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 3753b54..bf4055e 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -34,9 +34,12 @@ "outputs": [], "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", + "from copy import deepcopy\n" ] }, { @@ -54,7 +57,350 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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" + ] + }, + { + "data": { + "text/html": [ + "
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bot_question_idtitleresolutionscheduled_close_timeactual_close_timequestion_weight_xtypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weight_y
23691635705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23691735705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23691835705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23691935705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23692035705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
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" + ], + "text/plain": [ + " bot_question_id title \\\n", + "236916 35705 Which podcast will be ranked higher on Spotify... \n", + "236917 35705 Which podcast will be ranked higher on Spotify... \n", + "236918 35705 Which podcast will be ranked higher on Spotify... \n", + "236919 35705 Which podcast will be ranked higher on Spotify... \n", + "236920 35705 Which podcast will be ranked higher on Spotify... \n", + "\n", + " resolution scheduled_close_time actual_close_time \\\n", + "236916 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236917 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236918 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236919 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236920 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "\n", + " question_weight_x type options \\\n", + "236916 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236917 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236918 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236919 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236920 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "\n", + " range_min range_max open_upper_bound open_lower_bound \\\n", + "236916 NaN NaN False False \n", + "236917 NaN NaN False False \n", + "236918 NaN NaN False False \n", + "236919 NaN NaN False False \n", + "236920 NaN NaN False False \n", + "\n", + " pro_question_id question_weight_y \n", + "236916 NaN NaN \n", + "236917 NaN NaN \n", + "236918 NaN NaN \n", + "236919 NaN NaN \n", + "236920 NaN NaN " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# @title Create df_bot_resolved_questions, df_pro_resolved_questions, df_pro_bot_resolved_questions, df_bot_question_weights\n", "\n", @@ -74,7 +420,7 @@ "This is done by matching the title and scheduled_close_time.\n", "\n", "We remove early closers from the analysis. 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", @@ -87,7 +433,7 @@ "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", @@ -95,8 +441,8 @@ " 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", @@ -104,6 +450,7 @@ " 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", @@ -114,6 +461,7 @@ "# 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", @@ -182,154 +530,13 @@ "# 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()}')" ] }, { "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", @@ -346,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -354,11 +561,12 @@ "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" } @@ -369,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -404,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -419,12 +627,14 @@ "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" } @@ -435,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -467,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -499,11 +709,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "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", @@ -514,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -551,6 +761,8 @@ " 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 +784,8 @@ " [\"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 +805,8 @@ " [\"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 +826,8 @@ " [\"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 +847,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.59,0.35,0.044,0.015]\n", " False\n", @@ -648,6 +868,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.623,0.336,0.03,0.01]\n", " False\n", @@ -685,22 +907,29 @@ "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 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", - "\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 " + " 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", + "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", + "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", + "3 False \n", + "4 False " ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -711,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -734,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -747,15 +976,15 @@ " '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)" + " '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", + " 'KevinTestBot', '4Shadower', 'swingswish', 'jonahsingerbot',\n", + " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -767,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -821,11 +1050,11 @@ " \n", " 14\n", " bot_median\n", - " 6.926374\n", - " 2618.307732\n", + " 8.143307\n", + " 3078.332902\n", " 409\n", - " 3.779645\n", - " 1.600741\n", + " 5.471228\n", + " 1.359286\n", " \n", " \n", " 19\n", @@ -853,14 +1082,14 @@ " 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", + "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", "11 1.738353 \n", "12 2.298000 \n", - "14 1.600741 \n", + "14 1.359286 \n", "19 3.029040 \n", "5 2.309106 " ] @@ -976,7 +1205,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1025,7 +1254,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1449,7 +1678,7 @@ " np.int64(35705)}" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1470,7 +1699,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1520,7 +1749,7 @@ " \n", " 3\n", " bot_median\n", - " 8152.574861\n", + " 8721.511046\n", " \n", " \n", " 4\n", @@ -1541,7 +1770,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8152.574861\n", + "3 bot_median 8721.511046\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1647,7 +1876,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1666,7 +1895,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1710,13 +1939,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3472.028144\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2724.680171\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -1780,7 +2009,7 @@ " \n", " \n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 614.572462\n", " \n", " \n", @@ -1946,8 +2175,8 @@ " 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 3472.028144\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", @@ -1960,7 +2189,7 @@ "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", @@ -1994,7 +2223,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2036,7 +2265,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -2055,7 +2284,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2064,7 +2293,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2072,9 +2301,7 @@ "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" ] } ], @@ -2082,17 +2309,12 @@ "# 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", - "\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()}')" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2129,6 +2351,8 @@ " options\n", " range_min\n", " range_max\n", + " open_lower_bound\n", + " open_upper_bound\n", " post_id\n", " forecast\n", " is_median\n", @@ -2150,6 +2374,8 @@ " [\"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", @@ -2169,6 +2395,8 @@ " [\"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", @@ -2188,6 +2416,8 @@ " [\"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", @@ -2207,6 +2437,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.59,0.35,0.044,0.015]\n", " False\n", @@ -2226,6 +2458,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.623,0.336,0.03,0.01]\n", " False\n", @@ -2263,22 +2497,29 @@ "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 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", - "\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 " + " 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", + "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", + "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", + "3 False \n", + "4 False " ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2289,7 +2530,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2322,10 +2563,10 @@ " 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", @@ -2347,15 +2588,15 @@ " 0\n", " 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", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.2,0.25,0.35,0.15,0.05]\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", @@ -2372,14 +2613,14 @@ " 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", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2396,13 +2637,13 @@ " 1.0\n", " binary\n", " None\n", - " 0.013\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", " 0.1\n", - " 0.15\n", + " 0.05\n", " 0.1\n", " NaN\n", " 0.2\n", @@ -2419,15 +2660,15 @@ " 5-9\n", " 1.0\n", " multiple_choice\n", - " [\"0-4\",\"5-9\",\">9\"]\n", - " [0.16,0.44,0.4]\n", + " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", - " [0.16,0.47,0.37]\n", + " None\n", + " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.2,0.6,0.2]\n", - " [0.15,0.45,0.4]\n", + " [0.45,0.45,0.1]\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", @@ -2444,13 +2685,13 @@ " 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", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.0033333333,0.0066666667,0.01,0.01333333...\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", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2462,7 +2703,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -2473,39 +2714,39 @@ "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", - "\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", - "\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", - "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", + " 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", + "1 True True ... \n", + "2 False False ... \n", + "3 None None ... \n", + "4 False False ... \n", + "\n", + " metac-o1 \\\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 [0.02,0.7,0.2,0.07,0.01] \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", + "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 [0.2,0.25,0.35,0.15,0.05] NaN \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... 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 [0.15,0.45,0.4] 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", @@ -2529,7 +2770,7 @@ "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": {}, @@ -2562,10 +2803,10 @@ " 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", @@ -2588,10 +2829,10 @@ " 1.00\n", " binary\n", " None\n", - " 0.95\n", - " 0.9\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", " 0.9\n", " 0.9\n", @@ -2612,12 +2853,12 @@ " 1.00\n", " binary\n", " None\n", - " 0.05\n", - " 0.95\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.2\n", + " 0.4\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2636,13 +2877,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.97\n", - " 0.85\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.85\n", - " 0.9\n", + " 0.8\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2660,12 +2901,12 @@ " 0.85\n", " binary\n", " None\n", - " 0.666\n", - " 0.8\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.75\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2684,14 +2925,14 @@ " 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", " 0.05\n", + " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2702,7 +2943,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -2713,28 +2954,28 @@ "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 ... 0.9 \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", - "\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", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.05 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", - "[5 rows x 53 columns]" + " 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 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \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 0.05 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", + "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 57 columns]" ] }, "metadata": {}, @@ -2772,7 +3013,11 @@ "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", @@ -2793,14 +3038,15 @@ }, { "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", @@ -2808,14 +3054,14 @@ " '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": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2826,7 +3072,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2836,7 +3082,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2851,7 +3097,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2863,73 +3109,17 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "df_pro_bot_forecasts['options'] = df_pro_bot_forecasts['options'].apply(parse_options_array)" - ] - }, - { - "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, + "execution_count": 32, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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" + ] + }, { "data": { "text/html": [ @@ -2957,11 +3147,12 @@ " 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", " metac-perplexity\n", " minefrac1\n", @@ -2971,7 +3162,6 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", @@ -2983,174 +3173,209 @@ " 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", - " 299.573227\n", - " 529.831737\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", - " 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", - " NaN\n", - " NaN\n", - " 6.595797\n", + " numeric\n", + " None\n", + " 60.0\n", + " 100.0\n", + " True\n", + " True\n", " ...\n", - " 31.015493\n", - " 2.247286\n", + " 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5 rows × 54 columns

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5 rows × 57 columns

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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]" ] }, "metadata": {}, @@ -3183,11 +3408,12 @@ " 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", " metac-perplexity\n", " minefrac1\n", @@ -3197,391 +3423,517 @@ " 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", " NaN\n", - " -280.223742\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " -448.863637\n", - " -178.058617\n", - " -300.703183\n", - " -287.919846\n", - " -339.002408\n", + " 0.9\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", " NaN\n", - " -77.944110\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " -99.325177\n", - " -18.677591\n", - " -52.324814\n", - " 10.536052\n", - " 25.951120\n", + " 0.4\n", + " 0.9\n", + " NaN\n", + " NaN\n", + " 0.15\n", " NaN\n", " NaN\n", - " 27.650877\n", - " -64.460900\n", - " 27.650877\n", + " 0.1\n", + " 0.126\n", + " 0.95\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", + " 96\n", + " 35385\n", + " 35358\n", + " yes\n", + " 1.00\n", + " binary\n", + " None\n", " NaN\n", - " -70.227966\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " -132.175584\n", - " -26.570317\n", + " 0.8\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", " NaN\n", - " ...\n", - " -3.781749\n", - " -4.828879\n", " NaN\n", - " -12.482886\n", - " -8.037710\n", + " False\n", + " False\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", " NaN\n", - " ...\n", - " -170.474809\n", - " -290.872090\n", " NaN\n", - " -170.474809\n", - " -31.845373\n", + " False\n", + " False\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 × 57 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", - "\n", - " type \\\n", - "81 multiple_choice \n", - "82 multiple_choice \n", - "83 multiple_choice \n", - "91 multiple_choice \n", - "92 multiple_choice \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", - " 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", - " 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]" + " 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 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \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 0.05 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", + "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 57 columns]" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "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", + } + ], + "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", + " \"\"\"\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" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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" + ] + } + ], + "source": [ + "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "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", - " 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", + " range_min range_max open_upper_bound open_lower_bound ... \\\n", + "0 NaN NaN False False ... \n", + "3 NaN NaN None None ... \n", + "6 NaN NaN False False ... \n", + "9 NaN NaN None None ... \n", + "13 NaN NaN None None ... \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", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "0 2.302585 5.703782 NaN 2.292635 2.703087 \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", - "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", + "0 NaN NaN NaN NaN 4.605170 \n", + "3 NaN NaN -0.046520 NaN 0.310155 \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 54 columns]" + "[5 rows x 58 columns]" ] }, "metadata": {}, @@ -3614,10 +3966,10 @@ " 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-preview\n", " metac-perplexity\n", @@ -3633,177 +3985,179 @@ " \n", " \n", " \n", - " 94\n", - " 35380\n", - " 35345\n", - " yes\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.95\n", - " -5.406722\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", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - 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" binary\n", - " None\n", - " 0.97\n", - " -13.205972\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", " NaN\n", " NaN\n", + " None\n", + " None\n", " ...\n", - " -7.490131\n", + " -0.899761\n", + " -0.405465\n", " NaN\n", + " -0.182322\n", " NaN\n", - " -7.490131\n", " NaN\n", " NaN\n", - " -13.205972\n", - " -15.828292\n", - " -13.205972\n", - " -13.205972\n", + " -0.178330\n", + " -0.567984\n", + " -0.693147\n", " \n", " \n", - " 97\n", - " 35386\n", - " 35364\n", - " no\n", - " 0.85\n", - " binary\n", - " None\n", - " 0.666\n", - " -51.282363\n", + " 91\n", + " 35377\n", + " 35334\n", + " Jimmy Patronis\n", + " 1.0\n", + " multiple_choice\n", + " [Jimmy Patronis, Gay Valimont, Someone else]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " -80.050570\n", - " 73.993934\n", + " -0.054625\n", + " -0.102356\n", + " NaN\n", + " -0.124829\n", + " -0.080377\n", " NaN\n", - " -80.050570\n", - " -80.050570\n", + " -0.113529\n", " NaN\n", - " -10.735852\n", - " 95.505072\n", - " 73.993934\n", - " -10.735852\n", + " -0.147818\n", + " -0.124829\n", " \n", " \n", - " 98\n", - " 35387\n", - " 35367\n", - " no\n", - " 0.85\n", - " binary\n", - " None\n", - " 0.03\n", - " -32.621574\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", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " -7.490131\n", - " -2.083409\n", + " -1.704748\n", + " -4.007333\n", + " NaN\n", + " -1.704748\n", + " -0.318454\n", " NaN\n", - " -13.205972\n", - " -2.083409\n", + " -0.480973\n", " NaN\n", - " -19.268434\n", - " -28.425154\n", - " -19.268434\n", - " -13.205972\n", + " -0.749237\n", + " -0.318454\n", " \n", " \n", "\n", - "

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\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 ... \\\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", + " 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", - " 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", + " type \\\n", + "81 multiple_choice \n", + "82 multiple_choice \n", + "83 multiple_choice \n", + "91 multiple_choice \n", + "92 multiple_choice \n", "\n", - "[5 rows x 54 columns]" + " options range_min \\\n", + "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] NaN \n", + "82 [0, 1, 2, 3 or more] NaN \n", + "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] NaN \n", + "91 [Jimmy Patronis, Gay Valimont, Someone else] NaN \n", + "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 ... -0.076961 \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 -2.186051 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", + "83 -0.405465 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 -2.186051 \n", + "82 0.276509 -0.644609 -0.587787 \n", + "83 -0.178330 -0.567984 -0.693147 \n", + "91 NaN -0.147818 -0.124829 \n", + "92 NaN -0.749237 -0.318454 \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": 37, - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -3825,99 +4179,521 @@ " \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", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", + " ...\n", + " metac-o1-preview\n", + " metac-perplexity\n", + " minefrac1\n", + " mmBot\n", + " pgodzinai\n", + " pianobot\n", + " swingswish\n", + " twsummerbot\n", + " wunderplumb\n", + " bot_team_median\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", - " \n", - " \n", - " 4\n", - " manticAI\n", - " 2142.538438\n", + " 31270\n", + " 31264\n", + " no\n", + " 1.0\n", + " binary\n", + " None\n", + " NaN\n", + " NaN\n", + " False\n", + " False\n", + " ...\n", + " -0.038208\n", + " -0.092275\n", + " NaN\n", + " -0.210058\n", + " -0.059485\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.149434\n", " \n", " \n", " 5\n", - " metac-Gemini-Exp-1206\n", - " 2072.216227\n", - " \n", - " \n", - " 6\n", - " acm_bot\n", - " 1876.466009\n", - " \n", - " \n", - " 7\n", - " twsummerbot\n", - " 1763.532046\n", + " 31282\n", + " 31276\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " NaN\n", + " NaN\n", + " None\n", + " None\n", + " ...\n", + " -0.251314\n", + " 0.441833\n", + " NaN\n", + " 0.510826\n", + " 0.320472\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 0.367725\n", " \n", " \n", " 8\n", - " metac-perplexity\n", - " 1697.555196\n", - " \n", - " \n", - " 9\n", - " GreeneiBot2\n", - " 1603.998618\n", - " \n", - " \n", - " 10\n", - " cookics_bot_TEST\n", - " 1140.390796\n", - " \n", - " \n", - " 11\n", - " metac-claude-3-5-sonnet-latest\n", - " 1134.209821\n", + " 31294\n", + " 31288\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " NaN\n", + " NaN\n", + " False\n", + " False\n", + " ...\n", + " -0.054067\n", + " 0.000000\n", + " NaN\n", + " -0.111226\n", + " -0.147158\n", + " NaN\n", + " NaN\n", + " -0.398124\n", + " NaN\n", + " -0.147158\n", " \n", " \n", " 12\n", - " SynapseSeer\n", - " 1066.533051\n", - " \n", - " \n", - " 13\n", - " CumulativeBot\n", - " 1030.716475\n", - " \n", - " \n", - " 14\n", - " pgodzinai\n", - " 926.081448\n", - " \n", - " \n", - " 15\n", - " jkraybill_bot\n", - " 627.932509\n", + " 31338\n", + " 31334\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " NaN\n", + " NaN\n", + " False\n", + " False\n", + " ...\n", + " -0.057158\n", + " 0.000000\n", + " NaN\n", + " 0.054067\n", + " -0.057158\n", + " NaN\n", + " NaN\n", + " -0.499776\n", + " NaN\n", + " -0.057158\n", " \n", " \n", " 16\n", - " metac-deepseek-r1\n", - " 614.572462\n", - " \n", - " \n", - " 17\n", - " question_weight\n", + " 33876\n", + " 33751\n", + " no\n", + " 1.0\n", + " binary\n", + " None\n", + " NaN\n", + " NaN\n", + " False\n", + " False\n", + " ...\n", + " 0.008457\n", + " 0.008457\n", + " NaN\n", + " -0.068083\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.076070\n", + " NaN\n", + " -0.096728\n", + " \n", + " \n", + "\n", + "

5 rows × 58 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 range_min range_max open_upper_bound open_lower_bound ... \\\n", + "2 None NaN NaN False False ... \n", + "5 None NaN NaN None None ... \n", + "8 None NaN NaN False False ... \n", + "12 None NaN NaN False False ... \n", + "16 None NaN NaN False False ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\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 0.367725 \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", + "\n", + "[5 rows x 58 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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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...-0.020834NaNNaN-0.074901NaNNaN-0.132060-0.158283-0.132060-0.158283
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 -0.020834 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", + "\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 -0.158283 \n", + "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", + "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", + "\n", + "[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": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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metac-o1-preview 3162.155445\n", - "3 bot_median 2724.680171\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", "6 acm_bot 1876.466009\n", @@ -4092,7 +4868,7 @@ "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", @@ -4126,7 +4902,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -4137,7 +4913,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -4146,13 +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: 26.0%\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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", + "image/png": 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", "text/plain": [ "
" ] @@ -4195,15 +4971,15 @@ "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", @@ -4221,14 +4997,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { "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_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" ] } @@ -4278,7 +5054,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -4291,7 +5067,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4354,7 +5130,7 @@ "
\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4449,7 +5225,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4711,7 +5487,7 @@ "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 2481.552010 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", @@ -4723,7 +5499,7 @@ "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", @@ -4806,7 +5582,7 @@ "46 52.10 " ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -4875,7 +5651,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -4957,17 +5733,17 @@ " \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", @@ -5124,7 +5900,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5580,7 +6356,7 @@ "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 2481.6 93.1 26.7 55.791339 \n", + "bot_median 2374.2 93.1 25.5 56.712830 \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", @@ -5592,7 +6368,7 @@ "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", @@ -5629,7 +6405,7 @@ "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 5.782185 4.609796 1.985277 38.1 \n", + "bot_median 5.877687 4.338745 1.985277 37.2 \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", @@ -5641,7 +6417,7 @@ "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", @@ -5678,7 +6454,7 @@ "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 15.2 0.999994 0.000013 \n", + "bot_median 13.8 0.999982 0.000037 \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", @@ -5690,7 +6466,7 @@ "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", @@ -5724,7 +6500,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 42, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -5740,7 +6516,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -5785,62 +6561,6 @@ " \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", - " \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", @@ -5869,716 +6589,772 @@ " \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", + " \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", + " \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", - 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botPeer Score
Rank
1metac-o13864.168122
2bot_median3472.028144
3metac-o1-preview3162.155445
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-r1+asknews614.572462
17question_weight378.020000
34bot_median2481.5520102374.2163389793.10
1516metac-deepseek-r1metac-deepseek-r1+asknews1518.3086255552.10
bot_median2481.62374.293.126.755.7913395.7821854.60979625.556.7128305.8776874.3387451.98527738.115.20.9999940.00001337.213.80.9999820.000037
acm_bot0.070922
metac-deepseek-r1metac-deepseek-r1+asknews1518.352.129.1
Grizeu_Bot487.940.012.2123.49852319.5390470.6251002.02031451.7-27.30.7322250.535551
acm_bot149.763.82.3123.16721915.4139760.1521161.99701833.1-28.40.5602090.879583
RPM_bot145.06.024.231.46890712.8471271.8809962.57058257.2-8.90.9406380.118725
X_bot20.75.04.119.7562378.8352580.4688972.77644528.7-20.40.6682210.663558
cobyj-bot0.00.0NA
jonahsingerbot-61.3bean_bot-0.64.7-13.05.4853692.530212-5.154842-0.10.0698490.032219-4.2651062.784843-6.0-20.10.0041410.008283-0.0-0.20.0076740.015349
bean_bot-70.7jonahsingerbot-0.64.7-15.18.8131374.065197-3.702222-0.10.0502720.023189-5.2736302.784843-3.7-26.40.0119250.023851-0.1-0.20.0038390.007677
jkraybill_bot-76.138.2-2.067.06547910.858048-0.1837062.02336020.0-24.00.4276220.855243X_bot-0.77.0-0.10.3540680.133825-0.7471952.4469120.2-0.40.2415940.483189
CumulativeBot-97.0-1.110.2-9.530.1210609.408238-1.005535-0.10.2577980.080522-1.3151322.23184811.5-30.50.1701090.3402180.1-0.30.1100660.220132
swingswish-109.06.7-16.315.1455315.851229-2.7797012.450387-1.9-30.60.0168960.033793-1.27.7-0.20.1402750.050552-3.0749472.367123-0.0-0.30.0094760.018953
RPM_bot-1.37.0-0.20.8031630.303567-0.6018022.4469120.6-0.90.2846660.569332
SynapseSeer-128.527.1-4.847.0810459.052373-0.5249592.04956913.8-23.30.3020260.604052-1.326.2-0.10.4525550.088498-0.5689102.0530760.1-0.20.2872310.574463
KevinTestBot-148.3-1.58.4-17.759.36966920.484482-0.861938-0.20.5894660.203385-0.8971162.31149629.7-65.00.2078890.4157770.3-0.70.1989520.397903
twsummerbot-237.247.0-5.079.50269011.596659-0.4351342.01121518.3-28.40.3327500.665500Grizeu_Bot-1.751.4-0.01.1733920.163747-0.2066162.0064470.3-0.40.4185710.837143
pianobot-272.2-2.74.7-57.992.18716542.522768-1.361786-0.60.9162040.422613-1.3843272.79898661.1-176.90.1251370.250274
annabot-316.024.8-12.743.7374108.782683-1.4506142.0613075.4-30.80.0799700.1599400.6-1.80.1219410.243882
CatrachoCaster-331.3-3.219.7-16.852.31505911.786737-1.426980-0.20.5209010.117361-1.3655322.0887777.8-41.40.0850350.1700710.1-0.40.0941440.188288
cookics_bot_TEST-413.324.6-16.872.42669414.602631-1.1504362.06084513.3-46.90.1307440.261488krm-bot-5.19.5-0.50.5115460.165967-3.2298462.264709-0.2-0.90.0055630.011127
GreeneiBot2-446.645.8-9.888.55320713.092083-0.7457052.01234016.6-36.10.2298720.459745annabot-6.229.3-0.20.5208690.096226-2.2117952.044183-0.0-0.40.0176100.035221
metac-o1-500.374.74Shadower-6.214.0-0.40.7673220.205075-2.1431942.1472390.0-0.90.0257970.051593
cookics_bot_TEST-6.7111.25524212.872419-0.5203391.99159718.9-32.30.3021940.60438727.4-0.20.7480500.142908-1.7220042.0495410.0-0.50.0483840.096767
krm-bot-521.09.5-54.850.62785616.425846-3.3389622.264709-17.6-92.00.0047000.009400jkraybill_bot-7.544.0-0.20.5128530.077272-2.1971332.014642-0.0-0.30.0167210.033441
4Shadower-527.812.2-43.380.79118223.130448-1.8702732.1816957.2-93.70.0438960.087792twsummerbot-8.958.4-0.20.6597100.086327-1.7583912.0008550.0-0.30.0420060.084012
MWG-766.429.5-26.087.75333816.156699-1.6080772.0435277.0-59.00.0594210.118842-9.628.6-0.30.7111600.132979-2.5353842.046561-0.1-0.60.0085950.017191
bot_median-780.675.7-10.385.1138919.782560-1.0541471.9911819.2-29.80.1476070.295213ProfessorSP-10.018.6-0.50.9362770.217094-2.4844802.095243-0.1-1.00.0116440.023289
Bot_Pepa-814.937.2-21.993.06728515.269248-1.4365512.0250989.0-52.90.0797220.159444acm_bot-10.580.2-0.10.9142650.102059-1.2877171.9893440.1-0.30.1007960.201592
metac-o1-10.891.1-0.10.8668240.090818-1.3030181.9858290.1-0.30.0979440.195889
ajf-bot-843.131.4-26.9104.85473318.727046-1.4360202.03766711.3-65.10.0806120.161224-10.934.2-0.31.0855890.185496-1.7223952.0307780.1-0.70.0471450.094289
manticAI-861.555.0-15.782.87386511.169634-1.4011472.0030646.7-38.00.0834430.166886metac-deepseek-r1+asknews-11.252.1-0.20.6342570.087871-2.4450432.005379-0.0-0.40.0089850.017970
ProfessorSP-997.216.8-59.496.91948823.645934-2.5102932.112371-9.4-109.30.0116720.023345GreeneiBot2-11.458.4-0.20.8462280.110781-1.7668112.0008320.0-0.40.0412900.082581
metac-perplexity-1072.972.7-14.8105.31560712.351666-1.1948081.9924629.9-39.40.1180500.236099Bot_Pepa-11.544.0-0.30.7375370.111125-2.3431662.014642-0.0-0.50.0119050.023810
wunderplumb-1159.023.8-48.890.74010618.619477-2.6209902.065034-10.4-87.30.0076770.015353metac-Gemini-Exp-1206-11.576.5-0.20.8952100.102351-1.4718491.9908220.1-0.40.0726090.145218
laylaps-1214.552.2-23.348.0199296.646397-3.5005872.005359-9.9-36.60.0004860.000971-12.964.1-0.20.6619050.082674-2.4404611.996907-0.0-0.40.0087440.017488
NextWorldLab-1224.163.8-19.298.66262212.347306-1.5526991.9970185.5-43.80.0627580.125517bot_median-13.392.1-0.10.7572010.078901-1.8300581.9855500.0-0.30.0352560.070512
metac-Gemini-Exp-1206-1250.565.1-19.294.99321111.773405-1.6315191.9963774.3-42.70.0538420.107685wunderplumb-13.625.6-0.50.9000510.178062-2.9840942.056603-0.2-0.90.0031740.006348
minefrac1-1289.443.5-29.6123.19979118.679504-1.5868582.0149188.0-67.30.0599790.119958metac-perplexity-14.489.1-0.21.1026010.116810-1.3849521.9864050.1-0.40.0847820.169564
pgodzinai-1330.462.0-21.598.40405312.497327-1.7169531.9981743.5-46.40.0455310.091062manticAI-14.669.4-0.20.6709460.080510-2.6133541.993968-0.0-0.40.0055070.011014
metac-deepseek-r1-1360.348.2-28.2108.35980215.607908-1.8082482.0091123.1-59.60.0384710.076941NextWorldLab-16.980.2-0.20.9069640.101244-2.0783931.989344-0.0-0.40.0204550.040909
metac-Llama-3.1-1412.173.7-19.297.48349911.355267-1.6873751.9920243.5-41.80.0479090.095818minefrac1-18.851.1-0.40.8747520.122370-3.0135812.006545-0.1-0.60.0020210.004043
metac-claude-3-5-sonnet-latest-1463.974.7-19.696.85591111.206393-1.7487371.9915972.7-41.90.0422500.084500-21.691.1-0.20.7840730.082148-2.8855811.985829-0.1-0.40.0024440.004888
mmBot-21.992.1-0.20.7250100.075546-3.1501041.985550-0.1-0.40.0011040.002208
metac-claude-3-5-sonnet-20240620-1649.975.1-22.0105.32409412.153679-1.8076161.9915362.2-46.20.0373620.074725-22.190.5-0.20.9921900.104297-2.3447131.986072-0.0-0.50.0106270.021254
metac-o1-preview-1830.674.7-24.5107.51540912.439714-1.9699551.9915970.3-49.30.0263010.052601metac-grok-2-1212-23.291.1-0.30.9691800.101542-2.5044381.985829-0.1-0.50.0070320.014063
mmBot-2006.475.7-26.578.5323519.026111-2.9364461.991181-8.5-44.50.0022050.004411pgodzinai-23.276.4-0.31.0029230.114742-2.6493171.990849-0.1-0.50.0049100.009821
VeritasAI-2024.567.7-29.963.2821037.691066-3.8881871.994849-14.6-45.20.0001180.000235-24.377.1-0.30.6607030.075245-4.1859101.990482-0.2-0.50.0000380.000076
metac-grok-2-1212-2154.674.7-28.8106.09460612.275325-2.3496851.991597-4.4-53.30.0107350.021470metac-o1-preview-24.491.1-0.30.8524320.089310-2.9993961.985829-0.1-0.40.0017490.003497
metac-gpt-4o-2196.674.7-29.4100.42168411.618958-2.5308441.991597-6.3-52.50.0067560.013513-25.191.1-0.30.8735970.091528-3.0097071.985829-0.1-0.50.0016960.003391
metac-exa-2249.172.7-30.991.72329010.757526-2.8758531.992462-9.5-52.40.0026510.005302-26.189.1-0.30.7919350.083898-3.4956951.986405-0.1-0.50.0003710.000743
InstitutPelFutur-2477.372.8-34.0102.04145411.959443-2.8453911.992461-10.2-57.90.0028880.005777-26.990.1-0.30.9737670.102587-2.9085241.986114-0.1-0.50.0022920.004584
metac-Llama-3.1-28.089.1-0.30.9072000.096109-3.2702001.986405-0.1-0.50.0007670.001534
\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", + " 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", + "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 -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 -6.7 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", + "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-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", + "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", + "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 -21.6 91.1 -0.2 0.784073 0.082148 \n", + "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \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-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", + "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 -2.211795 2.044183 -0.0 \n", + "4Shadower -2.143194 2.147239 0.0 \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", + "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-Gemini-Exp-1206 -1.471849 1.990822 0.1 \n", + "laylaps -2.440461 1.996907 -0.0 \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", + "NextWorldLab -2.078393 1.989344 -0.0 \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-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-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", - "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 " + "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 0.017610 0.035221 \n", + "4Shadower -0.9 0.025797 0.051593 \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", + "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-Gemini-Exp-1206 -0.4 0.072609 0.145218 \n", + "laylaps -0.4 0.008744 0.017488 \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", + "NextWorldLab -0.4 0.020455 0.040909 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.002444 0.004888 \n", + "mmBot -0.4 0.001104 0.002208 \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-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": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -6604,17 +7380,17 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "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)" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": { "cellView": "form", "colab": { @@ -6856,7 +7632,7 @@ " \n", " 12\n", " 13\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 516.8\n", " 277.9\n", " 1.9\n", @@ -7399,7 +8175,7 @@ "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", @@ -7528,7 +8304,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -7567,17 +8343,17 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "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)" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -7782,7 +8558,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 47, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -7793,7 +8569,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -7811,9 +8587,9 @@ "<>: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", + "/tmp/ipykernel_17143/2856056443.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", + "/tmp/ipykernel_17143/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\s'\n", " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n" ] }, @@ -7869,7 +8645,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -8291,7 +9067,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": { "cellView": "form", "colab": { @@ -8341,147 +9117,147 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", + " 5.9\n", " 7.2\n", - " 9.6\n", - " 12.0\n", - " 13.1\n", + " 9.5\n", + " 11.8\n", + " 12.9\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", - " 5.2\n", + " 3.5\n", + " 5.3\n", " 8.3\n", " 11.2\n", - " 12.6\n", + " 12.7\n", " \n", " \n", " manticAI\n", - " -0.2\n", - " 2.1\n", - " 5.5\n", + " 0.3\n", + " 2.2\n", + " 5.4\n", " 8.7\n", " 10.4\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", + " 2.2\n", + " 5.0\n", + " 7.7\n", + " 9.5\n", " \n", " \n", " acm_bot\n", - " 0.3\n", + " 0.4\n", " 1.9\n", - " 4.5\n", - " 7.5\n", + " 4.6\n", + " 7.4\n", " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -1.7\n", - " 0.5\n", - " 4.1\n", - " 7.7\n", - " 9.9\n", + " -1.8\n", + " 0.1\n", + " 4.2\n", + " 7.8\n", + " 9.5\n", " \n", " \n", - " twsummerbot\n", - " 0.3\n", - " 1.4\n", - " 3.9\n", - " 6.1\n", - " 7.5\n", + " GreeneiBot2\n", + " -0.6\n", + " 0.8\n", + " 4.0\n", + " 7.2\n", + " 8.7\n", " \n", " \n", - " GreeneiBot2\n", - " -1.0\n", - " 0.7\n", + " twsummerbot\n", + " 0.2\n", + " 1.4\n", " 3.8\n", - " 7.2\n", - " 8.8\n", + " 6.3\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 0.9\n", - " 3.1\n", - " 5.0\n", + " -0.2\n", + " 0.8\n", + " 3.0\n", + " 5.1\n", " 6.2\n", " \n", " \n", " pgodzinai\n", - " -3.1\n", + " -3.0\n", " -1.1\n", - " 2.8\n", - " 6.9\n", - " 8.7\n", - " \n", - " \n", - " CumulativeBot\n", - " -0.2\n", - " 0.8\n", - " 2.6\n", - " 4.4\n", - " 5.4\n", + " 3.0\n", + " 6.8\n", + " 9.0\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " 0.1\n", + " -1.2\n", + " 0.2\n", " 2.6\n", - " 4.9\n", - " 6.2\n", + " 5.2\n", + " 6.6\n", " \n", " \n", " SynapseSeer\n", " 0.4\n", " 1.1\n", - " 2.5\n", + " 2.6\n", " 4.0\n", - " 4.9\n", + " 4.8\n", + " \n", + " \n", + " CumulativeBot\n", + " -0.5\n", + " 0.6\n", + " 2.6\n", + " 4.5\n", + " 5.4\n", " \n", " \n", " jkraybill_bot\n", - " -3.5\n", - " -1.6\n", + " -3.2\n", + " -1.3\n", " 1.7\n", - " 4.8\n", - " 6.4\n", + " 4.9\n", + " 6.5\n", " \n", " \n", " metac-exa\n", - " -5.2\n", - " -2.7\n", + " -4.8\n", + " -2.6\n", " 1.7\n", - " 5.4\n", - " 7.6\n", + " 5.7\n", + " 7.4\n", " \n", " \n", - " metac-deepseek-r1\n", - " -1.9\n", - " -0.6\n", - " 1.5\n", + " metac-deepseek-r1+asknews\n", + " -1.7\n", + " -0.7\n", + " 1.4\n", " 3.6\n", - " 4.9\n", + " 4.6\n", " \n", " \n", " MWG\n", - " -1.6\n", - " -0.9\n", + " -1.5\n", + " -0.8\n", " 0.7\n", " 2.0\n", - " 2.7\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", - " -1.0\n", + " -0.9\n", " -0.6\n", " -0.0\n", " 0.6\n", - " 1.0\n", + " 0.9\n", " \n", " \n", " X_bot\n", @@ -8493,299 +9269,716 @@ " \n", " \n", " pianobot\n", - " -1.2\n", + " -1.3\n", " -0.8\n", " -0.0\n", - " 0.7\n", - " 1.1\n", + " 0.6\n", + " 1.0\n", " \n", " \n", " cobyj-bot\n", " -1.5\n", " -0.9\n", - " -0.1\n", + " -0.0\n", " 0.9\n", - " 1.4\n", + " 1.3\n", " \n", " \n", - " annabot\n", - " -3.6\n", - " -2.3\n", + " KevinTestBot\n", + " -4.1\n", + " -2.9\n", " -0.4\n", - " 1.2\n", - " 1.9\n", + " 1.5\n", + " 2.7\n", " \n", " \n", - " bean_bot\n", - " -3.0\n", - " -2.1\n", - " -0.4\n", + " annabot\n", + " -3.7\n", + " -2.3\n", + " -0.5\n", " 1.2\n", - " 2.0\n", + " 2.1\n", " \n", " \n", - " KevinTestBot\n", - " -3.8\n", - " -2.7\n", + " bean_bot\n", + " -3.1\n", + " -2.2\n", " -0.5\n", - " 1.6\n", - " 2.5\n", + " 1.1\n", + " 1.9\n", " \n", " \n", " CatrachoCaster\n", - " -2.4\n", + " -2.2\n", " -1.7\n", - " -0.8\n", + " -0.7\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", - " -3.0\n", - " -2.2\n", + " -2.9\n", + " -2.3\n", " -0.8\n", " 0.4\n", " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", - " -2.7\n", + " -3.5\n", + " -2.6\n", " -0.9\n", - " 0.8\n", - " 1.6\n", + " 0.6\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", - " -3.4\n", + " -4.4\n", + " -3.2\n", " -1.0\n", " 1.0\n", - " 2.1\n", + " 2.2\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.5\n", - " -4.7\n", + " -6.6\n", + " -4.8\n", " -1.4\n", " 1.8\n", - " 3.3\n", + " 3.1\n", " \n", " \n", " mmBot\n", - " -7.1\n", - " -5.2\n", + " -7.5\n", + " -5.4\n", " -1.6\n", - " 2.2\n", - " 4.1\n", + " 2.5\n", + " 4.7\n", " \n", " \n", " 4Shadower\n", - " -4.7\n", - " -3.6\n", - " -1.6\n", - " 0.3\n", + " -4.9\n", + " -3.8\n", + " -1.8\n", + " 0.1\n", " 1.2\n", " \n", " \n", - " swingswish\n", - " -5.2\n", - " -4.0\n", - " -1.9\n", - " -0.1\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.2\n", + " -4.8\n", + " -2.0\n", " 0.7\n", + " 2.0\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", - " -3.9\n", + " -4.7\n", + " -3.8\n", " -2.0\n", " -0.7\n", - " -0.1\n", + " -0.2\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -5.0\n", + " swingswish\n", + " -5.5\n", + " -4.3\n", " -2.1\n", - " 0.9\n", - " 2.4\n", + " -0.3\n", + " 0.5\n", " \n", " \n", " InstitutPelFutur\n", - " -9.2\n", - " -6.7\n", - " -2.5\n", - " 1.8\n", - " 3.6\n", + " -8.5\n", + " -6.5\n", + " -2.1\n", + " 1.9\n", + " 4.1\n", " \n", " \n", " metac-Llama-3.1\n", " -6.6\n", - " -5.5\n", - " -2.5\n", - " 0.2\n", + " -5.3\n", + " -2.6\n", + " 0.1\n", " 1.4\n", " \n", " \n", - " wunderplumb\n", - " -6.3\n", - " -5.2\n", - " -2.6\n", - " -0.3\n", + " wunderplumb\n", + " -6.2\n", + " -5.0\n", + " -2.7\n", + " -0.2\n", + " 0.6\n", + " \n", + " \n", + " NextWorldLab\n", + " -9.0\n", + " -6.8\n", + " -3.4\n", + " -0.4\n", + " 1.0\n", + " \n", + " \n", + " Bot_Pepa\n", + " -7.1\n", + " -5.8\n", + " -3.9\n", + " -2.0\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", + " -7.7\n", + " -6.4\n", + " -4.3\n", + " -1.7\n", + " -0.5\n", + " \n", + " \n", + " minefrac1\n", + " -7.9\n", + " -6.8\n", + " -4.5\n", + " -2.6\n", + " -1.7\n", + " \n", + " \n", + " Grizeu_Bot\n", + " -9.4\n", + " -7.5\n", + " -5.0\n", + " -2.4\n", + " -1.0\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -10.2\n", + " -8.9\n", + " -5.8\n", + " -2.9\n", + " -1.5\n", + " \n", + " \n", + " ajf-bot\n", + " -14.8\n", + " -12.6\n", + " -8.4\n", + " -4.6\n", + " -2.2\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", + "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, + "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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pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
0312683126201.0multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaNFalseFalse...2.3025855.703782NaN2.2926352.703087NaNNaNNaNNaN4.605170
1312693126386.821.0numericNone60.0100.0TrueTrue...-0.158842-0.616988NaN-0.050442-0.163369NaNNaNNaNNaN-1.512868
23127031264no1.0binaryNoneNaNNaNFalseFalse...-0.038208-0.092275NaN-0.210058-0.059485NaNNaNNaNNaN-0.149434
NextWorldLab-8.3-6.7-3.4-0.41.2331280312745-91.0multiple_choice[0-4, 5-9, >9]NaNNaNNoneNone...0.3901980.204794NaN0.1278330.152526NaNNaN-0.046520NaN0.310155
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" ], "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", - "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" + " 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 -0.020834 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", + "\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 -0.158283 \n", + "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", + "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", + "\n", + "[5 rows x 57 columns]" ] }, - "execution_count": 50, "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": 51, - "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", @@ -8828,31 +10021,7 @@ " \n", " \n", " \n", - " Grizeu_Bot\n", - " -9.7\n", - " -5.4\n", - " 4.4\n", - " 15.9\n", - " 22.2\n", - " \n", - " \n", - " RPM_bot\n", - " -0.1\n", - " 0.3\n", - " 1.4\n", - " 2.8\n", - " 3.7\n", - " \n", - " \n", - " X_bot\n", - " -0.4\n", - " -0.3\n", - " 0.2\n", - " 0.7\n", - " 1.2\n", - " \n", - " \n", - " andrewsiah\n", + " cobyj-bot\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -8860,7 +10029,7 @@ " 0.0\n", " \n", " \n", - " cobyj-bot\n", + " andrewsiah\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -8868,332 +10037,356 @@ " 0.0\n", " \n", " \n", - " acm_bot\n", - " -16.3\n", - " -11.3\n", - " -0.2\n", - " 14.8\n", - " 22.5\n", + " X_bot\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " jonahsingerbot\n", - " -1.4\n", - " -1.1\n", - " -0.6\n", - " -0.3\n", - " -0.1\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", " \n", " \n", " bean_bot\n", - " -1.6\n", - " -1.3\n", - " -0.7\n", - " -0.3\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -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", - " -2.9\n", - " -2.3\n", - " -1.0\n", - " 0.2\n", - " 1.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", " \n", " \n", " swingswish\n", - " -2.4\n", - " -1.9\n", - " -1.1\n", - " -0.5\n", - " -0.3\n", - " \n", - " \n", - " jkraybill_bot\n", - " -8.5\n", - " -6.2\n", - " -1.1\n", - " 4.6\n", - " 7.5\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", " \n", " \n", " KevinTestBot\n", - " -5.8\n", - " -3.9\n", - " -1.4\n", - " 0.4\n", - " 1.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " SynapseSeer\n", - " -6.3\n", - " -4.6\n", - " -1.5\n", - " 1.9\n", - " 3.9\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " pianobot\n", - " -8.0\n", - " -5.9\n", - " -2.6\n", + " Grizeu_Bot\n", " -0.2\n", + " -0.1\n", + " -0.0\n", " 0.1\n", + " 0.2\n", " \n", " \n", - " twsummerbot\n", - " -13.4\n", - " -10.3\n", - " -2.9\n", - " 4.6\n", - " 9.2\n", + " pianobot\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", " \n", " \n", " CatrachoCaster\n", - " -8.6\n", - " -6.8\n", - " -3.4\n", - " -0.3\n", - " 1.1\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " \n", + " \n", + " krm-bot\n", + " -0.1\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " \n", + " \n", + " 4Shadower\n", + " -0.1\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", " \n", " \n", " annabot\n", - " -8.4\n", - " -6.5\n", - " -3.4\n", - " -0.6\n", - " 0.9\n", + " -0.1\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", " \n", " \n", " cookics_bot_TEST\n", - " -12.1\n", - " -9.7\n", - " -4.2\n", - " 0.1\n", - " 2.1\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " 0.0\n", " \n", " \n", - " GreeneiBot2\n", - " -17.4\n", - " -13.2\n", - " -4.9\n", - " 3.6\n", - " 7.4\n", + " jkraybill_bot\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", " \n", " \n", - " krm-bot\n", - " -10.6\n", - " -8.6\n", - " -5.3\n", - " -2.6\n", - " -1.6\n", + " twsummerbot\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.0\n", " \n", " \n", - " 4Shadower\n", - " -12.8\n", - " -9.8\n", - " -5.3\n", - " -1.8\n", - " -1.1\n", + " MWG\n", + " -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", + " ajf-bot\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 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", - " metac-o1\n", - " -22.7\n", - " -18.5\n", - " -6.7\n", - " 8.5\n", - " 16.1\n", + " GreeneiBot2\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.0\n", " \n", " \n", - " MWG\n", - " -18.3\n", - " -14.9\n", - " -8.3\n", - " -2.2\n", - " 1.3\n", + " metac-deepseek-r1+asknews\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", " \n", " \n", - " ajf-bot\n", - " -22.3\n", - " -17.2\n", - " -8.8\n", - " -1.4\n", - " 2.5\n", + " metac-Gemini-Exp-1206\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", " \n", " \n", - " bot_median\n", - " -22.7\n", - " -18.3\n", - " -9.0\n", - " 2.1\n", - " 8.9\n", + " metac-o1\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " 0.0\n", + " 0.1\n", " \n", " \n", " Bot_Pepa\n", - " -20.9\n", - " -16.3\n", - " -9.0\n", - " -1.2\n", - " 2.7\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", " \n", " \n", - " manticAI\n", - " -22.1\n", - " -17.7\n", - " -9.5\n", - " -0.7\n", - " 4.9\n", + " laylaps\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", " \n", " \n", - " ProfessorSP\n", - " -20.7\n", - " -16.8\n", - " -10.1\n", - " -4.7\n", - " -2.4\n", + " wunderplumb\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", " \n", " \n", - " wunderplumb\n", - " -22.4\n", - " -19.1\n", - " -12.0\n", - " -5.8\n", - " -3.3\n", + " bot_median\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.0\n", " \n", " \n", " metac-perplexity\n", - " -29.1\n", - " -24.0\n", - " -12.0\n", - " 0.8\n", - " 8.0\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", " \n", " \n", - " laylaps\n", - " -21.0\n", - " -17.8\n", - " -12.8\n", - " -8.1\n", - " -5.8\n", + " manticAI\n", + " -0.3\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", " \n", " \n", " NextWorldLab\n", - " -28.4\n", - " -24.0\n", - " -13.6\n", - " -2.8\n", - " 4.0\n", - " \n", - " \n", - " pgodzinai\n", - " -31.7\n", - " -25.6\n", - " -14.0\n", - " -4.1\n", - " 1.9\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -28.1\n", - " -23.3\n", - " -14.0\n", - " -2.7\n", - " 3.2\n", - " \n", - " \n", - " metac-deepseek-r1\n", - " -30.7\n", - " -25.2\n", - " -14.6\n", - " -4.9\n", - " 0.5\n", + " -0.3\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " 0.0\n", " \n", " \n", " minefrac1\n", - " -29.8\n", - " -24.8\n", - " -14.9\n", - " -3.1\n", - " 4.1\n", + " -0.3\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -32.9\n", - " -26.8\n", - " -15.1\n", - " -3.3\n", - " 3.2\n", + " metac-claude-3-5-sonnet-latest\n", + " -0.4\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\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", + " mmBot\n", + " -0.4\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\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", + " -0.4\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-o1-preview\n", - " -38.9\n", - " -32.4\n", - " -19.3\n", - " -6.9\n", - " 0.3\n", + " pgodzinai\n", + " -0.4\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " \n", " \n", - " mmBot\n", - " -36.2\n", - " -30.9\n", - " -21.1\n", - " -11.7\n", - " -7.1\n", + " metac-grok-2-1212\n", + " -0.4\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " \n", " \n", " VeritasAI\n", - " -33.5\n", - " -28.9\n", - " -21.3\n", - " -14.4\n", - " -11.1\n", + " -0.4\n", + " -0.3\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " -41.8\n", - " -35.2\n", - " -23.4\n", - " -10.4\n", - " -3.8\n", + " metac-o1-preview\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", + " -0.1\n", " \n", " \n", - " metac-exa\n", - " -40.4\n", - " -34.4\n", - " -23.4\n", - " -13.8\n", - " -7.9\n", + " metac-gpt-4o\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", + " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", - " -41.7\n", - " -34.7\n", - " -23.8\n", - " -11.3\n", - " -5.3\n", + " metac-exa\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -43.6\n", - " -37.9\n", - " -26.5\n", - " -14.9\n", - " -6.6\n", + " -0.5\n", + " -0.4\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " -0.5\n", + " -0.4\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", " \n", " \n", "\n", @@ -9201,55 +10394,55 @@ ], "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", - "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" + "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 -0.0 -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", + "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", + "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\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", + "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-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-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-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" ] }, - "execution_count": 51, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -9258,6 +10451,7 @@ "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", @@ -9270,17 +10464,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": {}, "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')" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -9340,7 +10534,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": { "cellView": "form", "colab": { @@ -9476,7 +10670,7 @@ " 0.153662\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 0.8\n", " 225.8\n", " -4.2\n", @@ -9749,7 +10943,7 @@ "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", @@ -9795,7 +10989,7 @@ "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", @@ -9829,7 +11023,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 54, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -9850,16 +11044,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 54, "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)" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -9898,7 +11092,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 56, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -9908,506 +11102,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.05]\n", - 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" >>> Collected 3 forecasts: [0.75, 0.75, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\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: [0.05, 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: [0.8, 0.6, nan]\n", " >>> Collected 3 forecasts: [0.7, 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: [0.15, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\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: [0.7, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.35, 0.108]\n", - " >>> Collected 3 forecasts: [0.1, 0.15, 0.16]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", + " >>> Collected 3 forecasts: [0.6, 0.85, nan]\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, 0.25, 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: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 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: [0.15, 0.35, 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: [0.1, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.25, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", - 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" >>> Collected 3 forecasts: [0.3, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.8, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 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: [0.2, 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: [0.15, 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, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.75, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.07, 0.05]\n", - " >>> Collected 4 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.75, 0.75, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, 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: [0.05, 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: [0.8, 0.6, nan, nan]\n", " >>> Collected 4 forecasts: [0.7, 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: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, 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: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.35, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.15, 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: [0.95, 0.95, 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: [0.1, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.35, 0.226]\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: [0.6, 0.85, nan, 0.936]\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, 0.25, 0.15, 0.144]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\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: [0.35, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\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, 0.85, 0.99, 0.99]\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: [0.95, 0.25, 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: [0.3, 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: [0.3, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.8, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 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: [0.2, 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: [0.15, 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, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.1, 0.07, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.15, 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: [0.95, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, 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: [0.05, 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: [0.8, 0.6, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 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: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, 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: [0.7, 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: [0.05, 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: [0.95, 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: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.6, 0.85, nan, 0.936, 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, 0.25, 0.15, 0.144, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.15, 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: [0.35, 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: [0.7, 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, 0.85, 0.99, 0.99, 0.95]\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: [0.95, 0.25, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\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: [0.3, 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: [0.05, 0.05, 0.086, nan, 0.12]\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: [0.15, 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, 0.2, 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 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: [0.1, 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 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: [0.05, 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: [0.8, 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: [0.15, 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: [0.1, 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: [0.7, 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: [0.05, 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: [0.95, 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: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.85, 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, 0.3, 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, 0.25, 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: [0.15, 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: [0.35, 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: [0.7, 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, 0.85, 0.99, 0.99, 0.95, 0.5]\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: [0.95, 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: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\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: [0.3, 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: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\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: [0.15, 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, 0.2, 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 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: [0.1, 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 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: [0.05, 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: [0.8, 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: [0.15, 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: [0.1, 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: [0.7, 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: [0.95, 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: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan]\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: [0.15, 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: [0.35, 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: [0.7, 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: [0.95, 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: [0.3, 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, 0.9]\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, 0.85, 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, 0.95]\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 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: [0.1, 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 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: [0.05, 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: [0.1, 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: [0.7, 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: [0.95, 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: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, 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: [0.15, 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: [0.35, 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: [0.7, 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: [0.95, 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: [0.3, 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, 0.9, 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, 0.85, 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, 0.95, 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 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: [0.1, 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 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: [0.8, 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: [0.15, 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: [0.1, 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: [0.7, 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: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.85, 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, 0.35, 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: [0.15, 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: [0.35, 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: [0.7, 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, 0.95, 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 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: [0.1, 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 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: [0.8, 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: [0.15, 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: [0.1, 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: [0.7, 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: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", + " >>> Collected 10 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.15, 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: [0.15, 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: [0.35, 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: [0.7, 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, 0.95, 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: [0.9, 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" ] } ], @@ -10416,7 +11610,7 @@ "\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", @@ -10424,7 +11618,7 @@ "# 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", @@ -10440,7 +11634,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -10450,7 +11644,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -10488,45 +11682,45 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " 0.017463\n", - " 0.085\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.012462871287128714, 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", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\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", - " 0.15\n", + " 0.05\n", + " 0.063\n", " 0.085\n", - " 0.125\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\n", - " 0.5125\n", + " [0.15,0.65,0.2]\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, 0.00161112178, 0.0032277004800000003, 0....\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 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", " \n", " ...\n", @@ -10552,16 +11746,16 @@ " NaN\n", " no\n", " 0.9\n", - " 0.2\n", - " 0.1335\n", + " 0.4\n", + " 0.2335\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", - " 0.9\n", - " 0.85\n", + " 0.95\n", + " 0.8\n", " 0.775\n", " \n", " \n", @@ -10570,16 +11764,16 @@ " NaN\n", " no\n", " 0.85\n", - " 0.75\n", - " 0.73\n", + " 0.8\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", @@ -10602,48 +11796,48 @@ "364 binary NaN no \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", + "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 0.9 \n", + "355 0.95 \n", "361 0.85 \n", - "364 0.1 \n", + "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00161112178, 0.0032277004800000003, 0.... \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", "342 0.9 \n", - "351 0.2 \n", - "355 0.85 \n", - "361 0.75 \n", - "364 0.052 \n", + "351 0.4 \n", + "355 0.8 \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", - "2 0.125 \n", - "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \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 0.1335 \n", + "351 0.2335 \n", "355 0.775 \n", - "361 0.73 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 59, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -10654,7 +11848,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -10674,7 +11868,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 60, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -10683,6 +11877,22 @@ "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": [ @@ -10712,52 +11922,52 @@ " \n", " 0\n", " 1\n", - " 16.68\n", + " 1252.60\n", " \n", " \n", " 1\n", " 2\n", - " 26.29\n", + " 2269.15\n", " \n", " \n", " 2\n", " 3\n", - " 28.21\n", + " 2400.04\n", " \n", " \n", " 3\n", " 4\n", - " 26.98\n", + " 2413.81\n", " \n", " \n", " 4\n", " 5\n", - " 27.65\n", + " 2591.97\n", " \n", " \n", " 5\n", " 6\n", - " 26.39\n", + " 2483.23\n", " \n", " \n", " 6\n", " 7\n", - " 26.89\n", + " 2478.69\n", " \n", " \n", " 7\n", " 8\n", - " 27.15\n", + " 2536.53\n", " \n", " \n", " 8\n", " 9\n", - " 27.29\n", + " 2388.76\n", " \n", " \n", " 9\n", " 10\n", - " 26.71\n", + " 2370.53\n", " \n", " \n", "\n", @@ -10765,19 +11975,19 @@ ], "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 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": 61, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -10808,16 +12018,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" ] }, - "execution_count": 62, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -10831,7 +12041,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -10840,7 +12050,7 @@ "(424, 47)" ] }, - "execution_count": 63, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -10851,26 +12061,311 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 63, "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']]]" + "# 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": 64, + "metadata": {}, + "outputs": [ + { + "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_median
34235345Will the US Citizenship and Immigration Servic...yes2025-03-12 22:00:002025-03-12 22:00:00binaryNaNNaNNaNFalseFalse353801.000.90.95
35135354Will the United States impose any new tariffs ...no2025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353811.000.40.05
35535358Will ChatGPT rank in the top 10 global website...yes2025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353851.000.80.97
36135364Will Doge's Agency Efficiency Leaderboard have...no2025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaNFalseFalse353860.850.80.666
36435367Will the Project 2025 Tracker spreadsheet mark...no2025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaNFalseFalse353870.850.050.03
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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", - "[5 rows x 27 columns]" + " 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", + " 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 \n", + "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 " ] }, - "execution_count": 65, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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" + ] } ], - "source": [ - "df_bot_team_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Z3TTBVWoZVzU", - "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" - }, - "outputs": [], "source": [ "# @title Weighted team-vs-pro\n", "\n", @@ -11198,26 +12749,28 @@ " 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", "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_head_to_head, args=('bot_team_median', 'pro_median'), axis=1)" + "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": 68, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -14.9893\n" + "Weighted Total Score: -0.1312\n" ] } ], @@ -11227,7 +12780,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 67, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11239,7 +12792,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -11251,7 +12804,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -14.97\n" + "The average of 'head_to_head' is: -0.14\n" ] } ], @@ -11261,7 +12814,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11307,28 +12860,289 @@ " \n", " \n", " head_to_head\n", - " -1424.0\n", - " 93.1\n", - " -15.3\n", - " 90.635958\n", - " 9.393462\n", - " -1.628277\n", - " 1.985277\n", - " 3.4\n", - " -33.9\n", - " 0.053441\n", - " 0.106882\n", + " -12.5\n", + " 92.1\n", + " -0.1\n", + " 0.669453\n", + " 0.069757\n", + " -1.939479\n", + " 1.98555\n", + " 0.0\n", + " -0.3\n", + " 0.027769\n", + " 0.055537\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\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 0.0 -0.3 0.027769 0.055537 " + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_h2h = calculate_t_test(df_top_bot_pro_forecasts, ['head_to_head'])\n", + "\n", + "df_bot_team_h2h" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "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
279What will Kalshi's rank in the iPhone Top Free...[0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05][0.02,0.01,0.015,0.015,0.05,0.89]Not in top 50-2.9
121How many movies will be new on Netflix's top 1...[0.0001, 0.0001, 0.0001, 0.125][0.005,0.017,0.157,0.821]3 or more-1.9
47What will be Donald Trump's net worth, accordi...[0.16999999999999998, 0.0001, 0.0001, 0.0001, ...[0.6,0.2,0.1,0.075,0.025]0-$6 billion, inclusive-1.3
232How many movies will be new on Netflix's top 1...[0.0001, 0.0001, 0.0001, 0.2963039014373716][0.002,0.008,0.09,0.9]3 or more-1.1
247Will the 500th richest person on Bloomberg's B...0.7666670.333no-1.1
\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", + "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, 0.125] \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 -1.9 \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, + "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": 70, + "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
0For Q1 2025, how many banks will be listed on ...[0.012462871287128714, 0.0001, 0.0001, 0.0001,...[0.001,0.62,0.35,0.019,0.01]02.5
189What will the highest rank of metac-GPT4o or m...[0.0, 0.0369946063, 0.07475, 0.10485, 0.1198, ...[0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...34.02.8
151How many earthquakes of magnitude ≥ 4 will hap...[0.0, 0.0035714286, 0.0071428571, 0.0107142857...[0.0,0.0158237002,0.0235315723,0.0279864362,0....0.0NaN
211Will Nikola Corporation file for bankruptcy be...0.990.999annulledNaN
214Will the state of Rhode Island have any recrea...0.9280.95annulledNaN
\n", "
" ], "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", + " 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", + "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 [0.012462871287128714, 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 0.928 \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", - " 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 \n", + "0 2.5 \n", + "189 2.8 \n", + "151 NaN \n", + "211 NaN \n", + "214 NaN " ] }, "execution_count": 70, @@ -11337,29 +13151,56 @@ } ], "source": [ - "df_bot_team_h2h = calculate_t_test(df_top_bot_pro_forecasts, ['head_to_head'])\n", + "print(\"\\nBottom 5:\")\n", "\n", - "df_bot_team_h2h" + "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" ] }, { "cell_type": "code", - "execution_count": 73, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0I0myCHpl7FT", - "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" - }, + "execution_count": 71, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 5:\n" - ] - }, + "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": 71, + "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": 72, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -11381,121 +13222,317 @@ " \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", + " open_upper_bound\n", + " open_lower_bound\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", + " False\n", + " False\n", + " 31268\n", + " 1.0\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 2.522754\n", + " 2.522754\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", + " True\n", + " True\n", + " 31269\n", + " 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", " \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", + " False\n", + " False\n", + " 31270\n", + " 1.0\n", + " 0.063\n", + " 0.013\n", + " -0.051987\n", + " -0.051987\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", + " NaN\n", + " NaN\n", + " 31280\n", + " 1.0\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.16,0.44,0.4]\n", + " 0.152526\n", + " 0.152526\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", + " False\n", + " False\n", + " 31281\n", + " 1.0\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", + " 0.132210\n", + " 0.132210\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 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 [0.012462871287128714, 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] 2.522754 \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 2.522754 \n", + "1 -0.158842 \n", + "2 -0.051987 \n", + "3 0.152526 \n", + "4 0.132210 " ] }, - "execution_count": 73, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.set_option('display.max_colwidth', 50)\n", + "df_top_bot_pro_forecasts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "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[\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", - "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", + "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, + "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": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pro forecasts: 48\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", - "print(\"Top 5:\")\n", + "# Set axis limits\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", "\n", - "df_top5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + "# 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": 74, + "execution_count": 75, + "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})\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": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Bottom 5:\n" + "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": [ { "data": { "text/html": [ @@ -11517,140 +13554,396 @@ " \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", + " open_upper_bound\n", + " open_lower_bound\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", + " False\n", + " False\n", + " 31270\n", + " 1.0\n", + " 0.063\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", + " NaN\n", + " NaN\n", + " 31282\n", + " 1.0\n", + " 0.62\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", + " False\n", + " False\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", + " 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", + " False\n", + " False\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", - "\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", + " 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", - " 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", + " 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", - " head_to_head \n", - "170 158.1 \n", - "0 299.6 \n", - "189 502.6 \n", - "211 NaN \n", - "214 NaN " + " range_min range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "2 NaN NaN False False 31270 \n", + "5 NaN NaN NaN NaN 31282 \n", + "8 NaN NaN False False 31294 \n", + "10 NaN NaN NaN NaN \n", + "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 0.86 0.95 \n", + "10 1.0 NaN NaN \n", + "13 1.0 0.85 0.9 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_top_bot_pro_forecasts_all_binary.head()" + ] + }, + { + "cell_type": "code", + "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": 74, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"\\nBottom 5:\")\n", + "df_top_bot_pro_forecasts_binary.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pro forecasts: 48\n", + "Number of bot forecasts: 236\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", - "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + "# 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": 75, - "metadata": {}, + "execution_count": 81, + "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": [ - "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" } ], "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" + "# 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": 76, + "execution_count": 82, + "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): 0.4355\n", + "Pro average forecast difference (1 - 0): 0.5238\n", + "Difference between pro and bot differences: 0.0882\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": 83, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "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": 84, "metadata": {}, "outputs": [ { @@ -11683,6 +13976,8 @@ " options\n", " range_min\n", " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " pro_question_id\n", " question_weight\n", " bot_team_median\n", @@ -11700,15 +13995,17 @@ " 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", - " 0.02\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 299.573227\n", - " 299.573227\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -11721,12 +14018,14 @@ " NaN\n", " 60.0\n", " 100.0\n", + " True\n", + " True\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.0341314028, 0.03460208...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -57.286904\n", - " -57.286904\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -11739,12 +14038,14 @@ " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -11754,15 +14055,17 @@ " 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", - " 0.6\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 31.015493\n", - " 31.015493\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -11775,12 +14078,14 @@ " NaN\n", " 0.0\n", " 400.0\n", + " False\n", + " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0017047194333333333, 0.0034148989, 0.0...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 56.082092\n", - " 56.082092\n", + " 0.132210\n", + " 0.132210\n", " \n", " \n", "\n", @@ -11801,127 +14106,45 @@ "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", + " 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 [0.012462871287128714, 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] 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", + "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", - "0 299.573227 \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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", 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" + "0 2.522754 \n", + "1 -0.158842 \n", + "2 -0.051987 \n", + "3 0.152526 \n", + "4 0.132210 " ] }, "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": [ @@ -11952,207 +14175,163 @@ " options\n", " range_min\n", " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " pro_question_id\n", " question_weight\n", " bot_team_median\n", " pro_median\n", + " head_to_head\n", + " weighted_score\n", " \n", " \n", - " \n", - " \n", - " 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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 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 -0.054067 -0.054067 \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 " ] }, "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()", + "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[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", + "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", @@ -12160,12 +14339,13 @@ "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: 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,) " ] } ], "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", @@ -12176,88 +14356,6 @@ "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": {}, @@ -12276,8 +14374,8 @@ "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", - " \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", "\n", @@ -12400,10 +14498,10 @@ "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 +14513,16 @@ " 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 +14531,7 @@ " # 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 +14542,10 @@ " 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 +14600,7 @@ "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 +14689,7 @@ "# 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 +14799,7 @@ "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", @@ -13010,9 +15108,9 @@ "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)" ] }, { @@ -13035,9 +15133,9 @@ "\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", @@ -13189,7 +15287,7 @@ "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)" ] @@ -13206,7 +15304,7 @@ "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)" ] }, { @@ -13248,7 +15346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/df_top_bot_pro_cp_forecasts.csv b/archived/df_top_bot_pro_cp_forecasts.csv similarity index 100% rename from df_top_bot_pro_cp_forecasts.csv rename to archived/df_top_bot_pro_cp_forecasts.csv 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/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/weighted_baseline_bot_cp.csv b/archived/weighted_baseline_bot_cp.csv similarity index 100% rename from weighted_baseline_bot_cp.csv rename to archived/weighted_baseline_bot_cp.csv diff --git a/weighted_t_test_h2h_bot_vs_cp.csv b/archived/weighted_t_test_h2h_bot_vs_cp.csv similarity index 100% rename from weighted_t_test_h2h_bot_vs_cp.csv rename to archived/weighted_t_test_h2h_bot_vs_cp.csv diff --git a/bootstrapped_h2h_bot_vs_pros.csv b/bootstrapped_h2h_bot_vs_pros.csv deleted file mode 100644 index c536929..0000000 --- a/bootstrapped_h2h_bot_vs_pros.csv +++ /dev/null @@ -1,47 +0,0 @@ -,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 -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 diff --git a/functions.py b/functions.py index 29a05b2..08b3fd0 100644 --- a/functions.py +++ b/functions.py @@ -1,25 +1,31 @@ -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_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location, +) + 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: @@ -27,39 +33,40 @@ 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 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. - 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: @@ -69,22 +76,23 @@ 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 +105,21 @@ 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 +133,12 @@ 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 +146,28 @@ 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): @@ -166,40 +181,46 @@ 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 + # 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] 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 -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. """ + 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. @@ -215,8 +236,12 @@ 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): """ Calculates the weighted standard deviation using Claude (via Nikos) method - (C) from stack exchange post. @@ -233,11 +258,15 @@ 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): + +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. @@ -250,10 +279,11 @@ 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): + def single_bootstrap(df: pd.DataFrame): # 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 +301,35 @@ 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 = [] @@ -309,26 +342,32 @@ 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) + 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): """ - @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'] - + q_type = row["type"] + forecasts = [] for bot in bots: f_raw = row.get(bot) @@ -341,49 +380,49 @@ def get_median_forecast(row, bots): continue else: forecasts.append(f_raw) # Already parsed float or list - + if not forecasts: return np.nan - if q_type == 'numeric': - forecasts = [f for f in forecasts if isinstance(f, list)] + if q_type == "numeric": + 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 = [] + elif q_type == "binary": + probs: list[float] = [] for f in forecasts: try: val = float(f) probs.append(val) - except (ValueError, TypeError): - print(f' Invalid forecast: {f} — error {e}') + except (ValueError, TypeError) as 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_weighted_scores(df_bot_team_forecasts, teams): +def calculate_weighted_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.Series: """ - @BEN: check - Calculates weighted scores for each team based on their forecasts and question weights. Args: @@ -396,89 +435,58 @@ 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'] - for team in teams: - forecast = row[team] - - if forecast is None or (isinstance(forecast, float) and np.isnan(forecast)): + # @Check: that the row conversion is corret + cleaned_row = _prepare_new_row_for_scoring(row, [team]) + if _is_unscorable(cleaned_row, [team]): continue - baseline_score = None + 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: - 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_baseline_score( + 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): + 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 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. 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. @@ -494,35 +502,39 @@ 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) + 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) + 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 @@ -531,145 +543,50 @@ 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 - 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) - - return df_W_leaderboard - -def calculate_head_to_head(row, a, b): - """ - @BEN: 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] + # Drop the temporary sorting column + df_W_leaderboard = df_W_leaderboard.drop("_p_value_sort", axis=1) - 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 df_W_leaderboard - 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. @@ -690,23 +607,23 @@ 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() @@ -714,7 +631,85 @@ 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): + +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, 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] + 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. @@ -771,43 +766,6 @@ 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. - - 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): """ @@ -824,9 +782,11 @@ 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 @@ -834,6 +794,7 @@ 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. @@ -851,6 +812,7 @@ 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. @@ -866,40 +828,47 @@ 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]], - 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 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]], - 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 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() + def get_weighted_score(df_forecasts): """ Calculates the weighted total score for forecasts. @@ -911,13 +880,15 @@ 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 @@ -926,8 +897,10 @@ 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. @@ -962,6 +935,7 @@ 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. @@ -985,13 +959,17 @@ 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): + +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 @@ -1003,28 +981,16 @@ 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): """ @@ -1045,7 +1011,8 @@ 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] @@ -1053,8 +1020,10 @@ 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. @@ -1068,9 +1037,14 @@ 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)) - return (b - a) + 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 + def extract_year(title): """ @@ -1082,9 +1056,10 @@ 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. @@ -1095,9 +1070,13 @@ 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. @@ -1118,6 +1097,7 @@ 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 @@ -1130,61 +1110,224 @@ 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_via_question_dict(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): + +def parse_options_array(options_str: str) -> list[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 - + + 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('[]') + 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( + 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'. + + 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. + """ + # @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 + + 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": + is_unscorable = True + return is_unscorable + + +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 + + elif question_type == "multiple_choice": + resolution = resolution + elif question_type == "numeric": + 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}") + new_row["resolution"] = resolution + + range_min = original_row.get("range_min") + if range_min: + range_min = float(range_min) + new_row["range_min"] = range_min + + range_max = original_row.get("range_max") + if range_max: + range_max = float(range_max) + new_row["range_max"] = range_max + + question_weight = original_row["question_weight"] + if question_weight: + question_weight = float(question_weight) + 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"): + """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] = 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"] = df.apply( + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( + row, "bot_median", pro_col + ), + axis=1, + ) + + return df_peer 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/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv new file mode 100644 index 0000000..6b92b92 --- /dev/null +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -0,0 +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 +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,-0.0,-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 +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 +laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +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 +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-latest,-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-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-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/weighted_bot_ONLY_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv similarity index 97% rename from weighted_bot_ONLY_peer_leaderboard_t_test.csv rename to notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv index 76b7626..029f529 100644 --- a/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/weighted_bot_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv similarity index 97% rename from weighted_bot_peer_leaderboard_t_test.csv rename to notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv index 3a4a494..b32fa6b 100644 --- a/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 new file mode 100644 index 0000000..8eb9a70 --- /dev/null +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -0,0 +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 +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 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+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 +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-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 +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 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+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-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 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..1aaa3f4 --- /dev/null +++ b/refactored_notebook/data_models.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from datetime import datetime +from typing import Literal + +from pydantic import BaseModel + +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: ForecastType + 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..6660cc4 --- /dev/null +++ b/refactored_notebook/pseudocode_for_main.py @@ -0,0 +1,163 @@ +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 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 + 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/scoring.py b/refactored_notebook/scoring.py new file mode 100644 index 0000000..eec131c --- /dev/null +++ b/refactored_notebook/scoring.py @@ -0,0 +1,331 @@ +from enum import Enum +from typing import Literal + +import numpy as np +from scipy.stats.mstats import gmean + +from refactored_notebook.data_models import ForecastType, ResolutionType + + +class QuestionType(Enum): + BINARY = "binary" + MULTIPLE_CHOICE = "multiple_choice" + NUMERIC = "numeric" + +def calculate_peer_score( + 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, + 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) + forecast_for_resolution = _determine_probability_for_resolution( + question_type, forecast, resolution, options, range_min, range_max + ) + other_user_forecasts = [ + _determine_probability_for_resolution( + question_type, 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: shouldn't other q types get a divsor? + peer_score /= 2 + return peer_score * question_weight + + +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, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, +) -> 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 + """ + 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( + question_type, forecast, resolution, options, range_min, range_max + ) + baseline_prob = _determine_baseline( + question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound + ) + 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" + ) + + baseline_score = np.log(prob_for_resolution / baseline_prob) / divisor * 100 + + weighted_score = baseline_score * question_weight + + return weighted_score + + +def _determine_baseline( + question_type: QuestionType, + 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: + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) + if question_type == QuestionType.BINARY: + baseline_prob = 0.5 + 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 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") + 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}" + 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 + 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( + q_type: QuestionType, + forecast: ForecastType, + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, +) -> float: + """ + Returns a 0 to 1 probability for the resolution + Also returns the baseline probability used in baseline scoring + """ + resolution = _normalize_resolution(q_type, resolution, range_min, range_max) + + if forecast is None or resolution is None: + raise NotImplementedError( + "Havent decided how to handle null forecasts or anulled resolutions" + ) + + 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") + + if q_type == QuestionType.BINARY: + assert isinstance(resolution, bool) + prob_for_resolution = _binary_resolution_prob(forecast, resolution) + 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 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" + ) + 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" + return prob_for_resolution + + +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]) + if resolution: + prob_for_resolution = forecast_val + else: + prob_for_resolution = 1 - forecast_val + return prob_for_resolution + + +def _multiple_choice_resolution_prob( + forecast: list[float], resolution: str, options: list[str] +) -> float: + 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] # @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 + + +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") + + 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, 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] + ) + 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) + 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] + + return prob_for_resolution + + +def _determine_divisor_for_baseline_score( + question_type: QuestionType, options: list[str] | None = None +) -> float: + if question_type == QuestionType.BINARY: + return np.log(2) + 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 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) 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/test_end_to_end.py b/tests/test_end_to_end.py new file mode 100644 index 0000000..76bbe91 --- /dev/null +++ b/tests/test_end_to_end.py @@ -0,0 +1,19 @@ +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 +# - 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 +# ... continue through and consider other final outputs (e.g. calibration curve) + + 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 diff --git a/tests/test_scoring.py b/tests/test_scoring.py new file mode 100644 index 0000000..ca437bf --- /dev/null +++ b/tests/test_scoring.py @@ -0,0 +1,692 @@ +from dataclasses import dataclass + +import numpy as np +import pytest + +from refactored_notebook.data_models import ForecastType +from refactored_notebook.scoring import calculate_baseline_score, calculate_peer_score + +# 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 +# - 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() -> 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 = [] + for i in range(201): + if i < index: + cdf.append(0.0) + else: + cdf.append(forecast) + 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_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score - expected) == pytest.approx(0) + + +@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_baseline_score( + forecast=forecast, + resolution=resolution, + ) + assert score == pytest.approx(expected, abs=1e-1) + + +def test_numeric_baseline_when_perfect_forecast(): + 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_baseline_score( + forecast=forecast, + resolution=float(correct_answer), + range_min=0, + range_max=range_max, + open_upper_bound=False, + open_lower_bound=False, + ) + assert score == pytest.approx(183) + + +def test_numeric_baseline_if_completly_incorrect_forecast(): + 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, 0.01/200) + + score = calculate_baseline_score( + forecast=forecast, + resolution=float(correct_answer), + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(-230.25, abs=1e-1) + + +@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_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(expected, abs=1e-2) + + +@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, + ), + 50.0, + None, + -1, + 96, + ), + ], +) +def test_baseline_score_better_when_closer( + forecast_closer: list[float], + forecast_further: list[float], + resolution: bool | str | float | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + score_closer = calculate_baseline_score( + forecast=forecast_closer, + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, + ) + score_further = calculate_baseline_score( + forecast=forecast_further, + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, + ) + 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 + ( + 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( + 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_baseline_score( + forecast, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 + + +################################### 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, 0.09, 0.01], + [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 + ( + [ + 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, + ), + ], + 49, + None, + -1, + 96, # Not even range + ), + # Numeric: forecast CDFs with more mass near upper bound get better score + ( + [ + generate_cdf( # Best CDF + [ + Percentile(value=110, probability_below=0.1), + Percentile(value=130, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=True, + ), + generate_cdf( + [ + Percentile(value=90, probability_below=0.1), + Percentile(value=140, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=True, + ), + generate_cdf( # worst CDF + [ + 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=True, # No upper bound = no probability mass at upper bound + ), + ], + 120, + None, + 0, + 100, + ), + ], +) +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, +): + scores = [ + calculate_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 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( + 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", [0.25, 0.25, 0.25, 0.25], "A", ["A", "B", "C", "D"], None, None), + ("numeric", generate_cdf_with_forecast_at_index(100, 0.999), 100, None, 0, 100), + ("numeric", generate_uniform_cdf(), 50, None, 0, 100), + ], +) +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_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 + ( + [ + 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, + -1, + 96, + ), + ], +) +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_peer_score( + forecast, + [f for i, f in enumerate(forecasts) if i != idx], + resolution, + options, + range_min, + range_max, + ) + 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 + ( + [ + generate_uniform_cdf(), + generate_cdf_with_forecast_at_index(100, 0.999), + generate_cdf_with_forecast_at_index(101, 0.999), + ], + 50, + None, + 0, + 100, + 0.8, + ), + ], +) +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_peer_score( + forecast, other_forecasts, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_peer_score( + forecast, other_forecasts, resolution, options, range_min, range_max, weight + ) + assert score_weighted == pytest.approx(score_unweighted * weight) + + +# 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 diff --git a/weighted_t_test_h2h_bot_vs_pros.csv b/weighted_t_test_h2h_bot_vs_pros.csv deleted file mode 100644 index 96cf6b7..0000000 --- a/weighted_t_test_h2h_bot_vs_pros.csv +++ /dev/null @@ -1,47 +0,0 @@ -,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -Grizeu_Bot,487.9,40.0,12.2,123.49852344088487,19.53904680990783,0.6251000199360248,2.0203143354405637,51.7,-27.3,0.7322246430842996,0.535551 -acm_bot,149.7,63.8,2.3,123.1672185402655,15.413976167212882,0.1521157135047702,1.9970180928411654,33.1,-28.4,0.5602085330688682,0.879583 -RPM_bot,145.0,6.0,24.2,31.46890650801069,12.847127284662498,1.8809957274619813,2.570581835636314,57.2,-8.9,0.9406376166785096,0.118725 -X_bot,20.7,5.0,4.1,19.75623679424021,8.835257690300725,0.4688971268422159,2.7764451051977987,28.7,-20.4,0.668221204908144,0.663558 -cobyj-bot,0.0,0.0,,,,,,,,,NA -andrewsiah,0.0,0.0,,,,,,,,,NA -jonahsingerbot,-61.3,4.7,-13.0,5.485368611367634,2.5302118657643557,-5.15484234051559,2.7848427377534137,-6.0,-20.1,0.004141428880289339,0.008283 -bean_bot,-70.7,4.7,-15.1,8.81313702231215,4.065196971858859,-3.702222190036137,2.7848427377534137,-3.7,-26.4,0.01192534276282408,0.023851 -jkraybill_bot,-76.1,38.2,-2.0,67.06547883632598,10.85804803442324,-0.18370601441935402,2.023360215298298,20.0,-24.0,0.4276215664726116,0.855243 -CumulativeBot,-97.0,10.2,-9.5,30.12105998155594,9.408238498783877,-1.0055347747612828,2.2318482470257073,11.5,-30.5,0.17010877366473343,0.340218 -swingswish,-109.0,6.7,-16.3,15.145530939114826,5.8512290764953425,-2.779700630431383,2.4503873959101115,-1.9,-30.6,0.016896405137265973,0.033793 -SynapseSeer,-128.5,27.1,-4.8,47.08104512679923,9.052373408885058,-0.5249586045828704,2.0495688922222266,13.8,-23.3,0.3020257536154594,0.604052 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-GreeneiBot2,-446.6,45.8,-9.8,88.55320725176313,13.092082882350407,-0.7457050808617829,2.0123403544597687,16.6,-36.1,0.22987241625188587,0.459745 -metac-o1,-500.3,74.7,-6.7,111.25524179571492,12.872419395150438,-0.5203385298152786,1.9915966480791545,18.9,-32.3,0.3021936468001055,0.604387 -krm-bot,-521.0,9.5,-54.8,50.627856321510166,16.42584560255888,-3.3389622067030595,2.2647088573190035,-17.6,-92.0,0.004699854903992789,0.009400 -4Shadower,-527.8,12.2,-43.3,80.79118175671782,23.1304480505728,-1.870272754393436,2.181694676433973,7.2,-93.7,0.043896119135688104,0.087792 -MWG,-766.4,29.5,-26.0,87.753337992406,16.156699118332316,-1.6080774730154093,2.043526587895404,7.0,-59.0,0.059420840675107243,0.118842 -bot_median,-780.6,75.7,-10.3,85.11389082378146,9.782559637787905,-1.0541472762650386,1.991180868356605,9.2,-29.8,0.14760661430231808,0.295213 -Bot_Pepa,-814.9,37.2,-21.9,93.0672852336652,15.269247572172862,-1.4365511370924278,2.0250978379673494,9.0,-52.9,0.07972209366548037,0.159444 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-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