra6BFlWTmppKy5Ytuf3224stNxFCCFExsqZbCCGaoJdeeonc3NxyT/OtLa+//joAD8gaMyHq1JtvvonBYODFF1+s664IIUSDJ2u6hRCiCWrZsiWffPJJiWWXalNsbCxffPEFx44d4+uvv2b48OH069evrrslRJPm4+PDp59+SmhoaF13RQghGjwJuoUQoomaMGFCXXcBgLNnz/LUU0/h5ubGqFGj+Pjjj+u6S0I0eY899lhdd0EIIRqNerWme/v27bz++uvs27ePhIQEvv/++zIzZkZGRjJv3jyOHTtG8+bNefbZZ5laUhF5IYQQQgghhBCiFtWrNd3Z2dl069aN9957r1zHnzt3jltuuYWIiAgOHjzIo48+ysyZM/nll19quKdCCCGEEEIIIUTZ6tVId1E6na7Mke5//etfbNy4kaNHj5q33XXXXaSlpfHzzz/XQi+FEEIIIYQQQoiSNeg13bt27WLYsGEW24YPH86jjz5a4n3y8/PJz8833zYajVy+fBlfX190Ol1NdVUIIYQQQgghRD2nlCIzM5OQkBBsbKpnYniDDrovXrxIYGCgxbbAwEAyMjLIzc3F2dm52H0WLVrE888/X1tdFEIIIYQQQgjRwJw/f55mzZpVS1sNOuiujKeeeop58+aZb6enp9OiRQvOnTuHl5dX3XVMiGpiNBpJSUnBz8+v2r6dE6IuyTUtGpsqX9NGI1y4ACdPQl4eODhUS7+UgtRUiIuH3Fywb3KfEkXlKPI9FI4ZOkBmjZaHe8xR2n9R9iDgngXrSe08sNrPn5sLISHQq1e1N91g6fV6Pv74YzIzM7GxseGVV17B3d292tpv0G+nQUFBxWrMJiYm4uHhYXWUG8DR0RFHR8di2728vCToFo2C0WikoKAALy8vCVBEoyDXtGhsqnRNZ2fDqVMQHQ0eHtCyZbX0KTcP4s5DfCY4+oOfJ8iqO1EeCkW2bR6uBid0EnSXz7UtcP75A+wuJ1l9xhQ6CgOb4TVyJF62ttV++pQUcHMDCX0sjR49mr179zJkyBBeeeWVal163KA/vfTr14/NmzdbbPv111/p169fHfVICCGEEKIGKAUJCbBnD5w9CwEB4O1dLc0mp8Dfx+D8ee1DuJeXBNxC1CgbWxLvmW91l7oShp9/fCnUQMAt/pGSkkJsbKz5dqdOnbj33nurdYTbpF4F3VlZWRw8eJCDBw8CWkmwgwcPmp+Mp556invvvdd8/P3338/Zs2d54oknOHHiBO+//z5ff/01jz32WF10XwghhBCi+uXnw99/awF3Xh60aAFWZu1VptmzZ7WAOy8PgoKqpVkhRDnkXNsTZVt80nFhYDPOvvYNaUPG1UGvmo4jR46wbNky1qxZQ2Zmpnl7TSXWrlfTy/fu3UtERIT5tmnt9ZQpU1i1ahUJCQkW30a0atWKjRs38thjj/HWW2/RrFkzli9fzvDhw2u970IIIYQQ1S4lBU6cgKQk8PcHF5dqafbyZYiJ0dZwe/uAkwTbQtQqzx0/YmPQA5DeZyg57bqT3Wsw6f1Hygh3DSosLOTnn39m//79AISEhNTKeetV0B0eHk5pZcNXrVpl9T4HDhyowV5pDAYDhYWFNX4eIarKaDRSWFhIXl5emWsF7e3tsZU3diGEqH/0em3d9smTWuK0Zs2q5YN4QQHEX9DWb6ODwECQVAlC1DKl8Nrynflmyu33Y7RzILv7AAm4a1BKSgrffPONOSfYoEGDGDx4cK3ki6lXQXd9pJTi4sWLpKWl1XVXhCgXpRRGo5HMzMxyTZHx8vIiKChI6tQLIUR9kZ4OUVEQH6+t266m9YWpaRATrY1ye3lBCTlnhRA1zOX4PhwvarN3szv0piCoBXYpF+u4V43bkSNH+PHHHykoKMDV1ZVx48ZxzTXX1Nr5JegugyngDggIwMXFRQITUe8ppdDr9djZ2ZV6vSqlyMnJISkpCYDg4ODa6qIQQghrjEaIi9MC7pwcraaPXdU/qhXq4UK8lihNKS0HmwymCVF3vLZ8a/45dej4OuxJ03HmzBkKCgoICwtj3LhxNZIsrTQSdJfCYDCYA25fX9+67o4Q5VLeoBswl9ZLSkoiICBAppoLIURdycnRppLHxICrqzadvBqkp2tNpqSAp2e1LQkXQlSSbfplPPZuBUDv4UNmr/C67VATMXLkSIKCgrjuuuvqpPyoBN2lMK3hdpG/UKIRM13fhYWFEnQLIURtUwouXtRGt1NTtUXW1ZBCXK+HhItwPlb72T8A7OQtXog65/X7enRXEqilDRoFdvZgNNRxrxqfw4cPc+rUKcaNG4dOp8PBwYHrr7++zvojQXc5yJRy0ZjJ9S2EEHUkPx/OnYMzZ8DeHpo3r5YC2ZmZEB0DKcng5l4t5byFENXBaMRr6/fmm2kRY+uwM41TYWEhP/30kznRdrt27ejSpUsd90qCbiGEEEKI2peRAadPV2spMIMBEhO16eT5+eDnL6PbQtQnrsd245AUD0BWl+spDKieZSRCk5KSwtq1a835igYPHkynTp3quFcaKRIhKmzhwoUEBgai0+lYt25djZ2nptsvS2RkJDqdzpy5ftWqVXh5eZn3L1y4kO7du9dJ3yri6schhBCiDun12sj2qVNaGvFmzaol4M7K0paER53UkqQFBkrALUR9UzSBWtoQSaBWnQ4fPszHH39MUlISrq6uTJ48mfDw8DpZv21N/eiFqHZTp05Fp9OZ1zC0adOGF154Ab1eX6V2jx8/zvPPP89HH31EQkICN998c5X72lCC1zvvvJOTJ0/WyrkkUBZCiEYoIwMOHICjR8HBQctOXsVcGkajtnb72DG4mAh+vtVWYUwIUY3sUpNx378dgEIvPzK7D6zjHjUeW7du5fvvv6ewsJCwsDBmz55dq+XAykOmlzdiI0aMYOXKleTn57Np0yYeeugh7O3teeqppyrclsFgQKfTcebMGQBGjx7d5NYCOzs7m7N9V1ZBQQEODg7V1CMhhBANgtGo1dw+cULLUh4crI14V1FODsTGannYnJwgKLAa+iqEqBGe235AdyVhWvrg0dVSDlBo2rVrx86dOxkwYACDBg2qN6PbRdW/Holq4+joSFBQEC1btuSBBx5g2LBhrF+/HoD8/Hzmz59PaGgorq6u9O3bl8jISPN9TVOp169fT8eOHXF0dGT69OmMGjUKABsbG4uge/ny5XTo0AEnJyeuvfZa3n//fYu+xMXFMXHiRHx8fHB1daV379789ddfrFq1iueff55Dhw6ZR+ZXrVpV7LEMGTKEOXPmWGxLTk7GwcGBzZs3l/gcbNiwgT59+uDk5ISfnx9jx/6TsOKzzz6jd+/euLu7ExQUxKRJk8xrQKy5enq5yUcffUTz5s1xcXFhwoQJpKenm/dNnTqVMWPG8PLLLxMSEkL79u3LPHd0dDQREREAeHt7o9PpmDp1KgBGo5FFixbRqlUrnJ2d6datG998841FfzZt2kTHjh1xcXEhIiKC6OjoEh+TEEKIGpaTA4cPw/792u1mzar8Ydto1JaCHz0KCQng46OVAxNC1FNGA96R6wBQOhtSwyWBWlVdvnzZ/HNoaCiPPPJIvZpOfjX5iqWSCgoKStxnY2ODXZE/qKUdq9PpsLe3L/PY6hgddXZ25tKlSwDMmTOHv//+m6+++oqQkBC+//57RowYwZEjR2jbti0AOTk5LF68mOXLl+Pr60twcDDh4eFMmzaNhIQEc7uff/45//73v3n33Xfp0aMHBw4cYNasWbi6ujJlyhSysrIYPHgwoaGhrF+/nqCgIPbv34/RaOTOO+/k6NGj/Pzzz/z2228AeFr55DBz5kzmzJnDkiVLcLxSSmX16tWEhoYyZMgQq49348aNjB07lmeeeYZPP/2UgoICNm3aZN5fWFjIiy++SPv27UlKSmLevHlMnTrV4piynD59mq+//poNGzaQkZHBjBkzePDBB/n888/Nx2zevBkPDw9+/fXXcp27efPmfPvtt4wfP56oqCg8PDzMI+yLFi1i9erVfPjhh7Rt25bt27dzzz334O/vz+DBgzl//jzjx4/ngQceYPbs2ezbt4/HH3+83I9HCCFENVFKy2p24kS1lgLLzdPKgF24oDUXGFgtCc+FEDXI7fAu7C9dBCCr2w3o/YLquEcNlyk7+eHDh5k5cyZBQdpz6V7P19VI0F1JixYtKnFf27ZtmTRpkvn2G2+8Ya75fbWWLVuaRzEB3nrrLXJycoodt2DBgkr3VSnF5s2b+eWXX3j44YeJjY1l5cqVxMbGEhISAsD8+fP5+eefWblyJa+88gqgXdTvv/8+3bp1M7dlGuk1XeCmvi1ZsoRx48YB0KpVK/7++28++ugjpkyZwhdffEFycjJ79uzBx8cHgDZt2pjv7+bmhp2dnUWbVxs3bhxz5szhhx9+YMKECYA28mxau27Nyy+/zF133cXzzz9v3lb0sUyfPt388zXXXMPbb79Nnz59yMrKws3NrZRn9B95eXl8+umnhIaGAvDOO+9wyy23sGTJEvPjcXV1Zfny5RZfnJR1btPzFBAQYH7O8/PzeeWVV/jtt9/o16+f+b47duzgo48+YvDgwXzwwQe0bt2a1157DTs7O6699lqOHDnC4sWLy/V4hBBCVIP8fC1Z2pkz2qh2NZQCUwpSLkFMtFYSzNu7WmJ4IUQtsEigFjGuDnvSsF2dnfz8+fOlxg/1iQTdjdiPP/6Im5sbhYWFGI1GJk2axMKFC4mMjMRgMNCuXTuL4/Pz8/H19TXfdnBwoGvXrqWeIzs7mzNnzjBjxgxmzZpl3q7X680j1gcPHqRHjx7mQLIynJycmDx5MitWrGDChAns37+fo0ePmqfLW3Pw4EGLPl1t3759LFy4kEOHDpGamorRaAQgNjaWjh07lqtfLVq0MAfcAP369cNoNBIVFWV+E+jSpUuxmQqVOffp06fJycnhxhtvtNheUFBAjx49AC3R3XXXXWex3xSgCyGEqAWXLkFUlLbQuppKgeXnw/nz2ui2nZ2MbgvRkNhduojbwZ0AFPoGktW9fx33qGE6fPgwP/74I4WFhbi6ujJu3Lh6lyytNBJ0V1JpyciuXkswf/78Eo+9epT2kUceqVrHioiIiOCDDz7AwcGBkJAQ85T3rKwsbG1t2bdvH7ZXZU0tOsLr7OxcZrK0rKwsAJYtW0bfvn0t9pnarmryMZOZM2fSvXt34uLiWLlyJUOGDKFly5YlHl/aebOzsxk+fDjDhw/n888/x9/fn9jYWIYPH17qcoDKcHV1rZZzm57rjRs3WgT6gHnKvRBCiDqi12sFsk+e1H5u1qzKmcmV0qqKRUdDejp4+4CTvN0L0aB4Rf6ATmmDK2mDx4CN1PKrCNN08gMHDgDajNpx48aVe1ZqfSFBdyVVZI11TR1bFldXV4tp3CY9evTAYDCQlJTEwIFVK1cQGBhISEgIZ8+e5e6777Z6TNeuXVm+fDmXL1+2Otrt4OCAwWAo81xdunShd+/eLFu2jC+++IJ333231OO7du3K5s2bmTZtWrF9J06c4NKlS7z66qs0b94cgL1795bZh6vFxsZy4cIF8zT9P//8ExsbG3PCNGvKc27TdVD0eTEltIuNjWXw4MFW2+7QoUOx0f8///yzwo9LCCFEBWRkaKPbcXHg5QUeHlVusqBAay4+XhvVDgyEepofSAhREoMer23rAFA2tqQNHl23/WmADh48aA64Bw8eXG+zk5dFgu4mqF27dtx9993ce++9LFmyhB49epCcnMzmzZvp2rUrt9xyS4Xae/7555k7dy6enp6MGDGC/Px89u7dS2pqKvPmzWPixIm88sorjBkzhkWLFhEcHMyBAwcICQmhX79+hIWFce7cOQ4ePEizZs1wd3cvceTWlFDN1dXVIhO5NQsWLGDo0KG0bt2au+66C71ez6ZNm/jXv/5FixYtcHBw4J133uH+++/n6NGjvPjiixV63KBNe58yZQpvvPEGGRkZzJ07lwkTJpS6vqQ8527ZsiU6nY4ff/yRkSNH4uzsjLu7O/Pnz+exxx7DaDQyYMAA0tPT2blzJx4eHkyZMoX777+fJUuW8OSTTzJr1iz2799vNRu8EEKIanB1KbCQkGopA5SaCtExcPmStna7miaMCSFqmdvBHdinJgOQ1WMgep+AOu5Rw9OrVy/i4uLo3r07rVq1quvuVFrD+5pAVIuVK1dy77338vjjj9O+fXvGjBnDnj17aNGiRYXbmjlzJsuXL2flypV06dKFwYMHs2rVKvMLw8HBgf/9738EBAQwcuRIunTpwquvvmqefj5+/HhGjBhBREQE/v7+fPnllyWea+LEidjZ2TFx4kScnJxK7Vd4eDhr165l/fr1dO/enSFDhrB7924A/P39WbVqFWvXrqVjx468+uqrvPHGGxV+7G3atGHcuHGMHDmSm266ia5duxYrl3a18pw7NDSU559/nieffJLAwEBzubQXX3yR5557jkWLFtGhQwdGjBjBxo0bzc91ixYt+Oabb8yP+cMPPzQnxhNCCFGNaqAUWKFem0p+9BhkZWqj2xJwC9FweW/5zvxz6hBJoFYehYWFREZGmpNQ29jYMHbs2AYdcAPolFKqrjtRlzIyMvD09CQ1NbVYDea8vDzOnTtHq1atygzwRO2Ijo6mdevW7Nmzh549e9Z1d+olpRR6vR47O7sy1+SDXOei/jMajSQlJREQENAgp5SJRsZUCiwqSltwHRAAFXzvNCpFUl4eAU5O2Fx5n05P15aEp6RoNberIf+aELVCoci2zcPV4IQOyfBnYp98gdaPj0anFAV+IZxZsq7kNSJGA3YpF8nuMRCDh3eN9y0lRVsJc1U6pjqXnJzM2rVrSU5OplevXtx666110o+0tDS8vb1JT0/HoxqWC4FMLxcNRGFhIZcuXeLZZ5/l+uuvl4BbCCFE7SsogNOnq7UUmF4PCQladnK9HvwDwE7yLAnR4HlFfo/uythmWsRYScpQhkOHDrFx40ZzdvJOnTrVdZeqlQTdokHYuXMnERERtGvXjm+++aauuyOEEKKpqYFSYJmZEBsLyUng7qGt3xZCNAJ6PV7btMS2ytaWtEGj6rhD9VdhYSGbNm3i4MGDQMPNTl4WCbpFgxAeHk4TXwkhhBCiLphKgZ06VW2lwAwGLYZPPw8F+TK6LURj474/Erv0SwBk9orA4OVXxz2qny5dusSaNWtITtaSzYWHhzNw4MBGuZRMgm4hhBBCCGtMpcDOn9eGof39q9xkVpaWmfxCNnjaasnShBCNiyRQKx87OzuysrJwdXVl/PjxDT5ZWmkk6BZCCCGEKMpUCiwqCrKzITS0ypnJjUZITIKYaMjNA48gcJcJXEI0OvaJ53E9plXLKQhsTk6H3nXco/rFaDSaR7I9PT2566678PHxaXTTya/W+MbuhRBCCCEqKzcXjhzRSoEpVS2lwHJy4ORJrZy3TqeNbtvKJzAhGiWLUe6IcZJArYjk5GQ++ugjoqKizNtatGjR6ANukJFuIYQQQggtwE5K0iLjS5e0yLiKZRSNRkhO1paEZ+eArw/Y24MMcAvROOkKC/D8fQMARjt70iWBmlnR7OSbN2+mXbt25Spt21hI0C2EEEKIpq2gAM6e1cqB2dhAixZVLgWWmwexMVo5MEcnCAyocpNCiHrOfe8W7DLTAMjsMwSDu1ed9qc+uDo7+TXXXMPYsWObVMANEnQLIYQQoim7fFlbu52QAH5+4OpapeaUgpQUbXQ7M1PLv+boWE19FULUa14WCdTG12FP6ofk5GTWrl1LcnIyOp2OwYMHN9rs5GVpeo9YiBoUHR2NTqczf5sXGRmJTqcjLS2tTvslhBDiKgYDnDkDu3drUXKzZlUOuPPy4PQZ+PtvbfA8MFACbiGaCof4c7ie2A9Afkgrctv3qOMe1a309HSWLVtGcnIybm5u3HvvvQwePLhJBtwgQXetMRggMhK+/FL7v8FQs+ebOnUqOp3O/M/X15cRI0Zw+PDhCrczZsyYUo8peh5r/xYuXFj5B1KNFi5ciE6nY8SIEcX2vf766+h0OsLDw6v1nDfccAMJCQl4enpWa7tCCCGqIDMTDh6Ew4fBwQFCQqpUe1spbRn4sWNwPhY8PbUR7iY2e1KIJs1ra9EEamOb/BuAp6cnXbt25ZprrmH27NmEhYXVdZfqlEwvrwXffQePPAJxcf9sa9YM3noLxtVg6b4RI0awcuVKAC5evMizzz7LrbfeSmxsbLWeJyEhwfzzmjVr+Pe//22RlbA+ZSQMDg5m69atxMXF0axZM/P2FStW0KJFi2o/n4ODA0FBQdXerhBCiEowGuHCBS1ZWlYWBAdrmc2qoKBA+/seH/9PZvImOpAjRJOlK8jDa8dGAIz2jqQPuKWOe1Q3kpOTcXZ2Nn/2HzFiBDY2Nk12dLsoeQZq2Hffwe23WwbcoP1xvv12bX9NcXR0JCgoiKCgILp3786TTz7J+fPnSU5ONh9z5MgRhgwZgrOzM76+vtx3331kZWUB2sjwJ598wg8//GAetY6MjCx2HtM5goKC8PT0RKfTWWz76quv6NChA05OTlx77bW8//77Fvf/17/+Rbt27XBxceGaa67hueeeo7Cw0Lx/4cKFdO/e3RwYu7m58eCDD2IwGHjttdcICgoiICCAl19+ucznJCAggJtuuolPPvnEvO2PP/4gJSWFW24p/ga5fPnyUvu+e/duevTogZOTE7179+bAgQMW+6+eXn7p0iUmTpxIaGgoLi4udOnShS+//NLiPuHh4cydO5cnnngCHx8fgoKC6s1sASGEaLByc+HoUdi3Twu+mzevcsCdmgrH/oboaHBzA19fCbiFaIo8dv+GbXYGABl9h2F0a3ozHA8dOsSyZcv47rvvMBqNANjZ2UnAfYWMdNcgg0Eb4VZWaoMopX0j/uijMHp0lWa1lUtWVharV6+mTZs2+Pr6ApCdnc3w4cPp168fe/bsISkpiZkzZzJnzhxWrVrF/PnzOX78OBkZGeYRcx8fnwqd9/PPP+ff//437777Lj169ODAgQPMmjULV1dXpkyZAoC7uzurVq0iJCSEI0eOMGvWLNzd3XniiSfM7Zw5c4affvqJn3/+mTNnznD77bdz9uxZ2rVrx7Zt2/jjjz+YPn06w4YNo2/fvqX2afr06TzxxBM888wzgDbKfffdd1e471lZWdx6663ceOONrF69mnPnzvHII4+Ueu68vDx69erFv/71Lzw8PNi4cSOTJ0+mdevWXHfddebjPvnkE+bNm8dff/3Frl27mDp1Kv379+fGG28s93MvhBDiisTEai0FVliofXl+/soX6jK6LUTTVjSBWlpEDU5jrYcKCgr46aefzPmMdDodBQUFOFXxfbaxkaC7Enr3hosXyz4uP1/LzVISpeD8eQgKKl+ilaAg2Lu3/P388ccfzdM7srOzCQ4O5scffzR/4/TFF1+Ql5fHp59+iuuV5DHvvvsuo0aNYvHixQQGBuLs7Ex+fn6lp0gvWLCAJUuWMO7KPPpWrVrx999/89FHH5mD7meffdZ8fFhYGPPnz+err76yCLqNRiMrVqzA3d2djh07EhERQVRUFJs2bcLGxob27duzePFitm7dWmbQfeutt3L//fezfft2evXqxddff82OHTtYsWJFhfr+xRdfYDQa+e9//4uTkxOdOnUiLi6OBx54oMRzh4aGMn/+fPPthx9+mF9++YWvv/7aIuju2rUrCxYsAKBt27a8++67bN68WYJuIYSoiKtLgTVvXuXoOC0NYmO1v++enuDiUj1dFUI0TI7nT+NySsuZlNe8Dbltu9Zxj2rP1dnJw8PDGTBggIxuWyFBdyVcvKh9w11dSgvMqyIiIoIPPvgAgNTUVN5//31uvvlmdu/eTcuWLTl+/DjdunUzB9wA/fv3x2g0EhUVRWBgYJXOn52dzZkzZ5gxYwazZs0yb9fr9RaJxdasWcPbb7/NmTNnyMrKQq/X4+HhYdFWWFgY7u7u5tuBgYHY2tpavKgDAwNJSkoqs1/29vbcc889rFy50jxa3rWr5Rtkefp+/PhxunbtavFNXr9+/Uo9t8Fg4JVXXuHrr78mPj6egoIC8vPzcbnqU9vV/QkODi7XYxNCCHFFNZcCK9TDxQQt4DYaISCg5mepCSHqP68t35p/TosY12QSqB08eJBNmzZRWFiIm5sb48ePb/LJ0kojQXcllHfQt6yRbhM/v/KPdFeEq6srbdq0Md9evnw5np6eLFu2jJdeeqlijVWCaW34smXLio0+2175pLJr1y7uvvtunn/+eYYPH46npydfffUVS5YssTje/qp1dzqdzuo20xqSskyfPp2+ffty9OhRpk+fXqm+V8brr7/OW2+9xdKlS+nSpQuurq48+uijFBQUWBxXlccmhBBNmsGgFck+eVKbB96sWZWj44wMiImF5CTw8Khy/C6EaCR0ebl47twEgNHBifT+I+u4R7VDr9ezY8cOCgsLueaaaxg3bpzFIJ4oToLuSijvFG+DAcLCtFFxa+u6dTrts8C5c7XzbblOp8PGxobc3FwAOnTowKpVq8jOzja/UHbu3Gmerg1a9m1DJeubBQYGEhISwtmzZ62umQYtiVnLli3N66sBYmJiKnW+iujUqROdOnXi8OHDTJo0qdj+8vS9Q4cOfPbZZ+Tl5ZlHu//8889Sz7tz505Gjx7NPffcA2jT5k+ePEnHjh2r+IiEEEKQmakF27FX6nb5+1epOYNBGyg/f177It0/AOxkdFsIcYXHn79gm5sNQEa/4Rhd6k/FnppkZ2fHHXfcwcmTJxkwYAC6JjK6XxUy4b4G2dpqZcGg+EwT0+2lS2su4M7Pz+fixYtcvHiR48eP8/DDD5OVlcWoUaMAuPvuu3FycmLKlCkcPXqUrVu38vDDDzN58mTz1PKwsDAOHz5MVFQUKSkpFlnFy+P5559n0aJFvP3225w8eZIjR46wcuVK3nzzTUBbrxwbG8tXX33FmTNnePvtt/n++++r94kowZYtW0hISMDLy6tSfZ80aRI6nY5Zs2bx999/s2nTJt54441Sz9m2bVt+/fVX/vjjD44fP87s2bNJTEys7ocmhBBNi1JamZDdu7UIOThYC7qrICtLy7126hTY2WnJ0iTgFkIU5V20NveQxp1A7eDBg+zevdt8OzAwkIEDB0rAXU4SdNewcePgm28gNNRye7Nm2vaarNP9888/ExwcTHBwMH379mXPnj2sXbuW8PBwAFxcXPjll1+4fPkyffr04fbbb2fo0KG8++675jZmzZpF+/bt6d27N/7+/uzcubNCfZg5cybLly9n5cqVdOnShcGDB7Nq1SpatWoFwG233cZjjz3GnDlz6N69O3/88QfPPfdctT0HpXF1dS0x4C5P393c3NiwYQNHjhyhR48ePPPMMyxevLjUcz777LP07NmT4cOHEx4eTlBQEGPGjKnGRyWEEE1Mbi4cOaKVAjMYqlwKzDS6ffQoJCdrS8DcmsbglRCiApyiT+B89m8AcsOuJa9V45y1WFBQwLp16/jhhx/45ZdfZLCoknRKWZv43HRkZGTg6elJampqsQAsLy+Pc+fO0apVqyqnvTcY4PfftT/kwcEwcKAkYBE1QymFXq/Hzs6uXN8+Vud1LkRNMBqNJCUlERAQIBlRhaVqLgWWk6PNTE+4CC7O2vrtmqBQZNvm4WpwQoeMEomGralez0ErXsZ7qzY7M2Ha06RVZaTbaMAu5SLZPQZi8PCuph6WLCUFvLygjII/JCUl8c0331hkJ28Ko9tpaWl4e3uTnp5eLLlzZcma7lpiawtXBpiFEEIIURWmUmBnzmjrtapYCsxo1Ea1Y2IgOwd8fao0WC6EaORscrPx2PULAAYnFzL6Da/jHlW/gwcPsnHjRvR6vWQnrwYSdAshhBCi4ajmUmC5uVdGtxPA0QkCA5pMxR8hRCV57PoZ27wcADJuuBmjc+PK3L1hwwb2798PINnJq4kE3UIIIYSo/0ylwE6d0lKJh4ZqGc4qSSltimV0DGRlgo8PODhUY3+FEI2TUngXqc3dGBOo+fn5Nanp5LVBgm4hhBBC1G9XlwLz86tSc3l5cD4OLsRrgXZgoIxuCyHKx+nsMZxiTgKQe00n8lu2r+MeVY/c3FycnZ0BuP7662nVqhVBQUF13KvGQ4JuIYQQQtRPSkF8vDadPDNTy0RahcXWSmk512JiICMDvL3B0bEa+yuEaPS8txQpEzZ0fB32pHoUFBSwadMm4uLimDVrFo6Ojuh0Ogm4q5kE3UIIIYSof3Jztank586Bs7NWa7MKw9EFBVoJ7/h4LblpQECVcq8JIZogm+xMPP68kkDNxY2MvjfVcY+qJikpibVr15KSkoJOpyM6Opr27RvHyH19I0G3EEIIIeqXpCQ4flxLmhYQUOVSYJcvQ0wspF7WRrelOqIQojI8/9iETUE+AOn9R6IcG+abiVKKuLiD/PbbJvR6Pe7u7owfP56WLVvWddcaLQm6hRBCCFE/FBZqpcBOn9ZuN2tWpeHowkKIi4e4OO12YKCMbgshKkkpvIpMLU+LaJgJ1PT6As6c2URKyiEAWrduzdixYyU7eQ2ToFsIIYQQdS81FU6c0Gp3+fqCm1uVmktL09ZuX7qk5V5zcamebgohmibnU4dwijsDQE67buQ3b1PHPaqcv//+5UrArWPIkAgGDBgg2clrgXzfKxqE8PBwHn30UfPtsLAwli5dWmf9EUIIUU0MBm3d9u7dkJyslQKrQsBdqNeSnB87Bunp2ux0CbiFEFVVdJQ7dUjDTaDWrl0Erq7BXHfdFCkHVosk6K4tBgNERsKXX2r/Nxhq9HRTp05Fp9Oh0+lwcHCgTZs2vPDCC+j1+mo9T3R0NDqdDltbW+Lj4y32JSQkYGdnZ07MUJ327NnDfffdV61tCiGEqGVZWXDwIBw6pNXcrmLt7YwMOHFcm53u6Aj+/lrSNCGEqArbzDQ8dv8GgN7Nk8w+Q+u4R+Wn1xcQF3fIfNvJyY3OnWfh4yPrt2uTBN214bvvICwMIiJg0iTt/2Fh2vYaNGLECBISEjh16hSPP/44Cxcu5PXXX7d6bEFBQZXOFRoayqeffmqx7ZNPPiE0NLRK7ZbE398fFxm6EEKIhslUCuyvv7SU4kFB4OVV6eb0em3d9rFjWtI0/wCQ5YlCiOriuWMjNoXaZ+X0AbegHBpGrcGMjCR27FjGwYPruHDhqHm7jG7XPgm6a9p338Htt/+TxcUkPl7bXoOBt6OjI0FBQbRs2ZIHHniAYcOGsX79ekAbCR8zZgwvv/wyISEh5vIAR44cYciQITg7O+Pr68t9991HVlZWmeeaMmUKK1eutNi2cuVKpkyZUuzYo0ePcvPNN+Pm5kZgYCCTJ08mJSXFvD87O5t7770XNzc3goODWbJkSbE2rp5e/uabb9KlSxdcXV1p3rw5Dz74oEW/V61ahZeXF7/88gsdOnTAzc3N/KWEEEKIWpSXB0ePwt69WrTcrFmVam9nZmllvE+d0gbJAwLATka3hRDVRSm8thZJoDak/idQU0oRG3uAHTuWkZWVgqOjO46O7nXdrSZNgu6aZDDAI49o3+hfzbTt0UdrfKq5ibOzs8WI9ubNm4mKiuLXX3/lxx9/JDs7m+HDh+Pt7c2ePXtYu3Ytv/32G3PmzCmz7dtuu43U1FR27NgBwI4dO0hNTWXUqFEWx6WlpTFkyBB69OjB3r17+fnnn0lMTGTChAnmY/7v//6Pbdu28cMPP/C///2PyMhI9u/fX+r5bWxsePvttzl27BiffPIJW7Zs4YknnrA4JicnhzfeeIPPPvuM7du3Exsby/z588t8bEIIIapJUhLs2aNFyH5+2r9KjrgYDFrOtaNHtaXgfv5Vzr0mhBDFuJzYh2NCDADZHXpREBxWtx0qg15fwMGD6zh8eD1Gox5//9YMGjQbX1+ZTl6XJHt5ZfTuDRcvln1cfj4UGcEtRql/ptU5lmOaSlCQNjJQQUopNm/ezC+//MLDDz9s3u7q6sry5ctxcHAAYNmyZeTl5fHpp5+aywa8++67jBo1isWLFxMYGFjiOezt7bnnnntYsWIFAwYMYMWKFdxzzz3YXzV68e6779KjRw9eeeUV87YVK1bQvHlzTp48SUhICP/9739ZvXo1Q4dq62U++eQTmjVrVupjvDrJ2ksvvcT999/P+++/b95eWFjIhx9+SOvWrQGYM2cOL7zwQqntCiGEqAZXlwJr3rxKtbuys7VkaRcvatPIS/nzJIQQVdKQyoRlZCSxf/9asrJSAB3XXhtB69aSnbw+kKC7Mi5e1KaHV5fSAvMq+PHHH3Fzc6OwsBCj0cikSZNYuHCheX+XLl3MATfA8ePH6datm0Wdvv79+2M0GomKiio16AaYPn06N9xwA6+88gpr165l165dxRK3HTp0iK1bt+JmZTjizJkz5ObmUlBQQN++fc3bfXx8zNPfS/Lbb7+xaNEiTpw4QUZGBnq9nry8PHJycsxrv11cXMwBN0BwcDBJSUmltiuEEKKKUlO1+d8XLlS5FJjRqA2Wx8RATq7WXBVmpgshRKls0y/jsWcLAHp3bzJ7R9Rxj0qXm5tKVlYKTk7u9OgxXka36xEJuisjKKh8x5U10m3i51f+ke4KiIiI4IMPPsDBwYGQkBDsrsoI61rNWWa6dOnCtddey8SJE+nQoQOdO3fm4MGDFsdkZWWZR86vFhwczGnTKEgFREdHc+utt/LAAw/w8ssv4+Pjw44dO5gxYwYFBQXmoPvqUXedToeyNvVfCCFE1RkM2myuqCjt72EVM5Pn5mqj2wkJ4OQEQTK6LYSoYZ6/b0Bn0AaQ0gaNQtk7lHGP2qeUMo9kBwa2p1u32wgIaIejo2STrE8k6K6M8k7xNhi0LOXx8dbXdet0WgKZc+dqpKaJq6srbdq0KffxHTp0YNWqVWRnZ5sD8p07d2JjY1PmSLPJ9OnTefDBB/nggw+s7u/ZsyfffvstYWFhxb4EAGjdujX29vb89ddftGjRAoDU1FROnjzJ4MGDrba5b98+jEYjS5YswebKdMWvv/66XP0VQghRA7KuZDc7fx7c3bUvlytJKW3NdkyM1qyPDzjUv8+9QojGxmjEe+v35ptpEWPrsDPWZWQkcuTIRnr2HI+zsycAzZv3qONeCWskkVpNsrWFt97Sfr56LYXp9tKl9aaI6N13342TkxNTpkzh6NGjbN26lYcffpjJkyeXObXcZNasWSQnJzNz5kyr+x966CEuX77MxIkT2bNnD2fOnOGXX35h2rRpGAwG3NzcmDFjBv/3f//Hli1bOHr0KFOnTjUH09a0adOGwsJC3nnnHc6ePctnn33Ghx9+WKnnQAghRBVcXQosMLBKpcDy8rRl4MePa4nOAwMl4BZC1A6Xv/fgkKRVH8rq3JfCwOZ13KN/aNnJ97Njx3JSU89z7Ngvdd0lUQYJumvauHHwzTfatLqimjXTto+rPwkZXFxc+OWXX7h8+TJ9+vTh9ttvZ+jQobz77rvlbsPOzg4/Pz+ro9gAISEh7Ny5E4PBwE033USXLl149NFH8fLyMgfWr7/+OgMHDmTUqFEMGzaMAQMG0KtXrxLP2a1bN958800WL15M586d+fzzz1m0aFHFHrwQQoiqMZUC27fvn1JglYyQldJWZx07psXunp5a7C65gIQQtcV7y7fmn+tTmbB/spNvuJKdvA1dutxS190SZdCpJr6oNSMjA09PT1JTU/G66tv4vLw8zp07R6tWrXBycqraiQwG+P13bTFacDAMHFhvRrhF46KUQq/XY2dnV65sldV6nQtRA4xGI0lJSQQEBJQ660XUoaQkbTp5crI2HF2F95L8fIiL0wbMbW3B27vxBdsKRbZtHq4GJ3Q0sgcnmpzGeD3bpaXQ5tFb0BkM6D19ObV0Y5VyUpTJaMAu5SLZPQZi8PAu8bCMjET27VtLdvYldDod7dsPoXXr/hXOTp6Son2RWSRvsSgiLS0Nb29v0tPT8fDwqJY2ZU13bbG1hfDwuu6FEEIIUX2quRTY5cva2u3UVPD2Aady5BgVQojq5rntB3QGAwBpg0fXbMBdTpcuxfDXX6sxGvWSnbwBqvsrSAghhBANT1oanDhRLaXACgshLl4b4QZtsFwmNQgh6oTRgFfkOgCUTkdqPUmg5uUVgqurD05OHnTvPkaykzcwEnQLIYQQovyKlgLLy6tyKbDUNIiNgUuXtOmOzs7V1lMhhKgw1yN/4pCSAEB21xvQ+wXXWV+ysi7h6uqNTmeDra09119/Lw4OLhWeTi7qngTdQgghhCifrCw4dQqio8HDQ0uWVkmFeki4oNXeNhohIEBSnQgh6l7RBGqpdZRATSnF+fMHOHr0J9q2HUjbtoMAZHS7AZOgWwghhBClU0pLBHr8OGRmQlBQlWp3padra7dTUrTM5C4u1dhXIYSoJLvLibgd2AFAoXcAWd3613of9PoCjhzZSHz8YQBSU+NRSsnodgMnQXc5GI3Guu6CEDVGrm8hRKny8rTR7XPnwNFRG92u5Ic/vR4SLsL5WG0dt38A2MnothCinvCK/AGd0j4XpYWPAdvaDZUyslPYve/zItnJh9K69Q0ScDcCEnSXwsHBARsbGy5cuIC/vz8ODg5y0Yt6r7wlw5RSFBQUkJycjI2NDQ5VGLUSQjRSyclasrRqKAWWmaWt3U5KAjd3rRSYEELUGwZ9kQRqNqSFj661UyulOF2Qwt4DazAaDTg5udOz5+34+LSotT6ImiVBdylsbGxo1aoVCQkJXLhwoa67I0S5KKUwGo3Y2NiU60siFxcXWrRoIfWPhRD/KCzURrZPndJuV6EUmMEAiYkQEwv5eeDnL6PbQoj6x+3QTuxTkwDI6jEAvU9grZ07R5/L3vw4jCgCAtrSvfsYHBxk3U1jIkF3GRwcHGjRogV6vR7DlXp9QtRnRqORS5cu4evrW2YgbWtrW+aIuBCiiTGVAktIAB+fKpUCy87WEqVdTARXF22wXAgh6iPvLd+Zf04dMr5Wz+1q70Jvx2ZkhbSgVceh8rmsEZKguxx0Oh329vbY29vXdVeEKJPRaMTe3h4nJycZvRZClJ/BoBXKPnFCW8cdElLpUmBGozaNPCYGcnLBz7dKVcWEEKJG2SdfwPXwHwAU+AWT3eX6Gj2fUopzmefxdHDH10lba9PGwY/s5r0wSMDdKMmfQCGEEKKpy86GkyerpRRYbq4WbF+8qC0BD5LRbSFEPecVuQ6dUsCVBGo2NbcGptCoZ1/yYWKzLuBq58yNzQbhoJNBksZOgm4hhBCiqTKVAjtxQqvjVYVSYEpp+daio7UY3senSlXFhBCiduj1eG37AQBla0v64JpLoJaWn8GuxH1kFmajQ8c1Hi2xt7EDJZVkGjsJuoUQQoimKC8PzpzR/jk6asnSKjmtMTcP4s5DfLzWVGBgpZsSQoha5X5gG3bplwDI7DkYvZdftZ/DNJ38QMpRDMqIs60T/QJ74ufsc+WAaj+lqGck6BZCCCGamuRkiIrS0ooHBoKzc6WaUQpSLmmlwNLTtdFtR8dq7qsQQtQgryIJ1NIixlV7+wajgT3Jh4nNigcg2CWA6wK642grU4GaEgm6hRBCiKbi6lJgLVpUuhRYfr6Wdy0uDuzttZnpMrothGhI7BPP43b0LwAKApqR3em6aj+Hjc6GQmMhOnR08WlPe6/Wkp28CZKgWwghhGgK0tK00e34ePD1rVIpsMuXtWRpqang7QNOMrothGiAvLZ+b/45NWJspb+EvJpSCoXCRmeDTqfjuoDuZBZm4efkUy3ti4ZHgm4hhBCiMTMa4fx5LeDOzYXQ0ErX7yoogPgL2vptdNrMdKlMKIRoiHSFBXht3wCAsrUjfdBt1dKuKTu5Di3Y1ul0ONo64GgrAXdTJkG3EEII0ViZSoHFxIC7e5VKgaWmQUy0Nsrt5VXpZeBCCFEvuO/dil1mKgAZfYZg8PCucptp+Rn8kbiPrCvZya/1ao2no0eV2xUNnwTdQgghRGNjKgUWFaVNK69CKbBCPVyI1wbLlYKAALCtuRK2QghRK7y2FkmgNqRqCdSUUpzNjOVAyjGMpuzkQT0l4BZmEnQLIYQQjUl+Ppw+DWfPaoF2FUqBpadrg+QpKeDpCS4u1dxXIYSoAw4XonE9vg+A/OCW5Fzbq9JtmaaTx2ZdACQ7ubBOgm4hhBCisUhJgRMnIClJG5Ku5BxwvR4SLsL5WO1n/wCwk9FtIUQjYTHKHTGu0l9MKqX4PWE3KXmXr2Qnv5b2XtdIdnJRjATdQgghREOn1/9TCsxo1Ea3K5nhLDMTomMgJRnc3MG76sschRCi3tAV5OO5YyMARnsH0gbeWvm2dDo6ebdlT/Jhrg/sIdnJRYkk6BZCCCEasvR0bXS7iqXADAZITNSmk+fng5+/jG4LIRof9z2bsctKByDjumEY3TwrdP9Co570ggxzgB3o4s/NLcKx1ckbpiiZBN1CCCFEQ1SNpcCysrSmLiaCq4tWCkwIIRoj7y3fmn+uaAI1U3byPH0+NzYfiLu9K4AE3KJMEnQLIYQQDU3RUmBubpUuBWY0QmISxMZATi74+VY6bhdCiHrPIe4MLicPAZAXeg25bbuV635KKc5mxHLgkpad3MXOiUJDIdjXZG9FYyJ/WoUQQoiGQim4eFGbTp6Wpg1JOzpWqqmcHIiN1ZpzcoIgGd0WQjRy3luKJFAbOr5cCdQKjYXsTT7CeclOLqpAgm4hhBCiITCVAjtzpkqlwIxGLcl5dLQ2YO7rC/YyWiOEaOR0+Xl47rySQM3BkfQbRpZ5n9T8dHYl7ierMFvLTu57Le09JTu5qDgJuoUQQoj6rppKgeXmaWXALlzQBsgDAytdKUcIIRoUj7/+h21OFgAZ19+E0dW9zPvEZMaTVZiNi50T1wf2ws9JyjmIypGgWwghhKiv9HptSPrkySqVAlMKUi5BTLRWEszbu9Kz0oUQokHy3vxPArXUIePLdZ8uvtei08G1Xm1kOrmoEgm6hRBCiPqoaCkwHx9wL3tUxpr8fC0z+YULWpI0Gd0WQjQ1jjFROJ89BkBey3bkXdPJ6nGp+emcTDtHn4Cu2OhssNXZ0M23Y212VTRSEnQLIYQQ9YnRCHFxWsBdhVJgSsHly9pAeXo6ePuAk4xuCyGaoKIJ1FKHFE+gppTiTEYsB69kJ3d3cKWjd9va7qZoxCToFkIIIeqLaioFVlCgxe3x8dpny8DASs1KF0KIBs8mNxuPP34CwODkQka/ERb7rWUnb+3Rstb7KRo3CbqFEEKIumYqBRYVpQ1PBwVVetF1aipEx8DlS9ra7UrmXBNCiEbBY9cv2OblAJDRbzhGZ1fzPi07+T6yCnPQoaOr77W0k+zkogZI0C2EEELUpatLgbVoUalF14V6iI+D83GA0ka3bW2rv7tCCNFgKIX3FusJ1M5nXeCvpIMYlREXO2f6BfbEV7KTixoiQbcQQghRV4qWAvP3BxeXSjWTnq7NSE9JAU/PSjcjhBCNitO5v3GKiQIg95qO5Idda97n6eCBDTqCXALpE9BNspOLGiVBtxBCCFHbTKXATp0Cg0Fbu12JYWm9HhIStOzker1WwltGt4UQQmORQC1iHPmGAnNw7eHgxtBmA/Cwd5Pp5KLGSdAthBBC1Kb0dG3tdny8tui6kqXAMjO1tdvJSeDuoTUlhBBCY5OThceuXwAwOLtysOO17I/ZzKDg6/B39gXA06Fy779CVJQE3UIIIURtMJUCi4qCnBwICalUKTCDQcu5FhurLQf3DwA7Gd0WQggLnjs3YVOQB0BUjz7syTwNQExWvDnoFqK2SNAthBBC1LScnH9Kgbm6VroUWFaW1kRSktZMYGA191MIIRoDpfDa+s/U8m3dOlzJTt6Bdp6t6rBjoqmSoFsIIYSoKUVLgaWmalFyJUqBGY2QmAQx0ZCXB35+lRokF0KIJsHp1GGczmsj27HNm5MVGsYQyU4u6pD8yRZCCCFqQn6+VgbszBmwt4fmzStVCiwnR5tKnnARXJxldFsIIcri9OsX5p/P3jCEG5sNlOzkok5J0C2EEEJUt0uXtFJgiYmVLgVmNEJysjadPDsHfH202F0IIUTJbLLSCdy3A4ACF1cCb5wJEnCLOiZBtxBCCFFdipYC0+srXQosNw9iY7RyYI5OEBhQqUFyIYRoEpRSRGfGEeoaROCOjdgU5gOQOfA2cHSu494JIUG3EEIIUT0yMrS123FxlS4FphSkpGij25mZWjOVWAIuhBBNRqGxkL1JhzmfnUBC9kXuKVKbOy1iXB32TIh/SNAthBBCVIXRqNXcPnGiSqXA8vLgfBxciNemkQcGyui2EEKUJjU/nV0X95Glz0GHjnbxiTgmRAOQfW1PCkIlU7moHyToFkIIISqraCkwF5dKlQJTCi5f1malp6eDj4+MbgshRGmUUpzJiOFgyt8YMeJi50y/wJ50+eUN8zFpQ2SUW9QfEnQLIYQQFaWUliTtxIkqlQIrKNBmo8fHa6PagYFgY1MD/RVCiEaiwFDI3uTDxGUnABDiEkifgG645OTgvmcLAHp3LzJ7D6nLbgphQYJuIYQQoiKKlgKzs6t0KbDUVIiOgdTL2tptJ6ca6KsQQjQyCsWlvFR06Ojq24F2nq3Q6XR4bl+Djb4QgPSBo1D2krFc1B8SdAshhBDldemSlizt4sVKlwIrLNRGts/HabdldFsIIUqnlEJ35ctNR1sHbgjqBYCvk7d2gNGI99Z/EqilytRyUc9I0C2EEEKURa/X1m2fPFmlUmBpaRAbq2Uo9/SsVMwuhBBNimk6ebBLAK08mgNFgu0rXI7vxSHxPADZna6jMLB5rfdTiNJI0C2EEEKUxlQK7Px5bR64h0eFmyjUw8UELeA2GiEgoFIxuxBCNCmp+en8cXEf2focEnOTCXUNwsHWvthx3ltklFvUbxJ0CyGEENZcXQosNLRSpcAyMiAmFpKTtHjd1bUG+iqEEI2IUorTGTEcuio7ubWA2zYtBfd9WwHQe/qS2TO8lnsrRNkk6BZCCCGuVg2lwAwGSEjQBsjz88E/AOxkdFsIIUplLTv5dQHdcLC1nhjNa/sGdAYDAGmDbqvUl6NC1DS5KoUQQggTUymwqCiteHZAQKXSimdlafF6UhK4uYGXV/V3VQghGhu90cBv8b+TVZiDzZXs5G2vZCe3ymjEa+v3ACidjrTwMbXXWSEqQIJuIYQQArSi2adPV6kUmMGgBdoxMZCXB35+MugihBDlZWdjS3PXEGKz4rk+sGexhGlXcz36Jw4pFwDI7tKPwoDQ2uimEBUmHwWEEEKIaigFlpOjJUpLuAguzlopMCGEEKUrMBSiV3pc7JwB6OTTjvZera2u376aRQK1CEmgJuovCbqFEEI0XaZSYKdOVboUmNEIyclaM9k54OsD9mV/VhRCiCbvcn4auy7ux8HWniGhN2Crs8VGZ4ODrU2Z97W7nITbgd8BKPT2J6vHgJrurhCVJkG3EEKIpunqUmD+/hVuIjf3yuh2Ajg6QWBAhWekCyFEk6NlJ4/mUMpxjBgBZ3L1ebjZl7+8g9e2H9AZryRQGzwGbCWsEfWXXJ1CCCGaFlMpsKgoyM6uVCkwpSAlBaJjICsTfHzAwXpiXSGEEEVo2ckPEZd9EYBQ10D6+Jecndwqgx6vyHUAKJ0NaeGja6CnQlQfCbqFEEI0Hbm5Wimw6OhKlwLLy4PzcXAhXgu0AwNldFsIIcrDNJ08W1/O7OQlcDv0B/aXEwHI6t4fvW9QTXRXiGojQbcQQojGr2gpsEuXtEi5gqXAlNLuGhOjzUz39gZHxxrqrxBCNDJKKQ6mHCNbn4OrnXO5spOXxGvrPwnU0oaMr64uClFjJOgWQgjRuBUUwNmzWjkwGxto0aLCQ9MFBdrS7/h4Lc+ajG4LIUTF6HQ6rgvozrHLJ+nh16li08mLsEtJwO3QTgAKfYPI6tqvOrspRI2QoFsIIUTjdfmyNrqdkKAVzXYtf5Keok3ExELqZW10u4ID5EII0WRdzksjJe8y7byuAcDN3pW+gT2q1KZ35Dp0SgGQGj4GbCpWcUKIuiBBtxBCiMbHYNDWbZ86BYWFlSoFVlgIcfEQF6fdDgzUBsqFEEKU7p/s5H9jROHp4EGgi1/VG9br8dz2g3YOG1vSB0sCNdEw1LuPD++99x5hYWE4OTnRt29fdu/eXerxS5cupX379jg7O9O8eXMee+wx8vLyaqm3Qggh6p3MTDhwAA4f1jKdhYRUOOBOS4O//4ZzZ8HFGfx8JeAWQojyKDAU8kfiPg6kHMOIItQ1CG9Hz2pp2/3g79inpQCQ2XMQeu+Kl3oUoi7Uq5HuNWvWMG/ePD788EP69u3L0qVLGT58OFFRUQQEBBQ7/osvvuDJJ59kxYoV3HDDDZw8eZKpU6ei0+l488036+ARCCGEqDNGI1y4ACdOQFYWBAeDvX2FmijUQ8IFbf22wQABARWO14UQosm6nJfGrsSi2ck70tYzrMLZyUviteVb88+SQE00JPUq6H7zzTeZNWsW06ZNA+DDDz9k48aNrFixgieffLLY8X/88Qf9+/dn0qRJAISFhTFx4kT++uuvWu23EEKIOpabq00lP3cOnJ2hefMKN5GRoWUmT04GD49KLf8WQogm60x6DAevjG672jnTL7AXPk5e1da+fVIcbkf+BKAgIJTsTtdVW9tC1LR6M1muoKCAffv2MWzYMPM2Gxsbhg0bxq5du6ze54YbbmDfvn3mKehnz55l06ZNjBw5slb6LIQQoh5ITITdu7Xs5H5+4Otbobvr9dq67WPHtKRp/gEScAshREXZ6mzN08lvbDaoWgNuAK+t35t/TosYK2t+RINSb0a6U1JSMBgMBAYGWmwPDAzkxIkTVu8zadIkUlJSGDBgAEop9Ho9999/P08//XSJ58nPzyc/P998OyMjAwCj0YjRaKyGRyJE3TIajSil5HoWjUaJ13RBgTaybSoF1qyZ9v8rWW3LIzMLzsdCUhK4uYG/l7a9/C0IUXGqyH9CNGQGZcBGZ4NC0dIjFCc7RwKd/dDpdNV7fesL8dq+AQBla0fqwFGN7PVz5R1BKZSq+c9vSmn/5KOidTXxGbreBN2VERkZySuvvML7779P3759OX36NI888ggvvvgizz33nNX7LFq0iOeff77Y9uTkZAoKCmq6y0LUOKPRSHp6OkopbORbYNEIWL2mMzO1otmXL2tzwZ2ctCC83G1CWroWbBcWgHsw6Gwgu4YegxBFKRT5toUA6JCC76LhUUoRkxZHdNp5+rXohbLVrmN3d3dyyC/j3hXnvWcLdhmXAUjtM4gMHxegESVOtjFi6wJ5+ssY82o+HjEaIT9f+xsoiktPT6/2NutN0O3n54etrS2JiYkW2xMTEwkKCrJ6n+eee47Jkyczc+ZMALp06UJ2djb33XcfzzzzjNWA46mnnmLevHnm2xkZGTRv3hx/f3+8vLyq7wEJUUeMRiM6nQ5/f38JukWjYHFNKwWxsdrodmEhhIZWONNZdraWKO3iRW0aeYA72tC2oUa6L0QxphE6V4OTBN2iwSkwFLI3+RDx2dpn9sTUZJr7hdbo9Rz86wbzzxkRd+BqcKqR89QZowG7HNDZ+WCo5mn51mRlgaOjlixUFOfg4FDtbdaboNvBwYFevXqxefNmxowZA2gftDZv3sycOXOs3icnJ6dYUGF75cOXKmF6oaOjI46OjsW229jYSIAiGg2dTifXtGhUdDodNtnZ2Jw+rQXdnp4V/rRgNGrf6sfEQE6uVgasgsnNhag2uiL/CdFQaNnJ95Gtz8UGHd38OtLaoyU55NfY9eyQEIPr8b0A5Ae1ILdD70b4urny7Ol06HQ1/9lNp9P+ycdE62ri83O9CboB5s2bx5QpU+jduzfXXXcdS5cuJTs725zN/N577yU0NJRFixYBMGrUKN5880169Ohhnl7+3HPPMWrUKHPwLYQQooFTClJStKHpSpYCy83VYvWEBG0melBg2fcRQgihUUpxKj2aw5f+vpKd3IV+gT3xcfKq8bXVFgnUhozTokUhGph6FXTfeeedJCcn8+9//5uLFy/SvXt3fv75Z3NytdjYWItvHp599ll0Oh3PPvss8fHx+Pv7M2rUKF5++eW6eghCCCGqU24unDwJ0dFatFzBUmBKaSXAYmK0eN3HB2pg1pgQQjRqJ9PPcejS3wCEugbRx78bDrY1P1VIV5CP5+/a1HKjvQPpA26t8XMKURN0qqR52E1ERkYGnp6epKamyppu0SgYjUaSkpIICAiQ6eWiYUtMhBMnMF6+TJKnJwEeHthUYIQjL09bu33hghZoe3rKAImoHxSKbNs8WdMtGowCQyGb43fSxrMlbTzC0BV5M63J69lj50+EfqglR06/4WYuPPBitbZfbxgN2KVcJLvHQAwe3jV+upQU8PKCvn1r/FQNUlpaGt7e3qSnp+Ph4VEtbdarkW4hhBCCggI4exbOnNGi5NDQCmUmVwouXdJGt9PTtdFtK6k8hBBClEApRUJOEsEuAeh0Ohxs7RnefBA2tbDeuCjvrd+Zf04dMr5Wzy1EdZKgWwghRP1x+TJERWmLr319teLZFZiQlZ8PcXFaNTFbWwgKktFtIYSoiAJDIXuSDxGffZGefp1p4xkGUOsBt0PcGVyiDgCQH3oNue261er5hahOEnQLIYSoewaDNjR96pQWOYeGgl3F/kRdvqw1kZoK3j7gJKPbQghRIZbZyW2gDpc/eBdJoJYaIQnURMMmQbcQQoi6lZmpJUszlQLz86vQ3QsLIS5eG+EGCAyUMihCCFERWnbycxy+dPyf7ORBPfFx9KqT/ujy8/DcsREAo4Mj6QNG1kk/hKguEnQLIYSoG0pp88CjorTAuxKlwFLTIDZGW8Pt5QXOzjXSUyGEaLQKDAVXppMnAtDMNYjetZSdvCQef/2KbU4mABl9b8LoWj3JrISoKxJ0CyGEqH25udpU8nPntFJgzZpVaOpgoR4SLmiD40pBQIC2hlsIIUTFZBRkcSE7CRts6ObXkTYeLS2yk9cFL4sEauPqsCdCVA8JuoUQQtSupCQ4flwbng4M1ILuCkhP19Zup6Ros9FdXGqon0II0QT4OfvQ078z3o6edTadvCjHmJO4nD4CQF6LduS17lzHPRKi6iToFkIIUTuKlgIDaN68Qouv9XqtdPf5WG0dt38A2MnothBCVEiBoYD9KUfp6N0WDwd3AFp7tKzjXv3DokyYJFATjYQE3UIIIWpeaiqcOGFZCqwCcvPg5AVITgI3d/D2rqF+CiFEI3YpL5U/E/eTrc8lszCbYaED6nwqeVG6vBw8dv4EgNHRmYz+I+q4R0JUDwm6hRBC1JwqlgIzGODiRYhJBpUGfv4yui2EEBVlLTt5L/8u9SrgBvDc9Qu2edkApPcbgdG5Yl/QClFfSdAthBCiZmRlaZnJz58Hd/cKlwLLztYSpSUkgp03+AXWZcVYIYRomAoMBexOOsSFHFN28mB6+3et0+zkJSmaQC1NEqiJRkSCbiGEENVLKbhwQZtOnpkJQUEVKgVmNGq51mJiICcX/Hwh3xEw1FyXhRCiMcouzGHrhV3k6HPrVXZya5zO/o3zueMA5LbqSF6rDnXcIyGqjwTdQgghqk9uLpw+rSVMq0QpsNxcLdi+eFG7e1AgKCC/5noshBCNlrOdEy52zujQ0S+oZ73ITl4SGeUWjZkE3UIIIapHUpI2up2SUuFSYEpBcjJER2vTyn18wMGh5roqhBCNVYGhAFsbO2x1NtjobOgX2BNbnW29nE5uYpObheeuXwAwOLmSfv1NddwjIaqXBN1CCCGqprBQG9k+fVq7XcFSYLl5EHce4uPB0VGL1+vhzEchhKj3LuWlsitxP6GuQfTw6wRoo931ncfOn7HJzwUgvf9IlJNLHfdIiOolQbcQQojKS03VkqVduFDhUmBKQcoliI2B9HRtdNvRsQb7KoQQjZRSipNXspMrFAnZiXT2aY+9TQP4qK8U3lu+Nd+UqeWiMWoAr0QhhBD1jsGgZSWPiqpUKbD8fIiL0/7Z22u51mR0WwghKq6k7OQNIuAGnM4cxen8KQBy2nQhv0XbOu6RENWvYbwahRBC1B9ZWXDypFbPqxKlwC5f1pKlpaaCtw84yei2EEJUimk6uSk7eXe/jrSup9nJS+K9pWgCtfF12BMhao4E3UIIIcrn6lJggYEVynZWUADxF7T12+i0u1dg6bcQQogi9EY9vyfspsBYiJudC/2CeuHt6FnX3aoQm+wMPP78HwAGF3cy+g6r4x4JUTMk6BZCCFG2vDw4dQrOndMWXlewFFhqGsREa6PcXl7g7FxTHRVCiKbBzsaOXv5dOJ+VQG//rvU6O3lJPHdswqZQKwqZPuAWlEP9T/omRGVI0C2EEKJ0SUna2u3k5AqXAivUw4V4bfm3UhAQALa2NdhXIYRoxC7lpWJQBgKctWU9zd1CaOYa3KCmk5tdlUAtVRKoiUZMgm4hhBDWVbEUWHq6tnY7JQU8PcFFKsAIIUSlFM1O7mBrz03NBplLgTXIgBtwPnkQxwvnAMhp34OC0GvquEdC1BwJuoUQQhSXlqat3a5EKTC9HhIuwvlY7Wf/ALCT0W0hhKiUfEMBe4pkJw9w8sXOpuG/qRZNoJYaIaPconGToFsIIcQ/ipYCy8urcCmwzEyIjoGUZHBzB2/vGuyrEEI0co0hO7k1tplpuO/+DQC9myeZfYbUcY+EqFkSdAshhNBkZWnJ0qKjwcNDS5ZWTgYDJCZq08nz88HPX0a3hRCisopOJ1co3Oxd6BfY8LKTl8Tz9x+x0RcCkD5wFMpBakeKxk2CbiGEaOquLgUWFFShUmBZWdrg+MVEcHXRcq0JIYSomrT8DBSK5q7B9A7oir1Nw8tObpVSeG0tUps7YmwddkaI2iFBtxBCNGVVKAVmNEJiEsTGQE4u+PlWaCa6EEKIqyil0Ol06HQ6evl3JtDFj5ZuoQ1+OnlRLn/vxfFiLADZHftQENyyjnskRM2Tj0dCCNFUJSdro9uVKAWWkwOxsXDxolZzO0hGt4UQotK06eRnSclL5YbAXuh0Ouxs7AhzL/8yn4bCu8got5QJE02FBN1CCNHUFBZqI9unTmm3K1AKzGjUSoBFR0N2tpbY3L6RzHgUQoi6cHV28gs5iYS6BtVxr2qGbfol3PduBUDv4UNmr/C67ZAQtUSCbiGEaEpMpcASEsDHp0KlwHLztDJgFy5oM9EDA8s9E10IIYQVFtnJdTZ09+1EiEvjnTrktX0DOoMegLRBt4GdfGsrmgYJuoUQoikwGCAuTgu48/IgJKTcC7CVgpRLEBOt5Vnz9taCbiGEEJVjmk5++NKJRpmd3CqjEa+t35tvpkWMqbu+CFHLJOgWQojGrgqlwPLztczkFy5oMbqMbgshRNXtTznKmYwYAJq7hdDbv0vjyU5eAtejf+GQHA9AVpd+FAY0vvXqQpREgm4hhGislNKmkZ84AenpFSoFphRcvqzF6enp4O0DTjK6LYQQ1SLMvRkxmfF08+3ANR4tGlV28pJYlAmTBGqiiZGgWwghGqO8PDh9Gs6e1eaCN29e7iHqggJtJnp8vHaXwMBy51kTQghhhVKK9IJMvBw9APB18ubWlkNwsC3fF6ENnV1qMu77twNQ6OVHZveBddwjIWqXBN1CCNHYJCdDVBQkJmoRs7Nzue+amgrRMZB6Gby8KnRXIYQQVuQbCtiddJDE3BSGhQ4wB95NJeAG8Nr2AzqjAYC08DHlzikiRGMhV7wQQjQWhYXafPCTJ7XbLVqUe4i6UA/xcXA+TrsdEAC2tjXTTSGEaCpS8lL5M3EfOfo8bHQ2ZBQZ7W4yjAa8IrUEakpnQ9rgMXXbHyHqgATdQgjRGKSlaaPb8fFa8ewKlAJLT4eYGK3+tqcnuLjUXDeFEKIpUEoRlXaWI5dN2cld6RfYs3FnJy+B2+Fd2F/SapBndbsBvV/jrEEuRGkk6BZCiIbMaNTSi0dFQW4uhIaWe9qeXq/lWTt/XvtZRreFEKLqTNPJE3KSgKaTnbwkXpu/Nf+cNmR8HfZEiLojQbcQQjRU2dnaVPKYGHB3r1ApsMxMbe12chK4e2i1t4UQQlRddGYcCTlJ2Ohs6OHXiWvcm0Z2cmvsLl3E7dBOAAp9A8nqdkMd90iIuiFBtxBCNDRVKAVmMMDFixAbq9Xg9g8AOxndFkKIatPWsxWZhVm09mjZJKeTF+UV+QM6ZQQgLXws2MgfHNE0SdAthBANSV4enDmjlQJzcKhQKbCsLG1QPCkJXF21xOZCCCGqJt9QwN+pp+jicy12NrbY6HT09u9a192qewY9XtvWAaBsbEkbPLpu+yNEHZKgWwghGgpTKbCkJG0BdjnreRmNkJgEMdFazO73/+zdd3xb5fX48c/V8rblvRPHiZ3lbEgIECABCi1tWeXLKGW1lNL2236hk1mgLZQOWuhgtdD1g4ZCgLbMQgIECAESIJCdeMR7SvLSvvf3x5PYMUmIpEiWLZ93X3nVGlc6IbJ0j57nnJMn01qEECIa9u9Orhs6i/LnxDukMSP9vdexOjoB6F+wjEB2fpwjEiJ+5LRLCCHGun2jwHbuVBl0eXnIo8AGB9VW8tY2SE2R1W0hhIiGg3Unn5o5Od5hjSnZa1YN/eyQBmpigpOkWwghxjKXS9VuNzdDTo5qmBYCXVcL4w0NMDAIuTlgnZiNc4UQIqo+3p18UnoJi/LnYjXJafU+1o5m0j5cB4Avv5SBmiVxjkiI+JJ3ByGEGIuOYBSY2wN7GlSvtaRkKCwIuexbCCHEJ3B4Xbze+g7uoEe6k38C+ytPoRkGAM6Tzgp5d5YQiUqSbiGEGGv2HwWWnh7yKDDDgK4udVhfnxoDlpQU41iFEGICSTLbCBpB0q1pHFu4CHtSZrxDGnsCfuyvPg2AYTbjPPHzcQ5IiPiTpFsIIcYKw1DzvLZtA6dTFWCHmDV7PNDYBC3Naht5YaGsbgshRDQE9CCWvaOuUi0pnFC8hAxbumwnP4SMDa9i6e0BoG/RcoJZuXGOSIj4k3cLIYQYC7xe2LVLjQMLYxSYYUBPj+qz5nKpsm9Z3RZCiOjocvfwVsdGFuTVUJpWBEBOsj2+QY1xIxuonRPHSIQYOyTpFkKIeOvqUqvbYY4C8/mgqUn1WNM0tbotZXNCCHHkVHfy3XzYsx0Dg22OXZSkFkrt9mFY2/aQtvltALxFkxiceVScIxJibJCkWwgh4iUQUEvUO3aEPQrM4YD6BnD0qNrt5OTYhiqEEBPFobqTS8J9eNlrnhz62XnS2fJNsBB7SdIthBDxEOEoML9fHdLYpC7L6rYQQkRPl7uHde0bpTt5BDS/j6zX/gWAbrHiOuFzcY5IiLFDkm4hhBhNuq72hG/bFvYoMKcT9uxRu9GzsiA1NbahCiHERNLn62dNyzoMDDKsaSyV7uRhyXhnNZZ+FwB9R59MMMMe34CEGEMk6RZCiNES4SgwfwDaWlXCreuq7NtsjnGsQggxwWTY0qnMnIRf97Mof650Jw9T9mppoCbEoci7iRBCxNq+UWDbt6tW40VFIbcY7+2Fhj3Q2QGZmZCWFuNYhRBiAuly95BmTSXFohpjLMibjYYm28nDlNxcT9r29wDwlkzBPX1BnCMSYmyRpFsIIWLp46PAJk0KaRRYMAitrdDYqB4ivwAssrothBBRYRgG25y7+ahnO/kpOZxQfAwmTcOkSZOMSOS//K+hnx0rzgnpc06IiUSSbiGEiJX9R4Hl54dchN3fr3agd3SoXeh2e2zDFEKIicQb9LG+4z3aBjsBSDYnoRs6Jk2+2YyE5vOQ99pzAOjWJFzHnxHniIQYeyTpFkKIaNs3CmznTrVkXVYWUhF2MKgS7YYG8HggLy/kHmtCCCFC0Onu4a293cnNmokFeTVMySiX7eRHIPPtl7EM9gPQu+RU9DRpPifEx8npnBBCRJPLpZqlNTWpAdohjgIbHFSN0lrbIC1VjQITQggRHftvJ5fu5NGV/fITQz87pYGaEAclSbcQQkTDvlFg27erDLqkJKRlal2Hzk61uj0wCLk5YLWOQrxCCDGBBI0g9X2NGBhMSi+R7uRRkrRnJ6m7PgTAUz4N97Q5cY5IiLFJ3m2EEOJIDQ4OjwJLSwt5FJjbvXd1uxWSkqFIVreFECImLCYLSwsX0eN1ynbyKLJ/fEyY/HcV4qAk6RZCiEjtPwrM4VB7wkMYBWYYqsdafQP090FOjmpsLoQQIjr2bSc3ayaq7ZUA2JMyZTt5FGkeN1lvPgtAMCkZ17GfjnNEQoxdknQLIUQkgkGVbO/erbaRl5eH9A2/f2+PtZZmlWgXFsrCgBBCRJMn6OXt9vdpc3eioVGcWkCGLT3eYSWczLdewOweAKBn6cnoqenIx5kQBydJtxBCRKK2ViXdeXkhjwIzDGhqhMY9anU7hEVxIYQQYeh0d/NW+3sjupOnW9PiHVZCyt5va3nHKWdJwi3EJ5CkWwghwtXWpmq4c3JCTrhBNUxrbFRNzSXhFkKI6JHu5KMruX4bKXVbAHBXzGCwcgZpwTgHJcQYJkm3EEKEo68PNm9Wc7fTQ9+u2NcPdXWqM3lycgzjE0KICcYwDN5oe5eWwXYAJqeXsjB/jnQnjyH76uExYY7lZ8cxEiHGB1O8AxBCiHHD74ctW1TinZcX1mH1dapbud0eu/CEEGIi0jSN/JRczJqJo/LnsrhgviTcMWRy95P15vMABJPT6F16WpwjEmLsk3ckIYQIhWHAzp3Q3AylpSF3PzMMNRassxPyC2IcoxBCTBCGYeAJekmxqK1D1VlTKE0rlPrtUZD55vOYvG4Aeo89HT0lDfDENyghxjhZ6RZCiFA0Namku6BAdSsPUUeHOjQ7ByzmGMYnhBAThCfoZW3r27zSsg6/HgDUarck3KPAMEY0UHOsODeOwQgxfshKtxBCHI7DAVu3qqZpKSkhH9bXp+q4bUmQLI3ThBDiiKnu5BtxB72YNRMOr5OClNDLfcSRSd69meQ9OwBwT63BO7kaMOIblBDjgCTdQgjxSTwe1TjN64WSkpAP8/tVwu3xqFncQgghIjfcnXwbBpBhTWdp4ULpTj7Kstfs10BtxTlxjESI8UWSbiGEOBRdh23b1B7x8vKQD9tXx93VpXajCyGEiJwn6OXt9vdpc3cC0p08XkwDfWS+9SIAwdR0epd8Ks4RCTF+yLuVEEIcSn29Wq4uKgJT6C0w2tvVPO6cHDVZTAghROTe79pMm7sTs2ZiYd4cKjLK0EJsZimiJ+uNZzD5vAC4jjsDI0nmXwoRKkm6hRDiYDo61Cq33Q5JoRdk9/ZCXb0q/Q7jMCGEEIcwL3cWnoCXBXmzyZLt5PFhGNhHNFCTreVChEO6lwshxMcNDKh53IYBmaGf4Pl8amHc5w3rMCGEEPvxBL3sctUPXU6xJHNS6VJJuOMoZccHJDfXAjBYPR9f2dQ4RyTE+CIr3UIIsb9AQCXcTieUlYV8mK5Dwx7o7pY6biGEiNT+3cltZhuT0kNvYCliRxqoCXFkJOkWQoh9DAN27VIF2aWlEEbNYPveedxSxy2EEOEzDIOtzl1s7tmOAWRa08myZcQ7LAGY+5xkvP0yAIH0LPqOPjnOEQkx/kjSLYQQ+7S0wI4dkJcHltDfHl0uqK+DtFSp4xZCiHB5gl7Wt79P+1B38jIW5ddgke7kY0LW6//B5PcB4Fr2WQybfNAJES55NxNCCFCZ89atKmtOSwv5MK8XauvUXG67PXbhCSFEItp/O/m+7uRTMkMf0ShizDCwr3ly6KJz+dlxDEaI8UuSbiGE8HpVHffAQNh13Hv2gKMHCgtjGJ8QQiQovx7AHfSSaU1nadEi2VI+xqRu20BSawMAAzMX4SuuiG9AQoxTknQLISY2XVdbyltbw0q4AdraoKlZ1XGHMcZbCCEmNMMwhuZsl6QVckzhQkpSC2Q7+Rhkf3n/BmrnxjESIcY3OU0UQkxsjY1QW6uWqsPogOZ0Qn291HELIUQ4OtzdvNj0GoMB99B1k9JLJOEeg8yuHjLfXQNAICObvqOWxzkiIcYvSbqFEBNXV5eq405Ph+TkkA/zeNQ87mAQMmQnpBBCHJZhGGxx7OTVlnW4fH181LM93iGJw8ha+2+0YAAA54mfB4s1zhEJMX7J14pCiIlpcBA2b1ZzufPzQz5M16GhARwOqeMWQohQeAJe1neM7E6+IK8mzlGJT6TrZK9ZNXTReZI0UBPiSEjSLYSYeIJB2L4denrCruNuaVF/cnOljlsIIQ6nY293co90Jx9X0ja/ja2jGYD+miX4C8P7rBRCjCRJtxBi4qmtVfvDS0rCypwdTrXKnZ4ONlvswhNCiETQOtDO623vYIB0Jx9n7KuHG6g5pYGaEEcs5KT7tddeO+C6E044IarBCCFEzLW1qVXu3Fywhl6f5vZA7W7QDZV0CyGE+GT5KXlk2jLITspiYV6NNEsbJyzOLjI2qvP+QFYufQvkfF+IIxXyu99JJ52EpmkYhgGApmkEg8GYBSaEEFHX26vquC2WsDLnYBAa6tXhBQWxC08IIcY7h9dFli0Tk6ZhMZlZUXosVpM04BpPsl59Gk1X5/jOE89Un5lCiCMS8m9RXV1dLOMQQojY8vlUp/L+figtDevQllY1xlvquIUQ4uB0w2Cbcxebe7YzO2c6s7KrACThHm/0INlrngTA0DQcy6WBmhDREHLSPXny5FjGIYQQsWMYsHMnNDerhFvTQj60p0etcqdnhLUbXQghJgzVnfw92t1dAAz4BzEMAy2M91oxNqRtWoe1uw2AgbnHEsgrjnNEQiSGqO0XMQyDNWvW4PV6Of7448mQ4bVCiLGiqQl27VJ7w8PYJud2q35rBpCeFrvwhBBivOpwd/FW+3vSnTxB7D8mzLHinDhGIkRiiSjpvuGGG3jzzTdZs2YNoBLuT33qU6xevRrDMJg0aRIvv/wyU6dOjWqwQggRtp4e2LIF0tIgJSXkw4JBqG9Qddwyj1sIIUbafzu5dCdPDJbuNtLfex0Af04h/fOOi3NEQiSOiKoTn3jiCRYvXjx0+fHHH+fll1/mJz/5Cf/5z38IBoPccsst0YpRCCEi43arhNvng+zssA5taYG2VsjLC2s3uhBCTAj9/gG2OHZiABUZ5ZxSdrwk3OOc/dWn0Qwd2NtAzSwN1ISIloh+m5qbm5k2bdrQ5VWrVjFr1iyuu+46AK6++mruvffe6EQohBCRCAbVaLDOTigrC+vQ7m61yp2RIU1bhRDiYDJt6SzMq8GERoVsJx//ggHsrzwNgKGZcJ50ZpwDEiKxRLTSbbFY8Hq9gNpa/vLLL3P66acP3V5YWEhXV1d0IhRCiEjU16s/hYVhtRzfV8etoXakCyGEUNvJtzh20uNxDl1XmTlJEu4Ekf7+61gdHQD0L1hGIEfqqoSIpoiS7pqaGv7+97/jcDh4+OGH6e7u5owzzhi6vaGhgby8vKgFKYQQYenogG3bICsLkpJCPiwQUHl6Xz/k5MQuPCGEGE88AS9rW9fzUc921rVvIKAH4h2SiLLs1dJATYhYimjj5M0338znPve5ocT6uOOOY/ny5UO3P/PMMxx99NHRiVAIIcLR36/quDUNMjPDOrS5GdrapI5bCCH2Gdmd3MzsnOlYTFJ3k0isnS2kfbgOAF9eCQNzjolzREIknojeNU899VQ2btzIf//7X+x2O+eff/7QbQ6HgxNOOIEzz5RaECHEKPP7YetWcDrDruPu6oI9e1SeLnXcQoiJTjcMtjp2ssWxQ7qTJzj7K0+iGQYAzuVngckc34CESEARn1rOmjWLWbNmHXB9dnY2v/71r48oKCGECJthwO7d0NgIpaVhLVUPDkJdPZjNkJoauxCFEGI88OsB3mx7l3a36s9TkVHOwrzZssKdiAIB7K/+CwDDbMZ5wufjHJAQiemI3j3feust1qxZQ0dHB1//+tepqqpicHCQbdu2UV1dTXp6erTiFEKIT9bSAjt3Qn5+WEvVgYBqnNbfD4UFMYxPCCHGCYtmxqSZMGtmFuXPoSIjvJ1DYvzI2PgKFlc3AH0LTyJol55MQsRCREm3z+fjggsu4Omnn8YwDDRN43Of+xxVVVWYTCY+9alPcc0113DDDTdEO14hhDiQ06nquJOSwlqqNgxoaoL2dsgvkDpuIcTEpRsGhqFjNpnRNI3FBfPxBL2ynTzB2aWBmhCjIqLu5TfddBP/+c9/uPfee9m+fTvG3joQgOTkZM477zyefvrpqAUphBCH5PWqOu7BQcjNDevQrm5Vx223g0VK2IQQE5Qn4OW11vW827lp6JwuyWyThDvBWdsbSd/8NgC+gjIGZ0kTZCFiJaKk+9FHH+Xqq6/mq1/9KjkHmaszc+ZMamtrjzg4IYT4RLoO27dDaysUF4d16MAA1NWC1QopKTGKTwghxrj2wS5ebHqNDncXTQNtDAQG4x2SGCUHjAkzRZQWCCFCENH28o6ODubMmXPI281mM4OD8qYthIixPXugthYKC1UXtBD5A1BbpxbHC6SOWwgxAe3rTr7ZsQOATGsGxxYtJN2aFufIxGjQ/D6y1v4bAN1ixbXsc3GOSIjEFlHSXV5ezrZt2w55+xtvvMG0adMiDkoIIQ6rq0ttK8/IgOTkkA/bV8fd2SF13EKIickd8LC+4z063KqB1pSMchbk1WCRUVETRsa7a7D0OQHoO2o5wczs+AYkRIKLaB/JRRddxP3338+6deuGrtP2nrk++OCDPPbYY1xyySXRiVAIIT5ucBA2b1bby+32sA7t6oI9DZCdLXXcQoiJxzAM1ra+TYe7G7NmZnHBfI4umCcJ9wRjX/3E0M/OFefGMRIRD/u14xKjJOSV7g8//HBoS/kNN9zAW2+9xQknnMDMmTPRNI1rrrmGnp4empqa+MxnPsM111wTs6CFEBNYIKBWuLu7YdKksA7t71fjwWy2sBbHhRAiYWiaxrzcmbzfvYWlhQvJlGZpE46tpZ60bRsB8JZUMDhjYZwjEqMpEFBrFxUV8Y5kYgl5pXvRokVcd911eDwebDYbzz//PA8//DCVlZXMmDEDr9fL3Llz+fOf/8y///1vzGHUVwohRMhqa1Utd0lJWHvD/X6VcA8Ohr04LoQQ45o74KHD3TV0uTA1n1PLTpCEe4KyrxluoOY86Wyps5pAdB1aWmDyZJg6Nd7RTCwhr3R/+ctf5he/+AX//Oc/uffeezn11FO5+OKLufjii2MZnxBCDGtthR07ICdHtR0PkWFAYyN0dqo6biGEmCjaB7tY3/EeAT3IqeXLyNjbKM0kidaEpPk82Nf+BwDdasO57LNxjkiMppYWyM+HmTPDOo0SURDySve9997Lm2++SUZGBqeffjoXX3wxnZ2dsYxNCCGG9fbCli3qUyI9PaxDOzpU0p2dI3XcQoiJQTcMNvfs4NXWt/AEvaRZU6SQU5Dx9suYB3oB6F18Cnp6VpwjEqOlo0OdPtXUQGpqvKOZeMLqXr548WI2bNjA3XffzY9+9COee+45fvazn7Fo0aKD3n/hQqkREUJEgc+nEu7+figrC+vQvn113EmQnBSj+IQQYgyR7uTiULL331ouDdQmDKdTfedWUyMldvES9sgwk8nENddcw+c//3mWLFnC1772tQPuYxgGmqYRDAajEqQQYgIzDNi5U+2JKi0N61C/H+pqweNRo7yFECLR7dtO7gl6sWhmFuXPYXJGeF9WisSU1LiL1B0fAOApm4q7am6cIxKjYXBQrVnMmyfnQvEU0Zzul19+mauvvhqn08nVV1/N0UcfHe24hBBCaWyEXbugoAAsob9lGYbqt9bZKR8yQoiJo2WwHU/QS5Ytg6WFi8i0hVeOIxLXiAZqK86RBmoTgM+nzoNmzlTN00T8hJV0d3Z2cs011/Doo48yd+5c1q1bJwm3ECJ2enrUeLC0NEhJCevQ9naVr+fmggxTEEJMFHNzZ5JktlGdVSnbycUQzeMm6/VnANBtybiOOyPOEYlYCwahrQ2mTIGqKvmOJd5CbqT24IMPMmPGDJ566inuvPNO3n33XUm4hRCx43arOm6/H7Kzwzq0txfq6tUs7iSp4xZCJLD2wS7ebHsX3dABMGsmZmVXScItRshc/yJm9wAAvcd8Cj1VdkAkMsNQA1+KitQqdxgbBUWMhPxPcNVVV3H66adz7733Mln2JwghYikYhG3bVKvN8vKwDvX5oL4efF61I10IIRKRbhhscexgi2MnADtd9Uy3V8Y5KjFWZa8e3lruWHFOHCMRo6G9HTIzYfZstQAh4i/kpPvRRx/l/PPPj2UsQgih1NerP8XFYAp5Qw66Dg17oKtLEm4hROJyBzysb3+PDs9wd/KpmbIgIg4uqX4bKbWbAfBMno6ncnacIxKx5HCosrqaGpV4i7Eh5KRbEm4hxKhob1er3NnZYLOFd2gHNDdBTo7UcQshEpN0Jxfh2n9MmEMaqCW0/n7VrXzBAsjPj3c0Yn8hJ90rVqw44LrVq1dHNRghxATX36/quE0myMgI61CXC+rrIDVV6riFEIlpd28DGzo/BJDu5CIkJvcAmW8+D0AwOZXepafHOSIRK16v6j87axaUyfdwY07ISbfUcQshYsrvV53KXa6wPy28XqirUw9ht8cmPCGEiLf85Fwsmpny9BIW5NVIszRxWJnrnsfsGQSg99jT0VPS4hyRiIVAQHUqr6yEadNkM8NYFHLS/fDDD8cyDiHERGYYahZ3YyOUlob1aaHrah53T4/M4xZCJJ7BgJtUixqZmGlL57TyE0mzpsY5KjEuGMbIBmrLpYFaItrXqbykRHUql/K6sSn0DkVCCBErzc2wc6cqQApzrkV7uzo8JyesnmtCCDGm6YbBRz3bebZhNZ3u7qHrJeEWoUqu20Jyw3YA3JWz8FbMiHNEIhba2lQbnJoaKa8by2RqmxAivpxOta08OVkVZId5aH291HELIRLLx7uTtw12kp+SG+eoxHiT/fITQz87Vpwbx0hErHR3q56zNTWQLu0dxjRJuoUQ8ePxqMZpbrfaVh7moXX1UscthEgs7YOdvNXxHt6gT7qTi4iZBvvJfOtFAIIpafQu+VScIxLR1tcHPp/qVJ4r38mNeZJ0CyHiQ9dhxw61LyrMxmm6Dg0N4JA6biFEgtANgy2OHWxx7ASkO7k4MllvPIvJ5wHAdfwZGMkpcY5IRJPHo+Zxz5kT9pqFiBNJuoUQ8VFfD7W1KmsOs+tHSwu0tKpvdqWOWwiRCFoG2oYS7sqMSczPmy3dyUVkDAP76uGt5U5poJZQAgHVz6aqSnUrF+PDmDtd/f3vf09FRQXJycksWbKEt99++xPv73Q6+cY3vkFxcTFJSUlUV1fz7LPPjlK0QoiIdHbC9u2QmalqucPgcKpV7vQ0VcckhBCJoDStiCkZ5SwpmM9RBXMl4RYRS9m5ieSm3QAMVs3FWz4tzhGJaNF1tfBQXg4zZsjCw3gypla6V65cybXXXst9993HkiVL+M1vfsNpp53G9u3bKSgoOOD+Pp+PU089lYKCAh5//HFKS0tpaGjALgWeQoxdAwOqjlvXISsrrEM9HqirBd2QhiFCiPHNMAy2O3dTmTEZm9mKpmkcXTAv3mGJBLD/Krc0UEssbW2QlwezZ4PVGu9oRDhCSrpNJhNaBFPWg8FgWPe/6667uPLKK7n88ssBuO+++3jmmWd46KGH+OEPf3jA/R966CF6enp48803se595VVUVIQdpxBilAQCsG2bGqpdXh7WocGg2pHucsFBvoMTQohxwx3wsL5jIz1uJz0eJ0sLF0V0niXEx5n6XWS+/RIAwbRM+hafHOeIRLR0dUFKiupUHuawFzEGhJR033zzzQd8GDz55JNs3ryZ0047jenTpwOwbds2XnzxRWpqajjrrLPCCsTn87Fhwwauu+66oetMJhOnnHIK69atO+gx//rXv1i6dCnf+MY3ePrpp8nPz+eiiy7iBz/4AeZD1Ih6vV68Xu/Q5d7eXgB0XUfX9bBiFmIs0nUdwzDG3uvZMGDXLrU3vLh4+LoQNbdCS5uq49ZMEPqRYrwz9vufEONd+2An6zveH+pOXppWBBry+hZRkfX6fzD5fQA4jz8D3ZZErD8x5T06Gvb+FzQMDOPA87feXjWtZc4ctUlwrJ3iJZpYnEOHlHTfcsstIy4/8MADdHR08NFHHw0l3Pts3bqVFStWUFJSElYgXV1dBINBCj/WiriwsJBt27Yd9Jja2lpWr17NF7/4RZ599ll27drF17/+dfx+Pz/60Y8Oeswdd9zBrbfeesD1nZ2d+Hy+sGIWYizSdR2Xy4VhGJjGUrFPT49KujMz1bJ1GDth+vthTztYc8GXBPKbOrEYGHjNfgA0ZDVQjE+GYbCzu5ZdPfUApCelsaC4hgxbOgN44hucSAyGwZT9tpa3nHIGHnPsX1vyHn3kNMOHKU3DE+hB94w8y/H51HiwyZNVDXdHR5yCnEBcLlfUHzOimu5f/OIXfPOb3zwg4QaYOXMm3/zmN/n5z3/OlVdeecQBfhJd1ykoKOCBBx7AbDazaNEimpub+cUvfnHIpPu6667j2muvHbrc29tLeXk5+fn5UgsuEoKu62iaRn5+/thJunt7VecPmy3sodpuDzTtAYsbclOB8KpWRALYt3qSFkyWEzoxLnkCHt5qf59OTw8AUzLLqSqoJNNIQwvKa1pER+rWDaS07gFgYMZCzEXTSRuFz0x5jz5CAT/WTifesinoWZNHTHTx+8HphOnT1Z+xclqX6Gwx6NQbUdLd1NQ0VEN9MFarlaamprAeMy8vD7PZTHt7+4jr29vbKSoqOugxxcXFWK3WEVvJZ86cSVtbGz6f76D/wZKSkkhKSjrgepPJNHYSFCGOkKZpY+c17fOpOu6BgbDncQeD0NgAfb1qsph8lE9c2n7/E2K8MWlm+v2DWDQzi/LnMimjhAGTBy0or2kRPdlrnhz62bninFF9bcl7dIQCAaxdrfhKpuCtrEGzDOdXug6trWqFe/p0sIyp9teJLRbnzxE9Yk1NDX/4wx9obm4+4Lampib+8Ic/MGfOnLAe02azsWjRIl5++eWh63Rd5+WXX2bp0qUHPea4445j165dI/bd79ixg+Li4ph8QyGECJNhwI4d0NwMh/jy7JO0tKgPnLw8kB5DQojxxNivZ0WS2caxRYs4pWwZkzNK4xiVSFTmXgeZb6tz6ECGnb6jVsQ5InFYwQDWzmb8BeV4KmfBfgm3YahzoKIimDVLEu5EENE/4a9//WtOO+00qqurOfvss5k2Tc3/27lzJ0899RSGYfD3v/897Me99tprufTSSznqqKNYvHgxv/nNbxgYGBjqZn7JJZdQWlrKHXfcAcDVV1/N7373O7797W/zv//7v+zcuZPbb7+db33rW5H8tYQQ0dbYCLt3q0+NMD8xuruhvgEyMuTDRggxvrgDHt5q38iUjHIqMtWkhtzk7DhHJRJZ1tp/owUDALiWfQ7DKotPY5oexNrRjD+/FPe0ORi2kbtwOzrUaNTZs1XHcjH+RXQqe/zxx7N+/XpuuukmnnzySdxuNwApKSmcdtpp3HrrrWGvdAOcf/75dHZ2cvPNN9PW1sb8+fN5/vnnh5qr7dmzZ8Ryf3l5OS+88ALXXHMNc+fOpbS0lG9/+9v84Ac/iOSvJYSIpu5u2LpVfWokJ4d1qNsNdXVqO3laWmzCE0KIWGgb7GR9+3t4dR99/gHK0kuwmA4+UUWIqND1EVvLHSvOiWMw4rB0HWtHC4G8YtxVczGSRp4jOZ1qd9++TuUiMWiGEcbMnoPQdZ3Ozk6AsdW4KUS9vb1kZWXhcDikkZpICLqu09HRQUFBQfx+H91uePdd9ckR5iSDQAB27oS2tr113LKtfMIzMBgwe6RJjxjTdENnc88Otjp3AWC3ZbK0cCEZtvQD7iuvaRFNqR+tZ/Kd3wCgf/ZiGn/4h1F9fnk9h8EwsHY0E8jKxT1jAXrKyJWFgQFwOGD+fFXLLeLD6XSSnZ2Ny+UiMzMzKo95xJs2TSYTycnJpKenj7uEWwgRA8GgapzW2Qnl5WEf3tKiEm6p4xZCjBf7tpPv604+NXMy83NnYZYVbjEKslevGvrZKavcY5dhYOlsJpCVjbt63gEJt8+nNgnOnAmTJsUpRhEzEWfJ7777Lqeffjqpqank5uby6quvAmre9plnnskrr7wSrRiFEONJXR3U10NxcdizLbq6oGGPGuUtddxCiPHAF/Tz36a1dHp6sGhmjilYwKL8OZJwi1FhdnaRsfEVAAJZufQtPCmu8YhDs3S1oqdl4a6ah56WMeK2YFA1jp0yBaqqZNEhEUWUdL/55pscf/zx7Ny5k4svvnhE9/C8vDxcLhf3339/1IIUQowT7e2wfTtkZ6uZ3GEYHIS6ejBpkJoam/CEECLabGYrUzLKsdsyObVsGZOkO7kYRfbX/oUWVMO4nSd+Xr6xHqMsXW3oyalqhTt9ZKG2YaiEu7hYrXKb5fu6hBRR0n399dczc+ZMtmzZwu23337A7cuXL2f9+vVHHJwQYhzp64PNm9XqdkbG4e+/n0BALZD396t8XQghxjJ3wMOAf3Do8uycalaUHnfQ+m0hYkYPYl/zFACGpuE86ez4xiMOytLTjmFLwl09n2DmgSc57e2qYVpNDSQlHeQBREKIKOl+5513uPzyy0lKSkI7yP6H0tJS2trajjg4IcQ44ferTuW9vZCfH9ahhgFNTepDR+q4hRBjXdtgJy82vsab7RsIGmqF0aSZpEO5GHVpH76FrasFgIE5S/Hnh9e4VMSexdmFYTLjrp5H0J57wO09PWpzQk1N2OsVYpyJaA+K1WodsaX845qbm0lPl297hZgQDEO1G29qgtLSsLPmrm7YswfsdrDIOasQYoz6eHfyFCMZX9BPirxxiTjZv4GajAkbe8yubjAM3NXzCWQfuCDR36+GvSxcqBYdRGKLaKX7mGOO4fHHHz/obQMDAzz88MOceOKJRxSYEGKcaG6GXbvUCneYtWQDA1BXC1YrpKTEKD4hhDhC7oCHV1veGkq4p2ZO5uTS40ixJB/mSCFiw9LTTvr7rwPgzy6gf/7xcY5I7M/c60AL+HFPm0Mgr+iA2z0eNRps5ky1XiESX0Qr3bfeeisnnngiZ5xxBhdeeCEAH3zwAbW1tfzyl7+ks7OTm266KaqBCiHGIIcDtmxRGXOY3c/8e+u4BwehoCBG8QkhxBFqG+xkfft7eHUfFs3CUQVzmZQu23hFfNlfeRpN39tA7aQzwSwN1MYKU78LzefBXTUPf8GBGXUgoErqpk2Dykopq5soIvoNXbJkCc8++yxXX301l1xyCQDf+c53AJg6dSrPPvssc+fOjV6UQoixx+NRCbfHE/bXtPvquDs6IL9APnCEEGOTYRh81LMNr+7DbstkaeFCaZYm4i8YwP7q0wAYmgnnSWfFNx4xxDTQh8k9gKdqLv6i8gNu39epvLQUZsyQTuUTScRzulesWMH27dvZuHEjK1eu5NFHH+Xtt99mx44dsrVciESn67Btm/qqtujAbVOH09UFexqkjlsIMbZpmsYxhQupzprCydKdXIwR6R+8ibWnHYD++ccRyCmMc0QCwOTuxzTgwjNlFr6iSQe9T1ubmtJSU6Mmq553Hqxbp27Tdfjf/4WpU9Uq+O9+d+jnev55OOoomDsXjjkGPvhg+LbLL4fqapg3D447Dt55J7T4BwfhwgvVc1dXwyEqiQG1WDJnDsyfr/6sXXv42zweWLQIXK7Q4kk0Ea10u1wusrLUjLn58+czf/78aMYkhBjr6uvVn6KisL+m7e9X28ptNqnjFkKMPW2DHTi9vczIngZAujWN+Xmz4xyVEMOyVz8x9LNjxblxjETso3kGMfc68VTOwld28D3jXV1qJNicOZCWBm+/rbqXL12qbv/739UGwh07VGK6YAEsXw6zP/b243DAF78Ir72mblu7Vl3+6CN1+9lnw4MPqjY7//mPSuzr6w//d/jlL1V8u3ap87QlS9Tz5x7YdB1Qz2u3h35bcjJ86Uvwq1/BbbcdPp5EE9FKd0FBAWeeeSaPPPII/f390Y5JCDGWdXaqVe7MzLAHSvr9UFevvk091Bu1EELEg27ofNi9jdda32ZTzzY63N3xDkmIA1i6Wknb9CYA/twiBuYujXNEQvO4sTi78Uyejrds6kET7r4+dQ40ezbk5Kjr7r8fLrpo+D4rV8KVV6q1jJwcOP98ePTRA59v926VCO9LxpctU1NgNm5Ulz//+eG+tscco/rdBgKH/3usXAlf+5r6ecoUOOkkePLJ0P4bhOqCC9QXAoYR3ccdDyJKuq+99lo2b97MxRdfTEFBAeeeey7//Oc/cbvd0Y5PCDGWDAyor2ENA/budgmVYUBjI3R2QK6MxhBCjCGDATevfKw7eW6SPb5BCXEQ2a88hbY3Y3GcdBbIfPi40nweLM5OPJOq8E6uBtOBqZXHA06n6lResl8PxldeUavJ++zZA5MnD1+uqFDXfVxVFXR3w5vquxf+9S+V1B9sNfvuu+EznwltuEyoz7/PySerLezXXqtOD0O5rahI7XLcvPnw8SSaiJLuO+64g127drF+/Xq+/vWvs2HDBs4//3wKCgq48MILeeqpp/D5fNGOVQgRT4GAWuHu6YHC8OvHOjtV0p2dLXXcQoixo22wg/82rqXL04NFs7C0cCGL8udglmRGjDWBAPZXngLAMJlxSQO1uNL8Pizd7XjLpuGtmHHQhHtfp/KqKrV6vL+mpohOp8jKUvXW112naqRffBFmzTowsf773+Gxx+CBB8J/jsNpaIANG1Ti39kJ3/teaLeBSrybmqIf01gXcSM1gKOPPppf/vKX1NfX88Ybb/DlL3+ZtWvXcu6551IYyatICDE2GYbaz9TQAMXFYbcb7+uH2lqwJamaHiGEGAu2OHbyWuvbQ93JTy1fRrmMAxNjVMZ7r2FxqbKHvoUnELDLtrG4CfixdLXiLavEM2XmQfvb6Dq0tKjV4+qDLIKnpqpV8H0mTVKnWfvU16vrDmb5cnj1VZXc/upX6nlmzRq+feVKuPVW+O9/Q0/sw3n+fdenpcHXvz6ykdon3Qbq7zwRe/ocUdK9v6VLl/KNb3yDK6+8kvT0dHp7e6P10EKIeGttVZ098vLAag3rUL8f6mrVm6w9vB3pQggRU6lm9S3g1MzJqju5NS3OEQlxaPb9Gqg5pYFa/AQCWLta8JVMwVM5+5B7t1tbIT9fbSs/2KnT3Lmwffvw5fPOU/XOwaDaVLhyparrPtRj7/PjH8OKFarrOKjV7RtvhJdeOjBp/t3v1Ar5wZx3Htx3n/q5rk5tfz/rrAPv53Co3jygvlhYuVI1fTvcbaD+brt3q2ZyE01E3cv3V1dXx8qVK3nsscf44IMPMJlMLF++nPMP9SoRQowvLpeq47bZ1NeWYTAMVQ/U1QUFBTGKTwghwuDXA1hN6vSnIrOcDFs6ucnZcY5KiE9mbW8i/aP1APgKShmYvTjOEU1QwQDWzmb8heV4KmeB5eALEZ2d6pSppkataB/MF74AL7wAp5yiLn/pS2q8V1WV2lB47bXDyem//qX+/PGP6vLNN6sV5EBAdT//05+GH/eLX1RbuM88c/i6l19Wzde2bIHKyoPH873vwRVXqJFlZrNK0PP2bqa47z61mn7bbarS8KqrVIyBACxcqGrH4ZNvA3j9dTj66OFmchOJZhjh949rbGzkscceY+XKlWzYsAFN01i2bBnnn38+5557Lvn5+bGINSZ6e3vJysrC4XBgl3bKIgHouk5HRwcFBQWYDlJfFBavV7XD7OiAsrKwD29rU2/AWXZIDq/RuRBDDAwGzB7SgslohFfaIMQ+uqHzUc8O9vQ3cWrZCSSZbXGLRV7TIlz5K39L3n/+AkDH+d+k+7OXxTeg/UyY17MexNrehD+/FHf1PIykg9fLuVxqd9/ChSr5PZT+fjj2WDWnO8w1jYgdfzw89xxkZIzO833cBRfAl78Mp54an+cPldPpJDs7G5fLRWZmZlQeM6KV7smTJ6NpGscccwy//vWvOe+88yguLo5KQEKIMULX1Zby1taIEu6+PjUeLDlZEm4hRHwNBty81f4eXZ4eAJr6W5maNfkwRwkxRgT82F/9FwCG2YJz2efjHNAEpOtYO1oI5BXjrpp7yIR7cFCd/8yb98kJN0B6Ovz612ord01NDGI+iNdfH53nORiPB048cewn3LESUdL9i1/8gv/5n/+hvLw82vEIIcaKxkZVeFNYeNAGIZ/E51MfIj6vbCsXQsRX62AHb7e/j1f3YdEsHF0wV5qliXEl4901WPocAPQetZxg1gTcmxtPhoG1s4WAPU8l3MkH7wLm96tt5TNmjBy99UlOPjmKcY5xyclw9dXxjiJ+Ikq6v/Od70Q7DiHEWNLdrfaFZ2SE3W5c16FB6riFEHGmtpNvZ5tzNwB2WyZLixZJszQx7mSvXjX0szRQG2WGgaWzmUBWNu7qeegpB3//CAZVzfOUKapTeZhDXsQEEFLS/de//jWiB7/kkksiOk4IEUeDg6rTht8/3EEjDO0d0NykmmSEuUAuhBBRs8Wxcyjhnpo5mfm5s2T2thh3bK31pG19FwBv0SQGZy6Kc0QTi6WrFT0tC3fVPPS0gxdCG4aqxCsuVp3KD9HMXExwIb0sLrvssrAfWNM0SbqFGG+CQTW/oqsrojpulwvq69T8xSSp4xZCxFF1ViUtA+3MzJ4m28nFuGVf8+TQz84V58gS6iiydLWhJ6eqFe70Q888bW+HzEyYPTvszYFiAgkp6a6rq4t1HEKIsaC2FurrVfePMDufe72qjtvvV3MphRBiNOmGTmN/C5PSS9E0DZvZyqlly9AkSRHjlObzkrX2PwDoVhuu4z8b54gmDktPO4YtCXf1fIKZhx4p6HCoXX01NSrxFuJQQkq6J4faDUAIMX61talu5Tk5aiZ3GHRdzePu6VF914QQYjTt353crweYllUBIAm3GNcy3nkZS78LgL7FJxPMsMc3oAnC4uzCMJlxV88jaM895P0GBlRF3vz5stggDu+Iqw62bNlCQ0MDoJLzWbNmHXFQQohR1tcHmzerr2vT08M+vL0dmptVvn6ko8GFECIcrQMdrO94D5/ux6JZ4jp/W4ho2r+BmmO5NFAbDWZXNxgG7ur5BLIPnUl7varn7KxZMN6GOQWDsHbtcB36smXSg2c0RJx0P/3001x77bXU19ePuH7KlCncddddfP7zMkNQiHHB71eN0/r6IqrjdjrVjvTUVKnjFkKMno93J8+2ZXFM0ULpTi4Sgq1pN6k73gfAW1qJu3pefAOaAMy9DrSAXyXceYcesh0IqM2BlZUwbdr4KrNftQq+/W1oahq+rqwM7r4bzjknfnFNBBGtST377LOce676xu3222/nySef5Mknn+T222/HMAzOOeccnn/++agGKoSIAcOAnTvVMnVxcdifHF4v1NWrvD3j4E09hRAi6gYDbl5peWso4Z6WWcGKsmMl4RYJY8QqtzRQizlTvwvN58E9bS7+gtJD3m9fp/KSEtWpfDytEK9aBV/4wsiEG9Qp4Be+oG4XsaMZhmGEe9DSpUvxer2sXbuWtLSRH3ADAwMcf/zxJCcns27duqgFGiu9vb1kZWXhcDiw2+3xDkeII6brOh0dHRQUFGA63F7vxkbYuBFyc1XL8bCeR+XrLS1qHrdsKxexYmAwYPaQFkxGQ048BXS6u3mlZR0Wk4Wj8ueOu+7k8poWn0Tzeqj61umYB/vRbUnsvOf5Q46rGgvG++vZNNCHabAPT9VcfMWf3MeqtVUtMixaFFE1XtwEg1BRcWDCvY+mqRXvurrx9UVCrDidTrKzs3G5XGRGqUNeRNvLN23axO23335Awg2QlpbGZZddxvXXX3/EwQkhYsjhgK1b1b7wMBNuUB88La1Sxy2EGH35KbkcnT+PvJQc0mV1WySYzPUvYh7sB6D3mE+N6YR7vDO5+zENuPBU1uArmvSJ9+3uBqtVdSofywm3x6Oa29bXqz8NDbB+/aETblAr+I2Nqtb7pJNGKdAJJqKkOzk5mZ6enkPe3tPTQ7IMqhNi7PJ4VOM0r1ftkQqTw6nexNPTwm50LoQQYRsMuHmnYxML8maRaVMJSEXmOOteJESIpIHa6NA8g5h7nXgqZ+Erq/zELfz9/eqUaeFCtTkwngYH1TlYQ8PIxHrfz21tkT92a2t0YhQHiijpXrFiBXfffTenn346S5cuHXHb+vXrueeee/jUpz4VlQCFEFGm67BtG3R0RNRy0+OB+jq1VSn70KMrhRAiKloH2lnf8T4+3c+7nZtYXnKsjAITCSupYTspuz8CwDOpGs/U2XGOKDFpXg8WZzeeihl4y6Z+YsLt8aiRqHPmQOmhy72jpr//wKR6/8S6oyN2z11cHLvHnugiSrp//vOfs3TpUo4//ngWL17M9OnTAdi+fTtvv/02BQUF3HnnnVENVAgRJfX1qminqCjsfeHBoDrc6VR13EIIESsHdCdPymJxwXxJuEVCkwZqsaf5PFgcHXgmVeOdXP2J50KBgBqLWlWlupVHQ2/vcAJ9sMS6qyvyxy4uhsmTVf32vj+TJ6s1ltNOU314DtbNa19N97JlkT+3+GQRJd1Tpkxh06ZN3HHHHTz33HOsXLkSUHO6v/3tb/PDH/6QAjkjF2Ls6ehQq9x2e0TzvVpaobVNba2SOm4hRKwMBtysa99It8cBqO7k8/JmYtakw49IXJpnkMw31fQfPSmF3mNPj3NEiUfz+7B0t+Mtr8JbMeMTT2Z0XSWp5eUwfXro5z1O56G3ftfXq5Y6EcWuqYrA/ZPp/X+eNAk+qbr3nntUl3JNG5l47/te5ze/kSZqsRTxnO6CggJ+/etf8+tf/zqa8QghYmVgQM3jNgyIoBOjwwEN9ap5iNUa/fCEEALA5etjTfOb+HQ/1nHanVyISGStewGzZwAA19LT0VPGcLeu8Sjgx9LViresEs+Uw8/7amuDvDyYNWu4f41hqPOhjyfT+//sckUWnsmktq9/fJV638/l5UfWR+ecc+Dxxw8+p/s3v5E53bEWcdJ9MLW1tXi9XmbOnBnNhxVCHKlAQCXcTqd6dw2T2wO1tWCgmqcJIUSsZFjTyLSlEzR0lhYulO7kYsKwr35i6GfnCsmAoioQwNrVgq9kCp7K2WA5eApkGCpp3rIFOjvVIsNjj41MrPv6IgvBbFaJ88dXqPf9XFYW+0WNc86BM89UXcpbW9V29GXLZIV7NESUdN9zzz28+eab/OMf/xi67rLLLuNvf/sbAAsWLODZZ5+VLeZCjAWGAbt2qVkQpaVh14cFg2qF2+VSZeBCCBFtgwE3SeYkzJoJk2bi2KKjsJossp1cTBjJtVtIqd8GgHvKLLUSK6IjGMDa2Yy/sBz3lFn09Fppadk7+rRFrWjvu9zaCm53ZE9jsagt3gfb+l1RoU7BDpHrjyqzWcaCxUNE//R//OMfWb58+dDlF154gb/+9a9cddVVzJkzhxtvvJFbb72V3//+91ELVAgRoZYW2LFD7ZGK4N1+3wdRXp70cxFCRF/LQDtvd7xPRUYZ8/NUp+Zkc/g9J4QYz+xrhhuoySp35HQdunsttHQl0dxpo7nTSuseP02D02nqzaS1zYTXG9ljW62HXqWePFnVW8uKsTiUiJLuhoaGEVvIH3vsMaZMmcK9994LQFtb29CqtxAijlwu2LpVNU1LC3+LZk8P1DdARobUcQshoks3dD7s2cZ2Zy0AnZ4egnoQs0nOWsXEYhrsJ2tvA7VgShquY2Ts7qHoOnQ6rTR32WjpstHclURL597/77LR0m3D54+s06vNphYYKipg9myYMmVkYh3B0BchhkSUdBsf6zX/4osvcuaZZw5drqiooO1IJrMLIY6c16uKkgYGIqvjdqs6bo2I8nUhhDikwYCbdW0b6fbu7U6eVcG8XOlOLiamrDefw+TzAOA69jMYyalxjih+gjp0OKxDyfS+xHpfkt3abcMfiCzzTUpSq9HFxcN/9l0uLITBQZVcz50rCw0i+iJKuqurq3nyySf52te+xgsvvEBLSwuf/vSnh25vamrCbrdHK0YhRLh0HXbuVPvCI0i4A4HhZiGFhdEPTwgxce3bTr6vO/nR+fMoSy+Od1hj1v/dU8lln25nftUAug63/72ctR9kAQaXnN7BF0/tPOhxPr/Gzx8p4/UPM0myGkyfNMjPr64fcZ9Vr+Vy44MV3PPtXZxy1OFbLne7LFx3fwV7OpKwWQxuvmwPR83oP+B+zZ02TvtODVXlw8Wxd39rN5MKfQC88l4Wv3i0jKAO1eVubv9qPekpOl0uC9+4axr/7+ZtWCbK9y+GMaEaqAWC0O6w7V2dttHcmURLt21otbqtx0YgGFktW0pSkNI8H6V5XkrTHJSUGOTXFFM0JYXiYsjOPnSZXFOTWsmeNUsSbhEbESXd3/3ud7nooovIzs5mYGCAmTNnctpppw3dvnr1aubPnx+tGIUQ4erqgro6lTFHUGC0r7GI1HELIaLJF/SxvuM9/HqA7KQs6U5+GJt2p+IaMDO/So2R+vebOexuTubZX3xE36CZc2+cyeKZfVSVeQ449q6VpaDBc7/YjKZBp3PkKV9zp43H1+Qxb9qBSfOh3PVYKXOnDfDA93fxYW0q3/rNVF6860OsBzmbTEsJ8uRPtx5w/YDHxE1/nMxfbthOZYmXn/ylnHufKuZ7FzaTlxVgflU/T7+ey7kndocc13iWsutDkht3ATA4bS7eSVVxjujI+APQ1rPfCnWnjZa9W7+bu2y099gI6pGdWKQlBynN91Ka56Mkz0dJnpfSfN9Qop2VHkTTwNLZgp6aweCMBejpKYd93I4ONQ61pgZSDn93ISISUdJ9wQUXkJuby7PPPovdbufrX/86lr0Nmnp6esjJyeFLX/pSVAMVQoSou1t1Kk9Lg+TksA/v6oKGPWqU91josimESBw2s42j8ufS6emR7eQheGx1Pp9d2jN0+bm3cjjvpC7MJrCnB/n0EgfPrsvh2+e1jDhu0GPiiVfzWHPPpqEvTvPtgaHbdR1u+tNkbrikkZ8/EvpuqOfXZ/P8Lz8CYE7lIAXZft7ZlsGxNaHPUFr7QSYzJw9SWaK6WV1wSidX3lnF9y5sBuAzx/Rw+98mTZik2756fDVQ8/k1WntsNHfaaN1v6/eeLgvtXcl09NjQjciS6szUACX5PkpyfQdNrjNTg4ddCLB0taEnp+KunoeennXY53Q61f/X1EDW4e8uRMQiPqU+9dRTOfXUUw+4Picnh1WrVh3kCCFEzA0OwubN6owqghKPwUGoqweTBqkTt6RMCBFFLQPtmDUzhal5AJSnl1CeXhLnqMaHd7ZlcOnp7UOXW7ttlOT5hi6X5vv4YNeBOwUaO5LISg/wwL+KWbc5gySrzjfOaWXpbJUc//m5QhZU9TN7ymDIsTj7zASC2ojkvSTPS2u37aD3d3vN/M/NMwjqGicvcnLVma2YTervULz/3yHPS6fTSiAIFjPMnjLIjsYU+t0m0lP0kOMbj0wDvWSu/y8AwdQMepecEueIwOvTaO0eufV732p1c5eNTqcVI8KkOis9oLZ+fyyZLslVlzPTgkcUu6WnHcOWhLt6PsHM7MPef3AQ+vth/nwppROxd0TrWM3Nzbz22mt0dHRw7rnnUlZWRjAYxOVykZWVhVn65gsxeoJB2L4dHA61LzxMgYDakd4vddxCiCjQDZ0Pu7ex3VVLktnGp8pOIMUS/u6biaytx0puVuDwd/yYoA4tXUlMLXVz7fnNbKlP4St3VvOvn23G0Wvhv+/a+esN22MQsZJv97Pm7k3kZgVw9pv5zu8q+fOzhXz5s+2HPdZihsy0AB0OK+kpEc52GieyXn8Gk1/9HV3LPothi/3vh9urqe3e+yXT+7Z+N3cm0eWKvKA5O8M/lFCX5nvV/+/d+l2S5yMthl+iWJxdGCYz7up5BO25h72/z6d29s2cqWZrCxFrEXcv/853vsPvfvc7AoEAmqYxZ84cysrK6O/vp6Kigttuu43/+7//i3K4QohDqq1VWXNxsUrAw9TUBO3tkF8gddxCiCMz4HfzVvtwd/JJ6aXYzNKdKFwpNh2vf/gNuTjXR0uXbajGu7nTRnGu74DjinN9mDSDzx6rtqbPqnBTlu9lR2MKe9qSaO5M4tPfqwGgy2Vl90OT6XK2cMEpXYeMxZ4RxGIy6HRahla7W7qSDvr8Nqsx9GWBPT3IOSd28cybOXz5s+0U5/pY91Hm0H2bu5LIt/tHNE7z+k0k2YyPP2xiMQyy99ta7lgena3lAx6TGp3VZRueVd01nFx390b+e5ib5R+x9bs0z0txno+SfC/2gl7yrDY0Rv8EwuzqBsPAXT2fQHb+Ye8fDKo+s1OmwLRpcs4jRkdESfcvfvEL7r77bn7wgx9w8sknj9hmnpWVxTnnnMMTTzwhSbcQo6WtTa1y5+aqtpthJt2dXaoMPCuLidMxVggRE9KdPHqqy93UtSZTnOsH4LTFDv75Sh6nLXHQN2jmufXZ3PudXQccl50R5JjZfby+KZMT5/fS1GGjqTOJqSUejq3pG5FcX/rTar50WvtQ9/K7VpZQmOM/aFf00xY7WLk6n2+e08qHtam0O6wcPePAeu5ul4XMtABWi6oB/u872cysUFvZl83t5Sd/mURtSxKVJV7+8VI+nz5muG69y2VB06A458BkPpGkbH+PpJY6AAamL8RXOiWk4/rdKqlu7hweo9Wy30gtR1/kSXW+3XfQJmUleV6Kc32kJB38ixADgwGzDke2Ozwi5j4nWsCvEu68osPe3zBUwl1crFa5pXeNGC0RvdQefPBBLrnkEm6//Xa6uw9sdDF37lyee+65Iw5OCBGC3l5Vx22xqPabRnirAwMDUFermpxLHbcQIlKGYbCpeyvbXbUA5CRlcUzhItKt8sYSqU8tdvDGh5lDjco+f3w3H9Wm8unv1qBpcNmnO6guV53LV2/MYs1GOz/+SgMAP7q8gZv+WMFdK8swaQa3XNFAYY7/sM+5fU8qs6ccfMX72gua+eF9Uzj9u7OxWgzu/FrdUOfy3z5RTL7dzwUnd7FxRzq/faIEs8kgoGssmdXHVZ9vAyAtRee2rzTwv7+ZRiAIVWUe7riqbug5Xt+UySmLnJgiG8U8bmQfooFa74B5aLv3vpVqtRVcJda9A5FliZpmUGD3qxrqvI9t/c73UZzjG3e7C0z9LjSvG3fVPPwFpSEd096uFhhqaiLqNStExCL6zW1sbOTYY4895O1paWn09vZGHJQQIkQ+H2zdqjqBlIb2gbM//9467oFBKCyIQXxCiAnFE1T1qVVZU5ibOxOzluCZU4ydfUI3X7xtOt84u5XUZB2zCW66rBFoPOC+Kxa6WLFweNZ2eYGPP1+/47DP8Zcbhu8T1MHRZ+HUo5wHvW9eVoA//mDnQW/733Nbh34+9Wgnpx598Mc4WKz7e/yVPG69Ys9h4x6PDANcA2a6GgaofvtlAPqsOXx53VeofyaDli4bfYORJdUmzaAwxzecTOd7VYOyfJVYF+X6sVnGV1L9SUwDfZjcA3iq5uIvKg/pmJ4etT5RUwMZGTEOUIiPieg3u6CggMbGA9/w99mwYQOTpCuBELFlGLBzJzQ3q4Q7zKIkw1B13B0dUscthIicbhiYNA1N01iYP4dJ6SUUp0k3xmhIS9b5wRebaO60UVV+4CzuaDOb4LHbtsX8eQ6ly2XhgpM7mVoa+79rLBgGOPvNI7Z+N3cl0bLfavWAx8x3+CWfQ+06uM9/BS+8f/jfF7PJoCjHt7fbt3dvMr13G3iej8Ic30HnpScik7sf04ALT2UNvqLQ8o3+fnC7YcGCiHrNCnHEIvr1POecc7jvvvu47LLLyNo71E7be8b+4osv8uc//5nvf//70YtSCHGgpibYtQsKCiIqSurqgj0NarKY1HELIcKlGzof9myjzzfAcUVHoWkaVpNFEu4o2zfmayLIywrw2WMd8Q7jkAwDenotqtP3vmR6v1nVLV023N7DfaAafJUHhi49wFcBsJgNinM/tvV7b8Oy4jwfhdk++awGNM8g5l4nnspZ+MoqQ1ox8HrVKvfs2VAW+lh6IaIqoqT71ltvZc2aNcyfP59ly5ahaRp33nknN910E+vWrWPBggVcf/310Y5VCLFPTw9s2QJpaZCSEvbh/f1qW7nNFtHhQogJ7uPdyTvc3UNzuIUYr3QdunstI7p+N+/t+r2vYZnHF1nJhNWiU5zr4/PJ/6W6QW3Rry9dyo8uD1Kav4l8ux+zVGN8Is3rweLsxlMxA2/Z1JAS7kBA9ZqdOlX9kV19Il4iSrqzsrJ46623+NWvfsXjjz9OcnIyr776KlOnTuVHP/oR3/ve90iRM3khYsPtVgm3zwclJWEf7g9AXT0MDso8biFE+A7WnVwSbjEe6Dp0Oq0HbP0eSqy7bfj8kWW+Nqs+tN3743XVpfk+8rL8mExQ+tt7QPW6w3L2mSyaPhDFv2Hi0nweLI4OPJOq8U6uJpROe/s6lZeWqk7lZtkpIOIo4uqPlJQUbrzxRm688caD3l5XV8eUKaGNPxBChCgYVKPBOjsj2iNlGNC4BzraoUASbiFEGHRD58PubdKdXIxZQR06HNaDbv1u7rTR1mPDH4gsqU626UP10yX7bf3eN14rNzNw2DzQ7OomY8MaAAKZOfQtOjGiWCYaze/D0t2Ot7wKb8WMkBJuUAl3drbaVm6zxThIIQ4j6i0XNm3axM9+9jMef/xxfL7EnrEoxKirr1d/CgtD/tDZX2enmsedkyN13EKI8Lzd8T57+lsA6U4u4iMQhHaHbe/qtGpM1tJtG1qtbuuxEQhGtn84JSl4QB116X6zqrMzAke8Ndn+6r/QgmqYtfOEz4Ml8pnaE0bAj6WrFW9ZJZ4poS9Xd3VBUhLMmaOmqQoRb2El3Zs3b+bee+9l9+7dZGdnc95553H22WcDsHHjRm688UZeeOEFrFYrF198cUwCFmLC6uiAbdvUgMmkpLAP79tXx50ksymFEOGrzqqk3d3Forw5lKUXxzsckYD8AWjrGU6mmztVHfW+udXtPTaCemSZb1pycMTqdGn+yFnVWenB2Nb76jr2V54CwNA0nMvPiuGTJYhAAGtXC76SKXgqZ4fcNLavD/x+WLhQLTIIMRaEnHS/9dZbrFixAo9neIzDypUrueuuuwgEAvzgBz8gIyOD733ve3z729+muFg+kIWImv5+VcetaZCZGfbhfj/U1apycKnjFkKEQjd0erxO8pLVWWtOsp0zJp2MxSTbZBJZUIcN29PpdFrJt/tZNL0/ag2+fH6N1p59ybRKqPdt/W7pTqKjx4puRJb5ZqYGKMn37a2h9o6sr873kZka46T6MNI+Wo+tsxmAgZpj8BdIG+1PFAxg7WzGX1iOp3JWyLsCPB5wOtUKdwRtb4SImZCT7ttuu43k5GSefPJJli1bRl1dHZdffjk333wzbreba6+9lhtuuGFohJgQIkr8fti6VX2KRFjHvWeP2mqVXxD98IQQiWfAP8hb7Rtx+Ho5ufQ4spPUZ7sk3Intv+/Yuf3v5bT3DBfAFub4uP7iRk492nnY470+jdbukVu/961WN3fZ6HRaMSJMqrPSA2q793511KX7zarOSNUjetzRkr36iaGfHSvOjWMk44AexNrRjD+/FPe0ORi20Hb37etUPn06SFspMdaEnHSvX7+eb3zjG5x22mkAzJ49m7vuuosTTjiBa6+9lp///OcxC1KICcswYPduVYhdWhrRrIuODnV4ttRxCyFC8PHu5J6gN94hiVHw33fs/N89lRgfu76jx8r/3VPJb75Vy/FzXWq790G2fjd3JtHlirxGOSfDvzeZPnDrd0mej7SUsZ1UfxKLo5P099YC4M/Op3/B8XGOaAzTdaztzQTyinFXzcFICq0eTtehuRkmTVJJdwRtb4SIqZCTbqfTSXV19Yjr9l1esWJFdKMSQigtLbBzJ+Tnh1zLtL++PqitUzXcyeGXgQshJpDg3u7kO6Q7+YQT1OH2v5fvTbhHfrlroAEG1/y2MuKt3wC5Wf6hBHoomc7fu2qd6yM1efwm1Ydjf/VpNH1vA7UTzwRz1PsYJwbDwNrZQiA7H3fVXIzk0N97WluhoEB1KrdKfzoxBoX8W28YBuaPdQzcdzlZujIJEX1Op6rjTkqC1PBPen0+1TjN51UfREIIcSgD/kHWtW+kx+sEpDv5RLNhe/qILeUH0tA/vgT+Mfn2fbOpfRTnjtz6XZzrIyXpMA+QqPQg9leeBMDQTDhPPCu+8YxVhoGls5lAVjbu6nnoKWkhH9rZqU6TamoiOl0SYlSE9VXbs88+S1tb29DlwcFBNE3jn//8J++///6I+2qaxjXXXBOVIIWYcLxeVcc9OBhRHbduwJ5GVcctCbcQ4nD29LfQ43ViNVlZXDCP0rSieIckRlGnM7SlwYoiDzWVA8MdwPeuVhfn+EiyTdCk+jDSP3gTa3c7AP3zjiOQJ79bB2PpakVPy8JdNQ89LSPk41wuCAZh/nyw22MWnhBHLKyk+5FHHuGRRx454Pr777//gOsk6RYiQroO27ervVIRJNygFsnbm9SojBBHWgohJrDp9ql4gz6mZVXIdvIJJqjD+i2hJTm3XNHA4pn9MY4osdhXrxr62bninDhGMnZZutrQk1PVCnd66A2Z3W5VRjdvHhTJdxlijAs56a6rq4tlHEKIffbsUfvCCwsjyph7e6GjHVJSIhrnLYSYAAb8g2x27GRhXg0WkxmTpjE/b1a8wxKjrL41ieseqOCDXemfeD8Ng8IcNT5MhM7S1Ub6B28A4M8tpH/esXGOaOyx9LRj2JJwV88nmJkd8nF+v2oUO2MGTJ4cwwCFiJKQk+7J8ooWIva6umDbNsjIUN3PwuT1qnw9EIxonLcQYgJoHmjj7Y4P8O/tTr4gb3a8QxKjTNfhkZfyuWtlGR6fqtvXMDBgb9u04YZp2t72atdd3Bi1ed0Thf3Vp9AM1SDOedLZICP3RrA4uzBMZtzV8wjac0M+LhhUfWanTIHq6ogGuwgx6qR9ohBjxeAgbN6sPk0imHev62qRvKcHMkrggLkvQogJ7cDu5Haqs2SY7UTT3GXjxgcns37L8Dezkwo93PHVerpc1oPM6fZzXYhzusV+AgHsrzwFgGEyq67lYojZ1Q2Ggbt6PoHs/JCPMwxVfVdUBDNnRjTYRYi4kJeqEGNBIKAap/X0QHl5RA/R3q5mVObkQMAEBKMbohBi/Pp4d/LqrCnMke7kE4phwJNrc7njb+UMeIZXXC86tYNr/6d5aGTXikVONmxPp9NpJd+utpTLCnf4Mt5fi9XZBUDfwhPCSiwTnbnPiRbwq4Q7zMZyHR1qM2BNTUQbAoWIG0m6hRgLamvVMnVxcUT7pJxOqK9XozKSkiAQ9QCFEONVh7uLN9o27N1OLt3JJ6JOp4Uf/Wkyr7xvH7quKNfHT75Sz7E1fSPuazYhzdKiYEQDteXSQG0fU78LzevGXTUPf0FpWMc6nWAyqYRbSujEeCNJtxDx1toKO3aoJWpraGNb9uf1Ql29aipit8uuciHESGmWNDTUdvKlhQtJk+7kE8pz67O57c+TcPUPn/KdfUIXP/xiIxmpehwjS1zWjibSP1wHgC+/lIGaJXGOaGwwDfRhcg/gmTYHf1F4u/oGBtSf+fNlFKoYnyTpFiKeenthyxaVbKd/cvfYg9F1tcLt6FHNzoUQAsAX9GMzqy/x0qwpnFSylAxbumwnn0CcfWZ+8tdJPPtWztB1uVl+br2igRULXXGMLPHZ1zw19LNz+dlqeXaCM7n7MQ248FTW4CsOrzmz16v6zM6eHXEFnhBxF9G7wBVXXMH69esPefvbb7/NFVdcEXFQQkwIPp9KuPv7IS8voodobYWWVsjNlc90IYTSPNDGs3tW0zzQNnSdPSlTEu4J5NX3M/n8dbNHJNynLe7hX3dsloQ71gJ+7K/9CwDDbMZ5wufiHFD8aZ5BzL1OvFNm4SurDKuMLhBQ5zqVlTBtmnQqF+NXRJ/Af/7zn9m9e/chb6+rq+Mvf/lLxEEJkfAMA3buVDMviiKrrXQ4oaEB0tPAZjvs3YUQCS5o6LzftZk32t7Fp/vZ3dsQ75DEKOt3m7jxwclc/asqulxqp0NmWoBffr2Wu75ZR3aGdNiMtYwNr2Dp7QGg76gVBLNCH4WViDSvB4uzG8/k6XjLpoaVNe/rVF5aqjqVm2XimhjHYrK9vKWlhZSUlFg8tBCJobERdu1ShUkRzLvweKC+Tk0Xy86OQXxCiHHlwO7klczJnRHfoMSoWr8lnRserKClK2nouhPmubjtyw0UZPvjGNnEkr1fAzXHBG+gpvk8WBwdeCZV451cHfaWvLY2dY4ze7ZqEivEeBby2f7TTz/N008/PXT5gQce4KWXXjrgfk6nk5deeomjjz46OhEKkWh6etR4sLQ0iODLqWBQrXA7HFLHLYRQ28nf7vgAv+7HZrJytHQnn1DcXo1fP1bK318c/kBITQ7ywy82cu6J3bIddxTZWhtI2/IOAN6iSQzOOirOEcWP5vdh6W7HW16Ft2JG2Al3T49qd1NTo0aECTHehZx0b9myhX/+858AaJrG+vXr2bBhw4j7aJpGWloaJ5xwAnfddVd0IxUiEbjdqo7b7z/iOu68PKnjFmKic3hdvNH2LiDdySeiD3alcd39FdS3DQ8sXjyzj59eWU9pvi+OkU1M9jVPDv3sXH7OxC1ADvixdLXiLavEMyX8feH9/WpH34IFqmeNEIkg5KT7uuuu47rrrgPAZDLxpz/9iYsuuihmgQmRcIJB2LYNOjoibr/pcKhu5enpEU0XE0IkmOykLKZmTsasmZmTO0OapU0QPr/GH54q5o//LkI3VGKXZNW59vxmvnhqh3whGweaz0vW2n8DoFusuJZ9Ns4RxUkggLWrBV/JFDyVs8MuofN41Cr3nDmqlluIRBFRTbeuy1xHIcJWV6cy5uLiiJao3R6orVVzuNPToh6dEGKcaB5oIyfJTopFrW4uzKtBm6grahPQtoYUrru/gu2Nwzsa5lQO8LOv1TGl2BvHyCa2jHfXYOlXneH7jj6ZYIY9vgHFQzCAtbMFf2E5nspZYAlvdSAQgPZ21aW8Mrwm50KMeTKnW4jR0N4O27erjiARtBoPBqGhXo31ljpuISamoKGzqXsrO111FKTkcULxEkyaJgn3BBEIwp+eKeL3q4oJBNUXtxazzjfOaeXLZ7Rhkc7OcZW9+omhnx0nnxvHSOLE0LF2tODPL8U9bQ6GLbzOZ7quBrqUl8OM8EvAhRjzIk66n3vuOe666y42btyIy+XCMIwD7hMMymgKIejvV43TTKaIu4G0tKha7txc+eZXiImo3z/IW+0b6PGqlTS7LRO170XeECaCutYkrru/gk2704eum14+yB1X1TNjsjuOkQkAW3MtqdvfA8BbMgV39fz4BjTadB1LbzeBvCLcVXMwkpIPf8zHtLWpXjWzZskYVJGYIkq6n3jiCf7nf/6H2bNnc8EFF3Dvvfdy0UUXYRgGTz/9NFVVVZx11llRDlWIccjvVwm30wllZRE9RE8P1DeofF3quIWYeJr6W3mn8wP8egCbycrigvmUpMmWl4lA1+H//beAu1aW4vWrpT+TZvDlz7bxjbNbsVkPXPAQoy97vwZqjhUTrIGaYWDtbGUwPwv3lDkYyeE3cuzqguRk1ak8TcrnRIKKKOm+4447WLx4Ma+//joOh4N7772XK664ghUrVlBfX88xxxzDlClToh2rEOOLYahZ3I2NqhtIBB/CbrcqBdeQDyIhJpr9t5MD5CbZOUa6k08YzZ02bniwgre3Du+QqijycMdV9cybNhDHyMT+NJ+HrLX/AUC3JuE6/ow4RzSKDANLZzOBLDvekkkkpaSFvfemt1fVci9cqCrwhEhUEVVMbNmyhQsuuACz2Yxlb1dCv98PQEVFBV//+te58847oxelEONRczPs3An5+WF37wT1IVRfrz6QcnKiH54QYmzTDZ22wQ4ApmdVsrz0WEm4JwDDgCdeyeWs62eNSLgv/lQ7T/xkiyTcY0zm+pcwD/YB0LvkVPS0zDhHNHosXa3oaVm4p83FSE4J+3iPR53jzJypeswKkcgiWulOTU3Ftrfgwm63k5SURGtr69DthYWF1NXVRSdCIcYjp1NtK09OhtTITpJbWoZrnCbSTjUhhGI1WVhauIjBgFu2k08QHQ4rN/9pMq99kDV0XUmel59eWc+SWf1xjEwcin2/BmrOFefEMZLRZelqQ09OxV09Dz09CzwdYR3v96ses9OnQ0VFbGIUYiyJaKV7+vTpbNmyZejy/Pnz+dvf/kYgEMDj8fDII48wadKkqAUpxLji8cCWLWpveG5uRA/R3Q0NeyAzM6JFciHEOBQ0dN7r2sx2Z+3QdfakTEm4JwDDgGfXZfP562aNSLjPPbGLp27fIgn3GJW0Zyepuz4EwFNehXvanDhHNDosPe0YtiTc1fMJZoa/J1zXVXPYyZOhulo6lYuJIaLT+bPPPpt77rmHX/7ylyQlJXHDDTdw5plnYrfb0TSNgYEBHnrooWjHKsTYp+uwY4daoo6wcdrgINTWgUmLeJFcCDHO7N+d3IRGeXoxqZbwt2uK8cfRZ+a2P0/ihbeH64jysvzc9uUGTlrgimNk4nDsq1cN/eycIA3ULM4uDJMZd9VcgvbIFhZaWlTl3axZ0iBWTByacbBZXxFYu3Ytq1atwmw2c8YZZ7B8+fJoPGzM9fb2kpWVhcPhwG63xzscMd7V1sKmTerTJDn8kRmBgBrn3dEJhQWRfX4bGAyYPaQFk9FknJBIAIn+mpbu5BPPvtf02+8U8qOHKuh2DWcenzmmhxsv2YM9Q8aujmWaZ5Cq//00Zs8AelIKO3/7HHpK+uEPHMfMrm40XWewej6BvKKh6w1Dx+PpIDm5AE375GXrjg41EuyooyAr6xPvKkTcOJ1OsrOzcblcZGZGp09D1DauLlu2jGXLlkXr4YQYfzo7VcacmRlRwg3Q1KRqnPIjTLiFEOPHgd3Js/d2J5cV7kTXN2jmJ/+viv+8NvzlSlZ6gJsv28OnlzjiGJkIVdZbL2L2qKZ2rqWnJX7C3edEC/hxfyzhDofTqUopamok4RYTj1SLChENAwOqjlvXI/4k6exS08WyssBijnJ8QogxRTcMXm1ZR5dHJVjT7ZXMyZmB6TCrRGL8W7c5gxsenExbd9LQdSfNd3LrlxvItwfiGJkIx4it5csTu4Gaqd+F5nXjrpqHv6A0oscYHIT+fpg/HwplI4+YgCJKug3D4IEHHuBPf/oTtbW1OBwHfiuraRqBgHx4iAkgEIBt26CnB8rLI3qIgQGoqwWzWeq4hZgITJpGaVoxvb5+2U4+QQx6TNz1WCmP/Ldg6Lq05CDXfamRs5d1y+6mcSS5bispdaqhsHvKTDyVs+IcUeyYBvowuQfwTJuDvyiycxyfT20GnDULpM+ymKgiSrq///3vc9dddzF//nwuvvhismWavZioDAN274aGBigpiWhPuH/vPO6BQVXHLYRITEFDxxPwDM3ars6awqT0ElIskZWjiPHjvR1pXPdABXvah/+tF81y8rMr91Ca549jZCISE2WV2+Tux9zvwj21Bl/x5IgeIxhUncqnTIFp06R0TkxcESXdf/nLXzj33HN57LHHoh2PEONLa6vqVp6bG1ELTsOQOm4hJoJ+/wDr2jcS0AOcUrYMq8mCpmmScCc4n1/jd6tKeOiZQnRDvcEn23SuPb+Jz5+2hwwjGRKwOWAiM7n7yVr3PADB5DRcS0+Lc0SxoXkGMbsceKbOxldWGdEJimGo06TiYpg5U0agioktope/2+3mlFNOiXYsQowvLhds3aracKZH1kClqwsa94DdLnXcQiSqj3cn7/P1k5Nsj3dYIsa21Kdw3f1T2Nk03Bhv3rR+7vhqPZOLPQyYAGlQPu5kvvE8Jq8bgN7jPo2RnHg1YZrXg8XZjadiBt6yqRGvCLS3qz41s2dH3F9WiIQRUdJ98skn88477/DVr3412vEIMT74fCrh7u+PeB53fz/U1akF8hRpVixEwgkawb3dyesB1Z18adFCmb+d4AJBePDfRdz7VAmBoEpWrBad/z23hcs/047ZBFGZ1SpGn2GQvWZ4a7kjAbeWaz4PFkcHnknVeCdXgymy5o4Oh1rZnj1bDXURYqKLKOn+wx/+wGmnncbtt9/OVVddRW5ubrTjEmLsMgy1pby5OeKE2x+AunrVzVO6eAqRePZtJ3d4XYB0J58odjcnc/0DFXxYmzZ03fRJg9z5tTqqyz1xjExEQ/LuzSTv2QGAe2qNSkoTiOb3Yelux1tehbdiRsQJd38/uN2qU3l+fnRjFGK8CinpzsjIQPvY1pJAIMBNN93ETTfdRHJyMmbzyL2xmqbhcrmiF6kQY0Vjo2qeVlQUUYGSYagt5Z0dqo5bCJF4PujeisPrwmaySnfyCUDX4W8vFPCbf5bi9atExWwyuPJzbXztrFZsFlnbTgTZq58Y+tmx4tw4RhIDAT+Wrla8ZZV4psxU41Qi4PWqYS6zZ0e8LiFEQgopYzj33HMPSLqFmJC6u9W28vT0iAuUOjtV3p6dLXXcQiSqRXlzAFiQN1u2kye4pg4b1z9QwbvbM4auqyxxc/tX65k7dTCOkYloMg30krn+RQCCqen0Ljk1zhFFUSCAtasFX8kUPJWzI+54FghAWxtMnar+SOogxLCQfqv+/Oc/xzgMIcYBtxu2bFH13Hl5ET1E3946bluSNBURIpH0+wdoGmhjhn0qAMmWJI4rOirOUYlYMgz455o87nykDLd3+BvUS05r5//+p5lkm6xuJ5Ks15/F5PMC4DruDIykBPkQDwawdjbjLyxX88Yt4U9igeFO5aWlMGNGxAvlQiSsiIo1brvtNj766KND3r5582Zuu+22iIMSYswJBmHbNrVMXVQU0UP4/VBfp3J3e1aU4xNCxE1Tfyv/bVrLpu6tNPa3xDscMQrae6xc9ctp3PLw5KGEuzTPy5+v384PL26ShDvRfLyB2ooEaaCmB7F2NOPPL8U9bQ6GLSnih3I41A6+2bMhKfKHESJhRZR033LLLWzatOmQt3/00UfceuutEQclxJhTVwf19WrYZASNRQwD9uxROXtuZIvkQogxJmgE2dj1EW+2b8CvB8hNziY3OTveYYkYMgz49xs5nHndLF7fNPzt6XkndfLU7VtYPLM/jtGJWEnZ8QFJzbUADFbPx1c2Nc4RRYGuY21vJpBXjLtqzhGt3Hd3q0ksc+ZEPEFViIQXkzH1PT092Gy2WDy0EKOvvR22b1df4Ub4uu7o2FvHnSN13EIkggO7k09lTs506U6ewHp6Ldz250m8+M7wFyv5dh8//koDJ8zrjWNkItYSroGaYWDtbCGQnY+7au4RzRrv61NVd5WV6jRJCHFwISfdr732Gq+88srQ5VWrVrFr164D7ud0Olm5ciVz5syJSoBCxFVfH2zerFa3MzIOf/9DPERdnarhTpYtV0KMe80Dbbzd8T5+PYDNZGVJwXyKpTt5Qnt5Qxa3PDSZ7t7hetczlnZzwyWN2NODcYxMxJq5z0nGOy8DEEjPou/oFXGO6AgZBpbOZgJZ2bir56GnpB3+mEPweMDphJoaWeEW4nBCTrrXrFkztGVc0zRWrVrFqlWrDnrfWbNm8dvf/jY6EQoRL36/6lTe2xvx3AufTyXcHo/M4xYiUWhoQ9vJlxYulO7kCax3wMwdfy/n6ddzh66zpwf40eUNnLbYGb/AxKjJev0/mPw+AFzLPntEdc9jgaWrFT0tC3fVPPS0yBYTQHUqb2+HqiqoqICurujFKEQiCjnp/v73v883v/lNDMOgoKCA++67j3PPHbnFRtM0UlNTSZa2zGK8MwzYuROamlQrzgjmXhgGNOxRH0QFMo9biHFNN/ShreMlaYUcX3Q0Ran5sp08gb35YQY3/rGCtp7hsqLlC53cekUDeVmBOEYmRo1hYF89vMDkXH52HIM5cpauNvTkVLXCnR55R1ddh5YWKC9XncojaHUjxIQTctKdkpJCSor6Nr+uro78/HxSUyOvARFiTGtuhl27ID8/4nmVbe3Q3AQ5OTI6Q4jxrLG/hU3d21heunRoVbtEtpMnrEGPiV/+o5R/vDz8bWl6SpDrv7SHM4/vkdnDE0jq1g0kte0BYGDmUfiKK+Ib0BGw9LRj2JJwV88nmHlkxdetrer0aPZs1UBN16MUpBAJLKJsYvLkydGOQ4ixw+FQ87hTUiDCL5Z6e9V4sJQUGZ0hxHgVNIJ80LWFXb0NAGxz7mZhXk2coxKxtHFHGtfdX0Fjx/COvaWze/nJlfUU5/rjGJmIB/uIBmrjd0yYxdmFYTLjrppL0J57+AM+QWenOjWqqYn4FEmICSkm3cuFGLc8HpVwezxqW3kE9tVx+/3qm2AhxPjT7x9gXdtGHD7VnXyGfSo1OdPjHJWIFa9P47dPlPDwc4UYhlrKTrEF+e6FzZy/olO2z05AZlcPme+uASCQkU3fUcvjHFFkzK5uMAzc1fMJ5BxZrZvLBcEgzJ8PdntUwhNiwpCkW4h9dB22bVOdQSJsnKbr0NCgZlZKHbcQ41Njfwvvdm6S7uQTxJb6FH543xR2NQ83xFtQ1c/tV9UzudAbx8hEPNnX/gstqGr3nSd+HizWwxwx9pj7nGgBv0q484qO6LHcbjWNZe5cKDqyhxJiQpKkW4h96uvVn6KiiIuw29tVObjUcY+e0nt+QM+nv4i7ai7oOoV//yXpH7wBaPScfiGOU88/6HHld34Di6sbNBN6ciptX/ou3ooZAFjb9lBy/y2Y+53oKem0fPVH+MqmHjaWsI8zDCbdcTXJDdvYcf8rQ1env7eWgkd/g6breMqn0frVH6GnpGN2dVN+1zXU3/wQmOXtOxb29DXzVsd7AOQlZ3OMdCdPWP4APPCvYu7/VzGBoFrdtlp0vvWFFi77dDtmWd2euHQd+5onhy46Txp/DdRM/S40rxt31Tz8BZHt3NvH74eODtU0raIiOvEJMdHIR4oQoIqUtm+HzMyIi7BdLpWzp6ZKHfdoSd79EeaBXpVwA1lvPktScx27f7GKulv/Qu4zf8PWtPugxzZ/82fU3f4P6n76CD2f/iIlD9w6dFvxQ7fjXH42tb9YRfdnLxlx2ycJ97ic5/8fvsKRuyo0zyDFf/wxTf/3K3b/8kkC9jzynvoTAMGsXNxV88h6/ZmQ4hHhK0krwm7LZIZ9KieVLJWEO0Htak7mottm8PsnS4YS7pmTB3n8tq18+QxJuCe6tM1vY+toBqB/zjH4CyPb/RYvpoE+TO4BPFNr8BeVH9FjBYOqU3lFBVRXRzTMRQiBJN1CwMCAquPWdciKbISG1wu1dWpuZUbkYy9FmLJXr8K19LShy5lv/RfnSWeByYyenkXvklPJWvfCQY/dfz6pabB/6EzC7OohuW4rruM+DUDf0Sdj7WnH2t74ibGEe5ytaTcZG16l+7OXjbg+/YM38Uyejq+kAgDHKeeRud/fwXXMaWTvN8JGHLkOdxe6YQBgMZk5ufQ45ubOlHFgCSiow0PPFPKFm2ayuS4NALPJ4OqzWvjHLVupKvfEOUIxFuzfQM25fHw1UDO5+zH3u/BMmYWv+MgaHxuG6lReVAQzZ0Y8zEUIwRFsL9+6dSsPP/wwtbW1OBwOjL0nLPtomsbLL798xAEKEVOBgKrj7ulRAycjsK+O29EDhVL2OapSt22g5/SLhi5butvw5xUPXfbnl5C868NDHl98382kbd0AQON37wbA2tNOwJ47vH1b0/DnFmLtasNfeOjXSFjHBQIU/+mntH7lpgMGnFq72/DvV3vnzyvB4uyCYADMFjxTZpDUuAuTux89Jf3Q/3HEYQX1IB90q+7kNTnTmZVdBYDZJLUhiWhPu40bHqxgw/bhL9wqS9z87Kp6aioH4xiZGEssjk4yNr4GQCArl74FJ8Q5otBpnkHMLgeeqbPxlVUe8bJ0Rwekp6vRYCmy6UeIIxJR0v23v/2Nyy+/HKvVyvTp08nOPnDe38eTcCHGHMOA3btVxlxSEvGHU1sbNLdAbu4B+ZOIMUtPB4GsyMeftH7tNgCy1v6Hgn/cQ+P37olWaJ8o/8kH6DtqOb7SKVg7W8I72GwhmJaBxdGFT5LuiPX5B1jXtgGnrxdQCbhITIYBK1fn8YtHy3B71RcqmmZw6entfOsLLSTb5HxFDMt69Wm0ve8HzpPOGjfLu5rXg8XZjadiBt6yqUeccDud6iHmzIl4E6AQYj8RvZPccsstLFiwgOeee468vLxoxyTE6GhthR07IC8PrJF1JXU6VR13WirYbFGNToTAsCWj+Ye7Cwdyi7B2tQ7VeFs7WwjkHr7NqmvZZyl6+A7MfU78OYVYnN1DK8sYBtbu9hGrzwcTznGp2zZi7W4j+6XH0IJBTO4Bpl7zOepv/Sv+3CLSPlo/dF9rVwsBe96Ixmkmvw/dJo0DItXY38I7HZsIGHu7kxcuoDhVxg0korYeKzf9cTJvfDicNZTle7n9q/UcNaM/jpGJMUkPkv3KUwAYmobjpLPiGk6oNJ8Hi6MDz6RqvJOrj3gFYGBA/Zk/XyaxCBEtEf1WtrS0cMUVV0jCLcYvl0vVcdtskJYW0UN4PGoedzAoddzx4imvwtbaMHS5d/Ep2F95CvQgpn4Xmev/S+8xnzrgONNAHxZH59Dl9HdfIZiepf5k5eCpmE7WG88BkPHOy/hzCoa2iBffdzMZe2e37u9wx+2v4aY/sus3/2H3r/9Nw01/RE9JY/ev/00wM5uBuUtJrt+GraUegOyX/jni72B2dWNoGoEcqWUIV1APsqHzQ9a1byRgBMhLzuFT5SdIwp2ADAP+9XoOZ143a0TCff6KTp68fYsk3OKg0jetw9rdBkD/vOMI7FeuNFZpfh+W7na8ZdPUBI4jTLh9PujqUk3TIqy6E0IcREQr3XPnzqWlJcwtkUKMFV6vSrgHBiKexx0M7q3jdkgddzz1LV5B+odvMVizBADX8Z8huXYLU797DmjQ/ekv4i2fBkD6xlfJ2PgarV+5CZO7n7Lf/gDN5wXNRDAzm8bv/HpoO17bFddT/MCt5P77YfSUNFqv/NHQc6bUbcXxqQsOGs8nHVf8xx/Tt/AE+hee+Il/Jz0ljdav3EjZb76DFgziLZtKy1XDXdDTN62jf9FJUssQgX7/AHV9qrHdDPs0anKqpVlaAup2Wbj1z5N46d3h0rfCbB8//koDx8/tjWNkYqwbdw3UAn4sXa14yyrxTJl5xLNKg0G1CbCyEqqqpFO5ENGkGREUX7/xxhucd955PP744xx77LGxiGvU9Pb2kpWVhcPhwG63xzscEWu6Dps3w86dKuGO8AOqqUk9RE7O2NtWbmAwYPaQFkxGI7E/MTXPIBW3XUH9zQ9jJMe+y4u510HJH26g8Yd/iPlzHcrkH3+F1ituwFc6JW4xjLZovqbr+5pIMttkdTtB/fcdO7c8PAlH33DJ0OeO6+b6LzWSlTZ26vYn0vv0eGHpbmPaNZ9HM3T8OYXsuuvpEWU9Y04ggLWrGV/xFNzTasASWZncPoahzm2KimDBgvBGn+q6TkdHBwUFBZjkC2GRAJxOJ9nZ2bhcLjIzM6PymBG9m9x5551kZWWxbNkyZs2axaRJkzB/LHnRNI2nn346KkEKETWNjVBbq5anI0y4HQ61yp2eMfYS7onGSE6l/YvXYutsHlrRjqVgZnZcE26zqxvHyV+YUAn3kVDdybdSkVFGTrIdgIqM8TVvV4TGNWDm9r+V8+83hhsr5mT4+dHlezj1aGf8AhPjhv2Vp9EMHdjbQG0sJ9zBANbOZvyF5XgqZx1xwg3Q3g52u+pUHk7CLYQITUTvKJs2bULTNCZNmkR/fz9btmw54D6a7EkRY013txoPlp4OyckRPYTbo3J23YD0yErBRZQNzl4c7xBGTTArl95jT493GOPC/t3JWwc7OH3SSZhlK3lCen1TJjf9cTLtjuFvQU9e5OCWy/eQmxWIY2Ri3AgGsL/6FACGyYzzxDPjG88n0YNYO5rx55finjYHIwpNNXt6VJP2mhrpUSNErESUdNfX10c5DCFibHBQ1XH7/apbeQSCQWioh95eqeMWYizbvzt5ksnGwvwaSbgT0IDHxC8eLeOx1flD12WkBrjhkkY+d2yP1KOKkKW//zrWvc01++cfTyBnjJaf6DrW9mYCecW4q+ZgJEW2gLC//n7VGHbBgohPj4QQIRjDe2eEiJJgELZvV+04I2ycBtDSqhqM5OZKcxEhxqKgHuT97i3s7lUd7fOSczimcAGpltjX+4vR9e72dK6/v4KmzuFVvuPmuPjxVxooyvHHMTIxHmWvXjX0s+Pkc+MYyScwDDUGMzsfd9VcjOTUI35Ij0eVzNXUQGlpFGIUQhzSESXdr776Ks888wwNDeoEZ/LkyZxxxhmceOInd+cVYlTV1qph2kVFEXd87ulRD5GREfFIbyFEDHmDPl5teQunT3WnnmmfxmzpTp5wvD6Nux8v4S/PF2IY6tvPlKQg37uwifNXdMkXoiJs1o5m0j5cB4Avr4SBmmPiHNFBGAaWzmYCWdm4q+ehpxx5fVsgoOq4p01T3crld0eI2Ioo6fb5fFx44YU89dRTGIYx1PXb6XTyq1/9irPPPptHH30Uq2QnIt7a2mDHjiNqM+52q3ncEPFIbyFEjNlMVlIsybgDHpYUzqdIupMnnI9qU/nh/RXUtgzvXFhY3cftX61nUqEvjpGJ8cz+ylNoewf5OJefNSbHMVq6WtHTsnBXzUNPO/Kia11XO/fKymDGkY/2FkKEIKJfs1tvvZUnn3yS73znO7S2ttLT00NPTw9tbW1897vfZdWqVdx2223RjlWI8PT1qfFgZrNqnhaBQECtcPf2Qm5OdMMTQhyZoB4koKtGWZqmsbhgPqeWL5OEO8H4A/DbJ4q58NYZQwm3zarzvQsb+csNOyThFpELBLC/9i8ADLMZ5wmfj3NAB7J0taEnp6oV7vSsqDxmWxtkZ6tO5TKFRYjREdFK9yOPPMKll17Kz3/+8xHXFxQUcOedd9Le3s7f/vY3fvzjH0clSCHC5verxml9fUdWx92ivg3Oz5etV0KMJX2+fta1byTLlsHigvlomkaSWc4eE83OxmR+eP8UtjYM16/OnjLA7V+tp6rME8fIRCLI2PgKFlc3AH0LTyJoH1udxCw97Ri2JNzV8wlmZkflMbu61ACXOXNk954Qoymile7W1laWLFlyyNuXLFlCW1tbxEH9/ve/p6KiguTkZJYsWcLbb78d0nH/+Mc/0DSNs846K+LnFgnAMNSW8pYWKC6OOFvu7oaGPZCVpUZpCDFED5K69V0y1z1P6tZ3QQ/GO6IJZU9/C/9teh2nr5e2wU7cQUm+Ek1Qhz/9p5Av3DxzKOG2mA2+eU4Lj9y8TRJuERUjGqitOCeOkRzI4uzCMJlxV80laM89/AEh6O1VO/hmz1ZVd0KI0RNRKlFWVsYrr7zC1772tYPe/uqrr1IW4eriypUrufbaa7nvvvtYsmQJv/nNbzjttNPYvn07BQWH3jJYX1/Pd7/7XZYtWxbR84oE0tQEu3ap5ekIs+XBQVXHbdIg9cgbhIoEkvHOagr//kusPR1D1/lzCmi/+Lv0Hb0ijpElvqAe5IPurdKdPME1tCdx/f0VvLdzuCxoaqmbn11Vz+wpg3GMTCQSa3sjaZvVoo6vsJzBWUfHOaJhZlc3GAbu6vlRG1/m8aiku6YGSkqi8pBCiDBEtNJ96aWX8thjj/G1r32N7du3EwwG0XWd7du3c/XVV/PPf/6Tyy67LKKA7rrrLq688kouv/xyZs2axX333UdqaioPPfTQIY8JBoN88Ytf5NZbb6WysjKi5xUJwuGArVtVppwS2Yn4vjruvn5V8yTEPhnvrKb0nu9j2S/hBrD0dFB6z/fJeGd1nCJLfAO+QVY3vzmUcM+0T+OkkmMk4U4gug6PvpTPOdfPHEq4Nc3gis+08fhtWyXhFlE1YpV7+dljppuYuc+JFvDjnjaHQF5RVB5z/07lU6ZE5SGFEGGKaBnw+uuvZ/fu3TzwwAM8+OCDmPa+Uem6jmEYXHrppVx//fVhP67P52PDhg1cd911Q9eZTCZOOeUU1q1bd8jjbrvtNgoKCvjyl7/M2rVrw/8LicTg8ag6bq/3iL7GbWpSTUbyC6SOW+xHD1L4918C8PGXhQYYQOHff0XfohPBZB7t6BKaYRi80/w+g343SSabdCdPQK3dVm58sIJ1mzOHrisv8HDHVfUsrB6IY2QiEWl+H1lr/w2AbrHiWva5OEekmPpdaF437qp5+AuiMzhb11W13eTJUF09Zr5bEGLCiSjpNpvN/PnPf+baa6/l2WefHTGn+zOf+Qxz586NKJiuri6CwSCFhYUjri8sLGTbtm0HPeb111/nT3/6E++//35Iz+H1evF6vUOXe3vVTFdd19F1PaK4xRig62qFu71dNU7bO/4jXF1dsKcRMrNU0/PIHiW+jP3+J6Indft7I7aUf5wGWHvaSdn+HoMzF41eYBOBBrMLplPXvYdjCheQYkmW13eCMAx4+vVc7vhbOf3u4VOSC07u4NoLmkhL1hPyX1rep+Mr493VWPqcAPQdvYJApp14f+KbBvrQ3P24p87BV1gKRnTOSVtaIC9PjQYzm9XpUrTtW3ST82iRKGLxWj6i9lBz586NOMGOhr6+Pr70pS/x4IMPkpcXWsfJO+64g1tvvfWA6zs7O/H5ZOzIuAtcOIEAAM40SURBVNXWpvaEZ2dDhP+Og4PQ1ALBdDDSYLyurRgYeM1+ALQD1mRFpJJ6W0O6X7C3lQGzNHk6UgO+QQb9bvLTcjEwyMjM4KjUeegaDCD/fRNBt8vKHX+cxmsbhptEFeR4ufGrOzlmrhMYv+/DhyPv0/FVtubxoZ9bTzkj7u/Zms+DWR/AO2USgexk8Bz6C95wuFyqU3lxsRrm0tcXlYc9gK7ruFwuDMMY2v0qxHjmcrmi/phjqidzXl4eZrOZ9vb2Ede3t7dTVHRgXcvu3bupr6/nc58b3ha075sJi8XC9u3bmTp16ohjrrvuOq699tqhy729vZSXl5Ofn4/dbo/i30aMms5OaG5WbcYzMiJ6iIEBaKoHYwAK84Fx3Ix638pJWjBZTuaiyJxZHNL9Ul0DeIPJMY4msTX2t/BuxyZA49Ty40mzqm6Gabq8phPFC29nc9vDk3D2W4euO/P4Ln54cSOZaUFI8N8heZ+OH1tzHZlb3wfAW1KBXnUMacH4/RtonkEszn48U2cRLJ2KJUp1bU6n+v958+AT+hBHha7raJpGfn6+JN0iIdhiMMA+pKTbZDJhMpkYHBzEZrNhMpnQDvOmoGkagUAgrGBsNhuLFi3i5ZdfHhr7pes6L7/8Mt/85jcPuP+MGTP48MMPR1x344030tfXx9133015efkBxyQlJZGUlHTA9fv+jmKcGRhQ28o1TSXdEXB7oHY39PepD6ZEOP3R9vufiA739AXotmRMvk9eESn62y+xdbfTee7XMGwHvteIQwvqQd7v3jKiO7lZM494Pctrenxz9pv56V/LeWbd8Op2bqafH13ewClH7VtZmBj/xvKajo/sNU8O/exYfg6aFr9zP83rweLswVMxA1/ZNLQonYcODqrTo/nz4SBrVjGhaZqcS4uEEYvXcUhJ980334ymaVj2jl/adzkWrr32Wi699FKOOuooFi9ezG9+8xsGBga4/PLLAbjkkksoLS3ljjvuIDk5mZqamhHH71ut/vj1IgEFAqpxmtOp6rgj4POphLu7GwoLpXGaOLSMDa8OJdwGI9OCfZWA2t4/uc/+jfT319Ly1VvwTJX3olD0+fpZ174Rp0/12Zhpn8bsnGpMmknqXhPEax9kctMfJ9PpHF5B+NTRDm6+bA85meF9SS9EJDSfB/vrzwCgW224jj8jrrFYHB14JlXjnRy9Dmc+n9oAOGsWTJoUlYcUQkRBSEn3Lbfc8omXo+n888+ns7OTm2++mba2NubPn8/zzz8/1Fxtz5498i2aUN13du2CxkYoLY0oWw4EoLZW9V7LL5COnuLQLD0dFD3006HLelom5oHeocuBnELav3gN1s5W8p+4F5PfR1JLPRW3XkH35y6l66wrMazR36qUKPb0NfNu5yYCRnBvd/IFFKXmxzssESUDbhM/f6SMf74y/G+amRrgxkv3cMZSh3zZKUZN5tsvDb139y45FT09sh1yR0rz+7B0t+Mtr8JbMSNqJyDBILS2qrFg06bJQoIQY4lmGOG3eb7ttts455xzDrmavHnzZp544gluvvnmIw4w1np7e8nKysLhcEhN93jS3AwbNoDdDmlpYR+u61BXBw0Nqqun1Xr4Y8YLA4MBs0dqBaNF15n082+StvltAHqPXkHzN24ndcf7WJxdBOx5DE5fMDQmzNZcS8kDt5BSu2XoITzl02j56i3q5Eoc4P2uzexw1ZGfnMMxhQtJsYys55XX9Pj1ztZ0rn+gguau4VKL4+e6+PGXGyjM8ccxsviS13R8TL7tClJ3bgKg/qY/4a6eN/pBBPxYO1vwllXiqawBS3TaKxmGOjUqKIAFC1QDtdGi6zodHR0UFBTIwphICE6nk+zsbFwuF5mZmYc/IAQR/WbccsstbNq06ZC3f/TRRwftEC5EVLhcqo47KSmihNswYM8eNRosJyexEm4RfTkvPDKUcPuzC2i94gYwWxiceRS9S09ncOZRI+Zy+0orqb/5ITq+cDWGWZ1MJTfuYsotl5K36gG1xUKw//e9c3JnsjCvhhNLjjkg4Rbjk8en8bO/l3Hp7dOHEu6UpCC3XN7A/d/dNaETbhEfSY27hhJuT9lU3FVxmL4TCGDtasFXMgVP5eyoJdygdu1lZsLs2aObcAshQhOTr6N6enpi0vVNCLxeVcc9MKCWqCPQ0qKmi2VmqLxdiENJathB/mO/H7rcctUtoW1HNFvoPvPL1N32NzyTqwHQgkHyn3yAilsvI6lxV6xCHhf29DWztu1t9L1zaM2aiWlZFZji2NBIRM+m3amce+Ms/vpC4dB1R03v46nbt/A/K7pky6uIC/vqJ4Z+dq44d/T3XgcDWDub8ReU46mcBZbofePvcKgZ3DU1KvEWQow9IX/F9tprr/HKK68MXV61ahW7dh144uh0Olm5ciVz5syJSoBCDNF12LFDFSxF2Dito0PVcaelQWpqlOMTCUXzeSi990ZMAbUi1/3pixmcvTisx/BOqqLulr+Q9/RD5P3rITQ9SEr9Nipu/hJdZ3+V7jO+BOYxNbkxpgJ6kPe7N1PbuweA2t5GpmVNjnNUIlp8AY37nirmwX8XEdRVQmOz6lxzXjNfOq1D+maIuNE8brLeeBYA3ZaM67jPjG4AehBrRzP+/FLc0+ZEdbJFf7/qVr5gAeRLKwwhxqyQz/bWrFkztGVc0zRWrVrFqlWrDnrfWbNm8dvf/jY6EQqxT2OjypgLC9VXumHq6VG916xWSE+PQXwioRT847ckNdcC4JlUTed5X4/sgSxWus69iv6FJ1B8/49Ibq7FFPBT8M/fk7HhFVquuhVfSUX0Ah+jDuhOnl1FZeaBYx3F+LSjMZkf3j+FbQ3D32bOqRzg9q/WM7X0k8fsCRFrmW+9gNk9AEDvMZ9CTx3FkwBdx9reTCCvGHfVHIyk6O399nrVuc2sWRGvRQghRknIjdTcbjeDg4MYhkFBQQH33Xcf55577sgH0zRSU1NJHkfFJNJIbZzo6oJ331UZcwT/Ti4XbNumymlzcqIf3lgiDXqOXNoHbzDpl98GQLcmUffjv+ErrTzix9X8PvJWPUDuM39F27u1Wrcm0Xne1fScduGI2vBEcqTdyeU1PXYFdXjomUJ++0QJgaBayraYDb5+Vgtf+VwblsR8SR8xeU2ProofXTLU3LLu1r+oeurRYBhYO5oJZOXinrEAPSX8PjSHEgioxmmVlTBnTkRrEVEjjdREoolFI7WQV7pTUlJISUkBoK6ujoKCgqHLQsTU4CBs3qw+YSLYOzUwoFa4fb6Iy8DFBGJ29VDy4G1Dlzsu/HZUEm4Aw2qj8/xv0rfoREoeuIWk1gZMfi+Fj/yGjHdfoeWrP8JfmFirv1sdu/iwZxvAIbuTi/GpvjWJ6x6o4INdw6uGVWVu7riqjlkV7jhGJsSw5PptQwm3Z/J0PFNmjc4TGwaWzmYCmXbc1fOimnAbhqq0Ky2FmTPjm3ALIUIT0ddRuq7z0ksvHfL2f//739TX10cakxDDgkG1RN3To7aVh8ntUQl3Xx/k5sYgPpFYDIPiP/4Yi6sbgP55x+E45byoP41n2hzqfvL/6P70FzH2NvNJ3fE+lddfSPZ/V6r+BQmiNK0Ii2ZhVnaVdCdPELoOf38xn3NunDWUcJs0g698to1/3rZVEm4xpuzfQM0xig3ULF2t6GlZuKvno6dlRPWx29ogO1t1KpeGsEKMDxF18Pnud79Lb28vn/vc5w56++9//3vsdjv/+Mc/jig4IaitVa3GS0oItwuPzwe7d6l8vaBg9BuVivHHvvoJMt5fC0AgI5uWK2+O2QvHsCXTcdE19C06iZIHbsXW0YTJ56Hor78g4501tF55M/78kpg8d6z1+vrItKmTzExbOp+ZvJxks5wZJoLmLhs3PjiZ9VuGt9tNKvRwx1frWVA9EMfIhDiQyT1A5roXAAgmp9K79LRReV5LVxt6cqpa4Q5l4kUYurvBZlOdyqU/jRDjR0Qr3evWrePUU0895O0nn3wya9eujTgoIQD1Ve727WqJOsxh2oGAytc7OiAvP+x8XUxAtpZ6Ch/59dDl1itvJpgV++0R7ukLqP3po/Tst6KetvVdplx/AfbVq9Q+wnEioAd5t2MTLzS+Rqe7e+h6SbjHP8OAVa/lctZ1s0Yk3Bed2sGqn2yVhFuMSZnrnsfsGQSg99jTo7rF+1AsPe0YtiTc1fMJZmZH9bH7+tSCwuzZsntPiPEmopVuh8NBRsaht8qkp6fT3d19yNuFOKzeXlXHbbGE/VWurqvF8ZYWVcMtjXzEYQX8lNx7IyafF4Cek8+jf8GyUXt6IzmF9kt/QN9RKyj+423YuloxewYpfvh2Mt5dQ+tXbiSQE355xWjq9fWzrn0DLl8fAD1eF/kpclaYCDqdFn70p8m88r596LqiXB8/vbKepbP74heYEJ/EMMjef2v58nNi/pQWZxeGyYy7ai5Be3Tf/zwecDrVCndpaVQfWggxCiJa/5s0aRJvvPHGIW9fu3YtZTK7QETK54OtW9XwyTA7nxkG7NkDjU2qS3mYC+Rigsp/4j5S6lWzL29JBR0XfjsucQzOPpq62x/FcdLZQ9elf7iOyuvOJ+u1f4/ZVe+GvmZealqLy9dHktnGicVLmG6PTvM5EV/Prc/m89fNHpFwn31CF0/fvlkSbjGmJdduJrlhBwDuytl4K2bE9PnMrm4wDNxV8wjkFET1sQMBaG+HqVNVt3IhxPgTUdJ94YUX8uijj3LPPfeg79fwJxgMcvfdd7Ny5UouuuiiqAUpJhDDgJ071RyMoqKw62mbm9Uqd1amNBcRoUnd+i65z/wVAMNsofnqn0R1jmq49JR02r58A3u+91v82erEzTzYT8mDt1J217VYnF1xi+3j9m0nX9/xHgEjSH5yLp8qO4HCMMaBibHJ2Wfmu7+fwnd+V4mrX22Ky83y87trdvHTKxvISE2cZn8iMWWvXjX0s2NFbFe5zX1OtIAf97Q5BPKKovrYuq527pWXw4wZUi4nxHgV8pzu/Xm9Xs444wxWr15Nfn4+06dPB2D79u10dnZy0kkn8dxzz5E0DrIemdM9xjQ2wsaNqlgpzJF07e2wYwckp0B67Mu2xiyZ/xo600AvlddfiLWnHYD2C75FzxmXxDmqYaaBPgr//ivsr/9n6LpgWiZtl36f3mNOi3t3wIa+JtZ3vA/ArOwqZmVXY4pBTPKaHl2vvp/JTX+soMs1vFXotMU93HzZHrIzgnGMLHHIazq2TAN9VH3rdEw+L8HUdHbe/RxGcmzG3Jr6XZg8g7ir5uEviv7Ix5YWsNth0SJITY36w0eFzOkWiSauc7r3l5SUxIsvvshf/vIXVq1axe7duwFYvHgx5557Lpdccon80onw9fTAli2QlhZ2wt3dDbt3q+3kEznhFmEwDIofvmMo4R6YeRQ9n744zkGNpKdl0HrVLfQdvZzih27H4urGPNBL6R9uJOOd1bRd+kOCWTlxi29SeildHgelaUUUyer2uNfvNvGzv5ez6rXhsp7MtAA3X7qHzyx1xDEyIcKT9eazQz06XMd9JnYJ90AfJvcAnmlzYpJwd3Wp06GamrGbcAshQhNR0g1gMpm4/PLLufzyy6MZj5io3G6VcPt8ajxYGFwuNYvbMCArupM5RALLeuMZMtf/F1Crxy1X3TJm9+31LzyR2qp5FP7tF2TtHX+T+c5qUrdtpO3y6+g7+uRRiSOgB9nq2Ml0+1RsZiuaprEof86oPLeIrfVb0rnhwQpauoZ3qJ0wz8VtX26gINsfx8iECJNhYH95uIGaM0YN1Ezufsz9LtxTa/AVT47647tcqpZ77lw1k1sIMb5FnHQLETXBoBoN1tkJYTbg6+9XCbfXC/my0CZCZO1oovAvPx+63Hr59QRyo1uHF23BDDstX/8pfUevoOjhO7D0ObH0OSm75we4lp5G+5e+RzDDHrPn3787eb9/gKVFi2L2XGL0uL0av36slL+/ONwdPzU5yHVfbOScE7vjXcEgRNhSdn5AcnMtAIPV8/CWT4v6c2ieQcwuB56ps/GVVUa91MftVkNc5s6F4uKoPrQQIk4iTrrb2tr405/+xMaNG3G5XCMaqgFomsbLL798xAGKCaC+Xv0pLAxrpdHtUQl3Xx8URLdRqEhkwQAl9948NLvVuexz9C05Jc5Bha7v6JMZrF5A0Z/vIPPdNQBkrXuBtC3v0nrFDfQvPCHqz9nQ18SGzg8JGEGSzDYqMydF/TnE6PtgVxrX3V9Bfdtw48DFM/v46ZX1lOb74hiZEJGz799ALQar3JrXg8XZjadiBt6yqVFPuP1+6OhQTdMqKqL60EKIOIoo6d60aRMnnXQSbreb6dOn8+GHHzJr1iycTifNzc1MnTqV8vLo17aIBNTRAdu2qX3hYTTe8/lUwu1wqIRbVmNEqPL+9TCpuzYB4Csopf1L341zROELZuXQ/K2f0/fWCxT95eeYB3qxuLop//W1OI//LO0Xfwc9LeOInyegB3mv6yPq+hoBKEjOZUnhAlIs8evuLo6cz6/xh6eK+eO/i9AN9eaZZNW59vxmvnhqx1itshDisMx9TjLffglQZUN9i6NbeqP5PFgcHXgmVeOdXB31kqR9nconT4bq6D+8ECKOIvp1/uEPf0h6ejrbt2/npZdewjAM7r77bhobG1m5ciUOh4Of/exn0Y5VJJr+flXHrWkQRmdAfwBqa6GzA/Ly5UNJhC5514fkPfVHAAyTmZav/Rg9ZZx23tM0epeeTu3PHqNv/rKhq+2v/4fK687//+zdd3xUZdbA8d/U9GTSOykQehNBKfa6uK7dtXcE3XV117IrKkhR0dV1XXUXAXtZe9/X3hsqxQqIIATSk5lkJjOT6fe+f1yYgJQEmMlMkvPdD+vMzcy9B7jM3HOf5zmHlO+/2KfduwJu3q/7LJxwD8+s4pCiiZJw93I/bUrijJuHsvi1wnDCPXqgi5duXc15x0rCLXq3jM/+D31Am6VhP/h4VHPkPq90AT9GWxO+kkFaz+8I/2NRVS3hLiiA4cPBKAtAhehT9uoT4/PPP2fGjBkMGDAgXKV86/Ty008/nXPOOYfrrrsuclGKvicQgDVrwG7fo7nhoZA2E72+HnJywGiIWoSij9F73BQvvAmdorU8sp54CZ6q0TGOat8FLTnUXn039dPnEEpOBcDU1syAO6+k4KFb0Xvce7Vfk96EPxQg0ZDAoYUTGZk1JCrtwETPCIZg0WsF/P7moayt0cogGw0KV51ex5Oz1lJR6ItxhELsI1XF8mHn1PKIFlALBjBaG/CVVOKtGAaGyF98NDdDaiqMGLHHDVyEEL3AXt1HUxSF/Hyt6IrFYsFgMNDa2hr++ahRo3jooYciE6Hoe1RV6+9VW6tVKu/mhbyqam28a2shK0trDyZEd+U/cRfm5joAOgaNxnrixTGOKIJ0OhwHH497xAQKH5xP6g9fApD50cuk/PglDdNm0zFiQpe7CakKBp12IzXBYOagwgkkGhJkdLuX29iQwMxF5Xz/S2p425DSDhbMqGZomSeGkQkROck/rSChYRMA7mH74y8qj8yOg0FM1nr8RRV4K0dEZQjabtcuhUaNki4sQvRVezXSXVFRwcaNG7Ud6PVUVFTw3nvvhX/+xRdfYLFYIhKg6IPq62Hdui1D1d3/8qqr00a5M9L3aPm3EKR99R6WT18HIJSYTP3l88DQ9+buBbPyqbnuPhouuoFQojaaabY2UHb75eQ/dgc6764TrHa/i/dqP6W6vSa8LTMhQxLuXkxR4Im38zjlxuHhhFuvU5l+QgPPzv1JEm7Rp2xbQC1io9yhIKaWOgJ5pXgrh4Mx8nf73W5ttd2IEVIUVoi+bK+S7mOOOYbnn38+/Pzyyy/nwQcf5KijjuLII4/kscce4+yzz45YkKIPsdu1ddwJCZCc3O23NTXBxo2QkirTrsSeMbY2UfjIbeHnTef/lUDenrWm61V0OuxHnMLG257BPWx8eHPWe89TeeNZJK39doe3bHLW8l7tpzj8Tla1rUNRlR1eI3qXuhYzF98+mAVPluILaF/15QVenpr9E38+vR6zSY1xhEJEjsHRSvqyDwAIpllwjj9833eqhDA11xHILcYzaBSqOfJ3+/1+sNlgyBCQ+sNC9G17NdRz4403ctZZZxEIBDCZTPz5z3/G7Xbz4osvYjAYmDVrFjfccEOkYxW9nc+nrePu6Nijftw2mzYb3WSC1F5a80rEiKJQ9MDNGNztALQfeDSOg34b46B6RiC3iM3X/4fM958n75n70Pu9mJtrKbv1UlqPPYuW0/9AwGjavjp5UjYH5u2HXifVtHorVYWXPs7m9qdKcXs7152ee0wTf/l9HUkJkmyLvifj09fRhYIA2A85AdVk3rcdKgqmpjqCOYV4qkahJkR+xk8oBA0NUFkJVVXShUWIvk6nquoefQOrqorT6cRsNpOY2PunHba3t5ORkUFbW5tMiY8mRYEff9T6fJWUdLsIicOhdRQLBrV13KJrKipug5eUUCI6+ve3eNb/PU7+M/cCEMjKZ8NtT6OkdL9Sfl9haqqhaPEckn/+LrzNU1DKqyefzNp87R/WiMzBDMusistiaXJOd09zm4nZD5XxyXedi0KLcnzcemk1Bw53xTAy8WtyTkeQojDwulMwN9cCsP6ulwnk78Owsapiaq4jmJGNZ+h+Uelwoarakrm8PBg3rvcvmVMUhebmZvLy8sIFloXozex2O5mZmTgcDtL3oMPS7uzxvwy/309WVhb33ntvRAIQ/cTmzdr88Pz8bifcLpe29Nvnk4Rb7LmE6p/Ie/4/AKg6HfUz5vbLhBsgkF/KphsX03T2n1G2jAAlNdbw+wfu45j3P+SwnHGMyBoclwm36JqqwhtLMzlh5vDtEu5TD7Xyym2rJeEWfVry6mXhhNs14oB9TriNLXUE0y14Bo+JWkvJpiatYNrIkb0/4RZCdM8eTy9PSEigoKCABPmUEN1ltWrD1Wlp0M3ZER6vNijucklhEbHndD4vxQtnhacb2o47j47h47t4Vx+nN9A69VxcYw6iaNHNJG1YhV5VmfTpx3g31NAwY67WCkf0Km1OA/MeHcDbX3femczJCDB/WjWHjm2PYWRC9IzMD14MP7Yfeeo+7ctobUBJycAzeCxKStq+hrZTra1aDdmRI7XLIiFE/7BXc0AuvPBCHn/8cfx+f6TjEX1NRwesWqUtXupmHwy/X0u429ogN1fWOYk9l/f0v0io1zoseMqH0nLa5TGOKPba/S5cgQ78ReVUz36IptP/iLKlEm9i3QbK51xIzouLIBiIcaSiuz5cmcEJM0dsl3AfN7GV1xaskoRb9AtGu5W0lR8DEMzIxrnfoXu/L2sjSmKyNsKdGp2+XS4XeDxapfKcnKgcQggRp/aqkNqoUaN45ZVXGDFiBBdeeCHl5eUk7aSk9CmnRKhlg+idgkGtcFpra7fLcgaCsGEDtDRDbh7I0iCxp1K/+ZSs97XuCoo5gfrLb4lKm5feZJOzlhUtP5BmTuWI4skYDEZaT7gI934HU7ToZhI3rUWnhMh9ZQlp33xM/fS5+AZUxTpssQvODj0LnizllU87r9otqUFmXbiZqQe2xTAyIXpWxsevoguFALAfesJe99A2tjahmhPwDB5LKD0zkiGGeb3aYMKIEVBcHJVDCCHi2F59Op111lnhx7Nmzdrpa3Q6HaEtH4Sin9qwQVvLXVjYreHqUEjrw11fr41wG7u39FuIMIPDRuGSeeHnTWf9BX9ReewCirGgEtquOrlJbySohDBsqavgKx3ExjmPkfPaw+S89hC6UIjETT9TMfs8Wk6+FNvxF/TJfua92dJVady4pJxGW2d15sPG2pl7ySZyLcEYRiZED1NCWD56BdDqdtgPO3mvdmO0W1H1BjxVowlZsiMYYKdgUFvHPWiQVq1cZvAJ0f/s1dXUhx9+GOk4RF/T0AA//6xVQDN1PcqoqlBTA7U12lv28ma16M9UlaIl8zA6tZE+59iD93l9X2/W7nextGkFDr8T2E11cqMR6ynTcY07hMLFc0isWY8uFCTvhYWkrdRGvf3FFTH4HYhtdXj13P1cMf99t7PIRUpiiJnn1XDywTa5iBf9TsoPX2K2NgDgHjWJQG7RHu/D4LCBquIZPJZgVnQKyKiqdklUXAxDh3a7lqwQoo/pdmpzww03cOaZZzJ69GgOPXTv18yIfqC9HVav3tJYO7Vbb6mr00a5MzKkkqfYO5nvPU/qd58DEEzPomHarH47nFDtrGVlyw8E1RCJhgQOzNuP/OTdLyD0lg+leu7j5LyyhOzXH0OnKiRtWE3FrHNoOfUyWqeeA3q5WoyFb35OYebicjY3dRaiPHB4O7dcuoniHKmtIvqnbQuotR2x58sZDU47umBAS7hzCiIZ2nYaGyEzUyucZt7H9uFCiN6r2ytmb7/9dn788cfwc5vNhsFg4IMPPohKYKKX8vu1hNvl6naVkKYmrZtYSirspDSAEF0y120g7+l/hZ/XT7+ZUEb/7DOnqArrHBsJqiHykrI5uuTgLhPurVSTmZbT/0j1zQ/j2zItXx/wk//MvZTdcimmxs1RjFz8mj+g4+5niznvliHhhDvRrHDj+Zt56G/rJOEW/ZaxtYnUbz4DIJCZh2vsQXv0fr3Lgc7nwTNoNIG86C2wtlq1gYRRoyAlOt3HhBC9xD6VqVJVNVJxiL5AVbXG2vX1UNC9u8Y2G/zyy5ZBcflCEntBF/BT/J+b0Ad8ALQe/XvcY6bEOKrY0ev0TMofx8isIRxSOJEkY/fa9G3LO3AkG+c/ie2481C3zBZIXvc9lTeeRebbz4CiRDps8Surq5M4ffYwHvxfAYqq/R2MGeTipVtWc87RLVJkUvRrlo9eRadqn0P2w07co9oTercTvceNd+BIAgX70NO7C04nBAJa4bSs/nkPWAixDfnaFpFTU6P1+srL69aibIdDe7mqdrubmBA7yH1hIYmbfwbAV1xJ85lXxjiinlftrGV127rw81RTCsN3tn57D6jmRJrPuopNNy3Bn69dmOr9PgqevIsBCy7D1Fy7z3GLHQVDsPCVAs6cM4x1tdrUH5NR4eozanly1lrKC30xjlCIGAsFtymgpsd+2Endfqve48LgcuCtGI6/sCw68aFVKrfbYdgwKNrzpeZCiD5Ikm4RGa2tWnuwlJRuzRF3ubRBcb9f7gCLvZe86muy33gCAMVoou7yW1DNez6y21sFlRDLmr/j6+Zv+bF1LVZv5NtFeQaPZcMt/6X1mDPD21J+WknlDWdhef8F7a6ZiIhf6hI5Z95Q7nuxmGBIu2EytKyD5+etYdrxTRjkG1sIUr/7HFNbMwCu/Q4imJXfrffpvB0YHG14K4fjL4leCfGtlcqrqqBCalAKIbbYoxrR1dXVrFy5EgCHwwHAunXrsFgsO339uHHj9i060Tt4PNo67kCgW+u4PV5Yt15LvPOiUyxU9AN6l4OiRXPCz1tO/yO+ssGxC6iHOfxOljaupD3QWZ08K8ESlWOpiUk0nXctzvGHUbh4HmZrPXqfh8JHbydt+Yc0TJtFMDt6hYj6OkWBJ97O457ni/EFtMzaoFe59HeNXHZSA2aj3NgQYqvMD14KP247vHsF1HQ+L0a7DW/5UHwlA6OWcCuKtsKurAwGD0aWgQghwnRqNxdm6/V6dL/6kFJVdYdt227vDX2629vbycjIoK2tbZc3D8RuhELw/fdaJbTS0i6/Yfx+WPszWFu0hFu+kCJPRcVt8JISSkRHH63eraoU3/c30pdphRzdIw5g81/v7zcnVLWzlhUtPxDaUp18Yv5+5CV1r1javtJ73OQ986/tLnxDSSk0nXM1jkNOiMrFbF8+p2ubzdywuJzla9PC2yqLPNw2vZrRAztiGJmIpr58TkeTqaWegdeciE5V8ecU8ss/Xumyq4LO78XY2ox3wGB8FcOi+j1RV6fN3hs3DpKTo3aYuKMoCs3NzeTl5aHvJ9/Dom+z2+1kZmbicDhIT0+PyD67PdL9yCOPROSAoo/ZuBE2bYLCwi6/yAJB2LABWpohVxJusQ8yPn09nHAHUzOonz6n35xQK1t+ZH17NQB5STlMzNuPRGPP9dlTklJovOgGnOOPoPDB+ZhamzB43BQ9OJ/0ZR/QcMlNBDNzeyye3kpV4fkPc7jjvyV4fFrSoNOpnH9sM1edXkeiWUa3hfg1y0evoNsyVmQ/7KSuE+6AH6OtCV9pFb7yoVH9nmhp0VbYjRzZvxJuIUT3dDvpvuCCC6IZh+iN2tu1SmgWS5fNJ0MhrQ93fT3k5oJR2v2KvWRqqiH/ibvCzxsvvoFgVv9Zp5CVaEHXDsMzBzNsH4ul7Qv3qIlsWPAs+U/9A8snrwPaWsvK639P4/nX0T55ar/tk96VplYTsx4q47PvOytIFuf4uG16NROGuWIYmRBxLBjE8vGrAKgGA45DT+zi9QGM1gZ8JZV4K4aBIXoXHg6HNrV8xAjtkkgIIX5tj9Z0C7GdlhZtPXfu7ke1VBU210BtjTbtqhuFzYXYuWCQ4oWzMHi1abf2Q07AOeHIGAcVfd6Qj0SDNppdnlZCVkIG6ea0Lt4VfUpyKg2X3qyNej90C0aHDUOHk+IHZmuj3hfNJJSRHesw44aqwv++yOLWx0tp7+j8IDz9sBb+enYtKUnSik2IXUn75mOMDhsAznGHErTsZklNMIjJWo+/qAJv5YioXnh0dGjtwcaM6Xa3VCFEP9Q/5mOKyAsGobYWUlO7fGldHWyq1tqCJfTcLFjRB+W8+hBJv/wIgD+/lMbzro1xRNEVVIJ83fwt79Z8ii/kD2+Ph4R7W679DuaX25/DMXlqeFvaio+ovP73pH31Xgwjix+t7Ub+cl8lf3ugIpxw51r8PHDtOuZeslkSbiG6YNmmjoR9dwXUQkFMLXUE8krxVg4HoylqMQUC2vhDVZVWPE0IIXZFkm6xd2w2rQllF8UFGhu1Zd8pqd3qJCbELiX9/C05rz4EgKo3UHfZfNTEvrtwzuF38l7tZ1Q7a/GGvDR5rLEOabeU1AzqL59P7VV3EkzLBMDoclBy//UU3T8Tg9Me2wBj6P0VGZw4czjvLMsMb/vtJBuvLljNIWPaYxiZEL2DqamG1B+/AsCfV4x7xAE7f6ESwtRcRyC3GM+gUajm6N3pD4W0JXMVFVqlcllNI4TYHZnoK/ZOQ4P2DbObKVs2G/zyi7bcOzWlB2MTfY7e46Jo4Wx0qjYa2HLypXgHjYxxVNETy+rk+8o5/nA6Bo+l4NHbSV/2PgAZX71LypoVNFx8A679D4ttgD2o3W1gwZOlvPpZ5xR7S2qQmy/axLEH2GMXmBC9jOXDl8OP2w4/ZecF0RQFU1MdwZxCPFWjUBMSoxaPqmqXQYWFMGyYLJsTQnRNPibEnnO7oalJmy++C3Y7rFun5eURqrQv+rH8x+/EbK0HoGPwGGwnXBTjiKIjqARZaf2RamctAPlJORzYw9XJIyGUnkndlXfQ/uU7FDx2B0aXA2N7K6X3XIt9ynE0nXctSkrf/mD44oc0bnqwnMbWziKTh4+zM/fiTeRkBGMYmRC9iy7gDxdrVA1GHAf/bscXqSqmlnqCmbl4qkZHfRZUU5N2bTNiBCRGL7cXQvQhknSLPWe1aol3zs5H3lwurah5ILDLlwjRbWlfvoPls/8DIJSYQv2MeV22iemtfmz9mWpnLTpiX508EpwTj6Fj6DgKH7mNtJWfAGD5/A1SVi+j4ZKbcI+ZEuMII6/Dq+euZ4p55v3OivqpSSFuPH8zJ0xplSmoQuyhtOUfYnS2AdA+/nBCGVnbv0BVMbbUEUy34Bk8BiUpulPr2tq0QugjR8qgghCi+yTpFntGUbQCartoQunxwLr1WuKd13+6OIkoMVobKXxkQfh544V/I5BXHMOIomt4ZhWtvjZGZg3pNdPJuxKy5FD753+Q/vkbFDxxJ4YOF6a2FgbcdRX2Q0+k6Zy/oCR1XZCxN1j5cwozF5VT09w59DVpRDu3XFpNYXYghpEJ0XtZPtymgNqRp+7wc6O1ASUlA8/gsSgp0S0y6XZr1cr326/Lxi1CCLEdSbrFnmlrg9bWnX7b+P2w/hewt2kJt4zoiH2ihChaNBtDhxMAx8RjtN7PfUhQCVLtrGNg+gB0Oh1mg4nDiyaj62v/eHQ62g/6LR3DJ1D40C2kfv8FAJaPXyXlxy+pnzabjpEHxjjIvefz67jvxSIeeTMfVdX+7pLMIa49q44zjmjZ6fJTIUTXzPXVpKxZAYCvsIyOoftv93OjtRElMVkb4U7d9ZK3SPD5tFo1w4dDSUlUDyWE6IMk6RZ7pqlJG+02bd+CIxCEDRugpRly83Ze40SIPZH9xhOk/LQSgEB2AY0XzuxTd3IcfidLG1fQHnABKoMyygH6XsK9jWBWHjXX/gvLx6+S99Q/MXjdmGxNlN3xR9qOPI2mM6/sdRXpV1cncf0DFayv62zPMG6wi1unV1OW74thZEL0ftuNch9+ynbfAcbWJlRzAp7BYwmlZ+7s7RETDGrdWCorYdCgPvVVJIToIZJ0i+7zerX+GL9axBQKQXW19qPcXDD2zeW2ogclblxD7gsLAVB1OupnzI36tMGetLG9hpXWH8PVyeOt73ZU6XTYDzsJ18gDKVoyj5TVywDIfP8FUn5YSv2lN+MZOi7GQXYtEITFrxWy6LVCgiHtCtxkVLjytHounNqEQW48CrFPdH4fGVvqeSgmM/aDjw//zGi3ouoNeKpGE7Jk72oXEbG1UnlRkVap3CDXOEKIvSBJt+g+m01brL3NvCpVhc01UFsDWVnSNkPsO53XQ9HCm9CFQgDYjr+QjmH7d/Gu3qGvVCePhGBOIZv/9m8yP3iRvKf/hd7vxdxcR9ltM2g75kyaT/9jVFv+7Iv1dYnMXFTOqo2dBZuGlXVw+4yNVJV6YxiZEH1H2rL3MbocADgPODI8fdzgsIGq4hk8lmBW9IvHNDZCZqZWOC2h/31UCyEiRFIk0T2qCnV12rTybeZV1dbBpmqwWOTLSERG/tP/JKFhEwCeimG0nDI9xhFFxrbTyXXAiKwhDLMM6tPTybuk19N21Om4Rk2iaMlcktd+g05VyXr7aVK++5yG6XPwVI2OdZRhIQUeezOfe18swh/QhrINepUZJzYw44QGTPKNKkTEZH7wYvhx2xFaATWD044uGNAS7pyCqMdgs2mXPSNHQmrfqPcohIgRuUQQ3eNwQEuLll1v0dgIGzdoX0TSp1JEQurKj8n8QFvDp5gTqb98PhhNXbyrd/CH/DgDLhINCUzMH0deUnSnRPYmgfwSNt2wiMx3niHvuX+jD/hIaNxM2fxp2I47V7vxktT1fqJpc5OZG5eUs2Jt51KAyiIPt8+oZmRlRwwjE6LvSahZT/LP3wHgLa7EUzUGvcuBzufBUzWmR7pYuFxa8bRx4yBbPq6FEPtIkm7RPS0tWnnyLdm11Qq//KKNbqdEtyWm6CcMdiuFD84PP28652r8heWxCygCVFUNj2TnJmVzYP448hKz++V08i7p9bT95mzcYyZTuHguyet/QKcq5Pzf46R++ym/XH4DlI3t8bBUFZ79IIc7ny7B49MWc+p0Khf8pokrT6sn0az2eExC9HXbFVA74hT0HS70HjfeQaMIFJRG/fher9aoZdQoKO67XSqFED1ISr2IrgUC2tTyNG2Ex26H9eu1Wea/qqkmxN5RVYqWzMPotAPgHHco9sNPjm1M+8jhd/J+3We0+53hbQNSiyTh7oK/sJxNsx6k6Yw/oWyZ5ZBYt5Hhsy/TiusFe67fdWOriel3DmLeo2XhhLs0z8djN/7MX8+uk4RbiCjQ+bxkfP4GAIo5Aef+h2JwOfBWDMdfWBb14weDWqOWQYO0auVCCBEJknSLrtls2vTy9HScLi3hDgS0wiJCRELmu8+GezcHM7JpuOSmXt2TZWN7De/Vfkqrz8E31tWxDqf30RtoPf4CNs5/Ek/FMAB0SojcVx+mYvb5JGxaG9XDqyq89lkWJ84czuc/dPb+PeOIFl66dTXjh7iienwh+rP0r97B0KH9G2ufcCS6QBBv5XD8JZVR/15QFK0TS2kpDBki7U+FEJEjHyeiaw0NoNPh8RtYv15b5yTrm0SkJNSsJ++Ze8PP66fPiXrP1WgJKkG+bv6WZS3fEVIV8pNyOTBvbKzD6rX8JQOpnv0IzafOQNnSpyexZh0VN59PzisPakNSEWZzGLnq3kquX1SBs0NbgZWf6Wfxdeu4+aLNpCQqET+mEKJT5vudBdSc4w7FWz4UX8nAHrkR29gIOTkwfDiYzVE/nBCiH5E13WL3XC5obsaXZGH9L9rU8rzcXj0IKeKIzu+jaOEs9AE/AK3HnoV79KQYR7V3pDp5lBiNWE+aRvP+BzJo4QISa9ahC4XIffEBUld+TP30OfhLBkbkUO8uszDnkQG0OTuL950wxcbM82rISAlF5BhCiF1L2LSWpA2rAPAVVeCYchy+ssE9MuRstUJSklapXGrVCCEiTUa6xe5ZrQQcbjY0p9DSrN0BlulWIlJyn/83iTXrAPCWDKT591fEOKK90+q1817tp7QHXCQZEjisaBLDM6sk4Y4gT3kVG+Y9hvXES1D12qh30sY1VMw6l+z/PQrK3ifFDreBvz1QzlX3Dgwn3FlpAf515S/cflm1JNxC9JCt3SsAbL85B1/FsB656Ghv1ybOjBghS+eEENEhI91i10IhQptqqGlNocEJublgNMQ6KNFXpPzwJdlv/RcAxWSm/g+3opp7Z5ExS0I6mQkZGHRGDswbK8XSosVoouW0y3GOO4SixXNJqNuAPhgg79n7SV3xMQ3Tb97jiveffZ/OrAfLaGrrnEt61Pg2br5wM9kZkZ++LoTYOb3HTfoXbwIQSkii6Zy/gCH6Fx0ej5Z0jxoFhYVRP5wQop+SMUuxS6qtlfpVbWx2WMjKAqPcohERYnDaKVw8J/y8+fdX4CsdFLuA9kK730VI1db36nV6Dio4gEMKD5CEuwd4K0ewcd4TWH97PqpO+xpLXv8DFTeeQ9abT2nVkLrg9uqZ88gApt9ZFU6405KD3H7ZRv515QZJuIXoYemfv4nBq/W8b516DkpGVtSPGQhAc7NWqby8POqHE0L0Y5J0i12qWdFEfa1KRraRBMkjRKSoKgUP34rJbgXANWoibcecGeOguk9VVTa21/Bu7Sd8b1sT3m42mGQ6eQ9SzQm0nHklm2Y9iK9gAAD6gI/8//6TsttmYGqq3eV7l69N5eQbhvPcB7nhbVNGOXh1wWpOmNIqNSuE6GnBAJnvPht+2nLa5VE/pKJodWLLymBwzywbF0L0Y/IRI3aqdp2HumUNmHMzSEyMdTSiL8n4+FXSl38IQDA1g4bpc3rN1Y5Wnfy7cHVyp9+Foko161jyVI1m4y3/xXbsWahbsuXktd9QecOZZL73/Haj3j6/jr//t5gLbh1MbYt2JzEpIcTsCzex+Lr1FGT1XA9wIcQWSojUlZ+QWL8RAPfwCXiGjov6YevrIS9Pq1RuMnX9eiGE2BcyYVjsoLER1i21kqU4MecMiHU4og8xNW6m4Im7ws8bLplF0JITw4i6z+FrZ2nTSqlOHofUhESaz70G5/jDKVo8F3NLHXq/l4LH7iBt+YfUT5vFt+0DuX5RORvqk8LvGzfYyW3TqxmQ749h9EL0Y4qCqamOtG8/C29qOWVG1A/b3AypqVql8qSkrl8vhBD7SpJusR2bDX74XiXFVkdyViJBSShEpASDFC+8Cb3fC0DbYSfjGn9YbGPqBlVVqXbWstL6AyFVIcmQwMT8ceQmSbP6eOMZOo4Ntz1N/jP3kvn+CwCkrPqakuvO4s7Q3WxQhwJgNilcdVod5/+mGUPvmGQhRN+jqpha6lESkkhb9j4AoZR02o6N7nIju13778iRkJER1UMJIUSYXG6IMIcDfvgBlFY7uXoboVRLrEMSfUjuy4tJ2rAaAF/BAJrOuTrGEXWPL+TnW9sqQqpCflIuR5ccIgl3HFMTk2m88Ho2/e3fdFi0UsRJQReL1em8wXEcUfozL8xfw0XHScItRMyoKsaWOoLpFpI2rgoXULMddx5KUvSaZHd0gMulTSnPz4/aYYQQYgdyySEAcLu1hNvhgCJjM7pAoNe2bxLxJ2ntN2S//igAqsFA/eXzURN7x5y+RGMC43NHMzJriFQn7yVCCtxXfQKlztU8yCXh7VN5i3es+7P/xhdAVWMYoRD9m9HagJKSgadqDNn/eyy8veXU6E0t9/vBaoUhQ2CArJwTQvQwSboFXq+WcFutUJzrx2ytQ0lJi3VYoo/Qd7goemA2ui0Fx1pOmYG3ckSMo9o1VVXZ0L6Zpo6W8LbS1CKGZ1bJ+u1eYFNTAuffMoR/PFtCa8jCpTzIJdkv40nLA8DgcVG0aA4l91yDYUsFfSFEzzFaG1ESk/EMHkPShtUkr/8BANfoSXgHjYrKMUMhrVJ5ebnWHkw+yoUQPU2S7n4uEIBVq7QqnoWFYHLaMLjaCaWkxzo00UcUPHYHZmsDAB1D9sN2/AUxjmjXAkqQr5u/ZXnL93zZ/A3eoC/WIYluUhR4+r1cTrlhGN+sSwVAp1O5+LhG/vT3AWy+8xnsU44Lvz5t5SdUzjyD9KVvy6i3ED3E2NqEak7AM3gsofRMcl9aFP5ZyymXReWYqqol3IWFMGwYGKWakRAiBuSjpx8LhWDNGti0CYqKtC8iU0s9qtHYa1o4ifiW/sVbZHzxJgCh5FTqLpsHekOMo9o5h6+dL5pW4gy40KFjcEYlCQZzrMMS3dBgM3HTknKWruq8WVia52XBjGrGDXYDoJjTabhsHs4JR1D48G0Y21sxuhwU/+dG0pZ9QOOF1xNKz4zVb0GIPs9ot6LqDXiqRhOyZGNwtJL53nMABNMzaTvq9Kgct6lJK5g2ciTSAlUIETOSdPdTigLr1sEvv2jFREwm0LvaMbU2SwE1ERFGawMFjy4IP2+88HqCOYUxjGjnVFVlo7OGb6w/bqlOnsjE/P2kWFovoKrw6mdZ3PbEAFyezps5Zx3ZzDVn1pGcuGMPddf+h7Fh8FjyH7uDjK/eBSB92fsk/7SSxotvwDn+8B6LX4j+wuCwgariGTyWYJa21CP7/x5H79O6Wdh+e35U6ny0tmoDCiNHQpqsmhNCxJAk3f2QqsLGjbB2LeTkdN75Ndqt6H2e8BeiEHtNCVH8wGwMHm2U0TF5Ku2TfhPjoHakqArLmr9jk6sOgIKkXA7IH0uiQYqlxTurw8jND5fx4UpLeFtBlp9bplUzeZRzt+8NpVmov2IBzglHUvDoAowuB0ZnGyX/ug7H5Kk0nnctSqr0EhIiEgxOO7pgQEu4cwq0japKzjZTy61R6M3tcoHHA+PGadc6QggRS5J090O1tbB6tTbdKjl5y8ZgEFNzDaHk1JjGJvqG7P89RvLabwDw5xTSeMHfYhzRzunQbfl/HSOzhjDUMlCKpfUCb39tYe4jZdhdnV9hJx1k5fpza0lPCXV7P84Dj6Jj6H4UPrKAtBUfAZDxxZskr15G4yU34Rp7UKRDF6Jf0bsc6HwePFVjCOQVh7enfvMpSdU/AeAcdwjeimERPa7Pp41yjxwJxcVdv14IIaJNku5+prERfvxRS7a3nWplbG/F4LQTzC6IXXCiT0jcsCpcHEfV6am/bB5KHN3MUVUVRVUw6A3odDr2zx3JwIwychJlPW+8s7sM3Pb4AP5vaefU/+z0ADdftImjxjv2ap+hjGxqr7qT9C/eouDxv2PocGKyWyn9x5+xH/I7ms65Jq7OXyF6C73bid7jxjtoFIGC0u1+lvviA+HHLREe5Q4GtWudQYOgslIqlQsh4oNUy+pHbDatNZheDxbL9j8z2hq1Bwa5DyP2ns7bQdHCWehC2mij7XcX4hmyX4yj6rS1OvkXTStQt1SsNuqNknD3Al98m8lJM0dsl3AfM6GNVxes3uuEO0yno33KVDbc/hyuMVPCmy2fvE7lzDNI+eHLfdu/EP2M3uPC4HLgrRiOv7Bsu58Z7FYsH7wIQMCSg/2IUyN23K2VyouLYehQMMRn3U4hRD8kGVY/4XBoCbfPp7XN2JbO24HJ1ogiBdTEPsp/6p8kNG4GwFM5nJaTp8c4ok52XztLm1bgDLjRoaPVZydbku245/boueO/JbzwUW54W3pykJsu2MxvJ7VFdBQrmJlLzTX3kPHp6+Q/+Q8MHjem1iYG/P0K2o44heYzr0JJSoncAYXog3TeDgyONrwDR+Av2XGoOef1R9EH/IB2Y1Y1R66GRkMDZGbCiBFgluYTQog4Ikl3P+B2awm3w7HztU1GuxV9h4tAQVbPByf6jLTlH5L50csAKAlJ1F1+S1w0RN15dfJxknD3AsvWpHLD4nLqrJ0X5QeNdjD/kk3kZwWic1CdDschJ+AefgCFD80n9cevAMj84CVSvv+Shktn0zF8fHSOLUQvp/N5MdpteMuH4isZuOPcbkXZvoBaBG/MWq2QkACjRkGqrAgRQsSZ2F8Ri6jyerWE22rVEu4dRoUUBXNzHWpCkix8EnvN2NZCwUO3hJ83nnsNgYIBMYxIE1CCrGz5Ybvq5Afm7yf9t+Oc16/jnueKefzt/PC25MQgfz27ltMPs/XIR1Uwp4Cav96P5YMXyX/6X+h9HszWesoWXEbr0WfQ/PsrotLiSIjeSuf3YmxrxjtgML6ywdpatl9JW/4hiTXrAWg/4Eh8A6oicmynEwIBrVJ5lowfCCHikCTdfVggAKtWda5v2sn3HwanHYPDRihDehKLvaQoFC6Zi9GlrattH384jkNPjHFQmqWNK2j0tEh18l7k+1+Smbmogo0NieFt44c4ufHynxicrdtScb6H6HTYjzwN96iJFC6ZR8pPKwHIevdZUr//gvrpN+MZPLbn4hEiTukCfoy2JnylVfjKh+78ggO2G+WOVAE1rxfsdm2Eu6goIrsUQoiIk0JqfVQoBGvWwKZN2hruXRUTMbY2owsGUU0y8if2TuY7z5C6pdBUIDOXxotvjJtZEyOyBpNiTOawokkMyxwkCXcc8wd13PtCEefMGxpOuM0mhb+dXcMjN6ylOM8Xs9gCeSVsnvkAjedei7Jl/am5qYayWy4l77/3oPN7YxabEDEXDGC0NuArqdRaf+3igsNobSTzQ20JUiA7PyI3Z7etVF5Rsc+7E0KIqJGR7j5IUWDdOvjlFygoAJNp56/T+X2YWuoIpab3bICiz0jYvI68Z+8LP2+49GZCaZaYxRNQgrR67eQn5wCQnZjJ1AGHodfJ/cV49nNNItcvquCnTcnhbaMq3dw2vZqBxV7UGMYWptfTduyZuEdPonDxXJLXf49OVcl+80lSv/uM+ulz8A4cGesohehZwSAmaz3+ogq8lSN2W8cj5/VH0IWCAFhPuHifb/YrCtTVwYABMGTILgfXhRAiLshHVB+jqrBxI6xdCzk5WlGRXTHarRjcTpTktF2/SIhd0Pl9FC28CX1QK2hl+83ZuEdNjFk8dl8779V+yqeNX9Pm62whJQl3/AopsOT1fE6bNSyccBsNKleeWsdTs39iYHH8jSD7C8vYNGsJTWdehbIlaUior6Z87sXkPv9vdFuqMgvR54WCmFrqCOSV4q0cDsZd3OEHrYDay4sBUHU6rCddus+Hb2iAvDytUvmuBheEECJeyEh3H1NTA6tXQ0YGJCfv5oWqiqmlHtVoktvDYq/kPXc/ibW/AOAtraLl9D/GJA5VVdng3My31lXh6uSKqsQkFtF91Q0JzFxcznfrO8sMV5V4WDBjI8PLPTGMrBv0Blp/ex6usVMoWjyHpA2r0akKOa89Quo3n1I/fY62rlWIvkoJYWquI5BbjGfQqC7bfqV/+Q4J9dUAtE86Fn/xvs0Fb2mBlBQYObKLax0hhIgTkm31IQ0N8OOP2hdQWheD13p3O8a2lphOBRa9V8r3S8l6+2kAFJOZuj/cEtFeq90VUIJ81fwNK1p+IKQqFCbncUzpIdIOLI4pCjz5Ti6n3DQ8nHDrdSrTjm/g+Xlr4j/h3oa/uJLq2Q/TfPofUA3aPezEmvVUzLmAnJcWawtOhehrFAVTUx3BnEI8VaNQExK7fEskC6g5HFrdmhEjwGLZp10JIUSPkZHuPsLr1Qqn6fXd+xIytrWg83tRE/K7frEQ2zC0t1G0eE74efOZV+IvGdjjcdh97SxtWoEz4EaHjlFZQxgi1cnjWp3VzE1LyvhqdWcdibICLwumVzO2yh3DyPaBwYjthItxjT2YosU3k7jpZ3ShELkvLyZt5cfUz5iLr3RQrKMUIjK2zJILZubiqRqNmtj1MLOpuQ7Lp68D4M8twnHQ8Xt9eI9Haw82ZoxWs0YIIXoLGenuI0Ih8Pu7HuEGIBjE3Fwna7nFnlNVCh+aj9FhA8A1ejJtR58Rk1DqO5pwBtwkGRI5vGgSQ6U6edxSVXjpk2xOmjl8u4T77KObeXH+mt6bcG/DN6CKjXMeo+WkS1H1WvXmxE1rqZh1LtmvPQIhGfUWvZyqYmypI5huwTN4DEpSSrfelvPqQ+hCIQCsJ03bbbG13QkEoLkZqqqgrGyvdiGEEDEjI939kNFhw+C0E8iR28Riz1g+fJm0lZ8AEEyzUH/p7Ji1BxtqGYSiKlRlVJBgkJZ38arFbuTmh8r46FtLeFtBtp9bL61m0ghn7AKLBqMJ66kzcI07hMJFN5NYtwFdKEje8/8mbcVH1E+fs89rWYWIFaO1ASUlA8/gsSgp3bxpHwyS88oSAFS9HuuJ0/bq2KEQ1NdrbcEGD46brpRCCNFtMtLdDxltDah6PRjknovoPnNDNflP3R1+3jBtNiFLTo8d3+5r54vGFQQVbcREr9MxMmuIJNxx7M2vMjlh5ojtEu6TD7Hy6m2r+l7CvQ1vxTCq5z+J9fgLUbdUz0/asIqKWeeQ9caTsOUcFqK3MFobURKTtRHu1Ixuvy/jizcxN9UC4JhyHIGC0j0+tqpqNWsKCmDYsL0eKBdCiJiSj65+Ru9xY7I1oUhvbrEnggGKFs5C79daOLUdcSqucYf0yKF/XZ08pS2ZMdnDeuTYYu/YnQZueXwAb3yZFd6WnRFg3sWbOHycYzfv7DtUk5mWM67Auf+hFC2eQ0LDJvQBP/lP30Paig+pnz6HQP6eJyBC9DRjaxOqOQHP4LGE0vesSOV2BdROvWyvjt/crC2dGzkSEruu2SaEEHFJRrr7GaPdit7jRklK7frFQmyR+9JikjauAcBXWEbT2X/pkePurDr5UEvPF20T3ffxt+mcMHPEdgn3sQe08tqCVf0m4d6Wd9AoNt7yFLap56BumROb/PN3VN5wJpnvPKOVcxciThntVlS9AU/VaEKW7D16r7lhExmfvwGAr2AA7ZN+s8fHt9u1ArEjR0K6jBUIIXoxGenuTxQFU2MNajeLnwgBkLxmBdn/exQA1WCg/vJbutUiZl/Zfe180bQCV7g6+VCGWCqlWFqccnn03P5kKS990rnkICM1yOwLNjN1YlsMI4s91ZxI89l/wbn/YRQtnou5uRa930fBE3eRtvwjGi6dTSC3KNZhCrEdg8MGqopn8FiCWXl7/P6cVx5Ep6oAWE+6FAyGPXq/2639GjsW8vb88EIIEVdkpLsfMbS3YmhvJbgH67FE/6Z3OylaNDt84dRy2uV4K6I/tbvO3ch7dZ/h2lqdvHgSQzOlHVi8+mp1KifdMHy7hPuQMQ5eW7Cq3yfc2/IM2Y8Ntz5N69G/D29LWbOcihvOxPLBS9riVSHigMFpRxcM4Bk0iuDeFF0NBsh+9SFAu1lrO/HiPXq7zwdWKwwZAqWyCkMI0QfISHc/Ymxt1pInoynWoYjeQFUpeHQBJlsTAO6h47Add16PHNpizsCoM5CdlMMBeWOlWFqc8vh0/PO5Yp58Jz+8LTkxxMxzajjlUJtUGN4JNTGJpvP/inP/wyl8cB5mawMGbweFj9xG2vIPaLjkJoLZ0llCxI7e5UDn8+CpGkMgr3iv9mH55HXM1gYA7IecsEczOYJBrXDawIEwaJBUKhdC9A0y0t1P6HxezC31hKSAmuim9C/eJOPLdwAIJadRf9k80O/Z9MA94Ql6w49TTEkcWXIQBxVMkIQ7Tn23PoVTbxq+XcJ9wDAnr962mlMPk4S7Kx0jJrDxtqdpO+zk8LbUH76kcuYZZHzyuox6i5jQu53oO1x4B47cq0rjW+1tAbWtlcqLi7VK5Xs4I10IIeKWJN39hNFuRd/hREnuZm9N0a+ZmusoePSO8POGi2ZGbfRNVVV+ad/EG5s/oM7dGN6eZkqR6eRxyB/Qcc/zRZwzbwjVjdra/gSTwsxzN/Pw9T9TnOuPcYS9h5KUSuMlN7L5uvsIZGqLVg0eN0VL5lJy99UY7dYYRyj6E73HhcHlwFs5An9h2V7vx1y7IXzD1ldcifOAo7r93sZGyMzUCqclJOx1CEIIEXck6e4PVBVTcx2qySzztETXQkGKFs3G4HUDYD/otzgnHhOVQ/26OnmtqyEqxxGR8dOmJM64eSiLXytEUbXPktEDXbx062rOO7YFvXyj7BX36ElsWPAs9oOOD29L+/ZTKq//PelfvCWj3iLqdN4ODI42vJXD8ZdU7tO1Qu7Li8OPW06+lO5+MNhsYDJpCXeqNFgRQvQxsqa7HzC4HBgdVkJplliHInqB7NcfJfnn7wDw5xbTdP51UTnODtXJs4cyJKMyKscS+yYYgof+r4B/v1RIMKRdQBsNCn88pYFLftuIUaaA7jMlJY2GGXNwTjicwodvw+iwYXC3U7zwJtKWvU/jhTMJZWR1vSMh9pDO58Vot+EtH4qvZOA+Jdy6gJ/s1x8BQDGasP3uom69z+XSiqfttx9k71lnMiGE6BUk6e4HjG0t6AIBVHP02zyJ3i1x/Y/kvrwEAFWnp/6yeRHv6a6qKhucm/nGugpFVUg2JjIxfxw5iZJQxKONDQnMXFTO9790ngdDSjtYMKOaoWWeGEbWN7nGHcqGqjHkP3EnGUvfBiB9+Yckr/2Gxguv36OpukJ0Ref3YmxrxjtgML6ywd0eld4Vy0evYGptBsB++MkEs/O7eAd4vdDaCqNGaWu5hRCiL5Kku68LBjA116Iky1wtsXs6bwfFC29Cp4QAsJ54CZ7BYyJ+HJuvjRUtPwBQmJwn1cnjlKLAU+/mcfezxfgC2oW4Xqcy7XeN/OHkBsxGmfIcLaE0C/V/uBXnhCMoeGQBRqcdo9NOyX3X45h4DE3n/1VmLol9pgv4Mdqa8JVW4Ssfus8JN0DOiw+EH1tPmdHl64NBaGrSqpRX7tusdiGEiGuSdPdxRrsNg6udQG5hrEMRca7gibswN9cC4Bk4EutJl0TlODmJWQzKKCfZmMSQjEoplhaH6lrM3LiknK/XdBZeLC/wsmDGRsYM6ohhZP2Lc8KRdAzej4JHF5C+/EMAMr58h5Q1K2i4+EZc4w6JcYSi1woGMFob8JVU4q2ITJnwhE0/h89T74DBOMcfvtvXKwrU12t9uIdGJucXQoi4JR9xfZzJ2qB9k0Wx1ZPo/dKWvY/lk9cACCUmU3f5LWCIzD05VVXZ0L55u5Zg43JGMtQyUBLuOKOq8MJH2Zx4w/DtEu7zjm3ixVtWS8IdA6GMLOqu/Dt1f7iFUIrW8tHosFH6z6spXDQHvdsZ4whFrxMMYrLW4y+qwFs5AoyR+azP2baA2inTuxy2bmyEnBwYPhzMMtlJCNHHyUh3H6Z3OzG1NhGUaYhiN4ytzRQ+dGv4edN51xLIL4nIvgNKgOUtP1DjqicvKZtDCieil0Q7LjW3mZj9UBmffJcR3laU4+PWS6s5cLgrhpEJdDraJ/2GjmHjKXjoVtK+/RQAy2f/I2XV1zRMuwn36MkxDlL0CqEgppY6AvmleCuHg9EUkd3qfF5ythZQM5mxHX/Bbl9vtUJiolapPCUlIiEIIURck5HuPsxot6LzdaAmJsc6FBGvFIWixXMwuNsBaJ9wJI6DfxeRXbf5HLxb+yk1rnp06ChIzkPS7fijqvDG0kxOmDl8u4T71EOtvHLbakm440jQkkPt1XdTP30OoS11OkxtzQy480oKHroVvUf+rsRuKCFMzXUEcovxDBqFao5cI+zMD17E6GgFoO2o0wlZcnb52vZ2bS33yJFaT24hhOgPZKS7rwqFtAJqiXILWexa1lv/JWXV1wAEMvNouPiGfa5ks3U6+Te2bauT709OolxdxZs2p4F5jw7g7a87K8fnZASYP62aQ8e2xzAysUs6HY6Dj8c9YgKFD84n9YcvAcj86GVSf1hK/aU30zFiQoyDFHFHUTA11RHMKcRTNQo1IbLdTHJeWhR+vLsCah6PlnSPGgWFUmpGCNGPyEh3H2Vsb8XQ3kYo1RLrUEScStj0M7nP/xsAVaejfsZclNSMLt61ewElyJfN37DC+gOKqlCYnMfRJYdIwh2HPlyZwQkzR2yXcB83sZXXFqyShLsXCGblU3PdfTRcdAOhLbOZTLZGym6/nPzH7kDnlXZuYgtVxdRSTzAzF0/V6IjPfkv8ZRVp32hLHjyVw3GNPWinrwsEoLlZq1ReXh7REIQQIu7JSHcfZbQ2avNGI1QgRfQtOr+X4oU3oQ8GAGidem7ERsfsPgc6dIzKHirVyeOQs0PPgidLeeXTzumfltQgsy7czNQD22IYmdhjOh32I07BPWoihUvmkbJmOQBZ7z1P6vdfUD99Dp4h+8U4SBFTqoqxpY5gugXP4DEoSZGf/bZ9AbUZO50tpSjQ0ABlZTB439uBCyFEryMZWR+k83ow2Rr3edRS9F15z9xLQt0GALxlg2k57fK93peqav2adTodJr2RSfn7E1RDMrodh5auSuPGJeU02jpLBR821s7cSzaRawnGMDKxLwK5RWy+/j9kvv88ec/ch97vxdxcR9mt02k99ixaTv8Dqjmy04lF72C0NqCkZOAZPBYlJa3rN+whnbeD7P89BoCSkEjrceft9HX19ZCXp1UqN0WmdpsQQvQqknT3QUa7Fb3HRSC/NNahiDiU8u1nZL37HACKKYG6y29FNe1dv5at1cmzEiwMsVQCYElIj1isIjI6vHrufq6Y/76bF96Wkhhi5nk1nHywbV+X8Yt4oNfTdvQZuEZPpmjxHJJ//g6dqpL91n9J/fYz6mfMxTtoVKyjFD3IaG1ESUzWRrijdBM+693nMLocALQecyah9B1vtjY3Q2qqVjgtKSkqYQghRNyTpLuvUVXMLXXaqEYvvpL+872VXDi1ibFVbhQFbnuylE+/ywBUzv9NM+cc3bLT9936eCkffpNBvTWBF29ZzbCyznWNR/1lJGajSoJZAWD67xqZOrHr6bTVjQncsKicNpeRtKQQt06vpqrEu8PrvlyVxj+fK8bt1aPTwaFjHFx9Rl14Gt1H32Rw59MlhBQYXOrhtunVpCYpWB1G/nj3IJ6a/RPGKLdTNzhaKVoyL/y8+ayr8BdX7NW+2nwOljatwBXooN7dSFlaMYmGyFXDjba9Pcem3VGF1WFEp9MS1xvOq2F4uXae7e78251onmPrapL468Jy6qydI50HDm/nlks3UZzj37M/NBH3AvmlbLpxMVlvP03u8/9BH/CT0LiZ8nmXYPvteVhPmbHXN9lE72FsbUI1J+AZPHaniXCkdFVAzW7XVrqNHAkZMvlOCNGPSdLdxxicdgwOG6G03ju19/tfknG4DYytcgPw+hdZ/FKXyBt3/oizw8CpNw3jgGHOnSYlxx7QxiXHN3Lu/CE73fc/rtjQ7URoq7kPD+D0w62cfIiNt7+2cOPicp6b99MOr0tPCXLXHzdQmufH59dxyR2DefWzbE4+xIbbq2fWg2U8duNaKot83PJYKQtfKeS6s+rIyQgytsrFq59lc+qhtj2KbY+oKoUPzsfYrrV1cY2ZQttRp+/FblR+ad/Et7bV21Un700J976cY3dfsYH0lBAA7y3XzoeXb1sDdH3+7Uo0zrG5j5Ryye2DWbUxGUXVbsAZDQp/O6eWs45skTWVfZneQOvUc3GNOYiiRTeTtGEVOlUh53+PkfrNpzTMmIu3YlisoxRRYrRbUfUGPFWjCVmyo3acpJ+/C1fP76gajXvkgdv9vKMDXC4YOxby86MWhhBC9Apy2dXHmNqa0QUCEe2/2dOe+yCX4ye1hp+/+WUWpx9mxaAHS2qIqQe28cbSrJ2+d/xQFwVZgYjFYnMY+XFjCr+boiXDx0yw09BqZlPTjn++w8s9lOZpI4cJZpWhAzqos2ojSp9+l86wsg4qi3wAnHlUy3a/h+MmtvLcB7kRi3tnLO+/SNq3WoXZYFom9ZfO3uPZEAElwJdNK1lp/RFFVShKzu+V1cn35RzbmnADODsMbNt8fG/Ov2icY6urk1i6Kp0fNqSEE+6qkg4qi7ycc7Qk3P2Fv6ic6tkP0fz7K1CM2kLaxLoNlM+5kJwXF0Ewcp+VIj4YHDZQVTxVYwhm5XX9hn2w7Sh3y6mXbfd94veD1QpDhsCAAVENQwghegW59OpDdAE/5pbaqBRL6UnLfkpj9EB3+HmDzUzRNtNgi3P9NNj2bnrkzEXlnDhzODctKaO1veuJHo2tZnItgfC0b50OirL9NFh3f/wWu5G3l2Vy2FhH+PdQuO3vIcdHi91EcEv+NqKig59rknB5ovNP0lxfTf7T/ww/b5h+M6GMPRsBUVSF92s/p8bdgA4dY7KHM6VgPAmG3jdVdV/PsesfKOeIq0Zx34tF3HHZxn2KJZLnWEG2n4WvFHDmnGFsbtKmkxsNClefUctz89awqTExaueYiFMGI7bfXUj1vCfwlmkzMHRKiNxXllAx5wISNq+LcYAiUgxOO7pgAM+gUQRzCqJ6LH2Hi+w3nwQglJRC62/OCf8sFNIqlZeXa+3BevFKNyGEiBi5+upDzO1WjB3thFJ6dyGrxlYT2RmRr6T8+I1reeW2NbwwfzWZaUFmLiqP+DEAXB49f7x7EJf8tpGRlR3deo/RoE0dbm6LQlnXYIDi/9yI3q+Nsrceefou+6jujl6npyJ9AMnGJI4onswQS+9tB7av59jtl1Xzwb9+4MrT6vjHMyURjKx7dnaO2RwmPlhh4b4XiwmGtv69qDw7Zw3Tjm8iwRTFc0zEPV/pIDbOeYyWk6ejGrQ7PImbfqZi9nlkv/oQhKR6fW+mdznQ+Tx4Bo0mkFcc9eNlvf00BrcTgNZjz0JJ1a47VFVLuAsLYdgw6VoqhBBbSdLdV6gqZlsDisHU6xtgJpkVfIHOZK4w20/9NqN+dS1mCrP3vABUUY42ldJkhPN/08SKn1O7fE9Bln+7EWlVhfpfjVpvy+3RM/3vVRwxzs6FU5u3+z1sO3JZZ03YbnQTwBfQk2BW9/j31ZXcFxaSuGmtdoyiCprPuqrb7w0oAZyBzhHhwRkVHFNyCNm9bDr5r0XqHDvp4Fa+XpOG3bn3FfD29RxTFHjszTwefzuPVqeWUBv0Kmcc0UxeZoBh5Z3r0qN1jolewmjEesp0quc8hrd0EAC6UJC8FxZSPvdizFvaCIreRe92ou9w4R04kkBBz3Qt2a6A2qmXhR83NWkF00aOhETpUieEEGG9OzsTnZxOzI5mQqmWWEeyzwaXetjY0PltfewBbTz/UQ4hBewuA29+lcnUia272cOOOrx62t2didH/Lc1iWFnnKPT1D5Tz3nLLDu/LzggyvLyD1z/XpmK/s8xCQZafsnzfDq91e/VMv7OKg0a3c9lJjdv97ODR7ayuTmZDvbZO95n3crf7PWythl2YFdlq0smrl5P9xhMAqAYjdX+4BTWhe1dCbT4H79Z8ymcNXxNQtFEwnU6H2dD7R0r39hxrdxu2Gyl+b3kGltQgGamhHV77a9E4x2qbzVx422Du+G8pwZD2cV6S6+Wp2T9h0Gu1AraK1jkmeh9v+VCq5z6O9YSLUHXaeZO0cTUVs84l6/8eB6Xr81nEgVAQY2sT+g4n3soR+AvLeuSwyauXk7JmBQDuYfvTMWx/ANratJHtESMgrXevchNCiIiTiT99hM7agsHvQUmIbuGUnnDMAW18/kM6k0dqU9dOOMjGjxuSmXrtSHQ6uHBqM4NLtdG7D1Zm8OFKC/OnbQLg5ocH8Mm3GVgdJqb/vYrkxBBv/2MVtnYjV907EEUBVdVRkufj9hnV4WP+uDGZc49p3iEWgDkXb+KGxeUsfr2A1KQQt17a+b5ZD5Zx+Dg7R4xz8MTbefywIYUOn553tyRXxx7QxmUnNpKSpDBv2ib+dM8ggiGoKvGyYEbnWuDPvk/nqP3tEZ2koHe3U7ToZnSqNrLZfPof8JV1XVU7XJ3cuhoFhWRjEp6gB5O571xF7e055vQYuPq+Srx+PXqdSmZ6kP9csz68ZnFX5x9E/hx7/qMcmttMqFsKpel0Koft52BjfSLX/aeiR84x0XupJjMtp/8R57hDKVo8h4T6avQBP/nP3Eva8o9omH5zjyVxYg8pIYx2Gzq/j2BWHr7iCoLZBT22eHpnbcJcLvB4tErludGtCSqEEL2STlXVfj3XsL29nYyMDNra2rBYLLEOZ+8Eg3je/5wfl/swFWST0HsLlwPaaN4584bw39lrSU5Uon681nYj1/2ngoeuj11BoXPnD2buxZsZWLxji6o9paLi1nsY/K95ZHz9HgDu4RPY/Ld/d7n0IKAEWN78PTXuBgCKkvOZkDemVxZL253efI41tZqY9VAZn33f2fS2OMfHbdOrmTDMtcv3RfIc62kqKm6Dl5RQIjp6Zx2BeKbz+8h98QGy3nwyfJNOMSfQ/PsraDv6jF6/ZCke7dU5rSgY21vReTsIWnLwF1cSyMrv0YXTelc7o6cWYfC4CaWk8f2b9XgMqTQ1aSPcVVVSOK0/UhSF5uZm8vLy0MvnhegD7HY7mZmZOBwO0tMjUytLRrr7gtZWsNsJpBTQ+yf+Qkqi1ku4rsVMVWn0E4Ss9GBME26rw8iZR7ZENBnK/vStcMIdSkmnfsacLi+c23wOljauwBXsQIeO0dnDGJxR0WuLpe1ObzzHVBX+90UWtz5eSntH50f36Ye38NezaklJ2vXNg2icY6LvUM0JNJ91Fc79D6Vo8VzMTTXo/T4KnvwHacs/pOHS2QTyer5goNhCUTA429B3uAhasvFXDCeQUwDGnv/Gz3rrKQwerc6Hbeq5+M2pNNbBwIHarz74dSGEEBEhI919YaT7u+/wrt3EyqZikpPp9SPdYt8Ym2qovOlsDF4PALVX3I7zwKO6fN+nDV/T0NFMsjGJSfnjen2xtL6ktd3IvEcH8M6yzr+TXIuf+dM2cciY9hhG1jNkpLvn6Lwe8p7/N1nvPBPepiQk0XTWVdiPOFWyqgjp1jmtqhicdvTudkLpWfiLKwjkFKKaYjTzSFUZdvZYktd9D8Cqp77l56QxFBVp08rl2qP/kpFu0dfISLfYkdsNTU2o6RnQFOtgRMyFghQ/MDuccNsP/l23Em6A8bmj+bF1LaOzh/W56eS92fsrMpjzcBm29s5RreMn27jhvBos3SjgJsSeUBOTaDrvWpzjD6Nw8TzM1nr0Pg+Fj95O2rIPaJg2O+o9oPs9VcXgcqB3OwilZOAZPJZAXjGqObZZbcqPX4UTbteoiWxIG0NWujatXBJuIYTYPbkd1dvZbFrinZIS60hEHMh59WGS1/8AgD+vmKbzrt3la9t8Dla3dU55TjIm9sn1271Vu9vAzEXl/OmeQeGEOzMtwD//9At/v7xaEm4RVR3DxrPxtqdpO+KU8LbUVV9TecMZZHz8qrbeQUSc3t2OuXEzqCqeQaNxj56Mv6Qy5gk3QM6LD4Qfbzx6BgkJMGoUpHbdfVMIIfo9GenuzRQFamshKUmm/AmS1n1PzqsPAaDqDdRdPh8lacebMb+uTp5uSqUktbCnwxW78cUPadz0YDmNrZ03QI4YZ2fOxZvIyQjGMDLRnyhJKTRedAPO8UdQ+OB8TK1NGDxuih6cT/qyD2i45CaCmVKqOhL0HS4M7a0oSSl4KkcQyC/Z6ed3rBja28h691kAAqkWaib9njEjICsrxoEJIUQvIUl3b9bWpo105+SADHr1a3qPm6IHZqHb0l+3/pQL8AwatcNKQX8owPKW76ndpjp5XlJ2D0crdqXDq+euZ4p55v3O1n+pSSFuPH8zJ0xplXtrIibcoyayYcGz5D/1DyyfvA5A6nefU3n972k8/zraJ0+VG797Se9xY3S0oSQm4S0bQqBgAEpy/A0dZ73xBHqfVohx86HnM2S/ZIqLYxyUEEL0IpJ092bNzRAKgdkMnlgHI2Ip/4m7MDfXAdBRNZr6E8/j12MkrT47SxtX4t5SnXxM9jCq+mh18t5o5c8pzFxUTk1zYnjbpBHt3HJpNYXZgRhGJgQoyak0XHozzvFHUPDwrZjsVgwdToofmK2Nel80k1CG3MDrLp3XgzFgQx8w4ysZiL+wDCU1MsV6Ik5Vyd1manlo2gwGVcQwHiGE6IUk6e6tfD6or4cIVdTr70IKrFibSovdRK4lwP5DXBh6ScWDtK/ew/KpNvoUSkyh7rK5YDBuN/thQ/tmVrb8iIIi1cljaGfnWTCo474Xi3jkzXxUVbsBkmQOce1ZdZx5ZIsMIIq44trvYDYseJaCJ+4i44s3AUhb8RFJa7+h8cLrcR54dIwjjG86vxdDmxXVaCBQmI87dyhKenx/Fqd++xlJG9cA4Bx7MBW/HS6t24UQYg/FZdL973//mzvvvJPGxkbGjBnDfffdxwEHHLDT1y5ZsoTHH3+cH3/8EYD999+f2267bZev7zOsVmhvhxLpnbqv3l1m4bYnS2naZv1sfpafG86t4egJ9tgF1g1GWyOFD98aft50wXVb+ulu34850ZCAgkJRcj4H5I3BLMXSetzOzrPs9AAmo0Jja2eRpHGDXdw6vZqyfF8swhSiS0pqBvWXz8c54QgKHr4No7MNo8tByf0zaV/2AY0X/I1QmiXWYcYVnd+HwWEFnZ5AwQB8BQPwm/3okzLivglezkuLwo/Nf5qBqefbgwshRK8Xd/cqn332Wa6++mpuvvlmVq5cyZgxYzj22GNpbm7e6es/+ugjzjrrLD788EOWLl1KaWkpxxxzDHV1dT0ceQ9SVW2U22xGbjfvm3eXWfjzvZU0tW5/FdHcauLP91by7jJLbALrDkWhaNEcDB1OANoPPBrHlN+GfxxSOoe6i1LyObxoElMKxkvCHQO7Os9s7cZwwm0yKlxzZi2P3bhWEm7RKzjHH86G25+jfcKR4W3pX71L5fW/J3X5R7ELLJ4EA5ha6jHYrQRyi3GPmohnyFhCGZm9Yh28wW4l893nAVCyskk4+9QYRySEEL2TTlXjq+/HgQceyIQJE7j//vsBUBSF0tJS/vSnP3H99dd3+f5QKERmZib3338/559/fpevb29vJyMjg7a2NiwWy76G3zMcDvj8c8jIgERt/afHA998A8nJ0i+zu0IKHPWXUVsSoZ1d/KjkWAI8MvPnuJxqXvHRIwz9v7sB8GTk89nVLxJMzkBVFdZ3bKLGXs/k/P1JNCZ2sScRTSEFLrptMFbHrs4zMBoUnpu3hqEDvDv9eX+nouI2eEkJJaKL+3HB/inty3coeOwOjC5HeJtjylQaz7sOJaUfLoMKBjHaraAECWYV4C+uIGjJCd8oV1UFr7eZxMQ8dLo4/ILZIn3JP6hatKX15DXXwF13xTYgEZcURaG5uZm8vDz0Mhgk+gC73U5mZiYOh4P0CC3ljavp5X6/nxUrVjBz5szwNr1ez1FHHcXSpUu7tY+Ojg4CgQBZu+hj4fP58Pk6R5Ha29sB7QNDUZR9iL4HNTdra7oTEsK9UhUVVDp/ia4tX5u23VTfHemw2s387m8jeyym7tqPlXzJfQAo6JjqeIaPb56yzStGxyYwsVeCIT0OtxFV/vXulLrN/0R8ap94NO6h+1H0yALSVn4CQMbnb5K8ejkNF9+Ia+yULvbQR4SCGB02dIEgwaxcfIUVBLNyO2elqdp1hqoqqKqKqsbvdYenQ2XI651Ty5Vp07RWpUL8iqJo53OvuY4WogvROJfjKum2Wq2EQiHy8/O3256fn89PP/3UrX387W9/o6ioiKOOOmqnP1+wYAFz587dYXtLSwt+v3/Pg+5pwSBUV2sj3N7OUTFfAPxJoDND0BC78HqT2vb4a8vSHUl08BTnYEaraP13/srHHBbboMQ+q21XGWGQke6dUVHxGbTzXUa641h2Ku3X3EL2Z28z4LF/YexwYWprYcA//kzLYb+l5twrCMVhO6yIUBUMHU50gQAdmekEswoIpllArwO/dceXqwqBgANQ426kOxQCtxvSln9Gav06AHxTptBmsWg3/YX4FUVRcDgcqKoqI92iT3A4HF2/aA/FVdK9r26//XaeeeYZPvroIxITdz6ldubMmVx99dXh5+3t7ZSWlpKbm9s7ppc3N0NHBxQUgKEzu/aoUOuBJB0kyDVpt5Skd+8PavJIBzkZ8dOyacYvf2VYo3YTan3KaL4efSWHhTbR5nWgomLUG8hMspCsT5T0JA5YHSa++DGjy9eVpOtICclSgJ3ZOsIt08t7B+/kk9gwdDJFD91C6vfaLLXcj/4Pyw/LqZ82C/fIA2McYQQpCob2VgyeDgKZOfgHVBDKzsdoNO72Aksb4daRmJgbN0m33w+trdq9/awsGPf1c+Gfma64gry8vBhGJ+KZoijodDpyc3Ml6RZ9gtkc+fpHcZV05+TkYDAYaGpq2m57U1MTBQUFu33vXXfdxe233857773H6NG7nlqbkJBAwk4WPev1+t7xQdHYqBVfMW7/V6fXaatFt/4SXdt/sIukhBAe386nBuhQyc8KsOi69XGzpjv1m08p/fwRABRzArrZs7k49UtWtPwAQHFKPuNzxxAw2yVBiRNbawc0t5pQd/L3sfU8Gz/EJX9fu6Hb5n8i/oWy8qm59l4yPn6V/Kf+icHrxmRrouyOK2g78jSazrwSNTE51mHuPVXF0N6G3uMilJ5FR8VwAjmFYDR1+wzV6XTodPqYJ90ej5ZsA+TlQVkZ5CpNGN95JbxRf8opUrhV7JZOp+s919JCdCEa53Fc/cswm83sv//+vP/+++FtiqLw/vvvM2nSpF2+7+9//zvz58/nrbfeYvz48T0Ramy4XNDUBL1hRL4XePD1gm0S7u3Xiuq2PJ95bk3cJNwGh43CJfPCz5vO/gv+onLKUkvITMhgbPZwJuePx2yQfi7xxKCHG86tATrPq63i8TwTImJ0OhyHncSGBc/gHtHZxjPz/ReovOEsktesiGFwe0lVMTjtmBo3oxoMeIbsh3v0JAIFA8DYuz57nU6oqdG6j5aWwqRJcMABUFgIxicegcCWGV4XX6x1SxFCCLHX4u4y7+qrr2bJkiU89thjrFmzhssvvxy3281FF10EwPnnn79dobU77riDWbNm8fDDD1NeXk5jYyONjY24XK5Y/Raix2rVppanpMQ6kl7vhY+yuffF4vDzjJTQdj/Pzwpwz5Ub4qdPt6pStGQeRmcbAC2jDqD18FMAMOoNHFk8hcGWSnS9oAVNf3T0BDv3XLmBvKztlynE3XkmRBQEcwrZ/Nf7abzgbyhmbQmFuaWOsttmkP/kP9D5ekctA73LgalpMyrgqRqDe/Rk/EXlqKbek5AqCtjtsGmTNp188GCYPBn22w9yt9Z7UxRYvLjzTZdeGqtwhRCiz4ir6eUAZ5xxBi0tLcyePZvGxkbGjh3LW2+9FS6utnnz5u2G/BcuXIjf7+e0007bbj8333wzc+bM6cnQoysUgtparSeY2CcfrMxgzsNl4efXnlnLBVObWLE2lRa7iVxLgP2HuOJq5DHzvedJ/e5zADpS03n02EOodPzC8MwqAPRxsiZQ7NrRE+wcsb89rs8zIaJGr6ftqNNxjZpE0ZK5JK/9BoCst58m5bvPabj0ZjyDx8Q4yJ3Tu50YnG0oSal4B47Cn1fc66bGB4Nasu12a91Gx4zRSsPs9B7+u+/Cxo3a42OOgcrKngxVCCH6pLjr093Tek2fbqsVvvhCW3Bl2nEKm/Tp7p5vfk7h4tsH4wtomc4Fv2nir2fXEs8DxOa6DVTMOg99QGt199Q557ChajCjs4cz2FKxw+ulp7Hoa+Sc7mMUhcx3niHvuX+HP9dUnZ7WqefQcuplqOb4+BLTe1wY7DaUpBT8BWUECkpRkiIz06yn+nT/ujhaWRnk53dxnXDKKfDyy9rjl16Ck0+OWnyib5A+3aKv6fN9usVuNDVpPbl3knCL7vmlLpHL7x4UTrh/O6mV686K74RbF/BT/J8bwxemXx1wAA3DRnN4/jiyEzNjHJ0QQuwFvZ6235yNe8xkChfPJXn9D+hUhew3niD128+onzEHb+WImIWn83ZgtNtQExLxlg0hUDAAJSUtZvHsjW2Lo+Xnw4AB2vRxY1dXffX18Npr2uPCQjj++KjGKYQQ/YUk3b2B1wsNDRChOy39UWOriUvvrKLdrZ3yk0a0c+v06rgvxpr13H0kbtb6pDbn5rL6pHM4umgCZkPvWUMohBA74y8sZ9OsB8l640lyX3wAfTBAQv1GyudejO34C2g5+dIeLU6m83kx2q2oRhO+koH4CwegpHbd7i+eOJ3aNHKzWUu0S0q0Ee5uf9c99JC2nA3gkkvkRr8QQkSIJN29gdWqfZOWlsY6kl7J4TYw/e9VNNq0RHV4uZt7r/oFszG+V1Ykr/qavLeeBiBoMLDq4us4sGSyFEsTQvQdegOtx1+Aa+xBFC2eQ9LGNeiUEDmvPUzqN59SP2MOvrIhUQ1B5/disFvBYMRXVEagoIxQeu+ZSaQo4HBolwkpKVpxtKKivWh0EgrBkiXaY71eCqgJIUQExfk4n0BVtQJqCQnE9TzoOOX167jinwNZX5cEQGmejweuXU9KkhLjyHbP4LRTtGhO+PnmU6ZRMPIISbiFEH2Sv2Qg1bMfofnUy1AN2nhAYs06Km4+n5xXHtQWJUeYLuDH2FKPwdFKIH8A7pET8VaN6TUJdzCo3ZOvqdEuD0aPhilTYPjwvews+tZb2s4Apk7VhsqFEEJEhIx0xzuHA2w26c29F0IK/HVhBSvWamvxstMDLP7rOnIyIn/xFin+UIAVLd9zwtNPYmprBsA14gB8x18S48iEECLKjEZsJ03Dtd8hFC26mcSadehCIXJffIDUlR9TP30O/pKB+36cYACj3QqKQjCnEH9ROUFLTq+5sf3r4mjDhnWjOFp3PPBA5+MZM/ZxZ0IIIbYlSXe8a27WvmETE2MdSa+iqjD/sQG8t1wbsUhKCPHAtespy/fFOLJda/XaWdq0kqpln5Oz8lMAgqkZNMyYuwcL8oQQonfzlQ1m47zHyX3lQbJffxSdEiJp4xoqZp2L9dQZ2I47D/SGPd9xMIjRYUUXDBLIzsdfVEEwM7fXfL56PNo9eJ1uD4ujdUdNDbzxhva4tBSOOy4COxVCCLGVJN3xLBDQppZLAbU9tvCVQp77IBcAo0Hl3qt+YURFR4yj2jlVVVnfXs131tVk2KxMffPN8M8aL75RuygUQoj+xGii5bTLcY47hKLFc0mo24A+GCDv2ftJXfExDdNvxl9Y3r19hYIYHa3o/D6C2fn4isoJZuaBYS8S9x6mquBydRZHKyvbi+Jo3fHgg9ricIBp03rFn40QQvQmveP2bn9ltUJ7O6T1rlYlsfb8hznc/1JR+Pmtl1YzZZQzhhHtmj8U4IumFXxjXQWhIGe++j/Mfj8A9kNPxDnhiBhHKIQQseOtHMHGeU9g/e35qFv6WSev/4GKG88h682nOhPFnVFCGO1WTC31hJLT6BgxAfeIAwjmFMZ9Uqko0NYGmzdrk90GD4bJk2HsWMjJiXDCHQxqSTdofy6XyHImIYSINBnpjmcNDdoXYJxfHMST91dkMPeRzuIv151Vw++mtMYwol3zBL18UPcF7mAHenT8ftlq8jZvBMCfX0rjudfEOEIhhIg91ZxAy5lX4tr/MAoXzyGhcTP6gI/8//6TtBUfUX/pzQTySzrfoCgYnG3oPW6CGVn4K4YTyCmM0Dzs6AoGtVFtt1sr5TJmDBQUaFXJo+Z//9P6cwP87ndQXBzFgwkhRP8kI93xyunU1nNLAbVuW/lzCtf+uxJF1YrhXDi1kYuOa45xVLuWaEgg3ZxKijGZEz0ZDH77ZQBUvYG6y+ejJibHOEIhhIgfnqrRbLzlv9iOPQt1S9Gz5LXfUHnDmWS++xyEQhja2zA11aAazXiGjqNj1CQCBaVxn3D7/dDYqP1KToYJE7RK5AMHRjnhBimgJoQQPSC+v4X6M6tVq5qSK+t5u2NdbSJ/uHsQvoB2H+n4yTauPbMuxlHtyB8KoNPpMOmN6HQ6Dsgbi6HDzZB7LkCnatMkW06+FO/AkTGOVAgh4o+akEjzudfgHH84RYvnYm6pQ+/3UvD430n/4k0aLriejlETCeQVo5r3tZx39Hk8WiVyvR7y8iJcHK07Nm6Ed97RHpeXwzHH9NCBhRCif5GR7ngUCmkF1JJlpLM7GmwmZtxZRbtbu0qZPLKdWy7dFHcFaVu9dt6t/YSVLT+gqioACQYzpU/9E7NVm9rXMXgsthMuimWYQggR9zxDx7HhtqdpO/TE8Lbk9T9QvuAy0pZ/gGoyxzC63VNVbTLb1rItZWUwaZI2ul3Y07PglyzRAgKYPr3XVHIXQojeRj5d41Frq1ZBRaaWd8nhNjD9zioaW7ULrBEVbv515S+YjWqMI+ukqio/2zfwQd3nuIMerN42/EoAgPSlb2P5XGvTEkpKof6yeXvXCkcIIfoRfYcLY1sLLaf/gY2zH8GXXwqAocNJ+S2XMuiq4zA11cY4yu39ujhaUVEUi6N1h98PDz2kPTYa4SK54SuEENEiSXc8amzU7jzH+Rq0WPP6dfzx7oH8UpcEQGmel4XXrCclaTfVbHuYP+Tni6blfGtbjYJKcUoBR5ccTILBjNHaSMGjC8KvbbzgegK5RbvZmxBC9G96jxtT42Z0fi/eiuG4R0+m9YQLWf3sj1hP7Ky6nfHFWww/YyRZ/3u8cyQ3RoJBaGnRJrDp9VpxtMmTtankGRkxDOzVV7XaMQAnn6xVbBNCCBEVktXFm44OrWp5TL+J418wBNf+u5KVP2vt1LLTAyz56zpyMoIxjqyTzdvGl00rcQc96NExJmc4g9LL0el0oIQoWjQbQ4cLAMekY2mfMjXGEQshRHzSeT0Y7VZUcwLeAYMJ5JeipKaHf66kprNp1oO0HXEqZbdMw9xSj9HloGLOBWS+/wKbblxMMKdnk0q/X5u4FgxqfbVHjNDWbSckaKPebnePhrOjRYs6H0sBNSGEiCoZ6Y43Npv2TZyaGutI4paqwvzHBvDBSgsAyYkhFl23jgH5/tgGto2QqrC0aQXuoIcUYzJHFE+hKqNCS7iB7P97gpSfVgIQyC6g8YLrYxmuEELEJZ3Pi7GpFoPbga+kEvfoSfgGjtgu4d5W+5SprH72R2zHnRfeZvn0dUacMYLMt5/pkVHvjg5tVNtq1YqiHXCAtma7tFRLuOPCunXw/vva40GD4PDDYxuPEEL0cTLSHU8URfumTkyELcmZ2NF/Xi7k+Q+1qu5Gg8K9V/7C8HJPjKPankGnZ0LuGH5p38z43NGYDabwzxI3riH3xYUAqDoddZfNQ0lJi1WoQggRd3R+HwaHFXR6AgUD8BeWEcrI6tZ7Q+mZVM97XBv1vm06ptZmjI5WKm88i7YPXmTz9f8hmBnZziCqCi6XtmY7IUErjlZSoo1wx2VtssWLOx/PmBGnQQohRN8hSXc8sdu1ke6s7l1Y9EfPfpDDv1/uXPe8YEY1k0c5YxhRJ5u3DV/IT1FKPgD5ybnkJ29/Yafzeij6z43oQiHtPcdfiGfouB6PVQgh4pEu4NemkasqgbziLcl29l7diHYcdiKrxk5hwB1XkPXuswBkvv8CqSs/ZvPMB7Afcco+x6so4HBoVchTU2HIECgujvMVYj4fPPKI9thshgsvjGk4QgjRH0jSHU+am7XFX3Ez/yy+vLc8g/mPDgg//9vZNfx2UlsMI9Koqso6x0a+t61BrzNwTOnBpJpSdvra/Kf/SULjZgA8FcNpOWV6T4YqhBDxKRjEaLeCEiSQXYi/qFwbjd7HWV8hSw4bFzxD25GnUrbgcowOG6a2Fgb+9VRsvzmbmuvu6/YI+q/Cpa1N67OdkaEVRysogJSdf/THlxdf1G7wA5x6qlY6XQghRFRJ0h0vfD6oq4M0mWa8MyvWpnDdfypRVO0C7KLjGrlganOMo9Kqky9r+Y46dxMARck5mPU77w+buuIjMj94CQDFnEj95fPBaNrpa4UQol8IBTE6bOiCAQJZ+fgLywlm5UV8urP9qNNx7XcIAxZcRuZHrwCQ/dZ/SV/2AZtuWoLj4OO7tR+fT0u2g0HIzt6+OFqvsW0Btcsui10cQgjRj0jSHS9sNnA6tXlpYjvrahP5492D8AW0i7DfTbFxzRl1MY5Km06+tGklHUEPevRbqpOXhYulbctgt1L40C3h503nXoO/sKwnwxVCiPihhDA6WtH5vASz8vAXlRPIygeDIWqHDGbns+HOl8h667+U/v0KjE47Jlsjg/7yO6y/u5Caa+5BSd35vPCODq0SuV4P+flau6+cnF7Y2XPNGvjkE+3xsGFw8MGxjUcIIfqJ3vZ10TepKtTXa9/eUsxkOw02EzPurKK9QztVp4xyMH/appj/Mf1s38D3tjUoqKQYk5lUMI6sBMvOX6woFC2ei9FpB8A57lDsh53UU6EKIUT8UBQM7a3ovR0ELTn4B43Sku2eyl51OlqnnoNz/OGU3XIpGZ+/AUDO64+S/vV7VM96COfEY4Dti6MlJvaC4mjdse0o9/TpUrRVCCF6iCTd8cDphJYWsFhiHUlcsbsMXPr3KhpbtenaIyvc3HPlBszG6Ld86Yo76EFBpSSlgPG5Y7arTv5rme8+R+oPSwEIZmTTMG2WXOgIIfoXRcHgtKPvcBLKyKajYjiBnIKYLbEJ5Bax/p7/kf36o5T+488Y3O2Ym2oZfMWxNJ88nVUX3kVbMI3UVBg6FIqK4rw4Wnd4PPDYY9rjxEQ4//zYxiOEEP2IJN3xoKUFvF5tYZgAwOvX8ce7B7GhPgmAAfleFl6znpREJWYxqaoanjo+OnsYWQkWBqQW7XQ6+VYJNevJe/be8PP6GXMJpVmiHaoQQsQHVcXgcqB3txNKteAZtj+BnEJU085rX/QonQ7bCRfRfsCRlM+fRvpX7wKQ9/Ji0pa+Q+udD2M59vDeURytO557TuuSAvD730unFCGE6EG9dYJU3xEMar25U1NjHUncCIbgmn9X8s067c8kOz3Akr+uIzsjGJN4VFXlZ/sGPm74EkXVkn6DTk9ZWvFuE26d30fRwpvQB/wAtB57Fu5RE3skZiGEiClVRe9yYGrajAp4qsbgHj0Jf2FZfCTc23BlDuCTG9/m2xkLCSVpGXZSYzXF5x1Byswrwe2OcYQRIgXUhBAiZiTpjrXWVu3Oc3p6rCOJC6oK8x8dwIcrLQAkJ4ZYdN06SvP8MYnHH/LzeeNyvrWtptljo8ZV3+335j7/bxJr1gPgLRlI8++viFaYQggRN/RuJ+bGzehCITyDRuMePRl/SSVqQmKsQ9tOR4d2z9tmg7x8HQVzLkP99ns49NDOF913H4wdC59/HrM4I+L772GptsyJUaNgotwAFkKIniRJd6w1NGjre3tdCdTouP+lQp7/KBcAo0Hh3qt+YXi5Jyax2LxtvFP7KfUdTejRMy5nJANSu1ddPuWHL8l+678AKCYz9X+4FdXcm3rKCCHEntF3uDA1bkYX8OGpHIF7zGT8pYNQE5NiHVqYqkJ7O2zerBVJKyuDSZNgwgStz7ZxcCV88AH861+QtCXu9eu1Kt/XXquti+6Nth3lnjFD6ooIIUQPk0wvltxuaGzsA9VZIuOZ93NY+EpR+PntM6qZPNLZ43GoqsrPjo18b1uDikqqMZlJBfuTmdC9vyeD007h4jnh581n/Alf6aAoRSuEELGl97gxOFpRExLxlg0hkF+KkpIW67C2oyjgcGgJd2qq1i2rsHAXX796PVx5JfzmN3DRRfDFF1q2/o9/wP/9Hzz6KBx4YE//Fvae2w1PPqk9Tk6Gc8+NbTxCCNEPyUh3LFmt2vy2PlOlZe+9t9zCLY8NCD+//pwajpvUFpNYvret4TvbalRUSlIKOark4G4n3KgqBQ/fisluBcA1aiJtR58RxWiFECI2dF4PpqZa9B0ufCUDcY2ehK9yeFwl3MGgVqu0tlbLpceMgYMO0iqSd3m/e/Bgraf1nXdCwpaZSj/9BJMnww03gM8X9fgj4plntLsNAGedJTf6hRAiBiTpjhVFgbo6bfpaP5/mtXxtKtf+pwJF1f4cLvltI+f/pjlm8VSkD8CsNzEuZyST8sftth3Yr1k+fpX05R8CEEzNoGH6nF7c0FUIIXak83sxNtdicDvwFZXhHj0Jb9UolNT4SeZ8Pm31VmOjNrI9fjxMmQIDB2qDvd1mMGjTyleu1Oagg/b9vWCBttOVK6MSf0Q98EDn4xkzYheHEEL0Y5INxEpbm1a9pZ/fcV5Xk8gVdw/EH9BOxROm2PjL7+t6NAZVVbF5O0fV082p/LbsSAZllO+2OvmvmRs2kf/EXeHnDdNmEbTkRDRWIYSIFZ3fh7GlHkN7G4H8AbhHTcJbNYZQemasQwvbrjhanjYLfOJEKC3tHKzeK8OHa9PMb70VTFtuxP74o3aAOXMgEIhE+JG3ciUsX649HjdOu1EghBCix0nSHStNTRAKgTm+Wqf0pHqriel3VtHeoZUWOGiUg/nTqnt0YHhrdfIP6j6nxWMLbzfp97DcQTBI0QOz0Pu9ALQdfjKu/Q+LYKRCCBEjwQBGawMGh41ATiHuURPxDBlLKCMrLmZqdVkcLVLVa4xGbVr58uVaRXPQ5q/Pnasl3z/8EKEDRZAUUBNCiLggSXcseL3avLd+3CbM7jIw/c4qmtq0mw6jKt3888oNmHqwtN+21cl16HEH974qbe7Li0nasBoAX8EAms6+OlJhCiFEbASDGG2NGFubCFpy6Rh5IJ6h4whZcuIieVMUbdLY5s1a7jtsmLbceuxYyIlmiKNHw1dfwezZ2vRzgG++gf3206ad+/3wpz9pc9kHDYL779/1vnw+uOIKqKrSWnltLXLm9cJJJ2nryseMgaOP1qqod0dzs1YEbuBAWLJE25aaqq3n3pbLBcceq/1hWSzd/1lTExxwgPaHLoQQolsk6Y4Fm027LZ8WP8VmepLHp+MP/xjEhnqtHcuAfC8Lr1lPSqLSI8dXVZW19g18UPcFHUEPqcZkjiyZQnlayV7tL2ntN2S//oi2b4OB+stviasWOUIIsUdCQYytTZhsjYTSs+gYcSAdIyYQzMqLixoV2xZHMxi0JHvKlG4WR4sUs1kb4f7qK6io0LaFQtpI+LBhsGwZ/PwzfP21Voht1aqd7kY3c6Z2d+Dnn7WR8rs6lygxfTqsXQvffQcnngjTpnUvtuuv1+bUX3edNg0AtIT719ccJhP87W/w3ns77mN3P8vP1+5uPP549+IRQgghSXePU1WtgJrZHBcXLz0tGIJr/13Jt+tTAcjOCLDkr+vISu+ZO+Zbp5NvrU5emlLI0aV7UJ38V/RuJ8ULZ6HbcmHTcspleCuHRzJkIYToGUoIY1sLppYGQsnpdIyYgHvEAQRzCuLi+2pXxdEqK/ewOFok7b8/HHIITJ3a+We0YQOsWAH33KPdBTjjDHj66R3equvogIcf1taJbx2WLyjQ/puYCMcd17l94kSoru5eTM89p00l37aA2rhxO74uIQGOOGLHkeyufgZaEr/t1HUhhBC7Fftv0f7G4dBu0e/qi6wPU1WY+0gZH35jASAlMcSia9dRmufvsRjq3E3UdzSh1+kZlzOKifnjMOm7X5381woeuwOTrREA95Bx2I4/P1KhCiFEz1AUDHYrpuY6QolJdAwfj3vURAK5RZ3Tp2NoZ8XRJk3SiqPFRVmUTz+Fv/8dPv8chgzRtgWDWtXzQw/V7ghs3rzD2wzV1ZCVBbfdpt1BOPhgeP/9nR/jX//SRru7YrNpRd02b9ZGyEE7RqT/oPbfH77/vrMVmRBCiN3qwRW0AtB6c/v92l3sfub+lwp58WOtmrfRoHDfn39hePner6PeG+VpJbT7nQxIK97r0e2t0r94i4ylbwEQSk6l/rK5oI/9BaoQQnSLqmJob0Pf4dSmkVcMI5BTCMa9vxEZwdBwOsFu174uy8q0JDsrPmq3ba+2VptyPXKktra7tFRLfkFLxL/+WpsDryjbzxgIBtFt2qRVRr/9du29Rx+tTUXPz+983W23aeu5d5WQ78y2o9CVlfv029spoxEyM6G+vl/XpxFCiO6Ske6eFAhoX879cC33M+/lsPCVovDzOy6rZuIIZ9SP6wv5WdHyA/6Q1s5Fp9MxJmf4PifcppZ6Ch5dEH7eeOH1BHMK92mfQgjRI1QVg9OOqXEzql6PZ+g43KMnESgYEPOEW1GgtXXnxdGys+Mw4QZtJNurda4gKUkbir/5Zq2QGWjf/cuWwZFHwsaN4beFiotR9Xo45xxtw377aevDt62Cftdd8NJL8Oab3ZtDn52tJcRbp7NnZGh3MAYMiMBv9Fe8Xu33K4QQokuSdPckm02bXt7P7gq/u8zC/Mc7v/BnnlvD1Iltu3lHZNi8bbxb+ym/tG9ipTWCrVyUEEWLbsbgcQPgmDKV9km/idz+hRAiSvQuB6amzaiAp2oM7jFT8BeVo5r3pYn1vtu2OJrRGKPiaHtr9Git4NlWp5+uTTlfuXL74mcffaS9dtEiUFXU7Gxt3fTbb2s/37hR+zVsmPb87ru15Pndd3dckjZz5q6roo8c2XkT4NhjtYXwhx4aid9pp6Ym7Q5IaWlk9yuEEH2UJN09qbFR+5KKgzVyPWXZmlSuW1iBqmrDE9OOb+S8Y5ujekytOvkvndXJTckMsQyM2P6z//cYyWu/AcCfU0jj+X+L2L6FECIa9G4npoZN6EIhvANH4R49CX/pQNSE2C51isviaHvqtNM6E2eA887T7haMHQsffAB/+IM2Px60VlyXXYZu6lT0dXWoCxdq1c1HjdJahC1aBMXF2t2Ha67R5tcffri2rwMP7DzGd991Fl3blqpqN/e3WrECnnxSq0YOWpuzbQusjR6tLZBvb4eSEi327vzsrbfg5JPjosCeEEL0BjpV3dpPon9qb28nIyODtrY2LNEsbuZyaWu7kpIgJSXiu/d4tOVgycla0dF48HNNIufdMgRnh1Y64MSDbNw2vTqq0wN9IT/Lmr+jvqMJgNKUQsbnjd6nYmnbStywivJ5F6MLhVB1ejbduBjPkLER2XekqKi4DV5SQonoiMe5mELsGTmn957e48LgaEVJTMZfUEYgvwQlOTXWYdHRoU0jNxi05culpZCb20vvSbtc2hz4pUt3/f3e3q4VVtvaNxtQ0tLgnnvQX3TRns2bD4W0auZffbVj0vvZZ1pBNtDuXnz22R7+Zrrp4INh8eLOUXnRrymKQnNzM3l5eejlRozoA+x2O5mZmTgcDtIjNENZ/mX0FKsV3O6oJNzxqM5qZvqdVeGE++DRDuZdEt2E2+5r593aT7Xq5OgZlzNyn6uTb0vn7aBo4Sx0oRAAthMuiruEWwghQPu8MjXWoPd68A4YjHv0ZHwVQ2OacKuqlntu3qzlqeXl2kDq+PHaoG2vTLhBG6L/5z+3W6+9g/R0LUl96y1tJBvQO53oL7kETjhBG+7vLoNBWyO+s+Rm2wJql13W/X3uiaYmuPxySbiFEGIPSPXynhAKQU1Nv0m47U4DM+4cRHOb1qJkVKWbf/5pA6Yon21JxkRUVSXVlMyk/P33uVjar+U/dTcJjVrbF0/lCFpOujSi+xdCiH2l83kx2q2oRhO+kkr8hWUoqbFdFK0o2ixpp1OrIzpsGBQW9oK12nviyCO797pjj4Uff0S96ip0jz+ubfvf/2DECG2N9lln7X21OJsNnn9ee5yVpU17j4b8fDj77OjsWwgh+ihJuntCW5v2a9sWIH2Ux6fj8rsHsaFeq2haVuBl4TXrSU5UonK8gBLEpNdO4wSDmUMKDyDZlBSx0e2t0pZ/SOZHrwCgJCRRd/l8rdqPEELEAZ3fi8FuBYMRX1EZgYIyQumZMY0pGNS++jo6tBxw7FhtRLvXrNWOFosF9ZFHsB9xBJbrr0fX2Kj9QZ1zDrzwgrbmOi9vz/f72GPaInmACy7ol61JhRAiXsn08p7Q1KTNq+vjSVowBNfcX8l367XpizkZAZZct46s9GBUjmf1tvF2zcdsbK8Jb8tISI94wm1sa6HgoVvCzxvPvUZrrSOEEDGmC/gxttRjcLQSyCvBPXIi3qoxMU24f10c7YADtCXPvao4Wg/wHXss6vffa6PbW738sjbq/cILe7YzVdWmr281Y0ZkghRCCBERfTsLjAceD9TX97F5dDtSVZjzcBkffWsBICUxxKLr1lGS54/CsVR+dmzge9tPqKisc2ykLK0EfTQWjCsKhYvnYHRp1WDbxx+O49ATI38cIYTYE8EARrsVFIVgTiH+onKClpyYNrJ2u7UB2z5RHK2nZGfDf/8Lp56qrcG2WrVfp58OZ56pTTnPzu56Px9/3Nm27LDDYMiQqIYthBBiz8hId7TZbFrFmNTYV4uNpntfKOKlT3IAMBkV7v/LeoaVeSJ+HF/Iz2eNy/jOtgYVldLUIg4vnhSdhBvIfOcZUn/8CoBAZi6NF98Y04taIUQ/FwpitDVisjURtOTQMfJAOobtTzAzNyafTdsWR3O7+1BxtJ526qmwahWcckrntmee0Ua9X3ut6/dv2wZMRrmFECLuyEh3NKmq1mszIaFPJ2r/fTeXRa8VAqDTqdx+WTUHDndF/DhWbxtfNq2gI+hFr9OzX/YIKtMHoIvSn23C5nXkPXtf+Hn99DmE0ixROZYQQuyWEsJot6Hz+whm5eErriCYmRezrHZnxdGKirQi3WIv5eVp08qfeQb++Edt2kBTE5x4Ipx/PvzrX7Cz1qbNzfDSS9rj3Fytf7YQQoi4IiPd0WS3ayPd0ez/HWNvf23h1idKw89nnlvD1APbIn6cjqCHj+qW0hH0kmpK4cjiKQzMKItawq3zeylaeBP6YAAA29Rz6Bh5YFSOJYQQu6SEMNqtmJrrCCWn0TFiAu4RBxDMKYxJwh0MajlebS2YTFpxtClTYOhQSbgjQqfT1nivWgXHH9+5/fHHYeRIreXYVqEQfPQR/PnPENC+q7joIu1GvxBCiLgiI93R1NwMfn+f/QL8ek0qf11Ygapqie+04xs495iWqBwr2ZjEEEslrmAH43NHRbxY2q/lPXs/ibW/AOAtraLl9D9G9XhCCLEdRcHgbEPf4SJoycZfMZxATgEYo/vZtys+H7S2anledjaMGqUNzJrNMQmn7yss1KaVP/44XHUVOBxQVwdTp8K0aXDooTBzpnb3Y1sDpMinEELEI0m6o8Xv174g++it/7Wbk7jin4MIBLXJEicdZOUvv6+P6DGs3jYSDWZSTVp/85FZWmGYaI1ub5Xy/RdkvfMMAIopgbo/3IJqkitLIUQPUFUMTjt6dzuh9Cw8w/YnkFMYs88gKY4WQzqd1vrryCPhkkvgnXe07Q8+qP3amT/9SUvYt10bLoQQIuZkenm02GxadZm0tFhHEnF1VjPT7xyEy6NddR0yxsHcSzZFbNm6qqr81PYLH9Z9wdKmlYTUEKAl29FOuA3tbRQtnht+3nzmn/CXDIzqMYUQYmuybWrajKrT4Rk8FvfoSfgLy3o84d62OFpHhxRHi7mSEm1a+eLFkJLS9ev//GdtSoIQQoi4ISPd0VJfr12Z9LGrkzangel/r6LFrl0Ejh7o4u4rNmCK0JnkC/n5uvlbGjqaAUg1paCoKoaeqEOnqhQ+NB+jwwaAa/Rk2o4+owcOLIToz/TudoztbYRS0vEMGk0gtxg1ManH45DiaHFMp4NLL9X+Yrbt6/1rqgo1NfDpp1rrMCGEEHFBku5ocDqhpaXPFVDr8Oq5/B+D2NiQCEB5gZeF16wnOVGJyP53qE6eM4LKtOhVJ/81y4cvkbbyEwCCaRbqL53dp6vOCyFiS9/hwtDeipKUgqdyBP6CUtTE5B6PIxjU1mt7vZCZqRVHKyiA5J4PRXRFVbv3uoaG6MYhhBBij0jSHQ0tLdqcvNzcWEcSMYEgXH1/Jd//ovUbz7X4WfLXdWSm7fsUNlVVWWvfwA+tP6GikmpKYXL+/lgSem54xdxQTf5Td4efN0ybTciS02PHF0L0H3qPG4PDhpKYjLdsCIGCASjJqT0ex7bF0XJypDhar1BYGNnXCSGE6BGSdEdaKKRN7Urt+QuoaFFVmPNIGZ98lwFAalKIRdeupzjXH5H9K6jUuOtRURmQWsT+uaMx6Xvw1AwGKFo4C73fB0DbEafiGndIzx1fCNEv6LwejHYrqjkBX8kg/IVlKKk9P3f718XRBgzQku4+thqqbzr4YG2Nd13dzke9dTrt5wcf3POxCSGE2CVJuiPNZtMWxRUUxDqSiPnXC0W8/Ik26msyKtz/l/UMLfNEbP8GnZ5J+eNo9tioSCvtsenkW+W+uIikjWsA8BWW0XT2X3r0+EKIvk3n92Jos4LRiK+kgkBBGaE0S4/GsLU4msMBSUlacbSSEsjKklU0vYrBAP/6F5x2mvYXt23ivfUv8p575A6KEELEGUm6I62pSfuvsW/80T75Ti6LX9Omqel0KndctpEDhrn2aZ/adPJfCKqhcBuwVFNKuDVYT0pes4Ls/3tMi8tgpP4Pt6ImJPZ4HEKIvkfn92FwWEGnJ1AwAH9hGaH0zB7Ncn9dHG34cG3msRRH68VOOQVeeEHr371tn+6SEi3hlnZhQggRd/pGZhgvOjq04iV9pIDaW19ZWPBkafj5DefV8JsD7fu0z19XJy9OKSAzIWOf9rm39O52ihbNRrdlpKDltMvwlg+NSSxCiD4kGMDU1oKqqgTyirVkOyO7R5NtKY7Wx51yCpx4olalvKFBu5Ny8MEywi2EEHFKku5IslrB5dLm6/VyX61O5W8PVKCq2kXi9BMaOOfoln3ap9XTytKmlXhCndXJLeYYDbeoKgWPLMBk02YmuIftj+2482ITixCibwgGMdqtoAQJZBfiLyonmJnbo8m216sl24qi1fKU4mh9mMEgbcGEEKKXkKQ7UhRFK2ySlNTrF8j9tCmJP90ziEBQD8DJh1i56rT6vd7f1unkP7SuRUUlzZTCpB6uTv5r6Z+/QcZX7wIQSk6jfsZc0MsIgRBiL4SCGB02dIEAgew8/IUVBLPyQK/vsRC2LY5WUCDF0YQQQoh4Ikl3pLhc2hVPRmymSkdKXYuZ6XdW4fJoV2qHjrUz9+JN+3QfYWnTSmrdWs/QmFQn/xVTcx0Fj/09/Lzh4hsIZvedwndCiB6ihDA6WtF5PQSz8vAXVxDIyu+xTPfXxdEqKqC4WIqjCSGEEPFGku5IUVVQFE6fPZSrz21h0mg3igJX3VXKG59noNOp/PmsZq44Y+dTtNdtTuCCOeVY7UYyUkM8enM1IwZ6AfD5dVzzzxLe/jKdRLPKmMEdPDm/usuQqhsTuGFROW0uI2lJIW6dXk1ViXenr33xo2wWvVZIY6uJYEgbnRk90MWhYx2cPntY+HVNrWbGD3Vy71UbsDqM/PHuQTw1+yeMu7nGLErOo76jif1yRlCZNqDHq5NvJxSk6IFZGLxuAOwH/RbngUfHLh4hRO+jKBjaWzF43AQyc/EPHEkgu6DHCmiGQlqi7XRqBdGkOJoQQggR3yTpjqCvf7bQ2m5k0mgtoXvyjSxWb0zk55d+xOEysN85wzh8vDOcTG9rxm0DmH6ylQt/Z+OF9yxcOLecZY//BMD19xWj08HPL61Cp4NGa/f+2uY+PIDTD7dy8iE23v7awo2Ly3lu3k87vK622cy/XigmJyNAMJQAQHZ6gIXXrCczLcSZR1rDrz3h+uEcP7kVgJyMIGOrXLz6WTanHmoLv0ZVVTwhL8nGJADK00vJTcohxZTUrbijKef1R0le9z0A/txims6/LsYRCSF6DUXB4LSj73ASysjGXTGMQE4hGE09cvhfF0fbbz9tKnlS7D9ahRBCCLEbPbfgrB9Y9NYAzj6mM/l89t0sLj3JisEAWRkhzji6jaff3rHIWnOrkeVrUjh3qvbeU4+0U9NkZn1NAm6Pnodey+HWP9SFpwsW5AS7jMXmMPLjxhR+N0Xb5zET7DS0mtnUlLDDa9/8KhOjQeGnzVpZ2/SUAAXZfjLTQtu97rv1ybS2Gzl8P3t423ETW3nug9zwc1/Iz2eNy3i/9nN8IX94ezwk3InrfyTn5SUAqDo99ZfNQ0lKjXFUQoi4p6oYnHZMTTWoBgOeoeNwj55EoGBAjyTcXi/U10Njo7aCacIEmDxZm04uCbcQQggR/yTpjqCPfsjmwBGdPaw3N5opK+xMPMuL/Gxu3LGEbE2TmcLsQHhmok4HA/K11/5Sm0BWepDbHilk/HlDOXjaYN7/Oq3LWBpbzeRaAuFp3zodFGX7abBuf3xVhVc+zaaxVUvG05KD3DJtE7b2HS8kX/o4h98d1Ippm4H2ERUd/FyThMujx+pp5Z2aT2joaMav+Gn12buMs6foPW6KF96ETtFuJFhPugTP4DExjkoIEddUFb3LgalpMyrgqRqDe/Rk/EXlqKbolwN3u7U2zG1t2oj2xIlw4IFaO2apRi6EEEL0HjK9PIJqrYnkZwUius9gCDY1JDC8wsPtf6rjm5+SOPqPg1n13Crys7se8e7KP58rYmODNlRiMirc/+dfyEzfcb8dXj1vfJnF03O2n55uNEB6SpAvNzXQZloeN9XJfy3/yX9gbq4FoGPQKKwnXhLjiIQQ8UzvdmJwtqEkpeIdOAp/XglqYvSHlXdWHK2kRJtOLsXRhBBCiN5Jku4ISk4I4fXrAQWAAQV+NjWYw2u8q+vNDCjw7/C+0nw/DTYTwaBWh0dVYXOT9lpLWhC9XuWcqdo66v2Geqgo9vHD+iTys527jKUgy0+L3UQwpCXGqgr1NjOFOZ3Hf+LtXB78X+GWZyp/v3wjE4a5+PjbdIqyt4/z7a8zGVTiYVDx9uvRfSE/bp/CBvfPWCxqXFQn/7W0Ze9j+eQ1AEKJydRfNh8M8ROfECJ+6D0uDI5WlMRkvBXDCeSXoCSlRP24oRDY7VojDCmOJoQQQvQtMr08gkaXO1m7KTH8/PSj2ljySg6hELQ6DDz7biZnHNO6w/vysoKMG9LBk29mA/Di+xZK8vwMKvWRYwlx5AQnby/Vrrw21pnZWJfAsAot+T1/djkvf2jZYZ/ZGUGGl3fw+ufaPt9ZZqEgy09Zvg/Q1nHf/lRp+PWpSSHGDXahqvDcB7lMnbh9nC9+nM2ph1r5tc83bUJFITPDyfjc0RyYt19cJdzG1iYKH7o1/LzpvOsI5JfEMCIhRDzSeTswNdag93rwlg3BPXoyvvIhUU+4g0Foboa6Om3K+LhxMGUKDBkiCbcQQgjRV8RPdtQHnDalgbe/yuKoyR0AnHecjWWrk6k6ZSQ64Opzmhk1SEuWX/s4g9c+sfDgrE0ALLphExfOLee2RwpITwnxyM3V4f0+MHMTl8wv52/3laDXqyy6YRPFedo09uVrkrnyzOadxjPn4k3csLicxa8XkJoU4tZLtX1+uSqNa/9dgapqcxVnnNBAUY6fc+cPBWDCUCe/P7yztdnGhgR+2pzM1APX73CM1prxjBtZzdGlB8XVdHIAFIWixXMxuNsBaJ9wJI6Dj49xUEKIeKLzejA6bKhGE76SgfgLB6CkZkT9uF6vVolcUSA3F0aNgrw8WasthBBC9EU6VVXVWAcRS+3t7WRkZNDW1obFYtn7HTkcuN75gskzD2Xpo2tJSVIiFuOutLQZOfvGCt79zzo8HvjmG0hOhoQdC5SHrdmUxPm3DMHt1SqsnXKIlfnTNnV7raAv5Gdjew1DLJXodDrOnT+YuRdvZmDxzvt/x1LWG0+S//Q9AAQy89hw29M9cjEdayoqboOXlFAiOmQRqOj9onFO6/xeDHYrGIz484oJFJQRSs+MyL53x+3WCqMZDFpxtNJSyMnRnov+Q1EUmpubycvLQ6+XSYeid5PzWfQ1drudzMxMHA4H6RGadiYj3RGUmhTin3/exMY6MyMHRT8Jzf3/9u48vIry7OP49yxZIPu+EcIWNlmiKAgouCAUcUELIlYQK0VFFOXFiivghiIqLRVwoaCCQlVQWhFFFEuBuiCgVkWBRBRIQgLZT3K2ef8YORISIAnZ+X2uK1c7M8/M3JM8HM89zzP3RLhZN//HKrf/Jdufm59K9SXcF6TlMeOPVU+4DzoO8d+sL3F4SrFbbYTTgWsvPtgoE+6An3YS88ZzABgWC/tvnnlaJNwicmIWl9NMtgFXXGuc8a3xhEXWaZUyFUcTERE5vSnprmUXn1OA791fjcihAjt/eiqVnHzzVWA9OxTx9KQ9vleKnYhhGHyft5tvDu30VSePDowkPMDNZf0O13Hk1WdxlpI0/wGsbnMK/qGh11NyxjkNHJWINCi3C3teDni9uKMTcCa2wR0eXadZr4qjiYiICCjpPi0Ul1q59ekO/JRpFnlrl+hgwZRdtAg4+ZMFZR4nn2ZvI7PEfMa7dXASvWK6N6piaceKXf5XAvanA1Ca0pGDI25t4IhEpMG43djzc7C43bii4s1kOyIG6nAKpNttPq9dWgqRkdCxI8TFmaPcIiIicvppvJmT1AqXG6bMa8fXe8wKvLERTp6/exfhIZ6T7ptTeogtmeZ0cpvFypnR3WgbkoylEc+HDNr+HyLX/QMAr18A+259DMNPlYlETjseN/b8Q1icZbij4ihLbIM7Mq5Ok20VRxMREZHKKOluxgwDHlrUho1fmc8yh7R088LdP5IUXfFd4ZXvb1DqKSXEL4i+cb0aX3XyY9jyD5H44sO+5ezr7sSZ1LYBIxKReuf1YC84jKXUgTs8GmdqD1xR8XVaqexIcTS73Zw+ruJoIiIicjQl3c3Ys/9I4p3/mO/p9vfz8rc7d9Mx+cRFz7yGgfXXkeyYFlH0jz+HmBZRjXo6OQCGQcJLD2MvMN8vXph2HocvHtHAQYlIvfF6sRUexuooxh0WibNtV1zRCXVWY0PF0URERKSqGnkmJTX16vuxvPSveAAsFoPZt6ZzTpeiE+5z0JHLFwe/on/82YT6hwCQGBRX57HWhvD1bxGy/T8AuEMjOTD+QX3zFTkdGAa2wjysxQV4QiNxdO6EKzqhzh4rUXE0ERERqS4l3c3Qmi0RzFqa7Ft+cOxeBp+Td9z2x1Yn//rQTvrHn10PkdYO/33pxL32rG95/58ewhMW1YARiUidMwxsRflYi/LxBIfh6HQmrphEDP+AOjmd2w25uVBWpuJoIiIiUj1KupsBjwf+vS2Y/271I9/hx1/fTPJtu3X4fq4dlHPcfUs9ZXyWtZ1Mh1mdPCU4ibNiutd5zLXG7SJpwQNYXWUAHBo0kuK08xo4KBGpS9biAuwFh/EEheJI7YkrNgkjILBOznVscbSUFPN/VRxNREREqkpJdxO38qNwJs9J5pfsit8AR1xwkElXHzjuvgcdufw360scnrImU538WDFvLiDwp50AlCW2JXv05AaOSETqirWkCHv+Ibwtg3G074YzrhVGYMs6OVdRkTmN3G6HxETzeW0VRxMREZGaUNLdhK38KJwRf25H5W/bNujXreC4jzVnO3L4ZP+nGBiE+AXTN+6sRl+d/Fgtv/2CqDWvAmDY7Oyb+CiGf92MdolIw7E6irG7D2HxtKC0bVdcca3wtgyu9fMcWxytXTtISlJxNBERETk1SrqbKI8HJs9J/jXhrvzb4OzXkrnknDxslbyWNjowkqjAcILsLTkrpnvjr05+DGtRPokLH8JimL+B7JG3UZbSqYGjEpHaZCktwZ6Xi9ffH2diEkZ0R4yQ8Fo/T2XF0RITISSk1k8lIiIip6GmlWmJz8ZtwZVOKf+NhcxD/mzdGUzvX6uW55YeJjwgDJvFitViZUBCH2wWW5OaTg6YrwdbPAu/w9kAFHc9h0ND/9DAQYlIbbGUlWLLywG7nbJW7XDGJePyK8MWGHqcW4w143KZz2s7neZotoqjiYiISF1Q0t1EHcjxq1K7g3l+R1Un/57UsLakRZ8BgL2JjW4fEfafdwn97EMAPEGh7L95BlgrGc4XkSbF4izDlp8DVhuuhBSc8a3xhEViGF4oza618xxdHC02Flq3Nv/Xr2ofqyIiIiLV0jSzLiEh2lWldmEhJWw88JmvOnmZx4lhGE1vdPtXflm/EPfKbN/ygT/ehzuyabxLXEQqZ3E5seflYACumCScCSnma/9q+XNKxdFERESkISjpbqLOP7OIVrFO9mX7YVQy4dKCQUxEKbkt36XMUdpkq5OX43GTuPBBbKUlAOQNuJzC3oMaOCgRqTG3G3teDng9uKITcCa2wR0eXavJ9pHiaHl5EBSk4mgiIiJS/5R0N1E2G/xl6s+M+HM7LBjlEm9zGS68ZDVlRikhfsH0izuLsCZWnfxY0e8souWurwFwxrYi6/qpDRyRiNSIx409PxeLy4UrKg5nYlvcETG1+pjIkeJoxcVmQbRu3SAhQcXRREREpP4p6W7Crr4ojzdn76nwnu7YyDIuHvwvOnb+npTgpCZZnfxYLX78iui3FwFgWG3su/URvC2CGjgqEakWrwd7/iEsZaW4I2MpS2qLOyK2Vud3qziaiIiINDZNOxMTrr4ojysH5vHhp8H8d6sfSTEuzu1exIGSQFzenrQJadV0p5P/yuooInHBg1gMLwA5w8dT2qF7A0clIlXm9WIrOIS1tAR3eDTODt1xRcaZD1fXktJSyM01p5OrOJqIiIg0Jkq6mwGr1cAvfBtn9QgnOSwGmxVaBSc0dFi1Ju6VOfgf3AdASWoPcq64sYEjEpEq8XqxFR7GWlKEJyyKkrZdcUXHg732MuGji6MlJUFyMkRFqTiaiIiINB5Kupu44rIyVm3fzu6DB/G3+BMXcgEBnOj93U1LyKfrCP/PvwDwBAax/5ZHwKZuK9KoGQa2wjysxQV4QiJwdOmFKzoBw692PpsMA/LzzR8VRxMREZHGTtlLE5aRm8tbX35JUVkZdquV1BZd8Lc2n4TbnptJwt8f9y1n3vBnXLFJDRiRiJyQYWArysdanI8nKAxHxzRcMYkYAYG1cniPx0y0i4ogNFTF0URERKRpUNLdBBmGwcZdu9iwcycGEB0czBVn9OLnH5vRN0+vh8Tnp2MrKQQgv88lFPS/tIGDEpHjsRYXYC84jCcoFEeHHrhikjACa6d6mctlTiF3u1UcTURERJoeJd1NjMvjYcUXX7D74EEAerZqxaXduuFx2fm5gWOrTZFrlhL03VYAXFFxZN54r+aNijRC1pIibAWH8LYIwtHuDFxxrWrtzQJHF0cLC4MOHcxkW8XRREREpClR0t3E2K1Wgvz9sVutDOvenbTkZAAcrgYOrBYFZnxP7JsLADAsFvbfPBNvUNN+x7hIc2N1FGPLP4Q3sAWlKZ1wxbfG2zK4Vo59bHG0Vq3MqeXx8bX6Km8RERGReqGkuwkwDAOXx4O/3Y7FYmFY9+6c16EDMc3wQUZLWSmJ8+/H4nEDkDtsLCVdzm7gqETkCEupA3t+LoafP2Wt2uNMSMEbfOo3xU5UHM0wIDu7FoIXERERaQBKuhu54rIyVm7bht1q5dpzzsFiseBvtzfLhBsg7vW5BBz4CQBHm84c/P0tDRyRiABYnKXY8nLAZqcsMQVXQhs8IeGnfFyPxxzVLioyp5BXVhzNME75NCIiIiINRkl3I3ZsdfKDhYXEhjbfadbB2zYSsf5NALz+Aey/9dFafZ+viFSfxVmGLT8HLFZcca1xJqTgCT31d3O5XHDoEDidKo4mIiIizZuS7kbIaxj856jq5DHBwYzo1YvYZjq6DWDLzyXhxYd9y1l/mIIzsU3DBSRyunO78Dt80Hy8JTbJTLbDok452T5SHA0gJgZat4bYWBVHExERkeZLSXcjc2Q6+Z6cHADSWrViaLdu+Nub8Z/KMEh8YSb2wsMAFJ41gLwLr27goEROU2439rwc8LpxRcbjTGqLOzz6lCuYHVscLTkZoqLAZqudsEVEREQaq2acyTU9hmGw4osv+Pnw4QrVyZuziA//QfBXmwFwh0Vx4KYH9XowkfrmcWPPz8XicuGOjKUssS3uyNhTSrYrK47WqhWEh+ufuIiIiJw+lHQ3IhaLhSFdu/Kvr7/mqjPPbNbTyY/w/2U3sa//1be8f8J083lREakfXg/2/ENYykpxR8TgTGqLKzLulIagPR44fBiKi49fHE1ERETkdKGku4EVl5WxLy+PjnFxACRFRDDh/POxnAbDQBaXk6T5D2B1lQFw6JJRFPfo18BRiZwmvF5sBYewlpbgDovC2b4brqh4c/53DR1bHK1zZ/N5bRVHExERkdOZku4GlJGTw1vbtuFwuRjfvz/xYWEAp0XCDRDzxnwCf/4RgNKkdmRfe3sDRyRyGjAMbAWHsTqK8IRGUtK2C67ohFN6U0BpqZlsG4aKo4mIiIgcS0l3A6isOrntFIsUNTUtv/mUqPeWAuC1+7F/4mMY/oENHJVIM2YY2IrysRbl4wkJx9HpTFzRCRj+ATU+ZFGROY3czw8SE83iaNGnXnNNREREpFlR0l3PisrKWHW6VSc/hq0wj8QXZviWD14zibLWqQ0XkEgzZy3Kx1aUhycoDEdqT1yxSRgBNbvJdaQ4WkEBtGwJ7durOJqIiIjIiTS/TG/kSJgyBfr2Ba8XJk+GNWvMb4N33gmTJlW6m3X3brj9dsjJMSv/LFkCZ5xhzpu89lr49lvzwcTYWFiwADp0OHkshw7B9Onwyy/g58eBW2/lNbeborIy/Gw2Lu3W7bfq5Bs3wty5ZswdOpj7BQefeFtuLtx1F8THYx15PdADvF7iXplD8I5NgIVDvxvN4UtGVRqeX+ZeEp+fga0oD2+LYPZPmI6zVXsAgr7aTMybC7C4XRj+gRy48T7KUjqe9JJt+YdIfH46/tm/4LX7kTluGo7OZ/3WwDCIX/w4focPAuD1DyRi/Zu0+GE7ByZMx9vCvObgbRuJfX0uFq+X0uQOvm22/FySn7mLjIf+Drbm131FapO1uBBbwSG8LUMobd8dZ2wSRmDLGh2rsuJo8fEqjiYiIiJyMs1rEuBnn5mJbt++5vLSpWay/MMP5rannoL//a/SXVvcdRdMmGC2veceGDfut40TJsDOnbBjB1x5JYwfX7V45s0zv5muWgXTpxM5axYlJSXEBAfzp/PO+y3hLimBRx6Bp58220ZHw6JFJ98WFWUOMe3Zg7dbDwAi/ruGgH3p7H5qJekzXybq3Vfx/2V3peEl/P1x8i68ij1PrST3srEkvjATAGtxAYkLHmT/hBmkP76crNGTSVzwQJUuOfYf83B06MbuOas4MGE6SfMfALfbtz3s36sJ/fwjAAyLhb13/5Xdc1bhDo8m+m3zuiylJSS89Ai/3Pl0hW2esCgcqT0J+8+7VfsbiJyGrI4i/DL3YnGVUdq2K8U9+1HWOrVGCbfLBVlZsG8fBAZCr17Qvz+kpirhFhEREamK5pV0P/88XHfdb8srVsCf/mS++iYyEkaNgtdfr7BbDGDfvh2uv95c8fvfw88/w65d5rfMSy/9bd7kuedCRkbV4vnwQ/NYAGecgX98PL83DMafdx4xR39b3bwZOnWCNm3M5ZEj4f33T74NwOEwSwX/KvzzdeRdMBysNrzBYRT0uYSwLUe1/5Ut/xCB6d+R338oAIXnXIzfoSz8sn7GP+sXPMFhvlFvR6cz8cvNIjDj+5NecuinH3L4IvOaS9udgTsimpbfbwXAL+tn4l+d42tb1jrVNwp+eNBIQn+NM3jHZkpTOuFMbFNhG0D+uUOI+GjlSWMROd1YSkvwO7AXa6mD0tYdKe7Rj7K2nfG2CKr2sUpLYf9+yM42K5H37g39+kFKivmxKCIiIiJV07yS7g0boE+f35b37jW/IR7Rpo257hjJgDcu7rdX5VgsZvndStryl7+Yo90nk5eH4XazdPduXB6PedjERLpaLBWf387MNOdpHpGYaE5zd7tPvA1gzx44eBCKiwDwP5RpViL+lSsmEXtuZoXw/A5l4Q6P+m2KtsWCKyoOv5xMnPGtsRXl0+KHHQAEf/kJttJi/A7uP+El2wrzwOPGEx792/mjE/HLzQS323w9WJkDgJL2Z+Bod0a5dva8HPC48cvNxBUdX+k2gNK2nQn4eRdWR9EJ4xE5XVjKSvHL+gVbcSFlye0p6tGXsvZn4A2q/lB0UZF5zzEvz/y4Ofdc82M1KUnVyEVERERqonk9FPvLL/Dr+67rxOOPm6Pf69efsJnXMNiyezd9vF52HzzIpl27uKBTp7qJKTsbQkKw5OQAwbVySG/LYPbd/iQx/3gOa1kJjg7dKUtqh2Gz1fiY0e+8RIs95tR+Z1wyRWcNxC/nQM0OZrPjCQrBfjgHZ4vauWaRpsjiLMWWlwM2O2WJKbjiU/CERlT7OCqOJiIiIlJ3mlfS3bKlOSfyiNat4aeffnvGOyPDXHeMnwFrVpY5emy3m99A9+4t33bOHFi50pwy3vL4z0UWlZWx8uuvSc/J4RyrlT6hofRrb07TZv/+8qPWR8THw6ef/ra8f7/57LbdfuJtYM7zdDohwHztjzMyHr+cAzhSzWe8/Q7uxx1V8ZyuyDjsebnm6LHNvGa/3CzfCHNJ17PZ2/VsACwuJ6mThlCW1O641w3gCQkHqw1bXo5vtNsvZz/WkmKi3/k7AIbVxr5bH8EvJ5OW32/z7euXsx93eDTY7Lii4gn65tNKtx1hdTnxnsKrjkSaMovLaSbbgCu2Fc6ENnjCIqudIVdWHC0h4bcajiIiIiJy6prX9PIePcyCZ0eMHAkvvmh+szx0yHzGe1TFSt4HAU+PHmbhNYC33jKHeY5UKH/mGfNZ8HXrzKGfo917L/ztbwCkFxby/ObNpOfk4GezUXTeefzuu+/M6eT/+585DbxXr4px9+0L33//27Pib7wBgweffBuYU+YNAyPWHOHPP3sQ4RveBq8Ha1E+oZ+uo+Dco9r/yhMWSWmbToRteg+AkM/X44qMxRVnFnez//qFHiD67Zco7nq2b1vMir8RsW5FxesACnoPIuKjtwAI3PM/7IeyiXz/dSyG1/xdXz2B0vbdKO7Rl8CM7/Hfb15XxIdv+OI80TYAW34uhsWCO7IOZzWINEZuF/acA9gOH8QdFU9J93NxdD4LT3hUtRJul8t8cmX//orF0ZRwi4iIiNSu5jXSPWKEWWRs0CBzecwY+Pxz85ukxWK+Sqx7d3Pb6tXmzzPPAFDy7LOE3nGHOYU8NBQWLzbb/fIL/N//Qbt2cOGF5rqAgN9Gn3fsgF692PHtt7zzww8YQExwMCN79SKyd2946CG46irzYchHHvlthHrhQnPEesQICAqCBx4wz+PxmHM7Z5qVxE+4DcybAw4HWM37J4f6Xkrw3m9pP/VqsEDu0D9QlmzePAj+8hNCvvw3B8Y/CEDmH+8j4YWZRP1zMd4WQRz403TfYaPfWkjLnduweDw4UrtzYPxDvm2Be38gr23nSv8E2dfeTuLCh2g/9SoMux9lSe0I/t9ngDm67gkKBTDPN/4BWs39PyweD2Wt2rP/5pkn3QYQ/NUWinpd4LtmkWbP7caen4PF7cYVFYczsS3uiJhq/xsoLTXvPxoGxMSYJS9iYvSstoiIiEhdshiGYTR0ELWmqMgsr7tli5msVkFBQQFhYWEcPnyY8GNHsU/G4zGrDH36Kfn79/P8kiV0io9naPfuFYul1ZUbb4T8fBwvLWPb9y1o2dI307xueD20mXEjGTOWnPQLf+iW90mafz8AnhZBpD/2Oq6YxFMOIeWR8Rz44/04k9qe8rGaIwODYlspQZ5ALOiB3CbN68Gel4vFWYY7Ko6yxDa4I2LNNzJUQ1GROY3cz8+cPt6qlXnPr6nct/J6vWRnZxMbG4u1qQQtcgLq09KcqD9Lc5OXl0dERAT5+fmEhobWyjGb10h3cDA8+yykp5sPJ9axwwUFRHz+OQBhISHc2rUrIUlJv41m17XcXHO6fGQklgP7gA51f06rjYyHXzlpM3tOJvFLZvmWM2+YVisJty0/l8MXj1DCLc2b14O94DCW0hLc4TE4U3vgioyr1mfLscXRUlPNauQqjiYiIiJSv5pX0g1w8cV1fgqv18vGjRv55JNPGDVqFJ1+rUwe4u9f5+cuJyoKfvc7AAwHsO3EzeuN10PiwoewlZiv9Mrv+zsKfn0f+KnyhEVR0O93tXIskUbH68VWeBhrSRHu8CicbbuaxQ3tVZ//reJoIiIiIo1L80u661hRURErV64kPT0dgIyMDF/SLaaod18laOeXALii4sm84Z4GjkikkTMMbIV5WIsL8IRG4ujSC1d0AoZf1W/kuVzm5BeXCyIioHNn8w2KgYF1GLeIiIiInJSS7mpIT09n5cqVFBUV4efnx7Bhw+jZs2dDh9WoBO75lpi3FgBgWKzsu/URvEEhDRyVSCNlGNiK8rEW5eMJDsPRMQ1XbBJGNV6Hd3RxtNhY802HKo4mIiIi0ngo6a4Cr9fLv//9bz755BMAYmNjGTFiBDExMQ0cWeNiKXWQuOABLB4PALmX34Cj05kNHJVI42QtLsBecBhPUCiO1B64YpIwAltUef+ji6O1agVJSU2rOJqIiIjI6UJJdxVkZGT4Eu4zzzyToUOH4qdhpAriXnuGgMy9ADjaduXgVTc3cEQijY+1pAhbwSG8LYJwtDsDZ3wyRmDLKu17pDhafr75ggYVRxMRERFp/JR0V0G7du3o27cvcXFxmk5+HMFbNxDx8SoAvP6B7L/1kfqr4i7SBFgdxdjyc/EGtqQ0pROu+NZ4W1atutmxxdF69ID4eBVHExEREWkKlBVVwuv1snnzZtLS0gj+9Vvt4MGDGziqxsuel0PCokd9y1nX/x/OhJQGjEik8bCUOrDn5WD4B1CWnIozvjXe4Kq981HF0URERESavkb59N9zzz1HmzZtCAwMpE+fPnz22WcnbP/GG2/QuXNnAgMD6d69O2vWrKnxuYuKili6dCnr169n5cqVGIZx8p08Hti4ET75BLZuNZebO6+Hlt99Qeim92j1zBTshXkAFPa6gLwLhjdoaCKNgaWsFHvWL9iK8ylr1ZbiHn0p7dCtSgl3aSns2wfZ2eabAXv3hn79ICVFCbeIiIhIU9PoRrpXrFjBlClTWLhwIX369GHu3LkMGTKEnTt3EhsbW6H95s2bGT16NLNmzeKyyy7jtddeY/jw4Xz55Zd069atWufes2cPK1eupLi4GD8/P9LS0rCc7EHJlSth8mT45Zff1sXGwtSpcNFF1Tp/UxHy+UfELZ2D36HscuvdLUM4cNMDerhUTmsWZxm2/BywWHHFt8aZkIInLLJK+x5dHC052SyQFhWl4mgiIiIiTZnFqNJQbv3p06cP55xzDn/7298Ac6p3cnIyt99+O9OmTavQftSoURQXF/Ovf/3Lt+7cc88lLS2NhQsXnvR8BQUFhIWF8c9//pOtW7cCZnXykSNHEh0dfeKdV66EESPM6kaVmT273hJvhwO2bYOWLSGg6m8bqraQzz8i6a9/BuDY1NoA9t0xm8JzmufNhqbCwKDYVkqQJxBLhb+S1BWLy2lOIzcMXLFJvybbUSe9CXVscbRWrVQc7Vher5fs7GxiY2Ox6g6ENAPq09KcqD9Lc5OXl0dERAT5+fmEhlbtkcCTaVQj3U6nk61bt3Lvvff61lmtVgYNGsSWLVsq3WfLli1MmTKl3LohQ4bw9ttvV+vcmzZtIjAwsOrVyT0ec4T7RPcsHnwQ1q6tl2/O/h7omAd2Wx2OihkGwTs2ARUT7iPilj5NYa+BYLXVURAijYzbjT0vB7xuXFEJOBPb4I6IOem/+yPF0UpKIDRUxdFEREREmqtGlXTn5OTg8XiIi4srtz4uLo7vv/++0n0yMzMrbZ+ZmVlp+7KyMsrKynzL+fn5gHmX7uKLL6Zbt24UFxefPNiNG7EePaW88pPBRx+d/Fi1xL8ezlF0sgaHsij+cC2Fbc6oh2ikMgZQFuylsMiqce46ZnF7sHg9OMOiKY1NxRkUDQVWKMg/4X4eD7jdZiXyDh0gJsZ8Vtvthry8+om9KfF6vRQUFODv769RFGkW1KelOVF/luYm79cvY7U5IbxRJd31YdasWcycObPC+tmzZzN79uwGiKgZenV6Q0cgIiIiIiJSY7m5uYSFhdXKsRpV0h0dHY3NZiMrK6vc+qy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231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.050.013
531276Will the USDA-posted recall by Pork Dynasty In...1.02025-01-21 11:42:002025-01-21 11:42:00binaryNaNNaNNaNNaNNaN312821.00.20.45
831288Will Eric Adams be Mayor of New York City on t...1.02025-01-22 20:19:002025-01-22 20:19:00binaryNaNNaNNaNFalseFalse312941.00.90.95
1031318Will the S&P 500 index go up in January 2025?1.02025-01-23 23:23:002025-01-23 23:23:00binaryNaNNaNNaNNaNNaN<NA>1.0NaNNaN
1331334At the end of March 2025, will Wikipedia still...1.02025-01-24 14:23:002025-01-24 14:23:00binaryNaNNaNNaNFalseFalse313381.00.750.9
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NaN NaN NaN \n", + "13 NaN NaN False False 31338 \n", + "\n", + " question_weight bot_team_median pro_median \n", + "2 1.0 0.05 0.013 \n", + "5 1.0 0.2 0.45 \n", + "8 1.0 0.9 0.95 \n", + "10 1.0 NaN NaN \n", + "13 1.0 0.75 0.9 " + ] + }, + "execution_count": 352, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_top_bot_pro_forecasts_all_binary.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 353, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse312681.00.010417[0.001,0.62,0.35,0.019,0.01]234.340709234.340709
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0TrueTrue312691.0[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.0013749738,0.0014499743,0.001526641,0.00160...-101.083204-101.083204
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.050.013-3.820805-3.820805
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaNNaNNaN312801.00.65[0.16,0.44,0.4]39.01976439.019764
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0FalseFalse312811.0[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0,0.0005044914,0.0010323506,0.0015847475,0....45.54604145.546041
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" + ], + "text/plain": [ + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max open_upper_bound \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_lower_bound pro_question_id question_weight \\\n", + "0 False 31268 1.0 \n", + "1 True 31269 1.0 \n", + "2 False 31270 1.0 \n", + "3 NaN 31280 1.0 \n", + "4 False 31281 1.0 \n", + "\n", + " bot_team_median \\\n", + "0 0.010417 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 0.65 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", + "2 0.013 -3.820805 \n", + "3 [0.16,0.44,0.4] 39.019764 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "\n", + " weighted_score \n", + "0 234.340709 \n", + "1 -101.083204 \n", + "2 -3.820805 \n", + "3 39.019764 \n", + "4 45.546041 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
34235345Will the US Citizenship and Immigration Servic...1.02025-03-12 22:00:002025-03-12 22:00:00binaryNaNNaNNaNFalseFalse353801.000.90.95-5.406722-5.406722
35135354Will the United States impose any new tariffs ...0.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353811.000.40.05-45.953233-45.953233
35535358Will ChatGPT rank in the top 10 global website...1.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353851.000.90.97-7.490131-7.490131
36135364Will Doge's Agency Efficiency Leaderboard have...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaNFalseFalse353860.850.850.666-80.050570-68.042984
36435367Will the Project 2025 Tracker spreadsheet mark...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaNFalseFalse353870.850.050.03-2.083409-1.770897
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" + ], + "text/plain": [ + " bot_question_id title \\\n", + "342 35345 Will the US Citizenship and Immigration Servic... \n", + "351 35354 Will the United States impose any new tariffs ... \n", + "355 35358 Will ChatGPT rank in the top 10 global website... \n", + "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", + "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", + "\n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "342 1.0 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", + "351 0.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "355 1.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "361 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "\n", + " range_min range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "342 NaN NaN False False 35380 \n", + "351 NaN NaN False False 35381 \n", + "355 NaN NaN False False 35385 \n", + "361 NaN NaN False False 35386 \n", + "364 NaN NaN False False 35387 \n", + "\n", + " question_weight bot_team_median pro_median head_to_head weighted_score \n", + "342 1.00 0.9 0.95 -5.406722 -5.406722 \n", + "351 1.00 0.4 0.05 -45.953233 -45.953233 \n", + "355 1.00 0.9 0.97 -7.490131 -7.490131 \n", + "361 0.85 0.85 0.666 -80.050570 -68.042984 \n", + "364 0.85 0.05 0.03 -2.083409 -1.770897 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[354], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:853\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 843\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 844\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 853\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 856\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 857\u001b[0m bins\n\u001b[1;32m 858\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], "source": [ "# Calculate confidence scores for bot_team_median and pro_median\n", "display_head_and_tail(df_top_bot_pro_forecasts)\n", @@ -12310,9 +13875,9 @@ "\n", "# Recommend paying attention to the bot team h2h scores vs CP graph (further down) rather than pgodzinai (he was selected as the bot \"team\" vs the PROS)\n", "\n", - "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_cp'] = df_top_bot_pro_cp_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'forecast_values'), axis=1)\n", - "df_top_bot_pro_cp_forecasts['head_to_head_cp_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_head_to_head, args=('forecast_values', 'pro_median'), axis=1)\n", - "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'pro_median'), axis=1)\n", + "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_cp'] = df_top_bot_pro_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'forecast_values'), axis=1)\n", + "df_top_bot_pro_cp_forecasts['head_to_head_cp_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('forecast_values', 'pro_median'), axis=1)\n", + "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'pro_median'), axis=1)\n", "\n", "plot_head_to_head_distribution(df_top_bot_pro_cp_forecasts, 'head_to_head_bot_vs_cp', ('pgodzinai', 'CP'))\n", "plot_head_to_head_distribution(df_top_bot_pro_cp_forecasts, 'head_to_head_cp_vs_pro', ('CP', 'Pro median'))\n", @@ -12464,7 +14029,7 @@ "df_top_bot_cp_forecasts = df_top_bot_cp_forecasts.dropna(subset=['forecast_values'])\n", "\n", "# Add the head_to_head column\n", - "df_top_bot_cp_forecasts['head_to_head'] = df_top_bot_cp_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'forecast_values'), axis=1)\n", + "df_top_bot_cp_forecasts['head_to_head'] = df_top_bot_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'forecast_values'), axis=1)\n", "\n", "display_head_and_tail(df_top_bot_cp_forecasts)" ] diff --git a/functions.py b/functions.py index 8e894df..0d6593b 100644 --- a/functions.py +++ b/functions.py @@ -11,11 +11,9 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import ( - calculate_baseline_score, - calculate_peer_score, - nominal_location_to_cdf_location, -) +from refactored_notebook.scoring import (calculate_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location) def extract_forecast(df): @@ -193,7 +191,7 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): df_pivoted = df_pivoted[cols] all_columns = df_pivoted.columns.tolist() - ## Remove 'question_id' and 'bot_median' from the list if they exist + # Remove 'question_id' and 'bot_median' from the list if they exist all_columns = [col for col in all_columns if col not in ["bot_question_id"]] new_column_order = ["bot_question_id"] + all_columns df_pivoted = df_pivoted[new_column_order] @@ -432,17 +430,21 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - resolution = row["resolution"] - options = row["options"] - range_min = row["range_min"] - range_max = row["range_max"] - question_weight = row["question_weight"] - open_upper_bound = row["open_upper_bound"] - open_lower_bound = row["open_lower_bound"] - question_type = row["type"] - for team in teams: - forecast = row[team] + # @Check: that the conversion is corret + cleaned_row = _prepare_new_row_for_scoring(row, [team]) + if _is_unscorable(cleaned_row, [team]): + continue + + forecast = cleaned_row[team] + resolution = cleaned_row["resolution"] + options = cleaned_row["options"] + range_min = cleaned_row["range_min"] + range_max = cleaned_row["range_max"] + question_weight = cleaned_row["question_weight"] + open_upper_bound = cleaned_row["open_upper_bound"] + open_lower_bound = cleaned_row["open_lower_bound"] + question_type = cleaned_row["type"] try: weighted_score = calculate_baseline_score( @@ -576,114 +578,6 @@ def calculate_t_test(df_input, bot_list, weight_col="question_weight"): return df_W_leaderboard -def calculate_head_to_head(row, a, b): - """ - @Check:... - - Calculates the head-to-head score for two forecasters. - Positive if 'a' did better than 'b', negative if 'b' did better than 'a'. - - Args: - row (pandas.Series): Row containing 'resolution', 'type', and forecast columns. - a (str): Column name for first forecaster. - b (str): Column name for second forecaster. - - Returns: - float: Head-to-head score. - """ - q_type = row["type"] - resolution = row["resolution"] - options = row["options"] - range_min = row.get("range_min") - range_max = row.get("range_max") - - forecast_a = row[a] - forecast_b = row[b] - - if q_type == "binary": - if (resolution == "yes") or (resolution == 1): - return 100 * np.log(forecast_a / forecast_b) - elif (resolution == "no") or (resolution == 0): - return 100 * np.log((1 - forecast_a) / (1 - forecast_b)) - else: - return np.nan - - elif q_type == "multiple_choice": - # Parse forecast_a if it's a string - if isinstance(forecast_a, str): - forecast_a = ast.literal_eval(forecast_a) - options = ( - ast.literal_eval(row["options"]) - if isinstance(row["options"], str) - else row["options"] - ) - resolution_idx = options.index(str(row["resolution"])) - forecast_a = forecast_a[resolution_idx] - - # Parse forecast_b if it's a string - if isinstance(forecast_b, str): - forecast_b = ast.literal_eval(forecast_b) - options = ( - ast.literal_eval(row["options"]) - if isinstance(row["options"], str) - else row["options"] - ) - resolution_idx = options.index(str(row["resolution"])) - forecast_b = forecast_b[resolution_idx] - - # Now both are floats with the prob assigned to the correct bin - return 100 * np.log(forecast_a / forecast_b) - - elif q_type == "numeric": - # Ensure both forecasts are Python lists - if isinstance(forecast_a, str): - forecast_a = ast.literal_eval(forecast_a) - elif isinstance(forecast_a, np.ndarray): - forecast_a = forecast_a.tolist() - - if isinstance(forecast_b, str): - forecast_b = ast.literal_eval(forecast_b) - elif isinstance(forecast_b, np.ndarray): - forecast_b = forecast_b.tolist() - - if not forecast_a or not forecast_b: - return np.nan - - cdf_a = forecast_a - cdf_b = forecast_b - - pmf_a = [cdf_a[0]] + [cdf_a[i] - cdf_a[i - 1] for i in range(1, len(cdf_a))] - pmf_a.append(1 - cdf_a[-1]) - - pmf_b = [cdf_b[0]] + [cdf_b[i] - cdf_b[i - 1] for i in range(1, len(cdf_b))] - pmf_b.append(1 - cdf_b[-1]) - - bin_edges = np.linspace(range_min, range_max, 200) - - if resolution == "below_lower_bound": - resolution_idx = 0 - elif resolution == "above_upper_bound": - resolution_idx = len(pmf_a) - 1 # i.e., 200 - else: - try: - resolution_val = float(resolution) - resolution_idx = np.searchsorted( - bin_edges, resolution_val, side="right" - ) - except ValueError: - print(f"Bad resolution value: {resolution}") - return np.nan - - p_a = pmf_a[resolution_idx] - p_b = pmf_b[resolution_idx] - - if p_a <= 0 or p_b <= 0: - print(f"Invalid PMF values: p_a={p_a}, p_b={p_b}") - return np.nan - - return 100 * np.log(p_a / p_b) - - def plot_head_to_head_distribution( df_forecasts, col="head_to_head", vs=("Bot Team", "Pros") ): @@ -1079,7 +973,8 @@ def get_cdf_at(cdf, unscaled_location): if index_scaled_location.is_integer(): return cdf[int(index_scaled_location)] # linear interpolation step - left_index = int(index_scaled_location) # This is the floor, which is what we want + # This is the floor, which is what we want + left_index = int(index_scaled_location) right_index = left_index + 1 left_value = cdf[left_index] right_value = cdf[right_index] @@ -1245,39 +1140,86 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str) -> float: - question_type = row["type"] +def calculate_weighted_h2h_score_between_two_forecast_columns( + row: pd.Series, col_a: str, col_b: str +) -> float: + """ + Calculates the head-to-head score for two forecasters. + Positive if 'a' did better than 'b', negative if 'b' did better than 'a'. - forecast_a = row[ - col_a - ] - if isinstance(forecast_a, str): - forecast_a = [float(x) for x in forecast_a.strip('[]').split(',')] - elif isinstance(forecast_a, float) and math.isnan(forecast_a): - return np.nan + Args: + row (pandas.Series): Row containing 'resolution', 'type', and forecast columns. + a (str): Column name for first forecaster. + b (str): Column name for second forecaster. - forecast_b = row[col_b] - if isinstance(forecast_b, str): - forecast_b = [float(x) for x in forecast_b.strip('[]').split(',')] - elif isinstance(forecast_b, float) and math.isnan(forecast_b): + Returns: + float: Head-to-head score. + """ + # @Check: that the row conversion is corret + + cleaned_row = _prepare_new_row_for_scoring(row, [col_a, col_b]) + if _is_unscorable(cleaned_row, [col_a, col_b]): return np.nan - options = row["options_parsed"] if "options_parsed" in row else row["options"] + question_type = cleaned_row["type"] + forecast_a = cleaned_row[col_a] + forecast_b = cleaned_row[col_b] + resolution = cleaned_row["resolution"] + options = cleaned_row["options"] + range_min = cleaned_row["range_min"] + range_max = cleaned_row["range_max"] + question_weight = cleaned_row["question_weight"] + + score = calculate_peer_score( + q_type=question_type, + forecast=forecast_a, + forecast_for_other_users=[forecast_b], + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, + question_weight=question_weight, + ) + return score + + +def _is_unscorable(row: pd.Series, forecast_columns_to_check_null: list[str]): + is_unscorable = False + for col in forecast_columns_to_check_null: + forecast = row[col] + if forecast is None: + is_unscorable = True + elif isinstance(forecast, float) and math.isnan(forecast): + is_unscorable = True resolution = row["resolution"] if resolution == "annulled" or resolution == "ambiguous": - return np.nan + is_unscorable = True + return is_unscorable + - question_type = row["type"] +def _prepare_new_row_for_scoring( + original_row: pd.Series, forecast_columns: list[str] +) -> pd.Series: + new_row = original_row.copy() + question_type = original_row["type"] + + options = ( + original_row["options_parsed"] + if "options_parsed" in new_row + else new_row["options"] + ) + if isinstance(options, str): + options = options.strip("[]").split(",") + new_row["options"] = options + + resolution = original_row["resolution"] + question_type = original_row["type"] if question_type == "binary": if resolution == "yes": resolution = True elif resolution == "no": resolution = False - assert isinstance(forecast_a, float) - assert isinstance(forecast_b, float) - forecast_a = [forecast_a] - forecast_b = [forecast_b] elif question_type == "multiple_choice": resolution = resolution elif question_type == "numeric": @@ -1289,31 +1231,37 @@ def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, co raise ValueError(f"Unknown resolution type: {resolution}") else: raise ValueError(f"Unknown question type: {question_type}") + new_row["resolution"] = resolution - - range_min = row.get("range_min") + range_min = original_row.get("range_min") if range_min: range_min = float(range_min) + new_row["range_min"] = range_min - range_max = row.get("range_max") + range_max = original_row.get("range_max") if range_max: range_max = float(range_max) + new_row["range_max"] = range_max - question_weight = row["question_weight"] + question_weight = original_row["question_weight"] if question_weight: question_weight = float(question_weight) - - score = calculate_peer_score( - q_type=question_type, - forecast=forecast_a, - forecast_for_other_users=[forecast_b], - resolution=resolution, - options=options, - range_min=range_min, - range_max=range_max, - question_weight=question_weight, - ) - return score + new_row["question_weight"] = question_weight + + for col in forecast_columns: + forecast = original_row[col] + if isinstance(forecast, float) and math.isnan(forecast): + forecast = forecast + elif question_type == "binary": + if isinstance(forecast, str): + forecast = [float(forecast)] + forecast = [forecast] + elif isinstance(forecast, str): + forecast = [float(x) for x in forecast.strip("[]").split(",")] + + new_row[col] = forecast + + return new_row def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 9c027a3..4ece3f3 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 X_bot,-0.0,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 @@ -18,30 +18,30 @@ annabot,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.0,-0.0 +MWG,-0.2,-0.2,-0.1,-0.1,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.1,-0.0 -GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 -ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 +metac-o1,-0.3,-0.2,-0.1,0.0,0.1 acm_bot,-0.3,-0.2,-0.1,0.0,0.1 +ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +bot_median,-0.3,-0.2,-0.1,-0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-o1,-0.3,-0.2,-0.1,-0.0,0.1 -metac-perplexity,-0.3,-0.2,-0.1,0.0,0.1 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +metac-deepseek-r1,-0.3,-0.2,-0.1,-0.1,-0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-deepseek-r1,-0.3,-0.2,-0.2,-0.1,-0.0 metac-Gemini-Exp-1206,-0.3,-0.3,-0.2,-0.0,0.0 -NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 -bot_median,-0.4,-0.3,-0.2,-0.1,0.0 +metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,-0.0 +metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,0.0 +metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.0 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-grok-2-1212,-0.4,-0.4,-0.2,-0.1,-0.0 pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 -VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 -metac-claude-3-5-sonnet-latest,-0.4,-0.4,-0.3,-0.2,-0.1 -metac-Llama-3.1,-0.5,-0.4,-0.3,-0.1,-0.1 -metac-exa,-0.5,-0.4,-0.3,-0.2,-0.1 +VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 +metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-o1-preview,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 49d442c..8c1e7a0 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +RPM_bot,-0.5,7.0,-0.1,0.8401626602195374,0.31755163711190787,-0.22911491175620202,2.4469118511449692,0.7,-0.8,0.4131948210081994,0.826390 jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 -RPM_bot,-1.1,7.0,-0.2,0.824531966811415,0.3116437903151381,-0.5234058432057136,2.4469118511449692,0.6,-0.9,0.3097258948590483,0.619452 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 @@ -15,33 +15,33 @@ CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.36553170 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.9,27.4,-0.3,0.7446989876942366,0.14226742863646924,-1.7648756350756885,2.0495406495390753,0.0,-0.5,0.04457614500253557,0.089152 +cookics_bot_TEST,-6.5,27.4,-0.2,0.7478313737485887,0.14286584023204454,-1.6679327769704273,2.0495406495390753,0.1,-0.5,0.053574616968489516,0.107149 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 -GreeneiBot2,-10.4,58.4,-0.2,0.8498829222635632,0.11125990180982864,-1.5979756990286293,2.000831925930035,0.0,-0.4,0.05777205560013113,0.115544 +metac-o1,-10.4,91.1,-0.1,0.9315503207588304,0.09759939627192438,-1.1710037539243623,1.9858289388460384,0.1,-0.3,0.12234246603454144,0.244685 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 +GreeneiBot2,-10.6,58.4,-0.2,0.84933087242601,0.11118763184285871,-1.638405629664946,2.000831925930035,0.0,-0.4,0.053406273914708285,0.106813 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 -metac-o1,-11.5,91.1,-0.1,0.8882269503815736,0.09306036633541931,-1.3604682737460798,1.9858289388460384,0.1,-0.3,0.08853781411471767,0.177076 +bot_median,-11.1,92.1,-0.1,0.8343911715991652,0.08694405375037174,-1.3919418427248071,1.9855502432148115,0.1,-0.3,0.08366450804542999,0.167329 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-perplexity,-11.9,89.1,-0.1,0.9936685898993489,0.10526953628638332,-1.2647310023240792,1.9864049297707018,0.1,-0.3,0.10465157496376706,0.209303 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 +metac-deepseek-r1,-14.1,52.1,-0.3,0.8172087173883323,0.11321764813763505,-2.3937504961816116,2.0053789762011176,-0.0,-0.5,0.01019302014325762,0.020386 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-deepseek-r1,-14.6,52.1,-0.3,0.7315248397695878,0.10134684096084697,-2.7666887863373426,2.0053789762011176,-0.1,-0.5,0.003932133201892011,0.007864 -metac-Gemini-Exp-1206,-15.2,76.5,-0.2,0.9437969359023713,0.1079065594460612,-1.8467741127168467,1.9908217254774627,0.0,-0.4,0.034349204246702666,0.068698 +metac-Gemini-Exp-1206,-14.6,76.5,-0.2,0.9369300827202118,0.1071214557093134,-1.7806582480922164,1.9908217254774627,0.0,-0.4,0.03949550680306326,0.078991 +metac-perplexity,-16.1,89.1,-0.2,1.0694909108673796,0.11330217478335987,-1.5994893543452755,1.9864049297707018,0.0,-0.4,0.05664610517795549,0.113292 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -bot_median,-17.3,92.1,-0.2,0.9191222179799003,0.09577307891459154,-1.9639956837727752,1.9855502432148115,0.0,-0.4,0.02628954496851215,0.052579 -minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 -metac-claude-3-5-sonnet-20240620,-19.5,90.5,-0.2,1.0091380158423626,0.10607823314499117,-2.031064521471562,1.9860719790130024,-0.0,-0.4,0.0226076007974782,0.045215 +minefrac1,-18.5,51.1,-0.4,0.8782230217189723,0.1228554331463025,-2.94542136244705,2.0065449272360034,-0.1,-0.6,0.002440792164293176,0.004882 +metac-claude-3-5-sonnet-20240620,-20.8,90.5,-0.2,0.9854576682401628,0.10358901026916505,-2.2176587156495677,1.9860719790130024,-0.0,-0.4,0.01455504948064986,0.029110 +metac-Llama-3.1,-21.0,89.1,-0.2,1.131903405632652,0.11991417243449026,-1.9667104273244107,1.9864049297707018,0.0,-0.5,0.026181998267921627,0.052364 +metac-claude-3-5-sonnet-latest,-21.7,91.1,-0.2,0.8679924761244506,0.0909403815880937,-2.6147562800776485,1.9858289388460384,-0.1,-0.4,0.005233245635108678,0.010466 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-grok-2-1212,-22.9,91.1,-0.3,1.0488287270766499,0.10988676432631847,-2.2835278472341387,1.9858289388460384,-0.0,-0.5,0.012375199205885952,0.024750 -pgodzinai,-23.9,76.4,-0.3,0.9564523461011735,0.1094250257541138,-2.858685649756527,1.9908489732268309,-0.1,-0.5,0.0027488433046459902,0.005498 +pgodzinai,-23.5,76.4,-0.3,0.9735671748298226,0.11138308522466013,-2.763549748735371,1.9908489732268309,-0.1,-0.5,0.003590727855444895,0.007181 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-claude-3-5-sonnet-latest,-24.4,91.1,-0.3,0.7843146490917536,0.08217337757580902,-3.2658265155495396,1.9858289388460384,-0.1,-0.4,0.0007722051094024979,0.001544 -metac-Llama-3.1,-26.1,89.1,-0.3,0.9987986166118539,0.10581301279218377,-2.7685645488001787,1.9864049297707018,-0.1,-0.5,0.00343170739454993,0.006863 -metac-exa,-26.6,89.1,-0.3,0.8489741653993217,0.08994056732713923,-3.324096943280282,1.9864049297707018,-0.1,-0.5,0.0006469013238867488,0.001294 -InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 -metac-o1-preview,-27.8,91.1,-0.3,0.87743376179017,0.09192955389631036,-3.31497363379348,1.9858289388460384,-0.1,-0.5,0.0006608298367709141,0.001322 -metac-gpt-4o,-30.5,91.1,-0.3,0.9139398799143879,0.09575433395355178,-3.4928274283029523,1.9858289388460384,-0.1,-0.5,0.00037140113373772884,0.000743 +metac-exa,-24.7,89.1,-0.3,0.8121952445686516,0.08604419787326485,-3.2197865951234235,1.9864049297707018,-0.1,-0.4,0.0008985159820669422,0.001797 +metac-o1-preview,-25.5,91.1,-0.3,0.8498877252707713,0.08904352994884641,-3.1492143531875287,1.9858289388460384,-0.1,-0.5,0.0011106007145197491,0.002221 +InstitutPelFutur,-26.9,90.1,-0.3,0.9739711690022733,0.10260858670161008,-2.9043019887843187,1.9861137662360124,-0.1,-0.5,0.0023202343180469525,0.004640 +metac-grok-2-1212,-27.9,91.1,-0.3,1.0054085980592369,0.10533759689680534,-2.9038578245582283,1.9858289388460384,-0.1,-0.5,0.0023176059032990978,0.004635 +metac-gpt-4o,-28.8,91.1,-0.3,0.8198830654548765,0.08589991374463501,-3.67651905388223,1.9858289388460384,-0.1,-0.5,0.0002007468680573961,0.000401 diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 93927be..9e33cc3 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -16,11 +16,11 @@ def calculate_peer_score( forecast: ForecastType, forecast_for_other_users: list[ForecastType], resolution: ResolutionType, - q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, ) -> float: question_type = _determine_question_type(q_type, resolution) resolution = _normalize_resolution(question_type, resolution, range_min, range_max) @@ -44,13 +44,13 @@ def calculate_peer_score( def calculate_baseline_score( forecast: ForecastType, resolution: ResolutionType, - q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, open_upper_bound: bool = False, open_lower_bound: bool = False, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, ) -> float: """ Question type can be infered from resolution type diff --git a/tests/test_scoring.py b/tests/test_scoring.py index b42c719..3b31bf9 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -235,7 +235,7 @@ def test_numeric_baseline_when_perfect_forecast(): index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio - forecast = generate_cdf_with_forecast_at_index(correct_index, 0.999) + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.59) # As of May 3, 2025, 0.59 is max difference between 2 points on a cdf score = calculate_baseline_score( @@ -333,10 +333,18 @@ def test_baseline_score_better_when_closer( range_max: float | None, ): score_closer = calculate_baseline_score( - forecast_closer, resolution, options, range_min, range_max, 1.0 + forecast=forecast_closer, + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, ) score_further = calculate_baseline_score( - forecast_further, resolution, options, range_min, range_max, 1.0 + forecast=forecast_further, + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, ) assert score_closer > score_further @@ -512,6 +520,7 @@ def test_better_forecast_means_better_peer_score( ) for idx, forecast in enumerate(forecasts) ] + assert scores[1] > 0, "The first score should be positive" sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) assert len(scores) == len(set(scores)), "Scores should all be different" assert sorted_indices == list( From ae1eefc9ce684b7ed764d2e206b19bad537f903d Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 08:08:40 -0600 Subject: [PATCH 17/26] Small touchups --- AI_BENCHMARKING_ANALYSIS.ipynb | 4 +--- functions.py | 26 ++++++++++++-------------- refactored_notebook/scoring.py | 6 ++---- tests/test_scoring.py | 3 +-- 4 files changed, 16 insertions(+), 23 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index dc8f1ff..10d1981 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -3391,9 +3391,7 @@ } ], "source": [ - "from functions import *\n", - "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", - "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." + "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)" ] }, { diff --git a/functions.py b/functions.py index 0d6593b..99b0a94 100644 --- a/functions.py +++ b/functions.py @@ -11,9 +11,11 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import (calculate_baseline_score, - calculate_peer_score, - nominal_location_to_cdf_location) +from refactored_notebook.scoring import ( + calculate_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location, +) def extract_forecast(df): @@ -348,8 +350,6 @@ def get_median_forecast_multiple_choice(row, forecasts): def get_median_forecast(row, bots): """ - @Check: - Calculates the median forecast for a given set of bots, handling different question types properly. Args: @@ -378,18 +378,18 @@ def get_median_forecast(row, bots): return np.nan if q_type == "numeric": - forecasts = [f for f in forecasts if isinstance(f, list)] + numeric_forecasts: list[list[float]] = [f for f in forecasts if isinstance(f, list)] - if not forecasts: + if not numeric_forecasts: return np.nan - cdfs_array = np.array(forecasts, dtype=float) - mean_cdf = np.mean(cdfs_array, axis=0) + cdfs_array = np.array(numeric_forecasts, dtype=float) + median_cdf = np.median(cdfs_array, axis=0) - return mean_cdf + return median_cdf elif q_type == "binary": - probs = [] + probs: list[float] = [] for f in forecasts: try: val = float(f) @@ -416,8 +416,6 @@ def get_median_forecast(row, bots): def calculate_weighted_scores(df_bot_team_forecasts, teams): """ - @Check: - Calculates weighted scores for each team based on their forecasts and question weights. Args: @@ -431,7 +429,7 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): for _, row in df_bot_team_forecasts.iterrows(): for team in teams: - # @Check: that the conversion is corret + # @Check: that the row conversion is corret cleaned_row = _prepare_new_row_for_scoring(row, [team]) if _is_unscorable(cleaned_row, [team]): continue diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 9e33cc3..a79a02b 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -125,8 +125,7 @@ def _determine_baseline( # Version 3: # baseline_prob = ( # 1 / 202 - # ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins - # @Check: Should this be either 1, 0.9, or 0.95 based on whether open or closed bounds + # ) # len(pmf) # bins = 201 because of extra appended bin else: raise ValueError("Unknown question type") assert ( @@ -234,8 +233,7 @@ def _numeric_resolution_prob( [lower_bound_prob] + [cdf[i] - cdf[i - 1] for i in range(1, len(cdf))] + [upper_bound_prob] - ) # @Check: is this a correct conversion? - # pmf = np.diff(np.concatenate([[0], cdf])) + ) assert len(pmf) == 202, f"There should be 202 bins, but there are {len(pmf)}" resolution = float(resolution) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 3b31bf9..b9d91de 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -685,9 +685,8 @@ def test_peer_score_weighted( assert score_weighted == pytest.approx(score_unweighted * weight) -# TODO: Test the below +# TODO: Test the below for peer scores # Best score for MC and binary is 996 # Worst score for MC and binary is -996 # Best score for numeric is 408 # Worst score for numeric is -408 -# @Check: Can we even validate this (won't we need infinite other forecasters to get max score?) From 3f4d40e64ec00641d48c8d07bfd1caf7e90a99c5 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 08:14:38 -0600 Subject: [PATCH 18/26] Small touchup --- functions.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/functions.py b/functions.py index 99b0a94..62b8f3b 100644 --- a/functions.py +++ b/functions.py @@ -458,7 +458,8 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): ) team_scores[team] += weighted_score - except (ValueError, TypeError, IndexError): + except (ValueError, TypeError, IndexError) as e: + print(f" >>> Error calculating baseline score for question {row.get('bot_question_id')} — skipping: {e}") # @Check: Does skipping introduce any problems? continue # Be robust to bad/missing data From b2deadb82ff097a9a5f3be7fcaa215c048114914 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 15:46:06 -0600 Subject: [PATCH 19/26] Updated resolution types for numeric tests --- tests/test_scoring.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index b9d91de..ca437bf 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -240,7 +240,7 @@ def test_numeric_baseline_when_perfect_forecast(): score = calculate_baseline_score( forecast=forecast, - resolution=correct_answer, + resolution=float(correct_answer), range_min=0, range_max=range_max, open_upper_bound=False, @@ -259,7 +259,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): score = calculate_baseline_score( forecast=forecast, - resolution=correct_answer, + resolution=float(correct_answer), range_min=0, range_max=range_max, ) @@ -317,7 +317,7 @@ def test_multiple_choice_examples( open_lower_bound=False, open_upper_bound=False, ), - 50, + 50.0, None, -1, 96, @@ -327,7 +327,7 @@ def test_multiple_choice_examples( def test_baseline_score_better_when_closer( forecast_closer: list[float], forecast_further: list[float], - resolution: bool | str | None, + resolution: bool | str | float | None, options: list[str] | None, range_min: float | None, range_max: float | None, From aed12bffd0f0799eaebf0e53d9dba3d864e0459b Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 20:04:02 -0600 Subject: [PATCH 20/26] Fixed option parsing problem, and provided median MC question better --- AI_BENCHMARKING_ANALYSIS.ipynb | 4503 ++++++++++------- functions.py | 33 +- .../bootstrapped_h2h_bot_vs_pros.csv | 46 +- ...ghted_bot_ONLY_peer_leaderboard_t_test.csv | 2 +- .../weighted_bot_peer_leaderboard_t_test.csv | 2 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 42 +- refactored_notebook/scoring.py | 9 +- 7 files changed, 2662 insertions(+), 1975 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 10d1981..d830bc0 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,20 +27,11 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 1, "metadata": { "id": "ISzIoto4hnoG" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# @title Import libraries\n", "%load_ext autoreload\n", @@ -52,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -63,16 +54,350 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1932996/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_3762618/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] + }, + { + "data": { + "text/html": [ + "
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I do this by comparing actual close time to scheduled\n", - "close time in a later cell!\n", + "close time in a later cell! @Check: Do we want to do this now that tournament is closed? Are we still doing this?\n", "\n", "df_pro_bot_resolved_questions: Has pro_question_id, bot_question_id, title, resolution, scheduled_close_time, question_weight\n", "\"\"\"\n", @@ -124,6 +449,7 @@ " on=['title', 'scheduled_close_time'],\n", " how='left'\n", ")\n", + "display_head_and_tail(df_pro_bot_resolved_questions)\n", "\n", "df_pro_bot_resolved_questions['question_weight'] = df_pro_bot_resolved_questions['question_weight_x'].combine_first(df_pro_bot_resolved_questions['question_weight_y'])\n", "df_pro_bot_resolved_questions.drop(['question_weight_x', 'question_weight_y'], axis=1, inplace=True)\n", @@ -134,6 +460,7 @@ "# Cast both question ids to int64\n", "df_pro_bot_resolved_questions['pro_question_id'] = df_pro_bot_resolved_questions['pro_question_id'].astype('Int64')\n", "df_pro_bot_resolved_questions['bot_question_id'] = df_pro_bot_resolved_questions['bot_question_id'].astype('Int64')\n", + "df_pro_bot_resolved_questions['options'] = df_pro_bot_resolved_questions['options'].apply(parse_options_array)\n", "\n", "# Remove df_bot_resolved_questions and df_pro_resolved_questions to make sure you only ever use df_pro_bot_resolved_questions\n", "del df_bot_resolved_questions\n", @@ -142,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -160,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -186,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -207,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -225,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 284, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -238,7 +565,7 @@ " dtype='object')" ] }, - "execution_count": 284, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -284,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -306,7 +633,7 @@ "dtype: object" ] }, - "execution_count": 286, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -349,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 290, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -445,7 +772,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -466,7 +793,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -487,7 +814,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -508,7 +835,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -529,7 +856,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -551,19 +878,12 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "5 31268 darkives \n", - "6 31268 datscilly \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -601,7 +921,7 @@ "6 False " ] }, - "execution_count": 291, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -635,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -648,14 +968,14 @@ " 'metac-grok-2-1212', 'metac-gpt-4o', 'bot_median', 'pgodzinai',\n", " 'metac-exa', 'jkraybill_bot', 'VeritasAI', 'MWG', 'twsummerbot',\n", " 'CatrachoCaster', 'X_bot', 'manticAI', 'annabot', 'minefrac1',\n", - " 'metac-deepseek-r1', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", + " 'metac-deepseek-r1+asknews', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", " 'SynapseSeer', 'RPM_bot', 'cookics_bot_TEST', 'ProfessorSP',\n", " 'wunderplumb', 'CumulativeBot', 'pianobot', 'krm-bot',\n", " 'KevinTestBot', '4Shadower', 'swingswish', 'jonahsingerbot',\n", " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 293, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -667,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -712,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 8.520428\n", - " 3220.892206\n", + " 8.388094\n", + " 3170.867318\n", " 409\n", - " 5.620668\n", - " 1.475108\n", + " 5.494976\n", + " 1.471729\n", " \n", " \n", " 4\n", @@ -752,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.520428 3220.892206 409 5.620668 \n", + "15 bot_median 8.388094 3170.867318 409 5.494976 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.475108 \n", + "15 1.471729 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -876,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 19, "metadata": { "id": "BmAFBHIhK77X" }, @@ -925,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1349,7 +1669,7 @@ " np.int64(35705)}" ] }, - "execution_count": 296, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1370,7 +1690,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 21, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1409,18 +1729,18 @@ " \n", " \n", " 1\n", - " metac-o1\n", - " 8861.959039\n", + " bot_median\n", + " 8997.290873\n", " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 8849.559824\n", + " metac-o1\n", + " 8861.959039\n", " \n", " \n", " 3\n", - " bot_median\n", - " 8766.210698\n", + " metac-o1-preview\n", + " 8849.559824\n", " \n", " \n", " 4\n", @@ -1439,9 +1759,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 metac-o1 8861.959039\n", - "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8766.210698\n", + "1 bot_median 8997.290873\n", + "2 metac-o1 8861.959039\n", + "3 metac-o1-preview 8849.559824\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1547,7 +1867,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1566,7 +1886,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 23, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1611,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3504.379897\n", + " 3538.184052\n", " \n", " \n", " 3\n", @@ -1680,7 +2000,7 @@ " \n", " \n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 614.572462\n", " \n", " \n", @@ -1846,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3504.379897\n", + "2 bot_median 3538.184052\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -1860,7 +2180,7 @@ "13 CumulativeBot 1030.716475\n", "14 pgodzinai 926.081448\n", "15 jkraybill_bot 627.932509\n", - "16 metac-deepseek-r1 614.572462\n", + "16 metac-deepseek-r1+asknews 614.572462\n", "17 question_weight 378.020000\n", "18 metac-exa 265.384263\n", "19 MWG 215.551323\n", @@ -1894,7 +2214,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 299, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1936,7 +2256,7 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1955,7 +2275,7 @@ }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1964,7 +2284,7 @@ }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1985,7 +2305,7 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2034,7 +2354,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -2055,7 +2375,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -2076,7 +2396,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -2097,7 +2417,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -2118,7 +2438,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -2140,19 +2460,12 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "5 31268 darkives \n", - "6 31268 datscilly \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -2190,7 +2503,7 @@ "6 False " ] }, - "execution_count": 303, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2201,7 +2514,7 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 28, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2259,18 +2572,18 @@ " 0\n", " 1.0\n", " multiple_choice\n", - " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", " False\n", " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", - " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", - " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", + " [0.009900990099009901,0.39603960396039606,0.44...\n", + " [0.014925742574257425,0.5137871287128712,0.334...\n", " NaN\n", " NaN\n", " NaN\n", @@ -2289,12 +2602,12 @@ " True\n", " True\n", " ...\n", - 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" type options range_min range_max \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "1 numeric None 60.0 100.0 \n", - "2 binary None NaN NaN \n", - "3 multiple_choice [\"0-4\",\"5-9\",\">9\"] NaN NaN \n", - "4 numeric None 0.0 400.0 \n", + " type options range_min range_max \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] NaN NaN \n", + "1 numeric None 60.0 100.0 \n", + "2 binary None NaN NaN \n", + "3 multiple_choice [0-4, 5-9, >9] NaN NaN \n", + "4 numeric None 0.0 400.0 \n", "\n", " open_upper_bound open_lower_bound ... \\\n", "0 False False ... \n", @@ -2399,68 +2712,47 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", - 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"\n", - " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", - 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"\n", - " pianobot swingswish \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " twsummerbot \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 [0.116,0.42,0.464] \n", - "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0] \n", + " metac-o1 \\\n", + "0 [0.5,0.3,0.15,0.04,0.01] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", - " wunderplumb \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", + " metac-o1-preview \\\n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "\n", + " metac-perplexity minefrac1 \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "2 0.1 NaN \n", + "3 [0.15,0.6,0.25] NaN \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", + "\n", + " mmBot \\\n", + "0 [0.009900990099009901,0.39603960396039606,0.44... \n", + "1 [0.0215944348,0.0218024136,0.0220262706,0.0222... \n", + "2 0.2 \n", + "3 [0.25,0.5,0.25] \n", + "4 [0.0,0.0006552097,0.0013605064,0.0021151815,0.... \n", + "\n", + " pgodzinai pianobot swingswish \\\n", + "0 [0.014925742574257425,0.5137871287128712,0.334... NaN NaN \n", + "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... NaN NaN \n", + "2 0.07 NaN NaN \n", + "3 [0.27499999999999997,0.5125,0.21249999999999997] NaN NaN \n", + "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... NaN NaN \n", + "\n", + " twsummerbot wunderplumb \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 [0.116,0.42,0.464] NaN \n", + "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0... NaN \n", "\n", "[5 rows x 57 columns]" ] @@ -2526,8 +2818,8 @@ " False\n", " False\n", " ...\n", - " 0.95\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2550,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.65\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2574,8 +2866,8 @@ " False\n", " False\n", " ...\n", - " 0.85\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2598,7 +2890,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2622,7 +2914,7 @@ " False\n", " False\n", " ...\n", - " 0.1\n", + " 0.05\n", " 0.05\n", " 0.05\n", " NaN\n", @@ -2654,11 +2946,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.4 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.95 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -2730,7 +3022,7 @@ }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2746,14 +3038,14 @@ " 'cobyj-bot', 'cookics_bot_TEST', 'jkraybill_bot', 'jonahsingerbot',\n", " 'krm-bot', 'laylaps', 'manticAI', 'metac-Gemini-Exp-1206',\n", " 'metac-Llama-3.1', 'metac-claude-3-5-sonnet-20240620',\n", - " 'metac-claude-3-5-sonnet-latest', 'metac-deepseek-r1', 'metac-exa',\n", - " 'metac-gpt-4o', 'metac-grok-2-1212', 'metac-o1', 'metac-o1-preview',\n", - " 'metac-perplexity', 'minefrac1', 'mmBot', 'pgodzinai', 'pianobot',\n", - " 'swingswish', 'twsummerbot', 'wunderplumb'],\n", + " 'metac-claude-3-5-sonnet-latest', 'metac-deepseek-r1+asknews',\n", + " 'metac-exa', 'metac-gpt-4o', 'metac-grok-2-1212', 'metac-o1',\n", + " 'metac-o1-preview', 'metac-perplexity', 'minefrac1', 'mmBot',\n", + " 'pgodzinai', 'pianobot', 'swingswish', 'twsummerbot', 'wunderplumb'],\n", " dtype='object')" ] }, - "execution_count": 305, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2764,7 +3056,7 @@ }, { "cell_type": "code", - "execution_count": 306, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2774,7 +3066,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 306, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2789,7 +3081,7 @@ }, { "cell_type": "code", - "execution_count": 307, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2801,18 +3093,17 @@ }, { "cell_type": "code", - "execution_count": 308, - "metadata": {}, - "outputs": [], - "source": [ - "df_pro_bot_forecasts['options'] = df_pro_bot_forecasts['options'].apply(parse_options_array)" - ] - }, - { - "cell_type": "code", - "execution_count": 309, + "execution_count": 32, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3762618/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" + ] + }, { "data": { "text/html": [ @@ -2871,9 +3162,9 @@ " False\n", " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " 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\n", "\n", " pianobot swingswish \\\n", "0 NaN NaN \n", @@ -3054,12 +3345,12 @@ "3 NaN NaN \n", "4 NaN NaN \n", "\n", - " twsummerbot \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 [0.116,0.42,0.464] \n", - "4 [0.0, 0.001311947, 0.0026238939, 0.0039358409, 0.0052477878, 0.0065597348, 0.0078716817, 0.0091836287, 0.0104955756, 0.0118075226, 0.0131194695, 0.0144314165, 0.0157433634, 0.0170553104, 0.0183672573, 0.0196792043, 0.0209911512, 0.0223030982, 0.0236150451, 0.0249269921, 0.026238939, 0.027550886, 0.0288628329, 0.0301747799, 0.0314867268, 0.0327986738, 0.0341106207, 0.0354225677, 0.0367345146, 0.0380464616, 0.0393584085, 0.0406703555, 0.0419823024, 0.0432942494, 0.0446061963, 0.0459181433, 0.0472300902, 0.0485420372, 0.0498539841, 0.0511659311, 0.052477878, 0.053789825, 0.0551017719, 0.0564137189, 0.0577256658, 0.0590376128, 0.0603495597, 0.0616615067, 0.0629734536, 0.0642854006, 0.0655973475, 0.0669092945, 0.0682212414, 0.0695331884, 0.0708451353, 0.0721570823, 0.0734690292, 0.0747809762, 0.0760929231, 0.0774048701, 0.078716817, 0.080028764, 0.0813407109, 0.0826526579, 0.0839646048, 0.0852765518, 0.0865884987, 0.0879004457, 0.0902457862, 0.0933094828, 0.0978079399, 0.1023063969, 0.1068048539, 0.111303311, 0.115801768, 0.120300225, 0.124798682, 0.1292971391, 0.1338199508, 0.1388055027, 0.1440933779, 0.1496807808, 0.1571177226, 0.1652387403, 0.1753118263, 0.1904276903, 0.2058197291, 0.2212117678, 0.237030829, 0.2551785571, 0.273870758, 0.2925629589, 0.3115548313, 0.3307464845, 0.3499926649, 0.3692260274, 0.3884136416, 0.407661417, 0.4269091924, 0.4457073638, ...] \n", + " twsummerbot \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 [0.116,0.42,0.464] \n", + "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0] \n", "\n", " wunderplumb \n", "0 NaN \n", @@ -3132,8 +3423,8 @@ " False\n", " False\n", " ...\n", - " 0.95\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3156,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.65\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3180,8 +3471,8 @@ " False\n", " False\n", " ...\n", - " 0.85\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3204,7 +3495,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3228,7 +3519,7 @@ " False\n", " False\n", " ...\n", - " 0.1\n", + " 0.05\n", " 0.05\n", " 0.05\n", " NaN\n", @@ -3260,11 +3551,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.4 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.95 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3280,6 +3571,21 @@ "output_type": "display_data" } ], + "source": [ + "multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n", + "if len(multiple_choice_rows_with_empty_options) > 0:\n", + " display_head_and_tail(multiple_choice_rows_with_empty_options)\n", + " raise ValueError(\"Multiple choice questions with empty options found\")\n", + "\n", + "df_pro_bot_forecasts['options'] = df_pro_bot_forecasts['options'].apply(parse_options_array) # @Check: TODO: Refactor/move this (and other times parse_options_array is used) to one central area at beginning cell data normalization should happen together and be availabe at all times in notebook\n", + "display_head_and_tail(df_pro_bot_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], "source": [ "# Simple function to parse CDF strings for numeric questions\n", "def parse_numeric_forecasts(df):\n", @@ -3318,13 +3624,12 @@ " return df\n", "\n", "# Now parse the numeric forecasts\n", - "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)\n", - "display_head_and_tail(df_pro_bot_forecasts)" + "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)\n" ] }, { "cell_type": "code", - "execution_count": 310, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -3396,7 +3701,7 @@ }, { "cell_type": "code", - "execution_count": 311, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -3457,8 +3762,8 @@ " False\n", " False\n", " ...\n", - " 2.343407\n", - " 5.857933\n", + " 2.644992\n", + " 5.703782\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3466,7 +3771,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.010635\n", + " 4.656813\n", " \n", " \n", " 3\n", @@ -3481,8 +3786,8 @@ " None\n", " None\n", " ...\n", - " 0.390198\n", - " 0.022473\n", + " 0.107631\n", + " 0.310155\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -3506,7 +3811,7 @@ " False\n", " ...\n", " 0.298855\n", - " 0.096331\n", + " -0.106610\n", " NaN\n", " -0.184571\n", " 0.112526\n", @@ -3514,7 +3819,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.106610\n", + " -0.576613\n", " \n", " \n", " 9\n", @@ -3529,7 +3834,7 @@ " None\n", " None\n", " ...\n", - " -0.518794\n", + " -0.423484\n", " -1.211941\n", " NaN\n", " -0.806476\n", @@ -3553,7 +3858,7 @@ " None\n", " None\n", " ...\n", - " 0.441833\n", + " 0.575364\n", " 0.287682\n", " 0.021979\n", " 0.200671\n", @@ -3599,16 +3904,16 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.343407 5.857933 NaN 2.292635 2.703087 \n", - "3 0.390198 0.022473 NaN 0.127833 0.152526 \n", - "6 0.298855 0.096331 NaN -0.184571 0.112526 \n", - "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.441833 0.287682 0.021979 0.200671 0.253781 \n", + "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", + "3 0.107631 0.310155 NaN 0.127833 0.152526 \n", + "6 0.298855 -0.106610 NaN -0.184571 0.112526 \n", + "9 -0.423484 -1.211941 NaN -0.806476 -0.494101 \n", + "13 0.575364 0.287682 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "0 NaN NaN NaN NaN 5.010635 \n", + "0 NaN NaN NaN NaN 4.656813 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN -0.106610 \n", + "6 NaN NaN NaN NaN -0.576613 \n", "9 NaN NaN -0.624154 NaN -0.693147 \n", "13 NaN NaN NaN NaN -0.062598 \n", "\n", @@ -3676,16 +3981,16 @@ " False\n", " False\n", " ...\n", - " -3.795489\n", + " -2.879198\n", " -1.780586\n", " -3.007032\n", " -2.879198\n", - " -3.390024\n", + " -3.795489\n", " NaN\n", " NaN\n", " -2.348570\n", " -2.409195\n", - " -3.390024\n", + " -2.879198\n", " \n", " \n", " 82\n", @@ -3701,7 +4006,7 @@ " None\n", " ...\n", " -0.993252\n", - " -0.186776\n", + " 0.000000\n", " -0.523248\n", " 0.105361\n", " 0.259511\n", @@ -3709,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " 0.276509\n", + " -0.993252\n", " \n", " \n", " 83\n", @@ -3725,7 +4030,7 @@ " None\n", " ...\n", " -0.693147\n", - " -0.182322\n", + " -0.693147\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -3748,8 +4053,8 @@ " False\n", " False\n", " ...\n", - " -0.069566\n", - " -0.102356\n", + " -0.048289\n", + " -0.048289\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -3757,7 +4062,7 @@ " -0.113529\n", " NaN\n", " -0.147818\n", - " -0.124829\n", + " -0.121048\n", " \n", " \n", " 92\n", @@ -3772,8 +4077,8 @@ " False\n", " False\n", " ...\n", - " -0.606136\n", - " -4.007333\n", + " -1.704748\n", + " -1.011601\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -3781,7 +4086,7 @@ " -0.480973\n", " NaN\n", " -0.749237\n", - " -0.200671\n", + " -0.318454\n", " \n", " \n", "\n", @@ -3811,25 +4116,25 @@ "92 [0-24, 25-30, 31-49, 50-70, >70] NaN \n", "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", - "81 NaN False False ... -3.795489 \n", + "81 NaN False False ... -2.879198 \n", "82 NaN None None ... -0.993252 \n", "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.069566 \n", - "92 NaN False False ... -0.606136 \n", + "91 NaN False False ... -0.048289 \n", + "92 NaN False False ... -1.704748 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -1.780586 -3.007032 -2.879198 -3.390024 NaN NaN \n", - "82 -0.186776 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", - "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -4.007333 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "81 -1.780586 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "82 0.000000 -0.523248 0.105361 0.259511 NaN NaN \n", + "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", + "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -3.390024 \n", - "82 0.276509 -0.644609 0.276509 \n", + "81 -2.348570 -2.409195 -2.879198 \n", + "82 0.276509 -0.644609 -0.993252 \n", "83 -0.178330 -0.567984 -0.693147 \n", - "91 NaN -0.147818 -0.124829 \n", - "92 NaN -0.749237 -0.200671 \n", + "91 NaN -0.147818 -0.121048 \n", + "92 NaN -0.749237 -0.318454 \n", "\n", "[5 rows x 58 columns]" ] @@ -3895,8 +4200,8 @@ " False\n", " False\n", " ...\n", - " -0.038208\n", - " -0.149434\n", + " -0.092275\n", + " -0.092275\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -3904,7 +4209,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.179287\n", + " -0.149434\n", " \n", " \n", " 5\n", @@ -3928,7 +4233,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.287682\n", + " 0.320472\n", " \n", " \n", " 8\n", @@ -3944,7 +4249,7 @@ " False\n", " ...\n", " -0.054067\n", - " 0.000000\n", + " -0.054067\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -3952,7 +4257,7 @@ " NaN\n", " -0.398124\n", " NaN\n", - " -0.171850\n", + " -0.179379\n", " \n", " \n", " 12\n", @@ -3967,7 +4272,7 @@ " False\n", " False\n", " ...\n", - " -0.182322\n", + " -0.057158\n", " 0.000000\n", " NaN\n", " 0.054067\n", @@ -3992,7 +4297,7 @@ " False\n", " ...\n", " -0.045611\n", - " 0.039547\n", + " -0.045611\n", " NaN\n", " -0.068083\n", " NaN\n", @@ -4023,16 +4328,16 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", + "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", - "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", - "12 -0.182322 0.000000 NaN 0.054067 -0.057158 \n", - "16 -0.045611 0.039547 NaN -0.068083 NaN \n", + "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", + "16 -0.045611 -0.045611 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "2 NaN NaN NaN NaN -0.179287 \n", - "5 NaN NaN NaN NaN 0.287682 \n", - "8 NaN NaN -0.398124 NaN -0.171850 \n", + "2 NaN NaN NaN NaN -0.149434 \n", + "5 NaN NaN NaN NaN 0.320472 \n", + "8 NaN NaN -0.398124 NaN -0.179379 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", @@ -4100,7 +4405,7 @@ " False\n", " False\n", " ...\n", - " -0.054067\n", + " 0.000000\n", " NaN\n", " NaN\n", " 0.000000\n", @@ -4124,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -0.459532\n", + " -2.251292\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4148,7 +4453,7 @@ " False\n", " False\n", " ...\n", - " -0.074901\n", + " -0.020834\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -4228,9 +4533,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -0.459532 NaN NaN -0.111226 NaN \n", - "96 -0.074901 NaN NaN -0.074901 NaN \n", + "94 0.000000 NaN NaN 0.000000 0.000000 \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", "\n", @@ -4256,7 +4561,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -4298,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3504.379897\n", + " 3538.184052\n", " \n", " \n", " 3\n", @@ -4367,7 +4672,7 @@ " \n", " \n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 614.572462\n", " \n", " \n", @@ -4533,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3504.379897\n", + "2 bot_median 3538.184052\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4547,7 +4852,7 @@ "13 CumulativeBot 1030.716475\n", "14 pgodzinai 926.081448\n", "15 jkraybill_bot 627.932509\n", - "16 metac-deepseek-r1 614.572462\n", + "16 metac-deepseek-r1+asknews 614.572462\n", "17 question_weight 378.020000\n", "18 metac-exa 265.384263\n", "19 MWG 215.551323\n", @@ -4581,7 +4886,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 312, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -4592,7 +4897,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -4601,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 69.0%\n", - "mean metac-o1 forecast on questions that resolved no: 30.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", + "mean metac-o1 forecast on questions that resolved no: 27.0%\n" ] }, { 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", 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" ] @@ -4676,14 +4981,14 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1932996/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_3762618/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -4733,7 +5038,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -4746,7 +5051,7 @@ }, { "cell_type": "code", - "execution_count": 316, + "execution_count": 40, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4809,7 +5114,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2456.727963\n", + " 2475.479525\n", " 97\n", " 93.10\n", " \n", @@ -4904,7 +5209,7 @@ " \n", " 15\n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 1518.308625\n", " 55\n", " 52.10\n", @@ -5166,7 +5471,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2456.727963 97 \n", + "3 4 bot_median 2475.479525 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5178,7 +5483,7 @@ "12 13 metac-Gemini-Exp-1206 1595.682612 81 \n", "13 14 NextWorldLab 1583.026226 85 \n", "14 15 metac-o1-preview 1527.657141 96 \n", - "15 16 metac-deepseek-r1 1518.308625 55 \n", + "15 16 metac-deepseek-r1+asknews 1518.308625 55 \n", "16 17 laylaps 1500.567874 68 \n", "17 18 mmBot 1482.726445 97 \n", "18 19 Grizeu_Bot 1399.477718 55 \n", @@ -5261,7 +5566,7 @@ "46 52.10 " ] }, - "execution_count": 316, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -5330,7 +5635,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -5412,17 +5717,17 @@ " \n", " \n", " bot_median\n", - " 2456.7\n", + " 2475.5\n", " 93.1\n", - " 26.4\n", - " 58.198995\n", - " 6.031713\n", - " 4.374886\n", + " 26.6\n", + " 57.595415\n", + " 5.969158\n", + " 4.454476\n", " 1.985277\n", " 38.4\n", - " 14.4\n", - " 0.999984\n", - " 0.000032\n", + " 14.7\n", + " 0.999988\n", + " 0.000024\n", " \n", " \n", " acm_bot\n", @@ -5579,7 +5884,7 @@ " 0.070922\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 1518.3\n", " 52.1\n", " 29.1\n", @@ -6035,7 +6340,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2456.7 93.1 26.4 58.198995 \n", + "bot_median 2475.5 93.1 26.6 57.595415 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6047,7 +6352,7 @@ "metac-Gemini-Exp-1206 1595.7 77.5 20.6 67.099981 \n", "NextWorldLab 1583.0 81.2 19.5 66.411747 \n", "metac-o1-preview 1527.7 92.1 16.6 87.111568 \n", - "metac-deepseek-r1 1518.3 52.1 29.1 62.764970 \n", + "metac-deepseek-r1+asknews 1518.3 52.1 29.1 62.764970 \n", "laylaps 1500.6 65.1 23.1 74.457365 \n", "mmBot 1482.7 93.1 15.9 79.990502 \n", "Grizeu_Bot 1399.5 52.4 26.7 60.886905 \n", @@ -6084,7 +6389,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.031713 4.374886 1.985277 38.4 \n", + "bot_median 5.969158 4.454476 1.985277 38.4 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6096,7 +6401,7 @@ "metac-Gemini-Exp-1206 7.622046 2.701303 1.990426 35.8 \n", "NextWorldLab 7.367722 2.644427 1.988985 34.1 \n", "metac-o1-preview 9.077077 1.827344 1.985550 34.6 \n", - "metac-deepseek-r1 8.695578 3.351382 2.005379 46.6 \n", + "metac-deepseek-r1+asknews 8.695578 3.351382 2.005379 46.6 \n", "laylaps 9.228204 2.497799 1.996341 41.5 \n", "mmBot 8.290173 1.921090 1.985277 32.4 \n", "Grizeu_Bot 8.415222 3.176755 2.005555 43.6 \n", @@ -6133,7 +6438,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.4 0.999984 0.000032 \n", + "bot_median 14.7 0.999988 0.000024 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6145,7 +6450,7 @@ "metac-Gemini-Exp-1206 5.4 0.995749 0.008502 \n", "NextWorldLab 4.8 0.995080 0.009840 \n", "metac-o1-preview -1.4 0.964539 0.070922 \n", - "metac-deepseek-r1 11.7 0.999241 0.001519 \n", + "metac-deepseek-r1+asknews 11.7 0.999241 0.001519 \n", "laylaps 4.6 0.992463 0.015074 \n", "mmBot -0.5 0.971093 0.057813 \n", "Grizeu_Bot 9.9 0.998740 0.002521 \n", @@ -6179,7 +6484,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 317, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -6195,7 +6500,7 @@ }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6268,18 +6573,18 @@ " NA\n", " \n", " \n", - " RPM_bot\n", - " -0.5\n", - " 7.0\n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", " -0.1\n", - " 0.840163\n", - " 0.317552\n", - " -0.229115\n", - " 2.446912\n", - " 0.7\n", - " -0.8\n", - " 0.413195\n", - " 0.826390\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", " \n", " \n", " jonahsingerbot\n", @@ -6296,20 +6601,6 @@ " 0.007677\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", - " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", - " \n", - " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6352,6 +6643,20 @@ " 0.018953\n", " \n", " \n", + " RPM_bot\n", + " -1.3\n", + " 7.0\n", + " -0.2\n", + " 0.826978\n", + " 0.312568\n", + " -0.610596\n", + " 2.446912\n", + " 0.6\n", + " -1.0\n", + " 0.281933\n", + " 0.563865\n", + " \n", + " \n", " SynapseSeer\n", " -1.3\n", " 26.2\n", @@ -6436,18 +6741,32 @@ " 0.011127\n", " \n", " \n", + " metac-o1\n", + " -5.3\n", + " 91.1\n", + " -0.1\n", + " 0.908473\n", + " 0.095182\n", + " -0.611363\n", + " 1.985829\n", + " 0.1\n", + " -0.2\n", + " 0.271249\n", + " 0.542499\n", + " \n", + " \n", " annabot\n", - " -6.2\n", + " -5.9\n", " 29.3\n", " -0.2\n", - " 0.520869\n", - " 0.096226\n", - " -2.211795\n", + " 0.517575\n", + " 0.095618\n", + " -2.112203\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.017610\n", - " 0.035221\n", + " 0.021811\n", + " 0.043621\n", " \n", " \n", " 4Shadower\n", @@ -6465,17 +6784,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.5\n", + " -6.8\n", " 27.4\n", " -0.2\n", - " 0.747831\n", - " 0.142866\n", - " -1.667933\n", + " 0.747290\n", + " 0.142762\n", + " -1.737830\n", " 2.049541\n", - " 0.1\n", + " 0.0\n", " -0.5\n", - " 0.053575\n", - " 0.107149\n", + " 0.046947\n", + " 0.093894\n", " \n", " \n", " jkraybill_bot\n", @@ -6507,17 +6826,17 @@ " \n", " \n", " MWG\n", - " -9.8\n", + " -9.6\n", " 28.6\n", " -0.3\n", - " 0.705240\n", - " 0.131872\n", - " -2.589625\n", + " 0.711160\n", + " 0.132979\n", + " -2.535384\n", " 2.046561\n", " -0.1\n", " -0.6\n", - " 0.007581\n", - " 0.015163\n", + " 0.008595\n", + " 0.017191\n", " \n", " \n", " ProfessorSP\n", @@ -6534,20 +6853,6 @@ " 0.023289\n", " \n", " \n", - " metac-o1\n", - " -10.4\n", - " 91.1\n", - " -0.1\n", - " 0.931550\n", - " 0.097599\n", - " -1.171004\n", - " 1.985829\n", - " 0.1\n", - " -0.3\n", - " 0.122342\n", - " 0.244685\n", - " \n", - " \n", " acm_bot\n", " -10.5\n", " 80.2\n", @@ -6568,12 +6873,12 @@ " -0.2\n", " 0.849331\n", " 0.111188\n", - " -1.638406\n", + " -1.638794\n", " 2.000832\n", " 0.0\n", " -0.4\n", - " 0.053406\n", - " 0.106813\n", + " 0.053366\n", + " 0.106731\n", " \n", " \n", " ajf-bot\n", @@ -6590,20 +6895,6 @@ " 0.094289\n", " \n", " \n", - " bot_median\n", - " -11.1\n", - " 92.1\n", - " -0.1\n", - " 0.834391\n", - " 0.086944\n", - " -1.391942\n", - " 1.985550\n", - " 0.1\n", - " -0.3\n", - " 0.083665\n", - " 0.167329\n", - " \n", - " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6618,6 +6909,20 @@ " 0.023810\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -11.7\n", + " 52.1\n", + " -0.2\n", + " 0.669031\n", + " 0.092689\n", + " -2.432744\n", + " 2.005379\n", + " -0.0\n", + " -0.4\n", + " 0.009262\n", + " 0.018524\n", + " \n", + " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6646,60 +6951,46 @@ " 0.006348\n", " \n", " \n", - " metac-deepseek-r1\n", - " -14.1\n", - " 52.1\n", - " -0.3\n", - " 0.817209\n", - " 0.113218\n", - " -2.393750\n", - " 2.005379\n", - " -0.0\n", - " -0.5\n", - " 0.010193\n", - " 0.020386\n", - " \n", - " \n", - " manticAI\n", - " -14.6\n", - " 69.4\n", + " metac-perplexity\n", + " -13.6\n", + " 89.1\n", " -0.2\n", - " 0.670946\n", - " 0.080510\n", - " -2.613354\n", - " 1.993968\n", - " -0.0\n", + " 0.953801\n", + " 0.101046\n", + " -1.515249\n", + " 1.986405\n", + " 0.0\n", " -0.4\n", - " 0.005507\n", - " 0.011014\n", + " 0.066645\n", + " 0.133289\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " -14.6\n", + " -13.9\n", " 76.5\n", " -0.2\n", - " 0.936930\n", - " 0.107121\n", - " -1.780658\n", + " 0.960843\n", + " 0.109855\n", + " -1.650953\n", " 1.990822\n", " 0.0\n", " -0.4\n", - " 0.039496\n", - " 0.078991\n", + " 0.051451\n", + " 0.102902\n", " \n", " \n", - " metac-perplexity\n", - " -16.1\n", - " 89.1\n", + " manticAI\n", + " -14.6\n", + " 69.4\n", " -0.2\n", - " 1.069491\n", - " 0.113302\n", - " -1.599489\n", - " 1.986405\n", - " 0.0\n", + " 0.670946\n", + " 0.080510\n", + " -2.613354\n", + " 1.993968\n", + " -0.0\n", " -0.4\n", - " 0.056646\n", - " 0.113292\n", + " 0.005507\n", + " 0.011014\n", " \n", " \n", " NextWorldLab\n", @@ -6716,60 +7007,74 @@ " 0.040909\n", " \n", " \n", - " minefrac1\n", - " -18.5\n", - " 51.1\n", + " metac-claude-3-5-sonnet-latest\n", + " -17.7\n", + " 91.1\n", + " -0.2\n", + " 0.822269\n", + " 0.086150\n", + " -2.253410\n", + " 1.985829\n", + " -0.0\n", " -0.4\n", - " 0.878223\n", - " 0.122855\n", - " -2.945421\n", - " 2.006545\n", - " -0.1\n", - " -0.6\n", - " 0.002441\n", - " 0.004882\n", + " 0.013330\n", + " 0.026660\n", + " \n", + " \n", + " bot_median\n", + " -17.9\n", + " 92.1\n", + " -0.2\n", + " 0.829829\n", + " 0.086469\n", + " -2.248076\n", + " 1.985550\n", + " -0.0\n", + " -0.4\n", + " 0.013492\n", + " 0.026984\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -20.8\n", + " -18.2\n", " 90.5\n", " -0.2\n", - " 0.985458\n", - " 0.103589\n", - " -2.217659\n", + " 0.988222\n", + " 0.103880\n", + " -1.930829\n", " 1.986072\n", - " -0.0\n", + " 0.0\n", + " -0.4\n", + " 0.028335\n", + " 0.056670\n", + " \n", + " \n", + " minefrac1\n", + " -18.8\n", + " 51.1\n", " -0.4\n", - " 0.014555\n", - " 0.029110\n", + " 0.874752\n", + " 0.122370\n", + " -3.013581\n", + " 2.006545\n", + " -0.1\n", + " -0.6\n", + " 0.002021\n", + " 0.004043\n", " \n", " \n", " metac-Llama-3.1\n", - " -21.0\n", + " -21.3\n", " 89.1\n", " -0.2\n", - " 1.131903\n", - " 0.119914\n", - " -1.966710\n", + " 0.912804\n", + " 0.096703\n", + " -2.471743\n", " 1.986405\n", - " 0.0\n", - " -0.5\n", - " 0.026182\n", - " 0.052364\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-latest\n", - " -21.7\n", - " 91.1\n", - " -0.2\n", - " 0.867992\n", - " 0.090940\n", - " -2.614756\n", - " 1.985829\n", - " -0.1\n", + " -0.0\n", " -0.4\n", - " 0.005233\n", - " 0.010466\n", + " 0.007684\n", + " 0.015368\n", " \n", " \n", " mmBot\n", @@ -6786,18 +7091,32 @@ " 0.002208\n", " \n", " \n", + " metac-exa\n", + " -22.4\n", + " 89.1\n", + " -0.3\n", + " 0.812802\n", + " 0.086108\n", + " -2.923729\n", + " 1.986405\n", + " -0.1\n", + " -0.4\n", + " 0.002198\n", + " 0.004396\n", + " \n", + " \n", " pgodzinai\n", - " -23.5\n", + " -23.9\n", " 76.4\n", " -0.3\n", - " 0.973567\n", - " 0.111383\n", - " -2.763550\n", + " 0.991479\n", + " 0.113432\n", + " -2.755452\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003591\n", - " 0.007181\n", + " 0.003672\n", + " 0.007345\n", " \n", " \n", " VeritasAI\n", @@ -6814,74 +7133,60 @@ " 0.000076\n", " \n", " \n", - " metac-exa\n", - " -24.7\n", - " 89.1\n", - " -0.3\n", - " 0.812195\n", - " 0.086044\n", - " -3.219787\n", - " 1.986405\n", - " -0.1\n", - " -0.4\n", - " 0.000899\n", - " 0.001797\n", - " \n", - " \n", - " metac-o1-preview\n", - " -25.5\n", + " metac-grok-2-1212\n", + " -24.5\n", " 91.1\n", " -0.3\n", - " 0.849888\n", - " 0.089044\n", - " -3.149214\n", + " 1.013996\n", + " 0.106237\n", + " -2.526844\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.001111\n", - " 0.002221\n", + " 0.006627\n", + " 0.013254\n", " \n", " \n", - " InstitutPelFutur\n", - " -26.9\n", - " 90.1\n", + " metac-gpt-4o\n", + " -26.0\n", + " 91.1\n", " -0.3\n", - " 0.973971\n", - " 0.102609\n", - " -2.904302\n", - " 1.986114\n", + " 0.851645\n", + " 0.089228\n", + " -3.193010\n", + " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.002320\n", - " 0.004640\n", + " 0.000970\n", + " 0.001940\n", " \n", " \n", - " metac-grok-2-1212\n", - " -27.9\n", + " metac-o1-preview\n", + " -26.2\n", " 91.1\n", " -0.3\n", - " 1.005409\n", - " 0.105338\n", - " -2.903858\n", + " 0.914333\n", + " 0.095796\n", + " -2.997048\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.002318\n", - " 0.004635\n", + " 0.001761\n", + " 0.003522\n", " \n", " \n", - " metac-gpt-4o\n", - " -28.8\n", - " 91.1\n", + " InstitutPelFutur\n", + " -26.9\n", + " 90.1\n", " -0.3\n", - " 0.819883\n", - " 0.085900\n", - " -3.676519\n", - " 1.985829\n", + " 0.973767\n", + " 0.102587\n", + " -2.908524\n", + " 1.986114\n", " -0.1\n", " -0.5\n", - " 0.000201\n", - " 0.000401\n", + " 0.002292\n", + " 0.004584\n", " \n", " \n", "\n", @@ -6891,149 +7196,149 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "RPM_bot -0.5 7.0 -0.1 0.840163 0.317552 \n", - "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", + "RPM_bot -1.3 7.0 -0.2 0.826978 0.312568 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", + "metac-o1 -5.3 91.1 -0.1 0.908473 0.095182 \n", + "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.5 27.4 -0.2 0.747831 0.142866 \n", + "cookics_bot_TEST -6.8 27.4 -0.2 0.747290 0.142762 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", - "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", + "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", - "metac-o1 -10.4 91.1 -0.1 0.931550 0.097599 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", "GreeneiBot2 -10.6 58.4 -0.2 0.849331 0.111188 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", - "bot_median -11.1 92.1 -0.1 0.834391 0.086944 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", + "metac-deepseek-r1+asknews -11.7 52.1 -0.2 0.669031 0.092689 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "metac-deepseek-r1 -14.1 52.1 -0.3 0.817209 0.113218 \n", + "metac-perplexity -13.6 89.1 -0.2 0.953801 0.101046 \n", + "metac-Gemini-Exp-1206 -13.9 76.5 -0.2 0.960843 0.109855 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-Gemini-Exp-1206 -14.6 76.5 -0.2 0.936930 0.107121 \n", - "metac-perplexity -16.1 89.1 -0.2 1.069491 0.113302 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "minefrac1 -18.5 51.1 -0.4 0.878223 0.122855 \n", - "metac-claude-3-5-sonnet-20240620 -20.8 90.5 -0.2 0.985458 0.103589 \n", - "metac-Llama-3.1 -21.0 89.1 -0.2 1.131903 0.119914 \n", - "metac-claude-3-5-sonnet-latest -21.7 91.1 -0.2 0.867992 0.090940 \n", + "metac-claude-3-5-sonnet-latest -17.7 91.1 -0.2 0.822269 0.086150 \n", + "bot_median -17.9 92.1 -0.2 0.829829 0.086469 \n", + "metac-claude-3-5-sonnet-20240620 -18.2 90.5 -0.2 0.988222 0.103880 \n", + "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", + "metac-Llama-3.1 -21.3 89.1 -0.2 0.912804 0.096703 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "pgodzinai -23.5 76.4 -0.3 0.973567 0.111383 \n", + "metac-exa -22.4 89.1 -0.3 0.812802 0.086108 \n", + "pgodzinai -23.9 76.4 -0.3 0.991479 0.113432 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-exa -24.7 89.1 -0.3 0.812195 0.086044 \n", - "metac-o1-preview -25.5 91.1 -0.3 0.849888 0.089044 \n", - "InstitutPelFutur -26.9 90.1 -0.3 0.973971 0.102609 \n", - "metac-grok-2-1212 -27.9 91.1 -0.3 1.005409 0.105338 \n", - "metac-gpt-4o -28.8 91.1 -0.3 0.819883 0.085900 \n", + "metac-grok-2-1212 -24.5 91.1 -0.3 1.013996 0.106237 \n", + "metac-gpt-4o -26.0 91.1 -0.3 0.851645 0.089228 \n", + "metac-o1-preview -26.2 91.1 -0.3 0.914333 0.095796 \n", + "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "RPM_bot -0.229115 2.446912 0.7 \n", - "jonahsingerbot -5.273630 2.784843 -0.1 \n", "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", + "RPM_bot -0.610596 2.446912 0.6 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "annabot -2.211795 2.044183 -0.0 \n", + "metac-o1 -0.611363 1.985829 0.1 \n", + "annabot -2.112203 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.667933 2.049541 0.1 \n", + "cookics_bot_TEST -1.737830 2.049541 0.0 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", - "MWG -2.589625 2.046561 -0.1 \n", + "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", - "metac-o1 -1.171004 1.985829 0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.638406 2.000832 0.0 \n", + "GreeneiBot2 -1.638794 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", - "bot_median -1.391942 1.985550 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", + "metac-deepseek-r1+asknews -2.432744 2.005379 -0.0 \n", "laylaps -2.440461 1.996907 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "metac-deepseek-r1 -2.393750 2.005379 -0.0 \n", + "metac-perplexity -1.515249 1.986405 0.0 \n", + "metac-Gemini-Exp-1206 -1.650953 1.990822 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-Gemini-Exp-1206 -1.780658 1.990822 0.0 \n", - "metac-perplexity -1.599489 1.986405 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "minefrac1 -2.945421 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-20240620 -2.217659 1.986072 -0.0 \n", - "metac-Llama-3.1 -1.966710 1.986405 0.0 \n", - "metac-claude-3-5-sonnet-latest -2.614756 1.985829 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.253410 1.985829 -0.0 \n", + "bot_median -2.248076 1.985550 -0.0 \n", + "metac-claude-3-5-sonnet-20240620 -1.930829 1.986072 0.0 \n", + "minefrac1 -3.013581 2.006545 -0.1 \n", + "metac-Llama-3.1 -2.471743 1.986405 -0.0 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "pgodzinai -2.763550 1.990849 -0.1 \n", + "metac-exa -2.923729 1.986405 -0.1 \n", + "pgodzinai -2.755452 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-exa -3.219787 1.986405 -0.1 \n", - "metac-o1-preview -3.149214 1.985829 -0.1 \n", - "InstitutPelFutur -2.904302 1.986114 -0.1 \n", - "metac-grok-2-1212 -2.903858 1.985829 -0.1 \n", - "metac-gpt-4o -3.676519 1.985829 -0.1 \n", + "metac-grok-2-1212 -2.526844 1.985829 -0.1 \n", + "metac-gpt-4o -3.193010 1.985829 -0.1 \n", + "metac-o1-preview -2.997048 1.985829 -0.1 \n", + "InstitutPelFutur -2.908524 1.986114 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "RPM_bot -0.8 0.413195 0.826390 \n", - "jonahsingerbot -0.2 0.003839 0.007677 \n", "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", + "RPM_bot -1.0 0.281933 0.563865 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "annabot -0.4 0.017610 0.035221 \n", + "metac-o1 -0.2 0.271249 0.542499 \n", + "annabot -0.4 0.021811 0.043621 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.053575 0.107149 \n", + "cookics_bot_TEST -0.5 0.046947 0.093894 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", - "MWG -0.6 0.007581 0.015163 \n", + "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", - "metac-o1 -0.3 0.122342 0.244685 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.053406 0.106813 \n", + "GreeneiBot2 -0.4 0.053366 0.106731 \n", "ajf-bot -0.7 0.047145 0.094289 \n", - "bot_median -0.3 0.083665 0.167329 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", + "metac-deepseek-r1+asknews -0.4 0.009262 0.018524 \n", "laylaps -0.4 0.008744 0.017488 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "metac-deepseek-r1 -0.5 0.010193 0.020386 \n", + "metac-perplexity -0.4 0.066645 0.133289 \n", + "metac-Gemini-Exp-1206 -0.4 0.051451 0.102902 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-Gemini-Exp-1206 -0.4 0.039496 0.078991 \n", - "metac-perplexity -0.4 0.056646 0.113292 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "minefrac1 -0.6 0.002441 0.004882 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.014555 0.029110 \n", - "metac-Llama-3.1 -0.5 0.026182 0.052364 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.005233 0.010466 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.013330 0.026660 \n", + "bot_median -0.4 0.013492 0.026984 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.028335 0.056670 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-Llama-3.1 -0.4 0.007684 0.015368 \n", "mmBot -0.4 0.001104 0.002208 \n", - "pgodzinai -0.5 0.003591 0.007181 \n", + "metac-exa -0.4 0.002198 0.004396 \n", + "pgodzinai -0.5 0.003672 0.007345 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-exa -0.4 0.000899 0.001797 \n", - "metac-o1-preview -0.5 0.001111 0.002221 \n", - "InstitutPelFutur -0.5 0.002320 0.004640 \n", - "metac-grok-2-1212 -0.5 0.002318 0.004635 \n", - "metac-gpt-4o -0.5 0.000201 0.000401 " + "metac-grok-2-1212 -0.5 0.006627 0.013254 \n", + "metac-gpt-4o -0.5 0.000970 0.001940 \n", + "metac-o1-preview -0.5 0.001761 0.003522 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 " ] }, - "execution_count": 318, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -7059,7 +7364,7 @@ }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -7069,7 +7374,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 44, "metadata": { "cellView": "form", "colab": { @@ -7311,7 +7616,7 @@ " \n", " 12\n", " 13\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 516.8\n", " 277.9\n", " 1.9\n", @@ -7854,7 +8159,7 @@ "9 10 metac-claude-3-5-sonnet-latest 951.3 370.3 2.6 \n", "10 11 GreeneiBot2 1494.7 264.1 5.7 \n", "11 12 metac-perplexity 1558.4 354.4 4.4 \n", - "12 13 metac-deepseek-r1 516.8 277.9 1.9 \n", + "12 13 metac-deepseek-r1+asknews 516.8 277.9 1.9 \n", "13 14 pgodzinai 1106.7 325.4 3.4 \n", "14 15 metac-exa 599.9 365.3 1.6 \n", "15 16 MWG 253.8 113.4 2.2 \n", @@ -7983,7 +8288,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 320, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -8022,7 +8327,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -8032,7 +8337,7 @@ }, { "cell_type": "code", - "execution_count": 322, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -8237,7 +8542,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 322, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -8248,7 +8553,7 @@ }, { "cell_type": "code", - "execution_count": 323, + "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8310,7 +8615,7 @@ }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -8732,7 +9037,7 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 49, "metadata": { "cellView": "form", "colab": { @@ -8782,139 +9087,139 @@ " \n", " \n", " metac-o1\n", - " 6.1\n", + " 6.0\n", " 7.2\n", - " 9.6\n", - " 11.9\n", - " 13.1\n", + " 9.5\n", + " 11.8\n", + " 12.8\n", " \n", " \n", " metac-o1-preview\n", - " 3.7\n", - " 5.3\n", - " 8.3\n", - " 11.3\n", - " 12.7\n", + " 3.8\n", + " 5.2\n", + " 8.2\n", + " 11.1\n", + " 12.6\n", " \n", " \n", " manticAI\n", - " 0.0\n", + " 0.5\n", " 2.2\n", - " 5.7\n", + " 5.6\n", " 8.9\n", - " 10.6\n", + " 10.5\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.6\n", - " 2.2\n", - " 4.9\n", - " 7.8\n", - " 9.3\n", + " 0.7\n", + " 2.1\n", + " 4.8\n", + " 7.5\n", + " 8.9\n", " \n", " \n", " acm_bot\n", " 0.1\n", - " 1.7\n", - " 4.7\n", + " 1.8\n", + " 4.6\n", " 7.6\n", - " 8.8\n", + " 8.9\n", " \n", " \n", " metac-perplexity\n", - " -1.6\n", - " 0.2\n", + " -1.5\n", + " 0.5\n", " 4.2\n", - " 7.9\n", - " 9.5\n", + " 7.7\n", + " 9.3\n", " \n", " \n", " GreeneiBot2\n", - " -1.4\n", - " 0.6\n", - " 4.0\n", - " 7.3\n", - " 9.0\n", + " -1.2\n", + " 0.7\n", + " 4.1\n", + " 7.4\n", + " 9.7\n", " \n", " \n", " twsummerbot\n", " 0.3\n", - " 1.6\n", - " 3.7\n", - " 6.2\n", - " 7.4\n", + " 1.5\n", + " 3.8\n", + " 6.1\n", + " 7.5\n", " \n", " \n", " pgodzinai\n", - " -3.8\n", + " -2.9\n", " -1.0\n", " 3.1\n", - " 7.1\n", + " 7.2\n", " 9.4\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.3\n", - " 1.0\n", - " 3.1\n", + " -0.0\n", + " 1.1\n", + " 3.0\n", " 5.0\n", " 6.1\n", " \n", " \n", " CumulativeBot\n", - " -0.2\n", + " -0.1\n", " 0.8\n", - " 2.6\n", - " 4.4\n", + " 2.7\n", + " 4.5\n", " 5.4\n", " \n", " \n", " SynapseSeer\n", " 0.4\n", - " 1.1\n", + " 1.2\n", " 2.5\n", - " 4.1\n", - " 4.9\n", + " 4.0\n", + " 4.8\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.4\n", - " 0.1\n", - " 2.4\n", + " -1.3\n", + " -0.1\n", + " 2.5\n", " 4.9\n", - " 6.1\n", + " 6.3\n", + " \n", + " \n", + " metac-exa\n", + " -5.0\n", + " -2.6\n", + " 2.0\n", + " 5.8\n", + " 7.8\n", " \n", " \n", " jkraybill_bot\n", - " -3.4\n", + " -4.3\n", " -1.7\n", - " 1.8\n", + " 1.7\n", " 4.9\n", - " 6.2\n", - " \n", - " \n", - " metac-exa\n", - " -4.6\n", - " -2.3\n", - " 1.6\n", - " 5.5\n", - " 7.7\n", + " 6.6\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " -2.0\n", " -0.8\n", " 1.3\n", - " 3.4\n", - " 4.4\n", + " 3.3\n", + " 4.5\n", " \n", " \n", " MWG\n", - " -1.7\n", - " -0.8\n", - " 0.7\n", - " 2.1\n", - " 2.9\n", + " -1.5\n", + " -0.7\n", + " 0.8\n", + " 2.2\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", @@ -8925,17 +9230,9 @@ " 0.9\n", " \n", " \n", - " cobyj-bot\n", - " -1.4\n", - " -0.9\n", - " -0.0\n", - " 0.9\n", - " 1.4\n", - " \n", - " \n", " X_bot\n", " -0.4\n", - " -0.2\n", + " -0.3\n", " -0.0\n", " 0.1\n", " 0.2\n", @@ -8943,202 +9240,210 @@ " \n", " pianobot\n", " -1.3\n", - " -0.8\n", + " -0.9\n", " -0.0\n", " 0.7\n", " 1.1\n", " \n", " \n", + " cobyj-bot\n", + " -1.3\n", + " -0.9\n", + " -0.1\n", + " 0.8\n", + " 1.4\n", + " \n", + " \n", " annabot\n", - " -3.5\n", - " -2.4\n", - " -0.5\n", - " 1.1\n", + " -3.9\n", + " -2.5\n", + " -0.4\n", + " 1.3\n", " 2.0\n", " \n", " \n", - " bean_bot\n", - " -2.9\n", - " -2.1\n", + " KevinTestBot\n", + " -4.0\n", + " -2.7\n", " -0.5\n", - " 1.3\n", - " 2.1\n", + " 1.6\n", + " 2.7\n", " \n", " \n", - " KevinTestBot\n", - " -4.0\n", - " -2.6\n", + " bean_bot\n", + " -3.3\n", + " -2.2\n", " -0.5\n", - " 1.5\n", - " 2.6\n", + " 0.9\n", + " 1.7\n", " \n", " \n", " CatrachoCaster\n", - " -2.2\n", - " -1.7\n", - " -0.8\n", + " -2.3\n", + " -1.8\n", + " -0.7\n", " 0.2\n", - " 0.7\n", + " 0.6\n", " \n", " \n", " jonahsingerbot\n", - " -2.8\n", - " -2.3\n", - " -0.8\n", - " 0.5\n", - " 1.2\n", + " -2.9\n", + " -2.2\n", + " -0.9\n", + " 0.4\n", + " 0.9\n", " \n", " \n", " krm-bot\n", - " -3.4\n", - " -2.5\n", - " -1.0\n", - " 0.8\n", - " 1.6\n", + " -3.6\n", + " -2.6\n", + " -0.9\n", + " 0.7\n", + " 1.7\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", - " -3.3\n", - " -1.0\n", + " -4.2\n", + " -3.2\n", + " -1.1\n", " 1.0\n", - " 1.9\n", + " 2.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " -6.4\n", - " -4.9\n", - " -1.6\n", - " 1.8\n", - " 3.1\n", + " mmBot\n", + " -7.0\n", + " -5.2\n", + " -1.2\n", + " 2.3\n", + " 4.4\n", " \n", " \n", - " mmBot\n", - " -7.3\n", - " -5.5\n", - " -1.6\n", - " 2.2\n", - " 3.9\n", + " metac-grok-2-1212\n", + " -6.6\n", + " -5.0\n", + " -1.5\n", + " 1.7\n", + " 3.7\n", " \n", " \n", " 4Shadower\n", - " -5.0\n", - " -3.8\n", + " -4.6\n", + " -3.6\n", " -1.7\n", " 0.2\n", " 1.2\n", " \n", " \n", " swingswish\n", - " -5.4\n", - " -4.2\n", - " -2.0\n", - " -0.1\n", - " 0.9\n", + " -5.3\n", + " -3.9\n", + " -1.9\n", + " -0.2\n", + " 0.6\n", + " \n", + " \n", + " InstitutPelFutur\n", + " -8.7\n", + " -6.6\n", + " -2.1\n", + " 1.7\n", + " 4.0\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", - " -3.9\n", - " -2.0\n", + " -4.6\n", + " -3.7\n", + " -2.1\n", " -0.7\n", - " -0.1\n", + " -0.0\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -4.8\n", - " -2.0\n", - " 0.8\n", - " 2.7\n", - " \n", - " \n", - " InstitutPelFutur\n", - " -9.2\n", - " -6.7\n", + " -6.6\n", + " -5.0\n", " -2.2\n", - " 1.6\n", - " 4.0\n", + " 0.7\n", + " 2.4\n", " \n", " \n", " wunderplumb\n", - " -6.5\n", - " -5.1\n", + " -6.4\n", + " -5.0\n", " -2.6\n", - " -0.2\n", - " 0.7\n", + " -0.4\n", + " 0.8\n", " \n", " \n", " metac-Llama-3.1\n", " -6.9\n", - " -5.3\n", - " -2.7\n", - " -0.1\n", - " 1.4\n", + " -5.5\n", + " -2.8\n", + " -0.0\n", + " 1.7\n", " \n", " \n", " NextWorldLab\n", - " -8.6\n", - " -6.7\n", + " -8.8\n", + " -6.8\n", " -3.6\n", - " -0.6\n", - " 1.0\n", + " -0.4\n", + " 1.8\n", " \n", " \n", - " Bot_Pepa\n", - " -7.0\n", - " -5.9\n", + " laylaps\n", + " -9.6\n", + " -7.8\n", " -3.8\n", - " -1.9\n", - " -1.0\n", + " -0.2\n", + " 1.4\n", " \n", " \n", - " laylaps\n", - " -9.7\n", - " -7.7\n", - " -4.0\n", - " -0.1\n", - " 2.2\n", + " Bot_Pepa\n", + " -7.1\n", + " -6.0\n", + " -3.9\n", + " -2.1\n", + " -1.2\n", " \n", " \n", " VeritasAI\n", - " -7.7\n", - " -6.6\n", - " -4.2\n", - " -1.8\n", - " -0.5\n", + " -7.5\n", + " -6.5\n", + " -4.3\n", + " -1.9\n", + " -0.8\n", " \n", " \n", " minefrac1\n", - " -7.9\n", - " -6.8\n", + " -7.6\n", + " -6.7\n", " -4.6\n", " -2.5\n", - " -1.7\n", + " -1.6\n", " \n", " \n", " Grizeu_Bot\n", - " -9.0\n", - " -7.6\n", + " -9.2\n", + " -7.9\n", " -5.0\n", - " -2.2\n", - " -0.6\n", + " -2.5\n", + " -1.2\n", " \n", " \n", " metac-gpt-4o\n", - " -10.6\n", - " -8.9\n", + " -10.7\n", + " -9.0\n", " -6.0\n", - " -2.9\n", - " -1.6\n", + " -3.2\n", + " -1.8\n", " \n", " \n", " ajf-bot\n", - " -14.6\n", - " -12.6\n", + " -15.7\n", + " -12.9\n", " -8.5\n", - " -4.4\n", - " -2.4\n", + " -4.2\n", + " -2.0\n", " \n", " \n", "\n", @@ -9146,54 +9451,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.1 7.2 9.6 11.9 13.1\n", - "metac-o1-preview 3.7 5.3 8.3 11.3 12.7\n", - "manticAI 0.0 2.2 5.7 8.9 10.6\n", - "metac-Gemini-Exp-1206 0.6 2.2 4.9 7.8 9.3\n", - "acm_bot 0.1 1.7 4.7 7.6 8.8\n", - "metac-perplexity -1.6 0.2 4.2 7.9 9.5\n", - "GreeneiBot2 -1.4 0.6 4.0 7.3 9.0\n", - "twsummerbot 0.3 1.6 3.7 6.2 7.4\n", - "pgodzinai -3.8 -1.0 3.1 7.1 9.4\n", - "cookics_bot_TEST -0.3 1.0 3.1 5.0 6.1\n", - "CumulativeBot -0.2 0.8 2.6 4.4 5.4\n", - "SynapseSeer 0.4 1.1 2.5 4.1 4.9\n", - "metac-claude-3-5-sonnet-latest -1.4 0.1 2.4 4.9 6.1\n", - "jkraybill_bot -3.4 -1.7 1.8 4.9 6.2\n", - "metac-exa -4.6 -2.3 1.6 5.5 7.7\n", - "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.4\n", - "MWG -1.7 -0.8 0.7 2.1 2.9\n", + "metac-o1 6.0 7.2 9.5 11.8 12.8\n", + "metac-o1-preview 3.8 5.2 8.2 11.1 12.6\n", + "manticAI 0.5 2.2 5.6 8.9 10.5\n", + "metac-Gemini-Exp-1206 0.7 2.1 4.8 7.5 8.9\n", + "acm_bot 0.1 1.8 4.6 7.6 8.9\n", + "metac-perplexity -1.5 0.5 4.2 7.7 9.3\n", + "GreeneiBot2 -1.2 0.7 4.1 7.4 9.7\n", + "twsummerbot 0.3 1.5 3.8 6.1 7.5\n", + "pgodzinai -2.9 -1.0 3.1 7.2 9.4\n", + "cookics_bot_TEST -0.0 1.1 3.0 5.0 6.1\n", + "CumulativeBot -0.1 0.8 2.7 4.5 5.4\n", + "SynapseSeer 0.4 1.2 2.5 4.0 4.8\n", + "metac-claude-3-5-sonnet-latest -1.3 -0.1 2.5 4.9 6.3\n", + "metac-exa -5.0 -2.6 2.0 5.8 7.8\n", + "jkraybill_bot -4.3 -1.7 1.7 4.9 6.6\n", + "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.3 4.5\n", + "MWG -1.5 -0.7 0.8 2.2 2.8\n", "andrewsiah -0.9 -0.6 0.0 0.6 0.9\n", - "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", - "annabot -3.5 -2.4 -0.5 1.1 2.0\n", - "bean_bot -2.9 -2.1 -0.5 1.3 2.1\n", - "KevinTestBot -4.0 -2.6 -0.5 1.5 2.6\n", - "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", - "jonahsingerbot -2.8 -2.3 -0.8 0.5 1.2\n", - "krm-bot -3.4 -2.5 -1.0 0.8 1.6\n", - "ProfessorSP -4.5 -3.3 -1.0 1.0 1.9\n", - "metac-grok-2-1212 -6.4 -4.9 -1.6 1.8 3.1\n", - "mmBot -7.3 -5.5 -1.6 2.2 3.9\n", - "4Shadower -5.0 -3.8 -1.7 0.2 1.2\n", - "swingswish -5.4 -4.2 -2.0 -0.1 0.9\n", - "RPM_bot -4.9 -3.9 -2.0 -0.7 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -6.5 -4.8 -2.0 0.8 2.7\n", - "InstitutPelFutur -9.2 -6.7 -2.2 1.6 4.0\n", - "wunderplumb -6.5 -5.1 -2.6 -0.2 0.7\n", - "metac-Llama-3.1 -6.9 -5.3 -2.7 -0.1 1.4\n", - "NextWorldLab -8.6 -6.7 -3.6 -0.6 1.0\n", - "Bot_Pepa -7.0 -5.9 -3.8 -1.9 -1.0\n", - "laylaps -9.7 -7.7 -4.0 -0.1 2.2\n", - "VeritasAI -7.7 -6.6 -4.2 -1.8 -0.5\n", - "minefrac1 -7.9 -6.8 -4.6 -2.5 -1.7\n", - "Grizeu_Bot -9.0 -7.6 -5.0 -2.2 -0.6\n", - "metac-gpt-4o -10.6 -8.9 -6.0 -2.9 -1.6\n", - "ajf-bot -14.6 -12.6 -8.5 -4.4 -2.4" + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "pianobot -1.3 -0.9 -0.0 0.7 1.1\n", + "cobyj-bot -1.3 -0.9 -0.1 0.8 1.4\n", + "annabot -3.9 -2.5 -0.4 1.3 2.0\n", + "KevinTestBot -4.0 -2.7 -0.5 1.6 2.7\n", + "bean_bot -3.3 -2.2 -0.5 0.9 1.7\n", + "CatrachoCaster -2.3 -1.8 -0.7 0.2 0.6\n", + "jonahsingerbot -2.9 -2.2 -0.9 0.4 0.9\n", + "krm-bot -3.6 -2.6 -0.9 0.7 1.7\n", + "ProfessorSP -4.2 -3.2 -1.1 1.0 2.1\n", + "mmBot -7.0 -5.2 -1.2 2.3 4.4\n", + "metac-grok-2-1212 -6.6 -5.0 -1.5 1.7 3.7\n", + "4Shadower -4.6 -3.6 -1.7 0.2 1.2\n", + "swingswish -5.3 -3.9 -1.9 -0.2 0.6\n", + "InstitutPelFutur -8.7 -6.6 -2.1 1.7 4.0\n", + "RPM_bot -4.6 -3.7 -2.1 -0.7 -0.0\n", + "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.2 0.7 2.4\n", + "wunderplumb -6.4 -5.0 -2.6 -0.4 0.8\n", + "metac-Llama-3.1 -6.9 -5.5 -2.8 -0.0 1.7\n", + "NextWorldLab -8.8 -6.8 -3.6 -0.4 1.8\n", + "laylaps -9.6 -7.8 -3.8 -0.2 1.4\n", + "Bot_Pepa -7.1 -6.0 -3.9 -2.1 -1.2\n", + "VeritasAI -7.5 -6.5 -4.3 -1.9 -0.8\n", + "minefrac1 -7.6 -6.7 -4.6 -2.5 -1.6\n", + "Grizeu_Bot -9.2 -7.9 -5.0 -2.5 -1.2\n", + "metac-gpt-4o -10.7 -9.0 -6.0 -3.2 -1.8\n", + "ajf-bot -15.7 -12.9 -8.5 -4.2 -2.0" ] }, - "execution_count": 325, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -9216,7 +9521,7 @@ }, { "cell_type": "code", - "execution_count": 326, + "execution_count": 50, "metadata": { "cellView": "form", "colab": { @@ -9285,12 +9590,12 @@ " 0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", + " jonahsingerbot\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " X_bot\n", @@ -9301,7 +9606,7 @@ " 0.0\n", " \n", " \n", - " jonahsingerbot\n", + " bean_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", @@ -9309,12 +9614,12 @@ " -0.0\n", " \n", " \n", - " bean_bot\n", - " -0.0\n", - " -0.0\n", - " -0.0\n", + " RPM_bot\n", + " -0.1\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " CumulativeBot\n", @@ -9381,8 +9686,16 @@ " -0.0\n", " \n", " \n", - " 4Shadower\n", + " metac-o1\n", + " -0.2\n", + " -0.2\n", " -0.1\n", + " 0.1\n", + " 0.1\n", + " \n", + " \n", + " 4Shadower\n", + " -0.2\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9402,11 +9715,11 @@ " -0.1\n", " -0.1\n", " -0.0\n", - " 0.0\n", + " -0.0\n", " \n", " \n", " jkraybill_bot\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9425,7 +9738,7 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " -0.0\n", " \n", " \n", @@ -9433,51 +9746,51 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " -0.0\n", " \n", " \n", " GreeneiBot2\n", - " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " metac-o1\n", + " ajf-bot\n", " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " 0.0\n", - " 0.1\n", " \n", " \n", " acm_bot\n", " -0.3\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " 0.1\n", " \n", " \n", - " ajf-bot\n", - " -0.3\n", + " Bot_Pepa\n", " -0.2\n", + " -0.2\n", + " -0.1\n", " -0.1\n", " -0.0\n", - " 0.0\n", " \n", " \n", - " bot_median\n", - " -0.3\n", + " metac-deepseek-r1+asknews\n", + " -0.2\n", " -0.2\n", " -0.1\n", + " -0.1\n", " -0.0\n", - " 0.1\n", " \n", " \n", - " Bot_Pepa\n", + " laylaps\n", " -0.2\n", " -0.2\n", " -0.1\n", @@ -9493,20 +9806,20 @@ " -0.1\n", " \n", " \n", - " laylaps\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", + " metac-perplexity\n", + " -0.3\n", + " -0.3\n", " -0.1\n", " -0.0\n", + " 0.1\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-Gemini-Exp-1206\n", + " -0.3\n", " -0.3\n", - " -0.2\n", - " -0.1\n", " -0.1\n", " -0.0\n", + " 0.0\n", " \n", " \n", " manticAI\n", @@ -9517,63 +9830,63 @@ " -0.0\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", + " NextWorldLab\n", " -0.3\n", " -0.3\n", " -0.2\n", + " -0.1\n", " -0.0\n", - " 0.0\n", " \n", " \n", - " metac-perplexity\n", - " -0.4\n", + " metac-claude-3-5-sonnet-latest\n", + " -0.3\n", " -0.3\n", " -0.2\n", + " -0.1\n", " -0.0\n", - " 0.0\n", " \n", " \n", - " NextWorldLab\n", - " -0.3\n", + " metac-claude-3-5-sonnet-20240620\n", + " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " 0.0\n", " \n", " \n", - " minefrac1\n", + " bot_median\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.4\n", + " minefrac1\n", + " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", " metac-Llama-3.1\n", " -0.4\n", - " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " mmBot\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " mmBot\n", + " metac-exa\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9582,7 +9895,7 @@ " \n", " \n", " pgodzinai\n", - " -0.4\n", + " -0.5\n", " -0.4\n", " -0.2\n", " -0.1\n", @@ -9597,15 +9910,15 @@ " -0.1\n", " \n", " \n", - " metac-exa\n", - " -0.4\n", + " metac-grok-2-1212\n", + " -0.5\n", " -0.4\n", " -0.3\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " \n", " \n", - " metac-o1-preview\n", + " metac-gpt-4o\n", " -0.4\n", " -0.4\n", " -0.3\n", @@ -9613,23 +9926,15 @@ " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", - " -0.5\n", + " metac-o1-preview\n", " -0.4\n", - " -0.3\n", - " -0.2\n", - " -0.1\n", - " \n", - " \n", - " metac-grok-2-1212\n", - " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9644,10 +9949,10 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", @@ -9656,41 +9961,41 @@ "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", + "metac-o1 -0.2 -0.2 -0.1 0.1 0.1\n", + "4Shadower -0.2 -0.1 -0.1 -0.0 -0.0\n", "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", - "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", + "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 -0.0\n", + "jkraybill_bot -0.1 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", - "ProfessorSP -0.2 -0.2 -0.1 -0.1 -0.0\n", - "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", - "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", - "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", + "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", + "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", + "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", + "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", + "metac-deepseek-r1+asknews -0.2 -0.2 -0.1 -0.1 -0.0\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-deepseek-r1 -0.3 -0.2 -0.1 -0.1 -0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", + "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", + "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.3 -0.2 -0.0 0.0\n", - "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", - "NextWorldLab -0.3 -0.3 -0.2 -0.1 0.0\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "bot_median -0.3 -0.3 -0.2 -0.1 -0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 -0.0\n", - "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 0.0\n", - "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.0\n", + "metac-Llama-3.1 -0.4 -0.3 -0.2 -0.1 -0.0\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "metac-exa -0.4 -0.3 -0.2 -0.1 -0.1\n", + "pgodzinai -0.5 -0.4 -0.2 -0.1 -0.1\n", "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", - "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.1\n", + "metac-gpt-4o -0.4 -0.4 -0.3 -0.2 -0.1\n", "metac-o1-preview -0.4 -0.4 -0.3 -0.2 -0.1\n", - "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" + "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1" ] }, - "execution_count": 326, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -9711,7 +10016,7 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -9721,7 +10026,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -9781,7 +10086,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 53, "metadata": { "cellView": "form", "colab": { @@ -9917,7 +10222,7 @@ " 0.153662\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 0.8\n", " 225.8\n", " -4.2\n", @@ -10190,7 +10495,7 @@ "twsummerbot 4.9 181.9 -1.8 11.6 \n", "cookics_bot_TEST 5.8 135.2 -1.8 13.4 \n", "CumulativeBot 8.0 94.2 -3.0 18.9 \n", - "metac-deepseek-r1 0.8 225.8 -4.2 5.8 \n", + "metac-deepseek-r1+asknews 0.8 225.8 -4.2 5.8 \n", "MWG 3.6 84.8 -4.3 11.5 \n", "metac-perplexity 2.8 264.3 -4.8 10.3 \n", "metac-grok-2-1212 0.1 281.2 -5.7 6.0 \n", @@ -10236,7 +10541,7 @@ "twsummerbot 0.152393 \n", "cookics_bot_TEST 0.132509 \n", "CumulativeBot 0.153662 \n", - "metac-deepseek-r1 0.763142 \n", + "metac-deepseek-r1+asknews 0.763142 \n", "MWG 0.365354 \n", "metac-perplexity 0.470416 \n", "metac-grok-2-1212 0.961620 \n", @@ -10270,7 +10575,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 329, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -10291,7 +10596,7 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -10300,7 +10605,7 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -10339,7 +10644,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 56, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10349,506 +10654,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.2]\n", - 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" >>> Collected 2 forecasts: [0.05, 0.15]\n", - " >>> Collected 2 forecasts: [0.2, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.8]\n", - " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.7]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.85, 0.75]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.8, 0.6]\n", - " >>> Collected 2 forecasts: [0.75, 0.35]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.8, 0.4]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.35]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.8]\n", - " >>> Collected 2 forecasts: [0.15, 0.5]\n", - " >>> Collected 2 forecasts: [0.25, 0.1]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.25, 0.2]\n", + " >>> Collected 2 forecasts: [0.25, 0.15]\n", + " >>> Collected 2 forecasts: [0.2, 0.9]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - 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" >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.15]\n", + " >>> Collected 3 forecasts: [0.98, 0.95, 0.05]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", - " >>> Collected 3 forecasts: [0.15, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.25, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.15, 0.25, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.15, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.01, 0.05, 0.12]\n", - " >>> Collected 3 forecasts: [0.1, 0.7, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", - " >>> Collected 3 forecasts: [0.2, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.25, 0.4166666666666666]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", - " >>> Collected 3 forecasts: [0.25, 0.5, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", + " >>> Collected 3 forecasts: [0.3, 0.25, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.85, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.3, 0.16]\n", + " >>> Collected 3 forecasts: [0.65, 0.85, 0.67]\n", " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.15, 0.25, 0.3925]\n", " >>> Collected 3 forecasts: [0.02, 0.05, 0.086]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.4, 0.35, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999]\n", - " >>> Collected 4 forecasts: [0.75, 0.7, 0.85, 0.884]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.95]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.15, 0.5, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.1, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.2, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.2, 0.9, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.12]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.98, 0.95, 0.05, 0.918]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.25, 0.4, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.15, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.05, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.1, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.5, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.3, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.65, 0.85, 0.67, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.3925, nan]\n", " >>> Collected 4 forecasts: [0.02, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.35, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.5, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.1, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.2, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.9, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.25, 0.5, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.3, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.65, 0.85, 0.67, nan, 0.76]\n", " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.3925, nan, 0.38]\n", " >>> Collected 5 forecasts: [0.02, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.75, 0.35, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.8, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725]\n", " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675]\n", " >>> Collected 6 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95]\n", - " >>> Collected 7 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3]\n", - " >>> Collected 7 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", - " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", - " >>> Collected 7 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3]\n", - " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", + " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", + " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2]\n", + " >>> Collected 7 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765]\n", - " >>> Collected 8 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", - " >>> Collected 8 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", + " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", + " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8]\n", - " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4]\n", + " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", + " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4, 0.126]\n", + " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10881,7 +11186,7 @@ }, { "cell_type": "code", - "execution_count": 333, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -10891,7 +11196,7 @@ }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -10929,36 +11234,36 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " 0.012671\n", - " 0.097463\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", " \n", " \n", " 1\n", " numeric\n", " NaN\n", " 86.82\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", " \n", " 2\n", " binary\n", " NaN\n", " no\n", - " 0.05\n", - " 0.063\n", - " 0.11\n", + " 0.1\n", + " 0.085\n", + " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.15,0.65,0.2]\n", - " 0.6\n", - " 0.5125\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.49000000000000005, 0.0001]\n", " \n", " \n", " 4\n", @@ -10966,8 +11271,8 @@ " NaN\n", " 119.2\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0, 0.0019825503600000003, 0.003970557620000...\n", - " [0.0, 0.0020603651142857148, 0.004124627985714...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", " \n", " ...\n", @@ -10983,7 +11288,7 @@ " binary\n", " NaN\n", " yes\n", - " 0.9\n", + " 0.95\n", " 0.905\n", " 0.92\n", " \n", @@ -10992,17 +11297,17 @@ " binary\n", " NaN\n", " no\n", - " 0.4\n", - " 0.35\n", - " 0.2085\n", + " 0.9\n", + " 0.65\n", + " 0.3585\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", + " 0.95\n", " 0.9\n", - " 0.85\n", " 0.775\n", " \n", " \n", @@ -11011,8 +11316,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.85\n", - " 0.78\n", + " 0.8\n", + " 0.709\n", " \n", " \n", " 364\n", @@ -11043,48 +11348,48 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", - "342 0.9 \n", - "351 0.4 \n", - "355 0.9 \n", + "342 0.95 \n", + "351 0.9 \n", + "355 0.95 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.063 \n", - "3 0.6 \n", - "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.905 \n", - "351 0.35 \n", - "355 0.85 \n", - "361 0.85 \n", + "351 0.65 \n", + "355 0.9 \n", + "361 0.8 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.097463 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.11 \n", - "3 0.5125 \n", - "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.92 \n", - "351 0.2085 \n", + "351 0.3585 \n", "355 0.775 \n", - "361 0.78 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 334, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -11095,7 +11400,7 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -11115,7 +11420,7 @@ }, { "cell_type": "code", - "execution_count": 336, + "execution_count": 60, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11124,6 +11429,22 @@ "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n" + ] + }, { "data": { "text/html": [ @@ -11153,52 +11474,52 @@ " \n", " 0\n", " 1\n", - 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"execution_count": 336, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -11249,16 +11570,16 @@ }, { "cell_type": "code", - "execution_count": 337, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" ] }, - "execution_count": 337, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -11272,7 +11593,7 @@ }, { "cell_type": "code", - "execution_count": 338, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -11281,7 +11602,7 @@ "(424, 47)" ] }, - "execution_count": 338, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -11292,7 +11613,7 @@ }, { "cell_type": "code", - "execution_count": 339, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -11310,7 +11631,7 @@ }, { "cell_type": "code", - "execution_count": 340, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -11369,235 +11690,590 @@ " NaN\n", " False\n", " False\n", - 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2312641.035135354Will the United States impose any new tariffs ...no2025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353811.000.650.05...0.050.10.070.0630.0630.070.110.110.150.15
3312741.05-9multiple_choice[0-4, 5-9, >9]35535358Will ChatGPT rank in the top 10 global website...yes2025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353851.000.90.97
36135364Will Doge's Agency Efficiency Leaderboard have...no2025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN[0.15,0.65,0.2]...0.650.6250.60.610.60.556250.51250.51250.556250.5125FalseFalse353860.850.80.666
4312751.0119.2numeric36435367Will the Project 2025 Tracker spreadsheet mark...no2025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN0.0400.0FalseFalse[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,......[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0, 0.00342857145, 0.00685714285, 0.01028571...[0.0, 0.0023237670666666666, 0.004652994133333...[0.0, 0.00219737075, 0.0043988365, 0.006603060...[0.0, 0.0019825503600000003, 0.003970557620000...[0.0, 0.0019593148500000003, 0.0039231771, 0.0...[0.0, 0.0020603651142857148, 0.004124627985714...[0.0, 0.0020603651142857148, 0.004124627985714...[0.0, 0.0022194861375, 0.004442382825, 0.00666...[0.0, 0.002118648455555556, 0.0042403284999999...353870.850.050.03
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5 rows × 29 columns

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" ], "text/plain": [ - " bot_question_id question_weight resolution type \\\n", - "0 31262 1.0 0 multiple_choice \n", - "1 31263 1.0 86.82 numeric \n", - "2 31264 1.0 no binary \n", - "3 31274 1.0 5-9 multiple_choice \n", - "4 31275 1.0 119.2 numeric \n", - "\n", - " options range_min range_max open_lower_bound \\\n", - "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", - "1 NaN 60.0 100.0 True \n", - "2 NaN NaN NaN False \n", - "3 [0-4, 5-9, >9] NaN NaN NaN \n", - "4 NaN 0.0 400.0 False \n", - "\n", - " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", - "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", - "2 False 0.05 ... \n", - "3 NaN [0.15,0.65,0.2] ... \n", - "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", - "\n", - " median_forecast_1_bots \\\n", - "0 0.010417 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", - "\n", - " median_forecast_2_bots \\\n", - "0 0.205208 \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", - "2 0.1 \n", - "3 0.625 \n", - "4 [0.0, 0.00342857145, 0.00685714285, 0.01028571... \n", - "\n", - " median_forecast_3_bots \\\n", - "0 0.014926 \n", - "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", - "2 0.07 \n", - "3 0.6 \n", - "4 [0.0, 0.0023237670666666666, 0.004652994133333... \n", - "\n", - " median_forecast_4_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.063 \n", - "3 0.61 \n", - "4 [0.0, 0.00219737075, 0.0043988365, 0.006603060... \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.063 \n", - "3 0.6 \n", - "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", - "\n", - " median_forecast_6_bots \\\n", - "0 0.014926 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.07 \n", - "3 0.55625 \n", - "4 [0.0, 0.0019593148500000003, 0.0039231771, 0.0... \n", - "\n", - " median_forecast_7_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.11 \n", - "3 0.5125 \n", - "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", - "\n", - " median_forecast_8_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.11 \n", - "3 0.5125 \n", - "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + " bot_question_id title \\\n", + "342 35345 Will the US Citizenship and Immigration Servic... \n", + "351 35354 Will the United States impose any new tariffs ... \n", + "355 35358 Will ChatGPT rank in the top 10 global website... \n", + "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", + "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", "\n", - " median_forecast_9_bots \\\n", - "0 0.048475 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.55625 \n", - "4 [0.0, 0.0022194861375, 0.004442382825, 0.00666... \n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "342 yes 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", + "351 no 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "355 yes 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "361 no 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "364 no 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", "\n", - " median_forecast_10_bots \n", - "0 0.048475 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.5125 \n", - "4 [0.0, 0.002118648455555556, 0.0042403284999999... \n", + " range_min range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "342 NaN NaN False False 35380 \n", + "351 NaN NaN False False 35381 \n", + "355 NaN NaN False False 35385 \n", + "361 NaN NaN False False 35386 \n", + "364 NaN NaN False False 35387 \n", "\n", - "[5 rows x 29 columns]" + " question_weight bot_team_median pro_median \n", + "342 1.00 0.905 0.95 \n", + "351 1.00 0.65 0.05 \n", + "355 1.00 0.9 0.97 \n", + "361 0.85 0.8 0.666 \n", + "364 0.85 0.05 0.03 " ] }, - "execution_count": 340, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" + ] } ], - "source": [ - "df_bot_team_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 341, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Z3TTBVWoZVzU", - "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" - }, - "outputs": [], "source": [ "# @title Weighted team-vs-pro\n", "\n", @@ -11631,20 +12307,22 @@ "# Filter to only those rows where pro_median is not NA\n", "df_top_bot_pro_forecasts = df_top_bot_pro_forecasts.dropna(subset=['pro_median'])\n", "\n", + "display_head_and_tail(df_top_bot_pro_forecasts)\n", + "\n", "# Add the head_to_head column\n", "df_top_bot_pro_forecasts['head_to_head'] = df_top_bot_pro_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'pro_median'), axis=1)" ] }, { "cell_type": "code", - "execution_count": 342, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -30.1253\n" + "Weighted Total Score: -0.1175\n" ] } ], @@ -11654,7 +12332,7 @@ }, { "cell_type": "code", - "execution_count": 343, + "execution_count": 67, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11666,7 +12344,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -11678,7 +12356,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -31.65\n" + "The average of 'head_to_head' is: -0.12\n" ] } ], @@ -11688,7 +12366,7 @@ }, { "cell_type": "code", - "execution_count": 344, + "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11734,31 +12412,31 @@ " \n", " \n", " head_to_head\n", - " -2861.9\n", + " -11.2\n", " 92.1\n", - " -31.1\n", - " 105.682644\n", - " 11.012194\n", - " -2.821774\n", + " -0.1\n", + " 0.671397\n", + " 0.06996\n", + " -1.732732\n", " 1.98555\n", - " -9.2\n", - " -52.9\n", - " 0.002931\n", - " 0.005863\n", + " 0.0\n", + " -0.3\n", + " 0.043264\n", + " 0.086527\n", " \n", " \n", "\n", "" ], "text/plain": [ - " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -2861.9 92.1 -31.1 105.682644 11.012194 -2.821774 \n", + " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", + "head_to_head -11.2 92.1 -0.1 0.671397 0.06996 -1.732732 1.98555 \n", "\n", - " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.98555 -9.2 -52.9 0.002931 0.005863 " + " upper_bound lower_bound cdf p_value \n", + "head_to_head 0.0 -0.3 0.043264 0.086527 " ] }, - "execution_count": 344, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -11771,7 +12449,7 @@ }, { "cell_type": "code", - "execution_count": 345, + "execution_count": 69, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11817,44 +12495,44 @@ " \n", " \n", " \n", - " 228\n", - " Will Donald Trump grant executive clemency to ...\n", - " 0.99\n", - " 0.125\n", - " no\n", - " -447.2\n", - " \n", - " \n", " 279\n", " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.02\n", + " [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05]\n", " [0.02,0.01,0.015,0.015,0.05,0.89]\n", " Not in top 50\n", - " -379.5\n", + " -2.9\n", " \n", " \n", - " 12\n", - " What will be the monthly cargo volumes at the ...\n", - " [0.16, 0.1627, 0.1654, 0.1681, 0.1708, 0.1735,...\n", - " [0.001714054,0.0017985406,0.0018846914,0.00197...\n", - " 720283.0\n", - " -274.3\n", + " 121\n", + " How many movies will be new on Netflix's top 1...\n", + " [0.0001, 0.0001, 0.0001, 0.125]\n", + " [0.005,0.017,0.157,0.821]\n", + " 3 or more\n", + " -1.9\n", " \n", " \n", - " 291\n", - " How many registered Syrian refugees will be in...\n", - " [0.05, 0.05125, 0.0525, 0.05375, 0.055, 0.0562...\n", - " [0.001,0.00105,0.0011,0.00115,0.0012,0.00125,0...\n", - " 2807615.0\n", - " -243.6\n", + " 232\n", + " How many movies will be new on Netflix's top 1...\n", + " [0.0001, 0.0001, 0.0001, 0.2963039014373716]\n", + " [0.002,0.008,0.09,0.9]\n", + " 3 or more\n", + " -1.1\n", " \n", " \n", - " 208\n", - " Will the Trump administration impose new tarif...\n", - " 0.1\n", - " 0.8\n", - " yes\n", - " -207.9\n", + " 247\n", + " Will the 500th richest person on Bloomberg's B...\n", + " 0.766667\n", + " 0.333\n", + " no\n", + " -1.1\n", + " \n", + " \n", + " 87\n", + " How many movies will be new on Netflix's globa...\n", + " [0.0001, 0.0001, 0.335]\n", + " [0.01,0.064,0.926]\n", + " 2 or more\n", + " -1.0\n", " \n", " \n", "\n", @@ -11862,35 +12540,28 @@ ], "text/plain": [ " title \\\n", - "228 Will Donald Trump grant executive clemency to ... \n", "279 What will Kalshi's rank in the iPhone Top Free... \n", - "12 What will be the monthly cargo volumes at the ... \n", - "291 How many registered Syrian refugees will be in... \n", - "208 Will the Trump administration impose new tarif... \n", - "\n", - " bot_team_median \\\n", - "228 0.99 \n", - "279 0.02 \n", - "12 [0.16, 0.1627, 0.1654, 0.1681, 0.1708, 0.1735,... \n", - "291 [0.05, 0.05125, 0.0525, 0.05375, 0.055, 0.0562... \n", - "208 0.1 \n", - "\n", - " pro_median resolution \\\n", - "228 0.125 no \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", - "12 [0.001714054,0.0017985406,0.0018846914,0.00197... 720283.0 \n", - "291 [0.001,0.00105,0.0011,0.00115,0.0012,0.00125,0... 2807615.0 \n", - "208 0.8 yes \n", - "\n", - " head_to_head \n", - "228 -447.2 \n", - "279 -379.5 \n", - "12 -274.3 \n", - "291 -243.6 \n", - "208 -207.9 " + "121 How many movies will be new on Netflix's top 1... \n", + "232 How many movies will be new on Netflix's top 1... \n", + "247 Will the 500th richest person on Bloomberg's B... \n", + "87 How many movies will be new on Netflix's globa... \n", + "\n", + " bot_team_median \\\n", + "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", + "121 [0.0001, 0.0001, 0.0001, 0.125] \n", + "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", + "247 0.766667 \n", + "87 [0.0001, 0.0001, 0.335] \n", + "\n", + " pro_median resolution head_to_head \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", + "247 0.333 no -1.1 \n", + "87 [0.01,0.064,0.926] 2 or more -1.0 " ] }, - "execution_count": 345, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -11912,7 +12583,7 @@ }, { "cell_type": "code", - "execution_count": 346, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -11955,25 +12626,25 @@ " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " 0.010417\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 234.3\n", + " 2.7\n", " \n", " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0030510204, 0.0061020408, 0.0091530612...\n", + " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 401.1\n", + " 2.8\n", " \n", " \n", - " 123\n", - " Which party will win the 2nd highest number of...\n", - " NaN\n", - " [0.03,0.9,0.06,0.009,0.001]\n", - " Alternative for Germany\n", + " 151\n", + " How many earthquakes of magnitude ≥ 4 will hap...\n", + " [0.0, 0.0035714286, 0.0071428571, 0.0107142857...\n", + " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", + " 0.0\n", " NaN\n", " \n", " \n", @@ -11987,7 +12658,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.2\n", + " 0.954\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12000,33 +12671,33 @@ " title \\\n", "0 For Q1 2025, how many banks will be listed on ... \n", "189 What will the highest rank of metac-GPT4o or m... \n", - "123 Which party will win the 2nd highest number of... \n", + "151 How many earthquakes of magnitude ≥ 4 will hap... \n", "211 Will Nikola Corporation file for bankruptcy be... \n", "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "0 0.010417 \n", - "189 [0.0, 0.0030510204, 0.0061020408, 0.0091530612... \n", - "123 NaN \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", + "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.2 \n", - "\n", - " pro_median \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] \n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... \n", - "123 [0.03,0.9,0.06,0.009,0.001] \n", - "211 0.999 \n", - "214 0.95 \n", - "\n", - " resolution head_to_head \n", - "0 0 234.3 \n", - "189 34.0 401.1 \n", - "123 Alternative for Germany NaN \n", - "211 annulled NaN \n", - "214 annulled NaN " + "214 0.954 \n", + "\n", + " pro_median resolution \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", + "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... 0.0 \n", + "211 0.999 annulled \n", + "214 0.95 annulled \n", + "\n", + " head_to_head \n", + "0 2.7 \n", + "189 2.8 \n", + "151 NaN \n", + "211 NaN \n", + "214 NaN " ] }, - "execution_count": 346, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -12039,7 +12710,7 @@ }, { "cell_type": "code", - "execution_count": 347, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -12065,7 +12736,7 @@ "dtype: object" ] }, - "execution_count": 347, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -12079,7 +12750,7 @@ }, { "cell_type": "code", - "execution_count": 348, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -12131,17 +12802,17 @@ " 2025-01-20 03:27:00\n", " 2025-01-20 03:27:00\n", " multiple_choice\n", - " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", " False\n", " False\n", " 31268\n", " 1.0\n", - " 0.010417\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 234.340709\n", - " 234.340709\n", + " 2.674462\n", + " 2.674462\n", " \n", " \n", " 1\n", @@ -12158,10 +12829,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -101.083204\n", - " -101.083204\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -12178,10 +12849,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.05\n", + " 0.085\n", " 0.013\n", - " -3.820805\n", - " -3.820805\n", + " -0.075746\n", + " -0.075746\n", " \n", " \n", " 3\n", @@ -12191,17 +12862,17 @@ " 2025-01-21 11:42:00\n", " 2025-01-21 11:42:00\n", " multiple_choice\n", - " [\"0-4\",\"5-9\",\">9\"]\n", + " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " 31280\n", " 1.0\n", - " 0.65\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 39.019764\n", - " 39.019764\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -12218,10 +12889,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 45.546041\n", - " 45.546041\n", + " 0.243782\n", + " 0.243782\n", " \n", " \n", "\n", @@ -12242,12 +12913,12 @@ "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", "\n", - " options range_min range_max open_upper_bound \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", - "1 NaN 60.0 100.0 True \n", - "2 NaN NaN NaN False \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", - "4 NaN 0.0 400.0 False \n", + " options range_min range_max open_upper_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", "\n", " open_lower_bound pro_question_id question_weight \\\n", "0 False 31268 1.0 \n", @@ -12257,28 +12928,28 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 0.010417 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", - "2 0.013 -3.820805 \n", - "3 [0.16,0.44,0.4] 39.019764 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", + "2 0.013 -0.075746 \n", + "3 [0.16,0.44,0.4] 0.152526 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", "\n", " weighted_score \n", - "0 234.340709 \n", - "1 -101.083204 \n", - "2 -3.820805 \n", - "3 39.019764 \n", - "4 45.546041 " + "0 2.674462 \n", + "1 -0.158842 \n", + "2 -0.075746 \n", + "3 0.152526 \n", + "4 0.243782 " ] }, - "execution_count": 348, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -12289,7 +12960,7 @@ }, { "cell_type": "code", - "execution_count": 349, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -12301,7 +12972,7 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": 74, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12313,7 +12984,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -12357,7 +13028,7 @@ }, { "cell_type": "code", - "execution_count": 351, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -12367,7 +13038,7 @@ }, { "cell_type": "code", - "execution_count": 352, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -12424,7 +13095,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.05\n", + " 0.085\n", " 0.013\n", " \n", " \n", @@ -12442,7 +13113,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.2\n", + " 0.62\n", " 0.45\n", " \n", " \n", @@ -12460,7 +13131,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.9\n", + " 0.86\n", " 0.95\n", " \n", " \n", @@ -12496,7 +13167,7 @@ " False\n", " 31338\n", " 1.0\n", - " 0.75\n", + " 0.85\n", " 0.9\n", " \n", " \n", @@ -12526,14 +13197,14 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.05 0.013 \n", - "5 1.0 0.2 0.45 \n", - "8 1.0 0.9 0.95 \n", + "2 1.0 0.085 0.013 \n", + "5 1.0 0.62 0.45 \n", + "8 1.0 0.86 0.95 \n", "10 1.0 NaN NaN \n", - "13 1.0 0.75 0.9 " + "13 1.0 0.85 0.9 " ] }, - "execution_count": 352, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -12544,7 +13215,7 @@ }, { "cell_type": "code", - "execution_count": 353, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -12595,7 +13266,7 @@ }, { "cell_type": "code", - "execution_count": 354, + "execution_count": 78, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12653,17 +13324,17 @@ " 2025-01-20 03:27:00\n", " 2025-01-20 03:27:00\n", " multiple_choice\n", - " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", " False\n", " False\n", " 31268\n", " 1.0\n", - " 0.010417\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 234.340709\n", - " 234.340709\n", + " 2.674462\n", + " 2.674462\n", " \n", " \n", " 1\n", @@ -12680,10 +13351,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -101.083204\n", - " -101.083204\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -12700,10 +13371,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.05\n", + " 0.085\n", " 0.013\n", - " -3.820805\n", - " -3.820805\n", + " -0.075746\n", + " -0.075746\n", " \n", " \n", " 3\n", @@ -12713,17 +13384,17 @@ " 2025-01-21 11:42:00\n", " 2025-01-21 11:42:00\n", " multiple_choice\n", - " [\"0-4\",\"5-9\",\">9\"]\n", + " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " 31280\n", " 1.0\n", - " 0.65\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 39.019764\n", - " 39.019764\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -12740,10 +13411,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 45.546041\n", - " 45.546041\n", + " 0.243782\n", + " 0.243782\n", " \n", " \n", "\n", @@ -12764,12 +13435,12 @@ "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", "\n", - " options range_min range_max open_upper_bound \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", - "1 NaN 60.0 100.0 True \n", - "2 NaN NaN NaN False \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", - "4 NaN 0.0 400.0 False \n", + " options range_min range_max open_upper_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", "\n", " open_lower_bound pro_question_id question_weight \\\n", "0 False 31268 1.0 \n", @@ -12779,25 +13450,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 0.010417 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", - "2 0.013 -3.820805 \n", - "3 [0.16,0.44,0.4] 39.019764 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", + "2 0.013 -0.075746 \n", + "3 [0.16,0.44,0.4] 0.152526 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", "\n", " weighted_score \n", - "0 234.340709 \n", - "1 -101.083204 \n", - "2 -3.820805 \n", - "3 39.019764 \n", - "4 45.546041 " + "0 2.674462 \n", + "1 -0.158842 \n", + "2 -0.075746 \n", + "3 0.152526 \n", + "4 0.243782 " ] }, "metadata": {}, @@ -12859,10 +13530,10 @@ " False\n", " 35380\n", " 1.00\n", - " 0.9\n", + " 0.905\n", " 0.95\n", - " -5.406722\n", - " -5.406722\n", + " -0.048527\n", + " -0.048527\n", " \n", " \n", " 351\n", @@ -12879,10 +13550,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.4\n", + " 0.65\n", " 0.05\n", - " -45.953233\n", - " -45.953233\n", + " -0.998529\n", + " -0.998529\n", " \n", " \n", " 355\n", @@ -12901,8 +13572,8 @@ " 1.00\n", " 0.9\n", " 0.97\n", - " -7.490131\n", - " -7.490131\n", + " -0.074901\n", + " -0.074901\n", " \n", " \n", " 361\n", @@ -12919,10 +13590,10 @@ " False\n", " 35386\n", " 0.85\n", - " 0.85\n", + " 0.8\n", " 0.666\n", - " -80.050570\n", - " -68.042984\n", + " -0.435900\n", + " -0.370515\n", " \n", " \n", " 364\n", @@ -12941,8 +13612,8 @@ " 0.85\n", " 0.05\n", " 0.03\n", - " -2.083409\n", - " -1.770897\n", + " -0.017709\n", + " -0.015053\n", " \n", " \n", "\n", @@ -12971,11 +13642,11 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", - "342 1.00 0.9 0.95 -5.406722 -5.406722 \n", - "351 1.00 0.4 0.05 -45.953233 -45.953233 \n", - "355 1.00 0.9 0.97 -7.490131 -7.490131 \n", - "361 0.85 0.85 0.666 -80.050570 -68.042984 \n", - "364 0.85 0.05 0.03 -2.083409 -1.770897 " + "342 1.00 0.905 0.95 -0.048527 -0.048527 \n", + "351 1.00 0.65 0.05 -0.998529 -0.998529 \n", + "355 1.00 0.9 0.97 -0.074901 -0.074901 \n", + "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", + "364 0.85 0.05 0.03 -0.017709 -0.015053 " ] }, "metadata": {}, @@ -12983,13 +13654,13 @@ }, { "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "evalue": "operands could not be broadcast together with shapes (201,) (5,) ", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[354], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:853\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 843\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 844\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 853\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 856\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 857\u001b[0m bins\n\u001b[1;32m 858\u001b[0m )\n", + "Cell \u001b[0;32mIn[78], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:750\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 753\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 754\u001b[0m bins\n\u001b[1;32m 755\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", @@ -12997,7 +13668,7 @@ "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (201,) (5,) " ] } ], diff --git a/functions.py b/functions.py index 62b8f3b..0373237 100644 --- a/functions.py +++ b/functions.py @@ -10,7 +10,7 @@ from scipy import stats from scipy.optimize import minimize_scalar from scipy.stats import binom, norm - +import re from refactored_notebook.scoring import ( calculate_baseline_score, calculate_peer_score, @@ -345,7 +345,14 @@ def get_median_forecast_multiple_choice(row, forecasts): # print(f"NO PROBS collected for multiple-choice question {row.get('bot_question_id')} — returning np.nan") return np.nan - return np.nanmedian(probs) + median_forecast = [] # NOTE: This forecast will not add to 1, but we only need the median for the resolution + for i, _ in enumerate(options): + if i == resolution_idx: + median_forecast.append(np.nanmedian(probs)) + else: + median_forecast.append(0.0001) # this is filler @Check: This won't screw anything up right? Perviously we were just returning the probability of resolution + + return median_forecast def get_median_forecast(row, bots): @@ -1106,7 +1113,7 @@ def compute_bucket_forecast_value(row): return df -def parse_options_array(options_str): +def parse_options_array(options_str: str) -> list[str]: """ Parse options string that looks like an array into an actual array. @@ -1119,24 +1126,30 @@ def parse_options_array(options_str): if not isinstance(options_str, str): return options_str # Already parsed or None + if options_str == "[]": + return [] # This can happen for numeric/binary questions with no options + + options = [] try: # First try using eval (safer than literal_eval for this specific case) - options_array = eval(options_str) - return options_array + options = eval(options_str) except: # If that fails, try custom parsing # Strip brackets and split by comma cleaned = options_str.strip("[]") # Split by comma, but respect quotes - import re # Match items in quotes with commas inside parts = re.findall(r'"([^"]*)"', cleaned) if parts: - return parts - - # Simple fallback: just split by comma and strip quotes - return [p.strip().strip("\"'") for p in cleaned.split(",")] + options = parts + else: + # Simple fallback: just split by comma and strip quotes + options = [p for p in cleaned.split(",")] + stripped_options = [p.strip("\"' ") for p in options] + if len(stripped_options) == 0: + raise ValueError(f"No options found in {options_str}") + return stripped_options def calculate_weighted_h2h_score_between_two_forecast_columns( diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 4ece3f3..c42ccb5 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 @@ -13,35 +13,35 @@ Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 +metac-o1,-0.2,-0.2,-0.1,0.1,0.1 +4Shadower,-0.2,-0.1,-0.1,-0.0,-0.0 annabot,-0.1,-0.1,-0.1,-0.0,-0.0 -cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 -jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 +cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,-0.0 +jkraybill_bot,-0.1,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.1,-0.0 -ProfessorSP,-0.2,-0.2,-0.1,-0.1,-0.0 -GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 -metac-o1,-0.3,-0.2,-0.1,0.0,0.1 -acm_bot,-0.3,-0.2,-0.1,0.0,0.1 +MWG,-0.2,-0.2,-0.1,-0.0,-0.0 +ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 +GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -bot_median,-0.3,-0.2,-0.1,-0.0,0.1 +acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 +metac-deepseek-r1+asknews,-0.2,-0.2,-0.1,-0.1,-0.0 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-deepseek-r1,-0.3,-0.2,-0.1,-0.1,-0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 +metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 +metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.3,-0.2,-0.0,0.0 -metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 -NextWorldLab,-0.3,-0.3,-0.2,-0.1,0.0 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +bot_median,-0.3,-0.3,-0.2,-0.1,-0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,-0.0 -metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,0.0 -metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.0 +metac-Llama-3.1,-0.4,-0.3,-0.2,-0.1,-0.0 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +metac-exa,-0.4,-0.3,-0.2,-0.1,-0.1 +pgodzinai,-0.5,-0.4,-0.2,-0.1,-0.1 VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 -metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.1 +metac-gpt-4o,-0.4,-0.4,-0.3,-0.2,-0.1 metac-o1-preview,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv index 76b7626..029f529 100644 --- a/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv +++ b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv @@ -15,7 +15,7 @@ metac-perplexity,734.7,264.3,2.8,62.518732274252,3.8454321257670965,0.7228462253 metac-exa,470.9,275.2,1.7,63.38280444669259,3.8205989842983494,0.4478599398298826,1.9681111912388756,9.2,-5.8,0.6726960546336258,0.654608 MWG,307.0,84.8,3.6,36.6252501807067,3.976544679654517,0.9101477753110279,1.987508353566517,11.5,-4.3,0.8173229386375491,0.365354 jkraybill_bot,219.6,162.4,1.4,71.12529221576798,5.5817601187391634,0.24232123347298368,1.9740758524924067,12.4,-9.7,0.5955805198867354,0.808839 -metac-deepseek-r1,172.5,225.8,0.8,38.0431452483966,2.5318249833740962,0.3017230896257882,1.9700645882216863,5.8,-4.2,0.6184289375422699,0.763142 +metac-deepseek-r1+asknews,172.5,225.8,0.8,38.0431452483966,2.5318249833740962,0.3017230896257882,1.9700645882216863,5.8,-4.2,0.6184289375422699,0.763142 pianobot,101.0,14.8,6.8,41.27615494222523,10.711147680523258,0.6349321054235654,2.1450947126002333,29.8,-16.2,0.7320891967624292,0.535822 metac-grok-2-1212,40.0,281.2,0.1,49.508070078167286,2.952248394236147,0.04816426739476925,1.967947383995502,6.0,-5.7,0.5191901814794234,0.961620 andrewsiah,2.6,25.1,0.1,35.80509173037023,7.1467391327710805,0.014679458541325375,2.0603406998894913,14.8,-14.6,0.5057956215530941,0.988409 diff --git a/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv index 3a4a494..b32fa6b 100644 --- a/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv +++ b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv @@ -11,7 +11,7 @@ Rank,Bot,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_b 10,metac-claude-3-5-sonnet-latest,951.3,370.3,2.6,38.26306555715613,1.988342419831904,1.2919544880180496,1.966062599368744,6.5,-1.3,0.9014096170572055,0.197181 11,GreeneiBot2,1494.7,264.1,5.7,59.728354485253575,3.675051787269948,1.539810539883174,1.9685962808273842,12.9,-1.6,0.9375959149496895,0.124808 12,metac-perplexity,1558.4,354.4,4.4,59.58837847152926,3.1652094732771676,1.389181319604283,1.9663705248092669,10.6,-1.8,0.9171738658225362,0.165652 -13,metac-deepseek-r1,516.8,277.9,1.9,37.353209862667065,2.2407803261049724,0.8299752665727909,1.968164543586558,6.3,-2.6,0.7963661024103902,0.407268 +13,metac-deepseek-r1+asknews,516.8,277.9,1.9,37.353209862667065,2.2407803261049724,0.8299752665727909,1.968164543586558,6.3,-2.6,0.7963661024103902,0.407268 14,pgodzinai,1106.7,325.4,3.4,66.68615909814488,3.6966946914459644,0.9199538936245306,1.966948755554642,10.7,-3.9,0.8208598109837832,0.358280 15,metac-exa,599.9,365.3,1.6,63.45938884307718,3.3201611290993176,0.4946106204656042,1.9661417524889626,8.2,-4.9,0.6894134359021193,0.621173 16,MWG,253.8,113.4,2.2,40.6740836146038,3.819036516963852,0.5859361127584735,1.980468444487731,9.8,-5.3,0.7204535666937473,0.559093 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 8c1e7a0..746b52f 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -RPM_bot,-0.5,7.0,-0.1,0.8401626602195374,0.31755163711190787,-0.22911491175620202,2.4469118511449692,0.7,-0.8,0.4131948210081994,0.826390 -jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 +RPM_bot,-1.3,7.0,-0.2,0.8269776545743774,0.3125681734016113,-0.610595609477049,2.4469118511449692,0.6,-1.0,0.2819326101745987,0.563865 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 +metac-o1,-5.3,91.1,-0.1,0.9084726497398434,0.09518152714706545,-0.6113627344286646,1.9858289388460384,0.1,-0.2,0.27124945946442813,0.542499 +annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.5,27.4,-0.2,0.7478313737485887,0.14286584023204454,-1.6679327769704273,2.0495406495390753,0.1,-0.5,0.053574616968489516,0.107149 +cookics_bot_TEST,-6.8,27.4,-0.2,0.7472901092218875,0.14276243695944935,-1.737830063646217,2.0495406495390753,0.0,-0.5,0.04694721167123542,0.093894 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 -MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 +MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 -metac-o1,-10.4,91.1,-0.1,0.9315503207588304,0.09759939627192438,-1.1710037539243623,1.9858289388460384,0.1,-0.3,0.12234246603454144,0.244685 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.6,58.4,-0.2,0.84933087242601,0.11118763184285871,-1.638405629664946,2.000831925930035,0.0,-0.4,0.053406273914708285,0.106813 +GreeneiBot2,-10.6,58.4,-0.2,0.8493306622643327,0.11118760433016613,-1.638793797628407,2.000831925930035,0.0,-0.4,0.05336569544684546,0.106731 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 -bot_median,-11.1,92.1,-0.1,0.8343911715991652,0.08694405375037174,-1.3919418427248071,1.9855502432148115,0.1,-0.3,0.08366450804542999,0.167329 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 +metac-deepseek-r1+asknews,-11.7,52.1,-0.2,0.6690305553273252,0.09268876407541017,-2.4327442879372825,2.0053789762011176,-0.0,-0.4,0.009262209683005887,0.018524 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -metac-deepseek-r1,-14.1,52.1,-0.3,0.8172087173883323,0.11321764813763505,-2.3937504961816116,2.0053789762011176,-0.0,-0.5,0.01019302014325762,0.020386 +metac-perplexity,-13.6,89.1,-0.2,0.953800697354561,0.10104592028043681,-1.5152493493302568,1.9864049297707018,0.0,-0.4,0.06664452341402785,0.133289 +metac-Gemini-Exp-1206,-13.9,76.5,-0.2,0.9608427574536519,0.10985544896515206,-1.6509533909374279,1.9908217254774627,0.0,-0.4,0.051451032994077626,0.102902 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-Gemini-Exp-1206,-14.6,76.5,-0.2,0.9369300827202118,0.1071214557093134,-1.7806582480922164,1.9908217254774627,0.0,-0.4,0.03949550680306326,0.078991 -metac-perplexity,-16.1,89.1,-0.2,1.0694909108673796,0.11330217478335987,-1.5994893543452755,1.9864049297707018,0.0,-0.4,0.05664610517795549,0.113292 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -minefrac1,-18.5,51.1,-0.4,0.8782230217189723,0.1228554331463025,-2.94542136244705,2.0065449272360034,-0.1,-0.6,0.002440792164293176,0.004882 -metac-claude-3-5-sonnet-20240620,-20.8,90.5,-0.2,0.9854576682401628,0.10358901026916505,-2.2176587156495677,1.9860719790130024,-0.0,-0.4,0.01455504948064986,0.029110 -metac-Llama-3.1,-21.0,89.1,-0.2,1.131903405632652,0.11991417243449026,-1.9667104273244107,1.9864049297707018,0.0,-0.5,0.026181998267921627,0.052364 -metac-claude-3-5-sonnet-latest,-21.7,91.1,-0.2,0.8679924761244506,0.0909403815880937,-2.6147562800776485,1.9858289388460384,-0.1,-0.4,0.005233245635108678,0.010466 +metac-claude-3-5-sonnet-latest,-17.7,91.1,-0.2,0.822268712940962,0.08614986025763702,-2.253410401302691,1.9858289388460384,-0.0,-0.4,0.013329842987401584,0.026660 +bot_median,-17.9,92.1,-0.2,0.8298286106445787,0.0864686321994526,-2.248076238150116,1.9855502432148115,-0.0,-0.4,0.013491943459249906,0.026984 +metac-claude-3-5-sonnet-20240620,-18.2,90.5,-0.2,0.9882219785580354,0.10387958811855824,-1.9308293392916587,1.9860719790130024,0.0,-0.4,0.028334774283890096,0.056670 +minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 +metac-Llama-3.1,-21.3,89.1,-0.2,0.9128041314903421,0.0967027322983173,-2.471742593789836,1.9864049297707018,-0.0,-0.4,0.007684177160478823,0.015368 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -pgodzinai,-23.5,76.4,-0.3,0.9735671748298226,0.11138308522466013,-2.763549748735371,1.9908489732268309,-0.1,-0.5,0.003590727855444895,0.007181 +metac-exa,-22.4,89.1,-0.3,0.8128016858276886,0.08610844443471673,-2.92372894610568,1.9864049297707018,-0.1,-0.4,0.002197830440677215,0.004396 +pgodzinai,-23.9,76.4,-0.3,0.9914794382114891,0.11343237695345683,-2.755452219862641,1.9908489732268309,-0.1,-0.5,0.00367232305294701,0.007345 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-exa,-24.7,89.1,-0.3,0.8121952445686516,0.08604419787326485,-3.2197865951234235,1.9864049297707018,-0.1,-0.4,0.0008985159820669422,0.001797 -metac-o1-preview,-25.5,91.1,-0.3,0.8498877252707713,0.08904352994884641,-3.1492143531875287,1.9858289388460384,-0.1,-0.5,0.0011106007145197491,0.002221 -InstitutPelFutur,-26.9,90.1,-0.3,0.9739711690022733,0.10260858670161008,-2.9043019887843187,1.9861137662360124,-0.1,-0.5,0.0023202343180469525,0.004640 -metac-grok-2-1212,-27.9,91.1,-0.3,1.0054085980592369,0.10533759689680534,-2.9038578245582283,1.9858289388460384,-0.1,-0.5,0.0023176059032990978,0.004635 -metac-gpt-4o,-28.8,91.1,-0.3,0.8198830654548765,0.08589991374463501,-3.67651905388223,1.9858289388460384,-0.1,-0.5,0.0002007468680573961,0.000401 +metac-grok-2-1212,-24.5,91.1,-0.3,1.0139958650854732,0.10623729287533687,-2.5268442158424125,1.9858289388460384,-0.1,-0.5,0.006626896274566267,0.013254 +metac-gpt-4o,-26.0,91.1,-0.3,0.8516451147774127,0.08922765328715744,-3.193010060382893,1.9858289388460384,-0.1,-0.5,0.0009699028149533728,0.001940 +metac-o1-preview,-26.2,91.1,-0.3,0.9143330864911109,0.09579553057346926,-2.9970476132039527,1.9858289388460384,-0.1,-0.5,0.0017609124521279873,0.003522 +InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index a79a02b..eec131c 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -153,8 +153,11 @@ def _determine_probability_for_resolution( "Havent decided how to handle null forecasts or anulled resolutions" ) - if len(forecast) == 0: - raise ValueError("Forecast is empty") + try: + if len(forecast) == 0: + raise ValueError("Forecast is empty") + except Exception as e: + raise ValueError(f"Error encountered for question of type {q_type} with resolution {resolution} and forecast {forecast}: {e}") if not q_type == QuestionType.NUMERIC and any(p <= 0 or p >= 1 for p in forecast): raise ValueError("Forecast contains probabilities outside of 0 to 1 range") @@ -207,7 +210,7 @@ def _multiple_choice_resolution_prob( raise ValueError("Forecast and options have different lengths") pmf = [float(p) for p in forecast] - options = [str(opt) for opt in options] + options = [str(opt) for opt in options] # @Check: TODO: For whatever reason, options had " and ' surrounding them, and were not parsed at this point. This is the easier way to handle it, but should be dealt with earlier in the pipeline. resolution_idx = options.index(str(resolution)) prob_for_resolution = pmf[resolution_idx] return prob_for_resolution From 8eca5b006b5d0aae9c0ee22584ae38fd8bc71462 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 21:07:09 -0600 Subject: [PATCH 21/26] Moved community prediction comparison files to archived --- {notebook_outputs => archived}/df_top_bot_pro_cp_forecasts.csv | 0 {notebook_outputs => archived}/weighted_baseline_bot_cp.csv | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename {notebook_outputs => archived}/df_top_bot_pro_cp_forecasts.csv (100%) rename {notebook_outputs => archived}/weighted_baseline_bot_cp.csv (100%) diff --git a/notebook_outputs/df_top_bot_pro_cp_forecasts.csv b/archived/df_top_bot_pro_cp_forecasts.csv similarity index 100% rename from notebook_outputs/df_top_bot_pro_cp_forecasts.csv rename to archived/df_top_bot_pro_cp_forecasts.csv diff --git a/notebook_outputs/weighted_baseline_bot_cp.csv b/archived/weighted_baseline_bot_cp.csv similarity index 100% rename from notebook_outputs/weighted_baseline_bot_cp.csv rename to archived/weighted_baseline_bot_cp.csv From 3f6377163f277fe7b9992715fd15047a552cf088 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 21:25:43 -0600 Subject: [PATCH 22/26] Moved another cp comparison csv --- {notebook_outputs => archived}/weighted_t_test_h2h_bot_vs_cp.csv | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {notebook_outputs => archived}/weighted_t_test_h2h_bot_vs_cp.csv (100%) diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv b/archived/weighted_t_test_h2h_bot_vs_cp.csv similarity index 100% rename from notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv rename to archived/weighted_t_test_h2h_bot_vs_cp.csv From 3a9daee963a5ac8153807fb63d75a66928eb5dc8 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 22:14:41 -0600 Subject: [PATCH 23/26] Moved discrimination chart above failing cell --- AI_BENCHMARKING_ANALYSIS.ipynb | 2885 +++++++++-------- functions.py | 2 +- .../bootstrapped_h2h_bot_vs_pros.csv | 44 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 40 +- 4 files changed, 1498 insertions(+), 1473 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index d830bc0..a2b1b4e 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -61,7 +61,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3762618/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_3873332/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1032,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 8.388094\n", - " 3170.867318\n", + " 9.060773\n", + " 3425.153221\n", " 409\n", - " 5.494976\n", - " 1.471729\n", + " 6.048852\n", + " 1.532164\n", " \n", " \n", " 4\n", @@ -1072,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.388094 3170.867318 409 5.494976 \n", + "15 bot_median 9.060773 3425.153221 409 6.048852 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.471729 \n", + "15 1.532164 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1729,20 +1729,20 @@ " \n", " \n", " 1\n", - " bot_median\n", - " 8997.290873\n", - " \n", - " \n", - " 2\n", " metac-o1\n", " 8861.959039\n", " \n", " \n", - " 3\n", + " 2\n", " metac-o1-preview\n", " 8849.559824\n", " \n", " \n", + " 3\n", + " bot_median\n", + " 8602.129306\n", + " \n", + " \n", " 4\n", " acm_bot\n", " 7605.922314\n", @@ -1759,9 +1759,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 bot_median 8997.290873\n", - "2 metac-o1 8861.959039\n", - "3 metac-o1-preview 8849.559824\n", + "1 metac-o1 8861.959039\n", + "2 metac-o1-preview 8849.559824\n", + "3 bot_median 8602.129306\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3538.184052\n", + " 3398.202830\n", " \n", " \n", " 3\n", @@ -2166,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3538.184052\n", + "2 bot_median 3398.202830\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2578,8 +2578,8 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", @@ -2603,7 +2603,7 @@ " True\n", " ...\n", " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", @@ -2626,9 +2626,9 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.15\n", + " 0.05\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2650,8 +2650,8 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -2674,8 +2674,8 @@ " False\n", " False\n", " ...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2713,23 +2713,23 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.5,0.3,0.15,0.04,0.01] \n", + "0 [0.45,0.3,0.15,0.05,0.05] \n", "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "2 0.15 \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", - "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.05 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", "\n", " metac-perplexity minefrac1 \\\n", "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", + "2 0.15 NaN \n", "3 [0.15,0.6,0.25] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", @@ -2818,8 +2818,8 @@ " False\n", " False\n", " ...\n", - " 0.9\n", " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2842,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.4\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2867,7 +2867,7 @@ " False\n", " ...\n", " 0.9\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2946,9 +2946,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.95 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", - "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.4 0.15 NaN NaN 0.15 NaN \n", + "96 0.9 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", @@ -3100,7 +3100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3762618/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_3873332/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,8 +3162,8 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", @@ -3186,8 +3186,8 @@ " True\n", " True\n", " ...\n", - " 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@@ -3210,9 +3210,9 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.15\n", + " 0.05\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -3234,8 +3234,8 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -3258,9 +3258,9 @@ " False\n", " False\n", " ...\n", - " 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\n", + "2 0.05 \n", + "3 [0.2,0.6,0.2] \n", + "4 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\n", + "\n", + " metac-perplexity \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] \n", + "1 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\n", + "2 0.15 \n", + "3 [0.15,0.6,0.25] \n", + "4 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\n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3423,8 +3423,8 @@ " False\n", " False\n", " ...\n", - " 0.9\n", " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3447,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.4\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3472,7 +3472,7 @@ " False\n", " ...\n", " 0.9\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3551,9 +3551,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.95 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", - "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.4 0.15 NaN NaN 0.15 NaN \n", + "96 0.9 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", @@ -3762,7 +3762,7 @@ " False\n", " False\n", " ...\n", - " 2.644992\n", + " 2.343407\n", " 5.703782\n", " NaN\n", " 2.292635\n", @@ -3786,7 +3786,7 @@ " None\n", " None\n", " ...\n", - " 0.107631\n", + " 0.310155\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -3810,8 +3810,8 @@ " False\n", " False\n", " ...\n", - " 0.298855\n", - " -0.106610\n", + " 0.116534\n", + " 0.211844\n", " NaN\n", " -0.184571\n", " 0.112526\n", @@ -3819,7 +3819,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.576613\n", + " -0.704447\n", " \n", " \n", " 9\n", @@ -3843,7 +3843,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.693147\n", + " -0.518794\n", " \n", " \n", " 13\n", @@ -3858,7 +3858,7 @@ " None\n", " None\n", " ...\n", - " 0.575364\n", + " 0.330943\n", " 0.287682\n", " 0.021979\n", " 0.200671\n", @@ -3904,17 +3904,17 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", - "3 0.107631 0.310155 NaN 0.127833 0.152526 \n", - "6 0.298855 -0.106610 NaN -0.184571 0.112526 \n", + "0 2.343407 5.703782 NaN 2.292635 2.703087 \n", + "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", + "6 0.116534 0.211844 NaN -0.184571 0.112526 \n", "9 -0.423484 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.575364 0.287682 0.021979 0.200671 0.253781 \n", + "13 0.330943 0.287682 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "0 NaN NaN NaN NaN 4.656813 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN -0.576613 \n", - "9 NaN NaN -0.624154 NaN -0.693147 \n", + "6 NaN NaN NaN NaN -0.704447 \n", + "9 NaN NaN -0.624154 NaN -0.518794 \n", "13 NaN NaN NaN NaN -0.062598 \n", "\n", "[5 rows x 58 columns]" @@ -3982,10 +3982,10 @@ " False\n", " ...\n", " -2.879198\n", - " -1.780586\n", + " -0.933288\n", " -3.007032\n", " -2.879198\n", - " -3.795489\n", + " -3.390024\n", " NaN\n", " NaN\n", " -2.348570\n", @@ -4006,7 +4006,7 @@ " None\n", " ...\n", " -0.993252\n", - " 0.000000\n", + " -0.300105\n", " -0.523248\n", " 0.105361\n", " 0.259511\n", @@ -4014,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.993252\n", + " -0.941958\n", " \n", " \n", " 83\n", @@ -4053,7 +4053,7 @@ " False\n", " False\n", " ...\n", - " -0.048289\n", + " -0.037817\n", " -0.048289\n", " NaN\n", " -0.124829\n", @@ -4077,8 +4077,8 @@ " False\n", " False\n", " ...\n", - " -1.704748\n", - " -1.011601\n", + " -1.299283\n", + " -2.908721\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4119,19 +4119,19 @@ "81 NaN False False ... -2.879198 \n", "82 NaN None None ... -0.993252 \n", "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.048289 \n", - "92 NaN False False ... -1.704748 \n", + "91 NaN False False ... -0.037817 \n", + "92 NaN False False ... -1.299283 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -1.780586 -3.007032 -2.879198 -3.795489 NaN NaN \n", - "82 0.000000 -0.523248 0.105361 0.259511 NaN NaN \n", + "81 -0.933288 -3.007032 -2.879198 -3.390024 NaN NaN \n", + "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "92 -2.908721 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", "81 -2.348570 -2.409195 -2.879198 \n", - "82 0.276509 -0.644609 -0.993252 \n", + "82 0.276509 -0.644609 -0.941958 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.121048 \n", "92 NaN -0.749237 -0.318454 \n", @@ -4200,8 +4200,8 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", - " -0.092275\n", + " -0.038208\n", + " -0.149434\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -4233,7 +4233,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.320472\n", + " 0.367725\n", " \n", " \n", " 8\n", @@ -4248,7 +4248,7 @@ " False\n", " False\n", " ...\n", - " -0.054067\n", + " 0.000000\n", " -0.054067\n", " NaN\n", " -0.111226\n", @@ -4257,7 +4257,7 @@ " NaN\n", " -0.398124\n", " NaN\n", - " -0.179379\n", + " -0.171850\n", " \n", " \n", " 12\n", @@ -4297,7 +4297,7 @@ " False\n", " ...\n", " -0.045611\n", - " -0.045611\n", + " 0.008457\n", " NaN\n", " -0.068083\n", " NaN\n", @@ -4328,16 +4328,16 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", + "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", - "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "8 0.000000 -0.054067 NaN -0.111226 -0.147158 \n", "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", - "16 -0.045611 -0.045611 NaN -0.068083 NaN \n", + "16 -0.045611 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", - "5 NaN NaN NaN NaN 0.320472 \n", - "8 NaN NaN -0.398124 NaN -0.179379 \n", + "5 NaN NaN NaN NaN 0.367725 \n", + "8 NaN NaN -0.398124 NaN -0.171850 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", @@ -4405,7 +4405,7 @@ " False\n", " False\n", " ...\n", - " 0.000000\n", + " -0.054067\n", " NaN\n", " NaN\n", " 0.000000\n", @@ -4429,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -2.251292\n", + " -0.111226\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4453,7 +4453,7 @@ " False\n", " False\n", " ...\n", - " -0.020834\n", + " -0.074901\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -4533,9 +4533,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.000000 NaN NaN 0.000000 0.000000 \n", - "95 -2.251292 NaN NaN -0.111226 NaN \n", - "96 -0.020834 NaN NaN -0.074901 NaN \n", + "94 -0.054067 NaN NaN 0.000000 0.000000 \n", + "95 -0.111226 NaN NaN -0.111226 NaN \n", + "96 -0.074901 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", "\n", @@ -4603,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3538.184052\n", + " 3398.202830\n", " \n", " \n", " 3\n", @@ -4838,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3538.184052\n", + "2 bot_median 3398.202830\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", - "mean metac-o1 forecast on questions that resolved no: 27.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 74.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -4988,7 +4988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3762618/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_3873332/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5114,7 +5114,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2475.479525\n", + " 2477.274734\n", " 97\n", " 93.10\n", " \n", @@ -5471,7 +5471,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2475.479525 97 \n", + "3 4 bot_median 2477.274734 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5717,17 +5717,17 @@ " \n", " \n", " bot_median\n", - " 2475.5\n", + " 2477.3\n", " 93.1\n", " 26.6\n", - " 57.595415\n", - " 5.969158\n", - " 4.454476\n", + " 58.467357\n", + " 6.059526\n", + " 4.391227\n", " 1.985277\n", - " 38.4\n", - " 14.7\n", - " 0.999988\n", - " 0.000024\n", + " 38.6\n", + " 14.6\n", + " 0.999985\n", + " 0.000030\n", " \n", " \n", " acm_bot\n", @@ -6340,7 +6340,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2475.5 93.1 26.6 57.595415 \n", + "bot_median 2477.3 93.1 26.6 58.467357 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6389,7 +6389,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 5.969158 4.454476 1.985277 38.4 \n", + "bot_median 6.059526 4.391227 1.985277 38.6 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6438,7 +6438,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.7 0.999988 0.000024 \n", + "bot_median 14.6 0.999985 0.000030 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6573,18 +6573,18 @@ " NA\n", " \n", " \n", - " bean_bot\n", + " RPM_bot\n", " -0.6\n", - " 4.7\n", + " 7.0\n", " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", + " 0.820675\n", + " 0.310186\n", + " -0.269729\n", + " 2.446912\n", + " 0.7\n", + " -0.8\n", + " 0.398203\n", + " 0.796405\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,6 +6601,20 @@ " 0.007677\n", " \n", " \n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", + " -0.1\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", + " \n", + " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6643,20 +6657,6 @@ " 0.018953\n", " \n", " \n", - " RPM_bot\n", - " -1.3\n", - " 7.0\n", - " -0.2\n", - " 0.826978\n", - " 0.312568\n", - " -0.610596\n", - " 2.446912\n", - " 0.6\n", - " -1.0\n", - " 0.281933\n", - " 0.563865\n", - " \n", - " \n", " SynapseSeer\n", " -1.3\n", " 26.2\n", @@ -6741,32 +6741,18 @@ " 0.011127\n", " \n", " \n", - " metac-o1\n", - " -5.3\n", - " 91.1\n", - " -0.1\n", - " 0.908473\n", - " 0.095182\n", - " -0.611363\n", - " 1.985829\n", - " 0.1\n", - " -0.2\n", - " 0.271249\n", - " 0.542499\n", - " \n", - " \n", " annabot\n", - " -5.9\n", + " -6.2\n", " 29.3\n", " -0.2\n", - " 0.517575\n", - " 0.095618\n", - " -2.112203\n", + " 0.520869\n", + " 0.096226\n", + " -2.211795\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.021811\n", - " 0.043621\n", + " 0.017610\n", + " 0.035221\n", " \n", " \n", " 4Shadower\n", @@ -6784,17 +6770,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.8\n", + " -6.6\n", " 27.4\n", " -0.2\n", - " 0.747290\n", - " 0.142762\n", - " -1.737830\n", + " 0.747093\n", + " 0.142725\n", + " -1.683660\n", " 2.049541\n", - " 0.0\n", + " 0.1\n", " -0.5\n", - " 0.046947\n", - " 0.093894\n", + " 0.052019\n", + " 0.104037\n", " \n", " \n", " jkraybill_bot\n", @@ -6825,18 +6811,32 @@ " 0.084012\n", " \n", " \n", + " metac-o1\n", + " -9.3\n", + " 91.1\n", + " -0.1\n", + " 0.901141\n", + " 0.094413\n", + " -1.081897\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.141093\n", + " 0.282185\n", + " \n", + " \n", " MWG\n", - " -9.6\n", + " -9.8\n", " 28.6\n", " -0.3\n", - " 0.711160\n", - " 0.132979\n", - " -2.535384\n", + " 0.705240\n", + " 0.131872\n", + " -2.589625\n", " 2.046561\n", " -0.1\n", " -0.6\n", - " 0.008595\n", - " 0.017191\n", + " 0.007581\n", + " 0.015163\n", " \n", " \n", " ProfessorSP\n", @@ -6853,6 +6853,20 @@ " 0.023289\n", " \n", " \n", + " GreeneiBot2\n", + " -10.4\n", + " 58.4\n", + " -0.2\n", + " 0.849317\n", + " 0.111186\n", + " -1.601352\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.057397\n", + " 0.114793\n", + " \n", + " \n", " acm_bot\n", " -10.5\n", " 80.2\n", @@ -6867,20 +6881,6 @@ " 0.201592\n", " \n", " \n", - " GreeneiBot2\n", - " -10.6\n", - " 58.4\n", - " -0.2\n", - " 0.849331\n", - " 0.111188\n", - " -1.638794\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.053366\n", - " 0.106731\n", - " \n", - " \n", " ajf-bot\n", " -10.9\n", " 34.2\n", @@ -6909,18 +6909,32 @@ " 0.023810\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -11.7\n", - " 52.1\n", + " metac-perplexity\n", + " -12.3\n", + " 89.1\n", + " -0.1\n", + " 0.992894\n", + " 0.105187\n", + " -1.316799\n", + " 1.986405\n", + " 0.1\n", + " -0.3\n", + " 0.095661\n", + " 0.191321\n", + " \n", + " \n", + " metac-Gemini-Exp-1206\n", + " -12.6\n", + " 76.5\n", " -0.2\n", - " 0.669031\n", - " 0.092689\n", - " -2.432744\n", - " 2.005379\n", - " -0.0\n", + " 1.007464\n", + " 0.115186\n", + " -1.431098\n", + " 1.990822\n", + " 0.1\n", " -0.4\n", - " 0.009262\n", - " 0.018524\n", + " 0.078264\n", + " 0.156528\n", " \n", " \n", " laylaps\n", @@ -6951,32 +6965,18 @@ " 0.006348\n", " \n", " \n", - " metac-perplexity\n", - " -13.6\n", - " 89.1\n", - " -0.2\n", - " 0.953801\n", - " 0.101046\n", - " -1.515249\n", - " 1.986405\n", - " 0.0\n", - " -0.4\n", - " 0.066645\n", - " 0.133289\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -13.9\n", - " 76.5\n", + " bot_median\n", + " -14.4\n", + " 92.1\n", " -0.2\n", - " 0.960843\n", - " 0.109855\n", - " -1.650953\n", - " 1.990822\n", + " 0.806477\n", + " 0.084035\n", + " -1.864964\n", + " 1.985550\n", " 0.0\n", - " -0.4\n", - " 0.051451\n", - " 0.102902\n", + " -0.3\n", + " 0.032703\n", + " 0.065406\n", " \n", " \n", " manticAI\n", @@ -6993,6 +6993,20 @@ " 0.011014\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -15.8\n", + " 52.1\n", + " -0.3\n", + " 0.772503\n", + " 0.107024\n", + " -2.827984\n", + " 2.005379\n", + " -0.1\n", + " -0.5\n", + " 0.003337\n", + " 0.006674\n", + " \n", + " \n", " NextWorldLab\n", " -16.9\n", " 80.2\n", @@ -7007,74 +7021,46 @@ " 0.040909\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -17.7\n", - " 91.1\n", - " -0.2\n", - " 0.822269\n", - " 0.086150\n", - " -2.253410\n", - " 1.985829\n", - " -0.0\n", - " -0.4\n", - " 0.013330\n", - " 0.026660\n", - " \n", - " \n", - " bot_median\n", - " -17.9\n", - " 92.1\n", - " -0.2\n", - " 0.829829\n", - " 0.086469\n", - " -2.248076\n", - " 1.985550\n", - " -0.0\n", + " minefrac1\n", + " -19.4\n", + " 51.1\n", " -0.4\n", - " 0.013492\n", - " 0.026984\n", + " 0.878544\n", + " 0.122900\n", + " -3.095343\n", + " 2.006545\n", + " -0.1\n", + " -0.6\n", + " 0.001607\n", + " 0.003215\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -18.2\n", + " -20.5\n", " 90.5\n", " -0.2\n", - " 0.988222\n", - " 0.103880\n", - " -1.930829\n", + " 1.002602\n", + " 0.105391\n", + " -2.144815\n", " 1.986072\n", - " 0.0\n", - " -0.4\n", - " 0.028335\n", - " 0.056670\n", - " \n", - " \n", - " minefrac1\n", - " -18.8\n", - " 51.1\n", + " -0.0\n", " -0.4\n", - " 0.874752\n", - " 0.122370\n", - " -3.013581\n", - " 2.006545\n", - " -0.1\n", - " -0.6\n", - " 0.002021\n", - " 0.004043\n", + " 0.017338\n", + " 0.034677\n", " \n", " \n", - " metac-Llama-3.1\n", - " -21.3\n", - " 89.1\n", + " metac-o1-preview\n", + " -21.8\n", + " 91.1\n", " -0.2\n", - " 0.912804\n", - " 0.096703\n", - " -2.471743\n", - " 1.986405\n", - " -0.0\n", + " 0.778395\n", + " 0.081553\n", + " -2.928718\n", + " 1.985829\n", + " -0.1\n", " -0.4\n", - " 0.007684\n", - " 0.015368\n", + " 0.002155\n", + " 0.004310\n", " \n", " \n", " mmBot\n", @@ -7091,32 +7077,32 @@ " 0.002208\n", " \n", " \n", - " metac-exa\n", - " -22.4\n", - " 89.1\n", - " -0.3\n", - " 0.812802\n", - " 0.086108\n", - " -2.923729\n", - " 1.986405\n", + " metac-claude-3-5-sonnet-latest\n", + " -22.6\n", + " 91.1\n", + " -0.2\n", + " 0.807536\n", + " 0.084606\n", + " -2.930813\n", + " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.002198\n", - " 0.004396\n", + " 0.002142\n", + " 0.004284\n", " \n", " \n", " pgodzinai\n", - " -23.9\n", + " -23.4\n", " 76.4\n", " -0.3\n", - " 0.991479\n", - " 0.113432\n", - " -2.755452\n", + " 0.973824\n", + " 0.111413\n", + " -2.746500\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003672\n", - " 0.007345\n", + " 0.003765\n", + " 0.007529\n", " \n", " \n", " VeritasAI\n", @@ -7133,60 +7119,74 @@ " 0.000076\n", " \n", " \n", - " metac-grok-2-1212\n", - " -24.5\n", - " 91.1\n", + " metac-exa\n", + " -24.9\n", + " 89.1\n", " -0.3\n", - " 1.013996\n", - " 0.106237\n", - " -2.526844\n", - " 1.985829\n", + " 0.829710\n", + " 0.087900\n", + " -3.180190\n", + " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.006627\n", - " 0.013254\n", + " 0.001016\n", + " 0.002032\n", " \n", " \n", - " metac-gpt-4o\n", - " -26.0\n", + " InstitutPelFutur\n", + " -26.9\n", + " 90.1\n", + " -0.3\n", + " 0.973767\n", + " 0.102587\n", + " -2.908524\n", + " 1.986114\n", + " -0.1\n", + " -0.5\n", + " 0.002292\n", + " 0.004584\n", + " \n", + " \n", + " metac-grok-2-1212\n", + " -28.0\n", " 91.1\n", " -0.3\n", - " 0.851645\n", - " 0.089228\n", - " -3.193010\n", + " 1.005364\n", + " 0.105333\n", + " -2.923031\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000970\n", - " 0.001940\n", + " 0.002191\n", + " 0.004383\n", " \n", " \n", - " metac-o1-preview\n", - " -26.2\n", + " metac-gpt-4o\n", + " -28.0\n", " 91.1\n", " -0.3\n", - " 0.914333\n", - " 0.095796\n", - " -2.997048\n", + " 0.864425\n", + " 0.090567\n", + " -3.393460\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.001761\n", - " 0.003522\n", + " 0.000514\n", + " 0.001027\n", " \n", " \n", - " InstitutPelFutur\n", - " -26.9\n", - " 90.1\n", - " -0.3\n", - " 0.973767\n", - " 0.102587\n", - " -2.908524\n", - " 1.986114\n", + " metac-Llama-3.1\n", + " -28.2\n", + " 89.1\n", + " -0.3\n", + " 0.906064\n", + " 0.095989\n", + " -3.291937\n", + " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.002292\n", - " 0.004584\n", + " 0.000716\n", + " 0.001433\n", " \n", " \n", "\n", @@ -7196,146 +7196,146 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", - "RPM_bot -1.3 7.0 -0.2 0.826978 0.312568 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "metac-o1 -5.3 91.1 -0.1 0.908473 0.095182 \n", - "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", + "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.8 27.4 -0.2 0.747290 0.142762 \n", + "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", - "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", + "metac-o1 -9.3 91.1 -0.1 0.901141 0.094413 \n", + "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", + "GreeneiBot2 -10.4 58.4 -0.2 0.849317 0.111186 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", - "GreeneiBot2 -10.6 58.4 -0.2 0.849331 0.111188 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-deepseek-r1+asknews -11.7 52.1 -0.2 0.669031 0.092689 \n", + "metac-perplexity -12.3 89.1 -0.1 0.992894 0.105187 \n", + "metac-Gemini-Exp-1206 -12.6 76.5 -0.2 1.007464 0.115186 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "metac-perplexity -13.6 89.1 -0.2 0.953801 0.101046 \n", - "metac-Gemini-Exp-1206 -13.9 76.5 -0.2 0.960843 0.109855 \n", + "bot_median -14.4 92.1 -0.2 0.806477 0.084035 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", + "metac-deepseek-r1+asknews -15.8 52.1 -0.3 0.772503 0.107024 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "metac-claude-3-5-sonnet-latest -17.7 91.1 -0.2 0.822269 0.086150 \n", - "bot_median -17.9 92.1 -0.2 0.829829 0.086469 \n", - "metac-claude-3-5-sonnet-20240620 -18.2 90.5 -0.2 0.988222 0.103880 \n", - "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", - "metac-Llama-3.1 -21.3 89.1 -0.2 0.912804 0.096703 \n", + "minefrac1 -19.4 51.1 -0.4 0.878544 0.122900 \n", + "metac-claude-3-5-sonnet-20240620 -20.5 90.5 -0.2 1.002602 0.105391 \n", + "metac-o1-preview -21.8 91.1 -0.2 0.778395 0.081553 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-exa -22.4 89.1 -0.3 0.812802 0.086108 \n", - "pgodzinai -23.9 76.4 -0.3 0.991479 0.113432 \n", + "metac-claude-3-5-sonnet-latest -22.6 91.1 -0.2 0.807536 0.084606 \n", + "pgodzinai -23.4 76.4 -0.3 0.973824 0.111413 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-grok-2-1212 -24.5 91.1 -0.3 1.013996 0.106237 \n", - "metac-gpt-4o -26.0 91.1 -0.3 0.851645 0.089228 \n", - "metac-o1-preview -26.2 91.1 -0.3 0.914333 0.095796 \n", + "metac-exa -24.9 89.1 -0.3 0.829710 0.087900 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", + "metac-grok-2-1212 -28.0 91.1 -0.3 1.005364 0.105333 \n", + "metac-gpt-4o -28.0 91.1 -0.3 0.864425 0.090567 \n", + "metac-Llama-3.1 -28.2 89.1 -0.3 0.906064 0.095989 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "bean_bot -4.265106 2.784843 -0.0 \n", + "RPM_bot -0.269729 2.446912 0.7 \n", "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "bean_bot -4.265106 2.784843 -0.0 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", - "RPM_bot -0.610596 2.446912 0.6 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "metac-o1 -0.611363 1.985829 0.1 \n", - "annabot -2.112203 2.044183 -0.0 \n", + "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.737830 2.049541 0.0 \n", + "cookics_bot_TEST -1.683660 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", - "MWG -2.535384 2.046561 -0.1 \n", + "metac-o1 -1.081897 1.985829 0.1 \n", + "MWG -2.589625 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", + "GreeneiBot2 -1.601352 2.000832 0.0 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.638794 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-deepseek-r1+asknews -2.432744 2.005379 -0.0 \n", + "metac-perplexity -1.316799 1.986405 0.1 \n", + "metac-Gemini-Exp-1206 -1.431098 1.990822 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "metac-perplexity -1.515249 1.986405 0.0 \n", - "metac-Gemini-Exp-1206 -1.650953 1.990822 0.0 \n", + "bot_median -1.864964 1.985550 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", + "metac-deepseek-r1+asknews -2.827984 2.005379 -0.1 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "metac-claude-3-5-sonnet-latest -2.253410 1.985829 -0.0 \n", - "bot_median -2.248076 1.985550 -0.0 \n", - "metac-claude-3-5-sonnet-20240620 -1.930829 1.986072 0.0 \n", - "minefrac1 -3.013581 2.006545 -0.1 \n", - "metac-Llama-3.1 -2.471743 1.986405 -0.0 \n", + "minefrac1 -3.095343 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.144815 1.986072 -0.0 \n", + "metac-o1-preview -2.928718 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-exa -2.923729 1.986405 -0.1 \n", - "pgodzinai -2.755452 1.990849 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.930813 1.985829 -0.1 \n", + "pgodzinai -2.746500 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-grok-2-1212 -2.526844 1.985829 -0.1 \n", - "metac-gpt-4o -3.193010 1.985829 -0.1 \n", - "metac-o1-preview -2.997048 1.985829 -0.1 \n", + "metac-exa -3.180190 1.986405 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", + "metac-grok-2-1212 -2.923031 1.985829 -0.1 \n", + "metac-gpt-4o -3.393460 1.985829 -0.1 \n", + "metac-Llama-3.1 -3.291937 1.986405 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "bean_bot -0.2 0.007674 0.015349 \n", + "RPM_bot -0.8 0.398203 0.796405 \n", "jonahsingerbot -0.2 0.003839 0.007677 \n", + "bean_bot -0.2 0.007674 0.015349 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", - "RPM_bot -1.0 0.281933 0.563865 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "metac-o1 -0.2 0.271249 0.542499 \n", - "annabot -0.4 0.021811 0.043621 \n", + "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.046947 0.093894 \n", + "cookics_bot_TEST -0.5 0.052019 0.104037 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", - "MWG -0.6 0.008595 0.017191 \n", + "metac-o1 -0.3 0.141093 0.282185 \n", + "MWG -0.6 0.007581 0.015163 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", + "GreeneiBot2 -0.4 0.057397 0.114793 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.053366 0.106731 \n", "ajf-bot -0.7 0.047145 0.094289 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-deepseek-r1+asknews -0.4 0.009262 0.018524 \n", + "metac-perplexity -0.3 0.095661 0.191321 \n", + "metac-Gemini-Exp-1206 -0.4 0.078264 0.156528 \n", "laylaps -0.4 0.008744 0.017488 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "metac-perplexity -0.4 0.066645 0.133289 \n", - "metac-Gemini-Exp-1206 -0.4 0.051451 0.102902 \n", + "bot_median -0.3 0.032703 0.065406 \n", "manticAI -0.4 0.005507 0.011014 \n", + "metac-deepseek-r1+asknews -0.5 0.003337 0.006674 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.013330 0.026660 \n", - "bot_median -0.4 0.013492 0.026984 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.028335 0.056670 \n", - "minefrac1 -0.6 0.002021 0.004043 \n", - "metac-Llama-3.1 -0.4 0.007684 0.015368 \n", + "minefrac1 -0.6 0.001607 0.003215 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.017338 0.034677 \n", + "metac-o1-preview -0.4 0.002155 0.004310 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-exa -0.4 0.002198 0.004396 \n", - "pgodzinai -0.5 0.003672 0.007345 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.002142 0.004284 \n", + "pgodzinai -0.5 0.003765 0.007529 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-grok-2-1212 -0.5 0.006627 0.013254 \n", - "metac-gpt-4o -0.5 0.000970 0.001940 \n", - "metac-o1-preview -0.5 0.001761 0.003522 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 " + "metac-exa -0.5 0.001016 0.002032 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 \n", + "metac-grok-2-1212 -0.5 0.002191 0.004383 \n", + "metac-gpt-4o -0.5 0.000514 0.001027 \n", + "metac-Llama-3.1 -0.5 0.000716 0.001433 " ] }, "execution_count": 42, @@ -9087,363 +9087,363 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", - " 7.2\n", - " 9.5\n", + " 6.1\n", + " 7.4\n", + " 9.7\n", " 11.8\n", - " 12.8\n", + " 13.2\n", " \n", " \n", " metac-o1-preview\n", - " 3.8\n", - " 5.2\n", - " 8.2\n", - " 11.1\n", - " 12.6\n", + " 3.9\n", + " 5.4\n", + " 8.3\n", + " 11.4\n", + " 12.9\n", " \n", " \n", " manticAI\n", - " 0.5\n", - " 2.2\n", - " 5.6\n", - " 8.9\n", - " 10.5\n", + " 0.3\n", + " 2.0\n", + " 5.4\n", + " 8.8\n", + " 10.6\n", " \n", " \n", " metac-Gemini-Exp-1206\n", " 0.7\n", - " 2.1\n", - " 4.8\n", - " 7.5\n", - " 8.9\n", + " 2.2\n", + " 5.0\n", + " 7.8\n", + " 9.2\n", " \n", " \n", " acm_bot\n", - " 0.1\n", - " 1.8\n", - " 4.6\n", - " 7.6\n", - " 8.9\n", + " 0.6\n", + " 1.9\n", + " 4.7\n", + " 7.5\n", + " 8.7\n", " \n", " \n", " metac-perplexity\n", - " -1.5\n", - " 0.5\n", - " 4.2\n", - " 7.7\n", - " 9.3\n", + " -1.9\n", + " 0.3\n", + " 4.3\n", + " 7.9\n", + " 9.8\n", " \n", " \n", " GreeneiBot2\n", - " -1.2\n", + " -1.4\n", " 0.7\n", - " 4.1\n", - " 7.4\n", - " 9.7\n", + " 3.9\n", + " 7.0\n", + " 8.6\n", " \n", " \n", " twsummerbot\n", - " 0.3\n", - " 1.5\n", - " 3.8\n", - " 6.1\n", + " 0.1\n", + " 1.4\n", + " 3.9\n", + " 6.3\n", " 7.5\n", " \n", " \n", - " pgodzinai\n", - " -2.9\n", - " -1.0\n", - " 3.1\n", - " 7.2\n", - " 9.4\n", - " \n", - " \n", " cookics_bot_TEST\n", " -0.0\n", " 1.1\n", - " 3.0\n", + " 3.1\n", " 5.0\n", - " 6.1\n", + " 5.8\n", + " \n", + " \n", + " pgodzinai\n", + " -3.4\n", + " -1.1\n", + " 3.1\n", + " 7.3\n", + " 9.5\n", " \n", " \n", " CumulativeBot\n", - " -0.1\n", - " 0.8\n", + " 0.1\n", + " 0.9\n", " 2.7\n", " 4.5\n", - " 5.4\n", + " 5.3\n", " \n", " \n", " SynapseSeer\n", - " 0.4\n", - " 1.2\n", + " 0.1\n", + " 0.9\n", " 2.5\n", - " 4.0\n", + " 4.1\n", " 4.8\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " -0.1\n", - " 2.5\n", - " 4.9\n", - " 6.3\n", + " -1.6\n", + " -0.2\n", + " 2.4\n", + " 5.0\n", + " 6.2\n", + " \n", + " \n", + " jkraybill_bot\n", + " -3.9\n", + " -1.7\n", + " 1.9\n", + " 5.0\n", + " 7.0\n", " \n", " \n", " metac-exa\n", - " -5.0\n", + " -4.8\n", " -2.6\n", - " 2.0\n", + " 1.5\n", " 5.8\n", - " 7.8\n", - " \n", - " \n", - " jkraybill_bot\n", - " -4.3\n", - " -1.7\n", - " 1.7\n", - " 4.9\n", - " 6.6\n", + " 7.6\n", " \n", " \n", " metac-deepseek-r1+asknews\n", - " -2.0\n", + " -1.8\n", " -0.8\n", " 1.3\n", - " 3.3\n", + " 3.5\n", " 4.5\n", " \n", " \n", " MWG\n", " -1.5\n", " -0.7\n", - " 0.8\n", + " 0.7\n", " 2.2\n", - " 2.8\n", + " 3.0\n", + " \n", + " \n", + " pianobot\n", + " -1.2\n", + " -0.8\n", + " 0.0\n", + " 0.7\n", + " 1.1\n", " \n", " \n", " andrewsiah\n", " -0.9\n", - " -0.6\n", - " 0.0\n", + " -0.5\n", + " -0.0\n", " 0.6\n", - " 0.9\n", + " 1.0\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", - " pianobot\n", - " -1.3\n", - " -0.9\n", - " -0.0\n", - " 0.7\n", - " 1.1\n", - " \n", - " \n", " cobyj-bot\n", - " -1.3\n", + " -1.4\n", " -0.9\n", " -0.1\n", " 0.8\n", - " 1.4\n", + " 1.3\n", " \n", " \n", " annabot\n", - " -3.9\n", + " -3.4\n", " -2.5\n", " -0.4\n", - " 1.3\n", - " 2.0\n", + " 1.2\n", + " 2.1\n", " \n", " \n", " KevinTestBot\n", - " -4.0\n", - " -2.7\n", + " -3.9\n", + " -2.8\n", " -0.5\n", " 1.6\n", - " 2.7\n", + " 2.6\n", " \n", " \n", " bean_bot\n", - " -3.3\n", + " -3.2\n", " -2.2\n", " -0.5\n", - " 0.9\n", - " 1.7\n", + " 1.0\n", + " 1.9\n", " \n", " \n", " CatrachoCaster\n", " -2.3\n", " -1.8\n", - " -0.7\n", + " -0.8\n", " 0.2\n", - " 0.6\n", + " 0.8\n", " \n", " \n", " jonahsingerbot\n", - " -2.9\n", + " -3.0\n", " -2.2\n", " -0.9\n", - " 0.4\n", - " 0.9\n", + " 0.3\n", + " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", + " -3.5\n", " -2.6\n", " -0.9\n", - " 0.7\n", - " 1.7\n", + " 0.8\n", + " 1.6\n", " \n", " \n", " ProfessorSP\n", - " -4.2\n", - " -3.2\n", - " -1.1\n", + " -4.4\n", + " -3.3\n", + " -1.0\n", " 1.0\n", - " 2.1\n", + " 2.0\n", " \n", " \n", " mmBot\n", - " -7.0\n", - " -5.2\n", - " -1.2\n", - " 2.3\n", - " 4.4\n", + " -7.3\n", + " -5.5\n", + " -1.5\n", + " 2.4\n", + " 4.2\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.6\n", - " -5.0\n", + " -6.3\n", + " -4.7\n", " -1.5\n", - " 1.7\n", + " 2.0\n", " 3.7\n", " \n", " \n", " 4Shadower\n", - " -4.6\n", - " -3.6\n", - " -1.7\n", + " -4.9\n", + " -3.7\n", + " -1.6\n", " 0.2\n", " 1.2\n", " \n", " \n", " swingswish\n", - " -5.3\n", + " -5.4\n", + " -4.2\n", + " -2.0\n", + " -0.1\n", + " 0.7\n", + " \n", + " \n", + " RPM_bot\n", + " -4.9\n", " -3.9\n", - " -1.9\n", + " -2.1\n", + " -0.8\n", " -0.2\n", - " 0.6\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.7\n", + " -5.0\n", + " -2.2\n", + " 0.8\n", + " 2.5\n", " \n", " \n", " InstitutPelFutur\n", " -8.7\n", " -6.6\n", - " -2.1\n", - " 1.7\n", - " 4.0\n", - " \n", - " \n", - " RPM_bot\n", - " -4.6\n", - " -3.7\n", - " -2.1\n", - " -0.7\n", - " -0.0\n", + " -2.5\n", + " 1.6\n", + " 3.3\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.6\n", - " -5.0\n", - " -2.2\n", - " 0.7\n", - " 2.4\n", + " metac-Llama-3.1\n", + " -6.7\n", + " -5.3\n", + " -2.6\n", + " 0.3\n", + " 1.7\n", " \n", " \n", " wunderplumb\n", - " -6.4\n", + " -6.2\n", " -5.0\n", " -2.6\n", - " -0.4\n", - " 0.8\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " -6.9\n", - " -5.5\n", - " -2.8\n", - " -0.0\n", - " 1.7\n", + " -0.2\n", + " 1.3\n", " \n", " \n", " NextWorldLab\n", - " -8.8\n", - " -6.8\n", - " -3.6\n", - " -0.4\n", - " 1.8\n", + " -8.3\n", + " -6.7\n", + " -3.7\n", + " -0.6\n", + " 0.9\n", " \n", " \n", - " laylaps\n", - " -9.6\n", - " -7.8\n", - " -3.8\n", - " -0.2\n", - " 1.4\n", + " Bot_Pepa\n", + " -6.9\n", + " -5.7\n", + " -3.9\n", + " -2.0\n", + " -1.1\n", " \n", " \n", - " Bot_Pepa\n", - " -7.1\n", - " -6.0\n", + " laylaps\n", + " -10.1\n", + " -8.1\n", " -3.9\n", - " -2.1\n", - " -1.2\n", + " -0.5\n", + " 1.3\n", " \n", " \n", " VeritasAI\n", - " -7.5\n", + " -7.8\n", " -6.5\n", - " -4.3\n", - " -1.9\n", - " -0.8\n", + " -4.2\n", + " -1.8\n", + " -0.5\n", " \n", " \n", " minefrac1\n", - " -7.6\n", - " -6.7\n", + " -8.0\n", + " -6.8\n", " -4.6\n", " -2.5\n", - " -1.6\n", + " -1.5\n", " \n", " \n", " Grizeu_Bot\n", - " -9.2\n", - " -7.9\n", - " -5.0\n", - " -2.5\n", - " -1.2\n", + " -9.4\n", + " -7.7\n", + " -4.9\n", + " -2.4\n", + " -1.1\n", " \n", " \n", " metac-gpt-4o\n", - " -10.7\n", + " -10.6\n", " -9.0\n", - " -6.0\n", - " -3.2\n", - " -1.8\n", + " -5.9\n", + " -2.9\n", + " -1.3\n", " \n", " \n", " ajf-bot\n", - " -15.7\n", - " -12.9\n", - " -8.5\n", + " -15.4\n", + " -12.8\n", + " -8.3\n", " -4.2\n", - " -2.0\n", + " -2.1\n", " \n", " \n", "\n", @@ -9451,51 +9451,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.2 9.5 11.8 12.8\n", - "metac-o1-preview 3.8 5.2 8.2 11.1 12.6\n", - "manticAI 0.5 2.2 5.6 8.9 10.5\n", - "metac-Gemini-Exp-1206 0.7 2.1 4.8 7.5 8.9\n", - "acm_bot 0.1 1.8 4.6 7.6 8.9\n", - "metac-perplexity -1.5 0.5 4.2 7.7 9.3\n", - "GreeneiBot2 -1.2 0.7 4.1 7.4 9.7\n", - "twsummerbot 0.3 1.5 3.8 6.1 7.5\n", - "pgodzinai -2.9 -1.0 3.1 7.2 9.4\n", - "cookics_bot_TEST -0.0 1.1 3.0 5.0 6.1\n", - "CumulativeBot -0.1 0.8 2.7 4.5 5.4\n", - "SynapseSeer 0.4 1.2 2.5 4.0 4.8\n", - "metac-claude-3-5-sonnet-latest -1.3 -0.1 2.5 4.9 6.3\n", - "metac-exa -5.0 -2.6 2.0 5.8 7.8\n", - "jkraybill_bot -4.3 -1.7 1.7 4.9 6.6\n", - "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.3 4.5\n", - "MWG -1.5 -0.7 0.8 2.2 2.8\n", - "andrewsiah -0.9 -0.6 0.0 0.6 0.9\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "pianobot -1.3 -0.9 -0.0 0.7 1.1\n", - "cobyj-bot -1.3 -0.9 -0.1 0.8 1.4\n", - "annabot -3.9 -2.5 -0.4 1.3 2.0\n", - "KevinTestBot -4.0 -2.7 -0.5 1.6 2.7\n", - "bean_bot -3.3 -2.2 -0.5 0.9 1.7\n", - "CatrachoCaster -2.3 -1.8 -0.7 0.2 0.6\n", - "jonahsingerbot -2.9 -2.2 -0.9 0.4 0.9\n", - "krm-bot -3.6 -2.6 -0.9 0.7 1.7\n", - "ProfessorSP -4.2 -3.2 -1.1 1.0 2.1\n", - "mmBot -7.0 -5.2 -1.2 2.3 4.4\n", - "metac-grok-2-1212 -6.6 -5.0 -1.5 1.7 3.7\n", - "4Shadower -4.6 -3.6 -1.7 0.2 1.2\n", - "swingswish -5.3 -3.9 -1.9 -0.2 0.6\n", - "InstitutPelFutur -8.7 -6.6 -2.1 1.7 4.0\n", - "RPM_bot -4.6 -3.7 -2.1 -0.7 -0.0\n", - "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.2 0.7 2.4\n", - "wunderplumb -6.4 -5.0 -2.6 -0.4 0.8\n", - "metac-Llama-3.1 -6.9 -5.5 -2.8 -0.0 1.7\n", - "NextWorldLab -8.8 -6.8 -3.6 -0.4 1.8\n", - "laylaps -9.6 -7.8 -3.8 -0.2 1.4\n", - "Bot_Pepa -7.1 -6.0 -3.9 -2.1 -1.2\n", - "VeritasAI -7.5 -6.5 -4.3 -1.9 -0.8\n", - "minefrac1 -7.6 -6.7 -4.6 -2.5 -1.6\n", - "Grizeu_Bot -9.2 -7.9 -5.0 -2.5 -1.2\n", - "metac-gpt-4o -10.7 -9.0 -6.0 -3.2 -1.8\n", - "ajf-bot -15.7 -12.9 -8.5 -4.2 -2.0" + "metac-o1 6.1 7.4 9.7 11.8 13.2\n", + "metac-o1-preview 3.9 5.4 8.3 11.4 12.9\n", + "manticAI 0.3 2.0 5.4 8.8 10.6\n", + "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", + "acm_bot 0.6 1.9 4.7 7.5 8.7\n", + "metac-perplexity -1.9 0.3 4.3 7.9 9.8\n", + "GreeneiBot2 -1.4 0.7 3.9 7.0 8.6\n", + "twsummerbot 0.1 1.4 3.9 6.3 7.5\n", + "cookics_bot_TEST -0.0 1.1 3.1 5.0 5.8\n", + "pgodzinai -3.4 -1.1 3.1 7.3 9.5\n", + "CumulativeBot 0.1 0.9 2.7 4.5 5.3\n", + "SynapseSeer 0.1 0.9 2.5 4.1 4.8\n", + "metac-claude-3-5-sonnet-latest -1.6 -0.2 2.4 5.0 6.2\n", + "jkraybill_bot -3.9 -1.7 1.9 5.0 7.0\n", + "metac-exa -4.8 -2.6 1.5 5.8 7.6\n", + "metac-deepseek-r1+asknews -1.8 -0.8 1.3 3.5 4.5\n", + "MWG -1.5 -0.7 0.7 2.2 3.0\n", + "pianobot -1.2 -0.8 0.0 0.7 1.1\n", + "andrewsiah -0.9 -0.5 -0.0 0.6 1.0\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", + "cobyj-bot -1.4 -0.9 -0.1 0.8 1.3\n", + "annabot -3.4 -2.5 -0.4 1.2 2.1\n", + "KevinTestBot -3.9 -2.8 -0.5 1.6 2.6\n", + "bean_bot -3.2 -2.2 -0.5 1.0 1.9\n", + "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", + "jonahsingerbot -3.0 -2.2 -0.9 0.3 1.0\n", + "krm-bot -3.5 -2.6 -0.9 0.8 1.6\n", + "ProfessorSP -4.4 -3.3 -1.0 1.0 2.0\n", + "mmBot -7.3 -5.5 -1.5 2.4 4.2\n", + "metac-grok-2-1212 -6.3 -4.7 -1.5 2.0 3.7\n", + "4Shadower -4.9 -3.7 -1.6 0.2 1.2\n", + "swingswish -5.4 -4.2 -2.0 -0.1 0.7\n", + "RPM_bot -4.9 -3.9 -2.1 -0.8 -0.2\n", + "metac-claude-3-5-sonnet-20240620 -6.7 -5.0 -2.2 0.8 2.5\n", + "InstitutPelFutur -8.7 -6.6 -2.5 1.6 3.3\n", + "metac-Llama-3.1 -6.7 -5.3 -2.6 0.3 1.7\n", + "wunderplumb -6.2 -5.0 -2.6 -0.2 1.3\n", + "NextWorldLab -8.3 -6.7 -3.7 -0.6 0.9\n", + "Bot_Pepa -6.9 -5.7 -3.9 -2.0 -1.1\n", + "laylaps -10.1 -8.1 -3.9 -0.5 1.3\n", + "VeritasAI -7.8 -6.5 -4.2 -1.8 -0.5\n", + "minefrac1 -8.0 -6.8 -4.6 -2.5 -1.5\n", + "Grizeu_Bot -9.4 -7.7 -4.9 -2.4 -1.1\n", + "metac-gpt-4o -10.6 -9.0 -5.9 -2.9 -1.3\n", + "ajf-bot -15.4 -12.8 -8.3 -4.2 -2.1" ] }, "execution_count": 49, @@ -9590,20 +9590,20 @@ " 0.0\n", " \n", " \n", - " jonahsingerbot\n", - " -0.0\n", - " -0.0\n", - " -0.0\n", + " RPM_bot\n", + " -0.1\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " X_bot\n", + " jonahsingerbot\n", + " -0.0\n", + " -0.0\n", " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " bean_bot\n", @@ -9614,8 +9614,8 @@ " -0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", + " X_bot\n", + " -0.0\n", " -0.0\n", " -0.0\n", " 0.0\n", @@ -9686,16 +9686,8 @@ " -0.0\n", " \n", " \n", - " metac-o1\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", - " 0.1\n", - " 0.1\n", - " \n", - " \n", " 4Shadower\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9715,11 +9707,11 @@ " -0.1\n", " -0.1\n", " -0.0\n", - " -0.0\n", + " 0.0\n", " \n", " \n", " jkraybill_bot\n", - " -0.1\n", + " -0.2\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9738,16 +9730,16 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " -0.0\n", " \n", " \n", - " ProfessorSP\n", - " -0.2\n", + " metac-o1\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", - " -0.0\n", + " 0.0\n", + " 0.1\n", " \n", " \n", " GreeneiBot2\n", @@ -9758,6 +9750,14 @@ " 0.0\n", " \n", " \n", + " ProfessorSP\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " \n", + " \n", " ajf-bot\n", " -0.3\n", " -0.2\n", @@ -9770,7 +9770,7 @@ " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " 0.0\n", " 0.1\n", " \n", " \n", @@ -9782,12 +9782,12 @@ " -0.0\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", + " metac-perplexity\n", + " -0.3\n", + " -0.3\n", " -0.1\n", - " -0.0\n", + " 0.0\n", + " 0.1\n", " \n", " \n", " laylaps\n", @@ -9798,26 +9798,26 @@ " -0.0\n", " \n", " \n", - " wunderplumb\n", + " metac-Gemini-Exp-1206\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", - " -0.1\n", + " -0.0\n", + " 0.1\n", " \n", " \n", - " metac-perplexity\n", - " -0.3\n", + " wunderplumb\n", " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " -0.1\n", - " -0.0\n", - " 0.1\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", + " bot_median\n", " -0.3\n", " -0.3\n", - " -0.1\n", + " -0.2\n", " -0.0\n", " 0.0\n", " \n", @@ -9830,15 +9830,15 @@ " -0.0\n", " \n", " \n", - " NextWorldLab\n", + " metac-deepseek-r1+asknews\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " NextWorldLab\n", " -0.3\n", " -0.3\n", " -0.2\n", @@ -9846,36 +9846,28 @@ " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.4\n", - " -0.3\n", - " -0.2\n", - " -0.1\n", - " 0.0\n", - " \n", - " \n", - " bot_median\n", + " minefrac1\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " minefrac1\n", - " -0.3\n", + " metac-claude-3-5-sonnet-20240620\n", + " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " 0.0\n", " \n", " \n", - " metac-Llama-3.1\n", + " metac-o1-preview\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", " mmBot\n", @@ -9886,7 +9878,7 @@ " -0.1\n", " \n", " \n", - " metac-exa\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9895,7 +9887,7 @@ " \n", " \n", " pgodzinai\n", - " -0.5\n", + " -0.4\n", " -0.4\n", " -0.2\n", " -0.1\n", @@ -9905,36 +9897,44 @@ " VeritasAI\n", " -0.4\n", " -0.3\n", + " -0.3\n", " -0.2\n", + " -0.1\n", + " \n", + " \n", + " metac-exa\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", - " -0.1\n", + " -0.2\n", " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", - " -0.4\n", + " metac-grok-2-1212\n", + " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-o1-preview\n", - " -0.4\n", + " metac-gpt-4o\n", + " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", + " metac-Llama-3.1\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9949,10 +9949,10 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", @@ -9961,38 +9961,38 @@ "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "metac-o1 -0.2 -0.2 -0.1 0.1 0.1\n", - "4Shadower -0.2 -0.1 -0.1 -0.0 -0.0\n", + "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 -0.0\n", - "jkraybill_bot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", + "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", - "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", + "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", + "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", + "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-deepseek-r1+asknews -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-perplexity -0.3 -0.3 -0.1 0.0 0.1\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.2 -0.1 -0.0 0.1\n", "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", - "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", - "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.0\n", + "bot_median -0.3 -0.3 -0.2 -0.0 0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", + "metac-deepseek-r1+asknews -0.3 -0.3 -0.2 -0.1 -0.1\n", "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", - "bot_median -0.3 -0.3 -0.2 -0.1 -0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-Llama-3.1 -0.4 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-exa -0.4 -0.3 -0.2 -0.1 -0.1\n", - "pgodzinai -0.5 -0.4 -0.2 -0.1 -0.1\n", - "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.1\n", - "metac-gpt-4o -0.4 -0.4 -0.3 -0.2 -0.1\n", - "metac-o1-preview -0.4 -0.4 -0.3 -0.2 -0.1\n", - "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1" + "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", + "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" ] }, "execution_count": 50, @@ -10654,505 +10654,505 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.8]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 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forecasts: [0.1, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.6, 0.9, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.15, 0.12]\n", - " >>> Collected 4 forecasts: [0.98, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.2, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.25, 0.02, 0.12, 0.29]\n", " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.95, 0.15, 0.14, 0.2]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.7, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.65, 0.85, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.02, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.85, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.1, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.8, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.2, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.9, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.9, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05]\n", - " >>> Collected 5 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06]\n", " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.2, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.65, 0.85, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.02, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.6, 0.85, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.2, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.15, 0.1, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.744]\n", " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.8, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05]\n", " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8]\n", " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", + " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18]\n", - " >>> Collected 7 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3]\n", - " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", + " >>> Collected 7 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", - " >>> Collected 7 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", - " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2]\n", " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2]\n", - " >>> Collected 7 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9]\n", - " >>> Collected 7 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", + " >>> Collected 7 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", - " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615]\n", - " >>> Collected 8 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", - " >>> Collected 8 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223]\n", + " >>> Collected 8 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", + " >>> Collected 8 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4]\n", - " >>> Collected 9 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4]\n", - " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8]\n", + " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", + " >>> Collected 9 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", - " >>> Collected 10 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4, 0.126]\n", - " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", + " >>> Collected 10 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } @@ -11234,16 +11234,16 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", " numeric\n", " NaN\n", " 86.82\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", @@ -11252,25 +11252,25 @@ " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.085\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", + " 0.11\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.2,0.6,0.2]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", " numeric\n", " NaN\n", " 119.2\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", @@ -11288,27 +11288,27 @@ " binary\n", " NaN\n", " yes\n", - " 0.95\n", + " 0.9\n", " 0.905\n", - " 0.92\n", + " 0.9025\n", " \n", " \n", " 351\n", " binary\n", " NaN\n", " no\n", - " 0.9\n", - " 0.65\n", - " 0.3585\n", + " 0.15\n", + " 0.15\n", + " 0.1085\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", - " 0.95\n", " 0.9\n", - " 0.775\n", + " 0.9\n", + " 0.825\n", " \n", " \n", " 361\n", @@ -11317,7 +11317,7 @@ " no\n", " 0.85\n", " 0.8\n", - " 0.709\n", + " 0.755\n", " \n", " \n", " 364\n", @@ -11348,42 +11348,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", - "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.05 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", ".. ... \n", - "342 0.95 \n", - "351 0.9 \n", - "355 0.95 \n", + "342 0.9 \n", + "351 0.15 \n", + "355 0.9 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.905 \n", - "351 0.65 \n", + "351 0.15 \n", "355 0.9 \n", "361 0.8 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "2 0.11 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", - "342 0.92 \n", - "351 0.3585 \n", - "355 0.775 \n", - "361 0.709 \n", + "342 0.9025 \n", + "351 0.1085 \n", + "355 0.825 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11474,52 +11474,52 @@ " \n", " 0\n", " 1\n", - " 636.97\n", + " 1326.64\n", " \n", " \n", " 1\n", " 2\n", - " 2444.36\n", + " 2492.14\n", " \n", " \n", " 2\n", " 3\n", - " 2419.66\n", + " 2545.30\n", " \n", " \n", " 3\n", " 4\n", - " 2491.70\n", + " 2613.88\n", " \n", " \n", " 4\n", " 5\n", - " 2645.79\n", + " 2743.23\n", " \n", " \n", " 5\n", " 6\n", - " 2517.08\n", + " 2513.69\n", " \n", " \n", " 6\n", " 7\n", - " 2392.69\n", + " 2611.87\n", " \n", " \n", " 7\n", " 8\n", - " 2484.64\n", + " 2685.15\n", " \n", " \n", " 8\n", " 9\n", - " 2381.71\n", + " 2381.69\n", " \n", " \n", " 9\n", " 10\n", - " 2419.31\n", + " 2215.95\n", " \n", " \n", "\n", @@ -11527,16 +11527,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 636.97\n", - "1 2 2444.36\n", - "2 3 2419.66\n", - "3 4 2491.70\n", - "4 5 2645.79\n", - "5 6 2517.08\n", - "6 7 2392.69\n", - "7 8 2484.64\n", - "8 9 2381.71\n", - "9 10 2419.31" + "0 1 1326.64\n", + "1 2 2492.14\n", + "2 3 2545.30\n", + "3 4 2613.88\n", + "4 5 2743.23\n", + "5 6 2513.69\n", + "6 7 2611.87\n", + "7 8 2685.15\n", + "8 9 2381.69\n", + "9 10 2215.95" ] }, "execution_count": 60, @@ -11690,18 +11690,18 @@ " NaN\n", " False\n", " False\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " ...\n", - " [0.014083333333333333, 0.0001, 0.0001, 0.0001,...\n", - " [0.25704166666666667, 0.0001, 0.0001, 0.0001, ...\n", + " [0.010416666666666666, 0.0001, 0.0001, 0.0001,...\n", + " [0.23020833333333335, 0.0001, 0.0001, 0.0001, ...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.05, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.05, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", + " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -11714,18 +11714,18 @@ " 100.0\n", " True\n", " True\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " ...\n", - " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05...\n", - " [0.05, 0.05079411765, 0.0515882353, 0.05238235...\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", - " [0.05, 0.050679136250000006, 0.051358272499999...\n", - " [0.05, 0.050679136250000006, 0.051358272499999...\n", + " [0.05, 0.0506374696, 0.051274939150000004, 0.0...\n", + " [0.05, 0.0506374696, 0.051274939150000004, 0.0...\n", " \n", " \n", " 2\n", @@ -11738,18 +11738,18 @@ " NaN\n", " False\n", " False\n", - " 0.1\n", + " 0.05\n", " ...\n", + " 0.05\n", " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.085\n", - " 0.085\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.07\n", + " 0.063\n", + " 0.063\n", + " 0.07\n", + " 0.11\n", + " 0.11\n", + " 0.15\n", + " 0.15\n", " \n", " \n", " 3\n", @@ -11762,18 +11762,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.2,0.6,0.2]\n", " ...\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", - " [0.0001, 0.545, 0.0001]\n", + " [0.0001, 0.6, 0.0001]\n", + " [0.0001, 0.525, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5562499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.50125, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", - " [0.0001, 0.50125, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", @@ -11786,12 +11786,12 @@ " 400.0\n", " False\n", " False\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " ...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.0029090909, 0.0058181818, 0.0087272727...\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", + " [0.0, 0.00267857145, 0.00535714285, 0.00803571...\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", + " [0.0, 0.0021590909, 0.0043181818, 0.0064772727...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", @@ -11820,80 +11820,80 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.014083333333333333,0.6016666666666668,0.178... ... \n", - "1 True [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... ... \n", - "2 False 0.1 ... \n", - "3 NaN [0.37,0.49000000000000005,0.13999999999999999] ... \n", - "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", + "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", + "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", + "2 False 0.05 ... \n", + "3 NaN [0.2,0.6,0.2] ... \n", + "4 False [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 [0.014083333333333333, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 [0.010416666666666666, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 [0.0001, 0.6, 0.0001] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.25704166666666667, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.05079411765, 0.0515882353, 0.05238235... \n", + "0 [0.23020833333333335, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", "2 0.1 \n", - "3 [0.0001, 0.545, 0.0001] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "3 [0.0001, 0.525, 0.0001] \n", + "4 [0.0, 0.00267857145, 0.00535714285, 0.00803571... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", - "2 0.1 \n", + "2 0.07 \n", "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", "\n", " median_forecast_4_bots \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5562499999999999, 0.0001] \n", - "4 [0.0, 0.0029090909, 0.0058181818, 0.0087272727... \n", + "4 [0.0, 0.0021590909, 0.0043181818, 0.0064772727... \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " median_forecast_6_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.50125, 0.0001] \n", + "2 0.07 \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "2 0.11 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "2 0.11 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_9_bots \\\n", - "0 [0.05, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.050679136250000006, 0.051358272499999... \n", - "2 0.1 \n", - "3 [0.0001, 0.50125, 0.0001] \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", + "2 0.15 \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", "\n", " median_forecast_10_bots \n", - "0 [0.05, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.050679136250000006, 0.051358272499999... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", + "2 0.15 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -11973,7 +11973,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -12009,7 +12009,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -12082,9 +12082,9 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", @@ -12171,7 +12171,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.65\n", + " 0.15\n", " 0.05\n", " \n", " \n", @@ -12256,7 +12256,7 @@ "\n", " question_weight bot_team_median pro_median \n", "342 1.00 0.905 0.95 \n", - "351 1.00 0.65 0.05 \n", + "351 1.00 0.15 0.05 \n", "355 1.00 0.9 0.97 \n", "361 0.85 0.8 0.666 \n", "364 0.85 0.05 0.03 " @@ -12315,14 +12315,14 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1175\n" + "Weighted Total Score: -0.1182\n" ] } ], @@ -12344,7 +12344,7 @@ "outputs": [ { "data": { - "image/png": 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666/l4uKiBx980L6Ol5eXVq9erRUrVuiHH37Q4sWLNXv2bLVu3VpLly696s9UhquF6X+7/Ihydj300EP2+aGWLl2qcePGaezYsZo3b57atWt3w/sFkLcITgDyrUuXLkmSkpKSJGVeHH/27FmHo047duyw35/h3wMx3KiMQSWCgoKu+eHq9OnTiomJ0ejRozVy5Eh7f8YRq/wmMDBQvr6+SktLc/qh8ZtvvlGlSpU0b948h9f135OuVqhQQTExMUpKSnL4MLxz506H9SIjI7Vs2bIsH+tq71uFChX0+++/Kz093eED/L/f+//85z8OA4qUKFHims/taqx8fbIr40jtkSNHJGX+n/3zzz9VpUqVLLfJeL127tx5xdHUnTt3Zjly379VrlxZy5cvV7Nmza4rVNxxxx2644479MYbb2jGjBl65JFHNGvWLIdTVv+tePHi6tixo+bMmaN33nlHs2fPVvPmzVW2bFmH9VxcXHT33Xfr7rvv1jvvvKM333xTL730klasWJFrYfLy1/DfduzYodKlSzsM2V6mTBk9/fTTevrpp3Xs2DHdfvvteuONNwhOQAHCNU4A8qWLFy9q6dKlcnd3t5+O1b59e6WlpemDDz5wWHfixImy2WwOH0CKFy9+3cM8X0tkZKT8/Pz05ptvZnl9xvHjxyVlHin695GTy08pyk9cXV31wAMPaO7cufrzzz+vuD/jeWWsKzk+t19//VXr16932KZ9+/a6dOmSpkyZYu9LS0vT+++/77BemTJlFBER4bBkKF68eJanNbZv317x8fGaPXu2ve/SpUt6//335ePjoxYtWkiSatas6bDf+vXrX9fr8W9Wvj5X8/PPP2f5fzDjWpqMU8batGkjX19fjRkzRhcuXHBYN6PGBg0aKCgoSFOnTlVKSor9/h9//FHbt29Xhw4dnNbz0EMPKS0tTa+//voV9126dMn+83f69Okrfi4yRhq8/LGvpmvXrjp8+LD++9//auvWreratavD/adOnbpim+zs/0aVKVNGdevW1fTp0x1+1/z5559aunSp2rdvL8l8j//9fzooKEhly5bN1foA5DyOOAHIF3788Uf70YNjx45pxowZ+vvvvzVs2DD7dURRUVFq1aqVXnrpJe3fv1/h4eFaunSpFi5cqEGDBjkMOV6/fn0tX75c77zzjsqWLauKFSuqcePG2a7Lz89PU6ZM0WOPPabbb79dDz/8sAIDAxUXF6cffvhBzZo10wcffCA/Pz/dddddevvtt3Xx4kWVK1dOS5cutc8Fkx+99dZbWrFihRo3bqzHH39cNWvW1KlTp7R582YtX77c/oG0Y8eOmjdvnu677z516NBB+/bt09SpU1WzZk370UDJfH+aNWumYcOGaf/+/apZs6bmzZuXZRC6mvr162v27NkaPHiwGjZsKB8fH0VFRemJJ57QRx99pF69emnTpk0KCwvTN998o7Vr12rSpElXXPdWGF+fsWPHatOmTbr//vvtpwlu3rxZn3/+uUqWLGkfHt3Pz08TJ05Uv3791LBhQ3Xv3l0lSpTQ1q1bde7cOU2fPl3FihXT2LFj1bt3b7Vo0ULdunWzD0ceFham559/3mk9LVq00JNPPqkxY8YoNjZWbdq0UbFixfT3339rzpw5evfdd9WlSxdNnz5dH374oe677z5VrlxZZ8+e1SeffCI/Pz97uLiWjLmjhgwZYg+0l3vttde0evVqdejQQRUqVNCxY8f04Ycf6pZbbtGdd955Xa/tjRo3bpzatWunJk2aqG/fvvbhyP39/fXqq69KMudwuuWWW9SlSxeFh4fLx8dHy5cv14YNG3J1jjYAucCKofwAIENWw5F7enoadevWNaZMmWIfPjnD2bNnjeeff94oW7asUaxYMaNq1arGuHHjrlhvx44dxl133WUfvvl6hyb/93DkGVasWGFERkYa/v7+hqenp1G5cmWjV69exsaNG+3r/PPPP8Z9991nBAQEGP7+/saDDz5oHD582JBkjBo1yr5exnDk/x4+uWfPnkbx4sWvqKlFixZGrVq1nNZeoUIFo0OHDlnet2LFiiuG2zYMwzh69KjxzDPPGKGhoUaxYsWMkJAQ4+677zY+/vhj+zrp6enGm2++aVSoUMHw8PAw6tWrZ3z//fdGz549rxg6/OTJk8Zjjz1m+Pn5Gf7+/sZjjz1mbNmy5bqHI09KSjK6d+9uBAQEGJIc9n/06FGjd+/eRunSpQ13d3ejdu3a17XPDAX99Vm7dq3xzDPPGLfddpvh7+9vFCtWzChfvrzRq1cvY8+ePVes/+233xpNmzY1vLy8DD8/P6NRo0bGzJkzHdaZPXu2Ua9ePcPDw8MoWbKk8cgjjxj//POPwzpX+3+Z4eOPPzbq169veHl5Gb6+vkbt2rWN//znP8bhw4cNwzCMzZs3G926dTPKly9veHh4GEFBQUbHjh0dfnaceeSRRwxJRkRExBX3xcTEGJ06dTLKli1ruLu7G2XLljW6detm7Nq1y+l+r/V/IkPGcOTjxo3L8v7ly5cbzZo1s7/OUVFRxrZt2+z3p6SkGC+++KIRHh5u+Pr6GsWLFzfCw8ONDz/80Gl9APIXm2Hk8BXHAAAAAFDIcI0TAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcKLITYCbnp6uw4cPy9fXVzabzepyAAAAAFjEMAydPXtWZcuWlYvLtY8pFbngdPjwYYWGhlpdBgAAAIB84uDBg7rllluuuY6lwWn16tUaN26cNm3apCNHjmj+/Pnq3LnzNbdJSUnRa6+9pi+//FLx8fEqU6aMRo4cqT59+lzXY/r6+koyXxw/P7+bfQoAAAAACqjExESFhobaM8K1WBqckpOTFR4erj59+uj++++/rm0eeughHT16VJ9++qmqVKmiI0eOKD09/bofM+P0PD8/P4ITAAAAgOu6hMfS4NSuXTu1a9fuutdfvHixVq1apb1796pkyZKSpLCwsFyqDgAAAABMBWpUvW+//VYNGjTQ22+/rXLlyunWW2/VkCFDdP78+atuk5KSosTERIcFAAAAALKjQA0OsXfvXq1Zs0aenp6aP3++Tpw4oaefflonT57UtGnTstxmzJgxGj16dB5XCgAAAKAwsRmGYVhdhGSeV+hscIg2bdro559/Vnx8vPz9/SVJ8+bNU5cuXZScnCwvL68rtklJSVFKSoq9nXEBWEJCAtc4AQAAWMQwDF26dElpaWlWl4JCrlixYnJ1dc3yvsTERPn7+19XNihQR5zKlCmjcuXK2UOTJNWoUUOGYeiff/5R1apVr9jGw8NDHh4eeVkmAAAAriE1NVVHjhzRuXPnrC4FRYDNZtMtt9wiHx+fm9pPgQpOzZo105w5c5SUlGR/4rt27ZKLi4vTcdcBAABgvfT0dO3bt0+urq4qW7as3N3dr2tEM+BGGIah48eP2w+yXO3I0/WwNDglJSVp9+7d9va+ffsUGxurkiVLqnz58ho+fLgOHTqkzz//XJLUvXt3vf766+rdu7dGjx6tEydO6MUXX1SfPn2yPE0PAAAA+UtqaqrS09MVGhoqb29vq8tBERAYGKj9+/fr4sWLNxWcLB1Vb+PGjapXr57q1asnSRo8eLDq1aunkSNHSpKOHDmiuLg4+/o+Pj5atmyZzpw5owYNGuiRRx5RVFSU3nvvPUvqBwAAwI1xcSlQgzujAMupI5qWHnFq2bKlrjU2RXR09BV91atX17Jly3KxKgAAAABwRNQHAAAAACcITgAAAMBNatmypQYNGpRnjxcdHa2AgIA8e7zclNev3Y0iOAEAAADXoVevXrLZbFcsu3fv1rx58/T666/b1w0LC9OkSZMctrci7KxYsUIdO3ZUYGCgPD09VblyZXXt2lWrV6/O0zqu5d+vXX5FcAIAAACuU9u2bXXkyBGHpWLFiipZsqR8fX2tLs/Bhx9+qLvvvlulSpXS7NmztXPnTs2fP19NmzbV888/b3V5dvnxtcsKwQkAAAC4Th4eHgoJCXFYXF1dHU43a9mypQ4cOKDnn3/eflRq5cqV6t27txISEux9r776qiQpJSVFQ4YMUbly5VS8eHE1btxYK1eudHjc6OholS9fXt7e3rrvvvt08uTJa9YZFxenQYMGadCgQZo+fbpat26tChUqqE6dOnruuee0ceNG+7onT55Ut27dVK5cOXl7e6t27dqaOXOmw/6yOoJWt25d+3MwDEOvvvqqypcvLw8PD5UtW1bPPvusfd0PP/xQVatWlaenp4KDg9WlSxf7ff8+Ve+LL75QgwYN5Ovrq5CQEHXv3l3Hjh2z379y5UrZbDbFxMSoQYMG8vb2VtOmTbVz585rviY3q0BNgAsAAIBCqkEDKT4+7x83JES6LETkhHnz5ik8PFxPPPGEHn/8cUnmUZVJkyZp5MiR9g/4Pj4+kqQBAwZo27ZtmjVrlsqWLav58+erbdu2+uOPP1S1alX9+uuv6tu3r8aMGaPOnTtr8eLFGjVq1DVrmDt3ri5evKj//Oc/Wd5/+RDdFy5cUP369TV06FD5+fnphx9+0GOPPabKlSurUaNG1/Wc586dq4kTJ2rWrFmqVauW4uPjtXXrVknmFETPPvusvvjiCzVt2lSnTp3Szz//fNV9Xbx4Ua+//rqqVaumY8eOafDgwerVq5cWLVrksN5LL72kCRMmKDAwUP3791efPn20du3a66r3RhCcAAAAYL34eOnQIaurcOr777+3Bx5JateunebMmeOwTsmSJeXq6mo/YpLB399fNpvNoS8uLk7Tpk1TXFycypYtK0kaMmSIFi9erGnTpunNN9/Uu+++q7Zt29pD0K233qp169Zp8eLFV61z165d8vPzc3isuXPnqmfPnvb2+vXrVbt2bZUrV05Dhgyx9w8cOFBLlizR119/fd3BKS4uTiEhIYqIiFCxYsVUvnx5+7ZxcXEqXry4OnbsKF9fX1WoUME+j2tW+vTpY79dqVIlvffee2rYsKGSkpIcXvs33nhDLVq0kCQNGzZMHTp00IULF+Tp6XldNWcXwQkAAADWu+wDfn5+3FatWmnKlCn2dvHixW/q4f/44w+lpaXp1ltvdehPSUlRqVKlJEnbt2/Xfffd53B/kyZNrhmcpCsnfo2MjFRsbKwOHTqkli1bKi0tTZKUlpamN998U19//bUOHTqk1NRUpaSkyNvb+7qfx4MPPqhJkyapUqVKatu2rdq3b6+oqCi5ubnpnnvuUYUKFez3tW3bVvfdd99V979p0ya9+uqr2rp1q06fPq309HRJZgCrWbOmfb06derYb5cpU0aSdOzYMZUvX/66684OghMAAACsl8Ony+WW4sWLq0qVKjm2v6SkJLm6umrTpk1ydXV1uO/yoyvZVbVqVSUkJCg+Pt5+1MnHx0dVqlSRm5tjBBg3bpzeffddTZo0SbVr11bx4sU1aNAgpaam2tdxcXGRYRgO2128eNF+OzQ0VDt37tTy5cu1bNkyPf300xo3bpxWrVolX19fbd68WStXrtTSpUs1cuRIvfrqq9qwYcMVowwmJycrMjJSkZGR+uqrrxQYGKi4uDhFRkY61CNJxYoVs9/OCIkZISs3MDgEAAAAkMPc3d3tR3Su1VevXj2lpaXp2LFjqlKlisOSEXhq1KihX3/91WG7X3755ZqP36VLFxUrVkxjx451WuvatWvVqVMnPfroowoPD1elSpW0a9cuh3UCAwN15MgRezsxMVH79u1zWMfLy0tRUVF67733tHLlSq1fv15//PGHJMnNzU0RERF6++239fvvv2v//v366aefrqhlx44dOnnypN566y01b95c1atXdxgYwkoccQIA5Ftz9iTk6v4frOyfq/sHUHSFhYVp9erVevjhh+Xh4aHSpUsrLCxMSUlJiomJUXh4uLy9vXXrrbfqkUceUY8ePTRhwgTVq1dPx48fV0xMjOrUqaMOHTro2WefVbNmzTR+/Hh16tRJS5YscXqaXvny5TVhwgQ999xzOnXqlHr16qWKFSvq1KlT+vLLLyXJfoSratWq+uabb7Ru3TqVKFFC77zzjo4ePepwWlzr1q0VHR2tqKgoBQQEaOTIkQ5HyKKjo5WWlqbGjRvL29tbX375pby8vFShQgV9//332rt3r+666y6VKFFCixYtUnp6uqpVq5Zl3e7u7nr//ffVv39//fnnn/lmjieOOAEAAAA57LXXXtP+/ftVuXJlBQYGSpKaNm2q/v37q2vXrgoMDNTbb78tSZo2bZp69OihF154QdWqVVPnzp21YcMG+7U6d9xxhz755BO9++67Cg8P19KlS/Xyyy87rWHgwIFaunSpjh8/ri5duqhq1apq37699u3bp8WLF6t27dqSpJdfflm33367IiMj1bJlS4WEhKhz584O+xo+fLhatGihjh07qkOHDurcubMqV65svz8gIECffPKJmjVrpjp16mj58uX67rvvVKpUKQUEBGjevHlq3bq1atSooalTp2rmzJmqVavWFTUHBgYqOjpac+bMUc2aNfXWW29p/PjxN/Qe5DSb8e+TFQu5xMRE+fv7KyEhQX5+flaXAwC4Bo44AYXPhQsXtG/fPlWsWDHXRj8DLnet/3PZyQYccQIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAADkuSI2PhkslFP/1whOAAAAyDPFihWTJJ07d87iSlBUpKamSpLDvFM3gglwAQAAkGdcXV0VEBCgY8eOSZK8vb1ls9ksrgqFVXp6uo4fPy5vb2+5ud1c9CE4AQAAIE+FhIRIkj08AbnJxcVF5cuXv+mATnACAABAnrLZbCpTpoyCgoJ08eJFq8tBIefu7i4Xl5u/QongBAAAAEu4urre9HUnQF5hcAgAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnLA0OK1evVpRUVEqW7asbDabFixYcN3brl27Vm5ubqpbt26u1QcAAAAAksXBKTk5WeHh4Zo8eXK2tjtz5ox69Oihu+++O5cqAwAAAIBMblY+eLt27dSuXbtsb9e/f391795drq6u2TpKBQAAAAA3osBd4zRt2jTt3btXo0aNuq71U1JSlJiY6LAAAAAAQHYUqOD0999/a9iwYfryyy/l5nZ9B8vGjBkjf39/+xIaGprLVQIAAAAobApMcEpLS1P37t01evRo3Xrrrde93fDhw5WQkGBfDh48mItVAgAAACiMLL3GKTvOnj2rjRs3asuWLRowYIAkKT09XYZhyM3NTUuXLlXr1q2v2M7Dw0MeHh55XS4AAACAQqTABCc/Pz/98ccfDn0ffvihfvrpJ33zzTeqWLGiRZUBAAAAKOwsDU5JSUnavXu3vb1v3z7FxsaqZMmSKl++vIYPH65Dhw7p888/l4uLi2677TaH7YOCguTp6XlFPwAAAADkJEuD08aNG9WqVSt7e/DgwZKknj17Kjo6WkeOHFFcXJxV5QEAAACAJMlmGIZhdRF5KTExUf7+/kpISJCfn5/V5QAArmHOnoRc3f+Dlf1zdf8AgPwtO9mgwIyqBwAAAABWITgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOGFpcFq9erWioqJUtmxZ2Ww2LViw4Jrrz5s3T/fcc48CAwPl5+enJk2aaMmSJXlTLAAAAIAiy9LglJycrPDwcE2ePPm61l+9erXuueceLVq0SJs2bVKrVq0UFRWlLVu25HKlAAAAAIoyNysfvF27dmrXrt11rz9p0iSH9ptvvqmFCxfqu+++U7169XK4OgAAAAAwWRqcblZ6errOnj2rkiVLXnWdlJQUpaSk2NuJiYl5URoAAACAQqRADw4xfvx4JSUl6aGHHrrqOmPGjJG/v799CQ0NzcMKAQAAABQGBTY4zZgxQ6NHj9bXX3+toKCgq643fPhwJSQk2JeDBw/mYZUAAAAACoMCearerFmz1K9fP82ZM0cRERHXXNfDw0MeHh55VBkAAACAwqjAHXGaOXOmevfurZkzZ6pDhw5WlwMAAACgCLD0iFNSUpJ2795tb+/bt0+xsbEqWbKkypcvr+HDh+vQoUP6/PPPJZmn5/Xs2VPvvvuuGjdurPj4eEmSl5eX/P39LXkOAAAAAAo/S484bdy4UfXq1bMPJT548GDVq1dPI0eOlCQdOXJEcXFx9vU//vhjXbp0Sc8884zKlCljX5577jlL6gcAAABQNNgMwzCsLiIvJSYmyt/fXwkJCfLz87O6HADANczZk5Cr+3+wMmcrAEBRlp1sUOCucQIAAACAvEZwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHDC0uC0evVqRUVFqWzZsrLZbFqwYIHTbVauXKnbb79dHh4eqlKliqKjo3O9TgAAAABFm6XBKTk5WeHh4Zo8efJ1rb9v3z516NBBrVq1UmxsrAYNGqR+/fppyZIluVwpAAAAgKLMzcoHb9eundq1a3fd60+dOlUVK1bUhAkTJEk1atTQmjVrNHHiREVGRuZWmQAAAACKuAJ1jdP69esVERHh0BcZGan169dfdZuUlBQlJiY6LAAAAACQHQUqOMXHxys4ONihLzg4WImJiTp//nyW24wZM0b+/v72JTQ0NC9KBQAAAFCIFKjgdCOGDx+uhIQE+3Lw4EGrSwIAAABQwFh6jVN2hYSE6OjRow59R48elZ+fn7y8vLLcxsPDQx4eHnlRHgAAAIBCqkAdcWrSpIliYmIc+pYtW6YmTZpYVBEAAACAosDS4JSUlKTY2FjFxsZKMocbj42NVVxcnCTzNLsePXrY1+/fv7/27t2r//znP9qxY4c+/PBDff3113r++eetKB8AAABAEWFpcNq4caPq1aunevXqSZIGDx6sevXqaeTIkZKkI0eO2EOUJFWsWFE//PCDli1bpvDwcE2YMEH//e9/GYocAAAAQK6yGYZhWF1EXkpMTJS/v78SEhLk5+dndTkAgGuYsychV/f/YGX/XN0/ACB/y042KFDXOAEAAACAFQhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJ24oOO3duzen6wAAAACAfOuGglOVKlXUqlUrffnll7pw4UJO1wQAAAAA+YrNMAwjuxvFxsZq2rRpmjlzplJTU9W1a1f17dtXjRo1yo0ac1RiYqL8/f2VkJAgPz8/q8sBAFzDnD0JVpdwwx6s7G91CQAAJ7KTDW7oiFPdunX17rvv6vDhw/rss8905MgR3Xnnnbrtttv0zjvv6Pjx4zdUOAAAAADkRzc1OISbm5vuv/9+zZkzR2PHjtXu3bs1ZMgQhYaGqkePHjpy5EhO1QkAAAAAlrmp4LRx40Y9/fTTKlOmjN555x0NGTJEe/bs0bJly3T48GF16tQpp+oEAAAAAMu43chG77zzjqZNm6adO3eqffv2+vzzz9W+fXu5uJg5rGLFioqOjlZYWFhO1goAAAAAlrih4DRlyhT16dNHvXr1UpkyZbJcJygoSJ9++ulNFQcAAAAA+cENBadly5apfPny9iNMGQzD0MGDB1W+fHm5u7urZ8+eOVIkAAAAAFjphq5xqly5sk6cOHFF/6lTp1SxYsWbLgoAAAAA8pMbCk5Xm/opKSlJnp6eN1UQAAAAAOQ32TpVb/DgwZIkm82mkSNHytvb235fWlqafv31V9WtWzdHCwQAAAAAq2UrOG3ZskWSecTpjz/+kLu7u/0+d3d3hYeHa8iQITlbIQAAAABYLFvBacWKFZKk3r17691335Wfn1+uFAUAAAAA+ckNjao3bdq0nK4DAAAAAPKt6w5O999/v6Kjo+Xn56f777//muvOmzfvpgsDAAAAgPziuoOTv7+/bDab/TYAAAAAFBXXHZwuPz2PU/UAAAAAFCU3NI/T+fPnde7cOXv7wIEDmjRpkpYuXZpjhQEAAABAfnFDwalTp076/PPPJUlnzpxRo0aNNGHCBHXq1ElTpkzJ0QIBAAAAwGo3FJw2b96s5s2bS5K++eYbhYSE6MCBA/r888/13nvv5WiBAAAAAGC1GwpO586dk6+vryRp6dKluv/+++Xi4qI77rhDBw4cyPb+Jk+erLCwMHl6eqpx48b67bffrrn+pEmTVK1aNXl5eSk0NFTPP/+8Lly4cCNPBQAAAACcuqHgVKVKFS1YsEAHDx7UkiVL1KZNG0nSsWPHsj0p7uzZszV48GCNGjVKmzdvVnh4uCIjI3Xs2LEs158xY4aGDRumUaNGafv27fr00081e/ZsjRgx4kaeCgAAAAA4dUPBaeTIkRoyZIjCwsLUuHFjNWnSRJJ59KlevXrZ2tc777yjxx9/XL1791bNmjU1depUeXt767PPPsty/XXr1qlZs2bq3r27wsLC1KZNG3Xr1s3pUSoAAAAAuFE3FJy6dOmiuLg4bdy4UYsXL7b333333Zo4ceJ17yc1NVWbNm1SREREZkEuLoqIiND69euz3KZp06batGmTPSjt3btXixYtUvv27bNcPyUlRYmJiQ4LAAAAAGTHdc/j9G8hISEKCQlx6GvUqFG29nHixAmlpaUpODjYoT84OFg7duzIcpvu3bvrxIkTuvPOO2UYhi5duqT+/ftf9VS9MWPGaPTo0dmqCwAAAAAud0NHnJKTk/XKK6+oadOmqlKliipVquSw5KaVK1fqzTff1IcffqjNmzdr3rx5+uGHH/T6669nuf7w4cOVkJBgXw4ePJir9QEAAAAofG7oiFO/fv20atUqPfbYYypTpoxsNtsNPXjp0qXl6uqqo0ePOvQfPXr0iqNZGV555RU99thj6tevnySpdu3aSk5O1hNPPKGXXnpJLi6OWdDDw0MeHh43VB8AAAAASDcYnH788Uf98MMPatas2U09uLu7u+rXr6+YmBh17txZkpSenq6YmBgNGDAgy23OnTt3RThydXWVJBmGcVP1AAAAAEBWbig4lShRQiVLlsyRAgYPHqyePXuqQYMGatSokSZNmqTk5GT17t1bktSjRw+VK1dOY8aMkSRFRUXpnXfeUb169dS4cWPt3r1br7zyiqKiouwBCgAAAABy0g0Fp9dff10jR47U9OnT5e3tfVMFdO3aVcePH9fIkSMVHx+vunXravHixfYBI+Li4hyOML388suy2Wx6+eWXdejQIQUGBioqKkpvvPHGTdUBAAAAAFdjM27g/LZ69eppz549MgxDYWFhKlasmMP9mzdvzrECc1piYqL8/f2VkJCQ7cl6AQB5a86eBKtLuGEPVva3ugQAgBPZyQY3dMQp43okAAAAACgKbig4jRo1KqfrAAAAAIB864bmcZKkM2fO6L///a+GDx+uU6dOSTJP0Tt06FCOFQcAAAAA+cENHXH6/fffFRERIX9/f+3fv1+PP/64SpYsqXnz5ikuLk6ff/55TtcJAAAAAJa5oSNOgwcPVq9evfT333/L09PT3t++fXutXr06x4oDAAAAgPzghoLThg0b9OSTT17RX65cOcXHx990UQAAAACQn9xQcPLw8FBiYuIV/bt27VJgYOBNFwUAAAAA+ckNBad7771Xr732mi5evChJstlsiouL09ChQ/XAAw/kaIEAAAAAYLUbCk4TJkxQUlKSAgMDdf78ebVo0UJVqlSRr6+v3njjjZyuEQAAAAAsdUOj6vn7+2vZsmVau3attm7dqqSkJN1+++2KiIjI6foAAAAAwHLZDk7p6emKjo7WvHnztH//ftlsNlWsWFEhISEyDEM2my036gQAAAAAy2TrVD3DMHTvvfeqX79+OnTokGrXrq1atWrpwIED6tWrl+67777cqhMAAAAALJOtI07R0dFavXq1YmJi1KpVK4f7fvrpJ3Xu3Fmff/65evTokaNFAgAAAICVsnXEaebMmRoxYsQVoUmSWrdurWHDhumrr77KseIAAAAAID/IVnD6/fff1bZt26ve365dO23duvWmiwIAAACA/CRbwenUqVMKDg6+6v3BwcE6ffr0TRcFAAAAAPlJtoJTWlqa3NyuflmUq6urLl26dNNFAQAAAEB+kq3BIQzDUK9eveTh4ZHl/SkpKTlSFAAABd2cPQm5uv8HK/vn6v4BAI6yFZx69uzpdB1G1AMAAABQ2GQrOE2bNi236gAAAACAfCtb1zgBAAAAQFFEcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAE25WFwAAKNjm7EmwugQAAHIdR5wAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ES+CE6TJ09WWFiYPD091bhxY/3222/XXP/MmTN65plnVKZMGXl4eOjWW2/VokWL8qhaAAAAAEWNm9UFzJ49W4MHD9bUqVPVuHFjTZo0SZGRkdq5c6eCgoKuWD81NVX33HOPgoKC9M0336hcuXI6cOCAAgIC8r54AECe8z64X367d8rraLy8jh2R57F4eR09Is/jR+V1LF5uyclKKVlSKSUDlVKylFJKBSqlZGldKFVaF4LL6ET9O3S+TDmrnwYAoICxGYZhWFlA48aN1bBhQ33wwQeSpPT0dIWGhmrgwIEaNmzYFetPnTpV48aN044dO1SsWLFsP15iYqL8/f2VkJAgPz+/m64fAIq6OXsScnX/LhfOK/DXtSqzeplCVi2X7/49N73PxEpVdbRZSx1r2lLH7rhTl3z9c6DSvPVg5YJXMwDkN9nJBpYGp9TUVHl7e+ubb75R586d7f09e/bUmTNntHDhwiu2ad++vUqWLClvb28tXLhQgYGB6t69u4YOHSpXV9cr1k9JSVFKSoq9nZiYqNDQUIITAOSQ3AhO3gf3q+xPixWyarmCfl0j15QLTrdJd3XVhdLBulS8uDxOn5T7mdOyXcefuHRXV52qU1/HmrXQgc4PKymsck48hVxHcAKAm5ed4GTpqXonTpxQWlqagoODHfqDg4O1Y8eOLLfZu3evfvrpJz3yyCNatGiRdu/eraeffloXL17UqFGjrlh/zJgxGj16dK7UDwDIWSU3/6bqn7yrsssXZRl60l1ddfL2RjreqJnOlQnV+eAQXQgK0fngMkopUUq67As026VLcj9zSh4nj8vj1Al5njwh3907Fbx+tUrGbpBLWpokySUtTaW3/KbSW35Tjcnjdbh1W/3d+2kdb3ynZLPl2XMHAORvll/jlF3p6ekKCgrSxx9/LFdXV9WvX1+HDh3SuHHjsgxOw4cP1+DBg+3tjCNOAIB8Ij1dZVYsUbVP3lPgxvVX3H0uuKzi77pb8S0idKxpC130C7iu3RpubkopHaSU0o7Xy24bNEJuZxMV+NtaBa9bqaC1q+S/2/yyzmYYKhfzo8rF/KjTNetoV++ndbDD/TLc3W/6aQIACjZLg1Pp0qXl6uqqo0ePOvQfPXpUISEhWW5TpkwZFStWzOG0vBo1aig+Pl6pqaly/9cfNw8PD3l4eOR88QCAm+KSkqLy336tav99X357djncdz64jPZ076NDEe2VeGvNHD/yc8nXT0fubqcjd7eTJHnGH1b5775R1ekfyTv+kCSpxLbf1fjF/qoz7lXtfuwJ7Xmkz3WHNgBA4WPpcOTu7u6qX7++YmJi7H3p6emKiYlRkyZNstymWbNm2r17t9LT0+19u3btUpkyZa4ITQCAfMgwFPr9XLVrXU8Nhw90CE0JVarrt7GT9cOKrdr+zItKrFYrT06XuxBSVrsef1aLVsTql4n/1ana9ez3eR2LV+0Jr6ltRANVmDdDsnZMJQCARSyfx2nw4MH65JNPNH36dG3fvl1PPfWUkpOT1bt3b0lSjx49NHz4cPv6Tz31lE6dOqXnnntOu3bt0g8//KA333xTzzzzjFVPAQBwnYrH7VfzPl10x6C+8j562N5/vGFTrfl4lpYuWqcDDzxi2alxRrFiOhjVRTHzftKKmYv0T5uOMv4X3DxPnVCj/zytlt3by2/XdkvqAwBYx/JrnLp27arjx49r5MiRio+PV926dbV48WL7gBFxcXFyccnMd6GhoVqyZImef/551alTR+XKldNzzz2noUOHWvUUAABO2C5e1K2fTVbN98fK7cJ5e//hVpHa/vQQnarX0MLqsmCz6UTDpjrRsKmK79+r2hNeU+iPCyRJgRvW6557m2tXn2e0bcB/lOZd3NpaAQB5wvJ5nPIa8zgBQM5yNhx5yc2/qf4rgxSwc5u971xIOW0ZNVaH7+mY2+XlmOBVy3X76BflE7fP3neuzC3aMvItHY7okOcj8DEcOQDcvOxkA8tP1QMAFE5uSWdVb+QLat010h6aDBcX7erVX0sW/1KgQpMkHW0RoSWL1umvgUOVVsw8ldD7yD9q9tSjavLMYyqWcMbaAgEAuYrgBADIcb67d+ruB+5WlRmf2udjOl0rXDFzY7T15bd0ycfX4gpvTLqnl7Y9N1xLf1yv+Dtb2/tvWfq97ul0lwL+jLWuOABAriI4AQBy1C2LFujuB+62j5Z3ybu4Yl96UzFzY3T6stHqCrKksMr6edpcrX93mlICSkiSiv8Tp9YPtlGlmdMYeQ8ACiGCEwAgR9guXlT4GyPU5NleKpacJEk6U72Wln77s/7u/bQMN8vHI8pZNpv+6XCfln27WifDG0iSXC+mqv4rz6vRkCflei7Z4gIBADmJ4AQAuGmex+LVose9unXah/a+/Z276qc5y5QcVsnCynLf+bKhWjFzkf7u+aS9r8LCr3X3/XfLd/dOCysDAOQkghMA4KaU2rheEZ1aKHDDeklSerFi2jR6gjaMm6o0L2+Lq8sbhru7Yl8Zq/XvTtPF4j6SJP/dOxRxf2uFfj/X4uoAADmB4AQAuHEffaSWj0bJ6/hRSeYw4ytmLNLeR/rm+fDc+cE/He7T8vkrlHBrTUmS27lk3TGor6pPeYfrngCggCM4AQCyzzCk116T+veXy6VLkqSjTe7S8oUr899ktnksqVJVxcxdrv33PWzvqz3hNdX9v2FSerqFlQEAbgbBCQCQPenp0sCB0qhR9q6dfZ7Rz9PmKaVUoIWF5R9pXt7a8PYU/fHCSHtf1ekf6Y5BfeWSkmJhZQCAG0VwAgBcv9RUqXt3afJke9fWYa/r9xFvFL5R826WzaYdTw3Wb2MnK93VVZIUumi+7uz3oNzOJlpcHAAguwhOAIDrc/as1LGjNHu22XZ1laZP165+A62tK5878MAjWjt1hi55ekmSgtevVsvuHeTxv+vCAAAFA8EJAODc8ePS3XdLy5aZbS8vaeFCqUcPa+sqIOJbRWrVF9/aJ8stsf0PtX6ojXz277G4MgDA9SI4AQCu7cABqXlzacMGsx0QIC1fLnXoYGlZBc2peg21YtZiJZe9RZLkc/CAWj0UKb9d2yyuDABwPQhOAICri4uTWraUdv5vIteyZaWff5aaNrW0rILqbJVq+unrpfbhyj1PnVCLxzoxUS4AFAAEJwBA1g4fllq3lvbvN9u33iqtWyfddpulZRV0F0LKasWsRToZXl+S5HnyuFo8dq989v5tcWUAgGshOAEArnT0qHlN057/XYNTtaq0cqVUoYKlZRUWF/0C9PO0uTpdK1yS5HX8qFo+dq+K799rcWUAgKshOAEAHJ04IUVESDt2mO2KFaWffpLKlLG2rkLmol+AVk1foDM1zCN4XkePqOVj98r74H5rCwMAZIngBADIdPq01KaN9OefZjs01AxNt9xibV2F1MWAElo1faH9mifvI/+o5aNR8j4UZ3FlAIB/IzgBAEyJiVLbttKWLWa7bFkzNIWFWVpWYZdaspRWfbFQiZWrSZKKHzqoFo/eK68jhyyuDABwOYITAEBKSpLat5d++81sBwVJMTFSlSrW1lVEpJQK1KovFupsRfP19jm4Xy0ejWKSXADIRwhOAFDUpaRInTtLa9ea7VKlzNBUvbqlZRU1F4JCtPKLb5VUvqIkyffAXjXv+6DcziZaXBkAQCI4AUDRlp4u9e5tBiXJnNx22TKGHLfIhZCyWvnld/ZJckts+11NB/SQLTXV4soAAAQnACjKhg6VZs40b3t5ST/+KNWrZ21NRdz5srfo58/mKtU/QJIUvHalGg4bYIZcAIBlCE4AUFS9+640frx528VFmj1buuMOa2uCJOlslWpa8/EspXl4SpIqfPu1ao971dqiAKCIIzgBQFH0zTfS889ntqdMkaKirKsHVzhZ/w79Mum/MlzMP9XVP3lPVaKnWFwVABRdBCcAKGpWr5YefVQyDLP9yivSE09YWxOydPiejtr86nh7u+4bI3TLD/MtrAgAii6CEwAUJX/9JXXqZI6kJ5kDQ4webW1NuKa93fto2zNDJEk2w1CjIU8qcP1qi6sCgKKH4AQARcWhQ+YEt2fOmO22baWPPpJsNkvLgnN/DXpJ+7o8KklyvZiqZk89Kv3xh8VVAUDRQnACgKLg7Flzgtt//jHb9etLc+ZIxYpZWxeuj82mTa9P1JGWbSRJxZISzWvSjh2zuDAAKDoITgBQ2KWlSY88Iv3+u9muWFH64QfJx8faupAtRrFiWv/eNJ2q/b/h4g8ckO67T7pwwdrCAKCIIDgBQGE3YoT03Xfm7YAAc66m4GBLS8KNSfMurrVTZ+h8cBmzY9066fHHMwf6AADkGoITABRm0dHS22+bt11dzdPzqlWztCTcnAvBZbTmo5nmhMWS9OWX0ltvWVsUABQBBCcAKKzWrHEcZvy996SICOvqQY45c1tdMzBlGDFCmjfPsnoAoCggOAFAYbR/v3n9y8WLZvvpp80Fhcf990tvvJHZfuwxafNm6+oBgEKO4AQAhc3Zs+aIaydOmO2ICGnSJEtLQi4ZPtyczFiSzp2T7r1XOnzY2poAoJAiOAFAYZKWJnXvLv35p9m+9Vbp668ZdrywstmkTz6RmjY124cOmRMcnztnbV0AUAgRnACgMBk+XPr+e/N2QIA5ml6JEpaWhFzm6SnNny+VL2+2N26U+vZlpD0AyGEEJwAoLL78Uho3zrydMYLerbdaWxPyRlCQGZgz5uaaNUuaMMHamgCgkCE4AUBhsHmzOZ9PBkbQK3pq15a++CKzPXSotGyZdfUAQCHjZnUBAICbdOKEOYLehQtmu18/6amn7HfP2ZNgUWHIc507SyNHSq+9JqWnS127mqfuVapkdWUAUOBxxAkACrJLl8wPx3FxZrtxY+mDD8xBA1A0jRpljqooSadPm2EqOdnSkgCgMCA4AUBB9p//SD/9ZN4OCZHmzpU8PKytCdZycTFP2atWzWz/8YfUuzeDRQDATSI4AUBB9dVX0sSJ5u1ixaRvvpHKlbO2JuQP/v7SggWSr6/ZnjNHGjvW0pIAoKAjOAFAQbRli3ktU4b33pOaNbOuHuQ/1aubIy1mGDFCWrzYunoAoIAjOAFAQfPvwSD69pWefNLampA/3Xuv9Oqr5m3DkLp1k3bvtrQkACioCE4AUJBkDAZx4IDZbtxYmjyZwSBwda+8InXqZN4+c8YM3QwWAQDZRnACgIJkxAgGg0D2uLhIn39unronSX/+ac75xWARAJAt+SI4TZ48WWFhYfL09FTjxo3122+/Xdd2s2bNks1mU+fOnXO3QADID+bOlcaNM2+7uZkX/DMYBK6Hn580f77k42O2Z86U3n/f2poAoICxPDjNnj1bgwcP1qhRo7R582aFh4crMjJSx44du+Z2+/fv15AhQ9S8efM8qhQALLRjh9SrV2Z74kTpzjstKwcFUPXqUnR0ZvuFF6Q1aywrBwAKGsuD0zvvvKPHH39cvXv3Vs2aNTV16lR5e3vrs88+u+o2aWlpeuSRRzR69GhVcjIbekpKihITEx0WAChQzp41r0tJSjLbjzwiPfOMtTWhYHrgAXPuL8m8Xu7BB6UjR6ytCQAKCEuDU2pqqjZt2qSIiAh7n4uLiyIiIrR+/fqrbvfaa68pKChIffv2dfoYY8aMkb+/v30JDQ3NkdoBIE8YhtSnj3nESZJq15Y++ojBIHDj3nhDatXKvB0fbw42cvGitTUBQAFgaXA6ceKE0tLSFBwc7NAfHBys+Pj4LLdZs2aNPv30U33yySfX9RjDhw9XQkKCfTl48OBN1w0Aeeadd8yJbSVzUtN586Tixa2tCQWbm5s0a1bm9XE//5x5FAoAcFWWn6qXHWfPntVjjz2mTz75RKVLl76ubTw8POTn5+ewAECBsHKlNHRoZvvzz6UqVSwrB4VIUJAZyIsVM9uTJplhCgBwVW5WPnjp0qXl6uqqo0ePOvQfPXpUISEhV6y/Z88e7d+/X1FRUfa+9PR0SZKbm5t27typypUr527RAJAXDh0yT6FKSzPbL71kTmYK5JQ77pDee0966imz3beveSporVrW1gUA+ZSlR5zc3d1Vv359xcTE2PvS09MVExOjJk2aXLF+9erV9ccffyg2Nta+3HvvvWrVqpViY2O5fglA4ZCaal60nzG6aJs20ujR1taEwunJJ6WePc3b586Zg5AwiBIAZMnSI06SNHjwYPXs2VMNGjRQo0aNNGnSJCUnJ6t3796SpB49eqhcuXIaM2aMPD09ddtttzlsHxAQIElX9ANAgfXCC1LGADkVKkgzZkiurtbWhMLJZpOmTJG2bpViY6W//5Z69zZP42MAEgBwYHlw6tq1q44fP66RI0cqPj5edevW1eLFi+0DRsTFxcnFpUBdigUAN+6rr6QPPjBvu7ubH2BLlbK2JhRuXl7m5Mr160tnzpgDkIwfL734otWVAUC+YjMMw7C6iLyUmJgof39/JSQkMFAEgPzlzz+lxo3NU6Yk6eOPpccfv+ndztmTcNP7QP7zYGX/nN3h999LGdcQu7hIMTFSy5Y5+xgAkM9kJxtwKAcA8oPERHNy0ozQ1Lu31K+ftTWhaOnYUXr5ZfN2err08MPS4cPW1gQA+QjBCQCsZhhmUNq1y2zXqydNnsw1Jsh7r75qDkYiSUePSg89xOS4APA/BCcAsNr48eZ1JZIUEGBe1+TlZWlJKKJcXc3r7MqXN9tr13KtEwD8D8EJAKy0cqU0bFhm+4svpEqVLCsHUOnSZnh3dzfb777L5LgAIIITAFjn8GHzOpL/TeStl182rzMBrNawoRmYMvTrJ23bZl09AJAPEJwAwAoXL5rXjxw9arbbtDGvLwHyiyeflHr0MG8nJ0v338/kuACKNIITAFjhxRfN60ckKTTUvK6ESW6Rn2RMjhsebrZ37pT69DEHMwGAIojgBAB5bebMzNOgMia5LV3a2pqArHh7m5Pj+v9vzqi5c83BTACgCCI4AUBe+uMPx/mZ3ntPatTIunoAZypXlr78MrM9bJj000/W1QMAFiE4AUBeSUgwrxO5fJLbJ56wtibgenTsKL3yink7Y3Lcf/6xtiYAyGMEJwDIC+np5oX2u3eb7dtvZ5JbFCyjRklt25q3jx+XunSRUlKsrQkA8hDBCQDywltvSd9+a94uWdK8VoRJblGQZEyOGxZmtn/9VRo82NKSACAvEZwAILctXWrO0SSZR5hmzMj88AkUJBmh38PDbH/4ofT559bWBAB5hOAEALlp/36pW7fMIZxfe02KjLS0JOCm3H67OUx5hieflGJjLSsHAPIKwQkAcsuFC+Z1IKdOme2oKGnECGtrAnLC5QObXLhgDnqS8f8cAAopghMA5AbDkJ5+Wtq0yWxXrmye0uTCr10UEu+9JzVsaN7et888spqWZm1NAJCL+AsOALnhww+ladPM215e0rx5UkCApSUBOcrDw7zeKTDQbC9dKr30krU1AUAuIjgBQE5bvVoaNCiz/dlnUp06lpUD5JrQUGnOHHPEPUkaO1b6+mtrawKAXEJwAoCcdPCg9OCD0qVLZnvIEHOyUKCwatFCeuedzHbv3tIff1hXDwDkEoITAOSUCxekBx6Qjh0z2xER0pgx1tYE5IWBA80JniXp3Dmpc2cGiwBQ6BCcACAnGIb01FPShg1mu2JFadYsyc3N2rqAvGCzSVOnSvXrm+29e6Xu3RksAkChQnACgJwwebIUHW3e9vKS5s+XSpWytCQgT2UMglK6tNlesiRz4mcAKAQITgBws1avlp5/PrP92WdSeLh19QBWKV/ecbCIt94y2wBQCBCcAOBmHDxoTnKbMRjEf/7DYBAo2lq2lCZMyGz37i39/rtl5QBATiE4AcCNyrgI/vhxs92mjfTmm5aWBOQLzz4rPfqoeTs5Wbr33sxBUwCggCI4AcCNSE+XevWSNm8225UqSTNnZp6iBBRlNpv08cdSw4Zm+8ABc8TJ1FRr6wKAm0BwAoAb8frrmddu+PpK334rlSxpbU1AfuLlJS1YIJUta7bXrDFHnjQMS8sCgBtFcAKA7JozR3r1VfO2zWYeaapVy9KSgHypbFkzPHl6mu3PPpPefdfSkgDgRjHBCIBCY86ehFzb94OV/c0bmzZJPXtm3vH221KHDrn2uECB17ChNG2a1K2b2X7hBal6daltW2vrAoBs4ogTAFyvI0ekTp2k8+fNdq9e5odAANf28MPSSy+Zt9PTpa5dpR07rK0JALKJ4AQA1+P8eTM0HTpktps1k6ZONU/VA+Dca6+Zo1BKUmKiFBUlnTplaUkAkB0EJwBwxjCkvn2lDRvMdvny0rx5koeHtXUBBYmLi/TFF1KdOmZ7927poYekixetrQsArhPBCQCcqD5lgjkAhCQVLy59950UFGRtUUBB5ONjjkAZGGi2Y2KkZ55hpD0ABQLBCQCuIfTbOar9zv+ZDZtN+uqrzG/MAWRfhQrS/PmSu7vZ/uQTc5AVAMjnCE4AcBWlf1urhkOfyex4803zOicAN6dZM3OkvQzDhkmzZ1tXDwBcB4ITAGTBd88uNXvqEbleTDU7nnhCGjrU2qKAwqR7d+mNNzLbPXuak+QCQD5FcAKAf/E4cUzN+3aRe8IZSdKRuyKkyZMZQQ/IacOHmwOvSFJKinlE9++/ra0JAK6C4AQAl3E9l6w7n3hYxf+JkySdrlFbv7w3TXJjvnAgx9ls0pQp0j33mO1Tp6T27aUTJ6ytCwCyQHACgAxpaWr8fD+V/H2zJOlcSDmt+e/XuuTja3FhQCFWrJg0Z450221me/du88jThQvW1gUA/0JwAgBJMgzVfWO4ysX8KEm66OOnnz/9WheCy1hcGFAE+PtLP/wglfnfz9u6deY1T+np1tYFAJchOAGApKrTPlTVzz+WJKW7uWnd5M+VWK2WxVUBRUj58tL335tzpUnS119LL7zAHE8A8g2CE4Air/yC2ar75kv29sY33tWxZi2tKwgoqm6/3RyW3OV/H08mTZLeesvSkgAgA8EJQJEWsmKJGg592t7+a+BQHXjgEQsrAoq4Dh2kjz/ObI8Y4dgGAIsQnAAUWaU3rFPTAT3lkpYmSdr9SD9te3aYxVUBUN++0tixme2nnpLmzrWuHgCQxPi6AIok/22/687HH5ZrijlyV1zHB7Rl1NuWzdU0Z0+CJY8L5Fv/+Y90/Lg0frw5SET37tKPP0qtW1tdGYAiiiNOAIocn/17dFfvB1QsKVGSOcHtb29PybyuAkD+8PbbUq9e5u3UVHOY8o0bLS0JQNGVLz4lTJ48WWFhYfL09FTjxo3122+/XXXdTz75RM2bN1eJEiVUokQJRUREXHN9ALicZ/xh3dXrPnmePC5JOnF7Y63/YLoMd3eLKwNwBZtN+uQT6d57zXZSktSunbRjh7V1ASiSLA9Os2fP1uDBgzVq1Cht3rxZ4eHhioyM1LFjx7Jcf+XKlerWrZtWrFih9evXKzQ0VG3atNGhQ4fyuHIABU2xM6d1V+8HVPyfOElSwq01teaT2UrzLm5xZQCuys1NmjVLuusus33ihNSmjXTwoLV1AShybIZh7QQJjRs3VsOGDfXBBx9IktLT0xUaGqqBAwdq2DDnF2mnpaWpRIkS+uCDD9SjRw+n6ycmJsrf318JCQny8/O76foB5B/Xuk7I7Wyi7up1v0ptNU/zSQqtoBWzl+hCUMh17fvByv45UuPVcI0Tsiu3/0/mOwkJUsuWUmys2a5SRVq5UipXzsKiABR02ckGlh5xSk1N1aZNmxQREWHvc3FxUUREhNavX39d+zh37pwuXryokiVLZnl/SkqKEhMTHRYARYtb0lk17/ugPTRdKB2k1dELrjs0AcgH/P2lxYvNwCRJu3ebA0UcOWJtXQCKDEuD04kTJ5SWlqbg4GCH/uDgYMXHx1/XPoYOHaqyZcs6hK/LjRkzRv7+/vYlNDT0pusGUHBkhKbSm3+VJKUElNDq6HlKrlDR4soAZFtwsLRihVSpktnetcsMT9f5mQEAbobl1zjdjLfeekuzZs3S/Pnz5enpmeU6w4cPV0JCgn05yDnRQJHhmpykOx9/SKU3/SLJDE2rPl+ohOq3WVwZgBt2yy1meAoLM9s7dkh33y1d5dpoAMgplgan0qVLy9XVVUePHnXoP3r0qEJCrn0Kzfjx4/XWW29p6dKlqlOnzlXX8/DwkJ+fn8MCoPBzTU5S834PKXCDedpvqn+AVk9foISaV/99AaCAKF/eDE/ly5vtbdvM8HTihLV1ASjULA1O7u7uql+/vmJiYux96enpiomJUZMmTa663dtvv63XX39dixcvVoMGDfKiVAAFiOu5ZN35eFcFblgnSUr189eq6Qt0pla4xZUByDFhYWZ4yjgF/88/zfB08qSlZQEovCw/VW/w4MH65JNPNH36dG3fvl1PPfWUkpOT1bt3b0lSjx49NHz4cPv6Y8eO1SuvvKLPPvtMYWFhio+PV3x8vJKSkqx6CgDyEdfz53Tn410V9NtaSf8LTZ8v1Jnb6lpbGICcV6mS9NNPmSPr/f67FBEhnTplbV0ACiXLg1PXrl01fvx4jRw5UnXr1lVsbKwWL15sHzAiLi5ORy4bMWfKlClKTU1Vly5dVKZMGfsyfvx4q54CgHzCNTlJzZ54WEG/rpEkpfr6aXX0fEITUJhVqWIeeSpTxmzHxkqtWjFgBIAcZ/k8TnmNeZyAQurUKZ28u61KxW6QJF308dOq6fN1Orx+juyeeZyQ3xS5eZyc2bnTnOcpIzBVqSItXy5VqGBpWQDytwIzjxMA5IjDh6UWLeyhKdXPX6uj5+VYaAJQAFSrJq1enTlgxO7dUrNm0vbt1tYFoNAgOAEo2Pbske6807wwXObktitn/KBTdRk4BihyqlaV1qwxQ5QkHTok3XWXtHmztXUBKBQITgAKrt9/N0PTvn2SpKTQCvpp9hLmaQKKstBQ6eefpdtvN9snTpjXPK1ebW1dAAo8N6sLAIAbsm6d1KGDdOaM2a5VSys+/kYXgstYWhaQV3LzurgCf/1UYKA52l5UlBmiEhOlyEhp7lypfXurqwNQQHHECUDBs3ixOeRwRmi64w5p9WpCE4BM/v7m74p27cz2hQtSp07SV19ZWxeAAovgBKBgmTZNuvde6fx5s33PPdKyZVLJktbWBSD/8faWFiyQunY125cuSY8+Kr3+ulS0BhUGkAMITgAKhvR0adgwqU8f6eJFs69LF+m77yQfH2trA5B/ububR5n698/sGzlS6tlTSkmxri4ABQ7BCUD+l5wsPfigNHZsZt+AAdKsWZKHh3V1ASgYXF2lDz+U3n5bstnMvi++MI9YnzxpbW0ACgyCE4D87X9zNGnePLPt4iK9/765uLpaWxuAgsNmk158UfrmG8nLy+z7+WfzGsldu6ytDUCBQHACkH9t2SI1aiRt2mS2fX2lH34wjzYBwI24/35p1SopJMRs794tNWli9gHANRCcAORP335rztF06JDZDgszhyBv29bSsgAUAg0bSr/+KtWubbZPnTJP25s2zdq6AORrBCcA+UtamjR6tNS5s3TunNnXpIn5Iec2JrYFkEPKl5fWrMn8MubiRXPwmSefNIcuB4B/ITgByD+OHzcnp3z11cyhgrt1MyeyDAqytDQAhZCfnzky59NPZ/Z9/LHUrJm0b591dQHIlwhOAPKHdeukevWkpUvNtouL9Oab5jDCnp7W1gag8HJzkyZPlqKjMweN2LxZql/fvKYSAP6H4ATAWoYhTZxojpyXcT1TcLC0fLk0fHjm0MEAkJt69pR++UWqUsVsnz4tdewovfyyeQoxgCKP4ATAOgkJ5iS2gwdLly6ZfXfdZY6m16qVtbUBKHrq1JE2bpTuuy+z7403pMhI81RiAEUawQmANTZulBo0yJyfSZKGDpViYqQyZayrC0DR5u8vzZ0rjR+fOVdcTIwUHi79+KO1tQGwFMEJQN66eFEaNcqcdHL3brMvIMAcfvytt8zrDQDASjab9MIL5sA0GfM9HTliDl7z1FNSUpK19QGwBJ9QAOSdP/+UevQwT8XL0KCB9PXXUsWK1tUFAFnJOHW4T5/Mo01Tp0rLlkmffy41bWptfUAum7MnIdf2/WBl/1zbd27hiBOA3JeWJr39tjlKVUZocnU1jzytW0doApB/hYSYo+tNnSp5e5t9e/ZIzZtLI0ZIqanW1gcgzxCcAOSu3bvNEfOGDs38gFGzpjmh7auvSsWKWVoeADhls5kT427dak7ILUnp6dKYMVKjRtIff1hbH4A8QXACkDsuXpTeece8oHrtWrPPZpOGDJE2bTKPPgFAQVKlirR6tTnHXMaXPlu3Srffbk6fkJxsbX0AchXBCUDOW73a/CDxwgvSuXNmX6VK0qpV0rhxTGgLoOByczND0m+/SbVqmX2XLpmD29SqZQ50A6BQIjgByDlHj5qDP7RoYQ4EIZlHmZ56yvxWtnlza+sDgJxSt645rcIrr0ju7mbfgQNSp07SvfdK+/dbWR2AXEBwAnDzLl2S3n9fuvVW6YsvMvvr15d++UX68EPJx8e6+gAgN3h6Sq+9Zl7jFBGR2f/dd+a1nG++yeARQCFCcAJwc1avlho2lJ59VkpMNPsCAsyw9Ouv5oXTAFCY3XqrtHSpNHt25gTe589LL70k1a5tTvRtGNbWCOCmEZwA3JgtW6R27czT8mJjM/t795Z27TJPz3N1taw8AMhTNpv00EPSjh3SoEGSy/8+Yu3aJT3wgDka38qVVlYI4CYRnABkz99/Sw8/bA7+sHhxZn94uLRmjfTZZ1JgoHX1AYCV/PykiRPN0UMvv67z11+lVq3ML5y2brWuPgA3jOAE4PocOmTOY1Kjhnk6Soby5aVp08wPCc2aWVcfAOQndeuaI4l+9510222Z/YsXS/XqSY8+Ku3bZ1l5ALKP4ATg2uLipOefN+cv+fhjKS3N7A8MlN591zwNpVcvTssDgH+z2aSOHc3TmadPN79okszrnb76yrw2qmdP6a+/LC0TwPUhOAHI2tat5jeilSpJkyZJFy6Y/b6+0ujR0p495oAQHh6WlgkA+Z6rqzlVw86d5sTgpUqZ/ZcuSZ9/bh6RiooyT3cGkG8RnABkMgxp+XIpMtI8zeSrrzKPMHl6SoMHS3v3SiNHmgEKAHD9PD3NI/h79kijRkklS2be9/335jVRd95pnt6Xnm5dnQCyRHACYA6b+8UX5rxL99xjDquboVQp8w98XJw0YYJUurR1dQJAYeDvL736qjlh7sSJUmho5n1r15oT6NauLX3wgXTmjFVVAvgXghNQlP3+uzRwoFS2rHkayZYtmfdVrGj+0Y6LM//AM1IeAOQsHx9z6PI9e8xroGrWzLxv27bM38+9e5uTiTMXFGApN6sLgDRnT0Ku7fvByv65tm9cXW6+p9JNvq9JSeaoeJ98Yg6P+2/160svvmjOO+LGr4gMuf2eAsgZBfZntVknPfjHo9IPP0hvv515vdP581J0tLnUqSM98YR5/ak/f9+BvMYRJ6AouHTJPP2ub19zVvt+/RxDk6enecTp55+lDRukrl0JTQCQ11xczEEifv7ZPCNgwADHgJTRV6aM+Xt63jwzWAHIEwQnoLC6dEmKiTHnXgoJMQd8+Owz84hThvBw83S8I0fM00TuvNMcPhcAYK3ataX335cOHzbnymvSJPO+8+elr782zwwIDpYee8w8UpWaal29QBHAV8pAYZKSYp7eMXeuuRw7duU6Pj5St27S449LDRoQlAAgP/P2NufK69XLPOL08cfSrFnSyZPm/WfPSl9+aS4lSkj33Sd16iS1bm3+vgeQYwhOQEF34ID044/mEhMjJSdfuY63tzkJ40MPSe3bS15eeV8nAODm1KljniUwcaL000/m9arz5kkJ/7uu6/Rp88yCzz6T3N2lFi3M3/nt20tVq/JFGXCTCE5AAeOWdFZa8ou0ZIkZlnbsyHpFT0+pQwczLHXoIBUvnreFAgByR7Fi5unXkZHSlCnm34PZs6WFCzO/PEtNlZYtM5fnn5cqV5batZPuvts8LZupJYBsIzgB+ZzHyeMqvWGdSm/8RaU3rleJbb9ffWLEwECpbVvz28UOHZikFgAKOw8Pc96ne++Vzp0zzzxYtMhc4uIy19uzxzxa9cEHZrtWLemuuzKXsmWtqR8oQAhOQD7icuG8Anb8pRJ/xqrEn7EqvekX+e7bfY0NXKQ77jC/RWzXTqpXz+wDABQ93t7mqHxRUeacT9u2ZYaoNWvMQYMy/PWXuUyZYrYrVzb/ntSvb17/Wq8e10gB/0JwAixSLPGM/P7eoYC/fleJv8yg5Ld7p1zS0q66jWGzKeHWGgq4u6V57npEhFSyZN4VDQAoGGw286hSrVrm3HyJidKKFeZQ56tXS5s3S5f/vdmzx1y++ipz++rVM4NUnTrmvgIDuVYKRRbBCchNhiGPk8flt3un/HbvlO8e81+/PbvkdSze6ebpxYrp1G31dKJhE51o0EQn6t+hi/4BTGwMAMgePz9ztL1Oncz22bPS+vVmiPr5Z3Nuv5SUzPUNQ9q+3Vy+/DKzv1QpqWZNM0TVrGku1aqZp/pxxgMKOYITcLOSkqSDB6V9+6S9e6W9e9X0j50qfnC/fA4ekNu5LEa5y0K6q6sSq1bX6VrhOl2rrk7fVlcJNW5Tmpd3Lj8BAECR4+srtWljLpI5mMRff0kbN0qbNpn//v67dPGi43YnT5pB6+efHfs9PKSKFc1T/ipVyvy3YkXpllvMiXw5UoUCjuAEZMUwzEB07FjmEh8vHTok/fOP478Zw8BeppyT3aeUKKXEKtWUWKWazlS/TWdqhetM9VpK92SYcACABdzdzeua6tUz5/mTzCNQf/5pntaXcU3Utm3mpLz/lpJijvJ6tZFeixc3A9TlS7lyUlCQOYlvUJC5ELCQj+WL4DR58mSNGzdO8fHxCg8P1/vvv69GjRpddf05c+bolVde0f79+1W1alWNHTtW7du3z8OKUSAYhnThgnled2KieVpCYqIZdE6dMue7+Pe/J09mBqULF27q4dOLFVNyufJKvqWCzlauqsTKZlBKrFxNqaUYBhYAkM95eJjXONWv79h/+rR5Ct+2bWaY2r3bfsbFVf92JidLO3eay7W4u2eGqJIlzUl9S5bMXEqUMBd/f/P0w8sXb29CF3KV5cFp9uzZGjx4sKZOnarGjRtr0qRJioyM1M6dOxUUFHTF+uvWrVO3bt00ZswYdezYUTNmzFDnzp21efNm3XbbbRY8A1zBMMzhsi9dclwuXnRcMvpSU81vqrL698KFzOX8ecfb586Zv4iTkx1vJyebR4sSEx0vfM1pnp7mt2XlypnfnFWsaJ6WUKmSvnctrfPBZSVX19x7fAAArFCihNS0qblcLj3dPDtjzx4zRO3ZY57K/s8/5nLwYNaTtF8uNTVz/exydTVPQSxePHPx8XFse3mZf7+z+tfd3QyLHh5X3nZ3N+fPutri6iq5uZmLiwsBrpCyGYZhWFlA48aN1bBhQ33wv3kF0tPTFRoaqoEDB2rYsGFXrN+1a1clJyfr+++/t/fdcccdqlu3rqZOner08RITE+Xv76+EhAT5+fnl3BO5EcOGSdu26XDyRTNs/Isto88w7PfbMm5ntJV5W4bhcL/NMBTo6Zq5/uVLenrmv5ffvrwvLS3z9uV9Gf3/vn3pUua/BZWLizkpYMa3Xf9eMkJSuXLmN19X+cU4Z8+Vp+/lJAaHyFpuv+5AUVGQf8cU5N8DBfl1vy6GYX6pmRGi4uOlo0fNszyOHnW8feJE7n75mdvc3MwwldXi4uJ4O6OdcTtjsdmu/m/G8u/2vxfp6n2X33d532W3D5+7dOV9Mkf5/fe6V2sbV/msdEvxYlLHjlKfPtf3muaS7GQDS484paamatOmTRo+fLi9z8XFRREREVq/fn2W26xfv16DBw926IuMjNSCBQuyXD8lJUUpl40Sk/C/61ESExNvsvocsGqV9Msvys1ZEvLBs8w7Npv5bZK3t/mvn5/5zdO/Fz8/KSDA/MYsIMDxtr//9Y8KdPbsVe86dzZ3X/nERL7Jykpuv+5AUVGQf8cU5N8DBfl1v242mxQaai7XYhjm39nTp6UzZ8x/M5YzZ8z7Mk7Bz7id0T53LvNMFKvCV8bZNgVcrn9GDQ6WunTJxUe5jjr+lwmu51iSpcHpxIkTSktLU3BwsEN/cHCwdlzl4sL4+Pgs14+Pz3po5zFjxmj06NFX9Ic6+4FFwZMxoENSktWV5LpeVhcAoFDrZXUBRVQvqwsA8trUqeaSD5w9e1b+/tc+6mv5NU65bfjw4Q5HqNLT03Xq1CmVKlVKthw6/zQxMVGhoaE6ePCg9af/IdfxfhctvN9FC+930cL7XbTwfhct1/t+G4ahs2fPqmzZsk73aWlwKl26tFxdXXX06FGH/qNHjyokJCTLbUJCQrK1voeHhzw8PBz6AgICbrzoa/Dz8+MHsQjh/S5aeL+LFt7vooX3u2jh/S5aruf9dnakKYOlUzy7u7urfv36iomJsfelp6crJiZGTZo0yXKbJk2aOKwvScuWLbvq+gAAAABwsyw/VW/w4MHq2bOnGjRooEaNGmnSpElKTk5W7969JUk9evRQuXLlNGbMGEnSc889pxYtWmjChAnq0KGDZs2apY0bN+rjjz+28mkAAAAAKMQsD05du3bV8ePHNXLkSMXHx6tu3bpavHixfQCIuLg4uVw2ylnTpk01Y8YMvfzyyxoxYoSqVq2qBQsWWDqHk4eHh0aNGnXFKYEonHi/ixbe76KF97to4f0uWni/i5bceL8tn8cJAAAAAPI7S69xAgAAAICCgOAEAAAAAE4QnAAAAADACYITAAAAADhBcMph9957r8qXLy9PT0+VKVNGjz32mA4fPmx1WcgF+/fvV9++fVWxYkV5eXmpcuXKGjVqlFJTU60uDbnkjTfeUNOmTeXt7Z1rE2nDOpMnT1ZYWJg8PT3VuHFj/fbbb1aXhFyyevVqRUVFqWzZsrLZbFqwYIHVJSEXjRkzRg0bNpSvr6+CgoLUuXNn7dy50+qykEumTJmiOnXq2Ce+bdKkiX788ccc2TfBKYe1atVKX3/9tXbu3Km5c+dqz5496tKli9VlIRfs2LFD6enp+uijj/TXX39p4sSJmjp1qkaMGGF1acglqampevDBB/XUU09ZXQpy2OzZszV48GCNGjVKmzdvVnh4uCIjI3Xs2DGrS0MuSE5OVnh4uCZPnmx1KcgDq1at0jPPPKNffvlFy5Yt08WLF9WmTRslJydbXRpywS233KK33npLmzZt0saNG9W6dWt16tRJf/31103vm+HIc9m3336rzp07KyUlRcWKFbO6HOSycePGacqUKdq7d6/VpSAXRUdHa9CgQTpz5ozVpSCHNG7cWA0bNtQHH3wgSUpPT1doaKgGDhyoYcOGWVwdcpPNZtP8+fPVuXNnq0tBHjl+/LiCgoK0atUq3XXXXVaXgzxQsmRJjRs3Tn379r2p/XDEKRedOnVKX331lZo2bUpoKiISEhJUsmRJq8sAkA2pqanatGmTIiIi7H0uLi6KiIjQ+vXrLawMQG5ISEiQJP5eFwFpaWmaNWuWkpOT1aRJk5veH8EpFwwdOlTFixdXqVKlFBcXp4ULF1pdEvLA7t279f777+vJJ5+0uhQA2XDixAmlpaUpODjYoT84OFjx8fEWVQUgN6Snp2vQoEFq1qyZbrvtNqvLQS75448/5OPjIw8PD/Xv31/z589XzZo1b3q/BKfrMGzYMNlstmsuO3bssK//4osvasuWLVq6dKlcXV3Vo0cPcUZkwZHd91uSDh06pLZt2+rBBx/U448/blHluBE38n4DAAqmZ555Rn/++admzZpldSnIRdWqVVNsbKx+/fVXPfXUU+rZs6e2bdt20/vlGqfrcPz4cZ08efKa61SqVEnu7u5X9P/zzz8KDQ3VunXrcuQQIXJfdt/vw4cPq2XLlrrjjjsUHR0tFxe+jyhIbuTnm2ucCpfU1FR5e3vrm2++cbjOpWfPnjpz5gxnDRRyXONUdAwYMEALFy7U6tWrVbFiRavLQR6KiIhQ5cqV9dFHH93UftxyqJ5CLTAwUIGBgTe0bXp6uiQpJSUlJ0tCLsrO+33o0CG1atVK9evX17Rp0whNBdDN/HyjcHB3d1f9+vUVExNj//Ccnp6umJgYDRgwwNriANw0wzA0cOBAzZ8/XytXriQ0FUHp6ek58lmc4JSDfv31V23YsEF33nmnSpQooT179uiVV15R5cqVOdpUCB06dEgtW7ZUhQoVNH78eB0/ftx+X0hIiIWVIbfExcXp1KlTiouLU1pammJjYyVJVapUkY+Pj7XF4aYMHjxYPXv2VIMGDdSoUSNNmjRJycnJ6t27t9WlIRckJSVp9+7d9va+ffsUGxurkiVLqnz58hZWhtzwzDPPaMaMGVq4cKF8fX3t1y76+/vLy8vL4uqQ04YPH6527dqpfPnyOnv2rGbMmKGVK1dqyZIlN71vTtXLQX/88Yeee+45bd26VcnJySpTpozatm2rl19+WeXKlbO6POSw6Ojoq36o4seqcOrVq5emT59+Rf+KFSvUsmXLvC8IOeqDDz7QuHHjFB8fr7p16+q9995T48aNrS4LuWDlypVq1arVFf09e/ZUdHR03heEXGWz2bLsnzZtmnr16pW3xSDX9e3bVzExMTpy5Ij8/f1Vp04dDR06VPfcc89N75vgBAAAAABOcEEGAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAgT6xcuVI2m01nzpyxupQ8ExYWpkmTJlldBgAgBxCcAKCI6NWrlzp37nxFf34KNK+++qrq1q2bI/u61vPKL4Hm3LlzGj58uCpXrixPT08FBgaqRYsWWrhwodWlAQD+xc3qAgAAKKr69++vX3/9Ve+//75q1qypkydPat26dTp58mSuPWZqaqrc3d1zbf8AUFhxxAkAcIU1a9aoefPm8vLyUmhoqJ599lklJyfb7//iiy/UoEED+fr6KiQkRN27d9exY8cc9rFo0SLdeuut8vLyUqtWrbR///5rPmZ0dLRGjx6trVu3ymazyWazKTo6WpIUFxenTp06ycfHR35+fnrooYd09OjRHHu+Z86cUb9+/RQYGCg/Pz+1bt1aW7dutd+/Z88ederUScHBwfLx8VHDhg21fPlyh30cO3ZMUVFR8vLyUsWKFfXVV185fdxvv/1WI0aMUPv27RUWFqb69etr4MCB6tOnj32dlJQUDR06VKGhofLw8FCVKlX06aef2u9ftWqVGjVqJA8PD5UpU0bDhg3TpUuX7Pe3bNlSAwYM0KBBg1S6dGlFRkZKkv7880+1a9dOPj4+Cg4O1mOPPaYTJ07c8GsIAIUdwQkA4GDPnj1q27atHnjgAf3++++aPXu21qxZowEDBtjXuXjxol5//XVt3bpVCxYs0P79+9WrVy/7/QcPHtT999+vqKgoxcbGql+/fho2bNg1H7dr16564YUXVKtWLR05ckRHjhxR165dlZ6erk6dOunUqVNatWqVli1bpr1796pr16459pwffPBBHTt2TD/++KM2bdqk22+/XXfffbdOnTolSUpKSlL79u0VExOjLVu2qG3btoqKilJcXJx9H7169dLBgwe1YsUKffPNN/rwww+vCJP/FhISokWLFuns2bNXXadHjx6aOXOm3nvvPW3fvl0fffSRfHx8JEmHDh1S+/bt1bBhQ23dulVTpkzRp59+qv/7v/9z2Mf06dPl7u6utWvXaurUqTpz5oxat26tevXqaePGjVq8eLGOHj2qhx566EZfQgAo/AwAQJHQs2dPw9XV1ShevLjD4unpaUgyTp8+bRiGYfTt29d44oknHLb9+eefDRcXF+P8+fNZ7nvDhg2GJOPs2bOGYRjG8OHDjZo1azqsM3ToUIfHycqoUaOM8PBwh76lS5carq6uRlxcnL3vr7/+MiQZv/3221X3tWLFCkPSFc+3ePHihs1mMyZOnGh/bn5+fsaFCxcctq9cubLx0UcfXXX/tWrVMt5//33DMAxj586dV9Szfft2Q5L9cbKyatUq45ZbbjGKFStmNGjQwBg0aJCxZs0a+/0Z+122bFmW248YMcKoVq2akZ6ebu+bPHmy4ePjY6SlpRmGYRgtWrQw6tWr57Dd66+/brRp08ah7+DBg4YkY+fOnVetFwCKMo44AUAR0qpVK8XGxjos//3vfx3W2bp1q6Kjo+Xj42NfIiMjlZ6ern379kmSNm3apKioKJUvX16+vr5q0aKFJNmPwGzfvl2NGzd22G+TJk0c2pfvv3///letefv27QoNDVVoaKi9r2bNmgoICND27dslSbVq1bLvq127dg7b//zzz1c857Jlyzo836SkJJUqVcqhpn379mnPnj2SzCNOQ4YMUY0aNRQQECAfHx9t377d4fm6ubmpfv369v1Wr15dAQEBV31eknTXXXdp7969iomJUZcuXfTXX3+pefPmev311yVJsbGxcnV1tb++Wb02TZo0kc1ms/c1a9ZMSUlJ+ueff+x9l9eV8ZxXrFjh8HyrV68uSfbnDABwxOAQAFCEFC9eXFWqVHHou/wDtmSGhCeffFLPPvvsFduXL19eycnJioyMVGRkpL766isFBgYqLi5OkZGRSk1Nve5aYmNj7bf9/Pyy90T+ZdGiRbp48aIkycvLy+G+ihUrXhFg3Nwy//wlJSWpTJkyWrly5RX7zdhuyJAhWrZsmcaPH68qVarIy8tLXbp0ydbzvZpixYqpefPmat68uYYOHar/+7//02uvvaahQ4de8VxuVPHixR3aSUlJioqK0tixY69Yt0yZMjnymABQ2BCcAAAObr/9dm3btu2KgJXhjz/+0MmTJ/XWW2/ZjwJt3LjRYZ0aNWro22+/dej75ZdfHNpZ7d/d3V1paWlX7OvgwYM6ePCg/fG2bdumM2fOqGbNmpKkChUqZOMZOrr99tsVHx8vNzc3hYWFZbnO2rVr1atXL913332SzOBx+WAX1atX16VLl7Rp0yY1bNhQkrRz584bGuK9Zs2aunTpki5cuKDatWsrPT1dq1atUkRExBXr1qhRQ3PnzpVhGPajTmvXrpWvr69uueWWaz7nuXPnKiwszCFEAgCujlP1AAAOhg4dqnXr1mnAgAGKjY3V33//rYULF9oHhyhfvrzc3d31/vvva+/evfr222/tp5Zl6N+/v/7++2+9+OKL2rlzp2bMmGEfIe9awsLCtG/fPsXGxurEiRNKSUlRRESEateurUceeUSbN2/Wb7/9ph49eqhFixZq0KDBTT/fiIgINWnSRJ07d9bSpUu1f/9+rVu3Ti+99JI9EFatWlXz5s1TbGystm7dqu7duys9Pd2+j2rVqqlt27Z68skn9euvv2rTpk3q16+f0yNGLVu21EcffaRNmzZp//79WrRokUaMGKFWrVrJz89PYWFh6tmzp/r06aMFCxZo3759Wrlypb7++mtJ0tNPP62DBw9q4MCB2rFjhxYuXKhRo0Zp8ODBcnG5+p/4Z555RqdOnVK3bt20YcMG7dmzR0uWLFHv3r2vCK4AABPBCQDgoE6dOlq1apV27dql5s2bq169eho5cqT9uqDAwEBFR0drzpw5qlmzpt566y2NHz/eYR/ly5fX3LlztWDBAoWHh2vq1Kl68803nT72Aw88oLZt26pVq1YKDAzUzJkzZbPZtHDhQpUoUUJ33XWXIiIiVKlSJc2ePTtHnq/NZtOiRYt01113qXfv3rr11lv18MMP68CBAwoODpYkvfPOOypRooSaNm2qqKgoRUZG6vbbb3fYz7Rp01S2bFm1aNFC999/v5544gkFBQVd87EjIyM1ffp0tWnTRjVq1NDAgQMVGRlpD0aSNGXKFHXp0kVPP/20qlevrscff9w+NHy5cuW0aNEi/fbbbwoPD1f//v3Vt29fvfzyy9d83LJly2rt2rVKS0tTmzZtVLt2bQ0aNEgBAQHXDFwAUJTZDMMwrC4CAAAAAPIzvlYCAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACc+H935kcQfatlnAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -12415,25 +12415,25 @@ " -11.2\n", " 92.1\n", " -0.1\n", - " 0.671397\n", - " 0.06996\n", - " -1.732732\n", + " 0.640747\n", + " 0.066766\n", + " -1.826475\n", " 1.98555\n", " 0.0\n", " -0.3\n", - " 0.043264\n", - " 0.086527\n", + " 0.035527\n", + " 0.071054\n", " \n", " \n", "\n", "" ], "text/plain": [ - " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -11.2 92.1 -0.1 0.671397 0.06996 -1.732732 1.98555 \n", + " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", + "head_to_head -11.2 92.1 -0.1 0.640747 0.066766 -1.826475 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.0 -0.3 0.043264 0.086527 " + "head_to_head 0.0 -0.3 0.035527 0.071054 " ] }, "execution_count": 68, @@ -12527,12 +12527,12 @@ " -1.1\n", " \n", " \n", - " 87\n", - " How many movies will be new on Netflix's globa...\n", - " [0.0001, 0.0001, 0.335]\n", - " [0.01,0.064,0.926]\n", - " 2 or more\n", - " -1.0\n", + " 245\n", + " Will Nebraska have 1.7 million or more residen...\n", + " 0.9\n", + " 0.7\n", + " no\n", + " -0.9\n", " \n", " \n", "\n", @@ -12544,21 +12544,21 @@ "121 How many movies will be new on Netflix's top 1... \n", "232 How many movies will be new on Netflix's top 1... \n", "247 Will the 500th richest person on Bloomberg's B... \n", - "87 How many movies will be new on Netflix's globa... \n", + "245 Will Nebraska have 1.7 million or more residen... \n", "\n", " bot_team_median \\\n", "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", "121 [0.0001, 0.0001, 0.0001, 0.125] \n", "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", "247 0.766667 \n", - "87 [0.0001, 0.0001, 0.335] \n", + "245 0.9 \n", "\n", " pro_median resolution head_to_head \n", "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", "247 0.333 no -1.1 \n", - "87 [0.01,0.064,0.926] 2 or more -1.0 " + "245 0.7 no -0.9 " ] }, "execution_count": 69, @@ -12624,20 +12624,20 @@ " \n", " \n", " \n", - " 0\n", - " For Q1 2025, how many banks will be listed on ...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " 0\n", - " 2.7\n", - " \n", - " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", + " [0.0, 0.0030510204, 0.0061020408, 0.0102928751...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 2.8\n", + " 2.5\n", + " \n", + " \n", + " 0\n", + " For Q1 2025, how many banks will be listed on ...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 0\n", + " 2.5\n", " \n", " \n", " 151\n", @@ -12658,7 +12658,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.954\n", + " 0.95\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12669,29 +12669,29 @@ ], "text/plain": [ " title \\\n", - "0 For Q1 2025, how many banks will be listed on ... \n", "189 What will the highest rank of metac-GPT4o or m... \n", + "0 For Q1 2025, how many banks will be listed on ... \n", "151 How many earthquakes of magnitude ≥ 4 will hap... \n", "211 Will Nikola Corporation file for bankruptcy be... \n", "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", - "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", + "189 [0.0, 0.0030510204, 0.0061020408, 0.0102928751... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.954 \n", + "214 0.95 \n", "\n", " pro_median resolution \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... 0.0 \n", "211 0.999 annulled \n", "214 0.95 annulled \n", "\n", " head_to_head \n", - "0 2.7 \n", - "189 2.8 \n", + "189 2.5 \n", + "0 2.5 \n", "151 NaN \n", "211 NaN \n", "214 NaN " @@ -12809,10 +12809,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.674462\n", - " 2.674462\n", + " 2.539332\n", + " 2.539332\n", " \n", " \n", " 1\n", @@ -12831,8 +12831,8 @@ " 1.0\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.158842\n", - " -0.158842\n", + " -0.250003\n", + " -0.250003\n", " \n", " \n", " 2\n", @@ -12849,10 +12849,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", - " -0.075746\n", - " -0.075746\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -12891,8 +12891,8 @@ " 1.0\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.243782\n", - " 0.243782\n", + " 0.387623\n", + " 0.387623\n", " \n", " \n", "\n", @@ -12928,25 +12928,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", - "2 0.013 -0.075746 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.674462 \n", - "1 -0.158842 \n", - "2 -0.075746 \n", + "0 2.539332 \n", + "1 -0.250003 \n", + "2 -0.051987 \n", "3 0.152526 \n", - "4 0.243782 " + "4 0.387623 " ] }, "execution_count": 72, @@ -12984,7 +12984,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -13095,7 +13095,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -13197,7 +13197,7 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.085 0.013 \n", + "2 1.0 0.063 0.013 \n", "5 1.0 0.62 0.45 \n", "8 1.0 0.86 0.95 \n", "10 1.0 NaN NaN \n", @@ -13267,13 +13267,120 @@ { "cell_type": "code", "execution_count": 78, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "N26JZjCV9_jc", + "outputId": "eacb7626-54d0-47c7-8f21-48e95e709564" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Call the function with your DataFrame and column names\n", + "create_discrimination_histogram(df_top_bot_pro_forecasts,\n", + " 'bot_team_median',\n", + " 'pro_median',\n", + " 'resolution')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4dkNBotk_4e3", + "outputId": "d393a72e-997a-4025-ca7b-6f5328436286" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bot average forecast difference (1 - 0): 0.4365\n", + "Pro average forecast difference (1 - 0): 0.5238\n", + "Difference between pro and bot differences: 0.0873\n" + ] + } + ], + "source": [ + "# Calculate average forecasts for resolved 1 and 0 for bots\n", + "bot_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['bot_team_median'].mean()\n", + "bot_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['bot_team_median'].mean()\n", + "\n", + "# Calculate average forecasts for resolved 1 and 0 for pros\n", + "pro_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['pro_median'].mean()\n", + "pro_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['pro_median'].mean()\n", + "\n", + "# Calculate the differences\n", + "bot_difference = bot_avg_1 - bot_avg_0\n", + "pro_difference = pro_avg_1 - pro_avg_0\n", + "\n", + "print(f\"Bot average forecast difference (1 - 0): {bot_difference:.4f}\")\n", + "print(f\"Pro average forecast difference (1 - 0): {pro_difference:.4f}\")\n", + "\n", + "# Calculate the difference between pro and bot differences\n", + "pro_bot_difference = pro_difference - bot_difference\n", + "print(f\"Difference between pro and bot differences: {pro_bot_difference:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "lPPgorXB7omi", - "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" + "id": "bGnXswWOx_yw", + "outputId": "35a0e2a8-5831-43cf-a006-f8e0262666ec" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted number of 1 resolutions: 14.5\n", + "Weighted number of 0 resolutions: 31.35\n", + "Average 1 resolutions: 0.31624863685932386\n" + ] + } + ], + "source": [ + "# Calculate weighted number of 1 resolutions\n", + "weighted_ones = np.sum(\n", + " df_top_bot_pro_forecasts['resolution'] *\n", + " df_top_bot_pro_forecasts['question_weight']\n", + ")\n", + "\n", + "# Calculate weighted number of 0 resolutions\n", + "weighted_zeros = np.sum(\n", + " (1 - df_top_bot_pro_forecasts['resolution']) *\n", + " df_top_bot_pro_forecasts['question_weight']\n", + ")\n", + "\n", + "print(f\"Weighted number of 1 resolutions: {weighted_ones}\")\n", + "print(f\"Weighted number of 0 resolutions: {weighted_zeros}\")\n", + "\n", + "print(f\"Average 1 resolutions: {weighted_ones / (weighted_zeros + weighted_ones)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, "outputs": [ { "data": { @@ -13331,10 +13438,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.674462\n", - " 2.674462\n", + " 2.539332\n", + " 2.539332\n", " \n", " \n", " 1\n", @@ -13353,8 +13460,8 @@ " 1.0\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.158842\n", - " -0.158842\n", + " -0.250003\n", + " -0.250003\n", " \n", " \n", " 2\n", @@ -13371,10 +13478,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", - " -0.075746\n", - " -0.075746\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -13413,8 +13520,8 @@ " 1.0\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.243782\n", - " 0.243782\n", + " 0.387623\n", + " 0.387623\n", " \n", " \n", "\n", @@ -13450,25 +13557,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", - "2 0.013 -0.075746 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.674462 \n", - "1 -0.158842 \n", - "2 -0.075746 \n", + "0 2.539332 \n", + "1 -0.250003 \n", + "2 -0.051987 \n", "3 0.152526 \n", - "4 0.243782 " + "4 0.387623 " ] }, "metadata": {}, @@ -13550,10 +13657,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.65\n", + " 0.15\n", " 0.05\n", - " -0.998529\n", - " -0.998529\n", + " -0.111226\n", + " -0.111226\n", " \n", " \n", " 355\n", @@ -13643,7 +13750,7 @@ "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", "342 1.00 0.905 0.95 -0.048527 -0.048527 \n", - "351 1.00 0.65 0.05 -0.998529 -0.998529 \n", + "351 1.00 0.15 0.05 -0.111226 -0.111226 \n", "355 1.00 0.9 0.97 -0.074901 -0.074901 \n", "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", "364 0.85 0.05 0.03 -0.017709 -0.015053 " @@ -13659,7 +13766,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[81], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:750\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 753\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 754\u001b[0m bins\n\u001b[1;32m 755\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", @@ -13685,88 +13792,6 @@ "print(f\"Pro team is {interpret_confidence(pro_confidence)}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "N26JZjCV9_jc", - "outputId": "eacb7626-54d0-47c7-8f21-48e95e709564" - }, - "outputs": [], - "source": [ - "# Call the function with your DataFrame and column names\n", - "create_discrimination_histogram(df_top_bot_pro_forecasts,\n", - " 'bot_team_median',\n", - " 'pro_median',\n", - " 'resolution')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4dkNBotk_4e3", - "outputId": "d393a72e-997a-4025-ca7b-6f5328436286" - }, - "outputs": [], - "source": [ - "# Calculate average forecasts for resolved 1 and 0 for bots\n", - "bot_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['bot_team_median'].mean()\n", - "bot_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['bot_team_median'].mean()\n", - "\n", - "# Calculate average forecasts for resolved 1 and 0 for pros\n", - "pro_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['pro_median'].mean()\n", - "pro_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['pro_median'].mean()\n", - "\n", - "# Calculate the differences\n", - "bot_difference = bot_avg_1 - bot_avg_0\n", - "pro_difference = pro_avg_1 - pro_avg_0\n", - "\n", - "print(f\"Bot average forecast difference (1 - 0): {bot_difference:.4f}\")\n", - "print(f\"Pro average forecast difference (1 - 0): {pro_difference:.4f}\")\n", - "\n", - "# Calculate the difference between pro and bot differences\n", - "pro_bot_difference = pro_difference - bot_difference\n", - "print(f\"Difference between pro and bot differences: {pro_bot_difference:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bGnXswWOx_yw", - "outputId": "35a0e2a8-5831-43cf-a006-f8e0262666ec" - }, - "outputs": [], - "source": [ - "# Calculate weighted number of 1 resolutions\n", - "weighted_ones = np.sum(\n", - " df_top_bot_pro_forecasts['resolution'] *\n", - " df_top_bot_pro_forecasts['question_weight']\n", - ")\n", - "\n", - "# Calculate weighted number of 0 resolutions\n", - "weighted_zeros = np.sum(\n", - " (1 - df_top_bot_pro_forecasts['resolution']) *\n", - " df_top_bot_pro_forecasts['question_weight']\n", - ")\n", - "\n", - "print(f\"Weighted number of 1 resolutions: {weighted_ones}\")\n", - "print(f\"Weighted number of 0 resolutions: {weighted_zeros}\")\n", - "\n", - "print(f\"Average 1 resolutions: {weighted_ones / (weighted_zeros + weighted_ones)}\")" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/functions.py b/functions.py index 0373237..8de9f69 100644 --- a/functions.py +++ b/functions.py @@ -421,7 +421,7 @@ def get_median_forecast(row, bots): raise ValueError(f"Unknown question type: {q_type}") -def calculate_weighted_scores(df_bot_team_forecasts, teams): +def calculate_weighted_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.Series: """ Calculates weighted scores for each team based on their forecasts and question weights. diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index c42ccb5..6d552fc 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 @@ -13,35 +13,35 @@ Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -metac-o1,-0.2,-0.2,-0.1,0.1,0.1 -4Shadower,-0.2,-0.1,-0.1,-0.0,-0.0 +4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 annabot,-0.1,-0.1,-0.1,-0.0,-0.0 -cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,-0.0 -jkraybill_bot,-0.1,-0.1,-0.1,-0.0,-0.0 +cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 +jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.0,-0.0 -ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 +MWG,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-o1,-0.3,-0.2,-0.1,0.0,0.1 GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 +ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 +acm_bot,-0.3,-0.2,-0.1,0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-deepseek-r1+asknews,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-perplexity,-0.3,-0.3,-0.1,0.0,0.1 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.2,-0.1,-0.0,0.1 wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 -metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 -metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.0 +bot_median,-0.3,-0.3,-0.2,-0.0,0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 +metac-deepseek-r1+asknews,-0.3,-0.3,-0.2,-0.1,-0.1 NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 -bot_median,-0.3,-0.3,-0.2,-0.1,-0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-Llama-3.1,-0.4,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-exa,-0.4,-0.3,-0.2,-0.1,-0.1 -pgodzinai,-0.5,-0.4,-0.2,-0.1,-0.1 -VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.1 -metac-gpt-4o,-0.4,-0.4,-0.3,-0.2,-0.1 -metac-o1-preview,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 +pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 746b52f..4d49be6 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 -RPM_bot,-1.3,7.0,-0.2,0.8269776545743774,0.3125681734016113,-0.610595609477049,2.4469118511449692,0.6,-1.0,0.2819326101745987,0.563865 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -metac-o1,-5.3,91.1,-0.1,0.9084726497398434,0.09518152714706545,-0.6113627344286646,1.9858289388460384,0.1,-0.2,0.27124945946442813,0.542499 -annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 +annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.8,27.4,-0.2,0.7472901092218875,0.14276243695944935,-1.737830063646217,2.0495406495390753,0.0,-0.5,0.04694721167123542,0.093894 +cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 -MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 +metac-o1,-9.3,91.1,-0.1,0.9011413735401934,0.09441342249931468,-1.0818974297140194,1.9858289388460384,0.1,-0.3,0.14109261555912994,0.282185 +MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 +GreeneiBot2,-10.4,58.4,-0.2,0.8493165305196299,0.11118575431472652,-1.6013523121813948,2.000831925930035,0.0,-0.4,0.05739674059552304,0.114793 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.6,58.4,-0.2,0.8493306622643327,0.11118760433016613,-1.638793797628407,2.000831925930035,0.0,-0.4,0.05336569544684546,0.106731 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-deepseek-r1+asknews,-11.7,52.1,-0.2,0.6690305553273252,0.09268876407541017,-2.4327442879372825,2.0053789762011176,-0.0,-0.4,0.009262209683005887,0.018524 +metac-perplexity,-12.3,89.1,-0.1,0.9928936435472672,0.1051874382468964,-1.3167986298410923,1.9864049297707018,0.1,-0.3,0.09566061681542057,0.191321 +metac-Gemini-Exp-1206,-12.6,76.5,-0.2,1.0074640479435764,0.11518577253617869,-1.4310981247048116,1.9908217254774627,0.1,-0.4,0.0782642072080301,0.156528 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -metac-perplexity,-13.6,89.1,-0.2,0.953800697354561,0.10104592028043681,-1.5152493493302568,1.9864049297707018,0.0,-0.4,0.06664452341402785,0.133289 -metac-Gemini-Exp-1206,-13.9,76.5,-0.2,0.9608427574536519,0.10985544896515206,-1.6509533909374279,1.9908217254774627,0.0,-0.4,0.051451032994077626,0.102902 +bot_median,-14.4,92.1,-0.2,0.8064767886698918,0.08403535853352312,-1.8649643315938071,1.9855502432148115,0.0,-0.3,0.03270280660214449,0.065406 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 +metac-deepseek-r1+asknews,-15.8,52.1,-0.3,0.7725034544186158,0.1070240960803573,-2.8279843345318105,2.0053789762011176,-0.1,-0.5,0.0033369803575435406,0.006674 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -metac-claude-3-5-sonnet-latest,-17.7,91.1,-0.2,0.822268712940962,0.08614986025763702,-2.253410401302691,1.9858289388460384,-0.0,-0.4,0.013329842987401584,0.026660 -bot_median,-17.9,92.1,-0.2,0.8298286106445787,0.0864686321994526,-2.248076238150116,1.9855502432148115,-0.0,-0.4,0.013491943459249906,0.026984 -metac-claude-3-5-sonnet-20240620,-18.2,90.5,-0.2,0.9882219785580354,0.10387958811855824,-1.9308293392916587,1.9860719790130024,0.0,-0.4,0.028334774283890096,0.056670 -minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 -metac-Llama-3.1,-21.3,89.1,-0.2,0.9128041314903421,0.0967027322983173,-2.471742593789836,1.9864049297707018,-0.0,-0.4,0.007684177160478823,0.015368 +minefrac1,-19.4,51.1,-0.4,0.8785436286688769,0.12290028314991908,-3.0953430020106336,2.0065449272360034,-0.1,-0.6,0.0016073014389962144,0.003215 +metac-claude-3-5-sonnet-20240620,-20.5,90.5,-0.2,1.0026017690668347,0.10539115813794282,-2.144815075299298,1.9860719790130024,-0.0,-0.4,0.017338365150828438,0.034677 +metac-o1-preview,-21.8,91.1,-0.2,0.7783952357785447,0.08155319511998359,-2.9287175025862417,1.9858289388460384,-0.1,-0.4,0.0021550719003434007,0.004310 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-exa,-22.4,89.1,-0.3,0.8128016858276886,0.08610844443471673,-2.92372894610568,1.9864049297707018,-0.1,-0.4,0.002197830440677215,0.004396 -pgodzinai,-23.9,76.4,-0.3,0.9914794382114891,0.11343237695345683,-2.755452219862641,1.9908489732268309,-0.1,-0.5,0.00367232305294701,0.007345 +metac-claude-3-5-sonnet-latest,-22.6,91.1,-0.2,0.8075357879826596,0.08460627796346898,-2.930812576746788,1.9858289388460384,-0.1,-0.4,0.002141865770272775,0.004284 +pgodzinai,-23.4,76.4,-0.3,0.9738243593913162,0.11141250898777778,-2.746500218115244,1.9908489732268309,-0.1,-0.5,0.00376450038951266,0.007529 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-grok-2-1212,-24.5,91.1,-0.3,1.0139958650854732,0.10623729287533687,-2.5268442158424125,1.9858289388460384,-0.1,-0.5,0.006626896274566267,0.013254 -metac-gpt-4o,-26.0,91.1,-0.3,0.8516451147774127,0.08922765328715744,-3.193010060382893,1.9858289388460384,-0.1,-0.5,0.0009699028149533728,0.001940 -metac-o1-preview,-26.2,91.1,-0.3,0.9143330864911109,0.09579553057346926,-2.9970476132039527,1.9858289388460384,-0.1,-0.5,0.0017609124521279873,0.003522 +metac-exa,-24.9,89.1,-0.3,0.8297104160130679,0.08789976017509527,-3.180189674479708,1.9864049297707018,-0.1,-0.5,0.0010160377455861174,0.002032 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 +metac-grok-2-1212,-28.0,91.1,-0.3,1.0053639878633573,0.10533292304496032,-2.9230309952832156,1.9858289388460384,-0.1,-0.5,0.0021912955912464513,0.004383 +metac-gpt-4o,-28.0,91.1,-0.3,0.8644250725107907,0.09056662138298972,-3.3934602737720856,1.9858289388460384,-0.1,-0.5,0.0005136910361772879,0.001027 +metac-Llama-3.1,-28.2,89.1,-0.3,0.9060643910911743,0.0959887222614469,-3.291936866376594,1.9864049297707018,-0.1,-0.5,0.0007163844167320878,0.001433 From 2aea1593277c4fb8a767a4296e8ab9def0966f4a Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Thu, 22 May 2025 08:27:00 -0600 Subject: [PATCH 24/26] Debugging calibration curve --- AI_BENCHMARKING_ANALYSIS.ipynb | 3241 ++++++++++------- functions.py | 126 +- .../bootstrapped_h2h_bot_vs_pros.csv | 32 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 38 +- 4 files changed, 1956 insertions(+), 1481 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index a2b1b4e..942c3c1 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -61,7 +61,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3873332/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_691899/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1032,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 9.060773\n", - " 3425.153221\n", + " 8.319299\n", + " 3144.861339\n", " 409\n", - " 6.048852\n", - " 1.532164\n", + " 5.304507\n", + " 1.533625\n", " \n", " \n", " 4\n", @@ -1072,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 9.060773 3425.153221 409 6.048852 \n", + "15 bot_median 8.319299 3144.861339 409 5.304507 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.532164 \n", + "15 1.533625 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1740,7 +1740,7 @@ " \n", " 3\n", " bot_median\n", - " 8602.129306\n", + " 8575.707679\n", " \n", " \n", " 4\n", @@ -1761,7 +1761,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8602.129306\n", + "3 bot_median 8575.707679\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3398.202830\n", + " 3328.161138\n", " \n", " \n", " 3\n", @@ -2166,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3398.202830\n", + "2 bot_median 3328.161138\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2578,9 +2578,9 @@ " False\n", " False\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " [0.3,0.4,0.2,0.07,0.03]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2602,7 +2602,7 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2626,9 +2626,9 @@ " False\n", " False\n", " ...\n", - " 0.15\n", - " 0.05\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2650,8 +2650,8 @@ " None\n", " None\n", " ...\n", - " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.6,0.35,0.05]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -2674,8 +2674,8 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2713,23 +2713,23 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.45,0.3,0.15,0.05,0.05] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", - "2 0.15 \n", - "3 [0.45,0.45,0.1] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "2 0.1 \n", + "3 [0.6,0.35,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", + "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.15 NaN \n", + "2 0.1 NaN \n", "3 [0.15,0.6,0.25] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", @@ -2818,7 +2818,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2842,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.4\n", - " 0.15\n", + " 0.65\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2866,7 +2866,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.85\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2890,7 +2890,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2914,9 +2914,9 @@ " False\n", " False\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2946,11 +2946,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.4 0.15 NaN NaN 0.15 NaN \n", - "96 0.9 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.85 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3100,7 +3100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3873332/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_691899/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,9 +3162,9 @@ " False\n", " False\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", - " [0.3,0.4,0.2,0.07,0.03]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -3186,9 +3186,9 @@ " True\n", " True\n", " ...\n", - " 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@@ -3210,9 +3210,9 @@ " False\n", " False\n", " ...\n", - " 0.15\n", - " 0.05\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -3234,8 +3234,8 @@ " None\n", " None\n", " ...\n", - " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.6,0.35,0.05]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -3258,8 +3258,8 @@ " False\n", " False\n", " ...\n", - " 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\n", + "\n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3423,7 +3423,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3447,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.4\n", - " 0.15\n", + " 0.65\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3471,7 +3471,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.85\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3495,7 +3495,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3519,9 +3519,9 @@ " False\n", " False\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3551,11 +3551,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.4 0.15 NaN NaN 0.15 NaN \n", - "96 0.9 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.85 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3762,8 +3762,8 @@ " False\n", " False\n", " ...\n", - " 2.343407\n", - " 5.703782\n", + " 2.302585\n", + " 5.857933\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3786,7 +3786,7 @@ " None\n", " None\n", " ...\n", - " 0.310155\n", + " -0.228842\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -3811,15 +3811,15 @@ " False\n", " ...\n", " 0.116534\n", - " 0.211844\n", + " -0.106610\n", " NaN\n", " -0.184571\n", - " 0.112526\n", + " 0.111521\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " -0.704447\n", + " 0.298855\n", " \n", " \n", " 9\n", @@ -3834,8 +3834,8 @@ " None\n", " None\n", " ...\n", - " -0.423484\n", - " -1.211941\n", + " -0.518794\n", + " -0.806476\n", " NaN\n", " -0.806476\n", " -0.494101\n", @@ -3843,7 +3843,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.518794\n", + " -0.693147\n", " \n", " \n", " 13\n", @@ -3858,8 +3858,8 @@ " None\n", " None\n", " ...\n", - " 0.330943\n", - " 0.287682\n", + " -2.145931\n", + " 0.510826\n", " 0.021979\n", " 0.200671\n", " 0.253781\n", @@ -3904,17 +3904,17 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.343407 5.703782 NaN 2.292635 2.703087 \n", - "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", - "6 0.116534 0.211844 NaN -0.184571 0.112526 \n", - "9 -0.423484 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.330943 0.287682 0.021979 0.200671 0.253781 \n", + "0 2.302585 5.857933 NaN 2.292635 2.703087 \n", + "3 -0.228842 0.310155 NaN 0.127833 0.152526 \n", + "6 0.116534 -0.106610 NaN -0.184571 0.111521 \n", + "9 -0.518794 -0.806476 NaN -0.806476 -0.494101 \n", + "13 -2.145931 0.510826 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "0 NaN NaN NaN NaN 4.656813 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN -0.704447 \n", - "9 NaN NaN -0.624154 NaN -0.518794 \n", + "6 NaN NaN NaN NaN 0.298855 \n", + "9 NaN NaN -0.624154 NaN -0.693147 \n", "13 NaN NaN NaN NaN -0.062598 \n", "\n", "[5 rows x 58 columns]" @@ -3982,15 +3982,15 @@ " False\n", " ...\n", " -2.879198\n", - " -0.933288\n", + " -2.879198\n", " -3.007032\n", " -2.879198\n", - " -3.390024\n", + " -3.795489\n", " NaN\n", " NaN\n", " -2.348570\n", " -2.409195\n", - " -2.879198\n", + " -2.348570\n", " \n", " \n", " 82\n", @@ -4005,7 +4005,7 @@ " None\n", " None\n", " ...\n", - " -0.993252\n", + " -0.587787\n", " -0.300105\n", " -0.523248\n", " 0.105361\n", @@ -4014,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.941958\n", + " -0.498556\n", " \n", " \n", " 83\n", @@ -4029,7 +4029,7 @@ " None\n", " None\n", " ...\n", - " -0.693147\n", + " -0.899761\n", " -0.693147\n", " NaN\n", " -0.182322\n", @@ -4053,8 +4053,8 @@ " False\n", " False\n", " ...\n", - " -0.037817\n", - " -0.048289\n", + " -0.054625\n", + " -0.102356\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -4078,7 +4078,7 @@ " False\n", " ...\n", " -1.299283\n", - " -2.908721\n", + " -1.704748\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4117,21 +4117,21 @@ "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", "81 NaN False False ... -2.879198 \n", - "82 NaN None None ... -0.993252 \n", - "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.037817 \n", + "82 NaN None None ... -0.587787 \n", + "83 NaN None None ... -0.899761 \n", + "91 NaN False False ... -0.054625 \n", "92 NaN False False ... -1.299283 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -0.933288 -3.007032 -2.879198 -3.390024 NaN NaN \n", + "81 -2.879198 -3.007032 -2.879198 -3.795489 NaN NaN \n", "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", - "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -2.908721 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -2.879198 \n", - "82 0.276509 -0.644609 -0.941958 \n", + "81 -2.348570 -2.409195 -2.348570 \n", + "82 0.276509 -0.644609 -0.498556 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.121048 \n", "92 NaN -0.749237 -0.318454 \n", @@ -4200,8 +4200,8 @@ " False\n", " False\n", " ...\n", - " -0.038208\n", - " -0.149434\n", + " -0.092275\n", + " -0.092275\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -4224,8 +4224,8 @@ " None\n", " None\n", " ...\n", - " -0.810930\n", - " 0.200671\n", + " -0.251314\n", + " 0.287682\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -4233,7 +4233,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.367725\n", + " 0.287682\n", " \n", " \n", " 8\n", @@ -4248,8 +4248,8 @@ " False\n", " False\n", " ...\n", - " 0.000000\n", " -0.054067\n", + " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -4273,7 +4273,7 @@ " False\n", " ...\n", " -0.057158\n", - " 0.000000\n", + " -0.057158\n", " NaN\n", " 0.054067\n", " -0.057158\n", @@ -4305,7 +4305,7 @@ " NaN\n", " -0.076070\n", " NaN\n", - " -0.096728\n", + " -0.076070\n", " \n", " \n", "\n", @@ -4328,18 +4328,18 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", - "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", - "8 0.000000 -0.054067 NaN -0.111226 -0.147158 \n", - "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", + "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", + "5 -0.251314 0.287682 NaN 0.510826 0.320472 \n", + "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", + "12 -0.057158 -0.057158 NaN 0.054067 -0.057158 \n", "16 -0.045611 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", - "5 NaN NaN NaN NaN 0.367725 \n", + "5 NaN NaN NaN NaN 0.287682 \n", "8 NaN NaN -0.398124 NaN -0.171850 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", - "16 NaN NaN -0.076070 NaN -0.096728 \n", + "16 NaN NaN -0.076070 NaN -0.076070 \n", "\n", "[5 rows x 58 columns]" ] @@ -4429,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -0.111226\n", + " -1.845827\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4502,7 +4502,7 @@ " False\n", " ...\n", " -0.017709\n", - " -0.017709\n", + " 0.000000\n", " NaN\n", " -0.112251\n", " -0.017709\n", @@ -4534,10 +4534,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -0.111226 NaN NaN -0.111226 NaN \n", + "95 -1.845827 NaN NaN -0.111226 NaN \n", "96 -0.074901 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -4603,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3398.202830\n", + " 3328.161138\n", " \n", " \n", " 3\n", @@ -4838,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3398.202830\n", + "2 bot_median 3328.161138\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 74.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 70.0%\n", "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -4988,7 +4988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3873332/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_691899/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5114,7 +5114,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2477.274734\n", + " 2437.335374\n", " 97\n", " 93.10\n", " \n", @@ -5471,7 +5471,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2477.274734 97 \n", + "3 4 bot_median 2437.335374 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5717,17 +5717,17 @@ " \n", " \n", " bot_median\n", - " 2477.3\n", + " 2437.3\n", " 93.1\n", - " 26.6\n", - " 58.467357\n", - " 6.059526\n", - " 4.391227\n", + " 26.2\n", + " 60.692389\n", + " 6.290127\n", + " 4.162040\n", " 1.985277\n", - " 38.6\n", - " 14.6\n", - " 0.999985\n", - " 0.000030\n", + " 38.7\n", + " 13.7\n", + " 0.999965\n", + " 0.000071\n", " \n", " \n", " acm_bot\n", @@ -6340,7 +6340,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2477.3 93.1 26.6 58.467357 \n", + "bot_median 2437.3 93.1 26.2 60.692389 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6389,7 +6389,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.059526 4.391227 1.985277 38.6 \n", + "bot_median 6.290127 4.162040 1.985277 38.7 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6438,7 +6438,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.6 0.999985 0.000030 \n", + "bot_median 13.7 0.999965 0.000071 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6573,18 +6573,18 @@ " NA\n", " \n", " \n", - " RPM_bot\n", + " bean_bot\n", " -0.6\n", - " 7.0\n", + " 4.7\n", " -0.1\n", - " 0.820675\n", - " 0.310186\n", - " -0.269729\n", - " 2.446912\n", - " 0.7\n", - " -0.8\n", - " 0.398203\n", - " 0.796405\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,20 +6601,6 @@ " 0.007677\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", - " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", - " \n", - " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6671,6 +6657,20 @@ " 0.574463\n", " \n", " \n", + " RPM_bot\n", + " -1.4\n", + " 7.0\n", + " -0.2\n", + " 0.819543\n", + " 0.309758\n", + " -0.650313\n", + " 2.446912\n", + " 0.6\n", + " -1.0\n", + " 0.269789\n", + " 0.539577\n", + " \n", + " \n", " KevinTestBot\n", " -1.5\n", " 8.4\n", @@ -6773,14 +6773,14 @@ " -6.6\n", " 27.4\n", " -0.2\n", - " 0.747093\n", - " 0.142725\n", - " -1.683660\n", + " 0.745283\n", + " 0.142379\n", + " -1.694619\n", " 2.049541\n", " 0.1\n", " -0.5\n", - " 0.052019\n", - " 0.104037\n", + " 0.050957\n", + " 0.101914\n", " \n", " \n", " jkraybill_bot\n", @@ -6811,32 +6811,18 @@ " 0.084012\n", " \n", " \n", - " metac-o1\n", - " -9.3\n", - " 91.1\n", - " -0.1\n", - " 0.901141\n", - " 0.094413\n", - " -1.081897\n", - " 1.985829\n", - " 0.1\n", - " -0.3\n", - " 0.141093\n", - " 0.282185\n", - " \n", - " \n", " MWG\n", - " -9.8\n", + " -9.6\n", " 28.6\n", " -0.3\n", - " 0.705240\n", - " 0.131872\n", - " -2.589625\n", + " 0.711160\n", + " 0.132979\n", + " -2.535384\n", " 2.046561\n", " -0.1\n", " -0.6\n", - " 0.007581\n", - " 0.015163\n", + " 0.008595\n", + " 0.017191\n", " \n", " \n", " ProfessorSP\n", @@ -6853,20 +6839,6 @@ " 0.023289\n", " \n", " \n", - " GreeneiBot2\n", - " -10.4\n", - " 58.4\n", - " -0.2\n", - " 0.849317\n", - " 0.111186\n", - " -1.601352\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.057397\n", - " 0.114793\n", - " \n", - " \n", " acm_bot\n", " -10.5\n", " 80.2\n", @@ -6881,6 +6853,20 @@ " 0.201592\n", " \n", " \n", + " GreeneiBot2\n", + " -10.7\n", + " 58.4\n", + " -0.2\n", + " 0.849274\n", + " 0.111180\n", + " -1.642777\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.052951\n", + " 0.105902\n", + " \n", + " \n", " ajf-bot\n", " -10.9\n", " 34.2\n", @@ -6895,6 +6881,20 @@ " 0.094289\n", " \n", " \n", + " metac-o1\n", + " -11.3\n", + " 91.1\n", + " -0.1\n", + " 0.885302\n", + " 0.092754\n", + " -1.342987\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.091325\n", + " 0.182650\n", + " \n", + " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6909,34 +6909,6 @@ " 0.023810\n", " \n", " \n", - " metac-perplexity\n", - " -12.3\n", - " 89.1\n", - " -0.1\n", - " 0.992894\n", - " 0.105187\n", - " -1.316799\n", - " 1.986405\n", - " 0.1\n", - " -0.3\n", - " 0.095661\n", - " 0.191321\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -12.6\n", - " 76.5\n", - " -0.2\n", - " 1.007464\n", - " 0.115186\n", - " -1.431098\n", - " 1.990822\n", - " 0.1\n", - " -0.4\n", - " 0.078264\n", - " 0.156528\n", - " \n", - " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6951,6 +6923,20 @@ " 0.017488\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -13.3\n", + " 52.1\n", + " -0.3\n", + " 0.780892\n", + " 0.108186\n", + " -2.366308\n", + " 2.005379\n", + " -0.0\n", + " -0.5\n", + " 0.010898\n", + " 0.021795\n", + " \n", + " \n", " wunderplumb\n", " -13.6\n", " 25.6\n", @@ -6965,18 +6951,32 @@ " 0.006348\n", " \n", " \n", + " metac-Gemini-Exp-1206\n", + " -13.7\n", + " 76.5\n", + " -0.2\n", + " 0.956701\n", + " 0.109382\n", + " -1.640002\n", + " 1.990822\n", + " 0.0\n", + " -0.4\n", + " 0.052582\n", + " 0.105165\n", + " \n", + " \n", " bot_median\n", - " -14.4\n", + " -14.2\n", " 92.1\n", " -0.2\n", - " 0.806477\n", - " 0.084035\n", - " -1.864964\n", + " 0.806056\n", + " 0.083992\n", + " -1.829889\n", " 1.985550\n", " 0.0\n", " -0.3\n", - " 0.032703\n", - " 0.065406\n", + " 0.035269\n", + " 0.070537\n", " \n", " \n", " manticAI\n", @@ -6993,18 +6993,32 @@ " 0.011014\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -15.8\n", - " 52.1\n", - " -0.3\n", - " 0.772503\n", - " 0.107024\n", - " -2.827984\n", - " 2.005379\n", - " -0.1\n", - " -0.5\n", - " 0.003337\n", - " 0.006674\n", + " metac-claude-3-5-sonnet-20240620\n", + " -15.7\n", + " 90.5\n", + " -0.2\n", + " 0.957721\n", + " 0.100673\n", + " -1.726279\n", + " 1.986072\n", + " 0.0\n", + " -0.4\n", + " 0.043874\n", + " 0.087748\n", + " \n", + " \n", + " metac-perplexity\n", + " -16.1\n", + " 89.1\n", + " -0.2\n", + " 1.040224\n", + " 0.110202\n", + " -1.638549\n", + " 1.986405\n", + " 0.0\n", + " -0.4\n", + " 0.052437\n", + " 0.104874\n", " \n", " \n", " NextWorldLab\n", @@ -7022,45 +7036,31 @@ " \n", " \n", " minefrac1\n", - " -19.4\n", + " -18.8\n", " 51.1\n", " -0.4\n", - " 0.878544\n", - " 0.122900\n", - " -3.095343\n", + " 0.874752\n", + " 0.122370\n", + " -3.013581\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.001607\n", - " 0.003215\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -20.5\n", - " 90.5\n", - " -0.2\n", - " 1.002602\n", - " 0.105391\n", - " -2.144815\n", - " 1.986072\n", - " -0.0\n", - " -0.4\n", - " 0.017338\n", - " 0.034677\n", + " 0.002021\n", + " 0.004043\n", " \n", " \n", - " metac-o1-preview\n", - " -21.8\n", + " metac-claude-3-5-sonnet-latest\n", + " -21.9\n", " 91.1\n", " -0.2\n", - " 0.778395\n", - " 0.081553\n", - " -2.928718\n", + " 0.826778\n", + " 0.086622\n", + " -2.778813\n", " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.002155\n", - " 0.004310\n", + " 0.003320\n", + " 0.006640\n", " \n", " \n", " mmBot\n", @@ -7077,32 +7077,32 @@ " 0.002208\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -22.6\n", - " 91.1\n", - " -0.2\n", - " 0.807536\n", - " 0.084606\n", - " -2.930813\n", - " 1.985829\n", - " -0.1\n", - " -0.4\n", - " 0.002142\n", - " 0.004284\n", - " \n", - " \n", " pgodzinai\n", - " -23.4\n", + " -23.5\n", " 76.4\n", " -0.3\n", - " 0.973824\n", - " 0.111413\n", - " -2.746500\n", + " 1.001063\n", + " 0.114529\n", + " -2.684830\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003765\n", - " 0.007529\n", + " 0.004459\n", + " 0.008918\n", + " \n", + " \n", + " metac-exa\n", + " -24.1\n", + " 89.1\n", + " -0.3\n", + " 0.823877\n", + " 0.087282\n", + " -3.103268\n", + " 1.986405\n", + " -0.1\n", + " -0.4\n", + " 0.001286\n", + " 0.002573\n", " \n", " \n", " VeritasAI\n", @@ -7119,18 +7119,18 @@ " 0.000076\n", " \n", " \n", - " metac-exa\n", - " -24.9\n", + " metac-Llama-3.1\n", + " -26.6\n", " 89.1\n", " -0.3\n", - " 0.829710\n", - " 0.087900\n", - " -3.180190\n", + " 0.890468\n", + " 0.094336\n", + " -3.169730\n", " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.001016\n", - " 0.002032\n", + " 0.001049\n", + " 0.002099\n", " \n", " \n", " InstitutPelFutur\n", @@ -7147,46 +7147,46 @@ " 0.004584\n", " \n", " \n", - " metac-grok-2-1212\n", - " -28.0\n", + " metac-o1-preview\n", + " -27.3\n", " 91.1\n", " -0.3\n", - " 1.005364\n", - " 0.105333\n", - " -2.923031\n", + " 0.839685\n", + " 0.087975\n", + " -3.407500\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.002191\n", - " 0.004383\n", + " 0.000491\n", + " 0.000982\n", " \n", " \n", - " metac-gpt-4o\n", - " -28.0\n", + " metac-grok-2-1212\n", + " -28.3\n", " 91.1\n", " -0.3\n", - " 0.864425\n", - " 0.090567\n", - " -3.393460\n", + " 1.037474\n", + " 0.108697\n", + " -2.862896\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000514\n", - " 0.001027\n", + " 0.002610\n", + " 0.005220\n", " \n", " \n", - " metac-Llama-3.1\n", - " -28.2\n", - " 89.1\n", + " metac-gpt-4o\n", + " -28.7\n", + " 91.1\n", " -0.3\n", - " 0.906064\n", - " 0.095989\n", - " -3.291937\n", - " 1.986405\n", + " 0.893717\n", + " 0.093636\n", + " -3.366630\n", + " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000716\n", - " 0.001433\n", + " 0.000560\n", + " 0.001120\n", " \n", " \n", "\n", @@ -7196,13 +7196,13 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", - "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", + "RPM_bot -1.4 7.0 -0.2 0.819543 0.309758 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", @@ -7210,47 +7210,47 @@ "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", + "cookics_bot_TEST -6.6 27.4 -0.2 0.745283 0.142379 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", - "metac-o1 -9.3 91.1 -0.1 0.901141 0.094413 \n", - "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", + "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", - "GreeneiBot2 -10.4 58.4 -0.2 0.849317 0.111186 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", + "GreeneiBot2 -10.7 58.4 -0.2 0.849274 0.111180 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", + "metac-o1 -11.3 91.1 -0.1 0.885302 0.092754 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-perplexity -12.3 89.1 -0.1 0.992894 0.105187 \n", - "metac-Gemini-Exp-1206 -12.6 76.5 -0.2 1.007464 0.115186 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", + "metac-deepseek-r1+asknews -13.3 52.1 -0.3 0.780892 0.108186 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "bot_median -14.4 92.1 -0.2 0.806477 0.084035 \n", + "metac-Gemini-Exp-1206 -13.7 76.5 -0.2 0.956701 0.109382 \n", + "bot_median -14.2 92.1 -0.2 0.806056 0.083992 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-deepseek-r1+asknews -15.8 52.1 -0.3 0.772503 0.107024 \n", + "metac-claude-3-5-sonnet-20240620 -15.7 90.5 -0.2 0.957721 0.100673 \n", + "metac-perplexity -16.1 89.1 -0.2 1.040224 0.110202 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "minefrac1 -19.4 51.1 -0.4 0.878544 0.122900 \n", - "metac-claude-3-5-sonnet-20240620 -20.5 90.5 -0.2 1.002602 0.105391 \n", - "metac-o1-preview -21.8 91.1 -0.2 0.778395 0.081553 \n", + "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", + "metac-claude-3-5-sonnet-latest -21.9 91.1 -0.2 0.826778 0.086622 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-claude-3-5-sonnet-latest -22.6 91.1 -0.2 0.807536 0.084606 \n", - "pgodzinai -23.4 76.4 -0.3 0.973824 0.111413 \n", + "pgodzinai -23.5 76.4 -0.3 1.001063 0.114529 \n", + "metac-exa -24.1 89.1 -0.3 0.823877 0.087282 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-exa -24.9 89.1 -0.3 0.829710 0.087900 \n", + "metac-Llama-3.1 -26.6 89.1 -0.3 0.890468 0.094336 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", - "metac-grok-2-1212 -28.0 91.1 -0.3 1.005364 0.105333 \n", - "metac-gpt-4o -28.0 91.1 -0.3 0.864425 0.090567 \n", - "metac-Llama-3.1 -28.2 89.1 -0.3 0.906064 0.095989 \n", + "metac-o1-preview -27.3 91.1 -0.3 0.839685 0.087975 \n", + "metac-grok-2-1212 -28.3 91.1 -0.3 1.037474 0.108697 \n", + "metac-gpt-4o -28.7 91.1 -0.3 0.893717 0.093636 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "RPM_bot -0.269729 2.446912 0.7 \n", - "jonahsingerbot -5.273630 2.784843 -0.1 \n", "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", + "RPM_bot -0.650313 2.446912 0.6 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", @@ -7258,47 +7258,47 @@ "krm-bot -3.229846 2.264709 -0.2 \n", "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.683660 2.049541 0.1 \n", + "cookics_bot_TEST -1.694619 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", - "metac-o1 -1.081897 1.985829 0.1 \n", - "MWG -2.589625 2.046561 -0.1 \n", + "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", - "GreeneiBot2 -1.601352 2.000832 0.0 \n", "acm_bot -1.287717 1.989344 0.1 \n", + "GreeneiBot2 -1.642777 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", + "metac-o1 -1.342987 1.985829 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-perplexity -1.316799 1.986405 0.1 \n", - "metac-Gemini-Exp-1206 -1.431098 1.990822 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", + "metac-deepseek-r1+asknews -2.366308 2.005379 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "bot_median -1.864964 1.985550 0.0 \n", + "metac-Gemini-Exp-1206 -1.640002 1.990822 0.0 \n", + "bot_median -1.829889 1.985550 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-deepseek-r1+asknews -2.827984 2.005379 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -1.726279 1.986072 0.0 \n", + "metac-perplexity -1.638549 1.986405 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "minefrac1 -3.095343 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-20240620 -2.144815 1.986072 -0.0 \n", - "metac-o1-preview -2.928718 1.985829 -0.1 \n", + "minefrac1 -3.013581 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.778813 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-claude-3-5-sonnet-latest -2.930813 1.985829 -0.1 \n", - "pgodzinai -2.746500 1.990849 -0.1 \n", + "pgodzinai -2.684830 1.990849 -0.1 \n", + "metac-exa -3.103268 1.986405 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-exa -3.180190 1.986405 -0.1 \n", + "metac-Llama-3.1 -3.169730 1.986405 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", - "metac-grok-2-1212 -2.923031 1.985829 -0.1 \n", - "metac-gpt-4o -3.393460 1.985829 -0.1 \n", - "metac-Llama-3.1 -3.291937 1.986405 -0.1 \n", + "metac-o1-preview -3.407500 1.985829 -0.1 \n", + "metac-grok-2-1212 -2.862896 1.985829 -0.1 \n", + "metac-gpt-4o -3.366630 1.985829 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "RPM_bot -0.8 0.398203 0.796405 \n", - "jonahsingerbot -0.2 0.003839 0.007677 \n", "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", + "RPM_bot -1.0 0.269789 0.539577 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", @@ -7306,36 +7306,36 @@ "krm-bot -0.9 0.005563 0.011127 \n", "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.052019 0.104037 \n", + "cookics_bot_TEST -0.5 0.050957 0.101914 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", - "metac-o1 -0.3 0.141093 0.282185 \n", - "MWG -0.6 0.007581 0.015163 \n", + "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", - "GreeneiBot2 -0.4 0.057397 0.114793 \n", "acm_bot -0.3 0.100796 0.201592 \n", + "GreeneiBot2 -0.4 0.052951 0.105902 \n", "ajf-bot -0.7 0.047145 0.094289 \n", + "metac-o1 -0.3 0.091325 0.182650 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-perplexity -0.3 0.095661 0.191321 \n", - "metac-Gemini-Exp-1206 -0.4 0.078264 0.156528 \n", "laylaps -0.4 0.008744 0.017488 \n", + "metac-deepseek-r1+asknews -0.5 0.010898 0.021795 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "bot_median -0.3 0.032703 0.065406 \n", + "metac-Gemini-Exp-1206 -0.4 0.052582 0.105165 \n", + "bot_median -0.3 0.035269 0.070537 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-deepseek-r1+asknews -0.5 0.003337 0.006674 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.043874 0.087748 \n", + "metac-perplexity -0.4 0.052437 0.104874 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "minefrac1 -0.6 0.001607 0.003215 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.017338 0.034677 \n", - "metac-o1-preview -0.4 0.002155 0.004310 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.003320 0.006640 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.002142 0.004284 \n", - "pgodzinai -0.5 0.003765 0.007529 \n", + "pgodzinai -0.5 0.004459 0.008918 \n", + "metac-exa -0.4 0.001286 0.002573 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-exa -0.5 0.001016 0.002032 \n", + "metac-Llama-3.1 -0.5 0.001049 0.002099 \n", "InstitutPelFutur -0.5 0.002292 0.004584 \n", - "metac-grok-2-1212 -0.5 0.002191 0.004383 \n", - "metac-gpt-4o -0.5 0.000514 0.001027 \n", - "metac-Llama-3.1 -0.5 0.000716 0.001433 " + "metac-o1-preview -0.5 0.000491 0.000982 \n", + "metac-grok-2-1212 -0.5 0.002610 0.005220 \n", + "metac-gpt-4o -0.5 0.000560 0.001120 " ] }, "execution_count": 42, @@ -9087,197 +9087,197 @@ " \n", " \n", " metac-o1\n", - " 6.1\n", - " 7.4\n", - " 9.7\n", - " 11.8\n", - " 13.2\n", + " 5.9\n", + " 7.3\n", + " 9.6\n", + " 11.9\n", + " 12.9\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", - " 5.4\n", + " 3.8\n", + " 5.3\n", " 8.3\n", - " 11.4\n", - " 12.9\n", + " 11.3\n", + " 13.2\n", " \n", " \n", " manticAI\n", " 0.3\n", - " 2.0\n", + " 2.1\n", " 5.4\n", " 8.8\n", - " 10.6\n", + " 10.7\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.7\n", - " 2.2\n", - " 5.0\n", - " 7.8\n", - " 9.2\n", + " 0.5\n", + " 2.1\n", + " 5.1\n", + " 8.0\n", + " 9.6\n", " \n", " \n", " acm_bot\n", - " 0.6\n", - " 1.9\n", - " 4.7\n", - " 7.5\n", - " 8.7\n", + " 0.2\n", + " 1.4\n", + " 4.4\n", + " 7.4\n", + " 9.1\n", " \n", " \n", " metac-perplexity\n", - " -1.9\n", - " 0.3\n", - " 4.3\n", - " 7.9\n", - " 9.8\n", + " -1.8\n", + " 0.1\n", + " 4.2\n", + " 7.6\n", + " 9.9\n", " \n", " \n", " GreeneiBot2\n", - " -1.4\n", + " -1.1\n", " 0.7\n", - " 3.9\n", - " 7.0\n", - " 8.6\n", + " 4.0\n", + " 7.2\n", + " 9.4\n", " \n", " \n", " twsummerbot\n", " 0.1\n", - " 1.4\n", + " 1.5\n", " 3.9\n", " 6.3\n", - " 7.5\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.0\n", + " -0.2\n", " 1.1\n", " 3.1\n", " 5.0\n", - " 5.8\n", + " 6.3\n", " \n", " \n", " pgodzinai\n", - " -3.4\n", + " -3.5\n", " -1.1\n", " 3.1\n", - " 7.3\n", - " 9.5\n", + " 6.9\n", + " 8.9\n", " \n", " \n", " CumulativeBot\n", - " 0.1\n", - " 0.9\n", + " -0.2\n", + " 0.8\n", " 2.7\n", - " 4.5\n", - " 5.3\n", + " 4.6\n", + " 5.6\n", " \n", " \n", " SynapseSeer\n", - " 0.1\n", - " 0.9\n", + " 0.3\n", + " 1.0\n", " 2.5\n", " 4.1\n", - " 4.8\n", + " 4.9\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.6\n", - " -0.2\n", - " 2.4\n", - " 5.0\n", + " -1.1\n", + " -0.0\n", + " 2.5\n", + " 4.9\n", " 6.2\n", " \n", " \n", - " jkraybill_bot\n", - " -3.9\n", - " -1.7\n", - " 1.9\n", - " 5.0\n", - " 7.0\n", + " metac-exa\n", + " -5.1\n", + " -2.2\n", + " 1.7\n", + " 5.6\n", + " 7.8\n", " \n", " \n", - " metac-exa\n", - " -4.8\n", - " -2.6\n", - " 1.5\n", - " 5.8\n", - " 7.6\n", + " jkraybill_bot\n", + " -4.4\n", + " -1.7\n", + " 1.7\n", + " 4.8\n", + " 6.5\n", " \n", " \n", " metac-deepseek-r1+asknews\n", - " -1.8\n", + " -2.0\n", " -0.8\n", " 1.3\n", - " 3.5\n", - " 4.5\n", + " 3.4\n", + " 4.6\n", " \n", " \n", " MWG\n", - " -1.5\n", - " -0.7\n", - " 0.7\n", + " -1.6\n", + " -0.8\n", + " 0.6\n", " 2.2\n", " 3.0\n", " \n", " \n", - " pianobot\n", - " -1.2\n", - " -0.8\n", + " andrewsiah\n", + " -0.9\n", + " -0.6\n", " 0.0\n", - " 0.7\n", - " 1.1\n", + " 0.6\n", + " 1.0\n", " \n", " \n", - " andrewsiah\n", - " -0.9\n", - " -0.5\n", + " cobyj-bot\n", + " -1.4\n", + " -1.0\n", " -0.0\n", - " 0.6\n", + " 0.9\n", + " 1.4\n", + " \n", + " \n", + " pianobot\n", + " -1.3\n", + " -0.8\n", + " -0.0\n", + " 0.7\n", " 1.0\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.2\n", + " -0.3\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", - " cobyj-bot\n", - " -1.4\n", - " -0.9\n", - " -0.1\n", - " 0.8\n", - " 1.3\n", - " \n", - " \n", " annabot\n", " -3.4\n", - " -2.5\n", + " -2.4\n", " -0.4\n", " 1.2\n", " 2.1\n", " \n", " \n", - " KevinTestBot\n", - " -3.9\n", - " -2.8\n", - " -0.5\n", - " 1.6\n", - " 2.6\n", - " \n", - " \n", " bean_bot\n", - " -3.2\n", + " -3.3\n", " -2.2\n", " -0.5\n", " 1.0\n", " 1.9\n", " \n", " \n", + " KevinTestBot\n", + " -4.1\n", + " -2.7\n", + " -0.5\n", + " 1.6\n", + " 2.5\n", + " \n", + " \n", " CatrachoCaster\n", " -2.3\n", " -1.8\n", @@ -9288,250 +9288,667 @@ " \n", " jonahsingerbot\n", " -3.0\n", - " -2.2\n", + " -2.3\n", " -0.9\n", - " 0.3\n", - " 1.0\n", + " 0.5\n", + " 1.1\n", " \n", " \n", " krm-bot\n", - " -3.5\n", - " -2.6\n", - " -0.9\n", - " 0.8\n", - " 1.6\n", + " -3.6\n", + " -2.7\n", + " -1.0\n", + " 0.7\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.4\n", + " -4.5\n", " -3.3\n", - " -1.0\n", + " -1.1\n", " 1.0\n", - " 2.0\n", + " 2.1\n", " \n", " \n", - " mmBot\n", - " -7.3\n", - " -5.5\n", - " -1.5\n", - " 2.4\n", - " 4.2\n", + " metac-grok-2-1212\n", + " -6.5\n", + " -4.6\n", + " -1.4\n", + " 1.9\n", + " 3.5\n", " \n", " \n", - " metac-grok-2-1212\n", - " -6.3\n", - " -4.7\n", + " mmBot\n", + " -6.9\n", + " -5.2\n", " -1.5\n", - " 2.0\n", - " 3.7\n", + " 2.3\n", + " 4.3\n", " \n", " \n", " 4Shadower\n", - " -4.9\n", + " -4.8\n", " -3.7\n", - " -1.6\n", - " 0.2\n", - " 1.2\n", + " -1.7\n", + " 0.3\n", + " 1.4\n", " \n", " \n", " swingswish\n", - " -5.4\n", + " -5.3\n", " -4.2\n", " -2.0\n", - " -0.1\n", - " 0.7\n", - " \n", - " \n", - " RPM_bot\n", - " -4.9\n", - " -3.9\n", - " -2.1\n", - " -0.8\n", " -0.2\n", + " 0.7\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.7\n", - " -5.0\n", - " -2.2\n", + " -6.4\n", + " -4.8\n", + " -2.0\n", " 0.8\n", - " 2.5\n", + " 2.4\n", + " \n", + " \n", + " RPM_bot\n", + " -4.9\n", + " -3.8\n", + " -2.0\n", + " -0.7\n", + " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -8.7\n", - " -6.6\n", - " -2.5\n", + " -8.9\n", + " -6.4\n", + " -2.2\n", " 1.6\n", - " 3.3\n", + " 4.0\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.3\n", + " -6.9\n", + " -5.1\n", " -2.6\n", - " 0.3\n", - " 1.7\n", + " 0.1\n", + " 1.6\n", " \n", " \n", " wunderplumb\n", - " -6.2\n", + " -6.1\n", " -5.0\n", - " -2.6\n", - " -0.2\n", - " 1.3\n", + " -2.7\n", + " -0.1\n", + " 0.9\n", " \n", " \n", " NextWorldLab\n", - " -8.3\n", - " -6.7\n", - " -3.7\n", - " -0.6\n", - " 0.9\n", + " -8.7\n", + " -6.9\n", + " -3.6\n", + " -0.2\n", + " 1.4\n", " \n", " \n", " Bot_Pepa\n", - " -6.9\n", - " -5.7\n", - " -3.9\n", + " -6.8\n", + " -5.9\n", + " -3.8\n", " -2.0\n", - " -1.1\n", + " -0.9\n", " \n", " \n", " laylaps\n", - " -10.1\n", - " -8.1\n", - " -3.9\n", - " -0.5\n", - " 1.3\n", + " -10.2\n", + " -8.0\n", + " -3.8\n", + " -0.1\n", + " 1.9\n", " \n", " \n", " VeritasAI\n", - " -7.8\n", - " -6.5\n", + " -8.0\n", + " -6.6\n", " -4.2\n", - " -1.8\n", - " -0.5\n", + " -1.9\n", + " -0.7\n", " \n", " \n", " minefrac1\n", - " -8.0\n", - " -6.8\n", - " -4.6\n", - " -2.5\n", + " -7.8\n", + " -6.9\n", + " -4.7\n", + " -2.6\n", + " -1.6\n", + " \n", + " \n", + " Grizeu_Bot\n", + " -9.1\n", + " -7.6\n", + " -4.9\n", + " -2.3\n", + " -0.9\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -10.7\n", + " -9.1\n", + " -6.1\n", + " -3.0\n", " -1.5\n", " \n", " \n", - " Grizeu_Bot\n", - " -9.4\n", - " -7.7\n", - " -4.9\n", - " -2.4\n", - " -1.1\n", + " ajf-bot\n", + " -15.3\n", + " -12.9\n", + " -8.4\n", + " -4.3\n", + " -2.4\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", + "metac-o1 5.9 7.3 9.6 11.9 12.9\n", + "metac-o1-preview 3.8 5.3 8.3 11.3 13.2\n", + "manticAI 0.3 2.1 5.4 8.8 10.7\n", + "metac-Gemini-Exp-1206 0.5 2.1 5.1 8.0 9.6\n", + "acm_bot 0.2 1.4 4.4 7.4 9.1\n", + "metac-perplexity -1.8 0.1 4.2 7.6 9.9\n", + "GreeneiBot2 -1.1 0.7 4.0 7.2 9.4\n", + "twsummerbot 0.1 1.5 3.9 6.3 7.4\n", + "cookics_bot_TEST -0.2 1.1 3.1 5.0 6.3\n", + "pgodzinai -3.5 -1.1 3.1 6.9 8.9\n", + "CumulativeBot -0.2 0.8 2.7 4.6 5.6\n", + "SynapseSeer 0.3 1.0 2.5 4.1 4.9\n", + "metac-claude-3-5-sonnet-latest -1.1 -0.0 2.5 4.9 6.2\n", + "metac-exa -5.1 -2.2 1.7 5.6 7.8\n", + "jkraybill_bot -4.4 -1.7 1.7 4.8 6.5\n", + "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.4 4.6\n", + "MWG -1.6 -0.8 0.6 2.2 3.0\n", + "andrewsiah -0.9 -0.6 0.0 0.6 1.0\n", + "cobyj-bot -1.4 -1.0 -0.0 0.9 1.4\n", + "pianobot -1.3 -0.8 -0.0 0.7 1.0\n", + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "annabot -3.4 -2.4 -0.4 1.2 2.1\n", + "bean_bot -3.3 -2.2 -0.5 1.0 1.9\n", + "KevinTestBot -4.1 -2.7 -0.5 1.6 2.5\n", + "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", + "jonahsingerbot -3.0 -2.3 -0.9 0.5 1.1\n", + "krm-bot -3.6 -2.7 -1.0 0.7 1.5\n", + "ProfessorSP -4.5 -3.3 -1.1 1.0 2.1\n", + "metac-grok-2-1212 -6.5 -4.6 -1.4 1.9 3.5\n", + "mmBot -6.9 -5.2 -1.5 2.3 4.3\n", + "4Shadower -4.8 -3.7 -1.7 0.3 1.4\n", + "swingswish -5.3 -4.2 -2.0 -0.2 0.7\n", + "metac-claude-3-5-sonnet-20240620 -6.4 -4.8 -2.0 0.8 2.4\n", + "RPM_bot -4.9 -3.8 -2.0 -0.7 -0.1\n", + "InstitutPelFutur -8.9 -6.4 -2.2 1.6 4.0\n", + "metac-Llama-3.1 -6.9 -5.1 -2.6 0.1 1.6\n", + "wunderplumb -6.1 -5.0 -2.7 -0.1 0.9\n", + "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.4\n", + "Bot_Pepa -6.8 -5.9 -3.8 -2.0 -0.9\n", + "laylaps -10.2 -8.0 -3.8 -0.1 1.9\n", + "VeritasAI -8.0 -6.6 -4.2 -1.9 -0.7\n", + "minefrac1 -7.8 -6.9 -4.7 -2.6 -1.6\n", + "Grizeu_Bot -9.1 -7.6 -4.9 -2.3 -0.9\n", + "metac-gpt-4o -10.7 -9.1 -6.1 -3.0 -1.5\n", + "ajf-bot -15.3 -12.9 -8.4 -4.3 -2.4" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop 'bot_median' from all_bots list\n", + "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", + "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "\n", + "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", + "ITER = 1000\n", + "\n", + "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", + "average_df = result_df / NUM\n", + "\n", + "print(f'BOT LEADERBOARD\\n\\n')\n", + "df_rounded = average_df.round(1)\n", + "df_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + }, + "id": "MXAev2sNXdbZ", + "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ - " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.1 7.4 9.7 11.8 13.2\n", - "metac-o1-preview 3.9 5.4 8.3 11.4 12.9\n", - "manticAI 0.3 2.0 5.4 8.8 10.6\n", - "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", - "acm_bot 0.6 1.9 4.7 7.5 8.7\n", - "metac-perplexity -1.9 0.3 4.3 7.9 9.8\n", - "GreeneiBot2 -1.4 0.7 3.9 7.0 8.6\n", - "twsummerbot 0.1 1.4 3.9 6.3 7.5\n", - "cookics_bot_TEST -0.0 1.1 3.1 5.0 5.8\n", - "pgodzinai -3.4 -1.1 3.1 7.3 9.5\n", - "CumulativeBot 0.1 0.9 2.7 4.5 5.3\n", - "SynapseSeer 0.1 0.9 2.5 4.1 4.8\n", - "metac-claude-3-5-sonnet-latest -1.6 -0.2 2.4 5.0 6.2\n", - "jkraybill_bot -3.9 -1.7 1.9 5.0 7.0\n", - "metac-exa -4.8 -2.6 1.5 5.8 7.6\n", - "metac-deepseek-r1+asknews -1.8 -0.8 1.3 3.5 4.5\n", - "MWG -1.5 -0.7 0.7 2.2 3.0\n", - "pianobot -1.2 -0.8 0.0 0.7 1.1\n", - "andrewsiah -0.9 -0.5 -0.0 0.6 1.0\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "cobyj-bot -1.4 -0.9 -0.1 0.8 1.3\n", - "annabot -3.4 -2.5 -0.4 1.2 2.1\n", - "KevinTestBot -3.9 -2.8 -0.5 1.6 2.6\n", - "bean_bot -3.2 -2.2 -0.5 1.0 1.9\n", - "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.2 -0.9 0.3 1.0\n", - "krm-bot -3.5 -2.6 -0.9 0.8 1.6\n", - "ProfessorSP -4.4 -3.3 -1.0 1.0 2.0\n", - "mmBot -7.3 -5.5 -1.5 2.4 4.2\n", - "metac-grok-2-1212 -6.3 -4.7 -1.5 2.0 3.7\n", - "4Shadower -4.9 -3.7 -1.6 0.2 1.2\n", - "swingswish -5.4 -4.2 -2.0 -0.1 0.7\n", - "RPM_bot -4.9 -3.9 -2.1 -0.8 -0.2\n", - "metac-claude-3-5-sonnet-20240620 -6.7 -5.0 -2.2 0.8 2.5\n", - "InstitutPelFutur -8.7 -6.6 -2.5 1.6 3.3\n", - "metac-Llama-3.1 -6.7 -5.3 -2.6 0.3 1.7\n", - "wunderplumb -6.2 -5.0 -2.6 -0.2 1.3\n", - "NextWorldLab -8.3 -6.7 -3.7 -0.6 0.9\n", - "Bot_Pepa -6.9 -5.7 -3.9 -2.0 -1.1\n", - "laylaps -10.1 -8.1 -3.9 -0.5 1.3\n", - "VeritasAI -7.8 -6.5 -4.2 -1.8 -0.5\n", - "minefrac1 -8.0 -6.8 -4.6 -2.5 -1.5\n", - "Grizeu_Bot -9.4 -7.7 -4.9 -2.4 -1.1\n", - "metac-gpt-4o -10.6 -9.0 -5.9 -2.9 -1.3\n", - "ajf-bot -15.4 -12.8 -8.3 -4.2 -2.1" + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", + "\n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 -0.054067 NaN NaN 0.000000 0.000000 \n", + "95 -1.845827 NaN NaN -0.111226 NaN \n", + "96 -0.074901 NaN NaN -0.074901 NaN \n", + "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", + "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", + "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", + "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", + "\n", + "[5 rows x 57 columns]" ] }, - "execution_count": 49, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop 'bot_median' from all_bots list\n", - "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", - "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", - "\n", - "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", - "ITER = 1000\n", - "\n", - "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", - "average_df = result_df / NUM\n", - "\n", - "print(f'BOT LEADERBOARD\\n\\n')\n", - "df_rounded = average_df.round(1)\n", - "df_rounded" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 125 + "output_type": "display_data" }, - "id": "MXAev2sNXdbZ", - "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" - }, - "outputs": [ { "name": "stdout", "output_type": "stream", @@ -9590,14 +10007,6 @@ " 0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", - " -0.0\n", - " -0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", " jonahsingerbot\n", " -0.0\n", " -0.0\n", @@ -9606,20 +10015,20 @@ " -0.0\n", " \n", " \n", - " bean_bot\n", - " -0.0\n", - " -0.0\n", + " X_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " X_bot\n", + " bean_bot\n", + " -0.0\n", + " -0.0\n", " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " CumulativeBot\n", @@ -9638,7 +10047,7 @@ " -0.0\n", " \n", " \n", - " KevinTestBot\n", + " RPM_bot\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -9646,7 +10055,7 @@ " 0.0\n", " \n", " \n", - " SynapseSeer\n", + " KevinTestBot\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -9654,12 +10063,12 @@ " 0.0\n", " \n", " \n", - " Grizeu_Bot\n", - " -0.2\n", + " SynapseSeer\n", " -0.1\n", " -0.0\n", - " 0.1\n", - " 0.2\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " pianobot\n", @@ -9670,6 +10079,14 @@ " 0.0\n", " \n", " \n", + " Grizeu_Bot\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", + " 0.2\n", + " \n", + " \n", " CatrachoCaster\n", " -0.1\n", " -0.1\n", @@ -9730,64 +10147,72 @@ " -0.2\n", " -0.2\n", " -0.1\n", + " -0.0\n", + " -0.0\n", + " \n", + " \n", + " ProfessorSP\n", + " -0.2\n", + " -0.2\n", " -0.1\n", " -0.0\n", + " -0.0\n", " \n", " \n", - " metac-o1\n", + " GreeneiBot2\n", " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " 0.0\n", - " 0.1\n", " \n", " \n", - " GreeneiBot2\n", - " -0.2\n", + " ajf-bot\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " ProfessorSP\n", + " Bot_Pepa\n", " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " -0.0\n", " \n", " \n", - " ajf-bot\n", + " acm_bot\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", - " 0.0\n", + " 0.1\n", " \n", " \n", - " acm_bot\n", + " metac-o1\n", " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " 0.0\n", - " 0.1\n", " \n", " \n", - " Bot_Pepa\n", - " -0.2\n", + " metac-deepseek-r1+asknews\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " -0.0\n", " \n", " \n", - " metac-perplexity\n", - " -0.3\n", + " wunderplumb\n", " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " -0.1\n", - " 0.0\n", - " 0.1\n", " \n", " \n", " laylaps\n", @@ -9801,41 +10226,41 @@ " metac-Gemini-Exp-1206\n", " -0.3\n", " -0.2\n", - " -0.1\n", + " -0.2\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " wunderplumb\n", + " manticAI\n", " -0.3\n", " -0.2\n", + " -0.2\n", " -0.1\n", - " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", " bot_median\n", " -0.3\n", - " -0.3\n", " -0.2\n", - " -0.0\n", + " -0.2\n", + " -0.1\n", " 0.0\n", " \n", " \n", - " manticAI\n", + " metac-claude-3-5-sonnet-20240620\n", + " -0.3\n", " -0.3\n", - " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " 0.0\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -0.3\n", + " metac-perplexity\n", + " -0.4\n", " -0.3\n", " -0.2\n", - " -0.1\n", - " -0.1\n", + " -0.0\n", + " 0.0\n", " \n", " \n", " NextWorldLab\n", @@ -9854,15 +10279,7 @@ " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.4\n", - " -0.3\n", - " -0.2\n", - " -0.1\n", - " 0.0\n", - " \n", - " \n", - " metac-o1-preview\n", + " mmBot\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9870,7 +10287,7 @@ " -0.1\n", " \n", " \n", - " mmBot\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9878,20 +10295,20 @@ " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " pgodzinai\n", + " -0.4\n", " -0.4\n", - " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", - " pgodzinai\n", + " metac-exa\n", " -0.4\n", " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", " \n", " \n", " VeritasAI\n", @@ -9902,7 +10319,7 @@ " -0.1\n", " \n", " \n", - " metac-exa\n", + " metac-Llama-3.1\n", " -0.4\n", " -0.4\n", " -0.3\n", @@ -9910,7 +10327,7 @@ " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", + " metac-o1-preview\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9918,7 +10335,7 @@ " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9926,7 +10343,7 @@ " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", + " metac-grok-2-1212\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9934,7 +10351,7 @@ " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", + " metac-gpt-4o\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9949,16 +10366,16 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", + "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", - "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", + "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", @@ -9966,33 +10383,33 @@ "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", - "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", + "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", + "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-perplexity -0.3 -0.3 -0.1 0.0 0.1\n", - "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.2 -0.1 -0.0 0.1\n", + "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-o1 -0.3 -0.2 -0.1 -0.0 0.0\n", + "metac-deepseek-r1+asknews -0.3 -0.2 -0.1 -0.1 -0.0\n", "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", - "bot_median -0.3 -0.3 -0.2 -0.0 0.0\n", + "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.2 -0.2 -0.0 0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-deepseek-r1+asknews -0.3 -0.3 -0.2 -0.1 -0.1\n", + "bot_median -0.3 -0.2 -0.2 -0.1 0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.1 0.0\n", + "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", - "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", - "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" + "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" ] }, "execution_count": 50, @@ -10004,6 +10421,7 @@ "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", "ITER = 1000\n", "\n", + "display_head_and_tail(df_bot_vs_pro_peer)\n", "result_df = weighted_bootstrap_analysis(df_bot_vs_pro_peer, all_bots, NUM, ITER)\n", "average_df = result_df / NUM\n", "\n", @@ -10654,506 +11072,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.65]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.98]\n", " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.65]\n", - " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.01]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.99]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 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Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.85]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", - " >>> Collected 2 forecasts: [0.6, 0.9]\n", - " >>> Collected 2 forecasts: [0.25, 0.5]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.25]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.7, 0.8]\n", + " >>> Collected 2 forecasts: [0.65, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.15, 0.3]\n", " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.4]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", " >>> Collected 2 forecasts: [0.98, 0.97]\n", " >>> Collected 2 forecasts: [0.4, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", - " >>> Collected 2 forecasts: [0.65, 0.6]\n", - " >>> Collected 2 forecasts: [0.25, 0.02]\n", + " >>> Collected 2 forecasts: [0.4, 0.25]\n", + " >>> Collected 2 forecasts: [0.85, 0.6]\n", + " >>> Collected 2 forecasts: [0.01, 0.02]\n", " >>> Collected 2 forecasts: [0.7, 0.7]\n", - " >>> Collected 2 forecasts: [0.99, 0.7]\n", - " >>> Collected 2 forecasts: [0.95, 0.98]\n", - " >>> Collected 2 forecasts: [0.95, 0.15]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.7]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", + " >>> Collected 2 forecasts: [0.99, 0.9]\n", + " >>> Collected 2 forecasts: [0.97, 0.99]\n", + " >>> Collected 2 forecasts: [0.99, 0.1]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.6, 0.4]\n", " >>> Collected 2 forecasts: [0.8, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.3, 0.3]\n", - " >>> Collected 2 forecasts: [0.6, 0.85]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.2, 0.3]\n", - " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", + " >>> Collected 2 forecasts: [0.3, 0.2]\n", + " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.15, 0.2]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.1]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.95]\n", - " >>> Collected 2 forecasts: [0.15, 0.4]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.07]\n", - " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.65]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.7]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.82]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.25]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.6, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.5, 0.108]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", + " >>> Collected 3 forecasts: [0.65, 0.3, 0.108]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.95]\n", " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, 0.115]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.03]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.25, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.4, 0.25, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.85, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", - " >>> Collected 3 forecasts: [0.95, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.95, 0.15, 0.14]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.7, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.1, 0.14]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.3, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.6, 0.85, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.086]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", + " >>> Collected 3 forecasts: [0.3, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.7, 0.67]\n", + " >>> Collected 3 forecasts: [0.15, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.1, 0.02]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.884]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", + " >>> Collected 3 forecasts: [0.85, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.6, 0.9, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.5, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, 0.652]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.65, 0.3, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.95, 0.052]\n", " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.25, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.95, 0.15, 0.14, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.7, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.1, 0.14, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.85, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.7, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.1, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.85, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.9, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.5, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, 0.652, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.65, 0.3, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999]\n", " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.6, 0.85, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.2, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.7, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.15, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.1, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.85, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125]\n", " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", + " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02]\n", - " >>> Collected 7 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", - " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", - " >>> Collected 7 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", + " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", + " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3]\n", + " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", - " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223]\n", - " >>> Collected 8 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 8 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", + " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", + " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", + " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8]\n", - " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 9 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65]\n", + " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75]\n", + " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95]\n", + " >>> Collected 9 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", - " >>> Collected 10 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2, 0.574]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65, 0.155]\n", + " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35, 0.088]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95, 0.762]\n", + " >>> Collected 10 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -11234,9 +11652,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", - " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", " \n", " \n", " 1\n", @@ -11244,7 +11662,7 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", " \n", @@ -11252,25 +11670,25 @@ " binary\n", " NaN\n", " no\n", - " 0.05\n", - " 0.063\n", - " 0.11\n", + " 0.1\n", + " 0.085\n", + " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.2,0.6,0.2]\n", + " [0.6,0.35,0.05]\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", " numeric\n", " NaN\n", " 119.2\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", @@ -11289,7 +11707,7 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.905\n", + " 0.9\n", " 0.9025\n", " \n", " \n", @@ -11297,9 +11715,9 @@ " binary\n", " NaN\n", " no\n", - " 0.15\n", - " 0.15\n", - " 0.1085\n", + " 0.85\n", + " 0.65\n", + " 0.3585\n", " \n", " \n", " 355\n", @@ -11307,8 +11725,8 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.9\n", - " 0.825\n", + " 0.85\n", + " 0.772\n", " \n", " \n", " 361\n", @@ -11316,8 +11734,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.8\n", - " 0.755\n", + " 0.71\n", + " 0.709\n", " \n", " \n", " 364\n", @@ -11348,42 +11766,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "2 0.1 \n", + "3 [0.6,0.35,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", - "351 0.15 \n", + "351 0.85 \n", "355 0.9 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", - "342 0.905 \n", - "351 0.15 \n", - "355 0.9 \n", - "361 0.8 \n", + "342 0.9 \n", + "351 0.65 \n", + "355 0.85 \n", + "361 0.71 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.11 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.9025 \n", - "351 0.1085 \n", - "355 0.825 \n", - "361 0.755 \n", + "351 0.3585 \n", + "355 0.772 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11474,52 +11892,52 @@ " \n", " 0\n", " 1\n", - " 1326.64\n", + " 702.66\n", " \n", " \n", " 1\n", " 2\n", - " 2492.14\n", + " 2127.15\n", " \n", " \n", " 2\n", " 3\n", - " 2545.30\n", + " 2378.31\n", " \n", " \n", " 3\n", " 4\n", - " 2613.88\n", + " 2447.50\n", " \n", " \n", " 4\n", " 5\n", - " 2743.23\n", + " 2613.58\n", " \n", " \n", " 5\n", " 6\n", - " 2513.69\n", + " 2565.78\n", " \n", " \n", " 6\n", " 7\n", - " 2611.87\n", + " 2492.12\n", " \n", " \n", " 7\n", " 8\n", - " 2685.15\n", + " 2572.02\n", " \n", " \n", " 8\n", " 9\n", - " 2381.69\n", + " 2483.55\n", " \n", " \n", " 9\n", " 10\n", - " 2215.95\n", + " 2418.82\n", " \n", " \n", "\n", @@ -11527,16 +11945,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 1326.64\n", - "1 2 2492.14\n", - "2 3 2545.30\n", - "3 4 2613.88\n", - "4 5 2743.23\n", - "5 6 2513.69\n", - "6 7 2611.87\n", - "7 8 2685.15\n", - "8 9 2381.69\n", - "9 10 2215.95" + "0 1 702.66\n", + "1 2 2127.15\n", + "2 3 2378.31\n", + "3 4 2447.50\n", + "4 5 2613.58\n", + "5 6 2565.78\n", + "6 7 2492.12\n", + "7 8 2572.02\n", + "8 9 2483.55\n", + "9 10 2418.82" ] }, "execution_count": 60, @@ -11690,18 +12108,18 @@ " NaN\n", " False\n", " False\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", + " [0.01,0.7,0.2,0.07,0.02]\n", " ...\n", - " [0.010416666666666666, 0.0001, 0.0001, 0.0001,...\n", - " [0.23020833333333335, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.13, 0.0001, 0.0001, 0.0001, 0.0001]\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", - " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", - " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", - " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", + " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -11717,10 +12135,10 @@ " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", - " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", + " [0.05, 0.0505555556, 0.0511111111, 0.051666666...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", @@ -11738,18 +12156,18 @@ " NaN\n", " False\n", " False\n", - " 0.05\n", + " 0.1\n", " ...\n", - " 0.05\n", " 0.1\n", - " 0.07\n", - " 0.063\n", - " 0.063\n", - " 0.07\n", - " 0.11\n", - " 0.11\n", - " 0.15\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.085\n", + " 0.085\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " \n", " \n", " 3\n", @@ -11762,18 +12180,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.2,0.6,0.2]\n", + " [0.6,0.35,0.05]\n", " ...\n", - " [0.0001, 0.6, 0.0001]\n", - " [0.0001, 0.525, 0.0001]\n", + " [0.0001, 0.35, 0.0001]\n", + " [0.0001, 0.475, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5562499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.48124999999999996, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", - " [0.0001, 0.48124999999999996, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.47324999999999995, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.5048350576136786, 0.0001]\n", + " [0.0001, 0.49717011522735727, 0.0001]\n", " \n", " \n", " 4\n", @@ -11786,10 +12204,10 @@ " 400.0\n", " False\n", " False\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " ...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.00267857145, 0.00535714285, 0.00803571...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0032500000000000003, 0.006500000000000...\n", " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", " [0.0, 0.0021590909, 0.0043181818, 0.0064772727...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", @@ -11820,80 +12238,80 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", + "0 False [0.01,0.7,0.2,0.07,0.02] ... \n", "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", - "2 False 0.05 ... \n", - "3 NaN [0.2,0.6,0.2] ... \n", - "4 False [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... ... \n", + "2 False 0.1 ... \n", + "3 NaN [0.6,0.35,0.05] ... \n", + "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 [0.010416666666666666, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.01, 0.0001, 0.0001, 0.0001, 0.0001] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 [0.0001, 0.6, 0.0001] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "2 0.1 \n", + "3 [0.0001, 0.35, 0.0001] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.23020833333333335, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", + "0 [0.13, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", "2 0.1 \n", - "3 [0.0001, 0.525, 0.0001] \n", - "4 [0.0, 0.00267857145, 0.00535714285, 0.00803571... \n", + "3 [0.0001, 0.475, 0.0001] \n", + "4 [0.0, 0.0032500000000000003, 0.006500000000000... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", - "2 0.07 \n", + "1 [0.05, 0.0505555556, 0.0511111111, 0.051666666... \n", + "2 0.1 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", "\n", " median_forecast_4_bots \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5562499999999999, 0.0001] \n", "4 [0.0, 0.0021590909, 0.0043181818, 0.0064772727... \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " median_forecast_6_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.07 \n", - "3 [0.0001, 0.48124999999999996, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.47324999999999995, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.11 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.11 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_9_bots \\\n", - "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", - "2 0.15 \n", - "3 [0.0001, 0.48124999999999996, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5048350576136786, 0.0001] \n", "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", "\n", " median_forecast_10_bots \n", - "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", - "2 0.15 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.49717011522735727, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -11973,7 +12391,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -11991,7 +12409,7 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", " \n", " \n", @@ -12009,7 +12427,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.063\n", + " 0.085\n", " 0.013\n", " \n", " \n", @@ -12082,9 +12500,9 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", @@ -12153,7 +12571,7 @@ " False\n", " 35380\n", " 1.00\n", - " 0.905\n", + " 0.9\n", " 0.95\n", " \n", " \n", @@ -12171,7 +12589,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.15\n", + " 0.65\n", " 0.05\n", " \n", " \n", @@ -12189,7 +12607,7 @@ " False\n", " 35385\n", " 1.00\n", - " 0.9\n", + " 0.85\n", " 0.97\n", " \n", " \n", @@ -12207,7 +12625,7 @@ " False\n", " 35386\n", " 0.85\n", - " 0.8\n", + " 0.71\n", " 0.666\n", " \n", " \n", @@ -12255,10 +12673,10 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median \n", - "342 1.00 0.905 0.95 \n", - "351 1.00 0.15 0.05 \n", - "355 1.00 0.9 0.97 \n", - "361 0.85 0.8 0.666 \n", + "342 1.00 0.9 0.95 \n", + "351 1.00 0.65 0.05 \n", + "355 1.00 0.85 0.97 \n", + "361 0.85 0.71 0.666 \n", "364 0.85 0.05 0.03 " ] }, @@ -12315,14 +12733,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1182\n" + "Weighted Total Score: -0.1240\n" ] } ], @@ -12344,7 +12762,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -12356,7 +12774,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -0.12\n" + "The average of 'head_to_head' is: -0.13\n" ] } ], @@ -12412,17 +12830,17 @@ " \n", " \n", " head_to_head\n", - " -11.2\n", + " -11.8\n", " 92.1\n", " -0.1\n", - " 0.640747\n", - " 0.066766\n", - " -1.826475\n", + " 0.643536\n", + " 0.067057\n", + " -1.907958\n", " 1.98555\n", " 0.0\n", " -0.3\n", - " 0.035527\n", - " 0.071054\n", + " 0.029773\n", + " 0.059546\n", " \n", " \n", "\n", @@ -12430,10 +12848,10 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -11.2 92.1 -0.1 0.640747 0.066766 -1.826475 1.98555 \n", + "head_to_head -11.8 92.1 -0.1 0.643536 0.067057 -1.907958 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.0 -0.3 0.035527 0.071054 " + "head_to_head 0.0 -0.3 0.029773 0.059546 " ] }, "execution_count": 68, @@ -12505,34 +12923,34 @@ " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.125]\n", + " [0.0001, 0.0001, 0.0001, 0.13]\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -1.9\n", - " \n", - " \n", - " 232\n", - " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.2963039014373716]\n", - " [0.002,0.008,0.09,0.9]\n", - " 3 or more\n", - " -1.1\n", + " -1.8\n", " \n", " \n", " 247\n", " Will the 500th richest person on Bloomberg's B...\n", - " 0.766667\n", + " 0.8\n", " 0.333\n", " no\n", - " -1.1\n", + " -1.2\n", " \n", " \n", - " 245\n", - " Will Nebraska have 1.7 million or more residen...\n", - " 0.9\n", - " 0.7\n", - " no\n", - " -0.9\n", + " 232\n", + " How many movies will be new on Netflix's top 1...\n", + " [0.0001, 0.0001, 0.0001, 0.32130390143737164]\n", + " [0.002,0.008,0.09,0.9]\n", + " 3 or more\n", + " -1.0\n", + " \n", + " \n", + " 71\n", + " Will OpenAI, Anthropic, or Perplexity run an a...\n", + " 0.18\n", + " 0.55\n", + " yes\n", + " -1.0\n", " \n", " \n", "\n", @@ -12542,23 +12960,23 @@ " title \\\n", "279 What will Kalshi's rank in the iPhone Top Free... \n", "121 How many movies will be new on Netflix's top 1... \n", - "232 How many movies will be new on Netflix's top 1... \n", "247 Will the 500th richest person on Bloomberg's B... \n", - "245 Will Nebraska have 1.7 million or more residen... \n", + "232 How many movies will be new on Netflix's top 1... \n", + "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", "\n", " bot_team_median \\\n", "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", - "121 [0.0001, 0.0001, 0.0001, 0.125] \n", - "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", - "247 0.766667 \n", - "245 0.9 \n", + "121 [0.0001, 0.0001, 0.0001, 0.13] \n", + "247 0.8 \n", + "232 [0.0001, 0.0001, 0.0001, 0.32130390143737164] \n", + "71 0.18 \n", "\n", " pro_median resolution head_to_head \n", "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", - "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", - "247 0.333 no -1.1 \n", - "245 0.7 no -0.9 " + "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", + "247 0.333 no -1.2 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.0 \n", + "71 0.55 yes -1.0 " ] }, "execution_count": 69, @@ -12634,7 +13052,7 @@ " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", " 2.5\n", @@ -12658,7 +13076,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.95\n", + " 0.954\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12677,10 +13095,10 @@ "\n", " bot_team_median \\\n", "189 [0.0, 0.0030510204, 0.0061020408, 0.0102928751... \n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.95 \n", + "214 0.954 \n", "\n", " pro_median resolution \\\n", "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", @@ -12809,10 +13227,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.539332\n", - " 2.539332\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -12829,10 +13247,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.250003\n", - " -0.250003\n", + " -0.161101\n", + " -0.161101\n", " \n", " \n", " 2\n", @@ -12849,10 +13267,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.063\n", + " 0.085\n", " 0.013\n", - " -0.051987\n", - " -0.051987\n", + " -0.075746\n", + " -0.075746\n", " \n", " \n", " 3\n", @@ -12928,23 +13346,23 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", - "2 0.013 -0.051987 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.161101 \n", + "2 0.013 -0.075746 \n", "3 [0.16,0.44,0.4] 0.152526 \n", "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.539332 \n", - "1 -0.250003 \n", - "2 -0.051987 \n", + "0 2.522754 \n", + "1 -0.161101 \n", + "2 -0.075746 \n", "3 0.152526 \n", "4 0.387623 " ] @@ -12960,19 +13378,32 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows in calibration df: 48\n" + ] + } + ], "source": [ "# Make binary-only df_top_bot_pro_forecasts for calibration curves etc\n", - "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['type'] == 'binary'].copy()\n", + "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[\n", + " (df_top_bot_pro_forecasts['type'] == 'binary') &\n", + " (df_top_bot_pro_forecasts['resolution'].notna())\n", + "].copy()\n", + "print(f\"Rows in calibration df: {len(df_top_bot_pro_forecasts_binary)}\")\n", + "\n", "\n", "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all[df_top_bot_pro_forecasts_all['type'] == 'binary'].copy()" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 92, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12982,9 +13413,25 @@ "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count: 10\n", + "Count: 11\n", + "Count: 11\n", + "Count: 11\n", + "Count: 11\n", + "Count: 10\n", + "Count: 9\n", + "Count: 10\n", + "Count: 9\n", + "Count: 10\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -12996,7 +13443,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of pro forecasts: 50\n" + "Number of pro forecasts: 48\n" ] } ], @@ -13038,7 +13485,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -13131,7 +13578,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.86\n", + " 0.81\n", " 0.95\n", " \n", " \n", @@ -13199,7 +13646,7 @@ " question_weight bot_team_median pro_median \n", "2 1.0 0.063 0.013 \n", "5 1.0 0.62 0.45 \n", - "8 1.0 0.86 0.95 \n", + "8 1.0 0.81 0.95 \n", "10 1.0 NaN NaN \n", "13 1.0 0.85 0.9 " ] @@ -13215,7 +13662,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -13266,7 +13713,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13278,7 +13725,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -13297,7 +13744,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13310,9 +13757,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bot average forecast difference (1 - 0): 0.4365\n", + "Bot average forecast difference (1 - 0): 0.4288\n", "Pro average forecast difference (1 - 0): 0.5238\n", - "Difference between pro and bot differences: 0.0873\n" + "Difference between pro and bot differences: 0.0950\n" ] } ], @@ -13339,7 +13786,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13379,7 +13826,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -13438,10 +13885,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.539332\n", - " 2.539332\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -13460,8 +13907,8 @@ " 1.0\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.250003\n", - " -0.250003\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -13557,22 +14004,22 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.539332 \n", - "1 -0.250003 \n", + "0 2.522754 \n", + "1 -0.158842 \n", "2 -0.051987 \n", "3 0.152526 \n", "4 0.387623 " @@ -13637,10 +14084,10 @@ " False\n", " 35380\n", " 1.00\n", - " 0.905\n", + " 0.9\n", " 0.95\n", - " -0.048527\n", - " -0.048527\n", + " -0.054067\n", + " -0.054067\n", " \n", " \n", " 351\n", @@ -13657,10 +14104,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.15\n", + " 0.3\n", " 0.05\n", - " -0.111226\n", - " -0.111226\n", + " -0.305382\n", + " -0.305382\n", " \n", " \n", " 355\n", @@ -13677,10 +14124,10 @@ " False\n", " 35385\n", " 1.00\n", - " 0.9\n", + " 0.85\n", " 0.97\n", - " -0.074901\n", - " -0.074901\n", + " -0.132060\n", + " -0.132060\n", " \n", " \n", " 361\n", @@ -13749,9 +14196,9 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", - "342 1.00 0.905 0.95 -0.048527 -0.048527 \n", - "351 1.00 0.15 0.05 -0.111226 -0.111226 \n", - "355 1.00 0.9 0.97 -0.074901 -0.074901 \n", + "342 1.00 0.9 0.95 -0.054067 -0.054067 \n", + "351 1.00 0.3 0.05 -0.305382 -0.305382 \n", + "355 1.00 0.85 0.97 -0.132060 -0.132060 \n", "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", "364 0.85 0.05 0.03 -0.017709 -0.015053 " ] diff --git a/functions.py b/functions.py index 8de9f69..11257c4 100644 --- a/functions.py +++ b/functions.py @@ -10,7 +10,7 @@ from scipy import stats from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -import re + from refactored_notebook.scoring import ( calculate_baseline_score, calculate_peer_score, @@ -266,7 +266,7 @@ def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) ) -def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): +def weighted_bootstrap_analysis(df_bot_peer_wide: pd.DataFrame, bots: list[str], NUM: int, ITER: int): """ Performs weighted bootstrap analysis to calculate confidence intervals and medians. @@ -281,7 +281,7 @@ def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): """ # Function to perform a single bootstrap iteration - def single_bootstrap(df): + def single_bootstrap(df: pd.DataFrame): # Weighted sampling of questions sampled_df = df.sample(n=NUM, weights="question_weight", replace=True) # Calculate total weighted score for each bot @@ -632,7 +632,80 @@ def plot_head_to_head_distribution( print(f"The average of 'head_to_head' is: {mean:.2f}") -def calculate_calibration_curve(forecasts, resolutions, weights): +def plot_calibration_curve(df: pd.DataFrame, column_name: str, label: str, color: str): + """ + Plots a calibration curve with confidence intervals. + + Args: + df (pandas.DataFrame): DataFrame with forecast and resolution data. + column_name (str): Column name for forecast probabilities. + label (str): Label for the plot. + color (str): Color for the plot. + + Returns: + None + """ + _assert_calibration_dataframe_matches_assumptions(df) + # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) + df = df[df["resolution"].isin(["yes", "no", 1, 0])] + + y_true = df["resolution"] + y_pred = df[column_name] + weights = [1.0 for _ in y_true] + calibration_curve = _calculate_calibration_curve(y_pred, y_true, weights)[ + "calibration_curve" + ] + prob_true = [item["average_resolution"] for item in calibration_curve] + bin_center = [ + (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve + ] + ci_lower = [item["lower_confidence_interval"] for item in calibration_curve] + ci_upper = [item["upper_confidence_interval"] for item in calibration_curve] + + plt.plot(bin_center, prob_true, marker="o", linewidth=2, label=label, color=color) + plt.fill_between(bin_center, ci_lower, ci_upper, alpha=0.2, color=color) + for x, y in zip(bin_center, prob_true): + if x is None or y is None: + continue + plt.annotate( + f"({x:.2f}, {y:.2f})", + (x, y), + textcoords="offset points", + xytext=(0, 10), + ha="center", + color=color, + fontsize=8, + ) + +def _assert_calibration_dataframe_matches_assumptions(df: pd.DataFrame): + # 1. Only binary questions + assert (df['type'] == 'binary').all(), "DataFrame contains non-binary questions." + + # 2. Only valid resolutions (0, 1, 'yes', 'no') + valid_resolutions = {0, 1} + assert set(df['resolution'].unique()).issubset(valid_resolutions), ( + f"DataFrame contains invalid resolutions: {set(df['resolution'].unique()) - valid_resolutions}" + ) + + # 3. Each question_id appears only once (if grouped by question) + if 'question_id' in df.columns: + assert df['question_id'].is_unique, "Each question_id should appear only once." + + # 4. No missing values in key columns + for col in ['resolution', 'type']: + assert df[col].notnull().all(), f"Missing values found in column: {col}" + + # 5. Probabilities are between 0 and 1 for forecast columns + prob_cols = [col for col in df.columns if 'prob' in col or 'median' in col or 'forecast' in col] + for col in prob_cols: + if df[col].dtype.kind in {'f', 'i'}: + assert ((df[col] >= 0) & (df[col] <= 1)).all(), f"Column {col} contains values outside [0, 1]" + + # 6. DataFrame is not empty + assert not df.empty, "DataFrame is empty after filtering." + + +def _calculate_calibration_curve(forecasts: list[float], resolutions: list[int], weights: list[float]) -> dict: """ Calculates a calibration curve for forecasts. @@ -690,51 +763,6 @@ def calculate_calibration_curve(forecasts, resolutions, weights): } -def plot_calibration_curve(df, column_name, label, color): - """ - Plots a calibration curve with confidence intervals. - - Args: - df (pandas.DataFrame): DataFrame with forecast and resolution data. - column_name (str): Column name for forecast probabilities. - label (str): Label for the plot. - color (str): Color for the plot. - - Returns: - None - """ - # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) - df = df[df["resolution"].isin(["yes", "no", 1, 0])] - - y_true = df["resolution"] - y_pred = df[column_name] - weights = [1.0 for _ in y_true] - calibration_curve = calculate_calibration_curve(y_pred, y_true, weights)[ - "calibration_curve" - ] - prob_true = [item["average_resolution"] for item in calibration_curve] - bin_center = [ - (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve - ] - ci_lower = [item["lower_confidence_interval"] for item in calibration_curve] - ci_upper = [item["upper_confidence_interval"] for item in calibration_curve] - - plt.plot(bin_center, prob_true, marker="o", linewidth=2, label=label, color=color) - plt.fill_between(bin_center, ci_lower, ci_upper, alpha=0.2, color=color) - for x, y in zip(bin_center, prob_true): - if x is None or y is None: - continue - plt.annotate( - f"({x:.2f}, {y:.2f})", - (x, y), - textcoords="offset points", - xytext=(0, 10), - ha="center", - color=color, - fontsize=8, - ) - - def calculate_confidence(predictions, outcomes): """ Calculates over- or under-confidence for a set of predictions. diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 6d552fc..930eefb 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,16 +1,16 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 -bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 X_bot,-0.0,-0.0,-0.0,0.0,0.0 +bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 -Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 +Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 @@ -18,30 +18,30 @@ annabot,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-o1,-0.3,-0.2,-0.1,0.0,0.1 -GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 +MWG,-0.2,-0.2,-0.1,-0.0,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 +GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -acm_bot,-0.3,-0.2,-0.1,0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-perplexity,-0.3,-0.3,-0.1,0.0,0.1 -laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.2,-0.1,-0.0,0.1 +acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 +metac-o1,-0.3,-0.2,-0.1,-0.0,0.0 +metac-deepseek-r1+asknews,-0.3,-0.2,-0.1,-0.1,-0.0 wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 -bot_median,-0.3,-0.3,-0.2,-0.0,0.0 +laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.2,-0.2,-0.0,0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-deepseek-r1+asknews,-0.3,-0.3,-0.2,-0.1,-0.1 +bot_median,-0.3,-0.2,-0.2,-0.1,0.0 +metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.1,0.0 +metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 -metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 -VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 4d49be6..477882c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,13 +1,13 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 -jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 +RPM_bot,-1.4,7.0,-0.2,0.8195427278689026,0.3097580352475143,-0.650312775083108,2.4469118511449692,0.6,-1.0,0.26978865902437565,0.539577 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 @@ -15,33 +15,33 @@ CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.36553170 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 +cookics_bot_TEST,-6.6,27.4,-0.2,0.7452828646172052,0.14237897258891655,-1.694618782556622,2.0495406495390753,0.1,-0.5,0.05095705221638959,0.101914 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 -metac-o1,-9.3,91.1,-0.1,0.9011413735401934,0.09441342249931468,-1.0818974297140194,1.9858289388460384,0.1,-0.3,0.14109261555912994,0.282185 -MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 +MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 -GreeneiBot2,-10.4,58.4,-0.2,0.8493165305196299,0.11118575431472652,-1.6013523121813948,2.000831925930035,0.0,-0.4,0.05739674059552304,0.114793 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 +GreeneiBot2,-10.7,58.4,-0.2,0.8492744520587402,0.11118024573783404,-1.6427768404571312,2.000831925930035,0.0,-0.4,0.05295076167168595,0.105902 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 +metac-o1,-11.3,91.1,-0.1,0.885301596604543,0.09275387429075187,-1.342986841449772,1.9858289388460384,0.1,-0.3,0.09132478421461744,0.182650 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-perplexity,-12.3,89.1,-0.1,0.9928936435472672,0.1051874382468964,-1.3167986298410923,1.9864049297707018,0.1,-0.3,0.09566061681542057,0.191321 -metac-Gemini-Exp-1206,-12.6,76.5,-0.2,1.0074640479435764,0.11518577253617869,-1.4310981247048116,1.9908217254774627,0.1,-0.4,0.0782642072080301,0.156528 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 +metac-deepseek-r1+asknews,-13.3,52.1,-0.3,0.7808915178330472,0.10818619432038376,-2.3663082727832094,2.0053789762011176,-0.0,-0.5,0.010897575637344883,0.021795 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -bot_median,-14.4,92.1,-0.2,0.8064767886698918,0.08403535853352312,-1.8649643315938071,1.9855502432148115,0.0,-0.3,0.03270280660214449,0.065406 +metac-Gemini-Exp-1206,-13.7,76.5,-0.2,0.9567011955687134,0.10938193429612067,-1.6400021546672607,1.9908217254774627,0.0,-0.4,0.05258248904380755,0.105165 +bot_median,-14.2,92.1,-0.2,0.8060563380929024,0.08399154733464013,-1.8298886724683292,1.9855502432148115,0.0,-0.3,0.03526855952035323,0.070537 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-deepseek-r1+asknews,-15.8,52.1,-0.3,0.7725034544186158,0.1070240960803573,-2.8279843345318105,2.0053789762011176,-0.1,-0.5,0.0033369803575435406,0.006674 +metac-claude-3-5-sonnet-20240620,-15.7,90.5,-0.2,0.9577206882239262,0.10067336366115942,-1.726279013247091,1.9860719790130024,0.0,-0.4,0.043873862980955504,0.087748 +metac-perplexity,-16.1,89.1,-0.2,1.04022365857026,0.11020159365499146,-1.6385490214880174,1.9864049297707018,0.0,-0.4,0.052436941119456015,0.104874 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -minefrac1,-19.4,51.1,-0.4,0.8785436286688769,0.12290028314991908,-3.0953430020106336,2.0065449272360034,-0.1,-0.6,0.0016073014389962144,0.003215 -metac-claude-3-5-sonnet-20240620,-20.5,90.5,-0.2,1.0026017690668347,0.10539115813794282,-2.144815075299298,1.9860719790130024,-0.0,-0.4,0.017338365150828438,0.034677 -metac-o1-preview,-21.8,91.1,-0.2,0.7783952357785447,0.08155319511998359,-2.9287175025862417,1.9858289388460384,-0.1,-0.4,0.0021550719003434007,0.004310 +minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 +metac-claude-3-5-sonnet-latest,-21.9,91.1,-0.2,0.8267775869528969,0.08662225919479004,-2.7788128175615063,1.9858289388460384,-0.1,-0.4,0.0033198064428072906,0.006640 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-claude-3-5-sonnet-latest,-22.6,91.1,-0.2,0.8075357879826596,0.08460627796346898,-2.930812576746788,1.9858289388460384,-0.1,-0.4,0.002141865770272775,0.004284 -pgodzinai,-23.4,76.4,-0.3,0.9738243593913162,0.11141250898777778,-2.746500218115244,1.9908489732268309,-0.1,-0.5,0.00376450038951266,0.007529 +pgodzinai,-23.5,76.4,-0.3,1.0010628527586396,0.11452878848708839,-2.684829528603297,1.9908489732268309,-0.1,-0.5,0.004459201995123589,0.008918 +metac-exa,-24.1,89.1,-0.3,0.8238773759897631,0.08728180623689599,-3.103267575628089,1.9864049297707018,-0.1,-0.4,0.0012863793448356026,0.002573 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-exa,-24.9,89.1,-0.3,0.8297104160130679,0.08789976017509527,-3.180189674479708,1.9864049297707018,-0.1,-0.5,0.0010160377455861174,0.002032 +metac-Llama-3.1,-26.6,89.1,-0.3,0.8904683193506574,0.09433646993436098,-3.1697302934806575,1.9864049297707018,-0.1,-0.5,0.001049393935170647,0.002099 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 -metac-grok-2-1212,-28.0,91.1,-0.3,1.0053639878633573,0.10533292304496032,-2.9230309952832156,1.9858289388460384,-0.1,-0.5,0.0021912955912464513,0.004383 -metac-gpt-4o,-28.0,91.1,-0.3,0.8644250725107907,0.09056662138298972,-3.3934602737720856,1.9858289388460384,-0.1,-0.5,0.0005136910361772879,0.001027 -metac-Llama-3.1,-28.2,89.1,-0.3,0.9060643910911743,0.0959887222614469,-3.291936866376594,1.9864049297707018,-0.1,-0.5,0.0007163844167320878,0.001433 +metac-o1-preview,-27.3,91.1,-0.3,0.8396846352431687,0.0879745426868476,-3.4074998848675455,1.9858289388460384,-0.1,-0.5,0.0004908622706364246,0.000982 +metac-grok-2-1212,-28.3,91.1,-0.3,1.0374739049385253,0.10869710901649764,-2.862896131089403,1.9858289388460384,-0.1,-0.5,0.00261020744989918,0.005220 +metac-gpt-4o,-28.7,91.1,-0.3,0.8937174262561063,0.09363560861558237,-3.3666300493101518,1.9858289388460384,-0.1,-0.5,0.0005601224288125974,0.001120 From a309c7ec34f11017e1794391bfe54df53ce74c9b Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Thu, 22 May 2025 08:37:15 -0600 Subject: [PATCH 25/26] Fixed second calibration graph --- AI_BENCHMARKING_ANALYSIS.ipynb | 2626 ++++++++--------- .../bootstrapped_h2h_bot_vs_pros.csv | 38 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 38 +- 3 files changed, 1348 insertions(+), 1354 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 942c3c1..bb7044b 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -61,7 +61,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_691899/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1441081/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -1032,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 8.319299\n", - " 3144.861339\n", + " 8.546230\n", + " 3230.645695\n", " 409\n", - " 5.304507\n", - " 1.533625\n", + " 5.546573\n", + " 1.525925\n", " \n", " \n", " 4\n", @@ -1072,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.319299 3144.861339 409 5.304507 \n", + "15 bot_median 8.546230 3230.645695 409 5.546573 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.533625 \n", + "15 1.525925 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1740,7 +1740,7 @@ " \n", " 3\n", " bot_median\n", - " 8575.707679\n", + " 8674.761163\n", " \n", " \n", " 4\n", @@ -1761,7 +1761,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8575.707679\n", + "3 bot_median 8674.761163\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3328.161138\n", + " 3544.710382\n", " \n", " \n", " 3\n", @@ -2166,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3328.161138\n", + "2 bot_median 3544.710382\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2578,9 +2578,9 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.35000000000000003,0.30000000000000004,0.250...\n", + " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2602,9 +2602,9 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2650,9 +2650,9 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.6,0.35,0.05]\n", - " [0.15,0.6,0.25]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2674,7 +2674,7 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.0033333333,0.0066666667,0.01,0.01333333...\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", @@ -2713,24 +2713,24 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.25,0.3,0.3,0.1,0.05] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.01,0.7,0.25,0.03,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.6,0.35,0.05] \n", + "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", "2 0.1 NaN \n", - "3 [0.15,0.6,0.25] NaN \n", + "3 [0.1,0.6,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2842,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.85\n", + " 0.3\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2867,7 +2867,7 @@ " False\n", " ...\n", " 0.85\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2890,7 +2890,7 @@ " False\n", " False\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2915,7 +2915,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.05\n", + " 0.1\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -2947,10 +2947,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.85 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", + "95 0.3 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3100,7 +3100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_691899/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_1441081/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,9 +3162,9 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -3186,9 +3186,9 @@ " True\n", " True\n", " ...\n", - " 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@@ -3234,9 +3234,9 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.6,0.35,0.05]\n", - " [0.15,0.6,0.25]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3258,8 +3258,8 @@ " False\n", " False\n", " ...\n", - " 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\n", + " metac-perplexity \\\n", + "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.1,0.6,0.3] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3447,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.85\n", + " 0.3\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3472,7 +3472,7 @@ " False\n", " ...\n", " 0.85\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3495,7 +3495,7 @@ " False\n", " False\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3520,7 +3520,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.05\n", + " 0.1\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -3552,10 +3552,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.85 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", + "95 0.3 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3763,7 +3763,7 @@ " False\n", " ...\n", " 2.302585\n", - " 5.857933\n", + " 5.703782\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3771,7 +3771,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 4.656813\n", + " 5.521275\n", " \n", " \n", " 3\n", @@ -3786,7 +3786,7 @@ " None\n", " None\n", " ...\n", - " -0.228842\n", + " 0.310155\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -3819,7 +3819,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.298855\n", + " 0.111521\n", " \n", " \n", " 9\n", @@ -3843,7 +3843,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.693147\n", + " -0.518794\n", " \n", " \n", " 13\n", @@ -3858,7 +3858,7 @@ " None\n", " None\n", " ...\n", - " -2.145931\n", + " 0.441833\n", " 0.510826\n", " 0.021979\n", " 0.200671\n", @@ -3867,7 +3867,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.062598\n", + " 0.158111\n", " \n", " \n", "\n", @@ -3904,18 +3904,18 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.302585 5.857933 NaN 2.292635 2.703087 \n", - "3 -0.228842 0.310155 NaN 0.127833 0.152526 \n", + "0 2.302585 5.703782 NaN 2.292635 2.703087 \n", + "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", "6 0.116534 -0.106610 NaN -0.184571 0.111521 \n", "9 -0.518794 -0.806476 NaN -0.806476 -0.494101 \n", - "13 -2.145931 0.510826 0.021979 0.200671 0.253781 \n", + "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "0 NaN NaN NaN NaN 4.656813 \n", + "0 NaN NaN NaN NaN 5.521275 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN 0.298855 \n", - "9 NaN NaN -0.624154 NaN -0.693147 \n", - "13 NaN NaN NaN NaN -0.062598 \n", + "6 NaN NaN NaN NaN 0.111521 \n", + "9 NaN NaN -0.624154 NaN -0.518794 \n", + "13 NaN NaN NaN NaN 0.158111 \n", "\n", "[5 rows x 58 columns]" ] @@ -3982,7 +3982,7 @@ " False\n", " ...\n", " -2.879198\n", - " -2.879198\n", + " -0.933288\n", " -3.007032\n", " -2.879198\n", " -3.795489\n", @@ -3990,7 +3990,7 @@ " NaN\n", " -2.348570\n", " -2.409195\n", - " -2.348570\n", + " -2.879198\n", " \n", " \n", " 82\n", @@ -4005,7 +4005,7 @@ " None\n", " None\n", " ...\n", - " -0.587787\n", + " -0.076961\n", " -0.300105\n", " -0.523248\n", " 0.105361\n", @@ -4014,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.498556\n", + " -0.587787\n", " \n", " \n", " 83\n", @@ -4029,8 +4029,8 @@ " None\n", " None\n", " ...\n", - " -0.899761\n", " -0.693147\n", + " -0.182322\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -4053,8 +4053,8 @@ " False\n", " False\n", " ...\n", - " -0.054625\n", - " -0.102356\n", + " -0.069566\n", + " -0.080377\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -4062,7 +4062,7 @@ " -0.113529\n", " NaN\n", " -0.147818\n", - " -0.121048\n", + " -0.124829\n", " \n", " \n", " 92\n", @@ -4077,8 +4077,8 @@ " False\n", " False\n", " ...\n", - " -1.299283\n", - " -1.704748\n", + " -0.788457\n", + " -1.011601\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4117,23 +4117,23 @@ "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", "81 NaN False False ... -2.879198 \n", - "82 NaN None None ... -0.587787 \n", - "83 NaN None None ... -0.899761 \n", - "91 NaN False False ... -0.054625 \n", - "92 NaN False False ... -1.299283 \n", + "82 NaN None None ... -0.076961 \n", + "83 NaN None None ... -0.693147 \n", + "91 NaN False False ... -0.069566 \n", + "92 NaN False False ... -0.788457 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -2.879198 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "81 -0.933288 -3.007032 -2.879198 -3.795489 NaN NaN \n", "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", - "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", + "91 -0.080377 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -2.348570 \n", - "82 0.276509 -0.644609 -0.498556 \n", + "81 -2.348570 -2.409195 -2.879198 \n", + "82 0.276509 -0.644609 -0.587787 \n", "83 -0.178330 -0.567984 -0.693147 \n", - "91 NaN -0.147818 -0.121048 \n", + "91 NaN -0.147818 -0.124829 \n", "92 NaN -0.749237 -0.318454 \n", "\n", "[5 rows x 58 columns]" @@ -4225,7 +4225,7 @@ " None\n", " ...\n", " -0.251314\n", - " 0.287682\n", + " 0.200671\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -4248,8 +4248,8 @@ " False\n", " False\n", " ...\n", + " -0.111226\n", " -0.054067\n", - " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -4257,7 +4257,7 @@ " NaN\n", " -0.398124\n", " NaN\n", - " -0.171850\n", + " -0.147158\n", " \n", " \n", " 12\n", @@ -4273,7 +4273,7 @@ " False\n", " ...\n", " -0.057158\n", - " -0.057158\n", + " 0.000000\n", " NaN\n", " 0.054067\n", " -0.057158\n", @@ -4296,7 +4296,7 @@ " False\n", " False\n", " ...\n", - " -0.045611\n", + " 0.008457\n", " 0.008457\n", " NaN\n", " -0.068083\n", @@ -4305,7 +4305,7 @@ " NaN\n", " -0.076070\n", " NaN\n", - " -0.076070\n", + " -0.096728\n", " \n", " \n", "\n", @@ -4329,17 +4329,17 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", - "5 -0.251314 0.287682 NaN 0.510826 0.320472 \n", - "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", - "12 -0.057158 -0.057158 NaN 0.054067 -0.057158 \n", - "16 -0.045611 0.008457 NaN -0.068083 NaN \n", + "5 -0.251314 0.200671 NaN 0.510826 0.320472 \n", + "8 -0.111226 -0.054067 NaN -0.111226 -0.147158 \n", + "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", + "16 0.008457 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", "5 NaN NaN NaN NaN 0.287682 \n", - "8 NaN NaN -0.398124 NaN -0.171850 \n", + "8 NaN NaN -0.398124 NaN -0.147158 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", - "16 NaN NaN -0.076070 NaN -0.076070 \n", + "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", "[5 rows x 58 columns]" ] @@ -4429,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -1.845827\n", + " -2.251292\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4453,7 +4453,7 @@ " False\n", " False\n", " ...\n", - " -0.074901\n", + " -0.020834\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -4501,7 +4501,7 @@ " False\n", " False\n", " ...\n", - " -0.017709\n", + " -0.063666\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -4534,10 +4534,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -1.845827 NaN NaN -0.111226 NaN \n", - "96 -0.074901 NaN NaN -0.074901 NaN \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -4603,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3328.161138\n", + " 3544.710382\n", " \n", " \n", " 3\n", @@ -4838,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3328.161138\n", + "2 bot_median 3544.710382\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 70.0%\n", - "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" + "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", + "mean metac-o1 forecast on questions that resolved no: 27.0%\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -4988,7 +4988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_691899/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_1441081/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5113,20 +5113,20 @@ " \n", " 3\n", " 4\n", - " bot_median\n", - " 2437.335374\n", - " 97\n", - " 93.10\n", - " \n", - " \n", - " 4\n", - " 5\n", " acm_bot\n", " 2239.058675\n", " 85\n", " 81.25\n", " \n", " \n", + " 4\n", + " 5\n", + " bot_median\n", + " 2138.701789\n", + " 97\n", + " 93.10\n", + " \n", + " \n", " 5\n", " 6\n", " metac-claude-3-5-sonnet-20240620\n", @@ -5471,8 +5471,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2437.335374 97 \n", - "4 5 acm_bot 2239.058675 85 \n", + "3 4 acm_bot 2239.058675 85 \n", + "4 5 bot_median 2138.701789 97 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -5520,8 +5520,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 93.10 \n", - "4 81.25 \n", + "3 81.25 \n", + "4 93.10 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -5716,20 +5716,6 @@ " 0.000036\n", " \n", " \n", - " bot_median\n", - " 2437.3\n", - " 93.1\n", - " 26.2\n", - " 60.692389\n", - " 6.290127\n", - " 4.162040\n", - " 1.985277\n", - " 38.7\n", - " 13.7\n", - " 0.999965\n", - " 0.000071\n", - " \n", - " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5744,6 +5730,20 @@ " 0.000025\n", " \n", " \n", + " bot_median\n", + " 2138.7\n", + " 93.1\n", + " 23.0\n", + " 64.275382\n", + " 6.661466\n", + " 3.448504\n", + " 1.985277\n", + " 36.2\n", + " 9.7\n", + " 0.999574\n", + " 0.000852\n", + " \n", + " \n", " metac-claude-3-5-sonnet-20240620\n", " 2018.1\n", " 91.5\n", @@ -6340,8 +6340,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2437.3 93.1 26.2 60.692389 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", + "bot_median 2138.7 93.1 23.0 64.275382 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -6389,8 +6389,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.290127 4.162040 1.985277 38.7 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", + "bot_median 6.661466 3.448504 1.985277 36.2 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -6438,8 +6438,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 13.7 0.999965 0.000071 \n", "acm_bot 15.3 0.999987 0.000025 \n", + "bot_median 9.7 0.999574 0.000852 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -6573,18 +6573,18 @@ " NA\n", " \n", " \n", - " bean_bot\n", + " RPM_bot\n", " -0.6\n", - " 4.7\n", + " 7.0\n", " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", + " 0.820675\n", + " 0.310186\n", + " -0.269729\n", + " 2.446912\n", + " 0.7\n", + " -0.8\n", + " 0.398203\n", + " 0.796405\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,6 +6601,20 @@ " 0.007677\n", " \n", " \n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", + " -0.1\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", + " \n", + " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6657,20 +6671,6 @@ " 0.574463\n", " \n", " \n", - " RPM_bot\n", - " -1.4\n", - " 7.0\n", - " -0.2\n", - " 0.819543\n", - " 0.309758\n", - " -0.650313\n", - " 2.446912\n", - " 0.6\n", - " -1.0\n", - " 0.269789\n", - " 0.539577\n", - " \n", - " \n", " KevinTestBot\n", " -1.5\n", " 8.4\n", @@ -6742,17 +6742,17 @@ " \n", " \n", " annabot\n", - " -6.2\n", + " -5.9\n", " 29.3\n", " -0.2\n", - " 0.520869\n", - " 0.096226\n", - " -2.211795\n", + " 0.517575\n", + " 0.095618\n", + " -2.112203\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.017610\n", - " 0.035221\n", + " 0.021811\n", + " 0.043621\n", " \n", " \n", " 4Shadower\n", @@ -6773,14 +6773,14 @@ " -6.6\n", " 27.4\n", " -0.2\n", - " 0.745283\n", - " 0.142379\n", - " -1.694619\n", + " 0.747093\n", + " 0.142725\n", + " -1.683660\n", " 2.049541\n", " 0.1\n", " -0.5\n", - " 0.050957\n", - " 0.101914\n", + " 0.052019\n", + " 0.104037\n", " \n", " \n", " jkraybill_bot\n", @@ -6857,14 +6857,14 @@ " -10.7\n", " 58.4\n", " -0.2\n", - " 0.849274\n", - " 0.111180\n", - " -1.642777\n", + " 0.848714\n", + " 0.111107\n", + " -1.647027\n", " 2.000832\n", " 0.0\n", " -0.4\n", - " 0.052951\n", - " 0.105902\n", + " 0.052511\n", + " 0.105022\n", " \n", " \n", " ajf-bot\n", @@ -6881,20 +6881,6 @@ " 0.094289\n", " \n", " \n", - " metac-o1\n", - " -11.3\n", - " 91.1\n", - " -0.1\n", - " 0.885302\n", - " 0.092754\n", - " -1.342987\n", - " 1.985829\n", - " 0.1\n", - " -0.3\n", - " 0.091325\n", - " 0.182650\n", - " \n", - " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6909,6 +6895,48 @@ " 0.023810\n", " \n", " \n", + " metac-perplexity\n", + " -12.0\n", + " 89.1\n", + " -0.1\n", + " 1.000845\n", + " 0.106030\n", + " -1.269604\n", + " 1.986405\n", + " 0.1\n", + " -0.3\n", + " 0.103785\n", + " 0.207569\n", + " \n", + " \n", + " bot_median\n", + " -12.2\n", + " 92.1\n", + " -0.1\n", + " 0.875909\n", + " 0.091270\n", + " -1.448706\n", + " 1.985550\n", + " 0.0\n", + " -0.3\n", + " 0.075426\n", + " 0.150853\n", + " \n", + " \n", + " metac-o1\n", + " -12.4\n", + " 91.1\n", + " -0.1\n", + " 0.941303\n", + " 0.098621\n", + " -1.375036\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.086265\n", + " 0.172530\n", + " \n", + " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6924,17 +6952,31 @@ " \n", " \n", " metac-deepseek-r1+asknews\n", - " -13.3\n", + " -13.4\n", " 52.1\n", " -0.3\n", - " 0.780892\n", - " 0.108186\n", - " -2.366308\n", + " 0.686642\n", + " 0.095129\n", + " -2.702394\n", " 2.005379\n", - " -0.0\n", - " -0.5\n", - " 0.010898\n", - " 0.021795\n", + " -0.1\n", + " -0.4\n", + " 0.004660\n", + " 0.009321\n", + " \n", + " \n", + " metac-Gemini-Exp-1206\n", + " -13.5\n", + " 76.5\n", + " -0.2\n", + " 1.006606\n", + " 0.115088\n", + " -1.527727\n", + " 1.990822\n", + " 0.1\n", + " -0.4\n", + " 0.065380\n", + " 0.130759\n", " \n", " \n", " wunderplumb\n", @@ -6951,34 +6993,6 @@ " 0.006348\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " -13.7\n", - " 76.5\n", - " -0.2\n", - " 0.956701\n", - " 0.109382\n", - " -1.640002\n", - " 1.990822\n", - " 0.0\n", - " -0.4\n", - " 0.052582\n", - " 0.105165\n", - " \n", - " \n", - " bot_median\n", - " -14.2\n", - " 92.1\n", - " -0.2\n", - " 0.806056\n", - " 0.083992\n", - " -1.829889\n", - " 1.985550\n", - " 0.0\n", - " -0.3\n", - " 0.035269\n", - " 0.070537\n", - " \n", - " \n", " manticAI\n", " -14.6\n", " 69.4\n", @@ -6994,31 +7008,17 @@ " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -15.7\n", + " -14.7\n", " 90.5\n", " -0.2\n", - " 0.957721\n", - " 0.100673\n", - " -1.726279\n", + " 0.942980\n", + " 0.099124\n", + " -1.642585\n", " 1.986072\n", " 0.0\n", " -0.4\n", - " 0.043874\n", - " 0.087748\n", - " \n", - " \n", - " metac-perplexity\n", - " -16.1\n", - " 89.1\n", - " -0.2\n", - " 1.040224\n", - " 0.110202\n", - " -1.638549\n", - " 1.986405\n", - " 0.0\n", - " -0.4\n", - " 0.052437\n", - " 0.104874\n", + " 0.051989\n", + " 0.103978\n", " \n", " \n", " NextWorldLab\n", @@ -7035,32 +7035,46 @@ " 0.040909\n", " \n", " \n", + " metac-claude-3-5-sonnet-latest\n", + " -18.9\n", + " 91.1\n", + " -0.2\n", + " 0.731708\n", + " 0.076662\n", + " -2.699995\n", + " 1.985829\n", + " -0.1\n", + " -0.4\n", + " 0.004141\n", + " 0.008282\n", + " \n", + " \n", " minefrac1\n", - " -18.8\n", + " -19.2\n", " 51.1\n", " -0.4\n", - " 0.874752\n", - " 0.122370\n", - " -3.013581\n", + " 0.880990\n", + " 0.123242\n", + " -3.043641\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.002021\n", - " 0.004043\n", + " 0.001859\n", + " 0.003717\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -21.9\n", + " metac-o1-preview\n", + " -20.9\n", " 91.1\n", " -0.2\n", - " 0.826778\n", - " 0.086622\n", - " -2.778813\n", + " 0.802181\n", + " 0.084045\n", + " -2.728807\n", " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.003320\n", - " 0.006640\n", + " 0.003821\n", + " 0.007643\n", " \n", " \n", " mmBot\n", @@ -7077,32 +7091,46 @@ " 0.002208\n", " \n", " \n", - " pgodzinai\n", + " metac-Llama-3.1\n", + " -23.2\n", + " 89.1\n", + " -0.3\n", + " 1.031278\n", + " 0.109254\n", + " -2.379606\n", + " 1.986405\n", + " -0.0\n", + " -0.5\n", + " 0.009745\n", + " 0.019489\n", + " \n", + " \n", + " metac-grok-2-1212\n", " -23.5\n", - " 76.4\n", + " 91.1\n", " -0.3\n", - " 1.001063\n", - " 0.114529\n", - " -2.684830\n", - " 1.990849\n", - " -0.1\n", + " 1.068006\n", + " 0.111896\n", + " -2.303421\n", + " 1.985829\n", + " -0.0\n", " -0.5\n", - " 0.004459\n", - " 0.008918\n", + " 0.011778\n", + " 0.023556\n", " \n", " \n", - " metac-exa\n", - " -24.1\n", - " 89.1\n", + " pgodzinai\n", + " -24.0\n", + " 76.4\n", " -0.3\n", - " 0.823877\n", - " 0.087282\n", - " -3.103268\n", - " 1.986405\n", + " 0.976590\n", + " 0.111729\n", + " -2.811085\n", + " 1.990849\n", " -0.1\n", - " -0.4\n", - " 0.001286\n", - " 0.002573\n", + " -0.5\n", + " 0.003144\n", + " 0.006289\n", " \n", " \n", " VeritasAI\n", @@ -7119,18 +7147,32 @@ " 0.000076\n", " \n", " \n", - " metac-Llama-3.1\n", - " -26.6\n", + " metac-exa\n", + " -26.2\n", " 89.1\n", " -0.3\n", - " 0.890468\n", - " 0.094336\n", - " -3.169730\n", + " 0.830275\n", + " 0.087960\n", + " -3.341545\n", " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.001049\n", - " 0.002099\n", + " 0.000612\n", + " 0.001224\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -26.6\n", + " 91.1\n", + " -0.3\n", + " 0.879087\n", + " 0.092103\n", + " -3.165570\n", + " 1.985829\n", + " -0.1\n", + " -0.5\n", + " 0.001056\n", + " 0.002112\n", " \n", " \n", " InstitutPelFutur\n", @@ -7146,48 +7188,6 @@ " 0.002292\n", " 0.004584\n", " \n", - " \n", - " metac-o1-preview\n", - " -27.3\n", - " 91.1\n", - " -0.3\n", - " 0.839685\n", - " 0.087975\n", - " -3.407500\n", - " 1.985829\n", - " -0.1\n", - " -0.5\n", - " 0.000491\n", - " 0.000982\n", - " \n", - " \n", - " metac-grok-2-1212\n", - " -28.3\n", - " 91.1\n", - " -0.3\n", - " 1.037474\n", - " 0.108697\n", - " -2.862896\n", - " 1.985829\n", - " -0.1\n", - " -0.5\n", - " 0.002610\n", - " 0.005220\n", - " \n", - " \n", - " metac-gpt-4o\n", - " -28.7\n", - " 91.1\n", - " -0.3\n", - " 0.893717\n", - " 0.093636\n", - " -3.366630\n", - " 1.985829\n", - " -0.1\n", - " -0.5\n", - " 0.000560\n", - " 0.001120\n", - " \n", " \n", "\n", "" @@ -7196,146 +7196,146 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", - "RPM_bot -1.4 7.0 -0.2 0.819543 0.309758 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", + "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.6 27.4 -0.2 0.745283 0.142379 \n", + "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", - "GreeneiBot2 -10.7 58.4 -0.2 0.849274 0.111180 \n", + "GreeneiBot2 -10.7 58.4 -0.2 0.848714 0.111107 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", - "metac-o1 -11.3 91.1 -0.1 0.885302 0.092754 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", + "metac-perplexity -12.0 89.1 -0.1 1.000845 0.106030 \n", + "bot_median -12.2 92.1 -0.1 0.875909 0.091270 \n", + "metac-o1 -12.4 91.1 -0.1 0.941303 0.098621 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", - "metac-deepseek-r1+asknews -13.3 52.1 -0.3 0.780892 0.108186 \n", + "metac-deepseek-r1+asknews -13.4 52.1 -0.3 0.686642 0.095129 \n", + "metac-Gemini-Exp-1206 -13.5 76.5 -0.2 1.006606 0.115088 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "metac-Gemini-Exp-1206 -13.7 76.5 -0.2 0.956701 0.109382 \n", - "bot_median -14.2 92.1 -0.2 0.806056 0.083992 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-claude-3-5-sonnet-20240620 -15.7 90.5 -0.2 0.957721 0.100673 \n", - "metac-perplexity -16.1 89.1 -0.2 1.040224 0.110202 \n", + "metac-claude-3-5-sonnet-20240620 -14.7 90.5 -0.2 0.942980 0.099124 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", - "metac-claude-3-5-sonnet-latest -21.9 91.1 -0.2 0.826778 0.086622 \n", + "metac-claude-3-5-sonnet-latest -18.9 91.1 -0.2 0.731708 0.076662 \n", + "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", + "metac-o1-preview -20.9 91.1 -0.2 0.802181 0.084045 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "pgodzinai -23.5 76.4 -0.3 1.001063 0.114529 \n", - "metac-exa -24.1 89.1 -0.3 0.823877 0.087282 \n", + "metac-Llama-3.1 -23.2 89.1 -0.3 1.031278 0.109254 \n", + "metac-grok-2-1212 -23.5 91.1 -0.3 1.068006 0.111896 \n", + "pgodzinai -24.0 76.4 -0.3 0.976590 0.111729 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-Llama-3.1 -26.6 89.1 -0.3 0.890468 0.094336 \n", + "metac-exa -26.2 89.1 -0.3 0.830275 0.087960 \n", + "metac-gpt-4o -26.6 91.1 -0.3 0.879087 0.092103 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", - "metac-o1-preview -27.3 91.1 -0.3 0.839685 0.087975 \n", - "metac-grok-2-1212 -28.3 91.1 -0.3 1.037474 0.108697 \n", - "metac-gpt-4o -28.7 91.1 -0.3 0.893717 0.093636 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "bean_bot -4.265106 2.784843 -0.0 \n", + "RPM_bot -0.269729 2.446912 0.7 \n", "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "bean_bot -4.265106 2.784843 -0.0 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", - "RPM_bot -0.650313 2.446912 0.6 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "annabot -2.211795 2.044183 -0.0 \n", + "annabot -2.112203 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.694619 2.049541 0.1 \n", + "cookics_bot_TEST -1.683660 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.642777 2.000832 0.0 \n", + "GreeneiBot2 -1.647027 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", - "metac-o1 -1.342987 1.985829 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", + "metac-perplexity -1.269604 1.986405 0.1 \n", + "bot_median -1.448706 1.985550 0.0 \n", + "metac-o1 -1.375036 1.985829 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", - "metac-deepseek-r1+asknews -2.366308 2.005379 -0.0 \n", + "metac-deepseek-r1+asknews -2.702394 2.005379 -0.1 \n", + "metac-Gemini-Exp-1206 -1.527727 1.990822 0.1 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "metac-Gemini-Exp-1206 -1.640002 1.990822 0.0 \n", - "bot_median -1.829889 1.985550 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-claude-3-5-sonnet-20240620 -1.726279 1.986072 0.0 \n", - "metac-perplexity -1.638549 1.986405 0.0 \n", + "metac-claude-3-5-sonnet-20240620 -1.642585 1.986072 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "minefrac1 -3.013581 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-latest -2.778813 1.985829 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.699995 1.985829 -0.1 \n", + "minefrac1 -3.043641 2.006545 -0.1 \n", + "metac-o1-preview -2.728807 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "pgodzinai -2.684830 1.990849 -0.1 \n", - "metac-exa -3.103268 1.986405 -0.1 \n", + "metac-Llama-3.1 -2.379606 1.986405 -0.0 \n", + "metac-grok-2-1212 -2.303421 1.985829 -0.0 \n", + "pgodzinai -2.811085 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-Llama-3.1 -3.169730 1.986405 -0.1 \n", + "metac-exa -3.341545 1.986405 -0.1 \n", + "metac-gpt-4o -3.165570 1.985829 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", - "metac-o1-preview -3.407500 1.985829 -0.1 \n", - "metac-grok-2-1212 -2.862896 1.985829 -0.1 \n", - "metac-gpt-4o -3.366630 1.985829 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "bean_bot -0.2 0.007674 0.015349 \n", + "RPM_bot -0.8 0.398203 0.796405 \n", "jonahsingerbot -0.2 0.003839 0.007677 \n", + "bean_bot -0.2 0.007674 0.015349 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", - "RPM_bot -1.0 0.269789 0.539577 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "annabot -0.4 0.017610 0.035221 \n", + "annabot -0.4 0.021811 0.043621 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.050957 0.101914 \n", + "cookics_bot_TEST -0.5 0.052019 0.104037 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.052951 0.105902 \n", + "GreeneiBot2 -0.4 0.052511 0.105022 \n", "ajf-bot -0.7 0.047145 0.094289 \n", - "metac-o1 -0.3 0.091325 0.182650 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", + "metac-perplexity -0.3 0.103785 0.207569 \n", + "bot_median -0.3 0.075426 0.150853 \n", + "metac-o1 -0.3 0.086265 0.172530 \n", "laylaps -0.4 0.008744 0.017488 \n", - "metac-deepseek-r1+asknews -0.5 0.010898 0.021795 \n", + "metac-deepseek-r1+asknews -0.4 0.004660 0.009321 \n", + "metac-Gemini-Exp-1206 -0.4 0.065380 0.130759 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "metac-Gemini-Exp-1206 -0.4 0.052582 0.105165 \n", - "bot_median -0.3 0.035269 0.070537 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.043874 0.087748 \n", - "metac-perplexity -0.4 0.052437 0.104874 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.051989 0.103978 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "minefrac1 -0.6 0.002021 0.004043 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.003320 0.006640 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.004141 0.008282 \n", + "minefrac1 -0.6 0.001859 0.003717 \n", + "metac-o1-preview -0.4 0.003821 0.007643 \n", "mmBot -0.4 0.001104 0.002208 \n", - "pgodzinai -0.5 0.004459 0.008918 \n", - "metac-exa -0.4 0.001286 0.002573 \n", + "metac-Llama-3.1 -0.5 0.009745 0.019489 \n", + "metac-grok-2-1212 -0.5 0.011778 0.023556 \n", + "pgodzinai -0.5 0.003144 0.006289 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-Llama-3.1 -0.5 0.001049 0.002099 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 \n", - "metac-o1-preview -0.5 0.000491 0.000982 \n", - "metac-grok-2-1212 -0.5 0.002610 0.005220 \n", - "metac-gpt-4o -0.5 0.000560 0.001120 " + "metac-exa -0.5 0.000612 0.001224 \n", + "metac-gpt-4o -0.5 0.001056 0.002112 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 " ] }, "execution_count": 42, @@ -9087,139 +9087,139 @@ " \n", " \n", " metac-o1\n", - " 5.9\n", - " 7.3\n", - " 9.6\n", - " 11.9\n", - " 12.9\n", + " 6.0\n", + " 7.6\n", + " 9.7\n", + " 12.0\n", + " 13.2\n", " \n", " \n", " metac-o1-preview\n", - " 3.8\n", - " 5.3\n", + " 3.4\n", + " 5.2\n", " 8.3\n", - " 11.3\n", - " 13.2\n", + " 11.2\n", + " 12.5\n", " \n", " \n", " manticAI\n", - " 0.3\n", - " 2.1\n", - " 5.4\n", - " 8.8\n", - " 10.7\n", + " 0.2\n", + " 2.2\n", + " 5.3\n", + " 8.6\n", + " 10.3\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.5\n", + " 0.4\n", " 2.1\n", - " 5.1\n", - " 8.0\n", - " 9.6\n", + " 4.9\n", + " 7.7\n", + " 9.1\n", " \n", " \n", " acm_bot\n", - " 0.2\n", - " 1.4\n", - " 4.4\n", + " -0.0\n", + " 1.3\n", + " 4.7\n", " 7.4\n", - " 9.1\n", + " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -1.8\n", - " 0.1\n", - " 4.2\n", - " 7.6\n", - " 9.9\n", + " -2.0\n", + " 0.6\n", + " 4.3\n", + " 8.2\n", + " 9.8\n", " \n", " \n", " GreeneiBot2\n", - " -1.1\n", + " -1.5\n", " 0.7\n", " 4.0\n", - " 7.2\n", - " 9.4\n", + " 7.0\n", + " 8.8\n", " \n", " \n", " twsummerbot\n", - " 0.1\n", - " 1.5\n", - " 3.9\n", - " 6.3\n", - " 7.4\n", + " 0.2\n", + " 1.6\n", + " 3.7\n", + " 6.2\n", + " 7.3\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.2\n", + " 0.0\n", " 1.1\n", " 3.1\n", - " 5.0\n", - " 6.3\n", + " 5.1\n", + " 6.2\n", " \n", " \n", " pgodzinai\n", - " -3.5\n", + " -3.6\n", " -1.1\n", " 3.1\n", - " 6.9\n", - " 8.9\n", + " 6.5\n", + " 9.0\n", " \n", " \n", " CumulativeBot\n", - " -0.2\n", - " 0.8\n", - " 2.7\n", - " 4.6\n", - " 5.6\n", + " -0.1\n", + " 0.9\n", + " 2.6\n", + " 4.5\n", + " 5.4\n", " \n", " \n", " SynapseSeer\n", " 0.3\n", - " 1.0\n", - " 2.5\n", + " 1.1\n", + " 2.6\n", " 4.1\n", " 4.9\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.1\n", - " -0.0\n", - " 2.5\n", - " 4.9\n", - " 6.2\n", - " \n", - " \n", - " metac-exa\n", - " -5.1\n", - " -2.2\n", - " 1.7\n", - " 5.6\n", - " 7.8\n", + " -1.4\n", + " -0.2\n", + " 2.6\n", + " 5.1\n", + " 6.3\n", " \n", " \n", " jkraybill_bot\n", - " -4.4\n", + " -3.6\n", " -1.7\n", - " 1.7\n", - " 4.8\n", + " 1.8\n", + " 5.1\n", " 6.5\n", " \n", " \n", + " metac-exa\n", + " -4.8\n", + " -2.7\n", + " 1.8\n", + " 5.6\n", + " 7.3\n", + " \n", + " \n", " metac-deepseek-r1+asknews\n", - " -2.0\n", + " -2.1\n", " -0.8\n", " 1.3\n", - " 3.4\n", - " 4.6\n", + " 3.3\n", + " 4.5\n", " \n", " \n", " MWG\n", - " -1.6\n", + " -1.7\n", " -0.8\n", - " 0.6\n", - " 2.2\n", - " 3.0\n", + " 0.7\n", + " 2.0\n", + " 2.9\n", " \n", " \n", " andrewsiah\n", @@ -9230,20 +9230,12 @@ " 1.0\n", " \n", " \n", - " cobyj-bot\n", - " -1.4\n", - " -1.0\n", - " -0.0\n", - " 0.9\n", - " 1.4\n", - " \n", - " \n", " pianobot\n", " -1.3\n", " -0.8\n", " -0.0\n", " 0.7\n", - " 1.0\n", + " 1.1\n", " \n", " \n", " X_bot\n", @@ -9254,12 +9246,28 @@ " 0.2\n", " \n", " \n", + " cobyj-bot\n", + " -1.5\n", + " -0.9\n", + " -0.1\n", + " 0.8\n", + " 1.3\n", + " \n", + " \n", " annabot\n", - " -3.4\n", - " -2.4\n", - " -0.4\n", + " -3.2\n", + " -2.1\n", + " -0.3\n", " 1.2\n", - " 2.1\n", + " 2.0\n", + " \n", + " \n", + " KevinTestBot\n", + " -4.0\n", + " -2.6\n", + " -0.4\n", + " 1.6\n", + " 2.6\n", " \n", " \n", " bean_bot\n", @@ -9270,14 +9278,6 @@ " 1.9\n", " \n", " \n", - " KevinTestBot\n", - " -4.1\n", - " -2.7\n", - " -0.5\n", - " 1.6\n", - " 2.5\n", - " \n", - " \n", " CatrachoCaster\n", " -2.3\n", " -1.8\n", @@ -9290,96 +9290,96 @@ " -3.0\n", " -2.3\n", " -0.9\n", - " 0.5\n", - " 1.1\n", + " 0.4\n", + " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", + " -3.8\n", " -2.7\n", " -1.0\n", " 0.7\n", - " 1.5\n", + " 1.6\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", + " -4.6\n", " -3.3\n", " -1.1\n", - " 1.0\n", - " 2.1\n", + " 0.9\n", + " 1.9\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.5\n", - " -4.6\n", - " -1.4\n", - " 1.9\n", - " 3.5\n", + " -6.7\n", + " -4.8\n", + " -1.3\n", + " 1.7\n", + " 3.4\n", " \n", " \n", " mmBot\n", - " -6.9\n", - " -5.2\n", - " -1.5\n", - " 2.3\n", - " 4.3\n", + " -7.2\n", + " -5.5\n", + " -1.6\n", + " 2.4\n", + " 4.5\n", " \n", " \n", " 4Shadower\n", " -4.8\n", " -3.7\n", - " -1.7\n", - " 0.3\n", - " 1.4\n", + " -1.6\n", + " 0.2\n", + " 1.1\n", " \n", " \n", " swingswish\n", " -5.3\n", - " -4.2\n", + " -3.9\n", " -2.0\n", - " -0.2\n", - " 0.7\n", + " -0.1\n", + " 0.8\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.4\n", - " -4.8\n", - " -2.0\n", + " -6.6\n", + " -5.0\n", + " -2.1\n", " 0.8\n", " 2.4\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", + " -4.7\n", " -3.8\n", - " -2.0\n", + " -2.1\n", " -0.7\n", " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -8.9\n", + " -9.3\n", " -6.4\n", " -2.2\n", - " 1.6\n", - " 4.0\n", + " 1.8\n", + " 4.2\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.9\n", - " -5.1\n", + " -6.7\n", + " -5.4\n", " -2.6\n", " 0.1\n", - " 1.6\n", + " 1.4\n", " \n", " \n", " wunderplumb\n", - " -6.1\n", - " -5.0\n", - " -2.7\n", - " -0.1\n", - " 0.9\n", + " -6.3\n", + " -4.9\n", + " -2.6\n", + " -0.4\n", + " 0.7\n", " \n", " \n", " NextWorldLab\n", @@ -9387,63 +9387,63 @@ " -6.9\n", " -3.6\n", " -0.2\n", - " 1.4\n", - " \n", - " \n", - " Bot_Pepa\n", - " -6.8\n", - " -5.9\n", - " -3.8\n", - " -2.0\n", - " -0.9\n", + " 1.1\n", " \n", " \n", " laylaps\n", - " -10.2\n", - " -8.0\n", + " -10.4\n", + " -7.7\n", " -3.8\n", " -0.1\n", - " 1.9\n", + " 1.6\n", + " \n", + " \n", + " Bot_Pepa\n", + " -7.0\n", + " -5.8\n", + " -3.9\n", + " -2.0\n", + " -1.1\n", " \n", " \n", " VeritasAI\n", - " -8.0\n", - " -6.6\n", - " -4.2\n", - " -1.9\n", - " -0.7\n", + " -8.1\n", + " -6.8\n", + " -4.3\n", + " -1.7\n", + " -0.9\n", " \n", " \n", " minefrac1\n", " -7.8\n", - " -6.9\n", - " -4.7\n", + " -6.8\n", + " -4.6\n", " -2.6\n", - " -1.6\n", + " -1.5\n", " \n", " \n", " Grizeu_Bot\n", - " -9.1\n", - " -7.6\n", + " -9.4\n", + " -7.8\n", " -4.9\n", - " -2.3\n", + " -2.2\n", " -0.9\n", " \n", " \n", " metac-gpt-4o\n", - " -10.7\n", - " -9.1\n", - " -6.1\n", - " -3.0\n", - " -1.5\n", + " -10.3\n", + " -8.9\n", + " -5.9\n", + " -3.1\n", + " -1.6\n", " \n", " \n", " ajf-bot\n", - " -15.3\n", + " -14.8\n", " -12.9\n", - " -8.4\n", - " -4.3\n", - " -2.4\n", + " -8.3\n", + " -4.4\n", + " -2.1\n", " \n", " \n", "\n", @@ -9451,51 +9451,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 5.9 7.3 9.6 11.9 12.9\n", - "metac-o1-preview 3.8 5.3 8.3 11.3 13.2\n", - "manticAI 0.3 2.1 5.4 8.8 10.7\n", - "metac-Gemini-Exp-1206 0.5 2.1 5.1 8.0 9.6\n", - "acm_bot 0.2 1.4 4.4 7.4 9.1\n", - "metac-perplexity -1.8 0.1 4.2 7.6 9.9\n", - "GreeneiBot2 -1.1 0.7 4.0 7.2 9.4\n", - "twsummerbot 0.1 1.5 3.9 6.3 7.4\n", - "cookics_bot_TEST -0.2 1.1 3.1 5.0 6.3\n", - "pgodzinai -3.5 -1.1 3.1 6.9 8.9\n", - "CumulativeBot -0.2 0.8 2.7 4.6 5.6\n", - "SynapseSeer 0.3 1.0 2.5 4.1 4.9\n", - "metac-claude-3-5-sonnet-latest -1.1 -0.0 2.5 4.9 6.2\n", - "metac-exa -5.1 -2.2 1.7 5.6 7.8\n", - "jkraybill_bot -4.4 -1.7 1.7 4.8 6.5\n", - "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.4 4.6\n", - "MWG -1.6 -0.8 0.6 2.2 3.0\n", + "metac-o1 6.0 7.6 9.7 12.0 13.2\n", + "metac-o1-preview 3.4 5.2 8.3 11.2 12.5\n", + "manticAI 0.2 2.2 5.3 8.6 10.3\n", + "metac-Gemini-Exp-1206 0.4 2.1 4.9 7.7 9.1\n", + "acm_bot -0.0 1.3 4.7 7.4 8.8\n", + "metac-perplexity -2.0 0.6 4.3 8.2 9.8\n", + "GreeneiBot2 -1.5 0.7 4.0 7.0 8.8\n", + "twsummerbot 0.2 1.6 3.7 6.2 7.3\n", + "cookics_bot_TEST 0.0 1.1 3.1 5.1 6.2\n", + "pgodzinai -3.6 -1.1 3.1 6.5 9.0\n", + "CumulativeBot -0.1 0.9 2.6 4.5 5.4\n", + "SynapseSeer 0.3 1.1 2.6 4.1 4.9\n", + "metac-claude-3-5-sonnet-latest -1.4 -0.2 2.6 5.1 6.3\n", + "jkraybill_bot -3.6 -1.7 1.8 5.1 6.5\n", + "metac-exa -4.8 -2.7 1.8 5.6 7.3\n", + "metac-deepseek-r1+asknews -2.1 -0.8 1.3 3.3 4.5\n", + "MWG -1.7 -0.8 0.7 2.0 2.9\n", "andrewsiah -0.9 -0.6 0.0 0.6 1.0\n", - "cobyj-bot -1.4 -1.0 -0.0 0.9 1.4\n", - "pianobot -1.3 -0.8 -0.0 0.7 1.0\n", + "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "annabot -3.4 -2.4 -0.4 1.2 2.1\n", + "cobyj-bot -1.5 -0.9 -0.1 0.8 1.3\n", + "annabot -3.2 -2.1 -0.3 1.2 2.0\n", + "KevinTestBot -4.0 -2.6 -0.4 1.6 2.6\n", "bean_bot -3.3 -2.2 -0.5 1.0 1.9\n", - "KevinTestBot -4.1 -2.7 -0.5 1.6 2.5\n", "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.3 -0.9 0.5 1.1\n", - "krm-bot -3.6 -2.7 -1.0 0.7 1.5\n", - "ProfessorSP -4.5 -3.3 -1.1 1.0 2.1\n", - "metac-grok-2-1212 -6.5 -4.6 -1.4 1.9 3.5\n", - "mmBot -6.9 -5.2 -1.5 2.3 4.3\n", - "4Shadower -4.8 -3.7 -1.7 0.3 1.4\n", - "swingswish -5.3 -4.2 -2.0 -0.2 0.7\n", - "metac-claude-3-5-sonnet-20240620 -6.4 -4.8 -2.0 0.8 2.4\n", - "RPM_bot -4.9 -3.8 -2.0 -0.7 -0.1\n", - "InstitutPelFutur -8.9 -6.4 -2.2 1.6 4.0\n", - "metac-Llama-3.1 -6.9 -5.1 -2.6 0.1 1.6\n", - "wunderplumb -6.1 -5.0 -2.7 -0.1 0.9\n", - "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.4\n", - "Bot_Pepa -6.8 -5.9 -3.8 -2.0 -0.9\n", - "laylaps -10.2 -8.0 -3.8 -0.1 1.9\n", - "VeritasAI -8.0 -6.6 -4.2 -1.9 -0.7\n", - "minefrac1 -7.8 -6.9 -4.7 -2.6 -1.6\n", - "Grizeu_Bot -9.1 -7.6 -4.9 -2.3 -0.9\n", - "metac-gpt-4o -10.7 -9.1 -6.1 -3.0 -1.5\n", - "ajf-bot -15.3 -12.9 -8.4 -4.3 -2.4" + "jonahsingerbot -3.0 -2.3 -0.9 0.4 1.0\n", + "krm-bot -3.8 -2.7 -1.0 0.7 1.6\n", + "ProfessorSP -4.6 -3.3 -1.1 0.9 1.9\n", + "metac-grok-2-1212 -6.7 -4.8 -1.3 1.7 3.4\n", + "mmBot -7.2 -5.5 -1.6 2.4 4.5\n", + "4Shadower -4.8 -3.7 -1.6 0.2 1.1\n", + "swingswish -5.3 -3.9 -2.0 -0.1 0.8\n", + "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.1 0.8 2.4\n", + "RPM_bot -4.7 -3.8 -2.1 -0.7 -0.1\n", + "InstitutPelFutur -9.3 -6.4 -2.2 1.8 4.2\n", + "metac-Llama-3.1 -6.7 -5.4 -2.6 0.1 1.4\n", + "wunderplumb -6.3 -4.9 -2.6 -0.4 0.7\n", + "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.1\n", + "laylaps -10.4 -7.7 -3.8 -0.1 1.6\n", + "Bot_Pepa -7.0 -5.8 -3.9 -2.0 -1.1\n", + "VeritasAI -8.1 -6.8 -4.3 -1.7 -0.9\n", + "minefrac1 -7.8 -6.8 -4.6 -2.6 -1.5\n", + "Grizeu_Bot -9.4 -7.8 -4.9 -2.2 -0.9\n", + "metac-gpt-4o -10.3 -8.9 -5.9 -3.1 -1.6\n", + "ajf-bot -14.8 -12.9 -8.3 -4.4 -2.1" ] }, "execution_count": 49, @@ -9591,7 +9591,7 @@ " False\n", " ...\n", " 2.302585\n", - " 5.857933\n", + " 5.703782\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -9599,7 +9599,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 4.656813\n", + " 5.521275\n", " \n", " \n", " 1\n", @@ -9615,7 +9615,7 @@ " True\n", " ...\n", " -0.270414\n", - " -0.505416\n", + " -0.616988\n", " NaN\n", " -0.050442\n", " -0.163369\n", @@ -9623,7 +9623,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.478371\n", + " -1.512868\n", " \n", " \n", " 2\n", @@ -9662,7 +9662,7 @@ " None\n", " None\n", " ...\n", - " -0.228842\n", + " 0.310155\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -9718,10 +9718,10 @@ "4 numeric None 0.0 400.0 \n", "\n", " open_upper_bound open_lower_bound ... metac-o1-preview metac-perplexity \\\n", - "0 False False ... 2.302585 5.857933 \n", - "1 True True ... -0.270414 -0.505416 \n", + "0 False False ... 2.302585 5.703782 \n", + "1 True True ... -0.270414 -0.616988 \n", "2 False False ... -0.092275 -0.092275 \n", - "3 None None ... -0.228842 0.310155 \n", + "3 None None ... 0.310155 0.310155 \n", "4 False False ... 0.243782 -0.102791 \n", "\n", " minefrac1 mmBot pgodzinai pianobot swingswish twsummerbot \\\n", @@ -9732,8 +9732,8 @@ "4 NaN 0.265372 0.041050 NaN NaN -0.771754 \n", "\n", " wunderplumb bot_team_median \n", - "0 NaN 4.656813 \n", - "1 NaN -1.478371 \n", + "0 NaN 5.521275 \n", + "1 NaN -1.512868 \n", "2 NaN -0.149434 \n", "3 NaN 0.310155 \n", "4 NaN 0.184891 \n", @@ -9826,7 +9826,7 @@ " False\n", " False\n", " ...\n", - " -1.845827\n", + " -2.251292\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -9850,7 +9850,7 @@ " False\n", " False\n", " ...\n", - " -0.074901\n", + " -0.020834\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -9898,7 +9898,7 @@ " False\n", " False\n", " ...\n", - " -0.017709\n", + " -0.063666\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -9931,10 +9931,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -1.845827 NaN NaN -0.111226 NaN \n", - "96 -0.074901 NaN NaN -0.074901 NaN \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -10007,6 +10007,14 @@ " 0.0\n", " \n", " \n", + " RPM_bot\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", " jonahsingerbot\n", " -0.0\n", " -0.0\n", @@ -10015,27 +10023,27 @@ " -0.0\n", " \n", " \n", - " X_bot\n", + " bean_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " bean_bot\n", " -0.0\n", " -0.0\n", + " \n", + " \n", + " X_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " CumulativeBot\n", " -0.0\n", " -0.0\n", " -0.0\n", - " -0.0\n", + " 0.0\n", " 0.0\n", " \n", " \n", @@ -10047,7 +10055,7 @@ " -0.0\n", " \n", " \n", - " RPM_bot\n", + " KevinTestBot\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -10055,7 +10063,7 @@ " 0.0\n", " \n", " \n", - " KevinTestBot\n", + " SynapseSeer\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -10063,12 +10071,12 @@ " 0.0\n", " \n", " \n", - " SynapseSeer\n", + " Grizeu_Bot\n", + " -0.2\n", " -0.1\n", " -0.0\n", - " -0.0\n", - " 0.0\n", - " 0.0\n", + " 0.1\n", + " 0.2\n", " \n", " \n", " pianobot\n", @@ -10079,14 +10087,6 @@ " 0.0\n", " \n", " \n", - " Grizeu_Bot\n", - " -0.2\n", - " -0.1\n", - " -0.0\n", - " 0.1\n", - " 0.2\n", - " \n", - " \n", " CatrachoCaster\n", " -0.1\n", " -0.1\n", @@ -10103,7 +10103,7 @@ " -0.0\n", " \n", " \n", - " 4Shadower\n", + " annabot\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10111,7 +10111,7 @@ " -0.0\n", " \n", " \n", - " annabot\n", + " 4Shadower\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10159,7 +10159,7 @@ " -0.0\n", " \n", " \n", - " GreeneiBot2\n", + " ajf-bot\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10167,7 +10167,7 @@ " 0.0\n", " \n", " \n", - " ajf-bot\n", + " GreeneiBot2\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10175,6 +10175,14 @@ " 0.0\n", " \n", " \n", + " acm_bot\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", + " \n", + " \n", " Bot_Pepa\n", " -0.2\n", " -0.2\n", @@ -10183,7 +10191,15 @@ " -0.0\n", " \n", " \n", - " acm_bot\n", + " metac-perplexity\n", + " -0.3\n", + " -0.3\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", + " \n", + " \n", + " bot_median\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10193,10 +10209,10 @@ " \n", " metac-o1\n", " -0.3\n", - " -0.2\n", + " -0.3\n", " -0.1\n", " -0.0\n", - " 0.0\n", + " 0.1\n", " \n", " \n", " metac-deepseek-r1+asknews\n", @@ -10207,16 +10223,16 @@ " -0.0\n", " \n", " \n", - " wunderplumb\n", - " -0.3\n", + " laylaps\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", - " laylaps\n", - " -0.2\n", + " wunderplumb\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", @@ -10225,40 +10241,24 @@ " \n", " metac-Gemini-Exp-1206\n", " -0.3\n", - " -0.2\n", - " -0.2\n", - " -0.0\n", - " 0.0\n", - " \n", - " \n", - " manticAI\n", " -0.3\n", - " -0.2\n", - " -0.2\n", " -0.1\n", " -0.0\n", + " 0.1\n", " \n", " \n", - " bot_median\n", + " manticAI\n", " -0.3\n", " -0.2\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", " -0.3\n", " -0.3\n", " -0.2\n", - " -0.1\n", - " 0.0\n", - " \n", - " \n", - " metac-perplexity\n", - " -0.4\n", - " -0.3\n", - " -0.2\n", " -0.0\n", " 0.0\n", " \n", @@ -10271,7 +10271,7 @@ " -0.0\n", " \n", " \n", - " minefrac1\n", + " metac-claude-3-5-sonnet-latest\n", " -0.3\n", " -0.3\n", " -0.2\n", @@ -10279,15 +10279,15 @@ " -0.1\n", " \n", " \n", - " mmBot\n", - " -0.4\n", + " minefrac1\n", + " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " metac-o1-preview\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -10295,55 +10295,55 @@ " -0.1\n", " \n", " \n", - " pgodzinai\n", - " -0.4\n", + " mmBot\n", " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", - " metac-exa\n", + " metac-Llama-3.1\n", " -0.4\n", " -0.4\n", - " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " \n", " \n", - " VeritasAI\n", + " pgodzinai\n", + " -0.4\n", " -0.4\n", " -0.3\n", - " -0.3\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -0.4\n", + " metac-grok-2-1212\n", + " -0.5\n", " -0.4\n", " -0.3\n", - " -0.2\n", " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-o1-preview\n", - " -0.5\n", + " VeritasAI\n", " -0.4\n", " -0.3\n", + " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", - " -0.5\n", + " metac-exa\n", + " -0.4\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -10366,49 +10366,49 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", + "CumulativeBot -0.0 -0.0 -0.0 0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", - "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", + "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", - "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", + "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", - "metac-o1 -0.3 -0.2 -0.1 -0.0 0.0\n", + "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", + "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-o1 -0.3 -0.3 -0.1 -0.0 0.1\n", "metac-deepseek-r1+asknews -0.3 -0.2 -0.1 -0.1 -0.0\n", - "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.2 -0.2 -0.0 0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.1\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "bot_median -0.3 -0.2 -0.2 -0.1 0.0\n", - "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.1 0.0\n", - "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.0 0.0\n", "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.1\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", + "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", - "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", - "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 -0.0\n", + "pgodzinai -0.4 -0.4 -0.3 -0.1 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.0\n", "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", - "metac-Llama-3.1 -0.4 -0.4 -0.3 -0.2 -0.1\n", - "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" ] }, @@ -11023,7 +11023,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -11074,32 +11074,32 @@ "text": [ " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.65]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.97]\n", " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.01]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", " >>> Collected 1 forecasts: [0.97]\n", @@ -11108,470 +11108,470 @@ " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", " >>> Collected 2 forecasts: [0.85, 0.85]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.6, 0.4]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> 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0.8]\n", + " >>> Collected 2 forecasts: [0.99, 0.15]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.65]\n", " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.8, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", - " >>> Collected 2 forecasts: [0.3, 0.2]\n", - " >>> Collected 2 forecasts: [0.75, 0.7]\n", - " >>> Collected 2 forecasts: [0.15, 0.2]\n", + " >>> Collected 2 forecasts: [0.8, 0.9]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", + " >>> Collected 2 forecasts: [0.65, 0.75]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", " >>> Collected 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0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.7, 0.75, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.1, 0.14]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.99, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", - " >>> Collected 3 forecasts: [0.3, 0.2, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.7, 0.67]\n", - " >>> Collected 3 forecasts: [0.15, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.086]\n", + " >>> Collected 3 forecasts: [0.8, 0.9, 0.84]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", + " >>> Collected 3 forecasts: [0.65, 0.75, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.85, 0.65, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.05]\n", " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.794]\n", " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.65, 0.3, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.6, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.65, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.2, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.15, 0.2, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", + " >>> Collected 4 forecasts: [0.1, 0.25, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.65, 0.7, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.7, 0.75, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.1, 0.14, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.7, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.9, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.65, 0.75, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.85, 0.65, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.05, 0.02]\n", " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.794, nan]\n", " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.65, 0.3, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.65, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.2, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.65, 0.7, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.7, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.15, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.65, 0.75, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.85, 0.65, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052]\n", " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75]\n", " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15]\n", " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92]\n", " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27]\n", + " >>> Collected 7 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", - " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", - " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", - " >>> Collected 7 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2]\n", " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92]\n", + " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan]\n", " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", - " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513]\n", + " >>> Collected 8 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", - " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65]\n", - " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75]\n", - " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", + " >>> Collected 9 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95]\n", - " >>> Collected 9 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85, 0.638]\n", " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", + " >>> Collected 10 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95, 0.762]\n", - " >>> Collected 10 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -11652,9 +11652,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -11662,7 +11662,7 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", " \n", @@ -11679,9 +11679,9 @@ " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.6,0.35,0.05]\n", - " [0.0001, 0.5125, 0.0001]\n", + " [0.2,0.6,0.2]\n", " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", @@ -11715,18 +11715,18 @@ " binary\n", " NaN\n", " no\n", - " 0.85\n", - " 0.65\n", - " 0.3585\n", + " 0.9\n", + " 0.3\n", + " 0.1835\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", - " 0.9\n", + " 0.95\n", " 0.85\n", - " 0.772\n", + " 0.775\n", " \n", " \n", " 361\n", @@ -11734,16 +11734,16 @@ " NaN\n", " no\n", " 0.85\n", - " 0.71\n", - " 0.709\n", + " 0.8\n", + " 0.755\n", " \n", " \n", " 364\n", " binary\n", " NaN\n", " no\n", - " 0.05\n", - " 0.05\n", + " 0.1\n", + " 0.052\n", " 0.046\n", " \n", " \n", @@ -11766,42 +11766,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.01,0.7,0.25,0.03,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.6,0.35,0.05] \n", + "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", - "351 0.85 \n", - "355 0.9 \n", + "351 0.9 \n", + "355 0.95 \n", "361 0.85 \n", - "364 0.05 \n", + "364 0.1 \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.9 \n", - "351 0.65 \n", + "351 0.3 \n", "355 0.85 \n", - "361 0.71 \n", - "364 0.05 \n", + "361 0.8 \n", + "364 0.052 \n", "\n", " median_forecast_8_bots \n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.5125, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.9025 \n", - "351 0.3585 \n", - "355 0.772 \n", - "361 0.709 \n", + "351 0.1835 \n", + "355 0.775 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11892,52 +11892,52 @@ " \n", " 0\n", " 1\n", - " 702.66\n", + " 1399.41\n", " \n", " \n", " 1\n", " 2\n", - " 2127.15\n", + " 2492.32\n", " \n", " \n", " 2\n", " 3\n", - " 2378.31\n", + " 2451.57\n", " \n", " \n", " 3\n", " 4\n", - " 2447.50\n", + " 2407.46\n", " \n", " \n", " 4\n", " 5\n", - " 2613.58\n", + " 2500.43\n", " \n", " \n", " 5\n", " 6\n", - " 2565.78\n", + " 2492.29\n", " \n", " \n", " 6\n", " 7\n", - " 2492.12\n", + " 2620.65\n", " \n", " \n", " 7\n", " 8\n", - " 2572.02\n", + " 2688.63\n", " \n", " \n", " 8\n", " 9\n", - " 2483.55\n", + " 2505.22\n", " \n", " \n", " 9\n", " 10\n", - " 2418.82\n", + " 2396.81\n", " \n", " \n", "\n", @@ -11945,16 +11945,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 702.66\n", - "1 2 2127.15\n", - "2 3 2378.31\n", - "3 4 2447.50\n", - "4 5 2613.58\n", - "5 6 2565.78\n", - "6 7 2492.12\n", - "7 8 2572.02\n", - "8 9 2483.55\n", - "9 10 2418.82" + "0 1 1399.41\n", + "1 2 2492.32\n", + "2 3 2451.57\n", + "3 4 2407.46\n", + "4 5 2500.43\n", + "5 6 2492.29\n", + "6 7 2620.65\n", + "7 8 2688.63\n", + "8 9 2505.22\n", + "9 10 2396.81" ] }, "execution_count": 60, @@ -11994,7 +11994,14 @@ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" + "['metac-o1-preview',\n", + " 'metac-o1',\n", + " 'pgodzinai',\n", + " 'GreeneiBot2',\n", + " 'manticAI',\n", + " 'acm_bot',\n", + " 'metac-Gemini-Exp-1206',\n", + " 'SynapseSeer']" ] }, "execution_count": 61, @@ -12011,7 +12018,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -12108,18 +12115,18 @@ " NaN\n", " False\n", " False\n", - " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", " ...\n", " [0.01, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.13, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.20500000000000002, 0.0001, 0.0001, 0.0001, ...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", - " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -12135,10 +12142,10 @@ " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.051666666...\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", + " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", @@ -12180,18 +12187,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.6,0.35,0.05]\n", + " [0.2,0.6,0.2]\n", " ...\n", - " [0.0001, 0.35, 0.0001]\n", - " [0.0001, 0.475, 0.0001]\n", + " [0.0001, 0.6, 0.0001]\n", + " [0.0001, 0.525, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5562499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.47324999999999995, 0.0001]\n", - " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.5048350576136786, 0.0001]\n", - " [0.0001, 0.49717011522735727, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.442, 0.0001]\n", + " [0.0001, 0.434, 0.0001]\n", " \n", " \n", " 4\n", @@ -12207,9 +12214,9 @@ " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " ...\n", " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.0032500000000000003, 0.006500000000000...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.0021590909, 0.0043181818, 0.0064772727...\n", + " [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0...\n", + " [0.0, 0.0033333333, 0.0066666667, 0.01, 0.0133...\n", + " [0.0, 0.00257575755, 0.00515151515, 0.00772727...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", @@ -12238,43 +12245,43 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.01,0.7,0.2,0.07,0.02] ... \n", + "0 False [0.01,0.7,0.25,0.03,0.01] ... \n", "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", "2 False 0.1 ... \n", - "3 NaN [0.6,0.35,0.05] ... \n", + "3 NaN [0.2,0.6,0.2] ... \n", "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", "\n", " median_forecast_1_bots \\\n", "0 [0.01, 0.0001, 0.0001, 0.0001, 0.0001] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", "2 0.1 \n", - "3 [0.0001, 0.35, 0.0001] \n", + "3 [0.0001, 0.6, 0.0001] \n", "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.13, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", + "0 [0.20500000000000002, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", "2 0.1 \n", - "3 [0.0001, 0.475, 0.0001] \n", - "4 [0.0, 0.0032500000000000003, 0.006500000000000... \n", + "3 [0.0001, 0.525, 0.0001] \n", + "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505555556, 0.0511111111, 0.051666666... \n", + "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", "2 0.1 \n", "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "4 [0.0, 0.0033333333, 0.0066666667, 0.01, 0.0133... \n", "\n", " median_forecast_4_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.085 \n", "3 [0.0001, 0.5562499999999999, 0.0001] \n", - "4 [0.0, 0.0021590909, 0.0043181818, 0.0064772727... \n", + "4 [0.0, 0.00257575755, 0.00515151515, 0.00772727... \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", @@ -12283,35 +12290,35 @@ "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.47324999999999995, 0.0001] \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.5125, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.5125, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_9_bots \\\n", - "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.5048350576136786, 0.0001] \n", + "3 [0.0001, 0.442, 0.0001] \n", "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", "\n", " median_forecast_10_bots \n", - "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.49717011522735727, 0.0001] \n", + "3 [0.0001, 0.434, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -12391,7 +12398,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -12409,7 +12416,7 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", " \n", " \n", @@ -12427,7 +12434,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", " \n", " \n", @@ -12445,7 +12452,7 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " [0.16,0.44,0.4]\n", " \n", " \n", @@ -12463,7 +12470,7 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", " \n", " \n", @@ -12500,11 +12507,11 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", - "2 0.085 \n", - "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.45, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " pro_median \n", "0 [0.001,0.62,0.35,0.019,0.01] \n", @@ -12571,7 +12578,7 @@ " False\n", " 35380\n", " 1.00\n", - " 0.9\n", + " 0.9025\n", " 0.95\n", " \n", " \n", @@ -12589,7 +12596,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.65\n", + " 0.1835\n", " 0.05\n", " \n", " \n", @@ -12607,7 +12614,7 @@ " False\n", " 35385\n", " 1.00\n", - " 0.85\n", + " 0.775\n", " 0.97\n", " \n", " \n", @@ -12625,7 +12632,7 @@ " False\n", " 35386\n", " 0.85\n", - " 0.71\n", + " 0.755\n", " 0.666\n", " \n", " \n", @@ -12643,7 +12650,7 @@ " False\n", " 35387\n", " 0.85\n", - " 0.05\n", + " 0.046\n", " 0.03\n", " \n", " \n", @@ -12673,11 +12680,11 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median \n", - "342 1.00 0.9 0.95 \n", - "351 1.00 0.65 0.05 \n", - "355 1.00 0.85 0.97 \n", - "361 0.85 0.71 0.666 \n", - "364 0.85 0.05 0.03 " + "342 1.00 0.9025 0.95 \n", + "351 1.00 0.1835 0.05 \n", + "355 1.00 0.775 0.97 \n", + "361 0.85 0.755 0.666 \n", + "364 0.85 0.046 0.03 " ] }, "metadata": {}, @@ -12740,7 +12747,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1240\n" + "Weighted Total Score: -0.1115\n" ] } ], @@ -12762,7 +12769,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -12774,7 +12781,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -0.13\n" + "The average of 'head_to_head' is: -0.12\n" ] } ], @@ -12830,17 +12837,17 @@ " \n", " \n", " head_to_head\n", - " -11.8\n", + " -10.6\n", " 92.1\n", " -0.1\n", - " 0.643536\n", - " 0.067057\n", - " -1.907958\n", + " 0.846125\n", + " 0.088167\n", + " -1.304254\n", " 1.98555\n", - " 0.0\n", + " 0.1\n", " -0.3\n", - " 0.029773\n", - " 0.059546\n", + " 0.097716\n", + " 0.195433\n", " \n", " \n", "\n", @@ -12848,10 +12855,10 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -11.8 92.1 -0.1 0.643536 0.067057 -1.907958 1.98555 \n", + "head_to_head -10.6 92.1 -0.1 0.846125 0.088167 -1.304254 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.0 -0.3 0.029773 0.059546 " + "head_to_head 0.1 -0.3 0.097716 0.195433 " ] }, "execution_count": 68, @@ -12923,7 +12930,7 @@ " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.13]\n", + " [0.0001, 0.0001, 0.0001, 0.14]\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", " -1.8\n", @@ -12931,26 +12938,26 @@ " \n", " 247\n", " Will the 500th richest person on Bloomberg's B...\n", - " 0.8\n", + " 0.833333\n", " 0.333\n", " no\n", - " -1.2\n", + " -1.4\n", " \n", " \n", " 232\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.32130390143737164]\n", + " [0.0001, 0.0001, 0.0001, 0.27130390143737165]\n", " [0.002,0.008,0.09,0.9]\n", " 3 or more\n", - " -1.0\n", + " -1.2\n", " \n", " \n", - " 71\n", - " Will OpenAI, Anthropic, or Perplexity run an a...\n", - " 0.18\n", - " 0.55\n", - " yes\n", - " -1.0\n", + " 47\n", + " What will be Donald Trump's net worth, accordi...\n", + " [0.185, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.6,0.2,0.1,0.075,0.025]\n", + " 0-$6 billion, inclusive\n", + " -1.2\n", " \n", " \n", "\n", @@ -12962,21 +12969,21 @@ "121 How many movies will be new on Netflix's top 1... \n", "247 Will the 500th richest person on Bloomberg's B... \n", "232 How many movies will be new on Netflix's top 1... \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", + "47 What will be Donald Trump's net worth, accordi... \n", "\n", " bot_team_median \\\n", "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", - "121 [0.0001, 0.0001, 0.0001, 0.13] \n", - "247 0.8 \n", - "232 [0.0001, 0.0001, 0.0001, 0.32130390143737164] \n", - "71 0.18 \n", - "\n", - " pro_median resolution head_to_head \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", - "247 0.333 no -1.2 \n", - "232 [0.002,0.008,0.09,0.9] 3 or more -1.0 \n", - "71 0.55 yes -1.0 " + "121 [0.0001, 0.0001, 0.0001, 0.14] \n", + "247 0.833333 \n", + "232 [0.0001, 0.0001, 0.0001, 0.27130390143737165] \n", + "47 [0.185, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "\n", + " pro_median resolution head_to_head \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", + "247 0.333 no -1.4 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.2 \n", + "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -1.2 " ] }, "execution_count": 69, @@ -13044,23 +13051,23 @@ " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0030510204, 0.0061020408, 0.0102928751...\n", + " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 2.5\n", + " 2.9\n", " \n", " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 2.5\n", + " 5.3\n", " \n", " \n", " 151\n", " How many earthquakes of magnitude ≥ 4 will hap...\n", - " [0.0, 0.0035714286, 0.0071428571, 0.0107142857...\n", + " [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0...\n", " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", " 0.0\n", " NaN\n", @@ -13076,7 +13083,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.954\n", + " 0.952\n", " 0.95\n", " annulled\n", " NaN\n", @@ -13094,11 +13101,11 @@ "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "189 [0.0, 0.0030510204, 0.0061020408, 0.0102928751... \n", - "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", + "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "151 [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0... \n", "211 0.99 \n", - "214 0.954 \n", + "214 0.952 \n", "\n", " pro_median resolution \\\n", "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", @@ -13108,8 +13115,8 @@ "214 0.95 annulled \n", "\n", " head_to_head \n", - "189 2.5 \n", - "0 2.5 \n", + "189 2.9 \n", + "0 5.3 \n", "151 NaN \n", "211 NaN \n", "214 NaN " @@ -13227,10 +13234,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.522754\n", - " 2.522754\n", + " 5.334952\n", + " 5.334952\n", " \n", " \n", " 1\n", @@ -13247,10 +13254,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.161101\n", - " -0.161101\n", + " -0.250003\n", + " -0.250003\n", " \n", " \n", " 2\n", @@ -13267,10 +13274,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", - " -0.075746\n", - " -0.075746\n", + " -0.092275\n", + " -0.092275\n", " \n", " \n", " 3\n", @@ -13287,10 +13294,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 0.152526\n", - " 0.152526\n", + " 0.022473\n", + " 0.022473\n", " \n", " \n", " 4\n", @@ -13307,10 +13314,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.387623\n", - " 0.387623\n", + " -0.102791\n", + " -0.102791\n", " \n", " \n", "\n", @@ -13346,25 +13353,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", - "2 0.085 \n", - "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.45, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.161101 \n", - "2 0.013 -0.075746 \n", - "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 5.334952 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "2 0.013 -0.092275 \n", + "3 [0.16,0.44,0.4] 0.022473 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... -0.102791 \n", "\n", " weighted_score \n", - "0 2.522754 \n", - "1 -0.161101 \n", - "2 -0.075746 \n", - "3 0.152526 \n", - "4 0.387623 " + "0 5.334952 \n", + "1 -0.250003 \n", + "2 -0.092275 \n", + "3 0.022473 \n", + "4 -0.102791 " ] }, "execution_count": 72, @@ -13378,7 +13385,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -13403,7 +13410,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 93, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13413,25 +13420,9 @@ "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Count: 10\n", - "Count: 11\n", - "Count: 11\n", - "Count: 11\n", - "Count: 11\n", - "Count: 10\n", - "Count: 9\n", - "Count: 10\n", - "Count: 9\n", - "Count: 10\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -13475,17 +13466,20 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "# Map resolution to 0 and 1\n", - "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" + "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})\n", + "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all_binary[\n", + " df_top_bot_pro_forecasts_all_binary['resolution'].notna()\n", + "]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -13542,7 +13536,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.063\n", + " 0.1\n", " 0.013\n", " \n", " \n", @@ -13560,7 +13554,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.62\n", + " 0.5\n", " 0.45\n", " \n", " \n", @@ -13578,7 +13572,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.81\n", + " 0.835\n", " 0.95\n", " \n", " \n", @@ -13644,14 +13638,14 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.063 0.013 \n", - "5 1.0 0.62 0.45 \n", - "8 1.0 0.81 0.95 \n", + "2 1.0 0.1 0.013 \n", + "5 1.0 0.5 0.45 \n", + "8 1.0 0.835 0.95 \n", "10 1.0 NaN NaN \n", "13 1.0 0.85 0.9 " ] }, - "execution_count": 76, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -13662,7 +13656,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -13679,8 +13673,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of pro forecasts: 50\n", - "Number of bot forecasts: 241\n" + "Number of pro forecasts: 48\n", + "Number of bot forecasts: 236\n" ] } ], diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 930eefb..7214749 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 +CumulativeBot,-0.0,-0.0,-0.0,0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 -pianobot,-0.1,-0.1,-0.0,-0.0,0.0 Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 +pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 annabot,-0.1,-0.1,-0.1,-0.0,-0.0 +4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 MWG,-0.2,-0.2,-0.1,-0.0,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 -GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 +GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 -metac-o1,-0.3,-0.2,-0.1,-0.0,0.0 +Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 +bot_median,-0.3,-0.2,-0.1,-0.0,0.1 +metac-o1,-0.3,-0.3,-0.1,-0.0,0.1 metac-deepseek-r1+asknews,-0.3,-0.2,-0.1,-0.1,-0.0 -wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.2,-0.2,-0.0,0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.1 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -bot_median,-0.3,-0.2,-0.2,-0.1,0.0 -metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.1,0.0 -metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 +metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.0,0.0 NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.1 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 +metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 -pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 -metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,-0.0 +pgodzinai,-0.4,-0.4,-0.3,-0.1,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.0 VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 -metac-Llama-3.1,-0.4,-0.4,-0.3,-0.2,-0.1 -metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 477882c..cd9448c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 -RPM_bot,-1.4,7.0,-0.2,0.8195427278689026,0.3097580352475143,-0.650312775083108,2.4469118511449692,0.6,-1.0,0.26978865902437565,0.539577 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 +annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.6,27.4,-0.2,0.7452828646172052,0.14237897258891655,-1.694618782556622,2.0495406495390753,0.1,-0.5,0.05095705221638959,0.101914 +cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.7,58.4,-0.2,0.8492744520587402,0.11118024573783404,-1.6427768404571312,2.000831925930035,0.0,-0.4,0.05295076167168595,0.105902 +GreeneiBot2,-10.7,58.4,-0.2,0.8487135517179298,0.11110681713348293,-1.6470273617836275,2.000831925930035,0.0,-0.4,0.052510863710317504,0.105022 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 -metac-o1,-11.3,91.1,-0.1,0.885301596604543,0.09275387429075187,-1.342986841449772,1.9858289388460384,0.1,-0.3,0.09132478421461744,0.182650 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 +metac-perplexity,-12.0,89.1,-0.1,1.0008449184534645,0.10602979859799266,-1.2696037636515303,1.9864049297707018,0.1,-0.3,0.10378462460698391,0.207569 +bot_median,-12.2,92.1,-0.1,0.8759085051927877,0.0912701844746672,-1.448706262693777,1.9855502432148115,0.0,-0.3,0.07542649485602951,0.150853 +metac-o1,-12.4,91.1,-0.1,0.9413031092818035,0.09862120502513756,-1.3750355923383297,1.9858289388460384,0.1,-0.3,0.08626502997859752,0.172530 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 -metac-deepseek-r1+asknews,-13.3,52.1,-0.3,0.7808915178330472,0.10818619432038376,-2.3663082727832094,2.0053789762011176,-0.0,-0.5,0.010897575637344883,0.021795 +metac-deepseek-r1+asknews,-13.4,52.1,-0.3,0.6866418388462276,0.09512866474982715,-2.7023938246614656,2.0053789762011176,-0.1,-0.4,0.0046603987010819335,0.009321 +metac-Gemini-Exp-1206,-13.5,76.5,-0.2,1.0066063915806054,0.11508771463432003,-1.5277274660739493,1.9908217254774627,0.1,-0.4,0.06537953017362978,0.130759 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -metac-Gemini-Exp-1206,-13.7,76.5,-0.2,0.9567011955687134,0.10938193429612067,-1.6400021546672607,1.9908217254774627,0.0,-0.4,0.05258248904380755,0.105165 -bot_median,-14.2,92.1,-0.2,0.8060563380929024,0.08399154733464013,-1.8298886724683292,1.9855502432148115,0.0,-0.3,0.03526855952035323,0.070537 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-claude-3-5-sonnet-20240620,-15.7,90.5,-0.2,0.9577206882239262,0.10067336366115942,-1.726279013247091,1.9860719790130024,0.0,-0.4,0.043873862980955504,0.087748 -metac-perplexity,-16.1,89.1,-0.2,1.04022365857026,0.11020159365499146,-1.6385490214880174,1.9864049297707018,0.0,-0.4,0.052436941119456015,0.104874 +metac-claude-3-5-sonnet-20240620,-14.7,90.5,-0.2,0.9429804683378815,0.09912390614679249,-1.6425851577449733,1.9860719790130024,0.0,-0.4,0.051988931836857315,0.103978 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 -metac-claude-3-5-sonnet-latest,-21.9,91.1,-0.2,0.8267775869528969,0.08662225919479004,-2.7788128175615063,1.9858289388460384,-0.1,-0.4,0.0033198064428072906,0.006640 +metac-claude-3-5-sonnet-latest,-18.9,91.1,-0.2,0.7317083930215759,0.07666177104402958,-2.699995118056715,1.9858289388460384,-0.1,-0.4,0.004140859358698023,0.008282 +minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 +metac-o1-preview,-20.9,91.1,-0.2,0.802181404225052,0.08404529418137442,-2.7288070523371224,1.9858289388460384,-0.1,-0.4,0.003821400227265772,0.007643 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -pgodzinai,-23.5,76.4,-0.3,1.0010628527586396,0.11452878848708839,-2.684829528603297,1.9908489732268309,-0.1,-0.5,0.004459201995123589,0.008918 -metac-exa,-24.1,89.1,-0.3,0.8238773759897631,0.08728180623689599,-3.103267575628089,1.9864049297707018,-0.1,-0.4,0.0012863793448356026,0.002573 +metac-Llama-3.1,-23.2,89.1,-0.3,1.0312779661924496,0.1092538844308646,-2.379606259857792,1.9864049297707018,-0.0,-0.5,0.009744516632283914,0.019489 +metac-grok-2-1212,-23.5,91.1,-0.3,1.0680060472571526,0.11189599005467826,-2.303421178504194,1.9858289388460384,-0.0,-0.5,0.011778139872058951,0.023556 +pgodzinai,-24.0,76.4,-0.3,0.9765897737398795,0.11172889227393508,-2.8110851156332464,1.9908489732268309,-0.1,-0.5,0.0031442974859602537,0.006289 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-Llama-3.1,-26.6,89.1,-0.3,0.8904683193506574,0.09433646993436098,-3.1697302934806575,1.9864049297707018,-0.1,-0.5,0.001049393935170647,0.002099 +metac-exa,-26.2,89.1,-0.3,0.8302752742001319,0.0879596014139391,-3.3415454501401167,1.9864049297707018,-0.1,-0.5,0.0006119018080970774,0.001224 +metac-gpt-4o,-26.6,91.1,-0.3,0.8790866786848435,0.09210273154158923,-3.165570176683145,1.9858289388460384,-0.1,-0.5,0.0010559673026657784,0.002112 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 -metac-o1-preview,-27.3,91.1,-0.3,0.8396846352431687,0.0879745426868476,-3.4074998848675455,1.9858289388460384,-0.1,-0.5,0.0004908622706364246,0.000982 -metac-grok-2-1212,-28.3,91.1,-0.3,1.0374739049385253,0.10869710901649764,-2.862896131089403,1.9858289388460384,-0.1,-0.5,0.00261020744989918,0.005220 -metac-gpt-4o,-28.7,91.1,-0.3,0.8937174262561063,0.09363560861558237,-3.3666300493101518,1.9858289388460384,-0.1,-0.5,0.0005601224288125974,0.001120 From 1e59b86f649912ac264616678848dae46b25e518 Mon Sep 17 00:00:00 2001 From: Molly Hickman Date: Thu, 22 May 2025 13:01:38 -0400 Subject: [PATCH 26/26] calibration bug fix :bug: --- AI_BENCHMARKING_ANALYSIS.ipynb | 3093 +++++++++-------- functions.py | 6 +- .../bootstrapped_h2h_bot_vs_pros.csv | 40 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 40 +- 4 files changed, 1653 insertions(+), 1526 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index bb7044b..bf4055e 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -38,7 +38,8 @@ "%autoreload 2\n", "from functions import *\n", "from IPython.display import display, clear_output\n", - "import pandas as pd\n" + "import pandas as pd\n", + "from copy import deepcopy\n" ] }, { @@ -61,7 +62,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1441081/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_17143/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -832,12 +833,12 @@ " False\n", " \n", " \n", - " 5\n", + " 3\n", " 31268\n", - " darkives\n", + " SpottedBear\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103907\n", + " 131523\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -849,16 +850,16 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.49,0.365,0.1,0.044]\n", + " [0.001,0.59,0.35,0.044,0.015]\n", " False\n", " \n", " \n", - " 6\n", + " 4\n", " 31268\n", - " datscilly\n", + " Zaldath\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103777\n", + " 139161\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -870,7 +871,7 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.56,0.36,0.059,0.02]\n", + " [0.001,0.623,0.336,0.03,0.01]\n", " False\n", " \n", " \n", @@ -878,47 +879,54 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "3 31268 SpottedBear \n", + "4 31268 Zaldath \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "1 For Q1 2025, how many banks will be listed on ... \n", + "2 For Q1 2025, how many banks will be listed on ... \n", + "3 For Q1 2025, how many banks will be listed on ... \n", + "4 For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "5 2025-01-17 19:06:22.013528+00 103907 1 \n", - "6 2025-01-17 19:06:22.013528+00 103777 1 \n", + "3 2025-01-17 19:06:22.013528+00 131523 1 \n", + "4 2025-01-17 19:06:22.013528+00 139161 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "\n", " open_lower_bound open_upper_bound post_id forecast \\\n", "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", - "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", - "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "3 False False 31736 [0.001,0.59,0.35,0.044,0.015] \n", + "4 False False 31736 [0.001,0.623,0.336,0.03,0.01] \n", "\n", " is_median \n", "0 False \n", "1 True \n", "2 False \n", - "5 False \n", - "6 False " + "3 False \n", + "4 False " ] }, "execution_count": 15, @@ -961,13 +969,14 @@ { "data": { "text/plain": [ - "array(['metac-Llama-3.1', 'metac-Gemini-Exp-1206', 'acm_bot',\n", - " 'NextWorldLab', 'metac-o1-preview', 'metac-perplexity', 'mmBot',\n", - " 'metac-claude-3-5-sonnet-latest', 'Grizeu_Bot', 'GreeneiBot2',\n", - " 'InstitutPelFutur', 'metac-claude-3-5-sonnet-20240620', 'metac-o1',\n", - " 'metac-grok-2-1212', 'metac-gpt-4o', 'bot_median', 'pgodzinai',\n", - " 'metac-exa', 'jkraybill_bot', 'VeritasAI', 'MWG', 'twsummerbot',\n", - " 'CatrachoCaster', 'X_bot', 'manticAI', 'annabot', 'minefrac1',\n", + "array(['GreeneiBot2', 'Grizeu_Bot', 'InstitutPelFutur', 'NextWorldLab',\n", + " 'acm_bot', 'metac-Gemini-Exp-1206', 'metac-Llama-3.1', 'mmBot',\n", + " 'metac-claude-3-5-sonnet-latest', 'metac-gpt-4o',\n", + " 'metac-grok-2-1212', 'metac-o1', 'metac-o1-preview',\n", + " 'metac-perplexity', 'bot_median',\n", + " 'metac-claude-3-5-sonnet-20240620', 'pgodzinai', 'jkraybill_bot',\n", + " 'metac-exa', 'manticAI', 'MWG', 'CatrachoCaster', 'twsummerbot',\n", + " 'VeritasAI', 'X_bot', 'annabot', 'minefrac1',\n", " 'metac-deepseek-r1+asknews', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", " 'SynapseSeer', 'RPM_bot', 'cookics_bot_TEST', 'ProfessorSP',\n", " 'wunderplumb', 'CumulativeBot', 'pianobot', 'krm-bot',\n", @@ -1021,7 +1030,7 @@ " \n", " \n", " \n", - " 12\n", + " 11\n", " metac-o1\n", " 9.674740\n", " 3631.123492\n", @@ -1030,16 +1039,7 @@ " 1.738353\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.546230\n", - " 3230.645695\n", - " 409\n", - " 5.546573\n", - " 1.525925\n", - " \n", - " \n", - " 4\n", + " 12\n", " metac-o1-preview\n", " 8.465638\n", " 3121.449998\n", @@ -1048,7 +1048,16 @@ " 2.298000\n", " \n", " \n", - " 24\n", + " 14\n", + " bot_median\n", + " 8.143307\n", + " 3078.332902\n", + " 409\n", + " 5.471228\n", + " 1.359286\n", + " \n", + " \n", + " 19\n", " manticAI\n", " 6.510835\n", " 2055.210309\n", @@ -1057,7 +1066,7 @@ " 3.029040\n", " \n", " \n", - " 1\n", + " 5\n", " metac-Gemini-Exp-1206\n", " 5.417367\n", " 1880.476418\n", @@ -1071,18 +1080,18 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.546230 3230.645695 409 5.546573 \n", - "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "24 manticAI 6.510835 2055.210309 337 0.552564 \n", - "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", + "11 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "12 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "14 bot_median 8.143307 3078.332902 409 5.471228 \n", + "19 manticAI 6.510835 2055.210309 337 0.552564 \n", + "5 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", - "12 1.738353 \n", - "15 1.525925 \n", - "4 2.298000 \n", - "24 3.029040 \n", - "1 2.309106 " + "11 1.738353 \n", + "12 2.298000 \n", + "14 1.359286 \n", + "19 3.029040 \n", + "5 2.309106 " ] }, "metadata": {}, @@ -1119,7 +1128,7 @@ " \n", " \n", " \n", - " 19\n", + " 23\n", " VeritasAI\n", " -4.854808\n", " -1602.183635\n", @@ -1137,7 +1146,7 @@ " 3.096816\n", " \n", " \n", - " 8\n", + " 1\n", " Grizeu_Bot\n", " -9.743831\n", " -1882.605577\n", @@ -1146,7 +1155,7 @@ " 3.931500\n", " \n", " \n", - " 14\n", + " 9\n", " metac-gpt-4o\n", " -5.987786\n", " -2235.360274\n", @@ -1169,17 +1178,17 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "19 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", + "23 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", "26 minefrac1 -9.333648 -1757.059251 202 -15.440064 \n", - "8 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", - "14 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", + "1 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", + "9 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", "30 ajf-bot -14.000701 -3208.260547 244 -24.482548 \n", "\n", " weighted_se \n", - "19 2.036820 \n", + "23 2.036820 \n", "26 3.096816 \n", - "8 3.931500 \n", - "14 2.255950 \n", + "1 3.931500 \n", + "9 2.255950 \n", "30 5.321344 " ] }, @@ -1740,7 +1749,7 @@ " \n", " 3\n", " bot_median\n", - " 8674.761163\n", + " 8721.511046\n", " \n", " \n", " 4\n", @@ -1761,7 +1770,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8674.761163\n", + "3 bot_median 8721.511046\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1940,7 @@ " \n", " 2\n", " bot_median\n", - " 3544.710382\n", + " 3472.028144\n", " \n", " \n", " 3\n", @@ -2166,7 +2175,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3544.710382\n", + "2 bot_median 3472.028144\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2414,12 +2423,12 @@ " False\n", " \n", " \n", - " 5\n", + " 3\n", " 31268\n", - " darkives\n", + " SpottedBear\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103907\n", + " 131523\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2431,16 +2440,16 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.49,0.365,0.1,0.044]\n", + " [0.001,0.59,0.35,0.044,0.015]\n", " False\n", " \n", " \n", - " 6\n", + " 4\n", " 31268\n", - " datscilly\n", + " Zaldath\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103777\n", + " 139161\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2452,7 +2461,7 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.56,0.36,0.059,0.02]\n", + " [0.001,0.623,0.336,0.03,0.01]\n", " False\n", " \n", " \n", @@ -2460,47 +2469,54 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "3 31268 SpottedBear \n", + "4 31268 Zaldath \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "1 For Q1 2025, how many banks will be listed on ... \n", + "2 For Q1 2025, how many banks will be listed on ... \n", + "3 For Q1 2025, how many banks will be listed on ... \n", + "4 For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "5 2025-01-17 19:06:22.013528+00 103907 1 \n", - "6 2025-01-17 19:06:22.013528+00 103777 1 \n", + "3 2025-01-17 19:06:22.013528+00 131523 1 \n", + "4 2025-01-17 19:06:22.013528+00 139161 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "\n", " open_lower_bound open_upper_bound post_id forecast \\\n", "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", - "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", - "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "3 False False 31736 [0.001,0.59,0.35,0.044,0.015] \n", + "4 False False 31736 [0.001,0.623,0.336,0.03,0.01] \n", "\n", " is_median \n", "0 False \n", "1 True \n", "2 False \n", - "5 False \n", - "6 False " + "3 False \n", + "4 False " ] }, "execution_count": 27, @@ -2578,9 +2594,9 @@ " False\n", " False\n", " ...\n", - " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", - " [0.01,0.7,0.25,0.03,0.01]\n", - " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2603,7 +2619,7 @@ " True\n", " ...\n", " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", @@ -2627,7 +2643,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.1\n", + " 0.05\n", " 0.1\n", " NaN\n", " 0.2\n", @@ -2651,8 +2667,8 @@ " None\n", " ...\n", " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.65,0.2]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2713,24 +2729,24 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 0.1 \n", "3 [0.45,0.45,0.1] \n", "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.25,0.03,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", + "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", "2 0.1 NaN \n", - "3 [0.1,0.6,0.3] NaN \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2842,7 +2858,7 @@ " False\n", " False\n", " ...\n", - " 0.3\n", + " 0.4\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2866,7 +2882,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -2914,8 +2930,8 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.05\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -2947,10 +2963,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.3 0.9 NaN NaN 0.15 NaN \n", - "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "95 0.4 0.9 NaN NaN 0.15 NaN \n", + "96 0.8 0.95 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", + "98 0.05 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3100,7 +3116,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1441081/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_17143/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,9 +3178,9 @@ " False\n", " False\n", " ...\n", - " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", - " [0.01,0.7,0.25,0.03,0.01]\n", - " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -3186,8 +3202,8 @@ " True\n", " True\n", " ...\n", - " 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@@ -3211,7 +3227,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.1\n", + " 0.05\n", " 0.1\n", " NaN\n", " 0.2\n", @@ -3235,8 +3251,8 @@ " None\n", " ...\n", " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.65,0.2]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3260,7 +3276,7 @@ " ...\n", " 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@@ -3296,26 +3312,26 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", - "1 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\n", + "\n", + " metac-perplexity \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.902,0.904,0.906,0.908,0.91,0.912,0.914,0.916,0.918,0.92,0.922,0.924,0.926,0.928,0.93,0.932,0.934,0.936,0.938,0.94,0.942,0.944,0.946,0.948,0.95,0.952,0.954,0.956,0.958,0.96,0.962,0.964,0.966,0.968,0.97,0.972,0.974,0.976,0.978,0.98,0.982,0.984,0.986,0.988,0.99,0.992,0.994,0.996,0.998,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3447,7 +3463,7 @@ " False\n", " False\n", " ...\n", - " 0.3\n", + " 0.4\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3471,7 +3487,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -3519,8 +3535,8 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.05\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -3552,10 +3568,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.3 0.9 NaN NaN 0.15 NaN \n", - "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "95 0.4 0.9 NaN NaN 0.15 NaN \n", + "96 0.8 0.95 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", + "98 0.05 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3636,61 +3652,61 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" ] } @@ -3771,7 +3787,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.521275\n", + " 4.605170\n", " \n", " \n", " 3\n", @@ -3786,8 +3802,8 @@ " None\n", " None\n", " ...\n", - " 0.310155\n", - " 0.310155\n", + " 0.390198\n", + " 0.204794\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -3810,16 +3826,16 @@ " False\n", " False\n", " ...\n", - " 0.116534\n", - " -0.106610\n", + " 0.298855\n", + " 0.211844\n", " NaN\n", " -0.184571\n", - " 0.111521\n", + " 0.112526\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 0.111521\n", + " 0.112526\n", " \n", " \n", " 9\n", @@ -3835,7 +3851,7 @@ " None\n", " ...\n", " -0.518794\n", - " -0.806476\n", + " -1.211941\n", " NaN\n", " -0.806476\n", " -0.494101\n", @@ -3843,7 +3859,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.518794\n", + " -0.681313\n", " \n", " \n", " 13\n", @@ -3858,7 +3874,7 @@ " None\n", " None\n", " ...\n", - " 0.441833\n", + " 0.330943\n", " 0.510826\n", " 0.021979\n", " 0.200671\n", @@ -3905,16 +3921,16 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "0 2.302585 5.703782 NaN 2.292635 2.703087 \n", - "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", - "6 0.116534 -0.106610 NaN -0.184571 0.111521 \n", - "9 -0.518794 -0.806476 NaN -0.806476 -0.494101 \n", - "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", + "3 0.390198 0.204794 NaN 0.127833 0.152526 \n", + "6 0.298855 0.211844 NaN -0.184571 0.112526 \n", + "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", + "13 0.330943 0.510826 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "0 NaN NaN NaN NaN 5.521275 \n", + "0 NaN NaN NaN NaN 4.605170 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN 0.111521 \n", - "9 NaN NaN -0.624154 NaN -0.518794 \n", + "6 NaN NaN NaN NaN 0.112526 \n", + "9 NaN NaN -0.624154 NaN -0.681313 \n", "13 NaN NaN NaN NaN 0.158111 \n", "\n", "[5 rows x 58 columns]" @@ -3982,7 +3998,7 @@ " False\n", " ...\n", " -2.879198\n", - " -0.933288\n", + " -2.186051\n", " -3.007032\n", " -2.879198\n", " -3.795489\n", @@ -3990,7 +4006,7 @@ " NaN\n", " -2.348570\n", " -2.409195\n", - " -2.879198\n", + " -2.186051\n", " \n", " \n", " 82\n", @@ -4029,8 +4045,8 @@ " None\n", " None\n", " ...\n", - " -0.693147\n", - " -0.182322\n", + " -0.899761\n", + " -0.405465\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -4053,8 +4069,8 @@ " False\n", " False\n", " ...\n", - " -0.069566\n", - " -0.080377\n", + " -0.054625\n", + " -0.102356\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -4077,8 +4093,8 @@ " False\n", " False\n", " ...\n", - " -0.788457\n", - " -1.011601\n", + " -1.704748\n", + " -4.007333\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4118,19 +4134,19 @@ " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", "81 NaN False False ... -2.879198 \n", "82 NaN None None ... -0.076961 \n", - "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.069566 \n", - "92 NaN False False ... -0.788457 \n", + "83 NaN None None ... -0.899761 \n", + "91 NaN False False ... -0.054625 \n", + "92 NaN False False ... -1.704748 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -0.933288 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "81 -2.186051 -3.007032 -2.879198 -3.795489 NaN NaN \n", "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", - "91 -0.080377 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "83 -0.405465 NaN -0.182322 NaN NaN NaN \n", + "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -4.007333 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -2.879198 \n", + "81 -2.348570 -2.409195 -2.186051 \n", "82 0.276509 -0.644609 -0.587787 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.124829 \n", @@ -4200,7 +4216,7 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", + " -0.038208\n", " -0.092275\n", " NaN\n", " -0.210058\n", @@ -4225,7 +4241,7 @@ " None\n", " ...\n", " -0.251314\n", - " 0.200671\n", + " 0.441833\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -4233,7 +4249,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.287682\n", + " 0.367725\n", " \n", " \n", " 8\n", @@ -4248,8 +4264,8 @@ " False\n", " False\n", " ...\n", - " -0.111226\n", " -0.054067\n", + " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -4328,15 +4344,15 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", - "5 -0.251314 0.200671 NaN 0.510826 0.320472 \n", - "8 -0.111226 -0.054067 NaN -0.111226 -0.147158 \n", + "2 -0.038208 -0.092275 NaN -0.210058 -0.059485 \n", + "5 -0.251314 0.441833 NaN 0.510826 0.320472 \n", + "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", "16 0.008457 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", - "5 NaN NaN NaN NaN 0.287682 \n", + "5 NaN NaN NaN NaN 0.367725 \n", "8 NaN NaN -0.398124 NaN -0.147158 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", "16 NaN NaN -0.076070 NaN -0.096728 \n", @@ -4462,7 +4478,7 @@ " -0.132060\n", " -0.158283\n", " -0.132060\n", - " -0.132060\n", + " -0.158283\n", " \n", " \n", " 97\n", @@ -4501,7 +4517,7 @@ " False\n", " False\n", " ...\n", - " -0.063666\n", + " -0.017709\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -4537,12 +4553,12 @@ "95 -2.251292 NaN NaN -0.111226 NaN \n", "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", - "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.158283 \n", "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", "\n", @@ -4603,7 +4619,7 @@ " \n", " 2\n", " bot_median\n", - " 3544.710382\n", + " 3472.028144\n", " \n", " \n", " 3\n", @@ -4838,7 +4854,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3544.710382\n", + "2 bot_median 3472.028144\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4922,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", - "mean metac-o1 forecast on questions that resolved no: 27.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.999999999999996%\n" ] }, { "data": { - "image/png": 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1Ouecc9SmTRv985//1Nlnn61PP/1UDz74YJnjoqOjdeWVV+rKK6/UkSNHNHLkSD366KOaMmWKIiIi/L5u6fdn48aNHq0PR44c0datW8t8vrt27SrTGrRp0yZJZubCyiguLpYkHT58uExMJ9qwYYNiY2Pd1z9xlsWq8vfz8KZjx45at26dBg0aVGF8HTt2lMvl0vr169WzZ0+vx3z++efav3+/3nzzTZ1zzjnu7aWzaZ6oe/fu6t69ux566CF988036t+/vxYtWqRHHnlEUsWfV5s2bXTbbbfptttuU3Z2tnr16qVHH32UQgpAuRgjBQBVcNFFF6mkpETPPfecx/annnpKNpvN7yTsvPPO06xZs/Tcc8+5p8X25oorrtDq1au1fPnyMvsOHjzoTs4vuugiFRcXe0xZXlJSomefffaksZS2MB3fouR0OrV48WKf34+v7Ha7LrvsMr333nv629/+puLiYo9ufZKZIe54YWFhSklJkWVZOnr0aKWuO3jwYIWFhemZZ57xeJ//93//J6fTqYsvvtjj+OLiYj3//PPu50eOHNHzzz+vli1bqnfv3pWK4b333pN0bEHcNm3aqGfPnlq6dKnHlOM//vij/vWvf+miiy5ybystqLxNZ18Z/n4e3lxxxRX69ddf9Ze//KXMvt9++035+fmSpBEjRshut2vmzJllWjdLr+3tZ/DIkSNasGCBx/F5eXnun/lS3bt3l91u95hyPTo6usxnVVJSUma8X1xcnOLj471O1w4ApWiRAoAqGD58uM477zw9+OCD+uWXX9SjRw/961//0jvvvKM777zTPb2yr+x2ux566KGTHnfPPffo3Xff1e9//3v3FOP5+fn64Ycf9Prrr+uXX35RbGyshg8frv79++v+++/XL7/8opSUFL355ptlEkdvhgwZorCwMA0fPly33HKLDh8+rL/85S+Ki4vT7t27/Xpfvrjyyiv17LPPavr06erevbt7nNnx8bRu3Vr9+/dXq1atlJmZqeeee04XX3xxmTFqvmrZsqWmTJmiGTNm6MILL9Qll1yijRs3asGCBerbt2+ZhWvj4+M1Z84c/fLLL+rcubN7PNsLL7zgMS14eX799Vf9/e9/l2QKgnXr1un5559XbGysR7e+uXPnatiwYerXr59uuOEG9/TnDofDo8tnafH24IMPavTo0WrUqJGGDx9e6fFT/n4e3lx77bV69dVX9cc//lGfffaZ+vfvr5KSEm3YsEGvvvqqli9frj59+igpKUkPPvigZs2apQEDBmjkyJEKDw/X2rVrFR8fr9mzZ+uss85Ss2bNlJaWpttvv102m01/+9vfynQX/fTTTzVhwgRdfvnl6ty5s4qLi/W3v/1NISEhGjVqlMfntWLFCs2bN0/x8fFq3769unTpooSEBF122WXq0aOHGjdurBUrVmjt2rXuSTgAwKuAzRcIAEGgdPrztWvXVnjc8dOUn+jQoUPWXXfdZcXHx1uNGjWyOnXqZM2dO9djCufKnLeUt+nPS687ZcoUKykpyQoLC7NiY2Ots846y3riiSc8puLev3+/de2111oxMTGWw+Gwrr32Wvd00Seb/vzdd9+1TjvtNCsiIsJq166dNWfOHOvFF18sM+V227ZtrYsvvrhM7AMHDnRPrX0yLpfLSkxM9DqdvGVZ1vPPP2+dc845VosWLazw8HCrY8eO1j333GM5nU6fzm9ZZac/L/Xcc89ZXbt2tRo1amS1atXKuvXWW60DBw6UeS/dunWzvv32W6tfv35WRESE1bZtW+u5557z6donTn9ut9utuLg466qrrvI6Tf6KFSus/v37W5GRkVZMTIw1fPhwa/369WWOmzVrlnXKKadYdrv9pFOh+/LzZln+fR7eHDlyxJozZ47VrVs3Kzw83GrWrJnVu3dva8aMGWW+Xy+++KKVmprqPm7gwIHWJ5984t6/atUq68wzz7QiIyOt+Ph4695773VPmV/6ffz555+t66+/3urYsaMVERFhNW/e3DrvvPOsFStWeFxrw4YN1jnnnGNFRkZakqy0tDSrqKjIuueee6wePXpYTZo0saKjo60ePXpYCxYsOOnnBKBhs1mWD6OAAQBo4M4991zl5OToxx9/DHQoAIAgwBgpAAAAAPAThRQAAAAA+IlCCgAAAAD8FNBC6ssvv9Tw4cMVHx8vm82mt99+22O/ZVmaNm2a2rRpo8jISA0ePFibN2/2OCY3N1djxoxRTEyMmjZtqhtuuMG9FgcAANXl888/Z3wUAMAtoIVUfn6+evTooT//+c9e9z/++ON65plntGjRIq1Zs0bR0dEaOnSoCgsL3ceMGTNGP/30kz755BO9//77+vLLL3XzzTfX1lsAAAAA0AAFzax9NptNb731lkaMGCHJtEbFx8dr8uTJuvvuuyWZhSBbtWqlJUuWaPTo0crMzFRKSorWrl2rPn36SJI+/vhjXXTRRdq5c6fi4+MD9XYAAAAA1GNBuyDv1q1btWfPHg0ePNi9zeFw6IwzztDq1as1evRorV69Wk2bNnUXUZJZld1ut2vNmjX6wx/+4PXcRUVFHquVu1wu5ebmqkWLFrLZbDX3pgAAAAAENcuydOjQIcXHx8tuL78DX9AWUnv27JEktWrVymN7q1at3Pv27NmjuLg4j/2hoaFq3ry5+xhvZs+erRkzZlRzxAAAAADqix07dighIaHc/UFbSNWkKVOmaNKkSe7nTqdTp556qrZt26aYmJgARuabLVuk++6TmjWTmjQpu//QIenAAWnOHKljx9qPr7JcLpdycnIUGxtbYfUPAABQ35EXBU5eXp7atm2rJt4S7eMEbSHVunVrSdLevXvVpk0b9/a9e/eqZ8+e7mOys7M9XldcXKzc3Fz3670JDw9XeHh4me1NmzatE4WUyyUdPSo5HFJISNn9MTFSdrY5rmnTWg+v0lwul44cOaKmTZvyBwMAADRo5EWBU/p5n2zIT9B+V9q3b6/WrVtr5cqV7m15eXlas2aN+vXrJ0nq16+fDh48qPT0dPcxn376qVwul84444xaj7m2OBxSRISUn+99f0GB2e9w1G5cAAAAQEMR0Bapw4cPKysry/1869at+v7779W8eXOdeuqpuvPOO/XII4+oU6dOat++vaZOnar4+Hj3zH7Jycm68MILddNNN2nRokU6evSoJkyYoNGjR9frGfuSkqTkZCk93TweXyxblrRzp9SnjzmuIXC5pKwsyek0xWNSkhSMN27qSpwAAAA4uYAWUt9++63OO+889/PScUtpaWlasmSJ7r33XuXn5+vmm2/WwYMHdfbZZ+vjjz9WRESE+zUvvfSSJkyYoEGDBslut2vUqFF65plnav291Ca7XUpLk7ZtkzIzpYQEKSrKtETt3CnFxkpjxzaMJD0jQ1q61HwOhYWmJS452Xw+qamBju6YuhInAAAAfBM060gFUl5enhwOh5xOZ50YI1XKW3KekmKKqLqYnLtcLmVnZysuLs6nvsAZGdLMmVJOjikmo6NNd8fSYnLatOD4HOpKnAAAIHj4mxeh+vhaGwTtZBM4udRUqUePhtldzOUyRWROjmf3xpgY8zwzU1q2zHw+gfw86kqcAAAA8A+FVB1nt0udOwc6itqXlXWsW+OJE6rYbGb7+vXmuEB+PnUlTgAAAPiHe+AISi6XtGmTtHateXS5PPc7naY7Y3S099dHRZn9TmfNx1qRuhInAAAA/EOLFILO99+b7m4VTcxw/BTw3rquBssU8HUlTgAAAPiHFikElS1bpEceMVO7N28udepkHtPTzYQNGRnmuNIp4HfuNFO+H690CviUlMBPAV9X4gQAAIB/KKQQNFwu6dNPj03MEBMjhYQcm5ghJ8e0VLlcx6aAj401LVd5eVJxsXnMzAyeKeDrSpwAAADwD+kbgsaWLdKOHb5NzCCZbn7Tpkm9e0u5uWZ7bq5ZjDiYphSvK3ECAADAd4yRQtBwOqWjRyuemGHXLs+JGerKFPB1JU4AANDwuFzkKJVBIYWg4XBIjRpJhw9LTZqU3V/exAx1ZQr4uhInAABoODIyzJqXFU3yBe+oNRE0OnaUEhOZmAEAAKA2ZGSYybxONskXvKOQQtCw26Xzz2diBgAAgJrmcpmWKF8m+YJ3dO1Duaqjv6wv5yg95uBB05z8wAPS3/9uiqddu8y2Pn1MEUUTMwAAgP9OzMlcLpNr+TLJF0MTvKOQglfV0V/Wl3Mcf0xRkflFjY6Wrr3WjJNi0CMAAEDVeMvJmjUzrU6Jid5f422SL3iikEIZpf1lc3LM3YjoaCk/3/SX3bbNtym7fTmH5HlM48ameFq7VvrlF3NM3741/nYBAADqrfJyss2bzbIzLVtKbduWfV15k3zhGO7xw0N19Jf15RxLl0pLlpQ9JiqKfrkAAADVoaKcLDXVzJb8ww9l8y0m+fINhRQ8ZGX53l+2Kuf49lvTOlWV6wAAAKB8FeVkdrvUvbtZwzMjg0m+KoOuffDgdJq+s/4siluZc+Tnm1/oqlwHAAAA5TtZTnbKKdK+fWba89xcJvnyF4UUPDgc5hcoP980+57Il/6yvpyj9Be6KtcBAABA+XzJyWJjpenTTcsTk3z5h48IHpKSTB/aqiyK68s5+vSRevdm8V0AAICa4mte17mz+erb1zxSRPmGFil4sNvN9OTbth3rUxsVZe5Y7NzpW39ZX86RlmaO3b792DHR0eYY+uUCAABUXXXkdSifzbJOrE8bnry8PDkcDjmdTsV4a/dsgLytN5CS4l9/WV/O4bmOlEudO2erceM4XXutnX65AACgwXK5XMrOzlZcXJzsVax0qiOva0h8rQ0opEQhVZ4TV8CuTH9ZX85ReszBgy6FhGSrR484hYZyawQAADRc1VlImfNVPa9rKHytDejah3LZ7aafbE2fo/QYl0vKzuaXGgAAoLpVR14HT6SsAAAAAOAnCikAAAAA8BOFFAAAAAD4iUIKAAAAAPxEIQUAAAAAfqKQAgAAAAA/UUgBAAAAgJ8opAAAAADATxRSAAAAAOAnCikAAAAA8BOFFAAAAAD4KTTQASD4uFxSVpbkdEoOh5SUJNkpuQEAAHxS1VzK5ZI2b5ays805OnX63+v9PHGl4iAR9BmFFDxkZEhLl0qZmVJhoRQRISUnS2lpUmpqoKMDAAAIblXNpUpfv2GD1KaNtHu31LWr9MczMtR1je8nrlQcJIJ+oZCCW0aGNHOmlJMjJSRI0dFSfr6Uni5t2yZNm8bvEAAAQHmqmksd//rERCk+XioqkvK+yNDBV2fqUKscNel68hNXKg4SQb/RTgdJphV36VLzu5OcLMXESCEh5jE52WxftswcBwAAAE9VzaW8vd5ulxxNXLq6eKkiD+foh6PJsppUfOJKxUEiWCkUUpBkusJmZpobEDab5z6bzWxfv94cBwAAAE9VzaXKe32sc4vaHMzU4WYJOuC0yZlX8YkrFQeJYKVQSEGSGU9YWGhacb2JijL7nc7ajQsAAKAuqGouVd7rI4841ai4UMXh0Soplo4cqfjElYqDRLBSKKQgyUzKEhFhusJ6U1Bg9jsctRsXAABAXVDVXKq81/8W5tDR0AiFFuUrJFQKC6v4xJWKg0SwUiikIMnMbJmcLO3cKVmW5z7LMttTUsxxAAAA8FTVXKq81+c4Omp302Q1PrBTzRyWHDEVn7hScZAIVgqFFCSZwYxpaVJsrOkim5cnFRebx8xMs33sWJYRAAAA8KaquZS315eUSM5Ddr0cmqbfGseqe6NM2Q5VfOJKxUEiWCk2yzqx7Gx48vLy5HA45HQ6FRMTc/IX1GPelg9ISTG/OzU946XL5VJ2drbi4uJk5xcVAADUQVXNpY6tI+VSmzbZ2r07TsnJdt1yupd1pCo4caXiCGQiGER8rQ0opEQhdaJALWhNIQUAAOqDquZSLpe0efOxvKhTJ7t5vZ8nrlQcgUoEg4ivtQEL8qIMu13q3DnQUQAAANRNVc2l7HapUydTx8TFHVfH+HniSsVBIuizhlVeAgAAAEA1oJACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAn0IDHQCCn8slZWVJTqfkcEhJSZKdEhwAAMBn1ZVP1VReRr7nPwopVCgjQ1q6VMrMlAoLpYgIKTlZSkuTUlMDHR0AAEDwq658qqbyMvK9yqGQQrkyMqSZM6WcHCkhQYqOlvLzpfR0ads2ado0frkAAAAqUl35VE3lZeR7lUeDHbxyucydiZwcc0ciJkYKCTGPyclm+7Jl5jgAAACUVV35VE3lZeR7VUMhBa+yskzzbkKCZLN57rPZzPb1681xAAAAKKu68qmaysvI96qGQgpeOZ2mj2x0tPf9UVFmv9NZu3EBAADUFdWVT9VUXka+VzUUUvDK4TADDfPzve8vKDD7HY7ajQsAAKCuqK58qqbyMvK9qqGQgldJSaZv7M6dkmV57rMssz0lxRwHAACAsqorn6qpvIx8r2oopOCV3W6mvIyNNX1n8/Kk4mLzmJlpto8dy/oCAAAA5amufKqm8jLyvaqxWdaJ9WfDk5eXJ4fDIafTqZiYmECHE1S8rSuQkmJ+qap7KkyXy6Xs7GzFxcXJzm8sAACoJyqTT3nLi2oqL6vNfK8u8LU2oJAShdTJ1NZK1xRSAACgvvI3nyovL6qpvKy28r26wNfagAV5cVJ2u9S5c6CjAAAAqLuqK5+qqbyMfM9/DbTOBAAAAIDKo5ACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPQV1IlZSUaOrUqWrfvr0iIyPVsWNHzZo1S5ZluY+xLEvTpk1TmzZtFBkZqcGDB2vz5s0BjBrlcbmkTZuktWvNo8sV6IgAAABqEclQvRIa6AAqMmfOHC1cuFBLly5Vt27d9O233+q6666Tw+HQ7bffLkl6/PHH9cwzz2jp0qVq3769pk6dqqFDh2r9+vWKiIgI8DtAqYwMaelSKTNTKiyUIiKk5GQpLU1KTQ10dAAAADWMZKjeCepC6ptvvtGll16qiy++WJLUrl07/eMf/9B//vMfSaY1av78+XrooYd06aWXSpKWLVumVq1a6e2339bo0aMDFjuOyciQZs6UcnKkhAQpOlrKz5fS06Vt26Rp0/j7AQAA6jGSoXopqLv2nXXWWVq5cqU2bdokSVq3bp2+/vprDRs2TJK0detW7dmzR4MHD3a/xuFw6IwzztDq1asDEjM8uVzm5ktOjrnpEhMjhYSYx+Rks33ZMlq2AQBAPUUyVG8FdYvU/fffr7y8PHXt2lUhISEqKSnRo48+qjFjxkiS9uzZI0lq1aqVx+tatWrl3udNUVGRioqK3M/z8vIkSS6XSy5+iKvV5s3Shg1SYqJkP6Fst9nM9sxMc1zHji5ZlsX3AAAA1B/+JEOdOrl3uVzkRYHi62ce1IXUq6++qpdeekkvv/yyunXrpu+//1533nmn4uPjlZaWVunzzp49WzNmzCizfd++fSosLKxKyDhBdrbUpo0UH1/2b4ckNW8uhYeb45o0ccnpdMqyLNm9HQwAAFDX+JMMORzuzS4XeVGgHDp0yKfjgrqQuueee3T//fe7xzp1795d27Zt0+zZs5WWlqbWrVtLkvbu3as2bdq4X7d371717Nmz3PNOmTJFkyZNcj/Py8tTYmKiWrZsqZiYmJp5Mw2U0ynt3i0VFZkW7BPl5Um5uVJcnBQX55LNZlPLli35gwEAAOoH/5Ih92aXi7woUHydsC6oC6mCgoIyPzghISHu5rb27durdevWWrlypbtwysvL05o1a3TrrbeWe97w8HCFh4eX2W632/lBrWadOkldu5qxlMnJpgW7lGVJO3ZIffoca8m22Wx8HwAAQP3hTzJ0Qv5DXhQYvn7eQV1IDR8+XI8++qhOPfVUdevWTRkZGZo3b56uv/56SeaH684779QjjzyiTp06uac/j4+P14gRIwIbPCSZvwdpaWZCmsxMM1FNVJRUUCDt3CnFxkpjx5rj6AIMAADqHX+SIdQpNuv41W2DzKFDhzR16lS99dZbys7OVnx8vK666ipNmzZNYWFhkswU6NOnT9cLL7yggwcP6uyzz9aCBQvUuXNnn6+Tl5cnh8Mhp9NJ174a4m3phJQU83ejdLZPl8ul7OxsxcXFcecFAADUL74kQ8chLwocX2uDoC6kaguFVO1wuaSsLNNV2OGQkpI8b77wBwMAANRrJ0uGPA4lLwoUX2uDoO7ah/rFbpf8aCgEAACoX0iG6hXKWwAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcW5EX94MdK4QAAAEBVUUih7svIkJYulTIzpcJCKSJCSk6W0tKk1NRARwcAAIB6iEIKdVtGhjRzppSTIyUkSNHRUn6+lJ4ubdsmTZtGMQUAAIBqRyGFusvlMi1ROTmmBcpmM9tjYszzzExp2TKpRw+6+QEAgICq7VEIjHqoeRRSqLuyskyxlJBwrIgqZbOZ7evXm+M6dw5MjAAAoMGr7VEIjHqoHRRSqLucTvPXITra+/6oKGnXLnMcAABAANT2KARGPdQeGvhQdzkc5hZLfr73/QUFZr/DUbtxAQAAqOwohJgYKSTk2CiEnBwzCsHlqpvXa+gopFB3JSWZvwo7d0qW5bnPssz2lBRzHAAAQC3zZxRCXbxeQ0chhbrLbjedfWNjzV+NvDypuNg8Zmaa7WPHMrISAAAEhC+jEAoLq28UQm1fr6Ejw0TdlppqOvv27i3l5ppbLLm5Up8+dAIGAAABVdujEBj1ULuYbAJ1X2qqmeKcOT4BAEAQKR2FkJ7uuVKLdGwUQp8+1TcKobav19BRSKF+sNuZ4hwAAASV0lEI27YdG7sUFWVahnburP5RCLV9vYaOjxEAAACoIbU9CoFRD7WHFikAAACgBtX2KARGPdQOCikAAACghtX2KARGPdQ86lIAAAAA8BOFFAAAAAD4iUIKAAAAAPxEIQUAAAAAfqKQAgAAAAA/UUgBAAAAgJ8opAAAAADATxRSAAAAAOAnCikAAAAA8BOFFAAAAAD4iUIKAAAAAPwUGugAUMe5XFJWluR0Sg6HlJQk2anPAQAA/HV8WhUTIzVpEuiIUBEKKVReRoa0dKmUmSkVFkoREVJyspSWJqWmBjo6AACAOuPEtCoyUurfX7r4YqlXr0BHB29oOkDlZGRIM2dK6elS8+ZSp07mMT3dbM/ICHSEAAAAdUJ5aVVWlvTII6RVwYpCCv5zucwtk5wc0wIVEyOFhJjH5GSzfdkycxwAAADKVVFalZhIWhXMKKTgv6ws0+6ckCDZbJ77bDazff16cxwAAADKRVpVd1FIwX9Op+m8Gx3tfX9UlNnvdNZuXAAAAHUMaVXdRSEF/zkcZmKJ/Hzv+wsKzH6Ho3bjAgAAqGNIq+ouCin4LynJdOLduVOyLM99lmW2p6SY4wAAAFAu0qq6i0IK/rPbzRTnsbGmU29enlRcbB4zM832sWNZTwoAAOAkKkqrduwgrQpmfEtQOamp0rRpUu/eUm6uGQGZmyv16WO2s44UAACAT8pLqzp1kh56iLQqWLEgLyovNVXq0ePYEtwOh2l35pYJAACAX05Mq2JipCZNpNatAx0ZykMhhaqx26XOnQMdBQAAQJ13fFrlcknZ2YGNBxWj6QAAAAAA/EQhBQAAAAB+opACAAAAAD8xRgrByeViEgsAAAAELQopBJ/vv5eWLTOLKRQWmuW8k5PNIgvM/wkAAIAgQCGF4LJli/Tkk9K+fVJCghQdLeXnS+np0rZtrFEFAACAoEBfKQQPl0v69FMpJ8e0QMXESCEh5jE52WxftswcBwAAAAQQhRSCx5Yt0o4dpiXKZvPcZ7OZ7evXm7FTAAAAQABRSCF4OJ3S0aOmO583UVFmzJTTWbtxAQAAACegkELwcDikRo3MmChvCgrMxBMOR+3GBQAAAJyAQgrBo2NHKTFR2rlTsizPfZZltqekmKnQAQAAgABi1r5g0tDXTrLbpfPPlzIyzNTnCQmmO19BgSmiYmOlsWMb1mcCAABwokDmjA09Xz0OhVSwyMiQli5l7aSOHaWHHjq2jtSuXeaz6NPHFFEN6bMAAAA4USBzRvJVDxRSwSAjQ5o500zvzdpJUs+e5ou7HQAAAMcEMmckXy2DzDTQXC5T2bN2kie7XercWerb1zxSRAEAgIYskDkj+apXZKeBlpV1bDwQaycBAADAm0DmjOSrXlFIBZrTafqYsnYSAAAAyhPInJF81SsKqUBzOMxAPdZOAgAAQHkCmTOSr3pFIRVoSUmmbylrJwEAAKA8gcwZyVe9opAKNLvdTBkZG2v6nublScXF5jEzk7WTAAAAENickXzVK5tlnVhWNjx5eXlyOBxyOp2KiYkJTBDe5uVPSWlQaye5XC5lZ2crLi5O9gb2iwgAAHC8cvOiQOaMDSRf9bU2YB2pYJGaKvXowdpJAAAAKF8gc0byVQ8UUsGkdO0kAAAAoDyBzBnJV90aZvkIAAAAAFVAIQUAAAAAfqKQAgAAAAA/UUgBAAAAgJ8opAAAAADATxRSAAAAAOAnCikAAAAA8BOFFAAAAAD4iUIKAAAAAPxEIQUAAAAAfqKQAgAAAAA/hQY6AKDWuFxSVpbkdEoOh5SUJNm5lwAAAOo3UqCaUaVCqqioSOHh4dUVi1e//vqr7rvvPn300UcqKChQUlKSFi9erD59+kiSLMvS9OnT9Ze//EUHDx5U//79tXDhQnXq1KlG40Idk5EhLV0qZWZKhYVSRISUnCylpUmpqYGODgAAoEaQAtUcv2rRjz76SGlpaerQoYMaNWqkqKgoxcTEaODAgXr00Ue1a9euag3uwIED6t+/vxo1aqSPPvpI69ev15NPPqlmzZq5j3n88cf1zDPPaNGiRVqzZo2io6M1dOhQFRYWVmssqMMyMqSZM6X0dKl5c6lTJ/OYnm62Z2QEOkIAAIBqRwpUs2yWZVknO+itt97Sfffdp0OHDumiiy7S6aefrvj4eEVGRio3N1c//vijvvrqK61evVrjxo3TrFmz1LJlyyoHd//992vVqlX66quvvO63LEvx8fGaPHmy7r77bkmS0+lUq1attGTJEo0ePdqn6+Tl5cnhcMjpdComJqbKcaNyXC6XsrOzFRcXJ3t1tTe7XNKkSeYvRnKyZLMd22dZ5vZMnz7Sk0/Sxg0AAIJGVfMiUqDK87U28Klr3+OPP66nnnpKw4YN8/qNvOKKKySZbnjPPvus/v73v+uuu+6qZOjHvPvuuxo6dKguv/xyffHFFzrllFN022236aabbpIkbd26VXv27NHgwYPdr3E4HDrjjDO0evXqcgupoqIiFRUVuZ/n5eVJMj+wLperynGjclwulyzLqt7vwebN0oYNUmJi2b8SNpvZnplpjqM7KAAACBJVzYtIgSrP18/cp0Jq9erVPp3slFNO0Z/+9CefjvXFzz//rIULF2rSpEl64IEHtHbtWt1+++0KCwtTWlqa9uzZI0lq1aqVx+tatWrl3ufN7NmzNWPGjDLb9+3bR5fAAHK5XHI6nbIsq/papLKzpTZtpPh477dbmjeXwsPNcQ5H9VwTAACgiqqaF5ECVd6hQ4d8Oq7Ks/bl5+erpKSkRrrEuVwu9enTR4899pgkKTU1VT/++KMWLVqktLS0Sp93ypQpmjRpkvt5Xl6eEhMT1bJlS7r2BZDL5ZLNZlPLli2rr5ByOqXdu6WiIsnb9zYvT8rNleLizBcAAEAQqGpeRApUeRERET4dV+lCav369Ro7dqy+++472Ww2paSkeMymVx3atGmjlJQUj23Jycl64403JEmtW7eWJO3du1dt2rRxH7N371717Nmz3POGh4d7nW3QbrdXXwKPSrHZbNX7fejUSeratfwOwjt2mA7CnTrRQRgAAASVquRFpECV5+vnXemP7ZZbbtGECRN0+PBh7d+/XyNHjqxSK5E3/fv318aNGz22bdq0SW3btpUktW/fXq1bt9bKlSvd+/Py8rRmzRr169evWmNBHWW3m/k9Y2NNR+C8PKm42DxmZprtY8fyFwQAANQrpEA1z+eP7tJLL9Wvv/7qfr5v3z5dcsklioqKUtOmTXXRRRdp79691RrcXXfdpX//+9967LHHlJWVpZdfflkvvPCCxo8fL8lU6XfeeaceeeQRvfvuu/rhhx80duxYxcfHa8SIEdUaC+qw1FRp2jSpd2/Thp2VZR779DHbWUQBAADUQ6RANcvnrn3XXHONzj//fI0fP14TJ07UhAkT1K1bNw0cOFBHjx7Vp59+qsmTJ1drcH379tVbb72lKVOmaObMmWrfvr3mz5+vMWPGuI+59957lZ+fr5tvvlkHDx7U2WefrY8//tjnvo1oIFJTpR49WNYbAAA0KKRANcendaRKOZ1O3XfffcrIyNCiRYsUGhqqzz//XCUlJerfv7/69u1bk7HWGNaRCg41so4UAABAHUReFDjVuo5UKYfDoUWLFunrr79WWlqaLrjgAs2aNUtRUVFVDhgAAAAA6gq/ytvc3Fylp6ere/fuSk9PV0xMjFJTU/Xhhx/WVHwAAAAAEHR8LqRefvllJSQk6OKLL1bbtm310Ucfafr06XrnnXf0+OOP64orrqj2ySYAAAAAIBj5XEhNmTJFL774ovbs2aOVK1dq6tSpkqSuXbvq888/1wUXXMCU4wAAAAAaBJ8LqcOHD6tLly6SpI4dO6qgoMBj/0033aR///vf1RsdAAAAAAQhnyebSEtL08UXX6xzzz1X3377ra699toyx8TFxVVrcAAAAAAQjHwupObNm6fzzjtPGzZs0Lhx4zRkyJCajAsAAAAAgpZf058PHz5cw4cPr6lYAAAAAKBO8GmM1CuvvOLzCXfs2KFVq1ZVOiAAAAAACHY+FVILFy5UcnKyHn/8cWVmZpbZ73Q69eGHH+rqq69Wr169tH///moPFAAAAACChU9d+7744gu9++67evbZZzVlyhRFR0erVatWioiI0IEDB7Rnzx7FxsZq3Lhx+vHHH9WqVauajhsAAAAAAsbnMVKXXHKJLrnkEuXk5Ojrr7/Wtm3b9Ntvvyk2NlapqalKTU2V3e7zbOqoIS6XlJUlOZ2SwyElJUl8WwAAAIDq5ddkE5IUGxurESNG1EAoqKqMDGnpUikzUyoslCIipORkKS1NSk0NdHQAAABA/eF3IYXglJEhzZwp5eRICQlSdLSUny+lp0vbtknTplFMAQAAANWFTl/1gMtlWqJyckwLVEyMFBJiHpOTzfZly8xxAAAAAKqOFql6ICvLdOdLSJBsNs99NpvZvn69Oa5z58DECAAAUFtcLpeOHDkS6DCqxOVy6ejRoyosLGQegmrWqFEjhYSEVPk8FFL1gNNpxkRFR3vfHxUl7dpljgMAAKjPjhw5oq1bt8pVx7viWJYll8ulQ4cOyXbinXJUWdOmTdW6desqfbZ+F1KfffaZzjvvvEpfENXP4TATS+Tnm+58JyooMPsdjtqPDQAAoLZYlqXdu3crJCREiYmJdbolx7IsFRcXKzQ0lEKqGlmWpYKCAmVnZ0uS2rRpU+lz+V1IXXjhhUpISNB1112ntLQ0JSYmVvriqB5JSWYsVHq6eTz+d82ypJ07pT59zHEAAAD1VXFxsQoKChQfH6+oqKhAh1MlFFI1JzIyUpKUnZ2tuLi4Snfz87tM//XXXzVhwgS9/vrr6tChg4YOHapXX321zvdDrcvsdjPFeWysGSuVlycVF5vHzEyzfexY1pMCAAD1W0lJiSQpLCwswJEg2JUW2kePHq30OfxOrWNjY3XXXXfp+++/15o1a9S5c2fddtttio+P1+23365169ZVOhhUXmqqmeK8d28pN9dMLJGba1qimPocAAA0JLTg4GSq42ekSpNN9OrVS61bt1aLFi30pz/9SS+++KIWLFigfv36adGiRerWrVuVA4TvUlOlHj1MEeV0mjFRSUm0RAEAAADVrVIp9tGjR/X666/roosuUtu2bbV8+XI999xz2rt3r7KystS2bVtdfvnl1R0rfGC3mynO+/Y1jxRRAAAAOJlx48ZpxIgRNX4dm82mt99+u8avUxv8TrMnTpyoNm3a6JZbblHnzp2VkZGh1atX68Ybb1R0dLTatWunJ554Qhs2bKiJeAEAAIB6Zdy4cbLZbLLZbGrUqJHat2+ve++9V4WFhYEOrdZYlqXBgwdr6NChZfYtWLBATZs21c6dOwMQWfn87tq3fv16Pfvssxo5cqTCw8O9HhMbG6vPPvusysEBAAAAtc3lqv2hEhdeeKEWL16so0ePKj09XWlpabIsS3Pnzq3ZCwcJm82mxYsXq3v37nr++ed1yy23SJK2bt2qe++9VwsXLlRCQkKAo/Tk94/E9OnTdfnll5cpooqLi/Xll19KkkJDQzVw4MDqiRAAAACoJRkZ0qRJ0sSJ0t13m8dJk8z2mhQeHq7WrVsrMTFRI0aM0ODBg7Vy5Ur3fpfLpdmzZ6t9+/aKjIxUjx499Prrr7v3HzhwQGPGjFHLli0VGRmpTp06afHixe79P/zwg84//3xFRkaqRYsWuvnmm3X48GGvsbzwwguKj48vs6jxpZdequuvv979/J133lGvXr0UERGhDh06aMaMGSouLnbv37x5s8455xxFREQoJSVFn3zySYWfQWJiop5++mndfffd2rp1qyzL0g033KAhQ4bo2muv1Y8//qhhw4apcePGatWqla699lrl5OS4X//666+re/fu7vc4ePBg5efnn+STrzy/C6nzzjtPubm5ZbY7nU4W6gUAAECdlZEhzZxp1uZs3lzq1Mk8pqeb7TVdTJX68ccf9c0333hM4z579mwtW7ZMixYt0k8//aS77rpL11xzjb744gtJ0tSpU7V+/Xp99NFHyszM1MKFCxUbGytJys/P19ChQ9WsWTOtXbtWr732mlasWKEJEyZ4vf7ll1+u/fv3e/Qwy83N1ccff6wxY8ZIkr766iuNHTtWd9xxh9avX6/nn39eS5Ys0aOPPirJFH4jR45UWFiY1qxZo0WLFum+++476XtPS0vToEGDdP311+u5557Tjz/+qOeff14HDx7U+eefr9TUVH377bf6+OOPtXfvXl1xxRWSpN27d+uqq67S9ddfr8zMTH3++ecaOXKkLMuqxHfAN3537bMsy+t0gfv371d0dHS1BAUAAADUJpdLWrpUysmRkpOl0nQ3JsY8z8yUli0zMyTXRDe/999/X40bN1ZxcbGKiopkt9s1f/58SVJRUZEee+wxrVixQv369ZMkdejQQV9//bWef/55DRw4UNu3b1dqaqr69OkjSWrXrp373C+//LIKCwu1bNkyd77+3HPPafjw4ZozZ45atWrlEUuzZs00bNgwvfzyyxo0aJAk09oTGxvrbjiZMWOG7r//fqWlpbnjmTVrlu69915Nnz5dK1as0IYNG7R8+XLFx8dLkh577DENGzbspJ/FCy+8oG7duunLL7/UG2+8oZYtW+qRRx5RamqqHnvsMfdxL774ohITE7Vp0yYdPnxYxcXFGjlypNq2bStJ6t69u1/fA3/5XEiNHDlSkum/OG7cOI+ufSUlJfrvf/+rs846q/ojBAAAAGpYVpYplhISjhVRpWw2s339enNc587Vf/3zzjtPCxcuVH5+vp566imFhIS48++srCwVFBToggsu8HjNkSNHlPq/xUJvvfVWjRo1St99952GDBmiESNGuHPzzMxM9ejRw6PRo3///nK5XNq4cWOZQkqSxowZo5tuukkLFixQeHi4XnrpJY0ePVr2/1WR69at06pVq9wtUJKpCQoLC1VQUKDMzEwlJia6iyhJ7iLwZOLi4nTLLbfo7bffds8kuG7dOn322Wdq3LhxmeO3bNmiIUOGaNCgQerevbuGDh2qIUOG6LLLLlOzZs18umZl+FxIORwOSaZFqkmTJoqMjHTvCwsL05lnnqmbbrqp+iMEAAAAapjTKRUWSuV1sIqKknbtMsfVhOjoaCUlJUkyLS09evTQ4sWLddNNN7nHMn3wwQc65ZRTPF5X2rgxbNgwbdu2TR9++KE++eQTDRo0SOPHj9cTTzxRqXiGDx8uy7L0wQcfqG/fvvrqq6/01FNPufcfPnxYM2bMcBd7x4uIiKjUNY8XGhqq0NBjpcrhw4fdLWgnatOmjUJCQvTJJ5/om2++0b/+9S89++yzevDBB7VmzRq1b9++yvF4jdHXA0sHq7Vr105333033fgAAABQbzgcUkSElJ9vuvOdqKDA7P9f20KNstvtmjJliiZPnqxrrrlGKSkpCg8P1/bt2yuc0K1ly5ZKS0tTWlqaBgwYoHvuuUdPPPGEkpOTtWTJEuXn57tz+FWrVslut6tLly5ezxUREaGRI0fqpZdeUlZWlrp06aJevXq59/fq1UsbN250F38nSk5O1o4dO7R79261adNGkvTvf/+7sh+JevXqpTfeeEPt2rXzKLCOZ7PZ1L9/f/Xv31/Tpk1T27Zt9dZbb2nSpEmVvm5FKjVrH0UUAAAA6pOkJDMWaudO6cT5CSzLbE9JMcfVhssvv1whISH685//rCZNmujuu+/WXXfdpaVLl2rLli367rvv9Oyzz2rp0qWSpGnTpumdd95RVlaWfvrpJ73//vtKTk6WZLrpRUREKC0tTT/++KM+++wzTZw4Uddee63Xbn2lxowZow8++EAvvviie5KJUtOmTdOyZcs0Y8YM/fTTT8rMzNQrr7yihx56SJI0ePBgde7cWWlpaVq3bp2++uorPfjgg5X+PMaPH6/c3FxdddVVWrt2rbZs2aLly5fruuuuU0lJidasWaPHHntM3377rbZv364333xT+/btc38GNcGnFqlevXpp5cqVatasmVJTU71ONlHqu+++q7bgAAAAgNpgt0tpadK2bcfGSkVFmZaonTul2Fhp7NiaX0+qVGhoqG699VbNnTtXt912m2bNmqWWLVtq9uzZ+vnnn9W0aVP16tVLDzzwgCQz1GbKlCn65ZdfFBkZqQEDBuiVV16RJEVFRWn58uW644471LdvX0VFRWnUqFGaN29ehTGcf/75at68uTZu3Kirr77aY9/QoUP1/vvva+bMmZozZ44aNWqkrl276sYbb5RkWtXeeust3XDDDTr99NPVrl07PfPMM7rwwgsr9XnEx8dr1apVuu+++zRkyBAVFRWpbdu2uvDCC2W32xUTE6Mvv/xS8+fPV15entq2basnn3zSp8ktKstm+TAn4IwZM3TPPfcoKipKM2bMqPDY6dOnV1twtSUvL08Oh0NOp1Mx3tpyUStcLpeys7MVFxfnHsgIAADgq8LCQm3dulXt27ev9DidjAwze19mphkzFRFhWqLGjpX+N69DrbAsS8XFxQoNDa2wEQOVU9HPiq+1gU8tUscXR3WxUKpXArHUdl3FZwUAAPyUmmqmOCeFwMn4vY4UAsjbLZLkZNMOXZu3SOoCPisAAFBJdnvNTHGO+sWnQqpZs2Y+Nynm5uZWKSCUo3Sp7Zwc02k3OtpMK5OebjrzTptGgVCqos/ql1+kceOkU07hFhMAAAAqzadCqnRVZQRIoJfarksq+qxatpTWrDGFVtu2UmQkrVQAAACoFJ8KqbS0tJqOAxUJ9FLbdUl5n1VOjimgjhwxxVarVlJoKC16AAAAqBSfCqm8vDz3jBV5eXkVHsusdzUg0Ett1yXePivLkjZtkn77TWreXMrLk0pKzL9p0QMAAEAl+DxGavfu3YqLi1PTpk29jpeyLEs2m00lJSXVHmSDF0xLbQc7b5+V0ykdPGiKq5IS0xIVFmb20aIHAACASvCpkPr000/VvHlzSdJnn31WowHBi9KlttPTPcf9SMeW2u7Tp/aW2g5m3j6rI0ek4mLTcpeXZ1bUO74gpUUPAAAAfvKpkBo4cKDXf6OWBNtS28HM22cVEmL25eZKjRubVqfji1Fa9AAAAOCnSq0jdeDAAf3f//2fMjMzJUkpKSm67rrr3K1WqAGpqWZChNK1kXbtMsl/nz61v9R2sDvxs/rtN9OVz243+2Jjjx1Lix4AAIDGjRungwcP6u23367R69hsNr311lsaMWJEjV6nNvjdhPHll1+qXbt2euaZZ3TgwAEdOHBAzzzzjNq3b68vv/yyJmJEqdRUad486dlnpSeeMI9PPkkR5c3xn9WTT0pPPy317Svt22e69xUXm8fMTFr0AABAQI0bN042m002m02NGjVS+/btde+996qwsDDQodW60s/iT3/6k8f2t99+2+d1bWuL3y1S48eP15VXXqmFCxcq5H9dpkpKSnTbbbdp/Pjx+uGHH6o9SByHpbZ9d/xn1bevWTuKFj0AAHAyLpeZhMrpNF3/k5Jq/IbrhRdeqMWLF+vo0aNKT09XWlqaLMvS3Llza/S6wSgiIkJz5szRLbfcombNmgU6nHL5/RORlZWlyZMnu4soSQoJCdGkSZOUlZVVrcEB1YoWPQAAcDIZGdKkSdLEidLdd5vHSZPM9hoUHh6u1q1bKzExUSNGjNDgwYO1cuVK936Xy6XZs2erffv2ioyMVI8ePfT666+79x84cEBjxoxRy5YtFRkZqU6dOmnx4sXu/T/88IPOP/98RUZGqkWLFrr55pt1+PBhr7G88MILio+Pl8vl8th+6aWX6vrrr3c/f+edd9SrVy9FRESoQ4cOmjFjhoqLi937N2/erHPOOUcRERFKSUnRJ5984tNnMXjwYLVu3VqzZ8+u8Lg33nhD3bp1U3h4uNq1a6cnn3zSp/NXF78LqV69ernHRh0vMzNTPXr0qJaggBpT2krVt695pDsfAAAolZEhzZxpZv9t3lzq1Mk8pqeb7TVcTJX68ccf9c033yisdLkWSbNnz9ayZcu0aNEi/fTTT7rrrrt0zTXX6IsvvpAkTZ06VevXr9dHH32kzMxMLVy4ULH/Gxeen5+voUOHqlmzZlq7dq1ee+01rVixQhMmTPB6/csvv1z79+/3mK07NzdXH3/8scaMGSNJ+uqrrzR27FjdcccdWr9+vZ5//nktWbJEjz76qCRT+I0cOVJhYWFas2aNFi1apPvuu8+n9x8SEqLHHntMzz77rHbu3On1mPT0dF1xxRUaPXq0fvjhBz388MOaOnWqlixZ4tM1qoNPXfv++9//uv99++2364477lBWVpbOPPNMSdK///1v/fnPfy7TlxEAAACoE1wuMwQgJ8dzuZmYGPM8M1Natkzq0aNGbsS+//77aty4sYqLi1VUVCS73a758+dLkoqKivTYY49pxYoV6tevnySpQ4cO+vrrr/X8889r4MCB2r59u1JTU9WnTx9JUrt27dznfvnll1VYWKhly5YpOjpakvTcc89p+PDhmjNnjlq1auURS7NmzTRs2DC9/PLLGjRokCTp9ddfV2xsrM477zxJ0owZM3T//fcrLS3NHc+sWbN07733avr06VqxYoU2bNig5cuXKz4+XpL02GOPadiwYT59Hn/4wx/Us2dPTZ8+Xf/3f/9XZv+8efM0aNAgTZ06VZLUuXNnrV+/XnPnztW4ceN8ukZV+VRI9ezZUzabTZZlubfde++9ZY67+uqrdeWVV1ZfdAAAAEBtyMo6tnTKiZMa2Gxm+/r15rgaGK9+3nnnaeHChcrPz9dTTz2lkJAQjRw58n+hZamgoEAXXHCBx2uOHDmi1P8NUbj11ls1atQofffddxoyZIhGjBihs846S9KxnmOlRZQk9e/fXy6XSxs3bixTSEnSmDFjdNNNN2nBggUKDw/XSy+9pNGjR8v+vyJy3bp1WrVqlbsFSjLzJhQWFqqgoECZmZlKTEx0F1GS3EWgr+bMmaPzzz9fd999d5l9mZmZuvTSSz229e/fX/Pnz1dJSYnHMKSa4lMhtXXr1pqOAwAAAAgcp1MqLJSOKzY8REWZyaqczhq5fHR0tJL+txTLiy++qB49emjx4sW66aab3GOZPvjgA51yyikerwsPD5ckDRs2TNu2bdOHH36oTz75RIMGDdL48eP1xBNPVCqe4cOHy7IsffDBB+rbt6+++uorPfXUU+79hw8f1owZM9zF3vEiIiIqdc0TnXPOORo6dKimTJlSa61M/vCpkGrbtm1NxwEAAAAEjsNhZvTNzzfd+U5UUGD2Oxw1HordbteUKVM0efJkXXPNNUpJSVF4eLi2b9+ugQMHlvu6li1bKi0tTWlpaRowYIDuuecePfHEE0pOTtaSJUuUn5/vbpVatWqV7Ha7unTp4vVcERERGjlypF566SVlZWWpS5cu6tWrl3t/r169tHHjRnfxd6Lk5GTt2LFDu3fvVps2bSSZ4UD++tOf/qSePXuWiTM5OVmrVq3y2LZq1Sp17ty5VlqjpEouyCtJ69ev1/bt23XkyBGP7ZdcckmVgwIAAABqVVKSGQuVnu45RkqSLEvaudMsm1JO4VDdLr/8ct17773685//rHvuuUd333237rrrLrlcLp199tlyOp1atWqVYmJilJaWpmnTpql3797q1q2bioqK9P777ys5OVmS6aY3ffp0paWl6eGHH9a+ffs0ceJEXXvttV679ZUaM2aMfv/73+unn37SNddc47Fv2rRp+v3vf69TTz1Vl112mex2u9atW6cff/xRjzzyiAYPHqzOnTsrLS1Nc+fOVV5enh588EG/P4fu3btrzJgxeuaZZzy2T548WX379tWsWbN05ZVXavXq1Xruuee0YMECv69RWX4XUj///LP+8Ic/6IcffvAYN1W6QFZJSUn1RggAAADUNLtdSkuTtm07NlYqKsq0RO3cKcXGmrUna2nG39DQUN16662aO3eubrvtNs2aNUstW7bU7Nmz9fPPP6tp06bq1auXHnjgAUlSWFiYpkyZol9++UWRkZEaMGCAXnnlFUlSVFSUli9frjvuuEN9+/ZVVFSURo0apXnz5lUYw/nnn6/mzZtr48aNuvrqqz32DR06VO+//75mzpypOXPmqFGjRuratatuvPFGSaZV7a233tINN9yg008/Xe3atdMzzzyjCy+80O/PYubMmfrnP//psa1Xr1569dVXNW3aNM2aNUtt2rTRzJkza7ULoM06fgYJHwwfPlwhISH661//qvbt2+s///mP9u/fr8mTJ+uJJ57QgAEDairWGpOXlyeHwyGn06kYb025qBUul0vZ2dmKi4tzD2QEAADwVWFhobZu3ar27dtXfpxORoaZvS8z04yZioiQUlJMEVWLa09alqXi4mKFhoa6GyxQfSr6WfG1NvC7RWr16tX69NNPFRsbK7vdLrvdrrPPPluzZ8/W7bffroxaml8fAAAAqHapqWaK86wsM7GEw2G683GTFyfwu5AqKSlRkyZNJEmxsbHatWuXunTporZt22rjxo3VHiAAAABQq+z2GpniHPWL34XU7373O61bt07t27fXGWecoccff1xhYWF64YUX1KFDh5qIEXWAy8WNGwAAADQcfhdSDz30kPLz8yWZgV+///3vNWDAALVo0aLMIDA0DN66Eicnm/GatdiVGAAAAKg1fhdSQ4cOdf87KSlJGzZsUG5urpo1a8ZAuAYoI0OaOVPKyTGT20RHm+UX0tPNpDfTplFMAQCA2uXnXGpogKrjZ6RKna927NihHTt2qHnz5hRRDZDLZVqicnJMC1RMjBQSYh6Tk832ZcvMcQAAADWtdCHWE9c5BU5UUFAgSWrUqFGlz+F3i1RxcbFmzJihZ555RocPH5YkNW7cWBMnTtT06dOrFAzqlqysY8ssnFhH22xm+/r15jjGawIAgJoWGhqqqKgo7du3T40aNarTy6kw/XnNsCxLBQUFys7OVtOmTd3Fd2X4XUhNnDhRb775ph5//HH169dPkpkS/eGHH9b+/fu1cOHCSgeDusXpNGOioqO974+KknbtMscBAADUNJvNpjZt2mjr1q3atm1boMOpEsuy5HK5ZLfbKaRqQNOmTdW6desqncPvQurll1/WK6+8omHDhrm3nXbaaUpMTNRVV11FIdWAOBxmYon8fNOd70QFBWa/w1H7sQEAgIYpLCxMnTp1qvPd+1wul/bv368WLVrU6Za1YNSoUaMqtUSV8ruQCg8PV7t27cpsb9++vcLCwqocEOqOpCQzFio93Twef7PEsqSdO6U+fcxxAAAAtcVutysiIiLQYVSJy+VSo0aNFBERQSEVpPz+rkyYMEGzZs1SUVGRe1tRUZEeffRRTZgwoVqDQ3Cz280U57GxZqxUXp5UXGweMzPN9rFjWU8KAAAA9Y9PLVIjR470eL5ixQolJCSoR48ekqR169bpyJEjGjRoUPVHiKCWmmqmOC9dR2rXLtOdr08fU0Qx9TkAAADqI58KKccJg1xGjRrl8TwxMbH6IkKdk5oq9ehhZudzOs2YqKQkWqIAAABQf/lUSC1evLim40AdZ7czxTkAAAAaDr8nmyi1b98+bdy4UZLUpUsXtWzZstqCAgAAAIBg5nfnq/z8fF1//fVq06aNzjnnHJ1zzjmKj4/XDTfc4F4hGAAAAADqM78LqUmTJumLL77Qe++9p4MHD+rgwYN655139MUXX2jy5Mk1ESMAAAAABBW/u/a98cYbev3113Xuuee6t1100UWKjIzUFVdcwYK8AAAAAOo9v1ukCgoK1KpVqzLb4+Li6NoHAAAAoEHwu5Dq16+fpk+frsLCQve23377TTNmzFC/fv2qNTgAAAAACEZ+d+2bP3++LrzwwjIL8kZERGj58uXVHiAAAAAABBu/C6nu3btr8+bNeumll7RhwwZJ0lVXXaUxY8YoMjKy2gMEAAAAgGDjVyF19OhRde3aVe+//75uuummmooJAAAAAIKaX2OkGjVq5DE2CgAAAAAaIr8nmxg/frzmzJmj4uLimogHAAAAAIKe32Ok1q5dq5UrV+pf//qXunfvrujoaI/9b775ZrUFBwAAANQJLpeUlSU5nZLDISUlSXa7v4dU+Rq1JphiCRC/C6mmTZtq1KhRNRHLSf3pT3/SlClTdMcdd2j+/PmSpMLCQk2ePFmvvPKKioqKNHToUC1YsMDrWlcAAABAtcvIkJYulTIzpcJCKSJCSk6W0tKk1FRfD6nyNWpNMMUSQH4XUosXL66JOE5q7dq1ev7553Xaaad5bL/rrrv0wQcf6LXXXpPD4dCECRM0cuRIrVq1KiBxAgAAoAHJyJBmzpRycqSEBCk6WsrPl9LTpW3bpGnTlKHUkx1Scf3hwzVqrYAJplgCzOf2N5fLpTlz5qh///7q27ev7r//fv322281GZvb4cOHNWbMGP3lL39Rs2bN3NudTqf+7//+T/PmzdP555+v3r17a/Hixfrmm2/073//u1ZiAwAAQAPlcpmWmZwc0yITEyOFhJjH5GQpJ0fW0mVatsRV0SFatsycqrLXqPgEtft+ay2WIOBzi9Sjjz6qhx9+WIMHD1ZkZKSefvppZWdn68UXX6zJ+CSZCS4uvvhiDR48WI888oh7e3p6uo4eParBgwe7t3Xt2lWnnnqqVq9erTPPPNPr+YqKilRUVOR+npeXJ8kUi64G8o0PRi6XS5Zl8T0AAAB1w+bN0oYNUmJi2fFBNpuUmKj89EzlabMSEzuVd4gyM82pOnU6ts+dF2VlnfQaXk9QE3x4v7UWSw3yNRf1uZBatmyZFixYoFtuuUWStGLFCl188cX661//KnsNDix75ZVX9N1332nt2rVl9u3Zs0dhYWFq2rSpx/ZWrVppz5495Z5z9uzZmjFjRpnt+/btY3r3AHK5XHI6nbIsq0Z/pgAAAKpFdrbUpo0UH+99ooXmzVVQEq54W7Zc7R3lHaLwcHMqh+PYdndetG+f7Ce5htcT1AQf3m+txVKDDh065NNxPhdS27dv10UXXeR+PnjwYNlsNu3atUsJCQn+R+iDHTt26I477tAnn3yiiIiIajvvlClTNGnSJPfzvLw8JSYmqmXLloqJiam268A/LpdLNptNLVu2pJACAADBz+mUdu+WiopM97YT5eUpf1eudilOv9njyjtEublSXJz5KuXOi2w22U9yDa8nqAk+vN9ai6UG+Vp3+FxIFRcXlzlpo0aNdPToUf8i80N6erqys7PVq1cv97aSkhJ9+eWXeu6557R8+XIdOXJEBw8e9GiV2rt3r1q3bl3uecPDwxUeHl5mu91uJ4EPMJvNxvcBAADUDZ06SV27mokWkpNN97ZSliXt2KHGvfsoxuqkTd/ZyztEffqYU5XtLWeTPSlJ9pNco9wTBOD91losNcjXPNTnQsqyLI0bN86jACksLNQf//hHj7WkqnMdqUGDBumHH37w2Hbdddepa9euuu+++5SYmKhGjRpp5cqV7inZN27cqO3bt6tfv37VFgcAAABQht1upvzets2MDUpIkKKipIICaedOKTZWtrSxGiu7ftle7iEaO7aCusOHa1R8gtp9v7UWSxCwWZZl+XLgdddd59MJa3p69HPPPVc9e/Z0ryN166236sMPP9SSJUsUExOjiRMnSpK++eYbn8+Zl5cnh8Mhp9NJ174Acrlcys7OVlxcHC1SAACg7vC2rlJKiikqKlhH6oRDPJTJi/w9QYDfb13ma23gc4tUoNaPOpmnnnpKdrtdo0aN8liQFwAAAKgVqalSjx5SVpYZR+RwSElJHi0zPhxS5WvUmmCKJYB8bpGqz2iRCg60SAEAABjkRYHja23AdwUAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD+FBjoA1ByXS8rKkpxOyeGQkpIkO6UzAABA0CBfq7sopOqpjAxp6VIpM1MqLJQiIqTkZCktTUpNDXR0AAAAqChf69Ej0NHhZCik6qGMDGnmTCknR0pIkKKjpfx8KT1d2rZNmjaNYgoAACCQTpavTZ0qxccHOkpUhIbDesblMnc2cnLMHY2YGCkkxDwmJ5vty5aZ4wAAAFD7fMnX/v538rVgRyFVz2RlmebhhATJZvPcZ7OZ7evXm+MAAABQ+3zN13bvDkx88A2FVD3jdJo+ttHR3vdHRZn9TmftxgUAAADD13ytoKB244J/KKTqGYfDDFTMz/e+v6DA7Hc4ajcuAAAAGL7ma1FRtRsX/EMhVc8kJZm+tTt3Spbluc+yzPaUFHMcAAAAap+v+VqbNoGJD76hkKpn7HYzZWZsrOl7m5cnFRebx8xMs33sWNYnqHYul7Rpk7R2rXlkdCgAACiHL/naNdeQrwU7m2WdWAc3PHl5eXI4HHI6nYqJiQl0ONXC27oEKSmmiArWqc9dLpeys7MVFxcne136y8GiXQAAoBIqytd69KijeVE94GttwDpS9VRqqlnIjZWyaxiLdgEAgEqqKF+jc0vwo5Cqx+x2qXPnQEdRj524CETp/KWli0BkZppFu3r0oIIFAABeka/VXWR3QGWxaBcAAECDRSEFVBaLdgEAADRYFFJAZbFoFwAAQINFIQVUFot2AQAANFgUUkBlsWgXAABAg0WGB1RFaqqZ4rx3byk310wskZsr9enD1OcAAAD1GNOfA1XFol0AAAANDoUUUB1YBAIAAKBB4ZY5AAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD8x2QRQC1wuJvUDAADynhRIZtuBA2YZlcOHJZtNatfOrEtZn5OGOpwkUUgBNSwjQ1q61KzRW1goRURIyclmLV+WmQIAoAHxlhS0aGH2bd0qbdkiHTpkiovISKl/f6lpU+nuu+tn0lDHkyQKKaAGZWRIM2dKOTlSQoIUHS3l50vp6dK2bazZCwBAg+EtKdi5U1q+XCoulkJCTJIgmdao4mKpoED6z3+kXbukefPqV9JQD5KkutFuBtRBLpe5yZKTY26uxMSYv5ExMeZ5To60bJk5DgAA1GPekgK7Xfr1Vyk0VDpyxLRE2e2mJSoy8thrQ0OlTZvM6+tL0lBPkiQKKaCGZGWZluqEBHNj6Xg2m9m+fr05DgAA1GPekoK8POngQSk8XLIsUzSEhBzb36iRaZWKiDD7vv22/iQN9SRJopACaojTabr7Rkd73x8VZfY7nbUbFwAAqGXekoIjR0yhVFpIWJbna2w2s630MT+//iQN9SRJopBC3eVymabutWvNY5A1/zoc5iZSaXfnExUUmP0OR+3GBQAAapm3pCAszHTbKy2gTmyZOb6IstlM0VFfkoZ6kiQx2QTqpjowy0tSkgkpPd08Hv/30bLM+NI+fY7NegoAAOopb0lBTIyZkS872zy326WSElNc2WzS0aPm34WFZl99ShrqSZJEixTqntJZXtLTpebNpU6dzGN6utmekRHoCCWZv3lpaWb5h8xM0xW6uNg8Zmaa7WPH1pmlEgAAQGV5SwpKSqRTTjHJQViY1KSJ6V3z22/mq1RxsdS5s3l9fUka6kmSZLOsEztkNjx5eXlyOBxyOp2KiYkJdDgNlsvlUnZ2tuLi4mQv7xfH5ZImTSr/DkZmprmD8eSTQfPL563xLCXF/H0IksYzAABQG7wlBbGxJoc5fh0py5IrIkLZ/fsrrlkz2SdPrp9JQ5AmSb7WBnTtQ93izywvnTsHJsYTpKZKPXrU2UW7AQBAdSkvKZDMtgMHpNxc6fBhk9e0ayf17Gm6+NVHdTxJqqffFdRbvszysmtX0M3yYrcHTV0HAAACqbyk4MRtLpcZP1VHiopKq8NJUj3/zqDeqSezvAAAAKBuo5BC3VI6y8vOnWXXWyid5SUlJehneQEAAEDdRiGFuqWezPICAACAuo1sE3VPaqo0bZrUu7cZkJmVZR779DHb6+OsNgAAAAgqTDaBuqmOz/ICAACAuo1CCnVXHZ7lBQAAAHUbt+8BAAAAwE8UUgAAAADgJwopAAAAAPAThRQAAAAA+IlCCgAAAAD8xKx9qBkuV2CnJg/09QEAAGpTdec+5FInRSGF6peRIS1dKmVmSoWFUkSElJwspaXVzmK5gb4+AABAbaru3IdcyicUUqheGRnSzJlSTo6UkCBFR0v5+VJ6urRtmzRtWs3+Agb6+gAAALWpunMfcimf0T6H6uNymbsXOTnmrkVMjBQSYh6Tk832ZcvMcfXx+gAAALWpunMfcim/UEih+mRlmSbghATJZvPcZ7OZ7evXm+Pq4/UBAABqU3XnPuRSfqGQQvVxOk0/2uho7/ujosx+p7N+Xh8AAKA2VXfuQy7lFwopVB+HwwxGzM/3vr+gwOx3OOrn9QEAAGpTdec+5FJ+oZBC9UlKMv1nd+6ULMtzn2WZ7Skp5rj6eH0AAIDaVN25D7mUXyikUH3sdjMtZmys6V+blycVF5vHzEyzfezYmluDINDXBwAAqE3VnfuQS/nFZlknlpsNT15enhwOh5xOp2JiYgIdTt3nbe2BlBTzi1fBdJkul0vZ2dmKi4uTvSq/oJW8PgAAQLDwKy+q7tyngedSvtYGFFKikKoRlVgNu9oKqUpeHwAAIFj4nRdVd+7TgHMpX2sDFuRFzbDbpc6dG+71AQAAalN15z7kUifVMMpKAAAAAKhGFFIAAAAA4CcKKQAAAADwE4UUAAAAAPiJQgoAAAAA/EQhBQAAAAB+opACAAAAAD9RSAEAAACAnyikAAAAAMBPFFIAAAAA4CcKKQAAAADwU1AXUrNnz1bfvn3VpEkTxcXFacSIEdq4caPHMYWFhRo/frxatGihxo0ba9SoUdq7d2+AIoabyyVt2iStXWseXa5ARwQAAOqxgKceFQUQ8OBQE0IDHUBFvvjiC40fP159+/ZVcXGxHnjgAQ0ZMkTr169XdHS0JOmuu+7SBx98oNdee00Oh0MTJkzQyJEjtWrVqgBH34BlZEhLl0qZmVJhoRQRISUnS2lpUmpqoKMDAAD1TMBTj4oCkMiL6imbZVlWoIPw1b59+xQXF6cvvvhC55xzjpxOp1q2bKmXX35Zl112mSRpw4YNSk5O1urVq3XmmWf6dN68vDw5HA45nU7FxMTU5Fuo/zIypJkzpZwcKSFBio6W8vOlnTul2Fhp2rRy/2i4XC5lZ2crLi5OdntQN5YCAIAgUYXUo+YDCP1fm0VxMXlRHeJrbVCnvitOp1OS1Lx5c0lSenq6jh49qsGDB7uP6dq1q0499VStXr06IDE2aC6XueOSk2PutMTESCEh5jE52WxftozmbAAAUC0CnnpUFEDXrqYb36ZN5t/kRfVOUHftO57L5dKdd96p/v3763e/+50kac+ePQoLC1PTpk09jm3VqpX27NlT7rmKiopUVFTkfp6Xl+e+hosf5srbvFnasEFKTJROvHNis5ntmZnmuE6dyrzc5XLJsiy+BwAAwCdVTD1qNoBDh479+/BhyeHwKzjyosDx9TOvM4XU+PHj9eOPP+rrr7+u8rlmz56tGTNmlNm+b98+FRYWVvn8DVZ2ttSmjRQfX/aPiSQ1by6Fh5vjjv9j8j8ul0tOp1OWZdGEDQAATqqKqUfNBtCokdStm/l3y5ZlAyAvClqHji+CK1AnCqkJEybo/fff15dffqmEhAT39tatW+vIkSM6ePCgR6vU3r171bp163LPN2XKFE2aNMn9PC8vT4mJiWrZsiVjpKrC6ZR275aKikyz9Yny8qTcXCkuznydwOVyyWazqWXLlvzBAAAAJ1XF1KNmAzh4UPrpJ/PvFi2kI0f8Co68KHAiIiJ8Oi6oCynLsjRx4kS99dZb+vzzz9W+fXuP/b1791ajRo20cuVKjRo1SpK0ceNGbd++Xf369Sv3vOHh4QoPDy+z3W6384NaFZ06mT7A6emm76/NdmyfZUk7dkh9+pjjyvmcbTYb3wcAAOCTakg9ai6AJk2O/btxYxOQn8GRFwWGr593UBdS48eP18svv6x33nlHTZo0cY97cjgcioyMlMPh0A033KBJkyapefPmiomJ0cSJE9WvXz+fZ+xDNbLbzVSe27aZPr8JCVJUlFRQcGx2mrFja+gvGQAAaGgCnnqcLIDOnc1xGzaQF9VDQT39ue34qv44ixcv1rhx4ySZBXknT56sf/zjHyoqKtLQoUO1YMGCCrv2nYjpz6uZt7UUUlLMH4sK5h9lmk8AAFAZlUw9aicAibyojvG1NgjqQqq2UEjVAJdLysoyfYcdDikp6aR3XPiDAQAAKqsSqUftBUBeVKf4WhsEddc+1GF2+7HmbAAAgBoW8NSjogACHhxqAuUtAAAAAPiJQgoAAAAA/ETXPtRNAe8IDQAAgIaMQgp1j7eZcZKTzfSjtTI1DwAAABo6CinULRkZ0syZUk6OWY8hOlrKzzcL4W3bJk2bRjEFAACAGkdfKNQdLpdpicrJMS1QMTFSSIh5TE4225ctM8cBAAAANYhCCnVHVtaxVcNPXKzZZjPb1683xwEAAAA1iEIKdYfTacZERUd73x8VZfY7nbUbFwAAABocCinUHQ6HmVgiP9/7/oICs9/hqN24AAAA0OBQSKHuSEoyY6F27pQsy3OfZZntKSnmOAAAAKAGUUih7rDbzRTnsbFmrFRenlRcbB4zM832sWNZTwoAAAA1junPUbekppopzkvXkdq1y3Tn69PHFFFMfQ4AAALF5TKTXjmdZqhBUlJgbvBWJo7qiD1Y3n8toZBC3ZOaKvXo0aB+UQEAQJDLyDh2o7ew0NzoTU42vWlq80ZvZeKojtiD5f3XIgop1E12u9S5c6CjAAAAMEXEzJlmTcuEBDPDcH6+lJ4ubdtmetPURjFRmTiqI/Zgef+1jFv49YzLJW3aJK1dax5ZmxYAAKAGuVymJSYnx7TAxMRIISHmMTnZbF+2rOaTssrEUR2xB8v7DwBapOqRBtiiCgAAEFhZWSb5SkiQbDbPfTab2b5+vTmuJnvTVCaO6og9WN5/ANAiVU+Utqimp0vNm0udOpnH9HSzPSMj0BECAADUQ06nuYMdHe19f1SU2e90Bl8c1RF7sLz/AKCQqgcacIsqAABAYDkcphtQfr73/QUFZr/DEXxxVEfswfL+A4BCqh7wp0UVAAAA1Sgpydy53rlTsizPfZZltqekmOOCLY7qiD1Y3n8AUEjVAw24RRUAACCw7HYzID021tzZzsuTiovNY2am2T52bM0v01KZOKoj9mB5/wFQ/95RA9SAW1QBAAACLzXVTPHdu7eUm2u6AeXmSn361O7U35WJozpiD5b3X8uYta8eKG1RTU83j8d37yttUe3Tp162qAIAAASH1FSpRw9TRDid5g52UlLtt8RUJo7qiD1Y3n8topCqB0pbVLdtOzZWKirKtETt3FmvW1QNl6tB/dICAIAgZbcHxxTflYmjOmIPlvdfSyik6onSFtXSdaR27TLd+fr0MUVUPW1RZfEsAAAABASFVD3S4FpUSxfPyskxzXDR0WagWHq6aZ6rx31yAQAAEFgUUvVMg2lRPXHxrNKBYaWLZ2VmmsWzevSox5UkAAAAAoUME3UTi2cBAAAggCikUDexeBYAAAACiEIKdROLZwEAACCAKKRQN5UunrVzp1ks63ili2elpLB4FgAAAGoEhRTqptLFs2JjzVipvDypuNg8ZmY2gMWzAAAAEEhkmai7ShfP6t1bys01E0vk5prFs5j6HAAAADWI6c9RtzW4xbMAAAAQDCikUPc1mMWzAAAAECwopNCwuVy0ZgEA0NDU1v//pdc5cEA6eFBq2lRq1qzi65W+5uBBKSTEjPsmNwlKFFJouDIypKVLzeQUhYVmuvTkZDOJBeOrAACon2rr///S66xZI+3YYa4VHi6deqp0xhner3d8bEVFpsdNdLSZQIvcJOhQSKFhysiQZs6UcnKkhATzRyo/X0pPl7ZtY7IKAADqo9r6/7/0Or/8Iu3bZ2YWjo42xVFpUXXi9U6MrXFjqUkTae1acx5yk6BDOyEaHpfL3O3JyTF3oGJiTNN5TIx5npMjLVtmjgMAAPVDbf3/X3qd0gKquNh06YuKMt36Skqko0fN/tLrlRdbVBS5SRCjkELDk5VlmswTEiSbzXOfzWa2r19vjgMAAPVDbf3/X3odh8OMc4qO9rxedLQZm9W06bHrkZvUSRRSaHicTtOkHh3tfX9UlNnvdNZuXAAAoObU1v//pdcJDTWtUaEnjKQp3R4Scux65CZ1EoUUGh6Hwwwszc/3vr+gwOx3OGo3LgAAUHNq6///0uuUFlHFxZ77S7eXlBy7HrlJnUQhhYYnKcn0N965U7Isz32WZbanpJjjAABA/VBb//+XXqe0+15+vuf18vOPdfsrvR65SZ1EIYWGx243U47Gxpr+yHl55u5QXp55HhtrphllzQYAAOqP2vr/v/Q6LVualqfQUFM0FRSY9aRCQqRGjcz+0uuVF1tBAblJELNZ1ollb8OTl5cnh8Mhp9OpmJiYQIfTYLlcLmVnZysuLk722vhD4W0diZQU/9dqqMxiewAAIDCq6/9/X69z/DpSERFSYqJ05pner3dcbK6iImV37qy4xo1lv/Zapj6vRb7WBhRSopAKFrVeSJmLVm1l8+P/SG7fbtaHKP0jWd5iewAAILCq+v+/v9fx52br/17jOnhQ2SEhiuvRQ/YTJ6xAjfK1NuC7gobNbjerhlfGiYvtlZScfLE9AAAQeFX5/7+mr1P6GpdLys6md0sQ4zsDVMbxi+0dPWqKqGbNzPSkTZseW4Dv+MX2AAAAUG9QSAGVUbpwXtOmplvA8es+2Gzm+cGDprsAC+gBAADUOxRSQGWULpwXEuL7YnsAAACoNyikgMooXTivpMT3xfYAAABQb1BIAZVRunBeafe941cityzzvLTbHwvoAQAA1DsUUkBlHL/YXqNGpgvfgQNm4byDB48twHf8YnsAAACoN8jugMpKTTVTmw8caNaNCgkxLVGhoeb5uecy9TkAAEA9xTpSwMlUtGhfaqrUo4f/i+0BAACgTqOQQnCqrRXHTyYjw6wXlZlpZt+LiDBjo9LSjrU01daifgAAAAgaFFIIPt9/bxaxrah4qQ0ZGdLMmVJOjpSQYNaGys+X0tOlbdvotgcAANCA0e8IwWXLFumRR0yx0ry51KmTeUxPN0VNRkbtxOFymZaonBxTxMXEmDFQMTHmeU6OKfZcrtqJBwAAAEGFFilUD3+64pV3rMslffrpseLFZjPHlxYvmZmmeOnRo+a7+WVlmeslJByLo5TNZravX2+Oo1sfAAB1S3UOISgullaulPbskVq3ls47T/rll6rlRLUVu7+CZehFkKCQQtX5Mo7Il2OjoqQdO4KjeHE6TXzR0d73R0VJu3aZ4wAAQN3hT95yMv/4h/T44yZ/OXrU5CuNGklt2piJpyqbE5UXR3XG7q9AXjtIUUihavwZR3SyY0eNMn+EgqF4cTjMH4j8fNMidqKCArPf4aj5WAAAQPWozvHP//iHNHmydPiwKZokKTtbOnTIbDvjjGPDE/zNiaZNMz1waip2fzFu3KuG2xaHqvNnHJEvx65cae7i5Od7v15tFi9JSSaunTsly/LcZ1lme0qKOQ4AAAS/6hz/XFxsWqIOH5bi483N3kOHTItU48ZSSYn044/m35XJiU6MI5Bjtxk3Xi4KKVSeP+OIfDl21y5zRycYihe73TRVx8aauPPyzB/NvDzzPDZWGju2QfcLBgCgTvEnbzmZlStNd75mzcxrjxwxX6GhJjcIDzdF1q5dlcuJ1q83E3DVROz+CuS1gxxZICrP6ZR++80UGPv2mefHF0BRUaYPrdPp25ijoiKpZ8/gKV5SU01Tde/eUm6u+QORmyv16dNgm7ABAKizfMlFSvOWk9mzxwxHCA83z0tbmkpzlNJJtAoKyp67MnFUZ+z+CuS1gxxjpFB5v/5q+sVu3myeh4ZKTZuaiSBiY8t2xfNlzFHnztJDDx1bR2rXLrO9Tx9TRNV28ZKaavooM0MNAAB1W3WOf27d2gxHKCoyhYTdfqx4Cgk5VlRFRXk/t79xBHLsNuPGy0UhhcrJyJCWLDEtRsXFZjBlSYnpJ3v4sClA9u0zBVBpV7zkZDMo8fipzaVj3fb69jWz3LRubVqmgqV4sduZ4hwAgLqudPxzRbnI8XlLRQYNkhITTfe7yEgpLMx8FRaa8xYVmaIjPt77uX2Jo2NHk1dVd+z+CuS1gxy31eG/0kGH+/ebGWkaNz7WrS8mxhRSa9ZILVoc64rny5ija67xbBLv3NkUV5070wIEAACqpjrHP4eGSvfea3KgXbtMq0yTJiYXOnzYtEr97nfm3yeeuzJxBHLsNuPGy2WzrBNH9Tc8eXl5cjgccjqdivHWZNkQVbTg2qZN0sSJphUqJsbcLdm0STp40PxiSeauzNNPSyNGeJ7vP/+RPvlE2r3b3K2JiDATSIwdK1ePHsrOzlZcXJzs/v4yskAcAADwhbf1kP6Xi1TLOlJhYaZ3Tek6UuWd+yRxuFyusnlRdcbur0Beu5b5WhvQtQ9lnWzBtRMHHcbGmtYnp9PMWBMSYtZROOUU7+cLDzdd+C64QDr99GNFT2WnzWSBOAAA4KvqHP981VXS5ZebWfz27DEF1HnnSb/8cvJzVyaOQI7dZtx4GRRS8OTLgmveBh3abGaiCck09UZGmuPKO9/PP0tvvCF161a1X0AWiAMAAP6qzvHPoaHS0KGe23w9d2XiCOTYbcaNe2i4JSTK8nXBtQ4dfFustkOHml3AjQXiAAAAECAUUjjG1wXXfv7Zt0GHP/9cswu4sUAcAAAAAoSufTjGlwXXdu0yx/Xta7rNlY5N8rbe09q1Zc9nWabgKh1L9dtvvi/gduKEEgcO+B5vbWPyCwAA4I8Tc4cOHQIdEU6CQgrH+Lvg2skGHZ54vvJm9/v1V1OYVcTbhBKtW5uCLNgWiGPyCwAA4I/yZsS77DIpLi7Q0aEcFFI4pjILrlU06PD487Vsaf5IlLYgRUVJubnm9UuWSG3bmqLMm/ImlNiyxcwOWFhoCrFgWCCOyS8AAIA/KsodiovNBF69egU6SnhBXyN4Ovdc0+Xuu+9MK1NVFlwrXcCtRQuzQO/hw6blyLLMuRs3Ngv67t9f/qQQFU0okZJiHvPyzFioQC8Qx+QXAADAHyfLHfLypL//ndwhSNEiBeP4JuVDh8wv9L59phiJjfUc++SP1FRp3DhzfpfL/EEIDTXN1J06mXOHh5tCaMuWst3wTjahRNeuZq2Gjh3N+g3exmrVFn8mv2DqUAAAcLLcITZW+ukncocgRSGFsk3KiYmm9Sgry7Qa3XqrdMkllW/ZOeUU03WvVSvTYhQWZgqm0j8Yx08KcWIh5csEGGFh0m23mRXEDxwwY7BiYky3vzVrzPbamOzBn8k6AAAATpY7hIeb/eQOQYlCqqE7sUm5tLhxOEx/3MxM6YsvTCFVWQ6H6d8bGio1b152f0WTQvg6AUazZuaYf/zDFE/bt0tFRWZfYqLpQljTkz34O1kHAABo2E6WO5TmMuQOQYkxUg1dbazFVDrpxIkL+FqWaUHKzJTatJHat/f9taWvL13899Ah06r2xRfSjh1SSYm5u1NcbJ5/8YXZn5FR+fdR2fd5Yqy1OfkFAAAIXifLHXJyyB2CGIVUQ+dLd7SqNimXTjpx/AK+e/ea4uaTT8wfj40bpfvuM+OkTvbaEyeUuOYa6W9/M2O6jh41RVSzZib2pk3N8cXFZn9NTvbgS6y1OfkFAAAIbifLHWJiTJ5D7hCU+K40dMc3KXtTXd3RUlPN1N+9e5vJIb7+2oxhiouTzj5batfOTPP5yivS99+X/9rcXNM6lptrJpSYNk1q0sT8sWna1BR8xxeFNpt5fvCgeQ9VbV3z5316i5WpzwEAwPEqyh1Gj5Z69gx0hChHvRkj9ec//1lz587Vnj171KNHDz377LM6/fTTAx1W8KvM2lGVlZoqde8u3XCD9NtvUpcunpNOlE5l/ve/mz8ax999qWjx37Vrjy1eV1xctnUtNNQUhCEhZhKNmh6webKFigEAAI7nLXfo0MF07UPQqheF1D//+U9NmjRJixYt0hlnnKH58+dr6NCh2rhxo+JYDbpipU3K27YdGysVFWUKj507q7872s8/m2nKS9dKON7Jpvksb/Hf0la1khJTNBUXS40aHdtfXGy2l5TU3oDNihYqBgAAONGJuQNrRwW9enGLfN68ebrpppt03XXXKSUlRYsWLVJUVJRefPHFQIdWN9Rmd7SamOaztFWttPve8d0ULcs8L+32x4BNAAAAVIM63yJ15MgRpaena8qUKe5tdrtdgwcP1urVq72+pqioSEVFRe7nzv8l7QcPHpSroVb/7dubomnr1mNNyu3bm7sjBw9W33XsdtNa5HSasU3HcdlsysvLU1ijRrL7e92RI81EFYcPmy58Bw6YoqyoyLRGhYSYCSj+8AfTfRAAACCIuVwukxeFhZm8CLUm73+5onXiTIonqPOFVE5OjkpKStSqVSuP7a1atdKGDRu8vmb27NmaMWNGme1t27atkRhRCX36VO/5fv1VWrdOev/96j0vAAAA6qVDhw7JUcGQkDpfSFXGlClTNGnSJPdzl8ul3NxctWjRQrYT11JCrcnLy1NiYqJ27NihGG+L0gEAADQQ5EWBY1mWDh06pPj4+AqPq/OFVGxsrEJCQrR3716P7Xv37lXr1q29viY8PFzh4eEe25o2bVpTIcJPMTEx/MEAAAAQeVGgVNQSVarOd7gMCwtT7969tXLlSvc2l8ullStXql+/fgGMDAAAAEB9VedbpCRp0qRJSktLU58+fXT66adr/vz5ys/P13XXXRfo0AAAAADUQ/WikLryyiu1b98+TZs2TXv27FHPnj318ccfl5mAAsEtPDxc06dPL9PtEgAAoKEhLwp+Nutk8/oBAAAAADzU+TFSAAAAAFDbKKQAAAAAwE8UUgAAAADgJwop1Cvt2rXT/Pnz3c9tNpvefvvtgMUDAACA+olCCvXa7t27NWzYsECHAQAAIEk699xzdeeddwY6DElSYWGhxo0bp+7duys0NFQjRowIdEh1CoUUKnTkyJFAh1AlrVu3ZtpQAAAAL0pKShQZGanbb79dgwcPDnQ4dQ6FVANz7rnnasKECZowYYIcDodiY2M1depUlc6C365dO82aNUtjx45VTEyMbr75ZknSG2+8oW7duik8PFzt2rXTk08+6fM127Vrp0ceeURjx45V48aN1bZtW7377rvat2+fLr30UjVu3FinnXaavv32W4/Xff311xowYIAiIyOVmJio22+/Xfn5+e792dnZGj58uCIjI9W+fXu99NJLZa59Yte+++67T507d1ZUVJQ6dOigqVOn6ujRo+79Dz/8sHr27Km//e1vateunRwOh0aPHq1Dhw75/H4BAED9cO6552rixIm688471axZM7Vq1Up/+ctflJ+fr+uuu05NmjRRUlKSPvroI/drfvzxRw0bNkyNGzdWq1atdO211yonJ0eSNG7cOH3xxRd6+umnZbPZZLPZ9Msvv6ikpEQ33HCD2rdvr8jISHXp0kVPP/10mXhefPFFdz7Wpk0bTZgwocL4Dxw4oLFjx6pZs2aKiorSsGHDtHnzZvf+6OhoLVy4UDfddJNat25dTZ9aw0Eh1QAtXbpUoaGh+s9//qOnn35a8+bN01//+lf3/ieeeEI9evRQRkaGpk6dqvT0dF1xxRUaPXq0fvjhBz388MOaOnWqlixZ4vM1n3rqKfXv318ZGRm6+OKLde2112rs2LG65ppr9N1336ljx44aO3asu6DbsmWLLrzwQo0aNUr//e9/9c9//lNff/21xx+McePGaceOHfrss8/0+uuva8GCBcrOzq4wjiZNmmjJkiVav369nn76af3lL3/RU0895XHMli1b9Pbbb+v999/X+++/ry+++EJ/+tOffH6vAACg/li6dKliY2P1n//8RxMnTtStt96qyy+/XGeddZa+++47DRkyRNdee60KCgp08OBBnX/++UpNTdW3336rjz/+WHv37tUVV1whSXr66afVr18/3XTTTdq9e7d2796txMREuVwuJSQk6LXXXtP69es1bdo0PfDAA3r11VfdcSxcuFDjx4/XzTffrB9++EHvvvuukpKSKox93Lhx+vbbb/Xuu+9q9erVsixLF110kcdNZFSBhQZl4MCBVnJysuVyudzb7rvvPis5OdmyLMtq27atNWLECI/XXH311dYFF1zgse2ee+6xUlJSfLpm27ZtrWuuucb9fPfu3ZYka+rUqe5tq1evtiRZu3fvtizLsm644Qbr5ptv9jjPV199Zdntduu3336zNm7caEmy/vOf/7j3Z2ZmWpKsp556yr1NkvXWW2+VG9vcuXOt3r17u59Pnz7dioqKsvLy8jze6xlnnOHTewUAAPXHwIEDrbPPPtv9vLi42IqOjrauvfZa97bSvGb16tXWrFmzrCFDhnicY8eOHZYka+PGje5z3nHHHSe99vjx461Ro0a5n8fHx1sPPvigz7Fv2rTJkmStWrXKvS0nJ8eKjIy0Xn311TLHp6WlWZdeeqnP54dl0SLVAJ155pmy2Wzu5/369dPmzZtVUlIiSerTp4/H8ZmZmerfv7/Htv79+3u85mROO+00979btWolSerevXuZbaUtSuvWrdOSJUvUuHFj99fQoUPlcrm0detWZWZmKjQ0VL1793afo2vXrmratGmFcfzzn/9U//791bp1azVu3FgPPfSQtm/f7nFMu3bt1KRJE/fzNm3anLSlCwAA1E/H5zAhISFq0aJFuTnMunXr9Nlnn3nkL127dpVkerxU5M9//rN69+6tli1bqnHjxnrhhRfcOUp2drZ27dqlQYMGeX3tH//4R49rSnLnSmeccYb7uBYtWqhLly7KzMysxCeBE4UGOgAEn+jo6Go/Z6NGjdz/Li3ivG1zuVySpMOHD+uWW27R7bffXuZcp556qjZt2uR3DKtXr9aYMWM0Y8YMDR06VA6HQ6+88kqZ8V7Hx1UaW2lcAACgYfGWF5SXwxw+fFjDhw/XnDlzypynTZs25V7jlVde0d13360nn3xS/fr1U5MmTTR37lytWbNGkhQZGVlhjDNnztTdd9/t83tC9aCQaoBKfylL/fvf/1anTp0UEhLi9fjk5GStWrXKY9uqVavUuXPncl9TVb169dL69evL7fvbtWtXFRcXKz09XX379pUkbdy4UQcPHiz3nN98843atm2rBx980L1t27Zt1Ro3AABouHr16qU33nhD7dq1U2io9zQ7LCysTI+eVatW6ayzztJtt93m3nZ8C1aTJk3Url07rVy5Uuedd16Zc8bFxSkuLs5jW3JysoqLi7VmzRqdddZZkqT9+/dr48aNSklJqfR7xDF07WuAtm/frkmTJmnjxo36xz/+oWeffVZ33HFHucdPnjxZK1eu1KxZs7Rp0yYtXbpUzz33XI3e+bjvvvv0zTffaMKECfr++++1efNmvfPOO+7JJrp06aILL7xQt9xyi9asWaP09HTdeOONFd6x6dSpk7Zv365XXnlFW7Zs0TPPPKO33nqrxt4DAABoWMaPH6/c3FxdddVVWrt2rbZs2aLly5fruuuucxdP7dq105o1a/TLL78oJydHLpdLnTp10rfffqvly5dr06ZNmjp1qtauXetx7ocfflhPPvmknnnmGW3evFnfffednn322XJj6dSpky699FLddNNN+vrrr7Vu3Tpdc801OuWUU3TppZe6j1u/fr2+//575ebmyul06vvvv9f3339fI59PfUMh1QCNHTtWv/32m04//XSNHz9ed9xxh3uac2969eqlV199Va+88op+97vfadq0aZo5c6bGjRtXYzGedtpp+uKLL7Rp0yYNGDBAqampmjZtmuLj493HLF68WPHx8Ro4cKBGjhypm2++uczdmONdcskluuuuuzRhwgT17NlT33zzjaZOnVpj7wEAADQs8fHxWrVqlUpKSjRkyBB1795dd955p5o2bSq73aTdd999t0JCQpSSkqKWLVtq+/btuuWWWzRy5EhdeeWVOuOMM7R//36P1ilJSktL0/z587VgwQJ169ZNv//97z2mMvdm8eLF6t27t37/+9+rX79+sixLH374oUfXxIsuukipqal677339Pnnnys1NVWpqanV/+HUQzbL+t9802gQzj33XPXs2VPz588PdCgAAABAnUWLFAAAAAD4iUIKVfLVV195TLd54hcAAABQH9G1D1Xy22+/6ddffy13/8lW3AYAAADqIgopAAAAAPATXfsAAAAAwE8UUgAAAADgJwopAAAAAPAThRQAAAAA+IlCCgAAAAD8RCEFAAAAAH6ikAIAAAAAP1FIAQAAAICf/h/egvNTdTRqUwAAAABJRU5ErkJggg==", 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", 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" ] @@ -4988,7 +5004,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1441081/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_17143/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5113,18 +5129,18 @@ " \n", " 3\n", " 4\n", - " acm_bot\n", - " 2239.058675\n", - " 85\n", - " 81.25\n", + " bot_median\n", + " 2374.216338\n", + " 97\n", + " 93.10\n", " \n", " \n", " 4\n", " 5\n", - " bot_median\n", - " 2138.701789\n", - " 97\n", - " 93.10\n", + " acm_bot\n", + " 2239.058675\n", + " 85\n", + " 81.25\n", " \n", " \n", " 5\n", @@ -5471,8 +5487,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 acm_bot 2239.058675 85 \n", - "4 5 bot_median 2138.701789 97 \n", + "3 4 bot_median 2374.216338 97 \n", + "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -5520,8 +5536,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 81.25 \n", - "4 93.10 \n", + "3 93.10 \n", + "4 81.25 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -5716,6 +5732,20 @@ " 0.000036\n", " \n", " \n", + " bot_median\n", + " 2374.2\n", + " 93.1\n", + " 25.5\n", + " 56.712830\n", + " 5.877687\n", + " 4.338745\n", + " 1.985277\n", + " 37.2\n", + " 13.8\n", + " 0.999982\n", + " 0.000037\n", + " \n", + " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5730,20 +5760,6 @@ " 0.000025\n", " \n", " \n", - " bot_median\n", - " 2138.7\n", - " 93.1\n", - " 23.0\n", - " 64.275382\n", - " 6.661466\n", - " 3.448504\n", - " 1.985277\n", - " 36.2\n", - " 9.7\n", - " 0.999574\n", - " 0.000852\n", - " \n", - " \n", " metac-claude-3-5-sonnet-20240620\n", " 2018.1\n", " 91.5\n", @@ -6340,8 +6356,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "bot_median 2374.2 93.1 25.5 56.712830 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", - "bot_median 2138.7 93.1 23.0 64.275382 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -6389,8 +6405,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "bot_median 5.877687 4.338745 1.985277 37.2 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", - "bot_median 6.661466 3.448504 1.985277 36.2 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -6438,8 +6454,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", + "bot_median 13.8 0.999982 0.000037 \n", "acm_bot 15.3 0.999987 0.000025 \n", - "bot_median 9.7 0.999574 0.000852 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -6573,18 +6589,18 @@ " NA\n", " \n", " \n", - " RPM_bot\n", + " bean_bot\n", " -0.6\n", - " 7.0\n", + " 4.7\n", " -0.1\n", - " 0.820675\n", - " 0.310186\n", - " -0.269729\n", - " 2.446912\n", - " 0.7\n", - " -0.8\n", - " 0.398203\n", - " 0.796405\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,20 +6617,6 @@ " 0.007677\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", - " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", - " \n", - " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6657,6 +6659,20 @@ " 0.018953\n", " \n", " \n", + " RPM_bot\n", + " -1.3\n", + " 7.0\n", + " -0.2\n", + " 0.803163\n", + " 0.303567\n", + " -0.601802\n", + " 2.446912\n", + " 0.6\n", + " -0.9\n", + " 0.284666\n", + " 0.569332\n", + " \n", + " \n", " SynapseSeer\n", " -1.3\n", " 26.2\n", @@ -6742,17 +6758,17 @@ " \n", " \n", " annabot\n", - " -5.9\n", + " -6.2\n", " 29.3\n", " -0.2\n", - " 0.517575\n", - " 0.095618\n", - " -2.112203\n", + " 0.520869\n", + " 0.096226\n", + " -2.211795\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.021811\n", - " 0.043621\n", + " 0.017610\n", + " 0.035221\n", " \n", " \n", " 4Shadower\n", @@ -6770,17 +6786,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.6\n", + " -6.7\n", " 27.4\n", " -0.2\n", - " 0.747093\n", - " 0.142725\n", - " -1.683660\n", + " 0.748050\n", + " 0.142908\n", + " -1.722004\n", " 2.049541\n", - " 0.1\n", + " 0.0\n", " -0.5\n", - " 0.052019\n", - " 0.104037\n", + " 0.048384\n", + " 0.096767\n", " \n", " \n", " jkraybill_bot\n", @@ -6853,18 +6869,18 @@ " 0.201592\n", " \n", " \n", - " GreeneiBot2\n", - " -10.7\n", - " 58.4\n", - " -0.2\n", - " 0.848714\n", - " 0.111107\n", - " -1.647027\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.052511\n", - " 0.105022\n", + " metac-o1\n", + " -10.8\n", + " 91.1\n", + " -0.1\n", + " 0.866824\n", + " 0.090818\n", + " -1.303018\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.097944\n", + " 0.195889\n", " \n", " \n", " ajf-bot\n", @@ -6881,6 +6897,34 @@ " 0.094289\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -11.2\n", + " 52.1\n", + " -0.2\n", + " 0.634257\n", + " 0.087871\n", + " -2.445043\n", + " 2.005379\n", + " -0.0\n", + " -0.4\n", + " 0.008985\n", + " 0.017970\n", + " \n", + " \n", + " GreeneiBot2\n", + " -11.4\n", + " 58.4\n", + " -0.2\n", + " 0.846228\n", + " 0.110781\n", + " -1.766811\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.041290\n", + " 0.082581\n", + " \n", + " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6895,46 +6939,18 @@ " 0.023810\n", " \n", " \n", - " metac-perplexity\n", - " -12.0\n", - " 89.1\n", - " -0.1\n", - " 1.000845\n", - " 0.106030\n", - " -1.269604\n", - " 1.986405\n", - " 0.1\n", - " -0.3\n", - " 0.103785\n", - " 0.207569\n", - " \n", - " \n", - " bot_median\n", - " -12.2\n", - " 92.1\n", - " -0.1\n", - " 0.875909\n", - " 0.091270\n", - " -1.448706\n", - " 1.985550\n", - " 0.0\n", - " -0.3\n", - " 0.075426\n", - " 0.150853\n", - " \n", - " \n", - " metac-o1\n", - " -12.4\n", - " 91.1\n", - " -0.1\n", - " 0.941303\n", - " 0.098621\n", - " -1.375036\n", - " 1.985829\n", + " metac-Gemini-Exp-1206\n", + " -11.5\n", + " 76.5\n", + " -0.2\n", + " 0.895210\n", + " 0.102351\n", + " -1.471849\n", + " 1.990822\n", " 0.1\n", - " -0.3\n", - " 0.086265\n", - " 0.172530\n", + " -0.4\n", + " 0.072609\n", + " 0.145218\n", " \n", " \n", " laylaps\n", @@ -6951,32 +6967,18 @@ " 0.017488\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -13.4\n", - " 52.1\n", - " -0.3\n", - " 0.686642\n", - " 0.095129\n", - " -2.702394\n", - " 2.005379\n", + " bot_median\n", + " -13.3\n", + " 92.1\n", " -0.1\n", - " -0.4\n", - " 0.004660\n", - " 0.009321\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -13.5\n", - " 76.5\n", - " -0.2\n", - " 1.006606\n", - " 0.115088\n", - " -1.527727\n", - " 1.990822\n", - " 0.1\n", - " -0.4\n", - " 0.065380\n", - " 0.130759\n", + " 0.757201\n", + " 0.078901\n", + " -1.830058\n", + " 1.985550\n", + " 0.0\n", + " -0.3\n", + " 0.035256\n", + " 0.070512\n", " \n", " \n", " wunderplumb\n", @@ -6993,6 +6995,20 @@ " 0.006348\n", " \n", " \n", + " metac-perplexity\n", + " -14.4\n", + " 89.1\n", + " -0.2\n", + " 1.102601\n", + " 0.116810\n", + " -1.384952\n", + " 1.986405\n", + " 0.1\n", + " -0.4\n", + " 0.084782\n", + " 0.169564\n", + " \n", + " \n", " manticAI\n", " -14.6\n", " 69.4\n", @@ -7007,20 +7023,6 @@ " 0.011014\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -14.7\n", - " 90.5\n", - " -0.2\n", - " 0.942980\n", - " 0.099124\n", - " -1.642585\n", - " 1.986072\n", - " 0.0\n", - " -0.4\n", - " 0.051989\n", - " 0.103978\n", - " \n", - " \n", " NextWorldLab\n", " -16.9\n", " 80.2\n", @@ -7035,46 +7037,32 @@ " 0.040909\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -18.9\n", - " 91.1\n", - " -0.2\n", - " 0.731708\n", - " 0.076662\n", - " -2.699995\n", - " 1.985829\n", - " -0.1\n", - " -0.4\n", - " 0.004141\n", - " 0.008282\n", - " \n", - " \n", " minefrac1\n", - " -19.2\n", + " -18.8\n", " 51.1\n", " -0.4\n", - " 0.880990\n", - " 0.123242\n", - " -3.043641\n", + " 0.874752\n", + " 0.122370\n", + " -3.013581\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.001859\n", - " 0.003717\n", + " 0.002021\n", + " 0.004043\n", " \n", " \n", - " metac-o1-preview\n", - " -20.9\n", + " metac-claude-3-5-sonnet-latest\n", + " -21.6\n", " 91.1\n", " -0.2\n", - " 0.802181\n", - " 0.084045\n", - " -2.728807\n", + " 0.784073\n", + " 0.082148\n", + " -2.885581\n", " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.003821\n", - " 0.007643\n", + " 0.002444\n", + " 0.004888\n", " \n", " \n", " mmBot\n", @@ -7091,46 +7079,46 @@ " 0.002208\n", " \n", " \n", - " metac-Llama-3.1\n", - " -23.2\n", - " 89.1\n", - " -0.3\n", - " 1.031278\n", - " 0.109254\n", - " -2.379606\n", - " 1.986405\n", + " metac-claude-3-5-sonnet-20240620\n", + " -22.1\n", + " 90.5\n", + " -0.2\n", + " 0.992190\n", + " 0.104297\n", + " -2.344713\n", + " 1.986072\n", " -0.0\n", " -0.5\n", - " 0.009745\n", - " 0.019489\n", + " 0.010627\n", + " 0.021254\n", " \n", " \n", " metac-grok-2-1212\n", - " -23.5\n", + " -23.2\n", " 91.1\n", " -0.3\n", - " 1.068006\n", - " 0.111896\n", - " -2.303421\n", + " 0.969180\n", + " 0.101542\n", + " -2.504438\n", " 1.985829\n", - " -0.0\n", + " -0.1\n", " -0.5\n", - " 0.011778\n", - " 0.023556\n", + " 0.007032\n", + " 0.014063\n", " \n", " \n", " pgodzinai\n", - " -24.0\n", + " -23.2\n", " 76.4\n", " -0.3\n", - " 0.976590\n", - " 0.111729\n", - " -2.811085\n", + " 1.002923\n", + " 0.114742\n", + " -2.649317\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003144\n", - " 0.006289\n", + " 0.004910\n", + " 0.009821\n", " \n", " \n", " VeritasAI\n", @@ -7147,32 +7135,46 @@ " 0.000076\n", " \n", " \n", - " metac-exa\n", - " -26.2\n", - " 89.1\n", + " metac-o1-preview\n", + " -24.4\n", + " 91.1\n", " -0.3\n", - " 0.830275\n", - " 0.087960\n", - " -3.341545\n", - " 1.986405\n", + " 0.852432\n", + " 0.089310\n", + " -2.999396\n", + " 1.985829\n", " -0.1\n", - " -0.5\n", - " 0.000612\n", - " 0.001224\n", + " -0.4\n", + " 0.001749\n", + " 0.003497\n", " \n", " \n", " metac-gpt-4o\n", - " -26.6\n", + " -25.1\n", " 91.1\n", " -0.3\n", - " 0.879087\n", - " 0.092103\n", - " -3.165570\n", + " 0.873597\n", + " 0.091528\n", + " -3.009707\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.001056\n", - " 0.002112\n", + " 0.001696\n", + " 0.003391\n", + " \n", + " \n", + " metac-exa\n", + " -26.1\n", + " 89.1\n", + " -0.3\n", + " 0.791935\n", + " 0.083898\n", + " -3.495695\n", + " 1.986405\n", + " -0.1\n", + " -0.5\n", + " 0.000371\n", + " 0.000743\n", " \n", " \n", " InstitutPelFutur\n", @@ -7188,6 +7190,20 @@ " 0.002292\n", " 0.004584\n", " \n", + " \n", + " metac-Llama-3.1\n", + " -28.0\n", + " 89.1\n", + " -0.3\n", + " 0.907200\n", + " 0.096109\n", + " -3.270200\n", + " 1.986405\n", + " -0.1\n", + " -0.5\n", + " 0.000767\n", + " 0.001534\n", + " \n", " \n", "\n", "" @@ -7196,146 +7212,146 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", - "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", + "RPM_bot -1.3 7.0 -0.2 0.803163 0.303567 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", + "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", + "cookics_bot_TEST -6.7 27.4 -0.2 0.748050 0.142908 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", - "GreeneiBot2 -10.7 58.4 -0.2 0.848714 0.111107 \n", + "metac-o1 -10.8 91.1 -0.1 0.866824 0.090818 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", + "metac-deepseek-r1+asknews -11.2 52.1 -0.2 0.634257 0.087871 \n", + "GreeneiBot2 -11.4 58.4 -0.2 0.846228 0.110781 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-perplexity -12.0 89.1 -0.1 1.000845 0.106030 \n", - "bot_median -12.2 92.1 -0.1 0.875909 0.091270 \n", - "metac-o1 -12.4 91.1 -0.1 0.941303 0.098621 \n", + "metac-Gemini-Exp-1206 -11.5 76.5 -0.2 0.895210 0.102351 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", - "metac-deepseek-r1+asknews -13.4 52.1 -0.3 0.686642 0.095129 \n", - "metac-Gemini-Exp-1206 -13.5 76.5 -0.2 1.006606 0.115088 \n", + "bot_median -13.3 92.1 -0.1 0.757201 0.078901 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", + "metac-perplexity -14.4 89.1 -0.2 1.102601 0.116810 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-claude-3-5-sonnet-20240620 -14.7 90.5 -0.2 0.942980 0.099124 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "metac-claude-3-5-sonnet-latest -18.9 91.1 -0.2 0.731708 0.076662 \n", - "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", - "metac-o1-preview -20.9 91.1 -0.2 0.802181 0.084045 \n", + "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", + "metac-claude-3-5-sonnet-latest -21.6 91.1 -0.2 0.784073 0.082148 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-Llama-3.1 -23.2 89.1 -0.3 1.031278 0.109254 \n", - "metac-grok-2-1212 -23.5 91.1 -0.3 1.068006 0.111896 \n", - "pgodzinai -24.0 76.4 -0.3 0.976590 0.111729 \n", + "metac-claude-3-5-sonnet-20240620 -22.1 90.5 -0.2 0.992190 0.104297 \n", + "metac-grok-2-1212 -23.2 91.1 -0.3 0.969180 0.101542 \n", + "pgodzinai -23.2 76.4 -0.3 1.002923 0.114742 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-exa -26.2 89.1 -0.3 0.830275 0.087960 \n", - "metac-gpt-4o -26.6 91.1 -0.3 0.879087 0.092103 \n", + "metac-o1-preview -24.4 91.1 -0.3 0.852432 0.089310 \n", + "metac-gpt-4o -25.1 91.1 -0.3 0.873597 0.091528 \n", + "metac-exa -26.1 89.1 -0.3 0.791935 0.083898 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", + "metac-Llama-3.1 -28.0 89.1 -0.3 0.907200 0.096109 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "RPM_bot -0.269729 2.446912 0.7 \n", - "jonahsingerbot -5.273630 2.784843 -0.1 \n", "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", + "RPM_bot -0.601802 2.446912 0.6 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "annabot -2.112203 2.044183 -0.0 \n", + "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.683660 2.049541 0.1 \n", + "cookics_bot_TEST -1.722004 2.049541 0.0 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.647027 2.000832 0.0 \n", + "metac-o1 -1.303018 1.985829 0.1 \n", "ajf-bot -1.722395 2.030778 0.1 \n", + "metac-deepseek-r1+asknews -2.445043 2.005379 -0.0 \n", + "GreeneiBot2 -1.766811 2.000832 0.0 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-perplexity -1.269604 1.986405 0.1 \n", - "bot_median -1.448706 1.985550 0.0 \n", - "metac-o1 -1.375036 1.985829 0.1 \n", + "metac-Gemini-Exp-1206 -1.471849 1.990822 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", - "metac-deepseek-r1+asknews -2.702394 2.005379 -0.1 \n", - "metac-Gemini-Exp-1206 -1.527727 1.990822 0.1 \n", + "bot_median -1.830058 1.985550 0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", + "metac-perplexity -1.384952 1.986405 0.1 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-claude-3-5-sonnet-20240620 -1.642585 1.986072 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "metac-claude-3-5-sonnet-latest -2.699995 1.985829 -0.1 \n", - "minefrac1 -3.043641 2.006545 -0.1 \n", - "metac-o1-preview -2.728807 1.985829 -0.1 \n", + "minefrac1 -3.013581 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.885581 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-Llama-3.1 -2.379606 1.986405 -0.0 \n", - "metac-grok-2-1212 -2.303421 1.985829 -0.0 \n", - "pgodzinai -2.811085 1.990849 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.344713 1.986072 -0.0 \n", + "metac-grok-2-1212 -2.504438 1.985829 -0.1 \n", + "pgodzinai -2.649317 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-exa -3.341545 1.986405 -0.1 \n", - "metac-gpt-4o -3.165570 1.985829 -0.1 \n", + "metac-o1-preview -2.999396 1.985829 -0.1 \n", + "metac-gpt-4o -3.009707 1.985829 -0.1 \n", + "metac-exa -3.495695 1.986405 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", + "metac-Llama-3.1 -3.270200 1.986405 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "RPM_bot -0.8 0.398203 0.796405 \n", - "jonahsingerbot -0.2 0.003839 0.007677 \n", "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", + "RPM_bot -0.9 0.284666 0.569332 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "annabot -0.4 0.021811 0.043621 \n", + "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.052019 0.104037 \n", + "cookics_bot_TEST -0.5 0.048384 0.096767 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.052511 0.105022 \n", + "metac-o1 -0.3 0.097944 0.195889 \n", "ajf-bot -0.7 0.047145 0.094289 \n", + "metac-deepseek-r1+asknews -0.4 0.008985 0.017970 \n", + "GreeneiBot2 -0.4 0.041290 0.082581 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-perplexity -0.3 0.103785 0.207569 \n", - "bot_median -0.3 0.075426 0.150853 \n", - "metac-o1 -0.3 0.086265 0.172530 \n", + "metac-Gemini-Exp-1206 -0.4 0.072609 0.145218 \n", "laylaps -0.4 0.008744 0.017488 \n", - "metac-deepseek-r1+asknews -0.4 0.004660 0.009321 \n", - "metac-Gemini-Exp-1206 -0.4 0.065380 0.130759 \n", + "bot_median -0.3 0.035256 0.070512 \n", "wunderplumb -0.9 0.003174 0.006348 \n", + "metac-perplexity -0.4 0.084782 0.169564 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.051989 0.103978 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.004141 0.008282 \n", - "minefrac1 -0.6 0.001859 0.003717 \n", - "metac-o1-preview -0.4 0.003821 0.007643 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.002444 0.004888 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-Llama-3.1 -0.5 0.009745 0.019489 \n", - "metac-grok-2-1212 -0.5 0.011778 0.023556 \n", - "pgodzinai -0.5 0.003144 0.006289 \n", + "metac-claude-3-5-sonnet-20240620 -0.5 0.010627 0.021254 \n", + "metac-grok-2-1212 -0.5 0.007032 0.014063 \n", + "pgodzinai -0.5 0.004910 0.009821 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-exa -0.5 0.000612 0.001224 \n", - "metac-gpt-4o -0.5 0.001056 0.002112 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 " + "metac-o1-preview -0.4 0.001749 0.003497 \n", + "metac-gpt-4o -0.5 0.001696 0.003391 \n", + "metac-exa -0.5 0.000371 0.000743 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 \n", + "metac-Llama-3.1 -0.5 0.000767 0.001534 " ] }, "execution_count": 42, @@ -8563,9 +8579,23 @@ "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", + "/tmp/ipykernel_17143/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\m'\n", + " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", + "/tmp/ipykernel_17143/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\s'\n", + " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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58+c3kiKFuggXLlyIEtJjkzNnTiPJnD171jFt06ZNRpIpWbKkkWRGjhz52GWiC90RYgo1ESI+JyN+0Pjvv/8c8+7evWvq169vJJlXX301zs/LJELoNnEIirEtG+FJ98tjx451/ED2cH8MCwszb7zxhuM1i8/nT8T++JNPPonzMtFZvny5OXfuXJTp27ZtM76+vsbd3d38+++/keY9aeiOT3+P6LtlypQxwcHBUdr/8ccfkb4zmFj6Z0K/v+TLl88cOnTIMe/+/fumW7duRpKpV69etK8BUjZCNxCLiJ1xXP/FNXQXL17cSDJXr16NUx2PC0WnTp0yLi4uxmazRflibIwx//77r/Hy8jKSzNatWx3TP/74YyPJVKxYMdr1vvvuu48N3R9//HGcnsOjVq1aZSSZoKCgSNMf/tBatmxZlOVGjRrlCKvRfSC+9dZbRpIZOnRonGuJ+GW8b9++0c5v0qRJtIEgMUL3o8/fPDhrIeI16NevX5T5O3fudPwC/7Bvv/3W8QU+Ojdv3jTZsmUzbm5ukfpeQp6HieW9HD16tNGDozgPh4aEeNL36lGJHbq9vb0jhYUIS5Yscfw49fDR7ojtu7u7m2PHjkVZLjw83BFu/vzzz2i3/fnnn0cJ0MHBwcbHx8e4uLhE+dIbGhpqsmbNaiRF+VEiuuDwpDVEd9T6zp07xtPT01SvXt3069fPSIp0tCemo+OxCQ0NNb1793acNfTwvyxZspg333wzymtgjHGcFfFomIxJ9+7djR4c2YpOz549jSRTt27dSNMjQneRIkWi7f/79+83NpvN5MyZM9qAYIwxjRo1MpLMkiVLHNN8fHxM+vTp41T740QctZ4yZYpj2kcffWQkmUWLFhk3NzfToEEDx7yYjo4nRui22Wxmz549Uebv2LHDSDIFChSI13N73Of3w5/LzgzdT7pfLlSokJFkvv322yjL3L171/j5+cV7v+3t7W0kme+++y7a+bNnz450tD7i36VLl+K8jQEDBhhJZty4cZGmP2nojk9/nzt3rpFk3n777TjX+7j+GZ24fH9ZvHhxlOX+++8/owdHu0NDQ+O8PaQM3EgNiIPYhhzRg2tuL1y4EOf1BQQEaP/+/erQoYM++OADvfDCC3Jze/I/x82bN8tut6ts2bIqWbJklPm5cuVS/fr1tWjRIm3YsEGVKlWSJMe1aB06dIh2vR06dNCXX34Z67Zbt24d6/yQkBCtXr1af/zxhy5evKiQkBAZY3Tz5k1J0qFDh6Jdzs3NTfXq1YsyPeKmZXnz5tVzzz0X4/xz587FWleE+/fvO+48HtMdbrt3766lS5dqw4YNcVpnfDRq1CjKtEyZMilz5sy6cuVKtPNjeo7Lli2TJLVt2zbabaVNm1blypXT8uXL9ccff0T7+sYmvu/lypUrpQevn6ura7y2FR2r36vY1KtXT35+flGmN2nSxPFe7tq1y/G3F6FMmTIqUKBAlOV2796tc+fOqWDBgnr++eej3WbEtY/btm1zTEuXLp1at26tadOmadq0aRowYIBj3rJly3Tp0iUFBATo2WeffexzetIaChQooPz58+vEiRM6duyYChYsqC1btigkJER169ZV+fLl9cUXX2jt2rWqUKGCjDFav369bDabateu/di6Iri7u+urr75S//79tXDhQm3ZskW7du3SoUOHdPnyZY0bN06zZs3S6tWrHfWfP39ee/bskYuLi7p37x6n7URc5xtbnxs7dqy2bNmi8PDwKH29RYsW0fb/5cuXyxijhg0bKl26dNGuu0aNGlq+fLm2bdvmGPYsICBAGzduVKdOndSrVy+VKVMmTtfmRqdOnTqaPn261q5d67hHxdq1a+Xj46MGDRqofPny2rJli0JDQ+Xh4eG4XtcZ49vnzZtXpUqVijK9WLFikqSzZ88+0Xpj+vx+Wjd+e5L98tmzZ3X06FFJUseOHaMs4+XlpTZt2mjMmDGJWusff/yhqVOnRpk+ZMgQZcmSJdK0K1euaNmyZdq3b5+uXbvmuMfCkSNHpFg+2+MrPv29bNmycnV11eTJk1WkSBHHfSieVEK+vzRo0CDKdD8/P2XMmFHXrl3TlStXov3MQMpF6Abi4HFDjtSoUSNeoXv48OH6+++/tWLFCq1YsULe3t4qW7asatSooQ4dOji+ZMRVxJeR/Pnzx9gm4g6dD39x+ffff6UHNxKJTlzGVI2tzY4dO9S2bVvHXb6jExwcHO30HDlyRPtDRNq0aaVY7jYb8eU1thtJPezKlSuOtjG9ftG9doklpueRNm1aXblyJdr5Ec8xJCQk0vSIG1C99NJLeumll2Ld7qVLl+JV55O8lxE3UIrrl9vPPvtMBw8ejDL9yy+/VJYsWZ76e7Vw4UItXLgwyvSXX35ZVapUiTQttr89f39/XblyxfH39ui86ES8lxHj6Mbm0feyW7dumjZtmqZMmRIpdP/444+SFOfxyhNSQ506dTRx4kStXbtWBQsWdIS1unXrqkSJEvL09NTatWv14Ycfavfu3bpy5YrKlCmjzJkzx6m2h/n5+en11193DAF34cIFzZw5U0OHDtXVq1fVqVMn/fPPP9JDIw7kyJFD6dOnj9P6H7d/jehz9+7d05UrV6Lc2O1x7/GkSZM0adKkWGt4+PUdP368mjRpounTp2v69OlKly6dypcvr1q1aumll16K1524I8LzunXrpAd/w3/88Yfq1q0rDw8P1alTR9u3b9f27dtVvXp1p4fu6Pj6+krR7O/iKi5DhjnTk+yXI/YVWbJkcXzmPSq2fU5MsmTJojNnzsS4///yyy8j/dDu5uam8PDwKO0mTpyoPn36xHrT0pg+2+MrPv29YMGC+uqrr9SvXz/17NlTPXv2VL58+VSxYkU1adJEQUFB8vDwiNN2E/r9Jabh83x9fXXt2rU4f0dBykHoBizg5+enP//8U5s2bdLatWu1detW/fbbb9q6dauGDRum4cOHq3///k+tnpi+UMflTtze3t7RTr9z545atGihCxcuqGvXrnrjjTdUqFAh+fr6ytXVVYcPH9YzzzwT5c64ER535OZJj+wkNYn5PCPuptqgQYPHjk0c3RBLMUnoexlXK1eudJx98bDojrI8DXv27In2qE+NGjWihO64iO71ienvJ+K99PPzU/369WNd76OvTbVq1VSwYEEdPnxY27ZtU6VKlXTx4kUtX75cXl5eateuXZzqTUgNEaF7zZo1eu2117R27VplzJhR5cqVk4uLi2Ns+zt37iR6kMuePbv69Okjf39/tWrVSvv379eRI0csG9rvce9x6dKloz3C+7AKFSo4/rtYsWI6dOiQVq9erfXr12vbtm3asmWL1q9fr48//liTJk2K9uhodHLmzKlixYrpwIED2rdvn44fP6779++rbt260oP35JNPPtGaNWtUuXJlbdq0SW5ubtHeYTqhUso+/VHO2i8/ibJly+rMmTMJGuVh586deu211+Tq6qoRI0aoadOmyps3r3x8fGSz2TRhwgS99tpr8f48iGkEjPj297feektt2rTR4sWL9euvv+rXX3/V7NmzNXv2bA0ePFhbtmx57NFvZ39/QepE6AYsEjE0RsSXl3v37mnKlCl688039cEHH6h169ZxHj8yV65c0kO/qEcnYl5E24j/PnToUJShMiLEND0uNm/erAsXLqhs2bKaPHlylPkRp6BZLXPmzPL09FRISIiOHz8e7en50b12SVGePHl08OBBde/e/bGn/cfHk76XefPm1YEDB3Tw4ME4BaqYhquK8LTfqyFDhsQ4vM2jTpw4EeO8iL+j3Llzx3nbefLkkR4850eHHnqciCGNPvroI/3444+qVKmSZsyYofv376tNmzbKkCGD02uoXbu2bDabNmzYoIsXL2rPnj1q2bKl48tonTp1tGHDBm3evNlpR08fvnzi8uXLKly4sOOo2H///acbN27E6Wh3rly5dOzYMR0/fjzaS1oi+lzEsGNxFfH6Vq5cWWPHjo3zcnpwBLJRo0aOy0+Cg4M1atQoDR06VK+99ppatmypNGnSxGldderU0YEDB7R27VrHc4l4LypWrKg0adJo7dq1atSokYKDg1WxYkXH0Wc83pPslyP2X5cvX9atW7eiPdr9JJ/PzZo106JFi7Rq1Spdvnz5iX7MnDdvnowxeuutt/Tee+9FmR/T50HEEeaIU7MfFd0wfRHi29+zZ8+uV155Ra+88or0YDjFbt26afv27Xr//fej/TH1Ycnl+wuSF36KAZIILy8vvf766ypZsqTsdrv+/vtvx7yID6v79+9Hu2y1atXk4uKiPXv26K+//ooy/7///nNcX1uzZs1Iy0nSrFmzol3vzJkzn/j5RIyFGdMpgzNmzHjidScmNzc3x1HLmIJFxIfuw69dUtSwYUNJ0ty5c+O13OP615O+lxHXtE2ePDnaUxTjKym/V6tXr9bFixejTF++fLmuXLmidOnSxXhddHTKly+vLFmyaP/+/Y5To+OjS5cucnFx0dy5c3Xnzp14n1qe0BoyZ86s0qVL6+rVq/riiy9kjHEcPdVDoW7p0qX69ddf5enpqapVq8Z5/XE5ivbwaaERIcbPz0+lSpWS3W6P9st0dCJ+GH1cn6tatWq87s0R8fe6ePHiBJ9q6uvrqyFDhihDhgy6c+eODh8+HOdlI96LNWvWaO3atfLz81OJEiWkB9fNV6tWTX/++ad+/vnnSO3j6nH7l6QsLrU/rs2T7Jdz587tuNdDdJ/DISEhmjdvXpzXF6Fjx47Kly+f7t27pzfffPOJzk6K+DyI7qj8vXv3NH/+/GiXi/gbjGnM9Yhr3+Mivv29aNGijrMH9+zZ89j1J5fvL0heCN2ABb788storxM6ePCg4xfUhz/QIo6Q7d+/P9r15c2bV0FBQTLG6LXXXtOVK1cc827fvq1XX31V9+7dU6VKlSLdyKl79+7y8fHRr7/+qnHjxkVa59atWzV+/Pgnfo4R16WvW7cuSt0TJkzQnDlznnjdie2dd96RJH377beOaxsjTJkyRYsXL5a7u7t69eplUYVx8+qrrypfvnyaN2+e+vfvH+0RhfPnz2vixImRpj2ufz3pe/nyyy8rd+7c2r17t1555ZUo1/8FBwc7jnLGVVJ9r+7evas33nhDd+/edUw7d+6co97XX39dXl5ecV6fu7u7Bg8eLGOMWrZsqV9//TVKm/DwcK1fv147duyIMi937tyqW7eugoOD9cEHH2jfvn3KmzevatWq9dRqiAhnEUdxHw7d5cqVU4YMGTRp0iTdvXtXlSpVivE07OjcuHFDZcuW1fTp03Xr1q0o848fP65u3bpJkipVqhTpy/PgwYMlSR9++GG0AWH//v2RgkGvXr3k5uamhQsXRvmyvXr1an3//feSpHfffTfO9evBTfQCAwN15swZtWrVKtojl7dv39ZPP/3kuGfInTt3NGrUqGivyd2yZYuuX78uV1fXeJ1VUaNGDbm5uWn9+vU6cOBAlFBdp04dhYeH69tvv3U8jo+IWp7kxyOrxaX2x7V50v1y7969pQdn3Dx8r4vw8HC9++67cb5Z6MM8PDw0b948eXl5ae7cuWrZsqXjhm2P2rZtW7ShPOLzYOrUqZGey71799SjR48Yz/qpVauWXFxctGrVqkiXERljNGbMmGj/FuPb39evX6/ly5c7bur28DaWLl0qxfEU/uT0/QXJiNW3TweSMmeN050+fXojyRQtWtS0bNnSvPjii6ZGjRrGzc3N6MGYxg8LCQlxDN1TpkwZ06lTJ9O9e3fz+eefO9pcvnzZMXZk+vTpTYsWLUzr1q0dQwTlz58/2iExpk+fblxcXIwejMvavn17U716dePi4uIYMszd3T3KcjENJfaw5s2bG0nGw8PD1KtXz7Rr184ULVrU2Gw28+GHH0Y7xExsQ8/E9ppGeNLhrwYOHOgYtqZKlSrmxRdfNGXLljWSjKurq5k0aVKibcvEYZiZxw1jEtPrv2/fPuPv728kmQwZMphq1aqZF1980bRo0cIUL17c2Gw2kz179kjLxKV/Pcl7aYwxu3btcgxtkyFDBtO4cWPTtm1bU6lSJePt7R3j+xibJ3mvjDGmQoUKjn8FChRwDOX18PSlS5fGq5aIYXA6depkMmXKZPz8/ExQUJBp2rSpY5z7ihUrRhq/28Sj70QMryXJPPvss6Z58+amXbt2pkaNGiZDhgwxDilkHgz/8/AQSYMGDYpxO7H1xyetIWJYneiGtzPGmJYtWzrmf/rpp7G+Do+6du2aY1lPT08TEBBggoKCTOvWrU2FChUc+7R8+fJFGkM8wqeffmpsNptjP9y2bVvTrFkzx3COj+7zv//+e8c6y5Yta1588UVTuXJlxzqGDBkSZRsRQ4bF9vkRHBzsGAbPw8PDlC9f3rRp08YEBQWZ8uXLO8YAP3DgQKTn7eLiYkqVKmVat25t2rdvbypWrOioJbb3OSYVK1Z0vJ5Tp06NNO+vv/5yzEuTJk20Qx3Ftt+OGG86bdq0plWrVqZ79+6me/fu5uDBg8bEYZ9u4vh586jEGKf7/Pnzjr/jypUrmy5dupju3bubyZMnO9osXbrU8f41adLEdOvWzXTv3j3S8JxPsl8ODw83TZs2day7fv36pl27diZ//vzGy8vLMVb3k3z+/PHHH47xr202m3nuuedMy5YtTceOHU3Tpk0jDbfWtGlTc/PmTcey165dc8zPnDmzadGihQkMDDTZsmUz6dKlM7169Yqxroh5rq6upkaNGqZVq1amYMGCxt3d3bz//vtR+kF8+3vE2Pa+vr6mRo0a5sUXXzQtW7Z01Js+fXqze/fuSDXF9FnrjO8vsW0PKR+hG4iFs0L3jBkzTNeuXc1zzz1nMmXKZDw9PU2+fPlMw4YNzYIFC4zdbo+yjb1795pmzZqZrFmzOr78Pbre27dvm+HDh5vSpUsbHx8f4+XlZYoVK2Y++OCDWMcE37hxo6lbt67x9fU1Pj4+pmzZsmbSpEnm9OnTRpLJkSNHlGXi8iUoNDTUfPHFF6ZEiRLGx8fHZMqUydSrV8+sXr06xg8nq0K3McasWLHCNGrUyGTOnNm4ubk5AtRvv/2W6NtyVug2D77If/7556ZixYomQ4YMxt3d3eTIkcOUL1/e9OvXz2zbti3KMo/rX0/yXka4dOmSGThwoClRooRJkyaN8fb2NgUKFDBt27Y1K1eujOMrFll836uHX7PY/sXlC/rDHh579vjx46Z9+/Yme/bsxsPDwxQqVMgMGjTI3L59O8py8ek7W7duNR06dDD58uUznp6eJl26dKZIkSKmRYsW5ocffojxb/vevXsmU6ZMji/Wx48fj3Ebj+uPT1JDxNjcimHc9HHjxjle99jet+jY7Xbz22+/mWHDhpl69eqZwoULm3Tp0hl3d3eTLVs2U7NmTTNq1Chz69atGNexfft20759e5MrVy7j7u5uMmXKZEqVKmXee+89c+rUqSjtd+zYYVq3bm38/PyMm5ubyZw5s2ncuLFZvXp1tOuPS+g2D8LVzJkzTaNGjUz27NmNu7u7yZw5s3nuuedM165dzYIFCxxBNywszHz33Xemffv2pmjRoiZ9+vTG29vbFCxY0AQGBpp169bF63WMEDE2tyRz9uzZSPPsdrvJli2bkWQaNmwY7fKx7QPCw8PN8OHDzbPPPmu8vLwc24noa0k5dBtjzObNm02dOnVMxowZHfvGR/9uJ06caMqWLWt8fHxi3Jc8yX45LCzMjBw50hQvXtx4enqazJkzm+bNm5s9e/Yk6PPHPNinT5061bRq1crkzZvXeHt7Gw8PD5MtWzZTrVo1M2DAALN3795ol7106ZLp0aOHKViwoPH09DQ5c+Y0HTt2NEeOHIm1LrvdbkaOHGmKFStmPDw8TKZMmUzTpk3Nzp07o+0H8e3vR48eNUOGDDG1a9c2efPmNV5eXiZjxoymZMmS5v333zdnzpyJUlNMn7XO+P4S2/aQ8tlMQm83CyDFmjZtmjp37qymTZtq8eLFVpcDJDlDhgzR0KFDNXjw4DjfdA0AAKQuXNMNpHKnT5/W+fPno0zfunWr4xrF+Nx8CQAAAMD/x5BhQCq3fv16de/eXaVKlVLevHnl6uqqY8eOOe6C3rVrV7Vs2dLqMgEAAIBkidANpHIvvPCCunbtqi1btmjjxo26ffu2MmTIoDp16qhbt25q37691SUCAAAAyRbXdAMAAAAA4CRc0w0AAAAAgJMQugEAAAAAcBKu6U5Edrtd586dU7p06WSz2awuBwAAAADgJMYY3bx5Uzlz5pSLS8zHswndiejcuXPKkyeP1WUAAAAAAJ6SM2fOKHfu3DHOJ3QnonTp0kkPXnRfX1+rywHixW6369KlS8qaNWusv9QByQ19GykR/RopFX0byUlwcLDy5MnjyIExIXQnoohTyn19fQndSHbsdrvu3bsnX19fPuSQotC3kRLRr5FS0beRHD3u0mJ6MgAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkblYXAGvcu3dP//33n+7duydjjNXlpCo2m01eXl7KkSOHvLy8rC4HAAAAgBMRulOZkydPatu2bTp27JjCw8OtLidVc3V1VaFChVSpUiXly5fP6nIAAAAAOAGhOxU5cuSIZs+erWzZsqlu3boqVKiQfHx85OLCVQZPk91u1507d3T06FHt2bNH06dPV7t27VSoUCGrSwMAAACQyAjdqcSNGzc0e/ZsFS5cWEFBQXJ1dbW6pFTNx8dHWbJkUbly5TR37lzNnj1bvXr1Urp06awuDQAAAEAi4hBnKrF//37ZbDa1bNmSwJ2EuLm5qWXLljLGaP/+/VaXAwAAACCREbpTiQMHDqhgwYLy9PS0uhQ8wtvbWwUKFNCBAwesLgUAAABAIiN0pxLXrl1Tjhw5rC4DMciRI4euXbtmdRkAAAAAEhmhO5UICwuTh4eH1WUgBh4eHgoNDbW6DAAAAACJjBupAQAApCDzjt1w6vqDCqZ36voBIKXhSDcAAAAAAE5C6AYAAAAAwEkI3UiypkyZIpvNFuu/2rVrx3l9drtdY8eOVdmyZeXj4yNfX19Vq1ZNixcvjtL23r176tu3r6pVq6acOXPKy8tLfn5+qly5sn788UeFhYUl8rMFAAAAkBJxTTeSrNKlS2vw4MHRzvv555/1zz//qH79+nFalzFGbdq00fz581WwYEF1795dISEhWrRokZo3b65vvvlGPXv2dLS/deuWvv32WwUEBKhx48bKmjWrrl27phUrVqhbt26aPXu2VqxYIRcXfrcCAAAAEDNCN5Ks0qVLq3Tp0lGmh4aGauzYsXJzc1Pnzp3jtK758+dr/vz5qly5stasWSNvb29J0rBhw1SuXDm9++67atKkifz9/SVJmTJl0o0bN6Lc8f3+/fuqW7euVq9erRUrVqhx48aJ8lwBAAAApEwcpkMkn3zyiWw2m1atWhVl3pIlS2Sz2TRy5EhLaouwcOFCXblyRU2aNFH27NnjtMyiRYskSR988IEjcEtSlixZ1KdPH4WEhOjHH390THdxcYl2iDU3Nze1bNlSknT06NFEeDYAAAAAUjJCNyLZvXu3JKls2bJR5u3atSvGeU/TDz/8IEl6+eWX47zM+fPnJUn58+ePMi9i2vr16x+7HrvdrpUrV0qSnnvuuThvHwAAAEDqxOnliGT37t3KnTu3smbNGmVeROiO7pTvCKNHj9b169fjvL0WLVrEur5HnTp1SuvWrVPu3LnVoEGDOC+XJUsWSdKJEydUrFixSPNOnDghSTp8+HCU5UJDQzVs2DAZY3TlyhWtW7dOBw8eVNeuXeN1EzcAAAAAqROhGw7Xrl3TyZMn1axZs2jn79q1S/7+/sqYMWOM6xg9erROnToV5236+/vHK3T/+OOPstvt6tKli1xdXeO8XMOGDTV79mx99tlnqlWrlry8vCRJV65c0ejRoyUp2h8LQkNDNXToUMdjm82md999V8OHD4/ztgEAAACkXoRuOOzZs0eK4fTxS5cu6d9//3VczxyTkydPOq0+u92uH3/8UTabTd26dYvXsi+++KKmTJmiDRs2qESJEmrQoIHCwsK0cOFCx3Xh0d2JPG3atDLGyG6369y5c1qyZIk++OADbd++XcuXL5evr2+iPT8AAAAAKQ/XdMMh4nruMmXKRJkXcWp5dPOelrVr1+r06dOqVatWtNdmx8bNzU0rVqzQkCFD5OLiogkTJuiXX35R8+bN9fPPP0uSsmXLFuPyLi4uyp07t9544w1NmDBBW7du1aeffprg5wQAAAAgZeNINxxiu4natm3bpDiEbmde0/0kN1B7mKenpwYPHhxl7O+NGzdKksqVKxen9dSrVy/ScgAAAAAQE0I3HHbv3i2bzaacOXNGmm632zV//nwpjqHbGdd0X7lyRYsWLVKmTJkee4p7fP3000+SpHbt2sWp/blz5yRJ7u7uiVoHAAAAgJSH0A1J0t27d3Xw4EEZY7R9+3ZVrlxZkmSM0eDBg/XPP/8oY8aMypUrV6zrcdY13dOnT1doaKg6duwoT0/PWNseO3ZMYWFhKliwYKRgHBwcHOUa7J9//lmTJ09W+fLl1apVK8f0/fv3y9/fXz4+PpHa37lzR3379pUkNWrUKJGeHQAAAICUitANSdLevXsVHh6ubNmyqWHDhgoMDJS3t7e2bdum4OBg2Ww2BQcHq3v37ho/fvxjg29imzRpkhTHU8tr166tU6dO6cSJE/L393dMr1ChgvLkyaNixYrJy8tLv//+uzZu3KgCBQpo3rx5ke6GPnfuXI0aNUpVqlSRv7+/fH19dfbsWa1YsUJXrlxR1apV1adPHyc9WwAAAAApBaEb0kPXc3/++efavn27Zs2aJUmqW7euvvrqK/Xv31+LFy/W3bt3n3rg/v3337Vv3z4FBASoRIkST7yetm3b6pdfftGOHTsUFham/Pnza+DAgerXr1+UI+BNmjTRuXPntG3bNm3fvl23bt1S+vTpVbJkSbVr107dunWTmxt/PgAAAABiR2qA9FDorlChgjp37qzvvvsu0vyZM2daVJkUEBAgY0yc28d0ivuQIUM0ZMiQOK2jXLlycb6xGgAAAADEhCHDID0I3T4+PipSpIjVpQAAAABAikHohsLDw7V3716VKFFCLi50CQAAAABILCQs6ODBg7p7926cx8sGAAAAAMQN13RDzz77bLyumQYAAAAAxA1HugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQjWRkxYoRsNptsNpt27NgRY7sFCxaobt26ypw5s7y8vJQ/f361b99eZ86cidN27t27p759+6patWrKmTOnvLy85Ofnp8qVK+vHH39UWFhYlGWMMfrll19Us2ZN5ciRQz4+PnrmmWf02muv6fjx4wl63gAAAACSJ4YMQ7Kxb98+DR48WGnSpNHt27ejbWOM0euvv64JEyaoYMGCateundKlS6dz585p06ZNOnXqlPLkyfPYbd26dUvffvutAgIC1LhxY2XNmlXXrl3TihUr1K1bN82ePVsrVqyQi8v//93q3Xff1ahRo5QjRw61aNFCvr6++uuvvzRx4kTNmjVL27Zt03PPPZeorwkAAACApC3JHukeN26c/P395eXlpQoVKuj333+Ptf28efNUtGhReXl5qUSJElq+fLljXlhYmPr3768SJUooTZo0ypkzpzp16qRz585FWsfVq1fVoUMH+fr6KkOGDOrevbtu3brltOeIuAsLC1Pnzp1VunRptWzZMsZ2Y8aM0YQJE9SjRw8dOnRI48aN02effaZp06bp1KlTeuGFF+K0vUyZMunGjRvatGmTJk6cqGHDhunbb7/V0aNHVaNGDa1evVorVqxwtD9//rxGjx6tfPny6cCBA/r22281YsQIrVy5UiNHjtTNmzc1atSoRHktAAAAACQfSTJ0z5kzR3379tXgwYO1a9culSpVSvXr19fFixejbb9t2za1b99e3bt31+7du9WiRQu1aNFC+/btkyTduXNHu3bt0kcffaRdu3bpl19+0aFDh9SsWbNI6+nQoYP++ecfrVmzRkuXLtXmzZv16quvPpXnnJRcvnxZ7733nooXLy4fHx/H6dwP/6tSpcpTrenTTz/VP//8o8mTJ8vV1TXaNnfv3tXQoUNVoEABff3119G2c3OL28kdLi4u8vDwiHb5iNB/9OhRx/STJ0/KbrercuXKSp8+faRlmjRpIkm6dOlSnLYNAAAAIOVIkqeXjxo1Sq+88oq6du0qSfruu++0bNkyTZ48We+//36U9l9//bUaNGigfv36SZI++eQTrVmzRmPHjtV3332n9OnTa82aNZGWGTt2rAICAnT69GnlzZtXBw4c0MqVK/XHH3+oXLlykqRvvvlGjRo10pdffqmcOXM+ledutVOnTqlq1ao6c+aMqlatqubNm+vSpUuaNWuW7ty5o/Tp0ytLliyqVavWU6tp165d+vTTT/Xxxx+rePHiMbZbvXq1rl27pq5duyo8PFyLFy/W4cOHlSFDBtWpU0eFChVKcC12u10rV66UpEinihcuXFgeHh7aunWrgoOD5evr65i3dOlSSVLt2rUTvH0AAAAAyUuSC92hoaHauXOnBgwY4Jjm4uKiOnXqaPv27dEus337dvXt2zfStPr162vhwoUxbufGjRuy2WzKkCGDYx0ZMmRwBG5JqlOnjlxcXPTbb79Fe0pzSEiIQkJCHI+Dg4OlB8HMbrfH63k7mzHG8S8mdrtdbdq00ZkzZ/TNN9/ozTffdMxr0aKFmjZtqmLFimnbtm2OdT5q9OjRun79epzratGihUqXLh3j/JCQEHXq1EmlS5dWv379Im3z0efz559/Sg/6S8mSJXX48GHHPBcXF/Xu3VtffvllnGvTg/44bNgwGWN05coVrV+/XgcPHlSXLl1Uq1Ytx/YzZcqk4cOH691331XRokXVrFkz+fr66u+//9b69ev1xhtv6M0334zx9Y94Llb2G7vdbnkNgDPQt5ESxdqvjXP7On9LcCb22UhO4tpPk1zovnz5ssLDw5U9e/ZI07Nnz66DBw9Gu8z58+ejbX/+/Plo29+7d0/9+/dX+/btHUckz58/r2zZskVq5+bmpkyZMsW4nuHDh2vo0KFRpl+6dEn37t17zDN9uu7cuaO7d+/GeAMySVqyZIl+//13NWvWTF26dInUtmrVqvL19dWff/6pW7duyWazRbuO0aNH6/Tp03GuK0eOHCpcuHCM8z/66CMdOXJEW7ZscbymEXcOf/T5RFyj/9VXX6l06dLauHGjnnnmGf311196++23NWrUKOXJk0cvv/xynOu7deuWPv74Y8djm82mt99+W0OHDo3yWr766qvKkiWLevbsqe+//94xvWLFimrZsmWUH2keFvFcYrqE4mmw2+26ceOGjDGRbhAHJHf0baREsfVr282YP+sTw8WL0X+WAYmBfTaSk5s3b8apXZIL3c4WFhamNm3ayBijb7/9NkHrGjBgQKQj7MHBwcqTJ4+yZs0a6fTipMDHx0fe3t5KkyZNjG0WLFggSerbt2+07by9vXX79m35+PjEuBM8efJkotW8fft2jRkzRoMHD1b58uUd093d3R31PFxnRE0eHh5atGiR45KAevXq6eeff1bp0qU1duxY9erVK841pEmTxnHmwrlz57RkyRJ9+OGH2rlzp5YtWxbpff7444/16aefaujQoerYsaMyZMigPXv2qG/fvmrUqJF+/vnnKPcRiBDxXB794edpstvtstlsypo1Kx9ySFHo20iJYuvX5tYNp247W7b0cWgFPBn22UhOvLy84tQuyYXuLFmyyNXVVRcuXIg0/cKFC/Lz84t2GT8/vzi1jwjcp06d0vr16yMFJj8/vyhHGe/fv6+rV6/GuF1PT095enpGme7i4pLkdhIP3wQtJr/++qvSpEmjKlWqRGl39+5dXb16Vfnz54/xRmaJ6f79++rSpYtKliypAQMGRFv3o88n4lKBcuXKKVeuXJHalihRQgUKFNDRo0d148YNR9u4cnV1VZ48edSjRw9lzZpVbdq00bBhwzRixAhJ0tq1azVkyBD16dMn0qURVatW1ZIlS1SgQAG9++67at68ebTrj3guVvebiBqsrgNIbPRtpEQx9mubc/s5f0dwNvbZSC7i2keTXOj28PDQ888/r3Xr1qlFixbSg1+81q1bp549e0a7TMWKFbVu3Tr17t3bMW3NmjWqWLGi43FE4D5y5Ig2bNigzJkzR1nH9evXtXPnTj3//POSpPXr18tut6tChQpOerZJR0hIiP77778YQ/Xq1asVFhb22BuoJdY13bdu3dKRI0ekB30iOhHv74IFC9SiRQs988wz0kPh+1ER0+/evRvv0P2wevXqSZI2btzomBYxfFjNmjWjtPfz81PRokW1e/du3bp1S2nTpn3ibQMAAABIXpJc6NaD05s7d+6scuXKKSAgQKNHj9bt27cddzPv1KmTcuXKpeHDh0uSevXqperVq2vkyJFq3LixZs+erT///FMTJkyQHgTu1q1ba9euXVq6dKnCw8Md12lnypRJHh4eKlasmBo0aKBXXnlF3333ncLCwtSzZ0+1a9cu1dy5XA9Okbfb7ZF+tTHGOMaYftz10KNHj9apU6fivD1/f/9oQ7enp6e6d+8e7TKbN2/WkSNH1KxZM2XNmlX+/v7SQ4H3wIEDUZYJCwvT0aNHlSZNGmXNmjXO9UUn4trxiNPc9eCGa4plWLBLly7JxcUl0jIAAAAAUr4kGbrbtm2rS5cuadCgQTp//rxKly6tlStXOm6Wdvr06UihsFKlSpo5c6YGDhyoDz74QIULF9bChQsdQzqdPXtWixcvlqQoAW/Dhg2qUaOGJOmnn35Sz549Vbt2bbm4uCgwMFBjxox5is/cOp6enipWrJgOHDigVatWqWHDho55H330kTZv3qxOnTpFurY6Ool1Tbe3t7d++OGHaOd16dJFR44c0YABA/TCCy84phcsWFD16tXT6tWr9cMPP0T6geCzzz7T9evX1bFjxyhjdR87dkxhYWEqWLCgIxTv379f/v7+8vHxidT2zp07juv4GzVq5JheuXJljR07VqNGjVJgYGCksbq/++47/fvvv6pcuXK0lyMAAAAASLmSZOiWpJ49e8Z4OvnDp/VGCAoKUlBQULTt/f39Yx0qK0KmTJk0c+bMJ6g2Zfjwww/VsWNHBQYGqkOHDsqcObPWrl2rnTt3ql69evruu++sLvGxxo8fr0qVKumVV17RwoULHad1r1+/Xvny5dMXX3wRZZnatWvr1KlTOnHihOOo+dy5czVq1ChVqVJF/v7+8vX11dmzZ7VixQpduXJFVatWVZ8+fRzrCAoK0rfffqvNmzerSJEiatasmTJkyKBdu3Zp/fr18vb2dpwtAAAAACD1SLKhG09fhw4d5OLiopEjR+qnn35yjHf9ww8/qGvXrsniZhYFCxbUn3/+qUGDBmnlypVavXq1/Pz89Oabb2rQoEFxvjt4kyZNdO7cOW3btk3bt2/XrVu3lD59epUsWVLt2rVTt27dIh0xd3V11erVq/XVV19p7ty5mjlzpkJDQ5U9e3Z17NhRH3zwgYoVK+bEZw4AAAAgKbKZuBwCRpwEBwcrffr0unHjRpIbMuyzzz5TtWrVVKlSJatLQTR+/fVXbd26Vf3797esBrvdrosXLypbtmzJ4gcWIK7o20iJYuvX8445d8iwoIIMGQbnYZ+N5CSu+Y+eDAAAAACAkxC6AQAAAABwEkI3AAAAAABOQuhOJVxcXBQeHm51GYhBeHi4XF1drS4DAAAAQCIjdKcSPj4+Cg4OtroMxCA4ODjKmOAAAAAAkj9CdypRoEABHTp0KE7jlePpstvtOnz4sAoUKGB1KQAAAAASGaE7lShevLiCg4O1b98+q0vBI/bu3aubN2/q2WeftboUAAAAAInMzeoC8HTky5dPzz33nBYsWKC7d++qZMmS8vLysrqsVO3evXv666+/tGrVKpUsWVK5c+e2uiQAAAAAiYzQnUrYbDa1atVKrq6uWr58uVatWqXcuXPLx8dHLi6c8PA02e123blzR//++6/Cw8NVpkwZNW3aVDabzerSAAAAACQyQncq4uLiopYtW6pWrVo6cOCAzpw5o3v37nGd91Nms9mUNm1a1a1bV8WLF5evr6/VJQEAAABwEkJ3KpQ+fXq98MILeuGFF6wuBQAAAABSNM4rBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMkydA9btw4+fv7y8vLSxUqVNDvv/8ea/t58+apaNGi8vLyUokSJbR8+fJI83/55RfVq1dPmTNnls1m0549e6Kso0aNGrLZbJH+vf7664n+3AAAAAAAqUeSC91z5sxR3759NXjwYO3atUulSpVS/fr1dfHixWjbb9u2Te3bt1f37t21e/dutWjRQi1atNC+ffscbW7fvq0qVapoxIgRsW77lVde0X///ef49/nnnyf68wMAAAAApB5JLnSPGjVKr7zyirp27arixYvru+++k4+PjyZPnhxt+6+//loNGjRQv379VKxYMX3yyScqW7asxo4d62jz0ksvadCgQapTp06s2/bx8ZGfn5/jn6+vb6I/PwAAAABA6uFmdQEPCw0N1c6dOzVgwADHNBcXF9WpU0fbt2+Pdpnt27erb9++kabVr19fCxcujPf2f/rpJ82YMUN+fn5q2rSpPvroI/n4+MTYPiQkRCEhIY7HwcHBkiS73S673R7v7QNWstvtMsbQd5Hi0LeREsXar41z+zp/S3Am9tlITuLaT5NU6L58+bLCw8OVPXv2SNOzZ8+ugwcPRrvM+fPno21//vz5eG37xRdfVL58+ZQzZ079/fff6t+/vw4dOqRffvklxmWGDx+uoUOHRpl+6dIl3bt3L17bB6xmt9t148YNGWPk4pLkToIBnhh9GylRbP3advO2U7d98WJIHFoBT4Z9NpKTmzdvxqldkgrdVnr11Vcd/12iRAnlyJFDtWvX1rFjx1SwYMFolxkwYECko+zBwcHKkyePsmbNyqnpSHbsdrtsNpuyZs3KhxxSFPo2UqLY+rW5dcOp286WLb1T14/UjX02khMvL684tUtSoTtLlixydXXVhQsXIk2/cOGC/Pz8ol3Gz88vXu3jqkKFCpKko0ePxhi6PT095enpGWW6i4sLOwkkSzabjf6LFIm+jZQoxn5tc24/5+8IzsY+G8lFXPtokurJHh4eev7557Vu3TrHNLvdrnXr1qlixYrRLlOxYsVI7SVpzZo1MbaPq4hhxXLkyJGg9QAAAAAAUq8kdaRbkvr27avOnTurXLlyCggI0OjRo3X79m117dpVktSpUyflypVLw4cPlyT16tVL1atX18iRI9W4cWPNnj1bf/75pyZMmOBY59WrV3X69GmdO3dOknTo0CHpwVFyPz8/HTt2TDNnzlSjRo2UOXNm/f333+rTp4+qVaumkiVLWvI6AAAAAACSvyQXutu2batLly5p0KBBOn/+vEqXLq2VK1c6bpZ2+vTpSIfxK1WqpJkzZ2rgwIH64IMPVLhwYS1cuFDPPfeco83ixYsdoV2S2rVrJ0kaPHiwhgwZIg8PD61du9YR8PPkyaPAwEANHDjwqT53AAAAAEDKYjPGGKuLSCmCg4OVPn163bhxgxupIdmx2+26ePGismXLxjVUSFHo20iJYuvX844590ZqQQW5kRqch302kpO45j96MgAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATpKg0H3u3LnEqwQAAAAAgBQmQaHb399fzZs319KlS2W32xOvKgAAAAAAUoAEhe4XXnhBS5YsUfPmzZU3b14NGjRIJ0+eTLzqAAAAAABIxhIUujdv3qyDBw+qb9++un//vv73v/+pUKFCatCggebPn6/79+8nXqUAAAAAACQzCb6RWpEiRfTFF1/o33//1bx581S3bl2tXbtWbdq0Ua5cudS/f38dPnw4caoFAAAAACAZSbS7l7u5uSkwMFArVqzQyZMnNXjwYLm4uOjLL79UsWLFVLNmTc2dO1fGmMTaJAAAAAAASVqiDxlmt9u1c+dO/fHHH7p06ZKMMcqTJ4+2bt2q9u3bq1SpUjpy5EhibxYAAAAAgCQn0UL38ePH9cEHHyhPnjxq1aqVVq9ercDAQK1bt04nT57U6dOn9e677+rgwYN64403EmuzAAAAAAAkWW4JWTgsLEzz58/XxIkTtWnTJtntduXPn1/Dhg1T165dlS1bNkdbPz8/jRgxQsHBwZo2bVpi1A4AAAAAQJKWoNCdM2dOXb16Va6urmrevLlee+011atXL9Zl8uXLp7t37yZkswAAAAAAJAsJCt0+Pj7q1auXunfvrhw5csRpmR49eqh9+/YJ2SwAAAAAAMlCgkL3yZMnZbPZ4rWMr6+vfH19E7JZAAAAAACShQTdSK1gwYL65ptvYm0zbtw4FShQICGbAQAAAAAgWUpQ6D558qSuXbsWa5vr16/r1KlTCdkMAAAAAADJUqKP0/2oGzduyNPT09mbAQAAAAAgyYn3Nd2bN2+O9PjkyZNRpklSeHi4zpw5o59++klFihRJWJUAAAAAACRD8Q7dNWrUcNw8zWazaerUqZo6dWq0bY0xstls+uyzzxJeKQAAAAAAyUy8Q/egQYNks9lkjNHHH3+s6tWrq0aNGlHaubq6KlOmTKpZs6aKFSuWWPUCAAAAAJBsxDt0DxkyxPHfmzZtUteuXdWpU6fErgsAAAAAgGQvQeN0b9iwIfEqAQAAAAAghXH63csBAAAAAEit4nWku0CBArLZbFq7dq3y58+vAgUKxGk5m82mY8eOPWmNAAAAAAAkS/EK3Xa73XHn8ugex8QY82TVAQAAAACQjMUrdJ88eTLWxwAAAAAA4P/jmm4AAAAAAJwkQXcvj0lwcLB+++03eXl5qUqVKnE6BR0AAAAAgJQmQUe6J06cqOrVq+vatWuOaX/99ZeKFi2qBg0aqEaNGqpataru3LmTGLUCAAAAAJCsJCh0T58+XSEhIcqYMaNj2jvvvKOLFy+qa9euatSokbZv365vv/02MWoFAAAAACBZSVDoPnz4sEqVKuV4fOXKFW3YsEEvv/yyfvjhBy1ZskTly5fXTz/9lBi1AgAAAACQrCQodF+/fl1Zs2Z1PN6yZYskqVWrVo5pVapU4S7nAAAAAIBUKUGhO3PmzPrvv/8cj9etWydXV1dVrlzZMc0Yo7CwsIRVCQAAAABAMpSg0F2yZEktWrRI+/bt09GjRzVz5kxVrlxZadKkcbQ5efKkcuTIkRi1AgAAAACQrCQodL/33nu6du2aSpUqpWeeeUbXr19X3759HfPtdrt+/fVXPf/884lRKwAAAAAAyUqCxumuWbOmFi9erB9//FGS1K5dOzVt2tQxf+vWrcqZM2eka7wBAAAAAEgtEhS6Jalx48Zq3LhxtPOqVq2q3bt3J3QTAAAASCLmHbvh9G0EFUzv9G0AwNOSoNPLAQAAAABAzBJ8pFuSfv/9d/3xxx+6fv26wsPDo8y32Wz66KOPEmNTAAAAAAAkGwkK3VevXlWLFi20detWGWNibEfoBgAAAACkRgkK3X379tWvv/6qGjVqqHPnzsqdO7fc3BLl4DkAAAAAAMleghLy0qVLFRAQoHXr1slmsyVeVQAAAAAApAAJupHa3bt3Va1aNQI3AAAAAADRSFDoLl26tE6ePJl41QAAAAAAkIIkKHQPHjxYixcv1o4dOxKvIgAAAAAAUogEXdN9/vx5NW7cWNWrV1eHDh1UtmxZ+fr6Rtu2U6dOCdkUAAAAAADJToJCd5cuXWSz2WSM0ZQpUzRlypQo13cbY2Sz2QjdAAAAAIBUJ0Gh+8cff0y8SgAAAAAASGESFLo7d+6ceJUAAAAAAJDCJOhGagAAAAAAIGaJEroXLFigNm3aqGTJkipUqJBj+sGDB/X555/r7NmzibEZAAAAAACSlQSdXm6329W+fXv9/PPPkiRvb2/dvXvXMT9jxoz68MMPFR4ergEDBiS8WgAAAAAAkpEEHen+6quvNG/ePL322mu6du2a3n333Ujzs2fPrqpVq2rZsmUJrRMAAAAAgGQnQaF7ypQpKl++vMaPHy9fX98ow4VJUqFChXTixImEbAYAAAAAgGQpQaH76NGjqlq1aqxtMmfOrCtXriRkMwAAAAAAJEsJCt3e3t66ceNGrG1OnTqlDBkyJGQzAAAAAAAkSwkK3WXKlNGqVat07969aOdfvXpVK1eu1AsvvJCQzQAAAAAAkCwlKHS//fbb+vfffxUYGKh///030rxjx46pZcuWunHjht5+++2E1gkAAAAAQLKToCHDmjdvrv79+2vEiBHKly+f0qRJI0nKli2brly5ImOMPvroI9WqVSux6gUAAAAAINlI0JFuSRo+fLhWrVqlJk2ayMfHR66urrLb7WrQoIFWrFihoUOHJk6lAAAAAAAkMwkO3ZJUt25dLVq0SOfPn1doaKguX76sZcuWqX79+k+0vnHjxsnf319eXl6qUKGCfv/991jbz5s3T0WLFpWXl5dKlCih5cuXR5r/yy+/qF69esqcObNsNpv27NkTZR337t3Tm2++qcyZMytt2rQKDAzUhQsXnqh+AAAAAACU0NB99uxZjRs3Tl26dFGTJk3UpEkTdevWTd9++63++++/J1rnnDlz1LdvXw0ePFi7du1SqVKlVL9+fV28eDHa9tu2bVP79u3VvXt37d69Wy1atFCLFi20b98+R5vbt2+rSpUqGjFiRIzb7dOnj5YsWaJ58+Zp06ZNOnfunFq1avVEzwEAAAAAAEmyGWPMkyw4ePBgff755woNDdWjq7DZbPL09NSAAQP00UcfxWu9FSpUUPny5TV27FhJkt1uV548efTWW2/p/fffj9K+bdu2un37tpYuXeqY9sILL6h06dL67rvvIrU9efKk8ufPr927d6t06dKO6Tdu3FDWrFk1c+ZMtW7dWpJ08OBBFStWTNu3b4/z3deDg4OVPn163bhxQ76+vvF63oDV7Ha7Ll68qGzZssnFJVFOggGSBPo2UqLY+vW8Y7EP55ocBBVMb3UJsAj7bCQncc1/T3QjtQ8//FDDhw+Xp6enOnbsqBo1aihnzpySpHPnzmnDhg2aN2+ehgwZovDwcA0ZMiRO6w0NDdXOnTs1YMAAxzQXFxfVqVNH27dvj3aZ7du3q2/fvpGm1a9fXwsXLozz89m5c6fCwsJUp04dx7SiRYsqb968sYbukJAQhYSEOB4HBwdLD3YWdrs9ztsHkgK73S5jDH0XKQ59GylRrP3aJP++zt9r6sU+G8lJXPtpvEP38ePH9fnnnyt//vxasWKFihQpEqVN165dNXDgQNWvX1/Dhg1T586dlT9//seu+/LlywoPD1f27NkjTc+ePbsOHjwY7TLnz5+Ptv358+fj/JzOnz8vDw8PZciQIV7rGT58eLQ3irt06VKMY5cDSZXdbteNGzdkjOGXZaQo9G2kRLH1a9vN25bVlVguXgyJQyukROyzkZzcvHkzTu3iHbqnTp0qu92u6dOnRxu4IxQpUkQzZsxQ1apVNW3aNA0ePDi+m0ryBgwYEOkoe3BwsPLkyaOsWbNyejmSHbvdLpvNpqxZs/IhhxSFvo2UKLZ+bW4l/9PLs2Xj9PLUin02khMvL684tYt36N66dauee+45VapU6bFtK1eurBIlSmjLli1xWneWLFnk6uoa5a7hFy5ckJ+fX7TL+Pn5xat9TOsIDQ3V9evXIx3tftx6PD095enpGWW6i4sLOwkkSzabjf6LFIm+jZQoxn5tS/79nL/V1I19NpKLuPbRePfkAwcOKCAgIM7tAwICYjw1/FEeHh56/vnntW7dOsc0u92udevWqWLFitEuU7FixUjtJWnNmjUxto/O888/L3d390jrOXTokE6fPh2v9QAAAAAA8LB4H+m+fv26smXLFuf22bJl0/Xr1+Pcvm/fvurcubPKlSungIAAjR49Wrdv31bXrl0lSZ06dVKuXLk0fPhwSVKvXr1UvXp1jRw5Uo0bN9bs2bP1559/asKECY51Xr16VadPn9a5c+ekB4FaD45w+/n5KX369Orevbv69u2rTJkyydfXV2+99ZYqVqwY5zuXAwAAAADwqHiH7rt370Z7SnVMPDw8dPfu3Ti3b9u2rS5duqRBgwbp/PnzKl26tFauXOm4Wdrp06cjHcavVKmSZs6cqYEDB+qDDz5Q4cKFtXDhQj333HOONosXL3aEdklq166d9GDYs4g7q3/11VdycXFRYGCgQkJCVL9+fY0fPz7OdQMAAAAA8KgnGjLM2Xr27KmePXtGO2/jxo1RpgUFBSkoKCjG9XXp0kVdunSJdZteXl4aN26cxo0b9wQVAwAAAAAQ1ROF7hkzZmjHjh1xanv06NEn2QQAAAAAAMneE4Xuo0ePxitM22y2J9kMAAAAAADJWrxD94kTJ5xTCQAAAAAAKUy8Q3e+fPmcUwkAAAAAACkMI84DAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAJAK2cPDFXL3juzh4VaXAgApmpvVBQAAACDx3LpxXQf/2Kb9v2/V4d1/6HbwdYWFhigsNFT3Q0MUGhKi+6EhCr9/X5Lk7umlvM8Ul3+xEv/3r3gJ5XvmWXmlSWv1UwGAFIHQDQAAkIxdvHhRmzdv1ubNm7Vp0ybt3btXxpg4Lx8Wck/H/t6lY3/vckyz2Wzyy1dA+YqVUKGSZVWpcStlyZnbSc8AAFI2QjcAAEAyc/XqVY0ZM0YLFy7UgQMHYm3rnSad3Dw85OHpJTcPD7l7eMrd0/P//t/DQ1cvntf5k8ciBXVjjP47eUz/nTymHSsW6qcvhqhM9bqq066LylSvK1c3vkICQFyxxwQAAHhK5h27kaDlL545qWVTvtX6eTMUcud2lPk2Fxf5FyuhYuUrqXiFyir6fEX5Zsr82PXeu31Lpw/t18kDex3/Th38R2Eh9yRJxm7Xrg2rtGvDKmX2y6WaQR1VK+gljn4DQBwQugEAAJK4o3/t1OIfvtFvqxbL2O2O6TabTYVKPq9iFSqrePlKKlruBfmkSx/v9XulSasiZQNUpGyAY1r4/fs6d/yIflu1WOvmTteV//6VJF05f1Y/fzNC88d98X9Hv9t2Vtma9eXi6ppIzxYAUhZCNwAAQBJkt9u1a/1KLZn0jQ78sT3SPA8vb9VsFqjGr/aWn38hp2zf1c1NeYoUU54ixdSqx7vas3mt1s6eop0bVsnY7ZGOfvsXK6HuQ7/UM2UrOKUWAEjOCN0AAABJzIl//tJ3H7ytE//8FWl6+sxZ1aDTq6rXvqt83SSTLtNTqcfF1VVla9ZX2Zr1deW/s9ow/yetmzPNcfT75IG9+qhNfdUIfFEd3huq9JmzPpW6ACA5YJxuAACAJCI05J5mfvmxBrSqFSlw5ypYRK99OkbjNu9V4Jv9lC7j0wnb0cmcI5da93xP4zb+pfcnzpF/sRKOeRvnz1SvOs9r5bQJjiHJACC1I3QDAAAkAQd37tB7Tatq4XejZA8PlyTlKVJc/SfM1sgVO1S7bSd5eHpZXaZDxNHv4Qs2qNugz+WTzleSdOdmsCZ//J7eb1lDB3fusLpMALAcoRsAAMBCd2/d1OSh/TS4XUOdO35EkuTq7q42vT/QiIUb9XytBnJxSbpf2Vzd3NSg06v6eu1O1Qh80TH91IF9GtS2gca994auX75oaY0AYKWkuwcHAABI4fZsXqd3GlXUyukTHeNkFy5VTp8v2qzWPd+Tm4eH1SXGWfrMWdVjxHh9MndVpFPON/0yS/0aV9Y/O7ZYWh8AWIXQDQAA8JTdu3Nb4/v30LBugbp87v9uRubp7aPOHw7TJ3NXKU+RYlaX+MSeKVtBny3cqG6Dv1Aa3/8bvuzGlUv6pHMLLf7hG8ePCwCQWhC6AQAAnqLL5/7V4HYNtXH+TMe0EpWq68tl29S4a48UMd61i6urGrz0ikav+VOlqtaWJNnDwzXjs4/01VtddPfWTatLBICnhtANAADwlBze9fv/3Zl8/9+SJO806fT6sDEaOHWhsuf1t7q8RJc+c1YN+GGuAnv2c0zbsXKRPgisrbPHDltaGwA8LYRuAACAp2D69Oka0qGJbjy4qVj2PP769Oc1qtWmk2w2m9XlOY2Lq6va9v5Q730/y3GH87PHDmtAq1rasXKR1eUBgNMRugEAAJwoPDxc77//vjp16qT7YaGSpGcrVNGwX9Yrd+GiVpf31JSr3VCfLdiovM8UlyTdu31Lo3p21owRgxjTG0CKRugGAABwkuDgYLVo0UIjRoxwTKv7Yjd9OGWB0mXMZGltVvDzL6D/zVujKs2CHNMWTxyjT7u20p2bNyytDQCchdANAADgBMePH1elSpW0dOlSSZKrq6u6D/lSr3w8Sm7u7laXZxkvnzR6a+QEdR00Qq5ubpKkfds3a2jHZgq+ctnq8gAg0RG6AQAAEtlvv/2mgIAA/fPPP5KkDBkyaOXKlarf8WWrS0sSbDabGnZ6TYN/Wqp0GTNLkk7885cGv9hYV8+fs7o8AEhUhG4AAIBEtG3bNtWtW1dXrlyRJD3zzDP6/fffVadOHatLS3KKPv+Chs5arkzZc0qSzh47pEHtG+r48eNWlwYAiYbQDQAAkEi2bNmi+vXr6+bN/xuHumbNmtqxY4cKFy5sdWlJVu5Cz+jj2cuVPc//DZl28cwpVa1aVfv377e6NABIFIRuAACARLBx40Y1aNBAt27dkiTVrVtXS5cuVYYMGawuLcnLlsdfQ2evUO5C/3c393Pnzql69eratWuX1aUBQIIRugEAABJo3bp1atSoke7cuSNJatCggRYtWiQfHx+rS0s2MmXPoSEzl6nAc6UlSZcvX1bNmjW1detWq0sDgAQhdAMAACTA6tWr1aRJE929e1eS1LhxYy1YsEDe3t5Wl5bs+GbKrEHTF6lKlSrSgyHX6tWrpzVr1lhdGgA8MUI3AADAE1qxYoWaNWume/fuSZKaNWum+fPny8vLy+rSki2fdOm1atUq1atXT5J0584dNWnSRMuXL7e6NAB4IoRuAACAJ7B06VK1aNFCISEhkqSWLVtq3rx58vT0tLq0ZM/Hx0eLFy9Wq1atJEmhoaFq3bo1p5oDSJYI3QAAAPG0dOlStWrVSqGhoZKk1q1ba86cOfLw8LC6tBTD09NTc+bMUVBQkCTp7t27atKkif7++2+rSwOAeCF0AwAAxMPWrVsVFBSksLAwSVK7du00a9Ysubu7W11aiuPm5qYZM2Y4TjW/fv266tevr2PHjlldGgDEGaEbAAAgjvbv36+mTZs6ruFu27atpk+fLjc3N6tLS7E8PDw0f/58VahQQZJ0/vx51atXT//995/VpQFAnBC6AQAA4uDs2bNq0KCBrl27JkmqU6eOpk2bRuB+CtKmTatly5apePHikqTjx4+rfv36jvcCAJIyQjcAAMBjXL9+XQ0aNNCZM2ckSWXKlNEvv/zCNdxPUebMmbV69Wrly5dPkrR37141bdrUMTY6ACRVhG4AAIBY3Lt3Ty1atNC+ffskSfnz59fy5cuVLl06q0tLdXLlyqXVq1cra9asUjTX1wNAUkToBgAAiEF4eLheeuklbdq0SZKUJUsWrVq1Sn5+flaXlmoVKVJEK1eudPzosXz5cnXp0kV2u93q0gAgWoRuAACAaBhj1Lt3b/3888/Sg7Gjly1bpsKFC1tdWqpXtmxZLVmyxDEm+syZM/Xuu+9aXRYARIvQDQAAEI0RI0Zo7NixkiRXV1f9/PPPCggIsLosPFC9enXNnTtXrq6ukqSvvvpKEyZMsLosAIiC0A0AAPCIadOmacCAAY7HP/zwgxo2bGhpTYiqWbNmGj9+vOPxm2++qQ0bNlhaEwA8itANAADwkC1btujll192PB42bJi6dOliaU2I2auvvqrevXtLku7fv6/AwEAdOXLE6rIAwIHQDQAA8MCpU6cUGBjouBt2jx499P7771tdFh7jyy+/dJyJcO3aNTVp0oQxvAEkGYRuAAAASbdv31bz5s116dIlSVKdOnX09ddfy2azWV0aHsPV1VWzZ89W8eLFJUmHDx9WmzZtGEoMQJJA6AYAAKme3W5X586d9ddff0mSChUqpDlz5sjNzc3q0hBHvr6+Wrp0qbJkySJJWrt2reO0cwCwEqEbAACkev/73/80f/58SVK6dOm0ePFiZcqUyeqyEE/58+fXggUL5O7uLkkaP368xo0bZ3VZAFI5QjcAAEjVfvnlFw0ePFiSZLPZNGvWLBUrVszqsvCEqlSpookTJzoe9+rVS6tXr7a0JgCpG6EbAACkWn/99Zdeeuklx+PPPvtMjRs3trQmJFznzp3Vv39/SVJ4eLjatGmjgwcPWl0WgFSK0A0AAFKlS5cuqXnz5rpz544kqWPHjurXr5/VZSGRDBs2TM2bN5ck3bhxQ02bNtX169etLgtAKkToBgAAqU5oaKhat26tU6dOSZICAgI0ceJE7lSegri4uGjGjBkqVaqUJOno0aPq1KmT7Ha71aUBSGUI3QAAIFUxxuitt97S5s2bJUk5cuTQggUL5OXlZXVpSGRp06bVwoULHTfFW7JkiYYPH251WQBSGUI3AABIVSZNmqQJEyZIkjw9PbVw4ULlzJnT6rLgJP7+/po1a5bjLIaPPvpIq1atsrosAKkIoRsAAKQaO3fuVM+ePR2PJ06cqICAAEtrgvPVq1dPn3zyifTgTIcXX3xRJ0+etLosAKkEoRsAAKQKV69eVevWrRUSEiJJ6tmzZ6Q7lyNlGzBggJo2bSo96AuBgYG6e/eu1WUBSAUI3QAAIMWz2+166aWXHEc3K1SooJEjR1pdFp4iFxcXTZs2TYUKFZIk7dq1S2+++aaMMVaXBiCFI3QDAIAUb9iwYVq+fLkkKUuWLJo3b548PDysLgtPWYYMGfTLL7/Ix8dHkvTjjz9q4sSJVpcFIIUjdAMAgBRtzZo1GjRokCTJZrNp1qxZypMnj9VlwSIlSpTQDz/84Hj81ltv6ffff7e0JgApm5vVBQAAADjL6dOn1b59e8cpxJ988onq1KljdVl4jHnHbjh1/W4BjfT2229rzJgxCg0NVWBgoHbu3Kls2bI5dbsAUieOdAMAgBQpJCREQUFBunLliiSpcePGGjBggNVlIYn48ssvVblyZUnSv//+q3bt2un+/ftWlwUgBSJ0AwCAFOmdd95xnDbs7++v6dOny8WFrz74P+7u7po3b578/PwkSRs2bNDQoUOtLgtACsQnDwAASHF++uknjRs3TpLk6emp+fPnK2PGjFaXhSQmR44cmjt3rlxdXSVJn376qVavXm11WQBSGEI3AABIUf755x+9+uqrjsfjxo1T2bJlLa0JSVfVqlX16aefSpKMMerYsaPOnTtndVkAUhBCNwAASDFu376toKAg3blzR5LUrVs3de/e3eqykMT169dPjRo1kiRdunSJ67sBJCpCNwAASDF69uypAwcOSJJKliypsWPHWl0SkgEXFxdNnTpVuXPnliRt2bJFgwcPtrosACkEoRsAAKQIU6dO1ZQpUyRJadKk0dy5c+Xt7W11WUgmsmTJojlz5jiu7x42bJhWrlxpdVkAUgBCNwAASPb279+vHj16OB5///33euaZZyytCclPpUqVNHz4cMfjjh076t9//7W0JgDJH6EbAAAka3fu3FGbNm0c13G//PLL6tChg9VlIZl655131KRJE0nSlStXuL4bQIIRugEAQLL29ttv659//pEkPffcc/r666+tLgnJWMT13Xnz5pUkbd26VQMHDrS6LADJGKEbAAAkWzNmzNCkSZMkST4+Ppo7d658fHysLgvJXKZMmTRnzhy5ublJkkaMGKFly5ZZXRaAZIrQDQAAkqWDBw/q9ddfdzz+9ttvVaxYMUtrQsrxwgsvaMSIEY7HnTp10pkzZyytCUDyROgGAADJzt27d9WmTRvdvn1bktS1a1d16tTJ6rKQwvTp00fNmjWTJF29elXt27fn+m4A8UboBgAAyU7v3r21d+9eSVLx4sX1zTffWF0SUiCbzaYpU6YoX7580oPru4cMGWJ1WQCSGUI3AABIVmbNmqUJEyZIkry9vTV37lylSZPG6rKQQmXMmFGzZs2KNH732rVrrS4LQDJC6AYAAMnG0aNH9eqrrzoejxs3Ts8++6ylNSHlq1ixoj799FNJkjFGHTt21IULF6wuC0AyQegGAADJQkhIiNq2batbt25Jkl566SV16dLF6rKQSvTr10/169eXJF24cEGdOnWS3W63uiwAyQChGwAAJAvvv/++du3aJUkqUqSIxo8fL5vNZnVZSCVcXFw0bdo0+fn5SZJWr16tzz//3OqyACQDhG4AAJDkLVmyRKNHj5YkeXp6as6cOUqbNq3VZSGVyZYtm2bMmOH4sWfgwIHaunWr1WUBSOII3QAAIEk7c+ZMpNPIR44cqdKlS1taE1Kv2rVr68MPP5QkhYeHq3379rp69arVZQFIwgjdAAAgybp//75efPFFR6hp2bKlevToYXVZSOUGDx6sqlWrSg9+FOrevbuMMVaXBSCJInQDAIAka+jQofr1118lSXnz5tWkSZO4jhuWc3Nz08yZM5UpUyZJ0sKFCzV27FirywKQRBG6AQBAkrR+/XrHME2urq6aNWuWMmbMaHVZgCQpd+7cmjJliuPxu+++67jRHwA8LMmG7nHjxsnf319eXl6qUKGCfv/991jbz5s3T0WLFpWXl5dKlCih5cuXR5pvjNGgQYOUI0cOeXt7q06dOjpy5EikNv7+/rLZbJH+ffbZZ055fgAAIGYXL15Uhw4dHKfs/u9//1OlSpWsLgspyLxjNxL8717xamrc9f8udwgNDVXjVkGa9te/mnfshtVPD0ASkiRD95w5c9S3b18NHjxYu3btUqlSpVS/fn1dvHgx2vbbtm1T+/bt1b17d+3evVstWrRQixYttG/fPkebzz//XGPGjNF3332n3377TWnSpFH9+vV17969SOv6+OOP9d9//zn+vfXWW05/vgAA4P+z2+3q1KmTzp8/L0mqV6+e3nvvPavLAqLVod8QFSxRRpJ0/tRxTRzUl+u7AUSSJEP3qFGj9Morr6hr164qXry4vvvuO/n4+Gjy5MnRtv/666/VoEED9evXT8WKFdMnn3yismXLOq6tMcZo9OjRGjhwoJo3b66SJUtq2rRpOnfunBYuXBhpXenSpZOfn5/jX5o0aZ7KcwYAAP/nyy+/1KpVqyRJfn5+mjZtmlxckuRXFkBuHh7qNXqyvNOkkyT9unieNvw8w+qyACQhSe4TLDQ0VDt37lSdOnUc01xcXFSnTh1t37492mW2b98eqb0k1a9f39H+xIkTOn/+fKQ26dOnV4UKFaKs87PPPlPmzJlVpkwZffHFF7p//34iP0MAABCT7du3O4ZjstlsmjFjhrJnz251WUCs/PLl12vDxjgeTx76nvbv329pTQCSDjerC3jU5cuXFR4eHuUDNnv27Dp48GC0y5w/fz7a9hGnpUX8f2xtJOntt99W2bJllSlTJm3btk0DBgzQf//9p1GjRkW73ZCQEIWEhDgeBwcHSw9Oi7Pb7fF85oC17Ha7jDH0XaQ49O3k49q1a2rXrp3jB+8BAwaoZs2aKeu9M4n0XIz5//+Ugl6fZKxSo+bat62z1s6ZqtB7d9WmTRvt2LFDPj4+VpeWrLDPRnIS136a5EK3lfr27ev475IlS8rDw0Ovvfaahg8fLk9Pzyjthw8frqFDh0aZfunSpSjXigNJnd1u140bN2SM4TROpCj07eTBGKNu3brp9OnTkqSAgAC98cYbMd7PJbmy3bydSGsyst29KdmkB/+DJKBLr346tHO7zhw9rH/++Uevv/66vvzyS6vLSlbYZyM5uXnzZpzaJbnQnSVLFrm6uurChQuRpl+4cEF+fn7RLuPn5xdr+4j/v3DhgnLkyBGpTenSpWOspUKFCrp//75OnjypZ555Jsr8AQMGRArqwcHBypMnj7JmzSpfX984P2cgKbDb7bLZbMqaNSsfckhR6NvJw9ixY7Vy5UpJUubMmTVv3jzlzJnT6rISnbmVSHe1NkYykkmbSWLc8iTDI53U55upGtCqtkLu3tFPP/2kRo0aqV27dlaXlmywz0Zy4uXlFad2SS50e3h46Pnnn9e6devUokUL6cEf37p169SzZ89ol6lYsaLWrVun3r17O6atWbNGFStWlCTlz59ffn5+WrdunSNkBwcH67ffftMbb7wRYy179uyRi4uLsmXLFu18T0/PaI+Au7i4sJNAsmSz2ei/SJHo20nbzp071a9fP8fjqVOnKm/evJbW5DS2xOqD9v8L2zZbIq4TiSF34WLqNvhzffv+/31vff311xUQEKBChQpZXVqywT4byUVc+2iSC916cJp3586dVa5cOQUEBGj06NG6ffu2unbtKknq1KmTcuXKpeHDh0uSevXqperVq2vkyJFq3LixZs+erT///FMTJkyQHvzh9u7dW//73/9UuHBh5c+fXx999JFy5szpCPbbt2/Xb7/9ppo1aypdunTavn27+vTpo44dOypjxowWvhoAAKRcwcHBatu2rUJDQyVJ77zzjho3bmx1WUCC1AjsoJv7dmjGjBm6efOm2rVrp61bt0Z7sAZAypckQ3fbtm116dIlDRo0SOfPn1fp0qW1cuVKx43QTp8+HelXhUqVKmnmzJkaOHCgPvjgAxUuXFgLFy7Uc88952jz3nvv6fbt23r11Vd1/fp1ValSRStXrnScEuDp6anZs2dryJAhCgkJUf78+dWnT59Ip48DAIDEY4zRq6++qmPHjkkPruMeNmyY1WUBCWaz2TR+/Hj99ttvOnLkiHbu3Kn3339fX331ldWlAbCAzRhjrC4ipQgODlb69Ol148YNrulGsmO323Xx4kVly5aN07mQotC3k66JEyfq1VdflR4M5bl7927lz5/f6rKcat6xxLqm2y7bzasy6TJxenkSFVQwvfbs2aMXXnjBMdrNokWL1KxZM6tLS9LYZyM5iWv+oycDAICnbu/evXr77bcdjydNmpTiAzdSn9KlS2vkyJGOx126dNGpU6csrQnA00foBgAAT9Xt27fVpk0bx/Cab775pgIDA60uC3CKHj16qFWrVtKDsegfvocBgNSB0A0AAJ6qN998UwcPHpQeHAlkHGOkZDabLdKZHL/99pvef/99q8sC8BQRugEAwFPz448/aurUqZKktGnTas6cOXEe5xRIrjJkyKB58+bJw8NDkvTVV19pwYIFVpcF4CkhdAMAgKfi77//Vo8ePRyPv//+exUpUsTSmoCn5fnnn9eoUaMcj7t27arjx49bWhOAp4PQDQAAnC44OFhBQUGO67hfe+01vfjii1aXBTxVPXr0UFBQkCTpxo0batOmjePO5gBSLkI3AABwqojxuA8fPixJKlOmjEaPHm11WcBTZ7PZ9MMPP6hQoUKSpJ07d+rdd9+1uiwATkboBgAATjV+/HjNmTNHkuTr66t58+ZxHTdSrYi/AU9PT0nS2LFjNW/ePKvLAuBEhG4AAOA0f/zxh/r06eN4/OOPP6pgwYKW1gRYrXTp0vr6668dj7t3766jR49aWhMA5yF0AwAAp7h27ZqCgoIUFhYmSerTp49jvGIgtXv11VfVvn17SdLNmzcj3fMAQMpC6AYAAInObrerc+fOOnXqlCSpYsWKGjFihNVlAUmGzWaLdAf/PXv2RDorBEDKQegGAACJbuTIkVqyZIkkKXPmzJozZ47c3d2tLgtIUtKlSxfpHgffffedpk+fbnVZABIZoRsAACSqLVu2aMCAAY7HM2bMUJ48eSytCUiqSpYsqXHjxjkev/baa/rrr78srQlA4iJ0AwCARHPx4kW1a9dO4eHhkqQPP/xQDRo0sLosIEnr1q2bXn75ZUnS3bt31apVK127ds3qsgAkEkI3AABIFGFhYWrTpo3OnTsnSapZs6aGDh1qdVlAsvDNN9/o+eeflyQdP35cnTp1kt1ut7osAImA0A0AABJF//79tWnTJklSjhw5NHPmTLm6ulpdFpAseHl5af78+cqUKZMkaenSpRo2bJjVZQFIBIRuAACQYDNnztRXX30lSXJ3d9f8+fPl5+dndVlAspIvXz7NmjVLNptNkjRo0CCtWrXK6rIAJBChGwAAJMjff//tuB5VksaMGaOKFStaWhOQXNWrV0+ffPKJJMkYoxdffFEnT560uiwACUDoBgAAT+zq1atq2bKl7t69K0nq2rWrXnvtNavLApK1AQMGqGnTptKDv7HAwEDdu3fP6rIAPCFCNwAAeCLh4eHq0KGDjh8/LkkqV66cxo8f7zg1FsCTcXFx0bRp01SwYEFJ0q5du9SzZ0+rywLwhAjdAADgiQwZMkQrV66UJGXJkkXz58+Xl5eX1WUBKUKGDBn0yy+/yNvbW5I0adIk/fDDD1aXBeAJELoBAEC8LVq0SP/73/+kB0fl5syZo7x581pdFpCilCxZUhMnTnQ8fvPNN/Xbb79ZWhOA+CN0AwCAeDl06JBeeuklx+MRI0aoVq1altYEpFQdOnTQW2+9JUkKDQ1Vy5YtdfbsWavLAhAPhG4AABBnN2/eVMuWLXXz5k1JUps2bfTOO+9YXRaQon355ZeqVq2aJOm///5TixYtHDcvBJD0EboBAECc2O12vfTSSzpw4IAk6bnnntOkSZO4cRrgZB4eHvr555/l7+8vSfrzzz/VvXt3GWOsLg1AHBC6AQBAnHzwwQdatGiRJCl9+vT65ZdflDZtWqvLAlKFrFmzatGiRUqTJo0kadasWfrss8+sLgtAHLhZXQAAAEj6pk6dqhEjRkiSXF1dNW/ePBUuXNjqshLVvGM3rC4BiFXJkiU1ffp0tWrVSpL04Ycf6tlnn1WzZs2sLg1ALDjSDQAAYvXrr7/qlVdecTz++uuvVbduXUtrAlKrli1b6pNPPpEkGWPUoUMH7du3z+qyAMSC0A0AAGJ08uRJtWzZUmFhYZKkHj166M0337S6LCBV+/DDD9W2bVtJ0q1bt9SsWTNduXLF6rIAxIDQDQAAohUcHKymTZvq8uXLkqQ6depo9OjRVpcFpHo2m02TJ09W2bJlJUknTpxQ69atHT+OAUhauKYbAABEER4erhdffNFx2mqRIkU0d+5cubu7W1YT11wD/5+Pj48WLlyo8uXL68KFC9q4caN69+6tcePGWV0agEdwpBsAAETx/vvva9myZZKkjBkzaunSpcqYMaPVZQF4SJ48ebRgwQJ5eHhIksaPH6+xY8daXRaARxC6AQBAJJMnT9aXX34pSXJzc9PPP/+c4u5UDqQUFStW1IQJExyPe/Xq5RjaD0DSQOgGAAAOmzdv1uuvv+54PHbsWNWqVcvSmgDErnPnznr//fclSXa7Xe3bt9dvv/1mdVkAHiB0AwAASdL+/fvVvHlzx82Y3n77bb322mtWlwUgDj799FO9+OKLkqS7d++qadOmOnbsmNVlASB0AwAASfr333/VoEEDXb9+XZJUv359jRw50uqyAMSRi4uLJk+erBo1akiSLl26pIYNGzpGHwBgHUI3AACp3PXr19WwYUOdOXNGkvT8889r3rx5cnNjkBMgOfH09NSCBQv07LPPSpKOHDmiZs2a6e7du1aXBqRqhG4AAFKxe/fuqXnz5o6hwQoUKKBly5YpXbp0VpcG4AlkyJBBy5cvV86cOSVJ27dvV4cOHRQeHm51aUCqRegGACCVCg8P10svvaTNmzdLkrJmzapVq1Ype/bsVpcGIAHy5s2rZcuWKW3atJKkBQsWqG/fvjLGWF0akCoRugEASIWMMerdu7d+/vlnSZKPj4+WLVumQoUKWV0agERQunRpzZ8/33GZyJgxY/TVV19ZXRaQKhG6AQBIhUaMGKGxY8dKD8binj9/vsqXL291WQASUb169TRx4kTH43feeUdz5861tCYgNSJ0AwCQykydOlUDBgxwPP7hhx/UoEEDS2sC4BxdunTRkCFDHI87duyo5cuXW1oTkNoQugEASEVWrlyp7t27Ox4PHz5cnTt3trQmAM41aNAgvfzyy5KksLAwBQYGasOGDVaXBaQahG4AAFKJzZs3KzAw0HEX4549e6p///5WlwXAyWw2m7777ju1bdtWejBqQdOmTbV9+3arSwNSBUI3AACpwPbt29W4cWPduXNHktS6dWuNHj1aNpvN6tIAPAWurq6aPn26mjZtKkm6ffu2GjZsqD179lhdGpDiEboBAEjh/vjjDzVo0EC3bt2SJDVq1EgzZsyQq6ur1aUBeIrc3d01d+5c1a5dW5J048YN1atXTwcOHLC6NCBFI3QDAJCC7d69W/Xq1VNwcLAkqW7dupo/f748PT2tLg2ABby8vLRw4UJVqlRJknTp0iXVqVNHx48ft7o0IMUidAMAkELt3btXdevW1fXr1yVJNWrU0MKFC+Xl5WV1aQAslDZtWi1btkxly5aVJJ07d061a9fWv//+a3VpQIpE6AYAIAU6cOCAateurStXrkiSKleurCVLlsjHx8fq0gAkARkyZNCqVatUvHhxSdLJkydVu3ZtXbhwwerSgBSH0A0AQApz+PBh1apVS5cuXZIkVahQQcuXL1fatGmtLg1AEpIlSxatWbNGBQsWlB7sO+rUqUPwBhIZoRsAgBTk+PHjqlWrls6fPy9JKlu2rFauXClfX1+rSwOQBOXMmVPr1q1T7ty5JUn79u1T9erVOdUcSESEbgAAUohjx46pVq1aOnv2rCSpZMmSWr16tTJkyGB1aQCSsHz58mn9+vXKkyePJOnQoUOqVq2aTpw4YXVpQIpA6AYAIAXYu3evqlSpolOnTkmSihcvrrVr1ypz5sxWlwYgGShcuLC2bNmiAgUKSJJOnDihqlWr6tChQ1aXBiR7hG4AAJK5HTt2qHr16o5Typ999lmtW7dOWbNmtbo0AMlIvnz5tGXLFhUrVkySdPbsWVWrVk1///231aUByRqhGwCAZGzNmjWqXbu2rl27JkkKCAjQpk2b5OfnZ3VpAJKhnDlzatOmTSpVqpQk6eLFi6pRo4b++OMPq0sDki1CNwAAydT8+fPVuHFj3blzR5JUu3ZtrVu3jlPKASRI1qxZtWHDBlWoUEGSdO3aNdWuXVu//vqr1aUByRKhGwCAZGjy5Mlq06aNwsLCJEktW7bUsmXLGBYMQKLImDGj1qxZo2rVqkmSbt68qfr162vt2rVWlwYkO4RuAACSmVGjRql79+6y2+2SpC5dumju3Lny9PS0ujQAKUi6dOm0YsUK1atXT5J0584dNW7cWLNnz7a6NCBZIXQDAJBMGGM0cOBAvfPOO45pvXv31qRJk+Tm5mZpbQBSJh8fHy1evFjNmzeXJIWGhqp9+/b69NNPZYyxujwgWeATGgCAZCAkJESvv/66pkyZ4pj2ySef6MMPP5TNZrO0NgBRzTt2w+oSEkVQwfTy9PTUvHnz1KNHD/3www+SpIEDB+ro0aP6/vvv5eHhYXWZQJLGkW4AAJK4S5cuqU6dOpEC9zfffKOBAwcSuAE8Fe7u7powYYKGDx/umDZlyhQ1bNhQ169ft7Q2IKkjdAMAkITt27dPAQEBjrsGe3t7a+7cuerZs6fVpQFIZWw2m95//33NmTPHcQ+J9evXq1KlSjpx4oTV5QFJFqEbAIAkaunSpapYsaJOnjwpPRg/d8uWLQoKCrK6NACpWJs2bbRhwwZlyZJFknTgwAFVqFBBv/32m9WlAUkS13QDAGCxR6/9NMZo6aSxmjFikONGRQVLlFG/72bqeIYcOh7Pa0WDCqZP1Hqjk1KuXwUQNxUrVtSOHTvUuHFjHTp0SJcuXVKNGjU0Y8YMBQYGWl0ekKRwpBsAgCTkfmiovh3QU9M/+8gRuCs2aqkhM5cpU/YcVpcHAA4FCxbUtm3bVL16dUnSvXv31Lp1aw0ePFjh4eFWlwckGYRuAACSiOArl/VJ5+ba+PNPjmmt3+qv3l9Plqe3j6W1AUB0MmXKpNWrV+ull15yTPv444/VoEEDXbx40dLagKSC0A0AQBKw/7df1a9ZVR34Y7skyd3TS72/nqw2vQZwh3IASZqHh4emTp2qzz77TC4u/xcv1q5dqzJlymjr1q1WlwdYjtANAICFwsPDNW/MZxr6UjNdu/CfJCljNj8NnbVclRq3sro8AIgTm82m/v37a926dcqePbsk6dy5c6pevbpGjhzpuFwGSI0I3QAAWOTcuXOqW7eu5o35TMZulyQ9V7GaPlu4UYVKlrW6PACItxo1amj37t2O67zDw8P17rvvKjAwkPG8kWoRugEAsMDKlStVunRpbdiwQZJkc3FR2z4fauCUBcqYzc/q8gDgieXIkUNr167VgAEDHNMWLFigcuXKac+ePZbWBliB0A0AwFMUFham9957Tw0bNtSlS5ckSZmy59SQn5Yq8M1+cnF1tbpEAEgwNzc3DRs2TEuWLFHGjBklSceOHdMLL7ygsWPHyv7g7B4gNSB0AwDwlJw8eVLVqlXTF1984ZjWpEkTfb5ki4qVr2RpbQDgDE2aNNGuXbtUvnx5SVJISIjeeust1a5dWydOnLC6POCpIHQDAOBkdrtdY8eO1XPPPacdO3ZIktzd3TVq1CgtXrxYvpkyW10iADiNv7+/tmzZorfeessxbePGjSpRooS+/fZbjnojxSN0AwDgRAcOHFDVqlX11ltv6fbt25KkAgUKaOvWrerTpw/DgQFIFTw9PTVmzBitW7dO+fLlkyTdvn1bPXr0UN26dXXy5EmrSwSchtANAIAThIaG6n//+59K/7/27j0qqmrxA/h3eA04vATk5QNRRHwFikKQeH0QqNzK6qJm5eNngqWmgWla2jvUkspXoOum1c2ukOsappFcrKQrgQoCEuCTi6IDIsLA8Bhgzu8P5VxHUFEZBobvZ62zZmbvPWf2gb0GvjPn7O3lhaNHj4rl4eHhGqdaEhF1JxMnTkROTg7Cw8PFssOHD2PEiBGIjY3l0mKklxi6iYiI2tmxY8cwevRorFmzBiqVCgDg5uaGX3/9FTExMbCystJ1F4mIdMbCwgIxMTE4dOgQ+vbtCwCorq7GwoULERwcjIsXL+q6i0TtiqGbiIiondTU1GD58uV49NFHkZOTAwAwNDTEypUrkZ2dLa5bS0REwOOPP45Tp07hpZdeEsuSk5Mxbtw4vPvuu6ipqdFp/4jaC0M3ERHRQxIEAXFxcRg2bBg2btwoTgrk5eWF9PR0rFu3DmZmZrruJhFRp2NpaYkdO3YgMTERffr0AQDU1dXhvffeg4eHB+Li4njKOXV5DN1EREQPITU1Ff7+/pgxY4Y4EZBUKkVUVBTS09MxatQoXXeRiKjTCw4OxqlTp7B06VIYGRkBAC5evIgZM2Zg/PjxOHnypK67SPTAGLqJiIgewPnz5zF9+nT4+/uLy4ABQGBgILKysvDGG2/A2NhYp30kIupKrKysEB0djeTkZAQFBYnlR44cgbe3NxYuXIirV6/qtI9ED4Khm4iI6D5cv34dy5cvx5AhQxAfHy+WDx06FAcPHsShQ4cwePBgnfaRiKgrc3d3x8GDB5GQkICBAwcCANRqNWJjY+Hu7o7o6GjU1tbquptEbcbQTURE1AZ1dXX4/PPP4ebmho0bN4qzktvb2yMmJgZZWVmYMmUK190mImoHEokETzzxBHJzc7F+/XqYm5sDACoqKhAZGYkBAwbg008/5WRr1CUwdBMREd2FQqHAhg0b0L9/fyxbtgzl5eUAAFNTU6xevRpnzpxBeHi4eA0iERG1H6lUihUrVuD06dOYO3euWC6XyxEREYEBAwYgOjqa4Zs6NYnA6QDbjUKhgJWVFSorK2Fpaanr7hDdF7VajdLSUtjb28PAgJ/Hkf540LFdVlaGTZs2YfPmzaioqNCoe+GFF/Dhhx+iX79+7dLH+HOV7bIf6kYENSRV5RAsbAAJ37NJe0IHWnXo693rPTs7Oxvvvfce9u7dq1Fub2+PFStWYOHChZDJZB3YY+rO2pr/+C5NRER0i+LiYkRERMDFxQXvv/++GLglEglCQ0ORkZGBb775pt0CNxERtd0jjzyC77//HllZWfjb3/4mlpeWlmL58uVwdXXFhg0bcP36dZ32k+hWDN1EREQATp06hbCwsBbXCRoZGWHevHnIy8tDXFwcRo4cqeuuEhF1e4888gji4+ORnZ2N0NBQcT6Nq1evYuXKlejduzfCwsKQlZWl664SMXQTEVH3VVtbi6+//hqPPfYYRowYgR07dogTpJmammLJkiU4d+4cvvzyS85ITkTUCY0YMQJxcXHIycnBjBkzxPBdW1uLHTt2wMvLCwEBAdizZw8aGhp03V3qpnhNdzviNd3UlfGabtJXrY3tvLw8xMbG4uuvv25xCqKlpSUWLVqEpUuXwsHBgddbU+fEa7pJXz3k2L58/gwS/7EDv+39DrXKKo06JycnhIeHIywsDE5OTu3Yaequ2pr/GLrbEUM3dWUM3aSvmse2hYUF9u3bh9jYWKSkpLRoN2zYMISHh+PFF1+EtbW1WM7QTZ0SQzfpq3Ya27XVVTiybw8Sv9mB4nMFGnVGRkYIDg7GrFmz8NRTT3HiNXpgDN06wNBNXRlDN+mjuro6HDx4EP/4xz9w6NAhKJVKjXqpVIrp06cjPDwc/v7+ra6xzdBNnRJDN+mrdh7bgiDArigDW7ZswQ8//AC1Wq1R36NHD0ybNg2zZs1CUFAQjI2NH/o1qftg6NYBhm7qyhi6SV/U19fj559/RlxcHBISElBVVdWijYeHB8LDwzF79mzY2NjcdX8M3dQpMXSTvtLC2G5e9qyoqEi8tOjSpUst2tna2iI0NBTPP/88/P39+f8Q3RNDtw4wdFNXxtDduXRE0NP22qsdGVZrqipxKvUI0pMO4FjSQdRWK1q0kVlZY8zjIRj/zCwMGdP6t9pEXQZDN+krLYbuZmq1GikpKdi9ezfi4+NbXV7M3t4eU6ZMwdSpUxEUFKRx2RFRM4ZuHWDopq6MobtzYei+O3VTE87nZiErJRlZKYdxOjMd6qamFu1kllYYEzgV/hMCMXxiCIykplrrE1GHYugmfdUBoftWKpUKiYmJ2L17NxISElBbW9uijaGhIcaOHYuQkBBMnToVQ4cO5Qe3BDB06wZDN3VlDN2dC0N3S9euFCPn6K84mXIYOf/5BVXXy1tt18PCEmMeD4Hf1KfxiP94GBkbMZyQ/mHoJn3VwaH7VlVVVfjhhx8QHx+P5OTkFvOANHNxccHjjz+OcePGISAgAC4uLgzh3RRDtw4wdFNXxtDduXT30N3Y0ID/5p9CQUYaTmekoyAjHdeutLz+rpmTqxs8AybCa1wgRvj9BcZS6f8qGU5IH3Fck77SYei+VX19PX777TccOHAABw4cwLlz5+7Ytk+fPggICEBAQADGjRuHIUOG8H+pboKhWwcYuqkrY+juXLpT6BYEAdeuXELhnzk4ffI4Tmek4Wx2BlR1LU/xa2ZmbokR/uPgGTAJngETYd/H5S4vwHBCeojjmvRVJwndtzt9+jQOHDiAgwcP4rfffkNDQ8Md29rY2MDPzw+jRo3CyJEjMWrUKPTr14/fhuuhtuY/ow7tFRERdWuqulpcPJOP/+afurHlncJ/C3KhrKy46/OkZj3g5ukNj9GPwnPsRLh5joYRl3UhIqIO4u7uDnd3d7z22mtQKpVIS0tDSkoKUlJSkJqaipqaGrFteXm5+A15MxsbG4wcOVIM4V5eXnBzc7uvJcq0/YG8tj+M784YuomIqF0JggC5XI4/0zJwpfA8rvz3HK5cOIvL58/i8oUzEG5bI7U1vfr0g/tIHwwe6QP3UT5w8RgOQyP+ySIiIt2TyWSYOHEiJk6cCABoaGhAZmamGMJ///13XLt2TeM55eXlSE5ORnJyslhmZGSEgQMHYvDgwfDw8ICHh4d4/17LWVLXwv9giIjovgiCgOvXr6OoqAhFRUW4ePEiioqKUFhYiDNnzuDMmTOorq5u8/56Ojihv8dwuHgMx4DhXnAf5QMbByetHgMREVF7MTY2ho+PD3x8fBAZGQlBEFBYWIjMzExkZGSIW0lJicbzGhsbUVBQgIKCAiQkJGjU2dnZwdXVFf3794eLiwv69++PiyZ26NWnH3o594WZuUUHHyU9jE4burdu3YqPP/4Ycrkcnp6e2Lx5M3x8fO7YPj4+HmvWrEFhYSEGDRqE9evXY+rUqWK9IAh4++23sWPHDlRUVOCxxx7DF198gUGDBoltysvLsWTJEuzfvx8GBgZ49tln8fnnn8Pc3Fzrx0tEpGtqtRrl5eWQy+UoKSlpsRUXF4sB+9bT6NrKyNgEfdwGw2XIcLgMHgaXISPg4jEclja2WjkeIiIiXZBIJHB1dYWrqyueeeYZsfzKlSvIzMzEiRMnkJubi/z8fBQUFKCurq7FPsrKylBWVoZjx461+hoyK2vYODijZy8HWNs7wLqXw437zbf2jrCytUMPCyteS94JdMqJ1Pbs2YPZs2cjJiYGvr6++OyzzxAfH4+CggLY29u3aH/06FGMGzcOUVFR+Otf/4rdu3dj/fr1yMjIwPDhwwEA69evR1RUFL766iu4urpizZo1yMnJwZ9//glT0xvrtk6ZMgVXrlxBbGwsGhoaMG/ePIwZMwa7d+9uU785kdqD04drVDpi4iutEtQIMK/nRGpt0Jl/140qFWqqq1BbXYXaagVqldWora5CTXUVlJUVqK68juqK6+KtUlEpPlaUl7W61vX9MDA0hH0fFzj1HwjH/gPg1N8NTv0Hwqn/ANg594WBoWG7HWubccIp0kcc16SvutnYVqvVuHblEorPncHl86dRfP4MLp8/g8sXzqKiVI6HjWoGhoYwt+oJi542MLe2gYV1T5j3tIFlT1vILK3Rw8JS3KYOdoKVlZW4WVhYwIiXdt1Vl5693NfXF2PGjMGWLVuAm4Oxb9++WLJkCd54440W7WfMmAGlUokff/xRLHv00Ufh5eWFmJgYCIIAZ2dnREZGYvny5QCAyspKODg4YNeuXZg5cyby8vIwdOhQHDt2DKNHjwYAJCYmYurUqbh06RKcnZ3v2W+G7gfH0N0JMHS32e2/a7VaDXVjI5qaGtHU2Ah1U9ON+w0NN8ua0NTYgMYGFRobbt6qbtxvLm9QqdBQXwdVfT0a6uvExw319VDV193YamtQX1uL+toa1NfWQFVXi7qaGqjqalBXU4Pa6io0Nqi0euxSsx6wdeoNO6c+sHPuc+O+cx/Y3nxs38el801w1s3+gaNuguOa9BXHtqhRpULZlWKUXS5C6aUilBVfRGnxjdurly/ieolc+3/3pVLIZDKYm5tDJpNpbObm5jAzM4OpqSlMTU3F+7eWSaVSSKVSmJiYaGzNZcbGxuJmZGTU6n1jY2MY6uJD+zbosrOXq1QqnDhxAqtWrRLLDAwMEBgYiNTU1Fafk5qaioiICI2y4OBg7Nu3DwBw4cIFyOVyBAYGivVWVlbw9fVFamoqZs6cidTUVFhbW4uBGwACAwNhYGCAtLQ0PP3001o42o71yiuv4OrVq7ruRqsuVd952YX2EGeu/RCg7WO4XXt8Xibgln0IAnYZqSGVSsXTkG5/jYd5fK/7d7u9dWut7E6bWq1u8bi57Nbb1rampiaN+7duqsamG+Fa3fTQ3wx3BqYyc8gsrWFpYwtrO3tY2fWCle2N2xuPb9zv2csR5tY9eZoaERFRBzAyMYGjiyscXVxbrRcEAUpFJSpK5bh+tQQVV0tu3N58XFV+DVUV5aiuKEdVxXXUKds+30qz+vp61NfXo7y8vB2O6MGEhoYiLi5OZ6/fHjpd6C4rK0NTUxMcHBw0yh0cHJCfn9/qc+Ryeavt5XK5WN9cdrc2t5+6bmRkBBsbG7HN7ZoHYbPKyhvfflVUVEDdhtl5O9qPP/6Iixcv6robRNReJBKYmvWAiakZpGY9YNpDBjNzc5jJLGAqM4eZTAZTmQXMZOYwlckgs7SCzNIaMitrmFtaQ2ZtDZmF1X19M11b1RXP6BAgqVJAgCEAfmBA+oLjmvQVx/b9MJAANg6OsHFwvGfbBpUK1ZXXb1xudvMSs1rljUvQaquq0NtIBYVCAYVCgaqqKigUCtTU1KCmpgZKpRI1NTX3NVFqe1Gr1aiouPvSorqiUCiANnwZ1ulCd1cSFRWFd999t0W5i4uLTvpDRN2MIKCuRom6GqWue0JERESkFXv37sXevXt13Y27qqqqgpXVnS9n7XSh287ODoaGhi2m1C8pKYGjY+uf4Dg6Ot61ffNtSUkJnJycNNp4eXmJbUpLSzX20djYiPLy8ju+7qpVqzROa2+e+dfW1panX1KXo1Ao0LdvX1y8eJFzEpBe4dgmfcRxTfqKY5u6EkEQUFVVdc/5vzpd6DYxMYG3tzeSk5Mxbdo04GaYTU5OxuLFi1t9jp+fH5KTk7Fs2TKxLCkpCX5+fgAAV1dXODo6Ijk5WQzZCoUCaWlpePnll8V9VFRU4MSJE/D29gYAHD58GGq1Gr6+vq2+bvPEALeytrZul58Dka5YWlryjxzpJY5t0kcc16SvOLapq7jbN9zNOl3oBoCIiAjMmTMHo0ePho+PDz777DMolUrMmzcPADB79mz07t0bUVFRAIClS5fiL3/5CzZu3IiQkBD885//xPHjx7F9+3bg5lp5y5YtwwcffIBBgwaJS4Y5OzuLwX7IkCGYPHkyFixYgJiYGDQ0NGDx4sWYOXNmm2YuJyIiIiIiIrpdpwzdM2bMwNWrV7F27VrI5XJ4eXkhMTFRnAitqKhIY0kjf39/7N69G2+99RZWr16NQYMGYd++feIa3QCwYsUKKJVKhIWFoaKiAmPHjkViYqK4RjcAfPvtt1i8eDEmTZoEAwMDPPvss9i0aVMHHz0RERERERHpi065TjcRdbz6+npERUVh1apVLS6bIOrKOLZJH3Fck77i2CZ9xNBNREREREREpCUGbWhDRERERERERA+AoZuIiIiIiIhISxi6iYiIiIiIiLSEoZuomyksLMT8+fPh6uoKMzMzDBw4EG+//TZUKpVGu+zsbAQEBMDU1BR9+/bFhg0bWuwrPj4eHh4eMDU1xYgRI3Dw4MEOPBIiTR9++CH8/f3Ro0cPWFtbt9qmqKgIISEh6NGjB+zt7fH666+jsbFRo82vv/6KUaNGQSqVws3NDbt27eqgIyBqu61bt6J///4wNTWFr68v0tPTdd0lojs6cuQInnjiCTg7O0MikWDfvn0a9YIgYO3atXBycoKZmRkCAwNx5swZjTbl5eV4/vnnYWlpCWtra8yfPx/V1dUdfCRED4ahm6ibyc/Ph1qtRmxsLHJzc/Hpp58iJiYGq1evFtsoFAoEBQXBxcUFJ06cwMcff4x33nkH27dvF9scPXoUzz33HObPn4/MzExMmzYN06ZNw6lTp3R0ZNTdqVQqhIaG4uWXX261vqmpCSEhIVCpVDh69Ci++uor7Nq1C2vXrhXbXLhwASEhIZgwYQJOnjyJZcuW4aWXXsLPP//cgUdCdHd79uxBREQE3n77bWRkZMDT0xPBwcEoLS3VddeIWqVUKuHp6YmtW7e2Wr9hwwZs2rQJMTExSEtLg0wmQ3BwMOrq6sQ2zz//PHJzc5GUlIQff/wRR44cQVhYWAceBdFDEIio29uwYYPg6uoqPt62bZvQs2dPob6+XixbuXKlMHjwYPHx9OnThZCQEI39+Pr6CuHh4R3Ua6LW7dy5U7CysmpRfvDgQcHAwECQy+Vi2RdffCFYWlqKY33FihXCsGHDNJ43Y8YMITg4uAN6TtQ2Pj4+wqJFi8THTU1NgrOzsxAVFaXTfhG1BQDhX//6l/hYrVYLjo6OwscffyyWVVRUCFKpVPjuu+8EQRCEP//8UwAgHDt2TGzz008/CRKJRCguLu7gIyC6f/ymm4hQWVkJGxsb8XFqairGjRsHExMTsSw4OBgFBQW4fv262CYwMFBjP8HBwUhNTe3AnhO1XWpqKkaMGAEHBwexLDg4GAqFArm5uWIbjmvqzFQqFU6cOKExTg0MDBAYGMhxSl3ShQsXIJfLNca0lZUVfH19xTGdmpoKa2trjB49WmwTGBgIAwMDpKWl6aTfRPeDoZuomzt79iw2b96M8PBwsUwul2sEEwDiY7lcftc2zfVEnc3DjGuFQoHa2toO7C1R68rKytDU1MT3X9IbzeP2bmNaLpfD3t5eo97IyAg2NjYc99QlMHQT6Yk33ngDEonkrlt+fr7Gc4qLizF58mSEhoZiwYIFOus70Z08yLgmIiIi6kyMdN0BImofkZGRmDt37l3bDBgwQLx/+fJlTJgwAf7+/hoTpAGAo6MjSkpKNMqaHzs6Ot61TXM9UXu433F9N46Oji1meG7ruLa0tISZmdl99p6o/dnZ2cHQ0JDvv6Q3msdtSUkJnJycxPKSkhJ4eXmJbW6fKLCxsRHl5eUc99QlMHQT6YlevXqhV69ebWpbXFyMCRMmwNvbGzt37oSBgeZJL35+fnjzzTfR0NAAY2NjAEBSUhIGDx6Mnj17im2Sk5OxbNky8XlJSUnw8/Nr1+Oi7u1+xvW9+Pn54cMPP0Rpaal4mmJSUhIsLS0xdOhQsc3tS99xXFNnYmJiAm9vbyQnJ2PatGkAALVajeTkZCxevFjX3SO6b66urnB0dERycrIYshUKBdLS0sTVKPz8/FBRUYETJ07A29sbAHD48GGo1Wr4+vrqtP9EbcHTy4m6meLiYowfPx79+vXDJ598gqtXr0Iul2tcEzVr1iyYmJhg/vz5yM3NxZ49e/D5558jIiJCbLN06VIkJiZi48aNyM/PxzvvvIPjx4/znz7SmaKiIpw8eRJFRUVoamrCyZMncfLkSXEd16CgIAwdOhQvvvgisrKy8PPPP+Ott97CokWLIJVKAQALFy7E+fPnsWLFCuTn52Pbtm2Ii4vDa6+9puOjI/qfiIgI7NixA1999RXy8vLw8ssvQ6lUYt68ebruGlGrqqurxfdk3Jw8rfn9WiKRYNmyZfjggw+QkJCAnJwczJ49G87OzuIHS0OGDMHkyZOxYMECpKen4z//+Q8WL16MmTNnwtnZWcdHR9QGup4+nYg61s6dOwUArW63ysrKEsaOHStIpVKhd+/ewrp161rsKy4uTnB3dxdMTEyEYcOGCQcOHOjAIyHSNGfOnFbH9S+//CK2KSwsFKZMmSKYmZkJdnZ2QmRkpNDQ0KCxn19++UXw8vISTExMhAEDBgg7d+7UwdEQ3d3mzZuFfv36CSYmJoKPj4/wxx9/6LpLRHf0yy+/tPr+PGfOHEG4uWzYmjVrBAcHB0EqlQqTJk0SCgoKNPZx7do14bnnnhPMzc0FS0tLYd68eUJVVZWOjojo/kiEG+vlEREREREREVE74+nlRERERERERFrC0E1ERERERESkJQzdRERERERERFrC0E1ERERERESkJQzdRERERERERFrC0E1ERERERESkJQzdRERERERERFrC0E1ERERERESkJQzdRERERERERFrC0E1ERKTnlEolPvroI4waNQrm5uaQSqXo06cPAgICsGrVKpw7d07XXSQiItJbRrruABEREWlPVVUVxo4di+zsbLi5ueGFF16Ara0tysrKkJ6ejnXr1mHgwIEYOHCgrrtKRESklxi6iYiI9Nhnn32G7OxsvPTSS9i+fTskEolG/YULF1BfX6+z/hEREek7nl5ORESkx1JTUwEAixYtahG4AcDV1RUeHh4aZaWlpYiMjMTgwYNhZmYGGxsb+Pr64pNPPmnx/P3792PChAmwsrKCmZkZPD09ER0djcbGRo12hYWFkEgkmDt3LvLy8vD000/D1tYWEokEhYWFYrsffvgBkyZNQs+ePWFqaorhw4fjk08+QVNTUzv+VIiIiDoOQzcREZEes7W1BQCcPn26Te0LCgrg5eWF6Oho2Nvb49VXX8WsWbPQo0cPfPTRRxpto6Oj8eSTTyI7OxuzZs3CokWLUFtbi8jISISGhkIQhBb7P3v2LB599FFcvXoVc+fOxZw5c2BiYgIAWLVqFaZNm4aCggI888wzeOWVV2BmZobXX38dM2fObJefBxERUUeTCK39RSQiIiK9kJCQgKeeegoWFhYICwtDUFAQvL29xTB+uzFjxuD48ePYvn07FixYoFF36dIl9OnTBwBw7tw5eHh4wMbGBsePH0ffvn0BAPX19QgMDMTvv/+Or7/+Gi+++CJw85tuV1dXAMDatWvx7rvvauw7KSkJQUFBCA4Oxt69eyGTyQAAgiDglVdeQUxMDL7//ns8++yzWvgpERERaQ+/6SYiItJjTz75JDZu3AhBELBx40YEBwfDzs4Obm5uWLx4Mc6cOSO2TU9Px/HjxzFu3LgWgRuAGLgBYPfu3WhsbERkZKQYuAFAKpVi/fr1AIBdu3a12IejoyPefPPNFuVbtmwBAGzfvl0M3AAgkUiwbt06SCQSfPfddw/1syAiItIFTqRGRESk5yIiIrBgwQIkJibi6NGjOH78ONLS0rB161b8/e9/x549e/Dkk08iPT0dABAUFHTPfWZmZgIAxo8f36LOz88PpqamOHnyZIs6T09P8XTyW/3xxx+QyWT48ssvW309MzMz5Ofnt+l4iYiIOhOGbiIiom7AwsICoaGhCA0NBQBUVlZi9erV2LZtG+bPn4/i4mJUVlYCAHr37n3P/SkUCgCAg4NDizqJRAIHBwcUFxe3qGutPQCUl5ejsbGxxWnnt1IqlffsFxERUWfD08uJiIi6ISsrK2zZsgUuLi4oKytDTk4OrK2tAaDVsHw7S0tLAEBJSUmLOkEQUFJSIra5VWszqDfvz9bWFoIg3HG7cOHCAxwpERGRbjF0ExERdVMSiUTj+mkfHx8AwKFDh+753JEjRwIAfv311xZ1aWlpqKurg5eXV5v74uvri2vXrmlcY05ERKQPGLqJiIj0WGxsLI4dO9Zq3b59+5CXlwdra2sMHz4cY8aMwZgxY3DkyBHs2LGjRftbvwGfNWsWjIyMEB0djcuXL4vlKpUKK1euBADMnTu3zf189dVXAQD/93//h2vXrrWol8vlyMvLa/P+iIiIOgte001ERKTHfvrpJyxcuBBubm547LHH4OzsDKVSiczMTKSkpMDAwADbtm2DVCoFAHz77bcYP348wsLC8M0338DPzw91dXXIzc1FZmamGIgHDhyI9evXIzIyEo888gimT58OmUyG/fv3o6CgAE899RReeOGFNvdz8uTJWLNmDd5//324ublh8uTJcHFxwbVr13D27FmkpKTggw8+wJAhQ7T2syIiItIGrtNNRESkxwoKCpCQkICkpCScPXsWV65cAW5OljZ27FgsWbIE3t7eGs8pKSlBVFQU9u/fj0uXLsHc3ByDBg3CjBkz8Nprr2m0TUhIQHR0NDIyMqBSqeDu7o65c+fi1VdfhZHR/z7bb16ne86cOa0uJdbs3//+NzZt2oQ//vgDFRUVsLW1haurK6ZOnYo5c+ZoLE9GRETUFTB0ExEREREREWkJr+kmIiIiIiIi0hKGbiIiIiIiIiItYegmIiIiIiIi0hKGbiIiIiIiIiItYegmIiIiIiIi0hKGbiIiIiIiIiItYegmIiIiIiIi0hKGbiIiIiIiIiItYegmIiIiIiIi0hKGbiIiIiIiIiItYegmIiIiIiIi0hKGbiIiIiIiIiItYegmIiIiIiIi0pL/B8nbfWlxjGPgAAAAAElFTkSuQmCC", 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" ] @@ -9087,363 +9117,363 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", - " 7.6\n", - " 9.7\n", - " 12.0\n", - " 13.2\n", + " 5.9\n", + " 7.2\n", + " 9.5\n", + " 11.8\n", + " 12.9\n", " \n", " \n", " metac-o1-preview\n", - " 3.4\n", - " 5.2\n", + " 3.5\n", + " 5.3\n", " 8.3\n", " 11.2\n", - " 12.5\n", + " 12.7\n", " \n", " \n", " manticAI\n", - " 0.2\n", + " 0.3\n", " 2.2\n", - " 5.3\n", - " 8.6\n", - " 10.3\n", + " 5.4\n", + " 8.7\n", + " 10.4\n", " \n", " \n", " metac-Gemini-Exp-1206\n", " 0.4\n", - " 2.1\n", - " 4.9\n", + " 2.2\n", + " 5.0\n", " 7.7\n", - " 9.1\n", + " 9.5\n", " \n", " \n", " acm_bot\n", - " -0.0\n", - " 1.3\n", - " 4.7\n", + " 0.4\n", + " 1.9\n", + " 4.6\n", " 7.4\n", " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -2.0\n", - " 0.6\n", - " 4.3\n", - " 8.2\n", - " 9.8\n", + " -1.8\n", + " 0.1\n", + " 4.2\n", + " 7.8\n", + " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -1.5\n", - " 0.7\n", + " -0.6\n", + " 0.8\n", " 4.0\n", - " 7.0\n", - " 8.8\n", + " 7.2\n", + " 8.7\n", " \n", " \n", " twsummerbot\n", " 0.2\n", - " 1.6\n", - " 3.7\n", - " 6.2\n", - " 7.3\n", + " 1.4\n", + " 3.8\n", + " 6.3\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 1.1\n", - " 3.1\n", + " -0.2\n", + " 0.8\n", + " 3.0\n", " 5.1\n", " 6.2\n", " \n", " \n", " pgodzinai\n", - " -3.6\n", + " -3.0\n", " -1.1\n", - " 3.1\n", - " 6.5\n", + " 3.0\n", + " 6.8\n", " 9.0\n", " \n", " \n", - " CumulativeBot\n", - " -0.1\n", - " 0.9\n", + " metac-claude-3-5-sonnet-latest\n", + " -1.2\n", + " 0.2\n", " 2.6\n", - " 4.5\n", - " 5.4\n", + " 5.2\n", + " 6.6\n", " \n", " \n", " SynapseSeer\n", - " 0.3\n", + " 0.4\n", " 1.1\n", " 2.6\n", - " 4.1\n", - " 4.9\n", + " 4.0\n", + " 4.8\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.4\n", - " -0.2\n", + " CumulativeBot\n", + " -0.5\n", + " 0.6\n", " 2.6\n", - " 5.1\n", - " 6.3\n", + " 4.5\n", + " 5.4\n", " \n", " \n", " jkraybill_bot\n", - " -3.6\n", - " -1.7\n", - " 1.8\n", - " 5.1\n", + " -3.2\n", + " -1.3\n", + " 1.7\n", + " 4.9\n", " 6.5\n", " \n", " \n", " metac-exa\n", " -4.8\n", - " -2.7\n", - " 1.8\n", - " 5.6\n", - " 7.3\n", + " -2.6\n", + " 1.7\n", + " 5.7\n", + " 7.4\n", " \n", " \n", " metac-deepseek-r1+asknews\n", - " -2.1\n", - " -0.8\n", - " 1.3\n", - " 3.3\n", - " 4.5\n", + " -1.7\n", + " -0.7\n", + " 1.4\n", + " 3.6\n", + " 4.6\n", " \n", " \n", " MWG\n", - " -1.7\n", + " -1.5\n", " -0.8\n", " 0.7\n", " 2.0\n", - " 2.9\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", " -0.9\n", " -0.6\n", - " 0.0\n", - " 0.6\n", - " 1.0\n", - " \n", - " \n", - " pianobot\n", - " -1.3\n", - " -0.8\n", " -0.0\n", - " 0.7\n", - " 1.1\n", + " 0.6\n", + " 0.9\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", + " pianobot\n", + " -1.3\n", + " -0.8\n", + " -0.0\n", + " 0.6\n", + " 1.0\n", + " \n", + " \n", " cobyj-bot\n", " -1.5\n", " -0.9\n", - " -0.1\n", - " 0.8\n", + " -0.0\n", + " 0.9\n", " 1.3\n", " \n", " \n", - " annabot\n", - " -3.2\n", - " -2.1\n", - " -0.3\n", - " 1.2\n", - " 2.0\n", - " \n", - " \n", " KevinTestBot\n", - " -4.0\n", - " -2.6\n", + " -4.1\n", + " -2.9\n", " -0.4\n", - " 1.6\n", - " 2.6\n", + " 1.5\n", + " 2.7\n", + " \n", + " \n", + " annabot\n", + " -3.7\n", + " -2.3\n", + " -0.5\n", + " 1.2\n", + " 2.1\n", " \n", " \n", " bean_bot\n", - " -3.3\n", + " -3.1\n", " -2.2\n", " -0.5\n", - " 1.0\n", + " 1.1\n", " 1.9\n", " \n", " \n", " CatrachoCaster\n", - " -2.3\n", - " -1.8\n", - " -0.8\n", + " -2.2\n", + " -1.7\n", + " -0.7\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", - " -3.0\n", + " -2.9\n", " -2.3\n", - " -0.9\n", + " -0.8\n", " 0.4\n", " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.8\n", - " -2.7\n", - " -1.0\n", - " 0.7\n", - " 1.6\n", + " -3.5\n", + " -2.6\n", + " -0.9\n", + " 0.6\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.6\n", - " -3.3\n", - " -1.1\n", - " 0.9\n", - " 1.9\n", + " -4.4\n", + " -3.2\n", + " -1.0\n", + " 1.0\n", + " 2.2\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.7\n", + " -6.6\n", " -4.8\n", - " -1.3\n", - " 1.7\n", - " 3.4\n", + " -1.4\n", + " 1.8\n", + " 3.1\n", " \n", " \n", " mmBot\n", - " -7.2\n", - " -5.5\n", + " -7.5\n", + " -5.4\n", " -1.6\n", - " 2.4\n", - " 4.5\n", + " 2.5\n", + " 4.7\n", " \n", " \n", " 4Shadower\n", - " -4.8\n", - " -3.7\n", - " -1.6\n", - " 0.2\n", - " 1.1\n", - " \n", - " \n", - " swingswish\n", - " -5.3\n", - " -3.9\n", - " -2.0\n", - " -0.1\n", - " 0.8\n", + " -4.9\n", + " -3.8\n", + " -1.8\n", + " 0.1\n", + " 1.2\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.6\n", - " -5.0\n", - " -2.1\n", - " 0.8\n", - " 2.4\n", + " -6.2\n", + " -4.8\n", + " -2.0\n", + " 0.7\n", + " 2.0\n", " \n", " \n", " RPM_bot\n", " -4.7\n", " -3.8\n", - " -2.1\n", + " -2.0\n", " -0.7\n", - " -0.1\n", + " -0.2\n", + " \n", + " \n", + " swingswish\n", + " -5.5\n", + " -4.3\n", + " -2.1\n", + " -0.3\n", + " 0.5\n", " \n", " \n", " InstitutPelFutur\n", - " -9.3\n", - " -6.4\n", - " -2.2\n", - " 1.8\n", - " 4.2\n", + " -8.5\n", + " -6.5\n", + " -2.1\n", + " 1.9\n", + " 4.1\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.4\n", + " -6.6\n", + " -5.3\n", " -2.6\n", " 0.1\n", " 1.4\n", " \n", " \n", " wunderplumb\n", - " -6.3\n", - " -4.9\n", - " -2.6\n", - " -0.4\n", - " 0.7\n", - " \n", - " \n", - " NextWorldLab\n", - " -8.7\n", - " -6.9\n", - " -3.6\n", + " -6.2\n", + " -5.0\n", + " -2.7\n", " -0.2\n", - " 1.1\n", + " 0.6\n", " \n", " \n", - " laylaps\n", - " -10.4\n", - " -7.7\n", - " -3.8\n", - " -0.1\n", - " 1.6\n", + " NextWorldLab\n", + " -9.0\n", + " -6.8\n", + " -3.4\n", + " -0.4\n", + " 1.0\n", " \n", " \n", " Bot_Pepa\n", - " -7.0\n", + " -7.1\n", " -5.8\n", " -3.9\n", " -2.0\n", - " -1.1\n", + " -1.0\n", + " \n", + " \n", + " laylaps\n", + " -9.9\n", + " -7.7\n", + " -4.0\n", + " -0.1\n", + " 1.6\n", " \n", " \n", " VeritasAI\n", - " -8.1\n", - " -6.8\n", + " -7.7\n", + " -6.4\n", " -4.3\n", " -1.7\n", - " -0.9\n", + " -0.5\n", " \n", " \n", " minefrac1\n", - " -7.8\n", + " -7.9\n", " -6.8\n", - " -4.6\n", + " -4.5\n", " -2.6\n", - " -1.5\n", + " -1.7\n", " \n", " \n", " Grizeu_Bot\n", " -9.4\n", - " -7.8\n", - " -4.9\n", - " -2.2\n", - " -0.9\n", + " -7.5\n", + " -5.0\n", + " -2.4\n", + " -1.0\n", " \n", " \n", " metac-gpt-4o\n", - " -10.3\n", + " -10.2\n", " -8.9\n", - " -5.9\n", - " -3.1\n", - " -1.6\n", + " -5.8\n", + " -2.9\n", + " -1.5\n", " \n", " \n", " ajf-bot\n", " -14.8\n", - " -12.9\n", - " -8.3\n", - " -4.4\n", - " -2.1\n", + " -12.6\n", + " -8.4\n", + " -4.6\n", + " -2.2\n", " \n", " \n", "\n", @@ -9451,51 +9481,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.6 9.7 12.0 13.2\n", - "metac-o1-preview 3.4 5.2 8.3 11.2 12.5\n", - "manticAI 0.2 2.2 5.3 8.6 10.3\n", - "metac-Gemini-Exp-1206 0.4 2.1 4.9 7.7 9.1\n", - "acm_bot -0.0 1.3 4.7 7.4 8.8\n", - "metac-perplexity -2.0 0.6 4.3 8.2 9.8\n", - "GreeneiBot2 -1.5 0.7 4.0 7.0 8.8\n", - "twsummerbot 0.2 1.6 3.7 6.2 7.3\n", - "cookics_bot_TEST 0.0 1.1 3.1 5.1 6.2\n", - "pgodzinai -3.6 -1.1 3.1 6.5 9.0\n", - "CumulativeBot -0.1 0.9 2.6 4.5 5.4\n", - "SynapseSeer 0.3 1.1 2.6 4.1 4.9\n", - "metac-claude-3-5-sonnet-latest -1.4 -0.2 2.6 5.1 6.3\n", - "jkraybill_bot -3.6 -1.7 1.8 5.1 6.5\n", - "metac-exa -4.8 -2.7 1.8 5.6 7.3\n", - "metac-deepseek-r1+asknews -2.1 -0.8 1.3 3.3 4.5\n", - "MWG -1.7 -0.8 0.7 2.0 2.9\n", - "andrewsiah -0.9 -0.6 0.0 0.6 1.0\n", - "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "cobyj-bot -1.5 -0.9 -0.1 0.8 1.3\n", - "annabot -3.2 -2.1 -0.3 1.2 2.0\n", - "KevinTestBot -4.0 -2.6 -0.4 1.6 2.6\n", - "bean_bot -3.3 -2.2 -0.5 1.0 1.9\n", - "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.3 -0.9 0.4 1.0\n", - "krm-bot -3.8 -2.7 -1.0 0.7 1.6\n", - "ProfessorSP -4.6 -3.3 -1.1 0.9 1.9\n", - "metac-grok-2-1212 -6.7 -4.8 -1.3 1.7 3.4\n", - "mmBot -7.2 -5.5 -1.6 2.4 4.5\n", - "4Shadower -4.8 -3.7 -1.6 0.2 1.1\n", - "swingswish -5.3 -3.9 -2.0 -0.1 0.8\n", - "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.1 0.8 2.4\n", - "RPM_bot -4.7 -3.8 -2.1 -0.7 -0.1\n", - "InstitutPelFutur -9.3 -6.4 -2.2 1.8 4.2\n", - "metac-Llama-3.1 -6.7 -5.4 -2.6 0.1 1.4\n", - "wunderplumb -6.3 -4.9 -2.6 -0.4 0.7\n", - "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.1\n", - "laylaps -10.4 -7.7 -3.8 -0.1 1.6\n", - "Bot_Pepa -7.0 -5.8 -3.9 -2.0 -1.1\n", - "VeritasAI -8.1 -6.8 -4.3 -1.7 -0.9\n", - "minefrac1 -7.8 -6.8 -4.6 -2.6 -1.5\n", - "Grizeu_Bot -9.4 -7.8 -4.9 -2.2 -0.9\n", - "metac-gpt-4o -10.3 -8.9 -5.9 -3.1 -1.6\n", - "ajf-bot -14.8 -12.9 -8.3 -4.4 -2.1" + "metac-o1 5.9 7.2 9.5 11.8 12.9\n", + "metac-o1-preview 3.5 5.3 8.3 11.2 12.7\n", + "manticAI 0.3 2.2 5.4 8.7 10.4\n", + "metac-Gemini-Exp-1206 0.4 2.2 5.0 7.7 9.5\n", + "acm_bot 0.4 1.9 4.6 7.4 8.8\n", + "metac-perplexity -1.8 0.1 4.2 7.8 9.5\n", + "GreeneiBot2 -0.6 0.8 4.0 7.2 8.7\n", + "twsummerbot 0.2 1.4 3.8 6.3 7.4\n", + "cookics_bot_TEST -0.2 0.8 3.0 5.1 6.2\n", + "pgodzinai -3.0 -1.1 3.0 6.8 9.0\n", + "metac-claude-3-5-sonnet-latest -1.2 0.2 2.6 5.2 6.6\n", + "SynapseSeer 0.4 1.1 2.6 4.0 4.8\n", + "CumulativeBot -0.5 0.6 2.6 4.5 5.4\n", + "jkraybill_bot -3.2 -1.3 1.7 4.9 6.5\n", + "metac-exa -4.8 -2.6 1.7 5.7 7.4\n", + "metac-deepseek-r1+asknews -1.7 -0.7 1.4 3.6 4.6\n", + "MWG -1.5 -0.8 0.7 2.0 2.8\n", + "andrewsiah -0.9 -0.6 -0.0 0.6 0.9\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", + "pianobot -1.3 -0.8 -0.0 0.6 1.0\n", + "cobyj-bot -1.5 -0.9 -0.0 0.9 1.3\n", + "KevinTestBot -4.1 -2.9 -0.4 1.5 2.7\n", + "annabot -3.7 -2.3 -0.5 1.2 2.1\n", + "bean_bot -3.1 -2.2 -0.5 1.1 1.9\n", + "CatrachoCaster -2.2 -1.7 -0.7 0.2 0.7\n", + "jonahsingerbot -2.9 -2.3 -0.8 0.4 1.0\n", + "krm-bot -3.5 -2.6 -0.9 0.6 1.5\n", + "ProfessorSP -4.4 -3.2 -1.0 1.0 2.2\n", + "metac-grok-2-1212 -6.6 -4.8 -1.4 1.8 3.1\n", + "mmBot -7.5 -5.4 -1.6 2.5 4.7\n", + "4Shadower -4.9 -3.8 -1.8 0.1 1.2\n", + "metac-claude-3-5-sonnet-20240620 -6.2 -4.8 -2.0 0.7 2.0\n", + "RPM_bot -4.7 -3.8 -2.0 -0.7 -0.2\n", + "swingswish -5.5 -4.3 -2.1 -0.3 0.5\n", + "InstitutPelFutur -8.5 -6.5 -2.1 1.9 4.1\n", + "metac-Llama-3.1 -6.6 -5.3 -2.6 0.1 1.4\n", + "wunderplumb -6.2 -5.0 -2.7 -0.2 0.6\n", + "NextWorldLab -9.0 -6.8 -3.4 -0.4 1.0\n", + "Bot_Pepa -7.1 -5.8 -3.9 -2.0 -1.0\n", + "laylaps -9.9 -7.7 -4.0 -0.1 1.6\n", + "VeritasAI -7.7 -6.4 -4.3 -1.7 -0.5\n", + "minefrac1 -7.9 -6.8 -4.5 -2.6 -1.7\n", + "Grizeu_Bot -9.4 -7.5 -5.0 -2.4 -1.0\n", + "metac-gpt-4o -10.2 -8.9 -5.8 -2.9 -1.5\n", + "ajf-bot -14.8 -12.6 -8.4 -4.6 -2.2" ] }, "execution_count": 49, @@ -9599,7 +9629,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.521275\n", + " 4.605170\n", " \n", " \n", " 1\n", @@ -9614,7 +9644,7 @@ " True\n", " True\n", " ...\n", - " -0.270414\n", + " -0.158842\n", " -0.616988\n", " NaN\n", " -0.050442\n", @@ -9638,7 +9668,7 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", + " -0.038208\n", " -0.092275\n", " NaN\n", " -0.210058\n", @@ -9662,8 +9692,8 @@ " None\n", " None\n", " ...\n", - " 0.310155\n", - " 0.310155\n", + " 0.390198\n", + " 0.204794\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -9719,9 +9749,9 @@ "\n", " open_upper_bound open_lower_bound ... metac-o1-preview metac-perplexity \\\n", "0 False False ... 2.302585 5.703782 \n", - "1 True True ... -0.270414 -0.616988 \n", - "2 False False ... -0.092275 -0.092275 \n", - "3 None None ... 0.310155 0.310155 \n", + "1 True True ... -0.158842 -0.616988 \n", + "2 False False ... -0.038208 -0.092275 \n", + "3 None None ... 0.390198 0.204794 \n", "4 False False ... 0.243782 -0.102791 \n", "\n", " minefrac1 mmBot pgodzinai pianobot swingswish twsummerbot \\\n", @@ -9732,7 +9762,7 @@ "4 NaN 0.265372 0.041050 NaN NaN -0.771754 \n", "\n", " wunderplumb bot_team_median \n", - "0 NaN 5.521275 \n", + "0 NaN 4.605170 \n", "1 NaN -1.512868 \n", "2 NaN -0.149434 \n", "3 NaN 0.310155 \n", @@ -9859,7 +9889,7 @@ " -0.132060\n", " -0.158283\n", " -0.132060\n", - " -0.132060\n", + " -0.158283\n", " \n", " \n", " 97\n", @@ -9898,7 +9928,7 @@ " False\n", " False\n", " ...\n", - " -0.063666\n", + " -0.017709\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -9934,12 +9964,12 @@ "95 -2.251292 NaN NaN -0.111226 NaN \n", "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", - "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.158283 \n", "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", "\n", @@ -10007,8 +10037,8 @@ " 0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", + " X_bot\n", + " -0.0\n", " -0.0\n", " -0.0\n", " 0.0\n", @@ -10031,8 +10061,8 @@ " -0.0\n", " \n", " \n", - " X_bot\n", - " -0.0\n", + " RPM_bot\n", + " -0.1\n", " -0.0\n", " -0.0\n", " 0.0\n", @@ -10043,7 +10073,7 @@ " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", + " -0.0\n", " 0.0\n", " \n", " \n", @@ -10103,7 +10133,7 @@ " -0.0\n", " \n", " \n", - " annabot\n", + " 4Shadower\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10111,7 +10141,7 @@ " -0.0\n", " \n", " \n", - " 4Shadower\n", + " annabot\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10160,30 +10190,30 @@ " \n", " \n", " ajf-bot\n", - " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " GreeneiBot2\n", + " acm_bot\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", " 0.0\n", + " 0.1\n", " \n", " \n", - " acm_bot\n", + " GreeneiBot2\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " Bot_Pepa\n", + " metac-deepseek-r1+asknews\n", " -0.2\n", " -0.2\n", " -0.1\n", @@ -10191,15 +10221,7 @@ " -0.0\n", " \n", " \n", - " metac-perplexity\n", - " -0.3\n", - " -0.3\n", - " -0.1\n", - " -0.0\n", - " 0.1\n", - " \n", - " \n", - " bot_median\n", + " metac-Gemini-Exp-1206\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10209,14 +10231,14 @@ " \n", " metac-o1\n", " -0.3\n", - " -0.3\n", + " -0.2\n", " -0.1\n", - " -0.0\n", + " 0.0\n", " 0.1\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -0.3\n", + " Bot_Pepa\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.1\n", @@ -10239,44 +10261,36 @@ " -0.0\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " -0.3\n", + " bot_median\n", " -0.3\n", + " -0.2\n", " -0.1\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " manticAI\n", + " metac-perplexity\n", + " -0.4\n", " -0.3\n", - " -0.2\n", - " -0.2\n", " -0.1\n", " -0.0\n", + " 0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.3\n", + " manticAI\n", " -0.3\n", " -0.2\n", - " -0.0\n", - " 0.0\n", - " \n", - " \n", - " NextWorldLab\n", - " -0.3\n", - " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " NextWorldLab\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " 0.0\n", " \n", " \n", " minefrac1\n", @@ -10287,7 +10301,7 @@ " -0.1\n", " \n", " \n", - " metac-o1-preview\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -10303,7 +10317,7 @@ " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", + " metac-claude-3-5-sonnet-20240620\n", " -0.4\n", " -0.4\n", " -0.2\n", @@ -10314,24 +10328,40 @@ " pgodzinai\n", " -0.4\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", " metac-grok-2-1212\n", - " -0.5\n", " -0.4\n", - " -0.3\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", " -0.1\n", - " -0.0\n", " \n", " \n", " VeritasAI\n", " -0.4\n", " -0.3\n", - " -0.3\n", " -0.2\n", + " -0.2\n", + " -0.1\n", + " \n", + " \n", + " metac-o1-preview\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", + " -0.1\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", " -0.1\n", " \n", " \n", @@ -10351,7 +10381,7 @@ " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", + " metac-Llama-3.1\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -10366,11 +10396,11 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", - "CumulativeBot -0.0 -0.0 -0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", @@ -10378,38 +10408,38 @@ "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", + "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", - "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "ajf-bot -0.2 -0.2 -0.1 -0.0 0.0\n", + "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", - "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-deepseek-r1+asknews -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", - "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", - "metac-o1 -0.3 -0.3 -0.1 -0.0 0.1\n", - "metac-deepseek-r1+asknews -0.3 -0.2 -0.1 -0.1 -0.0\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.1\n", + "bot_median -0.3 -0.2 -0.1 -0.0 0.0\n", + "metac-perplexity -0.4 -0.3 -0.1 -0.0 0.1\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.0 0.0\n", - "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.1\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", + "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 -0.0\n", - "pgodzinai -0.4 -0.4 -0.3 -0.1 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.0\n", - "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.4 -0.2 -0.1 -0.0\n", + "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "metac-grok-2-1212 -0.4 -0.4 -0.2 -0.1 -0.1\n", + "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", + "metac-o1-preview -0.4 -0.4 -0.3 -0.1 -0.1\n", + "metac-gpt-4o -0.4 -0.4 -0.3 -0.1 -0.1\n", "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" + "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" ] }, "execution_count": 50, @@ -10458,7 +10488,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -11023,7 +11053,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -11072,47 +11102,47 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.4]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.65]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.98]\n", " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", @@ -11121,457 +11151,457 @@ " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.35, 0.7]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", " >>> Collected 2 forecasts: [0.85, 0.85]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.25]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.8, 0.6]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + 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0.7]\n", + " >>> Collected 2 forecasts: [0.25, 0.02]\n", + " >>> Collected 2 forecasts: [0.85, 0.75]\n", + " >>> Collected 2 forecasts: [0.99, 0.99]\n", + " >>> Collected 2 forecasts: [0.2, 0.99]\n", + " >>> Collected 2 forecasts: [0.3, 0.15]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.75]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.25]\n", + " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.3, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.3]\n", - " >>> Collected 2 forecasts: [0.95, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.4]\n", + " >>> Collected 2 forecasts: [0.95, 0.8]\n", " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, 0.82]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.7, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", " >>> Collected 3 forecasts: [0.6, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.65, 0.108]\n", - " >>> Collected 3 forecasts: [0.25, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.15, 0.5, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.2, 0.15]\n", + " >>> Collected 3 forecasts: [0.15, 0.25, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.25, 0.125]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", + " >>> Collected 3 forecasts: [0.15, 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Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.8, 0.9, 0.84]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.65, 0.75, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.086]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.25, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.9, 0.7, 0.17]\n", + " >>> Collected 3 forecasts: [0.25, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.85, 0.75, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", + " >>> Collected 3 forecasts: [0.2, 0.99, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.3, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.75, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.25, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.7, 0.67]\n", + " >>> Collected 3 forecasts: [0.3, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.8, nan]\n", " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.794]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.7, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", " >>> Collected 4 forecasts: [0.6, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.65, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.15, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.2, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.144]\n", " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", - " >>> Collected 4 forecasts: [0.1, 0.25, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.97, 0.96, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.65, 0.7, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.7, 0.75, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.8, 0.9, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.65, 0.75, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.9, 0.7, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.25, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.85, 0.75, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.75, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.25, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.7, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", " >>> Collected 5 forecasts: [0.6, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.65, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.65, 0.7, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.65, 0.75, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.9, 0.7, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.25, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.7, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.3, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.9, 0.4, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27]\n", + " >>> Collected 7 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", " >>> Collected 7 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", - " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", + " >>> Collected 7 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38]\n", + " >>> Collected 7 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", + " >>> Collected 7 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78]\n", + " >>> Collected 7 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1]\n", " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92]\n", - " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", " >>> Collected 8 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513]\n", - " >>> Collected 8 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765]\n", + " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", + " >>> Collected 8 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", + " >>> Collected 8 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1, 0.073]\n", " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.9]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25]\n", " >>> Collected 9 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", - " >>> Collected 9 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.75]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15]\n", " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8]\n", + " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.9, 0.546]\n", " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25, 0.281]\n", " >>> Collected 10 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", - " >>> Collected 10 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2, 0.574]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.75, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15, 0.086]\n", " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -11652,16 +11682,16 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.057462871287128715, 0.0001, 0.0001, 0.0001,...\n", " \n", " \n", " 1\n", " numeric\n", " NaN\n", " 86.82\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", @@ -11670,16 +11700,16 @@ " binary\n", " NaN\n", " no\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", " 0.085\n", - " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.2,0.6,0.2]\n", + " [0.15,0.65,0.2]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.45, 0.0001]\n", " \n", @@ -11716,8 +11746,8 @@ " NaN\n", " no\n", " 0.9\n", - " 0.3\n", - " 0.1835\n", + " 0.4\n", + " 0.2335\n", " \n", " \n", " 355\n", @@ -11725,7 +11755,7 @@ " NaN\n", " yes\n", " 0.95\n", - " 0.85\n", + " 0.8\n", " 0.775\n", " \n", " \n", @@ -11735,15 +11765,15 @@ " no\n", " 0.85\n", " 0.8\n", - " 0.755\n", + " 0.709\n", " \n", " \n", " 364\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.052\n", + " 0.05\n", + " 0.05\n", " 0.046\n", " \n", " \n", @@ -11766,42 +11796,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.25,0.03,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", "351 0.9 \n", "355 0.95 \n", "361 0.85 \n", - "364 0.1 \n", + "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.9 \n", - "351 0.3 \n", - "355 0.85 \n", + "351 0.4 \n", + "355 0.8 \n", "361 0.8 \n", - "364 0.052 \n", + "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.085 \n", "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.9025 \n", - "351 0.1835 \n", + "351 0.2335 \n", "355 0.775 \n", - "361 0.755 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11892,52 +11922,52 @@ " \n", " 0\n", " 1\n", - " 1399.41\n", + " 1252.60\n", " \n", " \n", " 1\n", " 2\n", - " 2492.32\n", + " 2269.15\n", " \n", " \n", " 2\n", " 3\n", - " 2451.57\n", + " 2400.04\n", " \n", " \n", " 3\n", " 4\n", - " 2407.46\n", + " 2413.81\n", " \n", " \n", " 4\n", " 5\n", - " 2500.43\n", + " 2591.97\n", " \n", " \n", " 5\n", " 6\n", - " 2492.29\n", + " 2483.23\n", " \n", " \n", " 6\n", " 7\n", - " 2620.65\n", + " 2478.69\n", " \n", " \n", " 7\n", " 8\n", - " 2688.63\n", + " 2536.53\n", " \n", " \n", " 8\n", " 9\n", - " 2505.22\n", + " 2388.76\n", " \n", " \n", " 9\n", " 10\n", - " 2396.81\n", + " 2370.53\n", " \n", " \n", "\n", @@ -11945,16 +11975,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 1399.41\n", - "1 2 2492.32\n", - "2 3 2451.57\n", - "3 4 2407.46\n", - "4 5 2500.43\n", - "5 6 2492.29\n", - "6 7 2620.65\n", - "7 8 2688.63\n", - "8 9 2505.22\n", - "9 10 2396.81" + "0 1 1252.60\n", + "1 2 2269.15\n", + "2 3 2400.04\n", + "3 4 2413.81\n", + "4 5 2591.97\n", + "5 6 2483.23\n", + "6 7 2478.69\n", + "7 8 2536.53\n", + "8 9 2388.76\n", + "9 10 2370.53" ] }, "execution_count": 60, @@ -11994,14 +12024,7 @@ { "data": { "text/plain": [ - "['metac-o1-preview',\n", - " 'metac-o1',\n", - " 'pgodzinai',\n", - " 'GreeneiBot2',\n", - " 'manticAI',\n", - " 'acm_bot',\n", - " 'metac-Gemini-Exp-1206',\n", - " 'SynapseSeer']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" ] }, "execution_count": 61, @@ -12018,7 +12041,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -12115,16 +12138,16 @@ " NaN\n", " False\n", " False\n", - " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", " ...\n", " [0.01, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.20500000000000002, 0.0001, 0.0001, 0.0001, ...\n", + " [0.13, 0.0001, 0.0001, 0.0001, 0.0001]\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.057462871287128715, 0.0001, 0.0001, 0.0001,...\n", + " [0.057462871287128715, 0.0001, 0.0001, 0.0001,...\n", " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " \n", @@ -12139,10 +12162,10 @@ " 100.0\n", " True\n", " True\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " ...\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", + " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05...\n", + " [0.05, 0.05079411765, 0.0515882353, 0.05238235...\n", " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", @@ -12163,18 +12186,18 @@ " NaN\n", " False\n", " False\n", - " 0.1\n", + " 0.05\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.075\n", + " 0.07\n", + " 0.063\n", + " 0.063\n", + " 0.07\n", " 0.085\n", " 0.085\n", " 0.1\n", " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", " \n", " \n", " 3\n", @@ -12187,18 +12210,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.2,0.6,0.2]\n", + " [0.15,0.65,0.2]\n", " ...\n", - " [0.0001, 0.6, 0.0001]\n", - " [0.0001, 0.525, 0.0001]\n", + " [0.0001, 0.65, 0.0001]\n", + " [0.0001, 0.55, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.5562499999999999, 0.0001]\n", + " [0.0001, 0.5662499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.48124999999999996, 0.0001]\n", " [0.0001, 0.45, 0.0001]\n", " [0.0001, 0.45, 0.0001]\n", - " [0.0001, 0.442, 0.0001]\n", - " [0.0001, 0.434, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", @@ -12221,7 +12244,7 @@ " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", - " [0.0, 0.00217156865, 0.00434313725, 0.00651470...\n", + " [0.0, 0.002254902, 0.0045098039, 0.0067647059,...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", " \n", @@ -12245,65 +12268,65 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.01,0.7,0.25,0.03,0.01] ... \n", - "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", - "2 False 0.1 ... \n", - "3 NaN [0.2,0.6,0.2] ... \n", + "0 False [0.01,0.7,0.2,0.07,0.02] ... \n", + "1 True [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... ... \n", + "2 False 0.05 ... \n", + "3 NaN [0.15,0.65,0.2] ... \n", "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", "\n", " median_forecast_1_bots \\\n", "0 [0.01, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.1 \n", - "3 [0.0001, 0.6, 0.0001] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05... \n", + "2 0.05 \n", + "3 [0.0001, 0.65, 0.0001] \n", "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.20500000000000002, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", - "2 0.1 \n", - "3 [0.0001, 0.525, 0.0001] \n", + "0 [0.13, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "1 [0.05, 0.05079411765, 0.0515882353, 0.05238235... \n", + "2 0.075 \n", + "3 [0.0001, 0.55, 0.0001] \n", "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", - "2 0.1 \n", + "2 0.07 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0033333333, 0.0066666667, 0.01, 0.0133... \n", "\n", " median_forecast_4_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", - "3 [0.0001, 0.5562499999999999, 0.0001] \n", + "2 0.063 \n", + "3 [0.0001, 0.5662499999999999, 0.0001] \n", "4 [0.0, 0.00257575755, 0.00515151515, 0.00772727... \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " median_forecast_6_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.07 \n", "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.085 \n", "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.085 \n", "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", @@ -12311,14 +12334,14 @@ "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.442, 0.0001] \n", - "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", + "4 [0.0, 0.002254902, 0.0045098039, 0.0067647059,... \n", "\n", " median_forecast_10_bots \n", "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.434, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -12398,7 +12421,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -12416,7 +12439,7 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", " \n", " \n", @@ -12434,7 +12457,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -12452,7 +12475,7 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", " \n", " \n", @@ -12470,7 +12493,7 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", " \n", " \n", @@ -12507,11 +12530,11 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.45, 0.0001] \n", - "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.063 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median \n", "0 [0.001,0.62,0.35,0.019,0.01] \n", @@ -12578,7 +12601,7 @@ " False\n", " 35380\n", " 1.00\n", - " 0.9025\n", + " 0.9\n", " 0.95\n", " \n", " \n", @@ -12596,7 +12619,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.1835\n", + " 0.4\n", " 0.05\n", " \n", " \n", @@ -12614,7 +12637,7 @@ " False\n", " 35385\n", " 1.00\n", - " 0.775\n", + " 0.8\n", " 0.97\n", " \n", " \n", @@ -12632,7 +12655,7 @@ " False\n", " 35386\n", " 0.85\n", - " 0.755\n", + " 0.8\n", " 0.666\n", " \n", " \n", @@ -12650,7 +12673,7 @@ " False\n", " 35387\n", " 0.85\n", - " 0.046\n", + " 0.05\n", " 0.03\n", " \n", " \n", @@ -12680,11 +12703,11 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median \n", - "342 1.00 0.9025 0.95 \n", - "351 1.00 0.1835 0.05 \n", - "355 1.00 0.775 0.97 \n", - "361 0.85 0.755 0.666 \n", - "364 0.85 0.046 0.03 " + "342 1.00 0.9 0.95 \n", + "351 1.00 0.4 0.05 \n", + "355 1.00 0.8 0.97 \n", + "361 0.85 0.8 0.666 \n", + "364 0.85 0.05 0.03 " ] }, "metadata": {}, @@ -12694,7 +12717,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" ] } @@ -12726,7 +12749,7 @@ " how='left'\n", ")\n", "\n", - "# Copy with union (not just overlapping questions)\n", + "# Copy with union (not just questions at the intersection)\n", "df_top_bot_pro_forecasts_all = df_top_bot_pro_forecasts.copy()\n", "\n", "# Filter to only those rows where pro_median is not NA\n", @@ -12747,7 +12770,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1115\n" + "Weighted Total Score: -0.1312\n" ] } ], @@ -12769,7 +12792,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -12781,7 +12804,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -0.12\n" + "The average of 'head_to_head' is: -0.14\n" ] } ], @@ -12837,17 +12860,17 @@ " \n", " \n", " head_to_head\n", - " -10.6\n", + " -12.5\n", " 92.1\n", " -0.1\n", - " 0.846125\n", - " 0.088167\n", - " -1.304254\n", + " 0.669453\n", + " 0.069757\n", + " -1.939479\n", " 1.98555\n", - " 0.1\n", + " 0.0\n", " -0.3\n", - " 0.097716\n", - " 0.195433\n", + " 0.027769\n", + " 0.055537\n", " \n", " \n", "\n", @@ -12855,10 +12878,10 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -10.6 92.1 -0.1 0.846125 0.088167 -1.304254 1.98555 \n", + "head_to_head -12.5 92.1 -0.1 0.669453 0.069757 -1.939479 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.1 -0.3 0.097716 0.195433 " + "head_to_head 0.0 -0.3 0.027769 0.055537 " ] }, "execution_count": 68, @@ -12930,34 +12953,34 @@ " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.14]\n", + " [0.0001, 0.0001, 0.0001, 0.125]\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -1.8\n", + " -1.9\n", " \n", " \n", - " 247\n", - " Will the 500th richest person on Bloomberg's B...\n", - " 0.833333\n", - " 0.333\n", - " no\n", - " -1.4\n", + " 47\n", + " What will be Donald Trump's net worth, accordi...\n", + " [0.16999999999999998, 0.0001, 0.0001, 0.0001, ...\n", + " [0.6,0.2,0.1,0.075,0.025]\n", + " 0-$6 billion, inclusive\n", + " -1.3\n", " \n", " \n", " 232\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.27130390143737165]\n", + " [0.0001, 0.0001, 0.0001, 0.2963039014373716]\n", " [0.002,0.008,0.09,0.9]\n", " 3 or more\n", - " -1.2\n", + " -1.1\n", " \n", " \n", - " 47\n", - " What will be Donald Trump's net worth, accordi...\n", - " [0.185, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.6,0.2,0.1,0.075,0.025]\n", - " 0-$6 billion, inclusive\n", - " -1.2\n", + " 247\n", + " Will the 500th richest person on Bloomberg's B...\n", + " 0.766667\n", + " 0.333\n", + " no\n", + " -1.1\n", " \n", " \n", "\n", @@ -12967,23 +12990,23 @@ " title \\\n", "279 What will Kalshi's rank in the iPhone Top Free... \n", "121 How many movies will be new on Netflix's top 1... \n", - "247 Will the 500th richest person on Bloomberg's B... \n", - "232 How many movies will be new on Netflix's top 1... \n", "47 What will be Donald Trump's net worth, accordi... \n", + "232 How many movies will be new on Netflix's top 1... \n", + "247 Will the 500th richest person on Bloomberg's B... \n", "\n", - " bot_team_median \\\n", - "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", - "121 [0.0001, 0.0001, 0.0001, 0.14] \n", - "247 0.833333 \n", - "232 [0.0001, 0.0001, 0.0001, 0.27130390143737165] \n", - "47 [0.185, 0.0001, 0.0001, 0.0001, 0.0001] \n", + " bot_team_median \\\n", + "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", + "121 [0.0001, 0.0001, 0.0001, 0.125] \n", + "47 [0.16999999999999998, 0.0001, 0.0001, 0.0001, ... \n", + "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", + "247 0.766667 \n", "\n", " pro_median resolution head_to_head \n", "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", - "247 0.333 no -1.4 \n", - "232 [0.002,0.008,0.09,0.9] 3 or more -1.2 \n", - "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -1.2 " + "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", + "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -1.3 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", + "247 0.333 no -1.1 " ] }, "execution_count": 69, @@ -13049,25 +13072,25 @@ " \n", " \n", " \n", - " 189\n", - " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", - " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", - " 34.0\n", - " 2.9\n", - " \n", - " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 5.3\n", + " 2.5\n", + " \n", + " \n", + " 189\n", + " What will the highest rank of metac-GPT4o or m...\n", + " [0.0, 0.0369946063, 0.07475, 0.10485, 0.1198, ...\n", + " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", + " 34.0\n", + " 2.8\n", " \n", " \n", " 151\n", " How many earthquakes of magnitude ≥ 4 will hap...\n", - " [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0...\n", + " [0.0, 0.0035714286, 0.0071428571, 0.0107142857...\n", " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", " 0.0\n", " NaN\n", @@ -13083,7 +13106,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.952\n", + " 0.928\n", " 0.95\n", " annulled\n", " NaN\n", @@ -13094,29 +13117,29 @@ ], "text/plain": [ " title \\\n", - "189 What will the highest rank of metac-GPT4o or m... \n", "0 For Q1 2025, how many banks will be listed on ... \n", + "189 What will the highest rank of metac-GPT4o or m... \n", "151 How many earthquakes of magnitude ≥ 4 will hap... \n", "211 Will Nikola Corporation file for bankruptcy be... \n", "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", - "151 [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "189 [0.0, 0.0369946063, 0.07475, 0.10485, 0.1198, ... \n", + "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.952 \n", + "214 0.928 \n", "\n", " pro_median resolution \\\n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... 0.0 \n", "211 0.999 annulled \n", "214 0.95 annulled \n", "\n", " head_to_head \n", - "189 2.9 \n", - "0 5.3 \n", + "0 2.5 \n", + "189 2.8 \n", "151 NaN \n", "211 NaN \n", "214 NaN " @@ -13234,10 +13257,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 5.334952\n", - " 5.334952\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -13254,10 +13277,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.250003\n", - " -0.250003\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -13274,10 +13297,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", - " -0.092275\n", - " -0.092275\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -13294,10 +13317,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 0.022473\n", - " 0.022473\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -13314,10 +13337,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " -0.102791\n", - " -0.102791\n", + " 0.132210\n", + " 0.132210\n", " \n", " \n", "\n", @@ -13353,25 +13376,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.45, 0.0001] \n", - "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.063 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 5.334952 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", - "2 0.013 -0.092275 \n", - "3 [0.16,0.44,0.4] 0.022473 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... -0.102791 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", + "2 0.013 -0.051987 \n", + "3 [0.16,0.44,0.4] 0.152526 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.132210 \n", "\n", " weighted_score \n", - "0 5.334952 \n", - "1 -0.250003 \n", - "2 -0.092275 \n", - "3 0.022473 \n", - "4 -0.102791 " + "0 2.522754 \n", + "1 -0.158842 \n", + "2 -0.051987 \n", + "3 0.152526 \n", + "4 0.132210 " ] }, "execution_count": 72, @@ -13385,7 +13408,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -13410,7 +13433,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 74, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13422,7 +13445,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -13466,7 +13489,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -13479,7 +13502,35 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 0.0\n", + "5 1.0\n", + "8 1.0\n", + "10 1.0\n", + "13 1.0\n", + " ... \n", + "417 0.0\n", + "418 0.0\n", + "419 0.0\n", + "420 1.0\n", + "421 0.0\n", + "Name: resolution, Length: 236, dtype: float64\n" + ] + } + ], + "source": [ + "print(df_top_bot_pro_forecasts_all_binary['resolution'])" + ] + }, + { + "cell_type": "code", + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -13536,7 +13587,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -13554,7 +13605,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.5\n", + " 0.62\n", " 0.45\n", " \n", " \n", @@ -13572,7 +13623,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.835\n", + " 0.86\n", " 0.95\n", " \n", " \n", @@ -13638,14 +13689,14 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.1 0.013 \n", - "5 1.0 0.5 0.45 \n", - "8 1.0 0.835 0.95 \n", + "2 1.0 0.063 0.013 \n", + "5 1.0 0.62 0.45 \n", + "8 1.0 0.86 0.95 \n", "10 1.0 NaN NaN \n", "13 1.0 0.85 0.9 " ] }, - "execution_count": 95, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -13656,12 +13707,84 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "dtype: object" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_top_bot_pro_forecasts_all_binary.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "head_to_head float64\n", + "weighted_score float64\n", + "dtype: object" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_top_bot_pro_forecasts_binary.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -13707,7 +13830,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13719,7 +13842,7 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -13738,7 +13861,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13751,9 +13874,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bot average forecast difference (1 - 0): 0.4288\n", + "Bot average forecast difference (1 - 0): 0.4355\n", "Pro average forecast difference (1 - 0): 0.5238\n", - "Difference between pro and bot differences: 0.0950\n" + "Difference between pro and bot differences: 0.0882\n" ] } ], @@ -13780,7 +13903,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13820,7 +13943,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -13961,8 +14084,8 @@ " 1.0\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.387623\n", - " 0.387623\n", + " 0.132210\n", + " 0.132210\n", " \n", " \n", "\n", @@ -14009,14 +14132,14 @@ "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.132210 \n", "\n", " weighted_score \n", "0 2.522754 \n", "1 -0.158842 \n", "2 -0.051987 \n", "3 0.152526 \n", - "4 0.387623 " + "4 0.132210 " ] }, "metadata": {}, @@ -14098,10 +14221,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.3\n", + " 0.4\n", " 0.05\n", - " -0.305382\n", - " -0.305382\n", + " -0.459532\n", + " -0.459532\n", " \n", " \n", " 355\n", @@ -14118,10 +14241,10 @@ " False\n", " 35385\n", " 1.00\n", - " 0.85\n", + " 0.8\n", " 0.97\n", - " -0.132060\n", - " -0.132060\n", + " -0.192684\n", + " -0.192684\n", " \n", " \n", " 361\n", @@ -14191,8 +14314,8 @@ "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", "342 1.00 0.9 0.95 -0.054067 -0.054067 \n", - "351 1.00 0.3 0.05 -0.305382 -0.305382 \n", - "355 1.00 0.85 0.97 -0.132060 -0.132060 \n", + "351 1.00 0.4 0.05 -0.459532 -0.459532 \n", + "355 1.00 0.8 0.97 -0.192684 -0.192684 \n", "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", "364 0.85 0.05 0.03 -0.017709 -0.015053 " ] @@ -14207,15 +14330,15 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[81], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:750\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 753\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 754\u001b[0m bins\n\u001b[1;32m 755\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[84], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/metaculus/aib-analysis/functions.py:782\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 773\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 780\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 781\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 782\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 784\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 785\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 786\u001b[0m bins\n\u001b[1;32m 787\u001b[0m )\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/_core/_methods.py:49\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 48\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (201,) (5,) " ] } @@ -15209,7 +15332,7 @@ "provenance": [] }, "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -15223,7 +15346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/functions.py b/functions.py index 11257c4..08b3fd0 100644 --- a/functions.py +++ b/functions.py @@ -647,7 +647,10 @@ def plot_calibration_curve(df: pd.DataFrame, column_name: str, label: str, color """ _assert_calibration_dataframe_matches_assumptions(df) # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) - df = df[df["resolution"].isin(["yes", "no", 1, 0])] + df = df[df["resolution"].isin(["yes", "no", 1.0, 0.0])] + + # If any of df[column_name] are None, drop those rows + df = df[df[column_name].notnull()] y_true = df["resolution"] y_pred = df[column_name] @@ -655,6 +658,7 @@ def plot_calibration_curve(df: pd.DataFrame, column_name: str, label: str, color calibration_curve = _calculate_calibration_curve(y_pred, y_true, weights)[ "calibration_curve" ] + prob_true = [item["average_resolution"] for item in calibration_curve] bin_center = [ (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 7214749..6b92b92 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,11 +1,11 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 -CumulativeBot,-0.0,-0.0,-0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 @@ -13,35 +13,35 @@ Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -annabot,-0.1,-0.1,-0.1,-0.0,-0.0 4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 +annabot,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 MWG,-0.2,-0.2,-0.1,-0.0,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 -ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +ajf-bot,-0.2,-0.2,-0.1,-0.0,0.0 +acm_bot,-0.3,-0.2,-0.1,0.0,0.1 GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 -acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 +metac-deepseek-r1+asknews,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.2,-0.1,-0.0,0.1 +metac-o1,-0.3,-0.2,-0.1,0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 -bot_median,-0.3,-0.2,-0.1,-0.0,0.1 -metac-o1,-0.3,-0.3,-0.1,-0.0,0.1 -metac-deepseek-r1+asknews,-0.3,-0.2,-0.1,-0.1,-0.0 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.1 +bot_median,-0.3,-0.2,-0.1,-0.0,0.0 +metac-perplexity,-0.4,-0.3,-0.1,-0.0,0.1 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.0,0.0 -NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.1 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 +metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,-0.0 -pgodzinai,-0.4,-0.4,-0.3,-0.1,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.0 -VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-claude-3-5-sonnet-20240620,-0.4,-0.4,-0.2,-0.1,-0.0 +pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +metac-grok-2-1212,-0.4,-0.4,-0.2,-0.1,-0.1 +VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 +metac-o1-preview,-0.4,-0.4,-0.3,-0.1,-0.1 +metac-gpt-4o,-0.4,-0.4,-0.3,-0.1,-0.1 metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index cd9448c..8eb9a70 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 -jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 +RPM_bot,-1.3,7.0,-0.2,0.803162845690475,0.3035670217119917,-0.6018020851526737,2.4469118511449692,0.6,-0.9,0.2846659989090443,0.569332 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 -CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 +CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032240997,2.0887774106971415,0.1,-0.4,0.0941440217425653,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 +annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 +cookics_bot_TEST,-6.7,27.4,-0.2,0.7480496337801963,0.14290753666776426,-1.7220041694550487,2.0495406495390753,0.0,-0.5,0.048383645251144566,0.096767 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.7,58.4,-0.2,0.8487135517179298,0.11110681713348293,-1.6470273617836275,2.000831925930035,0.0,-0.4,0.052510863710317504,0.105022 +metac-o1,-10.8,91.1,-0.1,0.8668236222209089,0.09081791967404183,-1.3030182446846603,1.9858289388460384,0.1,-0.3,0.09794439270715757,0.195889 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 +metac-deepseek-r1+asknews,-11.2,52.1,-0.2,0.6342566612198152,0.08787112272667183,-2.4450432699738145,2.0053789762011176,-0.0,-0.4,0.008984924011519364,0.017970 +GreeneiBot2,-11.4,58.4,-0.2,0.8462281442135139,0.1107814473823621,-1.7668111287097124,2.000831925930035,0.0,-0.4,0.041290471840402215,0.082581 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-perplexity,-12.0,89.1,-0.1,1.0008449184534645,0.10602979859799266,-1.2696037636515303,1.9864049297707018,0.1,-0.3,0.10378462460698391,0.207569 -bot_median,-12.2,92.1,-0.1,0.8759085051927877,0.0912701844746672,-1.448706262693777,1.9855502432148115,0.0,-0.3,0.07542649485602951,0.150853 -metac-o1,-12.4,91.1,-0.1,0.9413031092818035,0.09862120502513756,-1.3750355923383297,1.9858289388460384,0.1,-0.3,0.08626502997859752,0.172530 +metac-Gemini-Exp-1206,-11.5,76.5,-0.2,0.8952097471246512,0.10235147002510721,-1.4718494129042066,1.9908217254774627,0.1,-0.4,0.07260889665750306,0.145218 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 -metac-deepseek-r1+asknews,-13.4,52.1,-0.3,0.6866418388462276,0.09512866474982715,-2.7023938246614656,2.0053789762011176,-0.1,-0.4,0.0046603987010819335,0.009321 -metac-Gemini-Exp-1206,-13.5,76.5,-0.2,1.0066063915806054,0.11508771463432003,-1.5277274660739493,1.9908217254774627,0.1,-0.4,0.06537953017362978,0.130759 +bot_median,-13.3,92.1,-0.1,0.7572006546947513,0.07890075621895877,-1.8300583290868744,1.9855502432148115,0.0,-0.3,0.03525575647024838,0.070512 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 +metac-perplexity,-14.4,89.1,-0.2,1.1026009344968866,0.11680986021222348,-1.3849519746718768,1.9864049297707018,0.1,-0.4,0.08478215225308733,0.169564 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-claude-3-5-sonnet-20240620,-14.7,90.5,-0.2,0.9429804683378815,0.09912390614679249,-1.6425851577449733,1.9860719790130024,0.0,-0.4,0.051988931836857315,0.103978 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -metac-claude-3-5-sonnet-latest,-18.9,91.1,-0.2,0.7317083930215759,0.07666177104402958,-2.699995118056715,1.9858289388460384,-0.1,-0.4,0.004140859358698023,0.008282 -minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 -metac-o1-preview,-20.9,91.1,-0.2,0.802181404225052,0.08404529418137442,-2.7288070523371224,1.9858289388460384,-0.1,-0.4,0.003821400227265772,0.007643 +minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 +metac-claude-3-5-sonnet-latest,-21.6,91.1,-0.2,0.7840729022099676,0.08214804952944678,-2.8855809804350296,1.9858289388460384,-0.1,-0.4,0.002444218354964672,0.004888 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-Llama-3.1,-23.2,89.1,-0.3,1.0312779661924496,0.1092538844308646,-2.379606259857792,1.9864049297707018,-0.0,-0.5,0.009744516632283914,0.019489 -metac-grok-2-1212,-23.5,91.1,-0.3,1.0680060472571526,0.11189599005467826,-2.303421178504194,1.9858289388460384,-0.0,-0.5,0.011778139872058951,0.023556 -pgodzinai,-24.0,76.4,-0.3,0.9765897737398795,0.11172889227393508,-2.8110851156332464,1.9908489732268309,-0.1,-0.5,0.0031442974859602537,0.006289 +metac-claude-3-5-sonnet-20240620,-22.1,90.5,-0.2,0.9921895725908227,0.10429665234389453,-2.3447130845077018,1.9860719790130024,-0.0,-0.5,0.010626881125878994,0.021254 +metac-grok-2-1212,-23.2,91.1,-0.3,0.9691804386011083,0.10154193882835436,-2.504438328301395,1.9858289388460384,-0.1,-0.5,0.007031732032192213,0.014063 +pgodzinai,-23.2,76.4,-0.3,1.00292283111273,0.11474158338495037,-2.6493172344887146,1.9908489732268309,-0.1,-0.5,0.004910376705596484,0.009821 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-exa,-26.2,89.1,-0.3,0.8302752742001319,0.0879596014139391,-3.3415454501401167,1.9864049297707018,-0.1,-0.5,0.0006119018080970774,0.001224 -metac-gpt-4o,-26.6,91.1,-0.3,0.8790866786848435,0.09210273154158923,-3.165570176683145,1.9858289388460384,-0.1,-0.5,0.0010559673026657784,0.002112 +metac-o1-preview,-24.4,91.1,-0.3,0.8524321835897993,0.08931011522099137,-2.9993955258512948,1.9858289388460384,-0.1,-0.4,0.0017486358986007922,0.003497 +metac-gpt-4o,-25.1,91.1,-0.3,0.8735971368751565,0.09152758712427154,-3.0097067040559993,1.9858289388460384,-0.1,-0.5,0.0016956535070904697,0.003391 +metac-exa,-26.1,89.1,-0.3,0.7919348200357222,0.08389780266944466,-3.4956946250034493,1.9864049297707018,-0.1,-0.5,0.0003713213076391189,0.000743 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 +metac-Llama-3.1,-28.0,89.1,-0.3,0.9072003561919431,0.09610906673103263,-3.2702003829748127,1.9864049297707018,-0.1,-0.5,0.0007672454772695423,0.001534