"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mean: 0.09311986003682822, std dev: 1.0015615474958925\n",
+ "=====\n",
+ "\n",
+ "\n",
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+ " 0.07695431, 0.09167685, -0.01352531, -0.02318744, 0.06600928,\n",
+ " -0.02465895, -0.03260975, 0.00561013, -0.01656378, -0.01239839,\n",
+ " 0.08470301, 0.01586901, -0.02012893, -0.02389339, -0.00040616,\n",
+ " -0.01115227, 0.0647352 , -0.00295478, 0.00839552, 0.06951854,\n",
+ " -0.00298807, 0.07299055, -0.01043141, -0.00976271, -0.00845791,\n",
+ " -0.00602178, 0.03951652, 0.06391563, -0.01430576, -0.00307329,\n",
+ " 0.09673586, 0.07370582, -0.00114735, 0.00124933, -0.01656937,\n",
+ " 0.01274578, 0.14309023, -0.00265139, -0.01987002, 0.10695819,\n",
+ " 0.01463732, 0.01401146, 0.12490686, 0.06220011, -0.0247361 ,\n",
+ " 0.05989785, -0.00784696, 0.03604079, -0.03053008, 0.0073778 ,\n",
+ " 0.0061978 , -0.00228165, 0.05472708, -0.01123143, 0.02096341,\n",
+ " -0.01168965, -0.0058858 , -0.01380564, 0.00796524], dtype=float32), array([1.3821796e-17, 1.3859970e-17, 1.2775576e-20, 7.9130921e-20,\n",
+ " 4.7580357e-23, 5.5200555e-17, 5.5283607e-17, 3.3155887e-21,\n",
+ " 8.5288170e-21, 9.6521127e-20, 8.0715953e-21, 1.1068176e-20,\n",
+ " 2.1583379e-19, 3.5634351e-21, 2.4064712e-21, 1.9989842e-19,\n",
+ " 6.0126042e-21, 3.0102548e-21, 3.4554227e-18, 8.4373240e-22,\n",
+ " 2.6094957e-21, 8.8007500e-20, 5.3963289e-20, 1.4600926e-21,\n",
+ " 7.6421310e-20, 2.0915425e-22, 7.2224151e-20, 8.6360256e-19,\n",
+ " 1.1120780e-21, 8.6212201e-19, 8.3251594e-22, 1.3798589e-17,\n",
+ " 5.5183684e-17, 8.6412539e-19, 1.9503330e-22, 5.5309041e-17,\n",
+ " 5.5212122e-17, 1.3459734e-20, 2.4470782e-23, 3.4491614e-18,\n",
+ " 5.6098707e-21, 2.4049222e-19, 5.3967544e-20, 3.6142950e-21,\n",
+ " 2.5076435e-19, 8.6643126e-19, 8.6672217e-19, 2.2084053e-16,\n",
+ " 1.3861142e-17, 8.6636442e-21, 1.3861096e-17, 1.2509430e-21,\n",
+ " 1.3798723e-17, 3.4603174e-18, 1.5593270e-21, 8.2841310e-22,\n",
+ " 5.3855846e-20, 1.3879485e-17, 8.6403384e-19, 3.4531234e-18,\n",
+ " 2.7471646e-21, 7.4573286e-22, 2.9690121e-21, 8.6193383e-19],\n",
+ " dtype=float32)]\n",
+ "[array([[ 1.58597717e-13, -8.96669078e-15, -1.24079925e-14, ...,\n",
+ " 1.53010444e-13, -1.32893151e-14, -1.34293770e-14],\n",
+ " [ 1.21298934e-12, -9.91346152e-14, -1.10004765e-13, ...,\n",
+ " 1.16435464e-12, -1.09233504e-13, -1.14652943e-13],\n",
+ " [ 1.28588308e-12, -1.15192564e-13, -9.96635500e-14, ...,\n",
+ " 1.22816796e-12, -1.14397044e-13, -7.60205023e-14],\n",
+ " ...,\n",
+ " [ 1.25341534e-12, -1.21802891e-13, -9.71339708e-14, ...,\n",
+ " 1.48245825e-12, -1.21749033e-13, -1.27836896e-13],\n",
+ " [-4.61572946e-13, 3.43781908e-14, 2.33851363e-14, ...,\n",
+ " -3.99487084e-13, 2.32301361e-14, 3.72497511e-14],\n",
+ " [ 1.86456666e-12, -1.11502292e-13, -1.57629077e-13, ...,\n",
+ " 1.96534932e-12, -1.60341182e-13, -1.52233274e-13]], dtype=float32), array([ 0.6307126 , -1.2567277 , -1.2765007 , -1.2699625 , 0.610062 ,\n",
+ " -1.2609938 , -1.2734647 , -0.30522463, -0.11935943, -1.2716926 ,\n",
+ " -1.2551875 , -0.00719464, -1.2752907 , -1.2621006 , -2.459898 ,\n",
+ " -0.00465 , -1.2489439 , -1.2576164 , -1.2615265 , -1.2815865 ,\n",
+ " -1.2828387 , -1.2307428 , 0.6061744 , -2.5135496 , 0.60928524,\n",
+ " 0.6120264 , -1.2590501 , -0.3005084 , -1.2783017 , 0.6096824 ,\n",
+ " -1.2672774 , -1.2676773 ], dtype=float32)]\n",
+ "[array([[ 5.3516484e-08, 7.4810643e-08, -2.5158499e-08, ...,\n",
+ " -1.5346723e-07, 4.5375998e-08, 1.0132624e-09],\n",
+ " [ 4.4612499e-08, 5.8842993e-08, 2.7628607e-07, ...,\n",
+ " 3.4787206e-07, 3.6385764e-08, 2.7892389e-07],\n",
+ " [ 4.3063558e-08, 5.7437259e-08, 2.8934596e-07, ...,\n",
+ " 3.4244687e-07, 3.4873544e-08, 2.9142421e-07],\n",
+ " ...,\n",
+ " [ 4.7188411e-08, 6.8237526e-08, -7.9174299e-08, ...,\n",
+ " -1.5837654e-07, 3.7890331e-08, -5.2270195e-08],\n",
+ " [ 4.3798011e-08, 5.8113265e-08, 2.8328517e-07, ...,\n",
+ " 3.4496867e-07, 3.5581795e-08, 2.8563392e-07],\n",
+ " [ 4.3766541e-08, 5.8084595e-08, 2.8354899e-07, ...,\n",
+ " 3.4485925e-07, 3.5551185e-08, 2.8588619e-07]], dtype=float32), array([-8.42727572e-02, -9.05588716e-02, -1.63633488e-02, -7.29920343e-02,\n",
+ " -1.52953183e-02, 3.27853850e-05, -7.49039426e-02, -1.18919306e-01,\n",
+ " -9.42348614e-02, -6.47953972e-02, -8.98051485e-02, -9.30344313e-02,\n",
+ " 3.72045697e-03, -6.26667356e-03, -1.27908051e-01, -6.20184429e-02,\n",
+ " -5.42562753e-02, -9.34698954e-02, -7.64255896e-02, -7.57683143e-02,\n",
+ " -1.76849251e-03, -1.54077187e-02, -9.60404053e-02, 2.42415871e-02,\n",
+ " -9.69895944e-02, -1.56701393e-02, -7.37119913e-02, -5.52383699e-02,\n",
+ " -7.05009922e-02, 2.31622681e-02, -6.50129840e-02, -1.70284305e-02],\n",
+ " dtype=float32)]\n",
+ "[array([[-3.9499823e-06],\n",
+ " [-4.6674209e-06],\n",
+ " [-1.9490331e-05],\n",
+ " [-3.3625729e-06],\n",
+ " [-2.0106405e-05],\n",
+ " [-4.5269454e-07],\n",
+ " [-3.3908570e-06],\n",
+ " [-1.0150693e-05],\n",
+ " [-5.1938996e-06],\n",
+ " [-3.6122235e-06],\n",
+ " [-4.5687207e-06],\n",
+ " [-5.0140575e-06],\n",
+ " [ 1.8189879e-05],\n",
+ " [-2.2365322e-05],\n",
+ " [-1.2337222e-05],\n",
+ " [-3.8416792e-06],\n",
+ " [-4.8940569e-06],\n",
+ " [-5.0784397e-06],\n",
+ " [-3.4352990e-06],\n",
+ " [-3.4138207e-06],\n",
+ " [-1.0288510e-05],\n",
+ " [-2.0042129e-05],\n",
+ " [-5.4781221e-06],\n",
+ " [ 1.0558367e-05],\n",
+ " [-5.6337799e-06],\n",
+ " [-1.9891379e-05],\n",
+ " [-3.3695774e-06],\n",
+ " [-4.7268595e-06],\n",
+ " [-3.3733374e-06],\n",
+ " [ 9.2980936e-06],\n",
+ " [-3.5973992e-06],\n",
+ " [-1.9103247e-05]], dtype=float32), array([-0.0968298], dtype=float32)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we need to test\n",
+ "ml_model.test_model()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2fbaf177",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model saved to: ../models/my_model\n",
+ "Scaler saved to: ../models/my_scaler.pkl\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we can save it\n",
+ "ml_model.save_model(f\"{model_output}.keras\")\n",
+ "\n",
+ "\n",
+ "# We need to save the scaling information\n",
+ "ml_model.save_scaler(str(model_output.parent.joinpath(Path(\"my_scaler.pkl\"))))\n",
+ "print(\"Model saved to: \", model_output)\n",
+ "print(\"Scaler saved to: \", model_output.parent.joinpath(Path(\"my_scaler.pkl\")))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "118f3407",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential\" \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ dense (Dense ) │ (None , 22 ) │ 484 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (Dense ) │ (None , 64 ) │ 1,472 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization │ (None , 64 ) │ 256 │\n",
+ "│ (BatchNormalization ) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_2 (Dense ) │ (None , 32 ) │ 2,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_3 (Dense ) │ (None , 32 ) │ 1,056 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_4 (Dense ) │ (None , 1 ) │ 33 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ " \n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m) │ \u001b[38;5;34m484\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,472\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m1,056\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 15,889 (62.07 KB)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m15,889\u001b[0m (62.07 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 5,253 (20.52 KB)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m5,253\u001b[0m (20.52 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 128 (512.00 B)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m128\u001b[0m (512.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Optimizer params: 10,508 (41.05 KB)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m10,508\u001b[0m (41.05 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[, , , , , , , , , , , , , ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "ml_model._model.summary()\n",
+ "\n",
+ "print(ml_model._model.weights)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/basic_random_forest.ipynb b/notebooks/basic_random_forest.ipynb
new file mode 100644
index 0000000..ac14e1f
--- /dev/null
+++ b/notebooks/basic_random_forest.ipynb
@@ -0,0 +1,430 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2121eaca",
+ "metadata": {},
+ "source": [
+ "## Welcome to the basic neural network tutorial!\n",
+ "The following notebook is designed to walk through the process of building and training your first scikit based algorithm using the standalone MaCh3 python utilities!\n",
+ "\n",
+ "If you're writing scripts this is encapsulate in the objects in `MaCh3PythonUtils/config_reader` however this guide aims to break apart the process of writing the code for yourself!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81a4f83c",
+ "metadata": {},
+ "source": [
+ "The first step is to load in a MaCh3 MCMC fit as input file. This is done using the ChainHandler class!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea515328",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# MaCh3Python Deps\n",
+ "from MaCh3PythonUtils.file_handling.chain_handler import ChainHandler\n",
+ "from MaCh3PythonUtils.machine_learning.ml_factory import MLFactory\n",
+ "\n",
+ "# Other imports\n",
+ "from matplotlib import pyplot as plt\n",
+ "from pathlib import Path\n",
+ "import gdown"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57b30cc7",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d7380ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Download file from google drive\n",
+ "\n",
+ "file_url=\"https://drive.google.com/file/d/1iE6xFhn3BH_HnLUfQ7KFGy2wfeH52Rwf/view?usp=sharing\"\n",
+ "\n",
+ "# download the file\n",
+ "input_file = Path(\"../models/demo_chain.root\")\n",
+ "\n",
+ "if not input_file.exists():\n",
+ " # download the file\n",
+ " input_file.parent.mkdir(parents=True, exist_ok=True)\n",
+ " gdown.download(file_url, str(input_file), quiet=False, fuzzy=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3587f5d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attempting to open ../models/demo_chain.root\n",
+ "Succesfully opened ../models/demo_chain.root:posteriors\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set up file properties\n",
+ "chain_name = 'posteriors'\n",
+ "verbose=False\n",
+ "\n",
+ "chain_handler = ChainHandler(str(input_file), chain_name, verbose=verbose)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39d31a84",
+ "metadata": {},
+ "source": [
+ "Okay, now we've loaded in a file we need to do a bit of processing. This means we need to select the variables we care about and apply parameter cuts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0cd2f396",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Firstly we set things we want to ignore, these are parameters we really don't care about/want to learn!\n",
+ "chain_handler.ignore_plots([\"LogL_systematic_xsec_cov\", \"Log\", \"LogL_systematic_osc_cov\"])\n",
+ "\n",
+ "# Now we add parameters we care about. Note this only need to be a substring of the full name\n",
+ "chain_handler.add_additional_plots([\"sin2th\", \"delm2\", \"delta\", \"xsec\"])\n",
+ "\n",
+ "# The fitting label is special, it's the thing we want to train our network to predict\n",
+ "fitting_label = \"LogL\"\n",
+ "# We need to make sure the chain handler knows this exists, passing true means it is looking for some with that exact name\n",
+ "chain_handler.add_additional_plots(fitting_label, True)\n",
+ "\n",
+ "# Finally we can do some cuts to get rid of things like burn-in\n",
+ "chain_handler.add_new_cuts([\"LogL<30\", \"LogL_systematic_xsec_cov<1234\", \"step>10000\"])\n",
+ "\n",
+ "# Last step is convert the chain into a pandas dataframe\n",
+ "chain_handler.convert_ttree_to_array()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d259501",
+ "metadata": {},
+ "source": [
+ "Let's quickly use the chain handler to make a plot of the trace and posterior!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd6b44ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from MaCh3PythonUtils.file_handling.chain_diagnostics import ChainDiagnostics\n",
+ "plotter=ChainDiagnostics(chain_handler)\n",
+ "\n",
+ "plotter(\"xsec_0\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58068596",
+ "metadata": {},
+ "source": [
+ "## Machine learning\n",
+ "Okay now we've done some very basic file manipulation, it's time for some machine learning!\n",
+ "All algorithms use the same common `MLFactory` interface so let's go about configuring it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23b769e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First step we need to initialise the factory\n",
+ "from pathlib import Path\n",
+ "\n",
+ "\n",
+ "model_output=Path(\"../models/my_model\")\n",
+ "# Make sure the model output directory exists\n",
+ "model_output.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# The factory produces ML models, we need to pass it the chain handler and the fitting label to get started\n",
+ "ml_factory = MLFactory(chain_handler, fitting_label, f\"{model_output}.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47842a01",
+ "metadata": {},
+ "source": [
+ "Now we need to define a model, for this demonstration we'll make a very simple BDT!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80a10c4f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We now want the actual neural network properties\n",
+ "# Usually you set this using a YAML file (see configs for some examples) but we'll do this manually for now\n",
+ "build_settings = {\n",
+ " # Loss function\n",
+ " 'loss': 'mse',\n",
+ " # Metrics to measure\n",
+ " 'metrics': ['mae', 'mse'],\n",
+ " # Learning rate\n",
+ " 'learning_rate': 0.001,\n",
+ "}\n",
+ "\n",
+ "# Settings used when the model is fitting\n",
+ "fit_settings = {\n",
+ " 'batch_size': 4096,\n",
+ " 'epochs': 100,\n",
+ " 'validation_split': 0.2,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "85fa21f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now we can make the model\n",
+ "ml_model = ml_factory.make_interface('SciKit', 'histboost', loss='absolute_error', max_iter=10000, verbose=2, max_leaf_nodes=400, learning_rate=0.01)\n",
+ "\n",
+ "# Let's use 20% of the data for testing\n",
+ "ml_model.set_training_test_set(0.2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "04c8e8c9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Model\n",
+ "Binning 0.017 GB of training data: 0.088 s\n",
+ "Binning 0.002 GB of validation data: 0.006 s\n",
+ "Fitting gradient boosted rounds:\n",
+ "[1/10000] 1 tree, 400 leaves, max depth = 33, train loss: 2.66150, val loss: 2.67616, in 0.293s\n",
+ "[2/10000] 1 tree, 400 leaves, max depth = 33, train loss: 2.63845, val loss: 2.65353, in 0.251s\n",
+ "[3/10000] 1 tree, 400 leaves, max depth = 35, train loss: 2.61594, val loss: 2.63115, in 0.214s\n",
+ "[4/10000] 1 tree, 400 leaves, max depth = 45, train loss: 2.59371, val loss: 2.60939, in 0.224s\n",
+ "[5/10000] 1 tree, 400 leaves, max depth = 46, train loss: 2.57181, val loss: 2.58798, in 0.219s\n",
+ "[6/10000] 1 tree, 400 leaves, max depth = 42, train loss: 2.55065, val loss: 2.56715, in 0.252s\n",
+ "[7/10000] 1 tree, 400 leaves, max depth = 39, train loss: 2.52979, val loss: 2.54660, in 0.262s\n",
+ "[8/10000] 1 tree, 400 leaves, max depth = 36, train loss: 2.50850, val loss: 2.52542, in 0.229s\n",
+ "[9/10000] 1 tree, 400 leaves, max depth = 41, train loss: 2.48772, val loss: 2.50477, in 0.282s\n",
+ "[10/10000] 1 tree, 400 leaves, max depth = 35, train loss: 2.46776, val loss: 2.48510, in 0.292s\n",
+ "[11/10000] 1 tree, 400 leaves, max depth = 35, train loss: 2.44729, val loss: 2.46510, in 0.306s\n",
+ "[12/10000] 1 tree, 400 leaves, max depth = 37, train loss: 2.42741, val loss: 2.44538, in 0.394s\n",
+ "[13/10000] 1 tree, 400 leaves, max depth = 35, train loss: 2.40794, val loss: 2.42598, in 0.291s\n",
+ "[14/10000] 1 tree, 400 leaves, max depth = 31, train loss: 2.38904, val loss: 2.40724, in 0.205s\n",
+ "[15/10000] 1 tree, 400 leaves, max depth = 41, train loss: 2.36992, val loss: 2.38853, in 0.219s\n",
+ "[16/10000] 1 tree, 400 leaves, max depth = 40, train loss: 2.35050, val loss: 2.36906, in 0.243s\n",
+ "[17/10000] 1 tree, 400 leaves, max depth = 36, train loss: 2.33227, val loss: 2.35116, in 0.247s\n",
+ "[18/10000] 1 tree, 400 leaves, max depth = 37, train loss: 2.31352, val loss: 2.33261, in 0.235s\n",
+ "[19/10000] 1 tree, 400 leaves, max depth = 38, train loss: 2.29497, val loss: 2.31419, in 0.254s\n",
+ "[20/10000] 1 tree, 400 leaves, max depth = 32, train loss: 2.27719, val loss: 2.29664, in 0.230s\n",
+ "[21/10000] 1 tree, 400 leaves, max depth = 39, train loss: 2.25924, val loss: 2.27871, in 0.257s\n",
+ "[22/10000] 1 tree, 400 leaves, max depth = 34, train loss: 2.24177, val loss: 2.26150, in 0.251s\n",
+ "[23/10000] 1 tree, 400 leaves, max depth = 37, train loss: 2.22417, val loss: 2.24398, in 0.295s\n",
+ "[24/10000] 1 tree, 400 leaves, max depth = 39, train loss: 2.20721, val loss: 2.22714, in 0.251s\n",
+ "[25/10000] 1 tree, 400 leaves, max depth = 42, train loss: 2.19041, val loss: 2.21043, in 0.258s\n",
+ "[26/10000] 1 tree, 400 leaves, max depth = 34, train loss: 2.17348, val loss: 2.19369, in 0.271s\n",
+ "[27/10000] 1 tree, 400 leaves, max depth = 39, train loss: 2.15657, val loss: 2.17683, in 0.304s\n",
+ "[28/10000] 1 tree, 400 leaves, max depth = 38, train loss: 2.14019, val loss: 2.16072, in 0.256s\n",
+ "[29/10000] 1 tree, 400 leaves, max depth = 34, train loss: 2.12379, val loss: 2.14449, in 0.251s\n",
+ "[30/10000] "
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Now we need to train our network\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mml_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/software/MaCh3/MaCh3-PythonUtils/src/MaCh3PythonUtils/machine_learning/scikit/scikit_interface.py:26\u001b[39m, in \u001b[36mSciKitInterface.train_model\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._training_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._training_labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo test data set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscaled_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_training_labels\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/software/MaCh3/MaCh3-PythonUtils/.venv/lib/python3.11/site-packages/sklearn/base.py:1389\u001b[39m, in \u001b[36m_fit_context..decorator..wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m 1382\u001b[39m estimator._validate_params()\n\u001b[32m 1384\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m 1385\u001b[39m skip_parameter_validation=(\n\u001b[32m 1386\u001b[39m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m 1387\u001b[39m )\n\u001b[32m 1388\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1389\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/software/MaCh3/MaCh3-PythonUtils/.venv/lib/python3.11/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:897\u001b[39m, in \u001b[36mBaseHistGradientBoosting.fit\u001b[39m\u001b[34m(self, X, y, sample_weight)\u001b[39m\n\u001b[32m 877\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m.n_trees_per_iteration_):\n\u001b[32m 878\u001b[39m grower = TreeGrower(\n\u001b[32m 879\u001b[39m X_binned=X_binned_train,\n\u001b[32m 880\u001b[39m gradients=g_view[:, k],\n\u001b[32m (...)\u001b[39m\u001b[32m 895\u001b[39m n_threads=n_threads,\n\u001b[32m 896\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m897\u001b[39m \u001b[43mgrower\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 899\u001b[39m acc_apply_split_time += grower.total_apply_split_time\n\u001b[32m 900\u001b[39m acc_find_split_time += grower.total_find_split_time\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/software/MaCh3/MaCh3-PythonUtils/.venv/lib/python3.11/site-packages/sklearn/ensemble/_hist_gradient_boosting/grower.py:388\u001b[39m, in \u001b[36mTreeGrower.grow\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 386\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Grow the tree, from root to leaves.\"\"\"\u001b[39;00m\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m.splittable_nodes:\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msplit_next\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 390\u001b[39m \u001b[38;5;28mself\u001b[39m._apply_shrinkage()\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/software/MaCh3/MaCh3-PythonUtils/.venv/lib/python3.11/site-packages/sklearn/ensemble/_hist_gradient_boosting/grower.py:605\u001b[39m, in \u001b[36mTreeGrower.split_next\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 600\u001b[39m \u001b[38;5;66;03m# We use the brute O(n_samples) method on the child that has the\u001b[39;00m\n\u001b[32m 601\u001b[39m \u001b[38;5;66;03m# smallest number of samples, and the subtraction trick O(n_bins)\u001b[39;00m\n\u001b[32m 602\u001b[39m \u001b[38;5;66;03m# on the other one.\u001b[39;00m\n\u001b[32m 603\u001b[39m \u001b[38;5;66;03m# Note that both left and right child have the same allowed_features.\u001b[39;00m\n\u001b[32m 604\u001b[39m tic = time()\n\u001b[32m--> \u001b[39m\u001b[32m605\u001b[39m smallest_child.histograms = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mhistogram_builder\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute_histograms_brute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 606\u001b[39m \u001b[43m \u001b[49m\u001b[43msmallest_child\u001b[49m\u001b[43m.\u001b[49m\u001b[43msample_indices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msmallest_child\u001b[49m\u001b[43m.\u001b[49m\u001b[43mallowed_features\u001b[49m\n\u001b[32m 607\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 608\u001b[39m largest_child.histograms = (\n\u001b[32m 609\u001b[39m \u001b[38;5;28mself\u001b[39m.histogram_builder.compute_histograms_subtraction(\n\u001b[32m 610\u001b[39m node.histograms,\n\u001b[32m (...)\u001b[39m\u001b[32m 613\u001b[39m )\n\u001b[32m 614\u001b[39m )\n\u001b[32m 615\u001b[39m \u001b[38;5;66;03m# node.histograms is reused in largest_child.histograms. To break cyclic\u001b[39;00m\n\u001b[32m 616\u001b[39m \u001b[38;5;66;03m# memory references and help garbage collection, we set it to None.\u001b[39;00m\n",
+ "\u001b[31mKeyboardInterrupt\u001b[39m: "
+ ]
+ }
+ ],
+ "source": [
+ "# Now we need to train our network\n",
+ "ml_model.train_model()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68ecf564",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Results!\n",
+ "[18.75503651 15.55911275 19.74723309 ... 12.55966201 9.22526319\n",
+ " 10.42062552]\n",
+ "Mean Absolute Error : 13.264392156892503\n",
+ "Line of best fit : y=0.2954008400341496x + -3.9183125856882457\n",
+ "Saving QQ to train_qq_plot.pdf\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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2r3f2QlgJ99bGnkfr9c7b8zVkjz0cl2J/PH7usCfA/rLMcs7sHxf729pjbweftxkzZpgeEuYLZSUn75Oc2LhxIx588EEzmYLPK3u+mDeY8T7ws5Sft3yNszeQrynmDrnzvU/MQ2rTpo3pKWdS/KOPPpr+vs5pe/kcMpeIidjsxWOvJF8b7B2yXnfEninmFXI/5o4xd8m+R3/06NGmt5N5eeyh5H1mDynzoYjvb/aWsceJIx38LuLIBr9TMvs+Ye4X/yZ7mfj8OfOe5GuFPaqcbMH3Cnvg2Db2omZ0/vx58xq67N6n/+6c+EkOFM/AeHb05JNPmt/vvfdec+aeWQ8Kz6QsPFPg2TCjeZ7RWLkkedEDdSnZ9UBlPN6ePXvMvvZnPjwbt++BYu7TjTfemOljcvbs2fQeF6tnL6uNf4t4BhoVFeXQDvZO8Szs22+/zfa+8ayJjzl7G7J6DbJngz0z8fHx6blI7G3Kyuuvv25ymohnlNyfbbE2niFaZ4nbt2+37dixI9Mz5Xbt2tkeeOCBHN9H5ixkzEWxevb4erJ6AjO2xbps/vz5preFP2fs6enfv7/J3SPul9nrgWehPIvOipUfyJ62nLQlJz1Q7GmyHktr437sEcmsN+bQoUO2M2fOmNcZ9+GZNE2fPt3czuoZsPBs/J133knvgcqYY0Z8Xj788MMs36/svWAPZGavd6tXksdgT4W9hx566JLvfftjffrppxf9ncOHD2d5W+ZZWvux1ygr7EVnLyI/27KT0/dJVj1QzAO66aabsnzNZIW5ZRl7tHPSA5WT97692bNnm+NZPYmX217ehj2zxPcbX1PslbfHnlzmTBI/I3hM9qTaY7vvuece8zNz9/h6Pn3acSSFuXpW7qJlw4YN5nWdscftct6TFn7uPP744xddzrytjD2yzsYBWgvPj/AsmGddjLyJuQ48m+OZAcfYs8KzHG6cFcGzFkbtPAtmDwLPDNzhco/F21FObmudefOsh/eHMk4jtqbgMvcqq14knsXZ50RlxpptxceLZ0A8C7R6xDgThWd3Gc/qM5598gyKZ83MW8pserH1nFp/i70SzGXgfcgK76+1KCZ7KKyzRAunhbNnij0nPBbzk9grwp5Aa8ow28bcpvvuuy/H95H5ORnP+K11pbgfX38Z28I8Gx6HuRvsleFzzeOzLdaMI97WvpeS17PNvIzlC4h5KOz9yK7XxDrbZs4Me38u1ZacYO9exlldfE55OWdnZcS/S8xn4fPN0gbEngbrfjD/iZhvwlmM7FGyMLeEPQDMibH25/PCvKms8LXC3kG+1rOaJcfb8/lmb4Ulq1IY9tgTa9m/f7/5G/aXZcf+fmXH6p3j/xnXKXPlfZIRX78ZPw+sv2efe5RZ+/iackZO3/sZ39dFixY1t7nc9vIzlK97q4eO7zf2aPJ1ZI8zV63nJz4+3vyf2Xvbek9ltQ9/t/Yh5l8x/5S91Oy9s5eTz4fMsHeVPal8z2X0/vvvm/xQ+x5Zp2Ubevm5/N4Dxd4UjmNz4/1gHgOjdqu35dy5c7bq1aubGWqcIbFz505z3bBhw8zZLn9n5M6xcp4RcjyaM9qsvBbmm7C3hWdRPFvNmEfhTA/U4MGDTZ4Kz8x4LOtM+lI5UDw75X3iWTbPJHgGn12PVsuWLU0OBB8PjuOzB86+B4pnqczd4ZkYz1B5hsWx9DvuuOOSZ7lZ4fPAfCzmojBvgY95nz59HO4D81msXBWeqTHHpH79+ubvW88hN/s2MG+GM7Z49secCJ5N2feC8DFhngdzYbix54xnguzRyEpmZ+DspWAvBHOn+Pd4feXKlR3eF5e6jzxD5PM0ZswYk8vAXLxu3bqZnhj2CmQmsx6TL774wuR78L7xOWQODNvG3hsLe1bZ4/T777/bVq1aZc7kuVnmzJljHgPOtuTrjWfvzINo06ZNlo9LZm3ha42vfasnz8oTsXofM5MxB4r4+ubjwdwO9hCyRyVjPgsf87p165rcELab+Sp16tRJz2lhDxSff76emQfF+83XOrfs7gN7ZjiLjZfz/c3Hg3+DPQDWrEo+5py1xseMbXz66afNbL28zoFiL9bMmTPN886eUf7MGZh9+/ZN34c9nnwv2bvU+8Tq+eBzxzwhfh5az6vF6uHjZx//Nl/j7Nmwcu+IvfS//vqruZ5tfOWVV8xtrNloVv4kj8vXIF8zfGz5O9/bOX3vcxYhj8nXAXvo2CbmKfF5caa9fC/yOef1fP2x54nPMx8L+8eTjxd7Ovm3+Fpljw97s4ivP/Yk8TOV7322mfeb73XeR+LnOb832LPP2Z18DT388MPmuNZsT94Xfu6y58z+PmfXO5TZ63nkyJHmc916HXfu3NnMLs14HN4XtpGzhV2JAxRA+XAAZT+9nx94DFC+/vprh/34IuUQCF9k/GLi1FcGM3wj80vp+uuvNwl/TMzlByDfpFZww4CJCa/8AsuqjEFOAyi+qfhhbyX1WmUM+Df5RskOPxQ5LMU3hH0Zg8wCKD6f/DLl3+H0VX7gZSxjwC/4G264wdwv7lerVi0zZGGfbO8MfmgymODwJxMsBw4cmB7o2XfnW22whiUz2+zLO3CKL6ch80OPH7pMoLTHIIOBAT9c+Xf54Wk/vT+nARTv9+jRo20xMTHmNcLueSuRM6f3kVj2gEEWg25+WHLYjYFdVrIaIuaHOAMkviatgMEe3688WWBZDt53PpfWFxQxsOJrwHrsGOwxwT7jsN+l2pLV82S9BnMaQPXr189M++b9adCgwUVTwonvR0615muS+/I+2ZcosKakf/PNN+Y9zOeJXx72wVxWjyeHi3jSxGCEX2rly5c3QYn98Rl88zOCzy/vH4dx8jqAYrBxxRVXmDbwNcQAkkGL/eezlUxv71LvE2LbMnsuM5YB4N/kZwI/E/kY2Q8vMjWCwQT/Dl97fI2xzfYyK//CzfqMy8l7n1/6/OyyHgc+D9OmTbto+PZS7eVnmvU+4nu7Z8+emSZ2c4jeul/8W999953D9Vu3bjXBEYfe+H7L7DXMYJxDmXzt8ruIn/X2z0Nm5W64Zfeayez1zBIH1vcVh4z5O4O6zMpb8HWe3QSEnMQBphDI5fdf+TYmUzOZkt2DOelGFRHxBCaqs5K/N1fkF/G1OECz8EREREScpABKRERExEkKoERE8jnWa9PwnUjeUgAlIiIi4iQFUCIiIiJOUgAlIiIi4iQFUCIiIiJOUgAlIiIi4iQFUCIiIiJOUgAlPq9Dhw6mSrOFi6a+9tprHqkWHRUVled/V0RE3E8BlJ8EDfL/Vq5cibvvvjtH+yroERGRzARneqn4PC6BmJqaiuDg/PESSEpKQoECBdxyrJIlS7rlOCIi4r/UA+WjVYkXLlyIyZMnIyAgwGzsSeH/P//8M5o0aYLQ0FAsXrzY7Hv99dc73J49V+zBsqSlpWHcuHFmMcWwsDA0bNgQX3/9tUvt498cM2aMCWYiIiJw7733miDJwr8/dOhQ05YSJUqgW7du5vL169ejR48eKFy4MGJiYtCvXz8cO3Ys/Xbnzp1D//79zfWlS5fGq6++etHfzziExwrO99xzjzkeF4usV68eZs+ejT/++AMDBw7E6dOn0x/HZ5991twmMTERDz/8MMqWLYtChQqhRYsWZn97fMwrVKiA8PBw3HDDDTh+/PhlP2YiIuJdFED5IAZOrVq1wuDBgxEbG2u28uXLm+sef/xxvPTSS9i0aRMaNGiQo+MxePr4448xbdo0bNiwAcOHD8ftt99ugjQLA5bsNgZI9ubPn2/awKDj888/x7fffmsCKnsfffSR6XVasmSJ+dsMdK666io0btwYq1atwty5c3H48GHcfPPN6bd55JFHTLu+//57/Prrr+b4a9asyfK+MThkQMa/8emnn2Ljxo3m8QkKCkLr1q1NoMUAz3ocGTQRg7ulS5fiiy++wLp16/C///0P3bt3x7Zt28z1y5cvx6BBg8x+a9euRceOHfHCCy/k6PEWERHvlz/Gb7zR5s1AsWJAfDwQGAiEhgKnTwMxMUBsLFC2LHDgAFC6NHD4MBAZyW4LfmMD4eHAiRP/v4/1P2/L3pTChYHUVI5bARERFy6rVSvHTYuMjDSBB3s+SpUq9V9zN5v/n3vuOXTp0iXHx2JPy9ixY/Hbb7+ZoIyqVKlieq/efvtttG/f3lzGICE7DELssX3Tp083baxbt65pF4Of559/HoF8PAFUr14d48ePT78NAxAGT2yPhcdgcLh161aUKVMG77//vgmEOnXqlB6ElStXLst28X6tWLHCBHM1atRIv3/2jyV7nqzHkfbu3YsPPvjA/M+/SQysGNDxcraPQSwDqkcffdRcz2P/9ddfZh8REcn/FED5maZNmzq1//bt2xEfH39R0MXhNgYzlmrVqjl1XA4DMniyMDg7e/Ys9u3bh4oVK5rLONRo759//sGCBQtMj1ZGO3bswPnz5027OJxmKVasGGrWrJllOxj4McCygqec+Pfff03+WMbbMNgsXry4+ZkBGYft7PE+KoASEfENCqAuV2Y9QuxBIvY22e9j/W4vOtpxH+v/okUv3rdECbgL83XssbeHCeX2kpOT039mUENz5swx+T72mEdlySyoscchPw7DudJWtuXaa6/Fyy+/fNG+zHdisOcs5nQ5i+3gEN/q1avN//Yu9TiIiIhvUADlozhExl6SS2ESNxOzM/bKhISEmJ/r1KljAiUOV1nDdZlxdgiPvUnsMbICmGXLlpngw8rVyswVV1yBb775xiSBZzZ7sGrVqqbdzD9i8jadPHnSDO9l1Xbmge3fv9/sk1kvVGaPI3veeNmRI0fQtm3bTI9bu3Zt0w57vI8iIuIbFED5KAYZ/ALfvXu3CUyYLJ0ZJmVPmDDBJIlziIn5QwyorOG5IkWKmPweJo7zGFdeeaWZlcakawZFAwYMuKwhPA61Mcn6qaeeMm185plnTMK1lf+UmSFDhuDdd99Fnz59TG4Rh+fY68RE7vfee8/cTx6TuVQcSouOjsaTTz6Z7TEZWLVr1w69e/fGxIkTzf1gvhjznpjDxMeRPU5MereGHRlo9e3b18z24yw/PlZHjx41+zAgu/rqq/HAAw+gTZs2eOWVV3Ddddfhl19+0fCdiIgP0Sw8H8Wgh8NL7EFiLxN7kDLD8gCjR482AUmzZs1w5swZExjYY2I39+FsPPasMLDgkB7LGlwuJnkzSZzByy233IJevXqllwjIChO2Gbix96dr166oX7++KXPAQpdWkMRgkL1CHOrr3LmzCfgy5lJlxF4t3ncGZny8+FhYvU6ciccZhGwjH0crqZ3J4nycRo4caXKsWJaBBTqtnq+WLVuaYI/J5Ay8OCOQwaKIiPiGAFvGBBhJl5CQgF27dplAgfWBxD1YB4olCb777jtPN0VEROSy4gD1QImIiIg4SQGUiIiIiJOURC55jkuciIiI5GfqgRIRERFxkgIoEREREScpgMqBrGooiYiIiH9+/ysHKhusQs36QgcPHjQ1gPg7CyyKiIiI72KFJxZ8ZpFkxgH8/s9IdaAugQ9gbGysWVBXRERE/AdXn+BaqwqgLhMfopSUlBytLSciIiL5H1fz4LqrWY08KYASERERcZKSyEVEREScpABKREREJD8FUG+99RYaNGiAiIgIs7Vq1Qo///yzwyJ+Q4YMQfHixVG4cGH07t0bhw8f9mSTRURERDybA/Xjjz+aJK3q1aubRO2PPvoIEyZMwN9//426devivvvuw5w5c8zSH5GRkRg6dKiZTrhkyRJPNVlERETE+5LIixUrZoKom266ydRemjFjhvmZNm/ejNq1a2Pp0qVo2bJljotgsY5TkSJFVMNJRCQf4tfUmTNnUKZMGXMSnRs44sGyNe7AKe8FCxZ0y7HEe3lNIU2WCPjqq69w7tw5M5S3evVqJCcno3Pnzun71KpVCxUqVMg2gEpMTDSb5cCBA6hTp06e3AcREck9+/btQ7ly5XIleKpcsTAOHXFPqZpSpUph165dCqJ8nMcDqH///dcETHwBM89p1qxZJuBZu3atieKjoqIc9o+JicGhQ4eyPN64ceMwZsyYTN94zLMSEZF8YudOVjJEXHg4ypcvb0YScgN7nhg87VlVCRFFXOvhijuThopNd5tjKoDybR4PoGrWrGmCpdOnT+Prr7/GgAEDsHDhwss+3qhRozBixIj03+Pi4swbz0pUFxGRfODUKeDIEaBrV36Qm4tyOw2jcJFAFC4S5NIxtHKq//B4AMVepmrVqpmfmzRpgpUrV2Ly5Mm45ZZbTAR/6tQph14ozsJj92hWQkNDzSYiIvnUhg1MYL0QPOUhZgS7mhXsXVnF4ld1oJj0zRwmBlMhISGYP39++nVbtmzB3r17zZCfiIj4IJaq4bpj9evn+Z9OQ4BbNvEPHu2B4nBbjx49TGI4Z1hwxt0ff/yBX375xZQtGDRokBmO48w8Dr8NGzbMBE85nYEnIiL5yN69wKZNQLdunm6JiHcHUEeOHEH//v0RGxtrAiYW1WTw1KVLF3P9pEmTzJRVFtBkr1S3bt3w5ptverLJIiKSW8N2hQt7NHhKc0MOk3Kg/IfX1YFyNyaRMzhjkrqSyEVEvFB8/IWepyZNPPI5bh3/4OZqiHAxiTzuTCrK1Nqu7xw/4PEkchER8WNbtgB79uR5wriIqxRAiYiI54btSpZkPRt4A5stwGyuHkP8g9fNwhMRET+QkgIcPQpER8NbpLlpE/+gHigREcn7nicGTx06eLolIpdNAZSIiOSdzZsv9DrVrQtv4446TqoD5T80hCciInnj/PkLtZ6Y9+SFNIQnzlAPlIiI5M2wHQMozbYTH6EASkREchfLFLAmkhcO2zlwwyw8HkP8g4bwREQk9xw5ciHvqXx5eDsN4Ykz1AMlIiK5N2wXHJxv1rZTErk4Qz1QIiLifidPXvjfS4pkiribeqBERMS9du4Etm/PdwnjabYAs7l6DPEP6oESERH3DtuFheW74IlsbtrEPyiAEhER90hMBOLigNKlPd0SkVynITwREXHdxo3AgQNAly7Ir5RELs5QACUiIq4P25UqBdSpg/zMHWUIVMbAf2gIT0RELl9yMnDoEFC8uKdbIpKnFECJiMjl9zytWAF06gRfYM3Cc3VzxqJFi3DttdeiTJkyCAgIwHfffZflvvfee6/Z57XXXnO4/MSJE+jbty8iIiIQFRWFQYMG4ezZsw77rFu3Dm3btkXBggVRvnx5jB8/3slHRzJSACUiIs5jmQL2OrVpA19hQ4BbNmecO3cODRs2xBtvvJHtfrNmzcKyZctMoJURg6cNGzZg3rx5mD17tgnK7r777vTr4+Li0LVrV1SsWBGrV6/GhAkT8Oyzz+Kdd95xqq3iSDlQIiLiHM60Y62nfFiqwNv06NHDbNk5cOAAhg0bhl9++QVXX321w3WbNm3C3LlzsXLlSjRt2tRcNnXqVPTs2ROvvPKKCbg+++wzJCUlYfr06ShQoADq1q2LtWvXYuLEiQ6BljhHPVAiIuLcsN3u3T4ZPLlzLTz2+thviSzxcDltSktDv3798Mgjj5jAJ6OlS5eaYTsreKLOnTsjMDAQy5cvT9+nXbt2JniydOvWDVu2bMFJq2K8OE0BlIiI5ExsLFCwINCgAXwRh9/SXNysITzmGUVGRqZv48aNu6w2vfzyywgODsYDDzyQ6fWHDh1CdHS0w2Xcv1ixYuY6a5+YmBiHfazfrX3EeRrCExGRS9u//0LvUz5ZGPhypNkubK4eg/bt22eSui2hoaFOH4v5SpMnT8aaNWtM8rh4F/VAiYhI9hg4paT4dPDkbgye7LfLCaD+/PNPHDlyBBUqVDC9Stz27NmDkSNHolKlSmafUqVKmX3spaSkmJl5vM7a5/Dhww77WL9b+4jzFECJiEjWOB0+KQn47wvbl3liFl52mPvE8gNM+LY2JoUzH4oJ5dSqVSucOnXK9FZZfv/9d5M71aJFi/R9ODMvmTW7/sMZezVr1kTRokXd1l5/oyE8ERHJ3NatPpsw7i1LubBe03aWhPjPrl27TKDEHCb2PBXPUKA0JCTE9Box+KHatWuje/fuGDx4MKZNm2aCpKFDh+LWW29NL3lw2223YcyYMaY+1GOPPYb169ebocFJkya5dF/9nQIoERHJfNiuZEm/CZ48ZdWqVejYsWP67yNGjDD/DxgwAB9++GGOjsEyBQyaOnXqZGbf9e7dG1OmTEm/nknsv/76K4YMGYImTZqgRIkSePrpp1XCwEUKoERExBHznY4fBzKZNu/L3JlEnlMdOnSAzZbzG+1mj2AG7K2aMWNGtrdr0KCByakS91EAJSIijj1PTDC+6ipPtyTPuSOHyZ05UOLdFECJiMgFmzezQJDf9TyJXA7NwhMRESAhgcWLgBIl4K/cWYlcfJ96oERE/B2H7c6dA7p0gT/zxCw8yb8UQImI+DMmJUdGathOxEkawhMR8VfHjgFbtgDlynm6JV41C8/VTfyDeqBERPx12C4oSMuz2NEsPHGGAigREX/DGk9cnLZWLU+3xKsoB0qcoSE8ERF/smsXwHXT6tTxdEtE8jX1QImI+NOwXVSUlmfJgs0NOUxOFBWXfE4BlIiIP0hM5Mq1mm2XjQt1nFwdwhN/oQBKRMTXbdp0oUimep5E3EYBlIiIrw/bcXmW2rU93RKvx9E3V0fgNILnP5RELiLiq1JSgEOH/Hp5lsuZhefqJv5BPVAiIr6IPU8nTgCdOnm6JSI+SQGUiIiv2bYNKFlSCeNOSrMFmM3VY4h/0BCeiIgvOXPmQq2n6GhPtySfzsJzfRP/oB4oERFfGrZLStJsOxFf74EaN24cmjVrhiJFiiA6OhrXX389tnBhSzsdOnRAQECAw3bvvfd6rM0iIl7pwAEgLAxo3NjTLcn3a+G5uol/8GgAtXDhQgwZMgTLli3DvHnzkJycjK5du+LcuXMO+w0ePBixsbHp2/jx4z3WZhERrwye1q8HqlTxdEvyNQ3hSb4Zwps7d67D7x9++KHpiVq9ejXatWuXfnl4eDhKlSrlgRaKiOSDYTv2PHXr5umW5HtKIpd8m0R++vRp83+xYsUcLv/ss89QokQJ1KtXD6NGjUJ8fHyWx0hMTERcXJzDJiLiswnjrPWknicR/00iT0tLw0MPPYQ2bdqYQMly2223oWLFiihTpgzWrVuHxx57zORJffvtt1nmVY0ZMyYPWy4i4qFSBZxtp4Rxt3FHIUwV0vQfATabd6wdfd999+Hnn3/G4sWLUa5cuSz3+/3339GpUyds374dVatWzbQHipuFPVDly5c3vVsRERG51n4RkTwdtmN1cS7R4gf4OR4ZGZlrn+PW8b9e0xLhRVzrV4g/k4Kbrlim7xw/4BU9UEOHDsXs2bOxaNGibIMnatGihfk/qwAqNDTUbCIiPolDdqwwriKZIv4bQLHza9iwYZg1axb++OMPVK5c+ZK3Wbt2rfm/dOnSedBCEREv63ni2nZaniX3hvBcTSLXEJ7f8GgAxRIGM2bMwPfff29qQR3iBwNgulLDwsKwY8cOc33Pnj1RvHhxkwM1fPhwM0OvQYMGnmy6iEje2rz5wpCdep5yjTvKEKiMgf/w6Cy8t956y4wTs1gme5SsbebMmeb6AgUK4LfffjO1oWrVqoWRI0eid+/e+PHHHz3ZbBGRvMW8zv37L+Q9iYhX8PgQXnaY/M1imyIifj1sd/Ys0Lmzp1vi89xRSVyVyP2HVySRi4hIJnbuBKKiNGyXRzSEJ/m2kKaIiPzn+PELtZ7KlvV0S0QkE+qBEhHxxmG7gAAtz5LHtJSLOEMBlIiINzl6FAgKAmrV8nRL/I4qkYszNIQnIuIt9uwB/v5bwZOH2Ny0iX9QACUi4i3Ddux50tp2IvmChvBERDwtIQGIj9dsOw/TEJ44QwGUiIinK4zv3aueJy+QZruwuXoM8Q8KoEREPDlsx+VZlPMkku8oB0pExBNSUoAjR7Q8ixcO4bm6OWPRokW49tprUaZMGQQEBOC7775Lvy45ORmPPfYY6tevj0KFCpl9+vfvj4MHDzoc48SJE+jbty8iIiIQFRWFQYMG4Syr19vhWrJt27ZFwYIFzSof48ePd/HREgVQIiKe6Hn66y+gY0dPt0QcXFjKxZWNx3DGuXPn0LBhQ7zxxhsXXRcfH481a9Zg9OjR5v9vv/0WW7ZsQa9evRz2Y/C0YcMGzJs3D7NnzzZB2d13351+fVxcnFlTtmLFili9ejUmTJiAZ599Fu+8844Lj5VoCE9EJC9t3QqULKmEcTF69OhhtsxERkaaoMje66+/jubNm2Pv3r2oUKECNm3ahLlz52LlypVo2rSp2Wfq1Kno2bMnXnnlFdNr9dlnnyEpKQnTp09HgQIFULduXaxduxYTJ050CLTEOeqBEhHJK+fOAbt3A9HRnm6JZJNE7upm9frYb4mJiW5p4+nTp81QH4fqaOnSpeZnK3iizp07IzAwEMuXL0/fp127diZ4snTr1s30Zp08edIt7fJHCqBERPJq2G7LFs2285McKOYZsQfJ2saNG+dy+xISEkxOVJ8+fUy+Ex06dAjRGQLy4OBgFCtWzFxn7RPDyQp2rN+tfcR5GsITEclt+/cDhQpp2M6P7Nu3Lz3IodDQUJeOx4Tym2++GTabDW+99ZYbWiiuUgAlIpKbYmMv9D5pYWCvl/bf5uoxiMGTfQDljuBpz549+P333x2OW6pUKRzhbE47KSkpZmYer7P2OXz4sMM+1u/WPuI8DeGJiOQWBk7Me1LwlC+4OgPv/2fiuY8VPG3btg2//fYbihcv7nB9q1atcOrUKTO7zsIgKy0tDS1atEjfhzPzeCwLk9Nr1qyJokWLurW9/kQBlIhIbjh9GkhNBapV83RLJIfSbAFu2ZzBek2cEceNdu3aZX7mLDsGPDfddBNWrVplZtKlpqaanCVunFVHtWvXRvfu3TF48GCsWLECS5YswdChQ3HrrbeaGXh02223mQRy1odiuYOZM2di8uTJGDFiRC48iv5DQ3giIu62fTuwc6cSxuWSGBx1tKsHZgU1AwYMMLWafvjhB/N7o0aNHG63YMECdOjQwfzM4IpBU6dOnczsu969e2PKlCnp+zKJ/ddff8WQIUPQpEkTlChRAk8//bRKGLhIAZSIiLuH7YoVU/Dk5zlQOcUgiInhWcnuOgtn3M2YMSPbfRo0aIA///zTydZJdhRAiYi4C3NMTp3SbLt86nKWYsnsGOIfFECJiLjDxo0XZtx16uTplohIHlAAJSLiqk2bWJkQqFPH0y0RF3CwzOaGY4h/UAAlIuIKzoY6eJDToTzdEnHR5cyiy+wY4h8UQImIuJIwHhenYTsRP6QASkTkcuzYcWG2nRLGfYbNDbPwNITnP1RIU0TEWZxpx1pPpUt7uiXi9jIGri4mLP5CPVAiIs4O27E2j5ZnEfFrCqBERHKKi7aGhAA1ani6JZILbLYAs7l6DPEPGsITEcmJvXsBrlem4MnnK5G7uol/UA+UiEhOhu2KFNHyLD5Olcj/nzMLDU+cOBH+SAGUiEh24uOB8+c12078yt9//52j/QICfCNgvBwKoEREsrJ584WhO/U8+QVVIv9/CxYs8HQTvJ4CKBGRrIbtoqOBWrU83RLJI6pEnr3t27djx44daNeuHcLCwmCz2fy6B0pJ5CIiGaWkAEePAiVLerolIh53/PhxdOrUCTVq1EDPnj0Ry0WzAQwaNAgjR46Ev1IAJSKSsedpyRKgQwdPt0TymOtFNF1PQvdGw4cPR0hICPbu3Yvw8PD0y2+55RbMnTsX/kpDeCIili1bLgzbKWHcL7mjDIEvljH49ddf8csvv6BcuXIOl1evXh179uyBv1IPlIiINduOXwYathNxcO7cOYeeJ8uJEycQGhoKf6UASkSEw3YbN2q2nZ+zIcAtm69p27YtPv744/TfmTielpaG8ePHo2PHjvBXGsITEf/GMgUskqlhO7+XZruwuXoMX8NAiUnkq1atQlJSEh599FFs2LDB9EAtYb6gn1IPlIj4r8OHgU2bgAoVPN0SEa9Vr149bN26FVdeeSWuu+46M6R34403mmKbVatWhb9SD5SI+O+wXYECQLdunm6JeAmbG2bR+eIQXkJCAiIjI/Hkk09edF1sbCxKly4Nf6QeKBHxP6dOXfi/enVPt0S8iMoYZO6KK67AWi6kncE333yDBg0awF8pgBIR/7JjB7BihXKe5CI2m3s2X9OhQwe0bNkSL7/8svmdQ3h33HEH+vXrhyeeeAL+SkN4IuJfw3bFimm2nYgT3nzzTVx99dW46667MHv2bDNsV7hwYaxYscLkR/krBVAi4h+SkoDTp9XzJFlyxxCcLw7hUY8ePUzi+FtvvYXg4GD8+OOPfh08eXwIb9y4cWjWrBmKFCmC6OhoXH/99djCSsAZkteGDBmC4sWLm4i3d+/eOMyZMyIiOcUaT4sWAa1be7olkg8qkbu6+RouINyqVSvT+8SK5Cxj0KtXL/N/cnIy/JVHA6iFCxea4GjZsmWYN2+eeSK6du1qxlft1+BhpPvVV1+Z/Q8ePGiiYBGRHAdPMTFA586ebolIvtSoUSNUrlwZ//zzD7p06YIXXngBCxYswLfffovmzZvDX3l0CC/jIoQffvih6YlavXo12rVrh9OnT+P999/HjBkzcNVVV5l9PvjgA9SuXdsEXUxqExHJEs+OuXJ8nTqebonkAzZbgNlcPYYv5kAxYdxe69atTR2ohx56CP7Kq3KgGDBRMSZ5AiaQYq9UZ7szx1q1aqFChQpYunRppgFUYmKi2SxxcXF50nYR8cKEcX6mdOrk6ZZIPuGOpVh8sQ5UxuDJUqRIEdPJ4a+8JoDiujqMZNu0aZOemHbo0CEUKFAAUVFRDvvGxMSY67LKqxozZkyetFlEvNT27Rdm2ylhXOSy/PDDDyZxPCQkxPyclYCAAFx77bXwR14TQDEXav369Vi8eLFLxxk1ahRGjBjh0ANVvnx5N7RQRPIF9jqx1pMqjIuT3FHHyVfqQHFSFzsqrAle2QVQqamp8EdeEUANHTrUZPcvWrQI5cqVS7+8VKlSZuHCU6dOOfRCcRYer8tMaGio2UTET4ft+GGu4Ekug4bwHEeFMvtZvGQWns1mM8HTrFmz8Pvvv5ssf3tNmjQx3Yfz589Pv4xlDvbu3WumVIqIpOOwPte28+OlJUTET3qgOGzHGXbff/+9SUaz8pq4aGFYWJj5f9CgQWZIjonlERERGDZsmAmeNANPRNLt33+hXIEqjIsLNAsva+zImDRpEjZt2mR+r127tslbtp/k5W882gPFiqacecd1drias7XNnDkzfR8+Yddcc40poMnSBhy6Y+0JERGHYTsFT+IirYWXdRmD7t27m46OBx980GwRERHo2bMn3njjDfirABvH0S6haNGiJlEsJ06cOAFvwiRy9mQxUOMTLiI+hEV3t24FGjf2dEskH3+OW8e/d+ENCC0c4tKxEs8mY1r7WT71ncPc5Mcff9yk3Nh74403MHbsWBw4cAD+KEc9UK+99prpCeL21FNPmcu6deuGZ5991mz8mUaPHp27rRURsXDZpyVLFDxJvsbJUywDUKZMGdNR8d133zlczz6Op59+2ozOMLWFQ2bbtm27qOOib9++JmDjhCumvpw9e9Zhn3Xr1qFt27YoWLCgmZk+fvz4HLeRE7nYA5VR165d0+s3+qMcBVADBgxI35YsWYLnnnsOn3/+OR544AGz8WdexqVWRETyZNiuaFEN20mu5EC5ujmDS5c1bNgwy6EwBjpTpkzBtGnTsHz5chQqVMh0WnCdWAuDpw0bNpgl0awZ7XfffbdDDxuDnYoVK5oC1RMmTDCdH++8806O2sh17zjZK6Pvv//epNj4qxwN4dnjgr5r165FtWrVHC7fvn27WS8nY9TraRrCE/ExKSkXep7at/d0S8THhvDu+eNGFHBxCC/pbDLe7vDtZbWVPVAMVKy6S/x6Zs/UyJEj8fDDD5vLeFwWk+bSZ7feeqtJ6q5Tpw5WrlyJpk2bpi+Txvyk/fv3m9sz3/jJJ59ML05NHJJjb9fmzZsv2S6ufffKK6+YQtfWDPhly5aZDhW2zf5+slPFXzg9C6948eIm6uSDZo+X8ToRkVzteTp6FOjQwdMtEclWxmXELqdG4a5du0zQYz/TjYFeixYtzHJmDKD4P4ftrOCJuH9gYKDpsbrhhhvMPpyEZQVPxF6sl19+GSdPnjR5ztnhci3cZ+PGjWazREVFOSzlwgBQAVQ2uEzKXXfdhT/++MM8icQniRHvu+++mxttFBEBeKYcE6PlWSRflDHIuALGM888Y4bNnGGV9mGPU1bLmVnVwu0FBweb0j/2+2Sss2gdk9ddKoBiICduCKDuuOMOU/+BY7JWOQH+ziVYrIBKRMStmO+xbx9XE/d0S8SHubMS+b59+xyGtrRChu+5rEKaDJQ+++wz97dGRCSzYbv4eKBLF0+3RCTHGDy5mq9lLVnG5cs4C8/C35lzbO1z5MgRh9ulpKSYmXnW7fk/b2PP+j2rZdEklwppcuHAb775xiSWcWPSm78uJigiuWjPHn4TAc2aebol4ge8rZAmh90Y4NgvZ8bcKqbNWMnc/J9lBji7zsKl0bh+nTUqxH04My85OTl9H87Yq1mz5iWH78SNARRn2zHjv3///mYIj9vtt9+OunXrYgdXQBcRcQcmizPvKUMuiUhuD+G5ujmDM9c5s52blW/En7nmK5OyuVwKOyp++OEH/Pvvv+a7lzPrrJl6TKFhjabBgwdjxYoVZmYcC14ywZz70W233WYSyFkfiuUOuNrH5MmTzTJpkocBFDPsq1SpYsZ316xZYzY+0YyU/Sn7XkRyediOqxr8V6RXxFetWrUKjRs3NhsxqOHPLJ5Jjz76qFkDlnWdmjVrZgIuTtpiQUwLU2pq1aqFTp06mfIFV155pUONJ87c+/XXX01w1qRJEzOLnse3rxWVFQ4Hss4jSyKIi3WgWMSL9R/q16/vcPk///xjakSoDpSIuISBE2cP1anj6ZaIn9WBunP+zW6pAzW905c+9Z3DNfDY+1WpUiVPNyV/90BxJsGZM2cuupyBk32NCRERp+3cyVNyBU/iEWncbC5u8D1XXXWVVhpxxyw8lm1ntx+LZzVv3txcxoS2e++915R7FxG57GG7qCgtzyIew+EY18sY+J4ePXqYyuXsheIQIEei7Pnrd7/TARTrP3FNPGb1h4SEpI+R8gFkUpqIiNMSEwH2bKtIpojXuf/++83/EydOvOi6gIAAv52F73QAxdLtXLaFq0FzDR4+eJwFkHFtPBGRHNm0CWCCquo8iQ9VIvclLIkgbiqkSdWrV08PmhhEiYg4jetqcRmK2rU93RKR/4bwXD+GL0tISHCYAejPLquQ5scff2xm4YWFhZmtQYMG+OSTT9zfOhHxXSkpF2bblSjh6ZaISDY4RPf888+jbNmyKFy4MHZysgeA0aNHOywm7G+cDqA4BnrfffeZWhNffvml2VjEi0nkkyZNyp1WiojvJYwvW8bpPZ5uichFQ3iubr7mxRdfxIcffojx48c7zLavV68e3nvvPfgrp4fwpk6dirfeestUQ7UwgZyVyLnS9PDhw93dRhHxJdu2Xeh1UsK4+PBiwr6Eo04szMlCnewssTRs2BCbuVqAn3K6Byo2NhatW7e+6HJexutERLLEmXa7dgExMZ5uiYjk0IEDBzKdKJaWluawvp6/cTqA4oPIYbuMuLYOE8tFRLIctmPuhOo8iZfytsWEvQXXv/3zzz8vuvzrr79OX4LGHzk9hDdmzBjccsstZmVnLt1CXLyQq0VnFliJiODgQSAsDKhSxdMtEclSGgLM5uoxfA3XzWP9R/ZEsdfp22+/xZYtW8zQ3uzZs+GvnO6B6t27t6k8XqJECXz33Xdm489cBfqGG27InVaKSP514ACwfr2CJ/F6SiLP3HXXXYcff/wRv/32m6lCzoBq06ZN5rIufly/7bLqQLGU+6effur+1oiI7w3bhYdr2E4kn2vbti3mzZvn6Wbk/wCKXXjbt2/HkSNHLqpQ2q5dO3e1TUTye8I4E0wrV/Z0S0RyRIU0s7dq1SrT82TlRTVp0gT+zOkAatmyZbjtttuwZ88e2DJky/nzmjgikqFUARPGu3XzdEtEcs4dQ3A+OIS3f/9+9OnTx+Q7czk3OnXqlJl9/8UXX6BcuXLwR07nQLEGRNOmTbF+/XqcOHECJ0+eTN/4u4j4OQ7bRUQoeBLxEXfddZcpV8DeJ37Pc9u0aZMZgeJ1/srpHiguIsypi1o8WEQyXZ6FJ1Iqkin5kBYTztzChQvx119/oWbNmumX1axZ0xTWZm6Uv3K6B6pFixYm/0lE5KKFgVkrxo8/UMU3cqBc3XxN+fLlMy2YmZqaijJlysBf5agHat26dek/Dxs2DCNHjsShQ4fMgsIhISEO+3JhYRHxM1zOgdXF69TxdEtExM0mTJhgvvvfeOMNk8JjJZQ/+OCDeOWVV+CvAmwZM8EzERgYaBLEs9rVus4bk8jj4uIQGRmJ06dPI4J5GSLiXomJwKJFgB/Xg5H8/TluHb/3T/0RUuj/F8u9HMnnkvBNz4996junaNGiiI+PR0pKCoKDL/S7pPz3M+tC2fOnXOgc9UDt4tpVIiKZJYyfPavgSXyCKpFn7rXXXvN0E/JvAFWxYsXcb4mI5C88sYqMVMK4iI/jMi5ymQHUDz/8gB49eph8J/6cnV69euXkkCKSnx0/DmzdqlIF4lPcsRiwLy4mLC4EUNdff71JGo+OjjY/Z8Ubc6BEJBeG7QIDFTyJz7EhwGyuHkP8Q44CKPvlWjIu3SIifuTYMSAoCKhVy9MtEXE71YGSXK0DJSJ+avduYM0aBU8iIjntgZoyZUqOD/jAAw+40h4R8dZhOyaMd+3q6ZaI5BotJpzzsg+///67qUZeu3Zt+KscBVCTJk3K0cGYA6UASsTHJCQA585ptp34PA3hZe7mm29Gu3btMHToUJw/f94U09y9e7ep/8jFhHv37g1/pDpQIpJ9hfG9e9XzJOLHFi1ahCeffNL8PGvWLBM4nTp1Ch999BFeeOEFvw2gLjsHKikpCVu2bDHVSEXER9e2K1FCwZP43Sw8Vzdfw6rqxYoVMz/PnTvXBEzh4eG4+uqrsW3bNvgrpwMolnMfNGiQefDq1q2LvTw7/W+NvJdeeik32igieY0nRocPXwigRPxEms09my8uJrx06VKcO3fOBFBd/zupOnnyJAoWLAh/5XQANWrUKPzzzz/4448/HB64zp07Y+bMme5un4h4ImF86VKgY0dPt0REvMBDDz2Evn37oly5cihTpgw6dOiQPrRXv359+Ksc5UDZ++6770yg1LJlS5M0bmFv1I4dO9zdPhHJS6wuXrKkEsbFLymJPHP3338/WrRoYUacunTpgkAW0gVQpUoVvPjii/BXTvdAHT161FQkz4hde/YBlYjkM1wUmLWeMnl/i/gD5UBl7rnnnjPlCm644QYULlw4/fKrrroKv/32G/yV0wEUpy/OmTMn/XcraHrvvffQqlUr97ZORPJu2I7JoEoYF5EMxowZg7M8wcokJ3rMmDHwV04HUGPHjsUTTzyB++67z8zAmzx5skko++CDD5zuyuP46bXXXmvGVBmIcXjQ3h133GEut9+6d+/ubJNFJDv79wPh4UDjxp5uiYhXLCbs6uZrWLYgsxGmf/75J312nj9yOoC68sorsXbtWhM8MXns119/NUN6zNBv0qSJU8fisF/Dhg3xxhtvZLkPA6bY2Nj07fPPP3e2ySKSldjYC+UKKlf2dEtEPE5DeI6KFi1qAiQGTzVq1DA/W1tkZKTJh2KRTX/ldBL5+vXrUa9ePbz77rsXXccepOuvvz7Hx+rRo4fZshMaGopSpUo520wRycmwXViYhu1E/qMkckevvfaa6X268847zVAdgyZLgQIFUKlSJb9O3XG6B6pbt26ZVib/5ptvzDRHd2O5BPZwcc0dDhseP3482/0TExPNOj32m4hkwPdFaiqn0Xi6JSJ+LTU1FaNHj0blypURFhaGqlWr4vnnnzeBi4U/P/300yhdurTZh2WDMhawPHHihPkOjoiIQFRUlKnXmFnekjMGDBhgUmkWLFhgvn/5u7X16dPHr4Ony+qBuuuuu8yTt2TJkvSeIZY1YIT64YcfurVxHL678cYbzQuLJRKYe8UeKw4XBgUFZXqbcePG+XVSm8gl8YN3506eDXm6JSJeJ69TmF5++WW89dZbZlkUlgNatWoVBg4caHp7rLVlx48fjylTpph9+H3IgIudGRs3bkyvx8jgiWku8+bNQ3JysjnG3XffjRkzZrjcxvbt2yMtLQ1bt27FkSNHzM/2uE6ePwqw2Ye5OcSq44xImQTOqqQMqj755BOX1sPhGCvX2MluCHDnzp0mOue0yU6dOmXZA8XNwh4oVlFlKXpG5iLw92G74sUBDYtLPsLPcQYUufU5bh2/07f3ILhQqEvHSjmXiPk3vp3jtl5zzTWIiYnB+++/n34Zv0vZ0/Tpp5+a3idOtBo5ciQefvhhcz2Pzduw0+LWW2/Fpk2bUKdOHaxcudLMlCd+N/fs2RP79+83t3fFsmXLcNttt2HPnj0OPWPWdzd70fzRZa2FN3XqVJP8zWKagwcPNondebGYIIt2lShRAtu3b882Z4ovWvtNRAAkJ3PtBQVPInkgYyqJ/Ym9vdatW2P+/Pmmd8ea2bZ48eL0/GCmzBw6dMiM/FgY7LGwJUdjiP9z2M4Knoj7s+Dl8uXLXb4v9957rzk2c6A5VMglXE7+t/F3f5WjIbwffvjhoss4tPbnn3+acVBGoNY+vXr1Qm5hJM0cKI4Di4gTONOOM+6y6LkVESANAWZz9RjEkQ97zzzzDJ599tmL9n/88cdNgFWrVi2TmsLeHJYEsnKKGTwRe5zs8XfrOv6fscB1cHCwmS1n7eMK5lt9/fXXqFatmsvH8rsAKrthtenTp5vtcrrymOBm35vESJslEqxpksxlYs8Wc62YA/Xoo4+aJ5BjvyKSQ5s28dMWqFPH0y0R8WruqONk3X7fvn0OIyAcHcnMl19+ic8++8zkKjEHit+BXHuOw25M1vYG7O3id7UCqMsIoDImjLkLk+U62i1YOmLECPM/XzRMqlu3bp1Jmjt16pR5MbFgJ2cnZPVCFJEMOGxw4ABQu7anWyLiV3KaQvLII4+YXijmMhHrKzLXiBOi+F1oTdY6fPiww+gLf2/UqJH5mfswudseazVyeM0dZYCY98wcLPZmsX0hISEO1zdo0AD+yOlZeO7EFZ2zy2H/5Zdf8rQ9Ij6XMM5yBXa5EyLiXXWguByKtTivhUN5VscFZ90xCGKelBUwcciPuU0sLUAsJ8COhtWrV6cXtP7999/NMdh75Corx5mz7S0BAQHpFcr9NYk8RwEUp09yOiSnS/Ln7FjTLkXEg1imoGhRoG5dT7dEJN9wRyVxZ2/P5cyY81ShQgUzhPf3339j4sSJ6cEKAxQO6b3wwguoXr16ehkDjspY6TVc6Jdlfzipa9q0aaaMwdChQ02vlqsz8Ciz2o+SwzIGfMI43Fa8eHHzc5YHCwgwpQb8afqriNfhrJiVK1XnSXxGXpUxaPf1/W4pY7Dopjdz3NYzZ86YgIhlfDgMx4CHk7NYOJPVvolf00xCf+edd0xPE5dUe/PNN83yKhYO1zFo+vHHH02PFnuN2OFRuHBhl+6PuLkOVH6iAEr8btiO1PMkPiSvAqi2DKDCXQyg4hPxpxMBlLfizHqWUmC+U2Yz8e3l5ux7v8iBYs8Ta0VwcWER8QAmkTK50+6sVES8ewjPW3F40CqPkN1M/ADlQLmO3ZBMchMRD9izB9iyRQsDi7hAiwlnPvs+t2bi+2UlchHxsmE7zuJR8CQikmcUQInkZ+fPX9gyVD0WEeeluWnzRQsXLjQzBllMk1uvXr3MaiT+TAGUSH61eTPADzC79a9ExPUhPFc3X8NFjbm2Xnh4uClVxC0sLAydOnUyFdT9VY5zoBo3bmySxbIrBiYieThsx7WvatXydEtExMexTtX48eMxfPjw9MseeOABU6+Kq4Pcdttt8Ec5DqCyy8IXkTyUkgIcPapSBSJupll4Wc+y5/BdRr169cITTzwBf5XjAIpFvETEC3qejh3jOkiebomIz3HnYsK+pHz58maWfcbFhH/77Tdznb/y6Fp4IuIElikoWVI9TyKSp7iQMIfs1q5di9atW5vLlixZgg8//BCTJ0+Gv1IAJZIfMMeQtZ5q1vR0S0R8lobwMsdFi7mg8auvvoovv/wyff29mTNn4rrrroO/UgAlkh+G7RISVOdJJJepkGbWbrjhBrPJ/1MAJeLN9u0DuBiohu1ExMNWrVqFTZs2mZ/r1KmDJk2awJ8pgBLxVocPA/ywUs+TSJ5QEnnm9u/fjz59+pi8p6ioKHPZqVOnTD7UF198gXLlysEfOR1ATZkyJdPLWSOqYMGCJku/Xbt2CAoKckf7RPx32K5AAQVPInmI+UtpyoG6yF133YXk5GTT+1TzvzzMLVu2YODAgea6uXPnwh85HUBNmjQJR48eNYUzixYtai47efKkqVBauHBhHDlyBFWqVMGCBQv8enqjyGU7derCaWz16p5uiYhfUQ5U1su4/PXXX+nBE9WsWRNTp05F27Zt4a+cXspl7NixaNasGbZt24bjx4+bbevWrWjRooWZzrh3716TrW9fsVREcmj7dmD5cqBePU+3RETEYGcIe6AySk1NRZkyZeCvnA6gnnrqKdMLVbVq1fTLOGz3yiuvYNSoUWYslCXfOVYqIk4O2xUqBHTr5umWiPglm5s2XzNhwgQMGzbMJJFbVq1ahQcffNB89/srp4fwYmNjkcKlJDLgZYcOHTI/MyI9c+aMe1oo4g94dnf6tGbbiXiQhvAyd8cdd5i0HY40BQcHp3/nBwcH48477zSb5cSJE/AXTgdQHTt2xD333IP33nvPLDBMf//9tym0ddVVV5nf//33X1SuXNn9rRXxRZxpd/Ag0KmTp1siInKR1157zdNN8I0A6v3330e/fv1M/YeQkJD0SLRTp07mOmIyOSuWikgOgqfoaJb19XRLRPyeKpFnbsCAAZ5ugm8EUEwQnzdvHjZv3mySx61sfPvsfPZSiUgOhu3Y86TgScQrqA6U5EkhzVq1aqUHTawBJSJOJowz50nDdiIi/jELjz7++GPUr18fYWFhZmvQoAE++eQT97dOxFdLFRQrBvy3qrmIeNcQnqub+AenA6iJEyeahPGePXuaVZm5de/eHffee68pbyAilyiSuWMHULq0p1siIlnMwnN18wXr1q1DWlqap5vhW0N4rDz61ltvoX///umX9erVC3Xr1sWzzz6rApoi2Q3bMUFCdZ5ExMtxlj3LFkVHR5vVRVauXInixYt7uln5uweKDygXEMyIl/E6EcliYWCubacK4yJeK83mns0XcNHgXbt2mZ93796t3ih39ECx6jiH7Z544gmHy2fOnInqWrtL5GL79l0oV6CFgUW8msoY/L/evXujffv2KF26tJko1rRpUwQFBWW6786dO+GPnA6gxowZg1tuuQWLFi1CmzZtzGVctmX+/PkmsBKRDMN2hQsreBLJB1SJ/P+98847uPHGG7F9+3Y88MADGDx4MIoUKeLpZuXvAIpR6fLly03C+HfffWcuq127NlasWJFemVxEAMTHAwkJWp5FRPIlThCj1atXm3XvFEC5oQ4Uq5B/+umnDpcdOXIEY8eOvWhoT8QvbdkC7NmjnieRfMQdiwH7SAqUgw8++CD95/3795v/y5UrB393WXWgMsME8tGjR7vrcCL5e9iuaFEFTyL5shK5q2UM4HOYQP7cc88hMjISFStWNFtUVBSef/55v04uv+xK5CKSiZQU4NgxDduJiM948sknzVq3L730Unru8+LFi03pooSEBLz44ovwRwqgRNzZ83T0KNChg6dbIiKXQbPwMvfRRx/hvffeMzUfLQ0aNEDZsmVx//33K4ASERds3gzExKjnSSQf02LCmTtx4oRZ/zajWrVqmev8VY4DqBEjRmR7/VGeeYv4o/Pngb17+Wni6ZaIiLhdw4YN8frrr2PKlCkOl7/++uvmOn+V4wDq77//vuQ+7dq1c7U9Ivlv2I7lCpQwLpLvcfgtTUN4Fxk/fjyuvvpq/Pbbb2jVqpW5bOnSpdi3bx9++ukn+KscB1ALFizI3ZaI5DcsU8C6KBq2E/EJKqSZOVYk37p1K9544w1sZroCYIpsMv+pTJky8FfKgRK5HEeOXMh70sLAIuIHGCj5a7J4rteBEvGrYbtTpxQ8ifhoIU1XN2cdOHAAt99+O4oXL46wsDDUr18fq1at+v922Wx4+umnzbp0vL5z587Ytm2bwzGYzN23b19ERESYGk2DBg3C2bNn3fCoSFYUQIk44+TJC//XqOHploiIm7leRNP5IcCTJ0+a2kohISH4+eefsXHjRrz66qsoymK8djlITOCeNm2aWUqtUKFC6Natm6nBZGHwtGHDBsybNw+zZ88269Xefffdbn18xJGG8ERyiiuOb9+uhHERX8Wi2q4W1nby9i+//DLKly/vsFxK5cqVHXqfXnvtNTz11FO47rrrzGUff/wxYmJizHq0t956KzZt2oS5c+di5cqVaNq0qdln6tSp6NmzJ1555RW/zlPKTeqBEsnpsF3BggqeRCRH4uLiHLbExMRM9/vhhx9M0PO///0P0dHRaNy4Md59993063ft2oVDhw6ZYTsLl1Rp0aKFmQlH/J/DdlbwRNw/MDDQ9Fi5ggHc3r17HXq7xIUA6s8//zTjtZzOyLFb+uSTT0xpdxGfww++uDhmUXq6JSKSiwJsAQhIc3H7bwiPvUoMdKxt3Lhxmf7NnTt34q233kL16tXxyy+/4L777sMDDzxgqn8Tgydij5M9/m5dx/8ZfNkLDg5GsWLF0vdxJYCqVq2aKVkgLgZQ33zzjRl7ZSIba0NZUfXp06cxduxYp47FMdprr73WdC8GBASY7kh7OUmcE8lVGzfyjAH4r/aJiPjBEJ6rG2ACDn4vWtuoUaMy/5NpabjiiivM9yd7n5i3NHjwYJPv5A3Yi8Xg7vjx455uSv4PoF544QXzxLKLkUlvFibBrVmzxqljnTt3zlQxZW2JzOQkcU4kV4MnnvXZdZ2LiOQEZ8PZb6GhoZnuxw6COnXqOFxWu3ZtM2xGpUqVMv8fPnzYYR/+bl3H/4+wtIqdlJQUMzPP2scVXET4kUcewfr1610+ll8nkW/ZsiXTiuPsojzFqd1O6NGjh9kyk5PEOZFck5wMxMYCGT7YRMSHcfjN1UKYTt6enQ/8XrXHopUVK1ZMTyhnEDR//nw0atTIXMacKnYqcLiPmE7D79/Vq1ejSZMm5rLff//d9G4xV8pV/fv3R3x8vOnwKFCggBkRsuev6+E5HUDxidy+fTsqVarkcDnzn6pUqeK2hl0qcS6rAIpDivbJenyhiVxWnadOnTzdEhHJQwFpFzZXj+GM4cOHo3Xr1mYI7+abb8aKFSvwzjvvmM0cLyAADz30kBn94VAaA6rRo0eb1Jfrr78+vceqe/fu6UN/ycnJGDp0qPmedMcMPHZmiBsCKD5BDz74IKZPn26e2IMHD5qA5uGHHzZPqrvkJHEuM0zUGzNmjNvaIX6GOXbFi2t5FhHJE82aNcOsWbNMjtRzzz1nAiQGLKzrZHn00UdNygvzo9jTdOWVV5qyBQU5M/g/n332mQmaOnXqZPKWevfufdHiv5drwIABbjkO/D2Aevzxx023IJ8kdulxOI9juwyghg0bBk/ji3DEiBEOPVCcDSFySeytZK0nVRgX8U8eqANF11xzjdmyws4KBlfcssIZdzNmzEBu2bFjh6lVxf8nT55sZv2x8GeFChVQ109POJ1OIucT+eSTT5oxTyaULVu2DEePHsXzzz/v1oblJHEuMwzmMibvieRo2G73bgVPIv4sLcA9m49ZuHChWV6GeVfffvtt+hIx//zzD5555hn4q8supMlEMs4caN68OQoXLuzeVmVInLNYiXNMmBNxGyaLsyu8QQNPt0RExOtw5Ik5WFwmht/9lquuusp0ovgrp4fwOnbsaHqhssLM/5xiFMuEdPvE8bVr15quSHYLXipxTsRl+/dfKFegCuMifs8TSeT5wb///pvp8GB0dDSOHTsGf+V0AGVNo7Qw259BD4fznE0042rTDMgsVu4Sj/Phhx/mKHFOxKVhu0KFFDyJyAXuGILzwSE8LhMTGxvrsEYfsZh22bJl4a+cDqAmTZqU6eXPPvts+rhoTnXo0MHUe3IlcU7ksvC1mpSk2XYi8v9sXM7F9WP4GpZDeOyxx/DVV1+Z72VOJFuyZImZPMYaUf7KbYsJc208ljYQ8XpbtwJLlgCNG3u6JSIiXo81qmrVqmVmtLOjhPnP7dq1M/WrWOzaXzndA5UV1oLS0Jrki2G7kiU1205EvKaMgbdj4jiXb2MeMtN1GEQ1btzY5Cf7M6cDqBtvvNHhdw7BcWyU+UzuLKQp4nYpKQAXxNSwnYhktQyLqzlMri4F48U4ucuqqxiQzWQyf+H0EB6XU7HfOGOOuUw//fSTX9eDEC/HmXZ//glkso6jiIhk7/3330e9evXMSBO3evXq4b333oM/c6oHKjU1FQMHDjQFtYoWLZp7rRJxp82bOd9WCwOLSLZUxiBzTz/9NCZOnGhWG7HqMC5dutSs47d3716/nejlVAAVFBSErl27YtOmTQqgJH9ISAD27QNq1fJ0S0TE2ykHKlNvvfWWyYHq06dP+mW9evVCgwYNTFDlrwGU00N47LbbyfXCRPJDwvg//wBduni6JSIi+RbrPTZt2vSiy5s0aYIU5pb6KacDKFYGZ+2H2bNnm+RxLq9iv4l4Ba5rFxkJtGjh6ZaISH6htfAy1a9fP9MLldE777yDvn37wl/leAiPXXQjR45Ez54907vv7LPwORuPvzNPSsSjuLTAli0qVSAiTglwQyFNlwtxeglrZRDidzsTxn/99Ve0bNnSXLZ8+XKT/+TPhTRzHECNGTMG9957LxYsWJC7LRJxddguKEjBk4iIC7hMS8bhOtqxY4f5v0SJEmbbwM9cP5XjAMpacqV9+/a52R6Ry8caT4GBShgXkcujtfDSqbPEzTlQKpwlXmvXLmD1aqB2bU+3RETy+yw8VzfxC06VMahRo8Ylg6gTJ0642iYR57ALmQnjXbt6uiUiko+pDlTmEhISMHXqVNMrdeTIEbOYsL01a9bAHzkVQDEPitXHRbxGYiJw9qyWZxERySWDBg0yCeQ33XQTmjdvrtGoywmgbr31VkSzorOIN9i0Cdi/X3WeRMQ9lAOVKZYt4nJtbdq08XRT8mcApYhTvG5tOwbzynkSEXdRJfJMlS1bFkWKFPF0M/JvErk1C0/E41j59vBhzqP1dEtERHzeq6++isceewx79uzxdFPyZw9UxqQxEY8ljHOiQseOnm6JiPiYAFsAAlwcguMxfA2XcWEieZUqVRAeHo6QkBCH6/118phTOVAiHrV164VeJyWMi0hu4ECLq4MtPjhYw0WEDxw4gLFjxyImJkYpPf9RACX5w5kzF2o9qcK4iEie+uuvv7B06VI0bNjQ003xKgqgJH8M2yUnK3gSkdylJPJM1apVC+fPn/d0M/J3JXKRPHfgABAWBjRq5OmWiIiPY/6TOzZf89JLL2HkyJH4448/cPz4ccTFxTls/ko9UOK9Dh680PukCuMiIh7TvXt383+nTp0ump0fEBCA1NRU+CMFUOKdGDix50nBk4jkFQ3hZUoLC2dOAZR4Z8I4az1VqeLploiIH9FaeJlr3769p5vglRRAiXfZtg3YuVMJ4yKS5wLSbGZz9Ri+ZtGiRdle365dO/gjBVDiXcN2rPOk4ElExGt06NDhossC7GpB+WsOlGbhiXfgkB2r2cbEeLolIuLnQ3iubr7m5MmTDtuRI0cwd+5cNGvWDL/++iv8lXqgxDsWBj50CLjqKk+3RET8mHKgMhcZGXnRZV26dEGBAgUwYsQIrF69Gv5IAZR41ubNF3qd6tTxdEtERMQJMTEx2LJlC/yVAijxnMREYP9+lrn1dEtERBBgc0MPlO/lkGPdunUX1X+KjY01BTYb+XGRYwVQ4rmEcZYr6NzZ0y0RETECUm1mc/UYvoZBEpPGGTjZa9myJaZPnw5/pSRyyXssUxAVxXefp1siIuI12KPDQOWhhx5KvywhIQFDhgxB8eLFUbhwYfTu3RuHDx92uN3evXtx9dVXIzw8HNHR0XjkkUeQwok5brJr1y7s3LnT/M9tz549iI+PN4sMc508f6UeKMlbx49fqPWkUgUi4m08WIl85cqVePvtt9GgQQOHy4cPH445c+bgq6++MsncQ4cOxY033oglS5aklxBg8FSqVCkT0HBorX///ggJCcHYsWPhDhUrVnTLcXyNAijJ22E71g5R8CQiXshTs/DOnj2Lvn374t1338ULL7yQfvnp06fx/vvvY8aMGbjqv1nKH3zwAWrXro1ly5aZITSWEdi4cSN+++03k9TN4bbnn38ejz32GJ599lkzU84d5s+fbzaWMEhLc7yT/jqMpyE8yRtHjwLBwZptJyJ+IS4uzmFL5KSZLHCIjr1InTPkhLI8QHJyssPlHDKrUKECli5dan7n//Xr1zfBk6Vbt27mb27gSasbjBkzBl27djUB1LFjxy6qC+Wv1AMluW/PHoBTXbUwsIj4SQ9U+fLlHS5/5plnTI9QRl988QXWrFljhvAyOnTokOlBimLOqB0GS7zO2sc+eLKut65zh2nTpuHDDz9Ev3793HI8X6EASnIXz4AiIhQ8iYhfBVD79u1DBD/7/hMaGnrRvtznwQcfxLx581CwYEF4q6SkJLRu3drTzfA6GsKT3HP+PHDuHE/FPN0SEZEcLybs6kYMnuy3zAIoDtExp+iKK65AcHCw2RYuXIgpU6aYn9mTxODl1KlTDrfjLDwmjRP/zzgrz/rd2sdVd911l8nDEkfqgZLcqzC+d696nkREstCpUyf8+++/DpcNHDjQ5DkxCZzDgJxNx9wjli8gVv5m2YJWrVqZ3/n/iy++aAIxljAg9mgxaKvjppxTllJ45513TKI6ZwmyTfYmTpwIf6QASnJn2I5j8H5cH0RE8p+8noVXpEgR1KtXz+GyQoUKmZpP1uWDBg0y680VK1bMBEXDhg0zQRNn4BGTuxkoMT9p/PjxJu/pqaeeMonpmfV6XW4lcqvi+Pr16x2uC+DMaj+lAErci8XbOOOubl1Pt0REJN8vJjxp0iQEBgaaHijO5OMMuzfffDP9+qCgIMyePRv33XefCawYgA0YMADPPfec29qwYMECtx3LlwTYMtZm9zGcysniY6ynYZ/QJ7nU88RCme3aebolIuJDcvtz3Dp+k5ufR1AB15K5U5MSsPrL0frO8QPqgRL3YJmCkiXV8yQi+ZcbFhPmMcQ/KIAS13Gm3e7dQM2anm6JiMjl4wy6/2bRuXQM8QsKoMT1YTtW2NXyLCIi4ke8ug4Uq7Yyw99+8+eVn73O/v2cMgJccYWnWyIi4rYkclc38Q9e3wNVt25dU3vCwuJi4gW4RMDGjarzJCI+wxtn4Yn38vpohAGTu6qpihuH7VhfRMGTiIj4Ka8ewqNt27ahTJkyqFKlCvr27WsqsGaHdTIyroItbnT6NJCaClSr5umWiIi4lYbwxGcCqBYtWpgVoOfOnYu33noLu3btQtu2bXHmzJksbzNu3DhTz8PaMq6ILS7Yvh1Ytgxo0MDTLRERH8OShNZGKWdTELc8Did/P4nTK07nSRsCUm1u2cQ/5KtCmlxQsWLFimbdHZa3z6oHipuFPVAMolTUzA3DdsWKAaVLe7olIuKDvp74I/6cuQwP33MvwkuHIyAkAGHVwhBWKSzPCmm27D4GwSGuFdJMSU7AsrnP6DvHD3h9DpS9qKgo1KhRA9vZE5IFrv3jrvV/5D/JyYxeVSRTRNzKlmrD6cWnEVw0GKV3lkSrBk0Q/b9ohBbRZ7h4v3wVQJ09exY7duwwiyZKHuFMu9hYLhvu6ZaISD6WlpqG08fOIDghECEBITg26xiK9SiGoIggFKpfCG1ev7A4ridpFp74TA7Uww8/jIULF2L37t3466+/cMMNN5iFE/v06ePppvmHTZuAmBgFTyLikoS9CdgxfgdGVB+NBaMWI6R4CMo9WA7hNcJRpHERU+PPK6S5aRO/4NU9UPv37zfB0vHjx1GyZElceeWVWLZsmflZcllSEnDwIFC7tqdbIiL5TOr5VJz95yxsKTacW3cO0X2iUWFoBQxtdhfKVC2FoEJBnm6iiG8HUF988YWnm+C/CeMsV6CeJxHJobTENByfcxwBQQEIigxCaNlQhFcPR9SVUen7NOns3TN4A2w2BLi4lh2PIf7BqwMo8YAdOy7MtlPCuIhkgxO445bFwRZoM71NhWsXRvFriyMwxKszQ7KlHChxRv59pYv7caYdZziqVIGIZCL5VDISDyZi32v7EL85HgHBAfjmuzl47s3XENY0PF8HTyLOUg+U/P+wHXXr5umWiIiHxR0/gxljv0Xb3i1RrXIlHPvuGKI6RJkep5I3lkT5h/6/QHHT+EYoU700gkPyf16TeqDEGQqgBDhyBChQAKhe3dMtEREPSU5KwcEdhxBVNBKnlp/Entl7UftEFdR4tQpKDSyFoIJBKFS70EW3a9i+rtl8AvOXXM1hUg6U31AA5e+4tuDmzVoYWMTPE8B3fbobb97zAbqPvApt+7fE2M1Pmeu8psSAiJdRAOXvw3aFCyt4EvEjh/ccxZibXkXfPjegbOXSmDlyFlrf0RzNR1yBYU0Ho2S54ihU/OKeJr/AGXguzsJz+faSbyiA8lfx8cD585ptJ+IHzp46h4WfLgMSbQhYkoYGVWqjcEQhRLaIQFiPcBTrVBShhUNRtWEl+DUN4YkTFED5Iw7ZcehOPU8iPrdcyrm48/wWR2BQIEISg00CeEK5JCx7ciW2Be9BsWpRmLL4hfSk7wfeGOzpZnsPBVDiBAVQ/jhsFx0N1Krl6ZaIiJvt23AAL7SYhCRbEhqUqI37V96JmNtiEBgWiNGHR1zYKSAAQcEqNyDiKgVQ/iQlBTh2TMN2Il4sdudhvPvYp7jl0etQs1m1S+5/ePsRfHnX92jVrinKty+Lfi/chIBKgQgrEoaCMQUREHghCbxAwQJ50Pp8TjlQ4gQFUP7U88TgqX17T7dEROzsXLcHKUkpqN6kipnxlpqSirgTZ0xZAcuu9fuQlJCEGk2qYM+GfTi77izKliuN5NhkxJ07g31Bh9C2YwhiOkYjplO0R+9PvqYhPHGCAih/yXnisJ16nkS8zvSnPsfpI6cxZelY83vZ6qUx/ten03Oa6NNnvsaxXSfwcP+7MWv2HBzZexzPr3scwQWDEY1ojL9ztEfvg4g/UgDlD7PtmDCunCcRr/TI9PuRlvr/vRa71+/DE9eOQ4HUYFSOL4fDBY/jpu5Xo/5HdVGyXnEM6ne76aUKCs3/lb+9cjFhF3uQtJiw/1AA5evDdixVoNl2Il4rskSE+d+WasNvL/+Fw8eP45Y61yC+fiJsBYBqBauheu9qiK5VwuwXUbyIW5ds+W7qz2jVqymqX1HFbcfNt5QDJU5QAOWr2OtUpIiG7US80InDp5B4Pgm2pDQE7w5C6r4UFGpYCNs27MOBoGN4fs79eVIBPP7Mefz++WJUrFtOAZSIkxRA+eradlqeRcQrndt8Du8P/gK7V+wFUlLR79ub0XJQE3PdvZ/enKfLp8RULIkPNk0GtFrLBew8cjmJ3F2NEW+nAMoXh+24MLCCJxGPzqybfP87uH/SQIQEhuDNIR+gT9vrUO36KggICcBNr1+NeA6vp9lQoXbZ9Nvl9bpz5u8peLLjhll4iqD8hgIoX3Lq1IX/q1f3dEtE/Nbfv/+Lf3/bjKAlAVj2/t+IPl0UpcvEoGi/YohsEGn2icCFvCcRyb8UQPmKHTuA7duBbt083RIRn8W8JXbYFAgrgMSEpP/KDATAlpaG8/+cR1jJgljxwBr8sWcpkgslY+s3B1G7ZQ2M/+GxPO9dksugOlDiBAVQvjJsV6yYgieRXGSz2fDsjRMQGBSAF34chef+9xqOLD6G6LQSiA04hJp1quCRP+/HHStuRb/UC7lMxDXpJJ/gDLoAzcKTnFEAld8lJQGnT2u2nXgl1it6/8nPUbF2WXS7oyPyu6CTgYjfmYB3e30B2/JUpJUH2j7aEgFpNhSNjjDLpoSGh3q6mXK5bGkXNlePIX5BAVR+tmkTcPAg0KmTp1sikilbmg0HtsWicGS487e12bDr370oGB6KMtVKIS+cPXUO+7ceNAvumjYkp8EWD6QtT0WZljEoX64sNgbuwD8J2xFQJwX1G9RCt75tNTwn4ocUQOVXGzcCMTFA7dqebolIloILBGPMt49c1m2ZX/Rin0mo2qgSnvjsIZfawWCMMgt0rOto07KtGN3jZdQKroFDAUdRKbkUthc8hCpdKmH8k4/hnk79XWqHeDnlQIkTFEDlR8nJQGwsUKeOp1sikmuYOzT2pycREhrieu5S7wkoGh2Jh6bdc9H1/yzdig/unYXUlASUOl4c4dFlcf34a1C9TYULX4YclgvTsJxfUA6UOEEBVH5MGGe5Ag3bySUkJSRjzrvzUK1RJdRvm3mwnZyUjDnv/IZK9SqgUQfvyqNjbxELPVpDgXM/XICoEkXQqlczp4/VqGM9nDp2BjMnzYGNnVCBwQg+GYoCW2yIr5KMpk1qI6FsAhAcgO6h5VGrU1WUKlc8F+6ViKNx48bh22+/xebNmxEWFobWrVvj5ZdfRs2aNdP3SUhIwMiRI/HFF18gMTER3bp1w5tvvokYjkL8Z+/evbjvvvuwYMECFC5cGAMGDDDHDg7W13xu0SObn7BMAWfbKWFcciA5MRmz356HLv3aZxlApSSl4qd3f0O7m1p5XQCVsRfpt08WokKtsjkKoLh/3LEzSGGZgcBAtLu5DVZ/+S++fvxnhEWEIiIkEluqxcOWloy6JSpi2NheZo25kAL6SPRrHhjCW7hwIYYMGYJmzZohJSUFTzzxBLp27YqNGzeiUKFCZp/hw4djzpw5+OqrrxAZGYmhQ4fixhtvxJIlS8z1qampuPrqq1GqVCn89ddfiI2NRf/+/RESEoKxY8e6dn8kSwE2+wQAHxQXF2decKdPn0ZERD4uXseZdsuWqVSB5Bjf2swj4sywwMDAy97HG5h2ptlMDaaclAXgvsOaP479206hauHaOJd6FvGBiTgWloBXvxmCmvXKpyeKr/plLZ6+/mVMWvQ86rSskQf3Rrztc9w6fts6DyI4yLXh2pTURPy5cfJlt/Xo0aOIjo42gVW7du3McUqWLIkZM2bgpptuMvuwt6p27dpYunQpWrZsiZ9//hnXXHMNDh48mN4rNW3aNDz22GPmeAW4OoW4nU638suwXWqqgidxeggsKDjI5X28JYCacv87OLznmMPlDPweeGMwjh09g0+n/mwW0I7ZVQjniiQgal8ZRFxRBjc+3x0Baf9NLQ8MQMWqMQ73uVbzanhx9hOmd0vEnUGZvdDQULNdCgMmKsbRBgCrV69GcnIyOnfunL5PrVq1UKFChfQAiv/Xr1/fYUiPw3wc0tuwYQMaN27sxnsmFgVQ3u7QIb7zgGrVPN0SEbdJS0vD2t/Xo0ixwqh+RZVs9z26/zi2rNqBlXP/wZF9xy6s3RYUBAQHISC0AJZ/vhGn4s4icHYytkZtx6GYQihfswwCrw9HqegIJB+PQ4P2dVA46sJwSEaRJSLQtGvDXLqn4q9J5OXLl3e4+JlnnsGzzz6b/U3T0vDQQw+hTZs2qFevnrns0KFDpgcpKirKYV8GS7zO2sc+eLKut66T3KEAypvt33+hXIEWBhYv6glKTU41PT+u9Fxx2PCNB6ejTquaGPnefdnu++/iTRh7+5QLa7QGBiAgIAjFQkuialBl7Cp9HnPeXYU9BQ8joFgiAo6cRMNmjfD0axfKDSz9cRWeu/lVTFn8Iqo3yT5QE3FnDtS+ffschvBy0vvEXKj169dj8eLFrrVB8oQCKG8etmMCoYIn8bKZfUNbjELzHo0x+OXbL/s4DL4mL34RQSGXDsJshcIQXq4ySqEkEsIDUByRWB/3N2p0CMZbUx7AyI7PoEWVGDw8/X5TEZy1pyxNuzXCzP3voNBlFPIUcQWDJ2dyoJgYPnv2bCxatAjlypVLv5yJ4UlJSTh16pRDL9Thw4fNddY+K1ascDger7euk9yhAMobnTsHJCZqtp1katns1Vi/ZDP6jb4pz5cNCQ4JQu/h1+DYgeOYNvIj9H2qN4oULZzpvhv+2oJFXy9F3yd7mxluGXOvChcthM3Lt+G3zxbZXwNbRCGkRhVEamQRlFiahqPHT6NwkZJo2b8hjp48jl9mLsZt93dGeIEgfPrMTJw6eBIVqpdG5H/t2LluD35+fz5ufuQ6lCxXHCEZ/rZIVgJsNrO5egxne3WHDRuGWbNm4Y8//kDlypUdrm/SpImZTTd//nz07t3bXLZlyxZTtqBVq1bmd/7/4osv4siRIyYBnebNm2cCuDqqF5hrFEB5my1bgN27lTAuWYrdeRjr/9yE1JS8X3OLQ3d1W9fE8jmrseS7Fbj5kV5Z7nv84An888cG3DTiWpw5eRYnYk+iTLXSplQAvzQObD+ENX9swHfvzoeN+UyBgaiRWgFxhdJQvGA0dtRKxZmzpxCYDAQWCULNRjGI2R2A7YVD0eN/LRG76zCmfbQARUtFIrp8ifS/e/poHNYuWI9r71XvrXh/IU0O23GG3ffff48iRYqk5yxxViDrQvH/QYMGYcSIESaxnEERAy4GTUwgJ5Y9YKDUr18/jB8/3hzjqaeeMsfOydChXB6VMfC2YbuSJYH/ziBEnF2WJLclxifijloPoPV1zTB0yqBLtoFt5T4/vTcfU4a8i+kbX0OZqqWQkpSCQfWGI/4QkBodherBVbC5whnER9iQvHkvgo6dQcCx08yq/f+DBQAdb2mDJz57MP3v2n982bfF+rviG/KqjEG7qve7pYzBoh1v5ritWb1OP/jgA9xxxx0OhTQ///xzh0Ka9sNze/bsMbPu2IvF+lEspPnSSy+pkGYuUgDlLVJSgL/+Atq183RLJB+Lj4vHk9e+hI63tkGv+9zfi8lZQvs2H0ShyDCUKHvpSt0MlJ6+cQLKViuFrgM6YOrsFUjdkYhKGwpiUdmDKLY7FUdit2H0lw+gZMUSsAUCQedT+C0EsAhmho8nzqSzqpOL//DlAEryL4Wm3tLzdPQo0KGDp1si+VxgcBBqt6juMKTlrB1rd2Pzyu3odFtbFCwUambdzZ/xJ8pUjUHtljWwZdV2lK4cjaIxUfh9xmKcP5tgiltedduVOLL3GNYv3oyr+lyJI8fPYM2Kndi9Ox6FzgLrqhxFkVnnsTR4OzadPIv4wiEoVLMYena9ErXrlEPRkvqyEQ/TYsLiBAVQnrZ5Mwt2KGFc3KJgeCjuHt/PpWMwQf2Ll2ah1bVNTQCVkpyCmeO/Q+tezUzNpg+e+hzNujU2+UxfjP8Oh3cfQWpqGmp1rI01f/6L95+cgWqNq2PLV/sxf9bfCC1bBquLx2P+5uUISNyFAkfOIywxEZEn09Dsxiq4Y9T1KBQRbpaeSYhPND/npNq4iNsx+HF1MWAFUH5DQ3ielJAALFqkUgXiVVJTUk2dJpYDYH4GPyI4FMeghv8zB+pE7CkULFwQb60abxLK3xj5IaJv6YCz5VJR4kwQjlZIwNEDBxH1+Um8u/RFxFQsgZTkVNzTcCTqX1kbD0272/ytBTMW4/UH3se0NROwecV2TLrnbby1erwphCmS50N4le9FcKCLQ3hpiVi0a5p3fueIW+k0z5PDduvWKXgS4+ypcxjbdzKWfO9Yy8Xd/vxmGcbdPhnnTsdnW6OJp1UTB0/Ds71fMe1KOJd4oXAmA6rikTjXMAbnrqqG99f+iN3/HEbJgZUQt2k7KgcF4OZH2sK2ZB9aJ5fCE+/egxnPf425785HwYIhePD1u3DDsB4oEBpitkZX1cPjnzyAYqWjUL9tLYz69AEUL1MU6xZtxHP/e9VUIRfJ8yE8VzfxCxrC84Q9e1hlTcN2ks6WZjNBTXJiisPlyUkp2LR0K6IrlkCpSq7Pzty/9SCW/rga9028MLvHcnjvURzcfsihYCbLFMTFnUdIkYK46uhGhCZF4cwOG6qhGs5GHUbxM1E4XSgeu3udxLENcbB9ewpVTtVG5zK1saZIJdSoXgWtr74CS75eisTziWaxYha2tMeEcCspPKxwGKIrXPiZvVV8PNgTJpJnGPu4nAPlrsaIt1MAldeYLM5aT+p5EjtcE+7F2aMuuvzcqXN4+oaXceuj1+PWx2+46Po05mtwyn4gh9qQ/rM19MbAjL1GnCnNn83+/+HvZrp/YAAWfrkU7zz26YUvD86qDuQNbAgtFIUKBSthxs8zUXhjRfxY5zySH0lBkQUnETBzK8bXnojlc9bg9Wenm/I5vCmH/lhqwDLq0///OSP7NthP576iU32ziYh4KwVQeT1sx5ocCp4kh1jB+/31kxBWJCzT67+Z9CN++WABJi950VTf/vn93zF58Qumynd83Hk81PYpdL69Ha69rxsebPMkjuw7DgRdCFR+nPYrZk2Zg9cWv5AeNNmKRSG1cXEUL1cGkWnAvlLnsQZpKPLWUeD0IZT9qQAmLRqDEj0ikDoqEc/eOAH12tbGjF1vmVPvgoUKOnX/Fn2zDO888jFe+f1ZlK7iuBiqSJ7TLDxxggKovHL8uOkJQM2anm6JXIa4E2fxzaTZaH1dU9RsWi3P/i4Tt4uXKZbl9TWbVTNDYyGhweZnDr19+cr3aNSxHupdWQvd77wK1RpXNj09TPw+FxePkNAQzHhpFnbsOY7YAiH4fv8cnD55EnV6VkRifAxOpMVj96GtCApNge1EIGwJgbAdSEZAQiqCCwagZIFCKFk0CqmFU9H9zo6IO34Ws9/+FTcNvxb7thw0SeVc7oVDglzMt/dD15getsyUr1XGBHeFogrl4qMokkOmhzZvK5FL/qUk8rywcyewahWgNYnyrYSzCVj01VLE7jwCb8DcoEO7j6By/QomWClQsAAq1SuPltc0wfKf1mDX+n0IDglGm+ubo2T5Eji05yhSwkOQVrQAEqOA+TMXYceOI2gYVBcrv1+DXTV2IuQp4MAjQGrDVNTaYkP1dUGosSEQNXbYEJz83/Tu/86umZ90ZP8xXHNPV9NztOjrZaYEwcEdh8z6d0w6P7jjsHnMEs4lZHk/qtSviFsfux4RWQRYIiLeSmUM8mLYjitoly2b939b3CarJUM8Je74GQyoPgw3Dr8a/Ub/z1z28XNfYdaUn/DRlimmx4dBzoCaD5h9udZcWvcmqJ9aFDu6HUTDYzasa3Aa+84XRPGhO3DL4J4Y/HK/LJdv4XGOHzyJ0LAC+HDLFPz1/Uq88+gn+GDzZLNg76V4w2Mm+VeelTEoexeCAwu4dKyUtCQsOvCeyhj4AfVA5abERODMGQVPPoABgLVlZuaE7/HMDePNrLkvX/nhws+Jyfh64o94+vqXzdBaTnz/+s948pqxpgcns1IHj3V9Dr98uADhEeF46Zen0H3gVWb5lse6PW96h57+ciSev20y7rtjCh559Stc27cI2j9fBB2ub4S4FslYUnQTUj/cg94Dr0fX2IaIGrMXAefSMP/zJXi0y3M4c+IsFnyxBI90HmMCL95f9m49//1jmLLkRZOrFFUyAm17t8S4n5/Ea/e8ja9e/dHh8bG2pT+swsgOz5hFhEXyBZUxEF/LgXrjjTcwYcIEs8J0w4YNMXXqVDRv3hxeX2F8/36gc2dPt0TyAIMKTsdnfBVZMgLRnJofEGCG2bat2Qmb/aK4mUg8n2R6dVbMXYutq3aaNee2/73LDIlx4V4OxzEfqlTlGLNcyh8zl5jj79sai/OJiViZdgjlSxVC0TIFkXAuCQXWB2DvmdM4VPMIjiEB+2tuQNqekgjdcgaBmxOwf04s4hYfR8HdyWh9fQsTJDE3in+jcGS4WQqG7anasBJqNK2KGk2qOrS3WKko08tVtkYZc38zEx4RZob3TP0okfxAOVDiS0N4M2fORP/+/TFt2jS0aNECr732Gr766its2bIF0dHR3jmEt3HjheVZil96aEN829uPfGwSqd/++xUz/MVkbuYKEXtpQsMLmOrexw6exAOtRiEhPglR0ZF4++8JmDH2W8yd/js+3jbVBDesjUSfjPsaX036AUH/nf6UDS6HkwMqon3J42hWYR/GFimMkxujkLQuDBE/bkCIDQgpEGwW5+UxUlJSEVowxKxxx5ID7/zzCkqUKWb+hoV5S3c3etj0NN01ri8S45MQFBzosI+Izw3hlbrTPUN4h6ZrCM8PeH0P1MSJEzF48GAMHDjQ/M5Aas6cOZg+fToef/xxeJ2UFODQISWMizHwhT7o/+zNKFDwQuBx4vAp3Nv4YSScT0KxUkXx9hoGSt+YYbN3/nnVrGXHniWuQWfv85dmmaEyhAQhqFtRRE6pjFu2BSKxVixORxzGhyuiEXJFJ9RtXgchjR5H2LGjCGO8lZqGG0f2Qr+nepvjfDXxRzOs+PbaVzH/00X46JmZuLvBSNz44NWmrZbQ8FC8vfYV03uUdD4J917xMFpf19zldfZEvFkAa5K52Kfg6u0l//DqACopKQmrV6/GqFH/X2CQU7Y7d+6MpUuXZnqbxMREs9mfWeRpwviJE8BVV+Xd3xSvxuVKzpxLxMSHPkD7/7VG/StrmSrgXHyXVcenPvA+Ni/fZvKE3hrx0X/DXReKX3IILz4tEVM2zkR8+Z1o374a9laORUhsGfy9IxG/7v0HAduTERRSEKOGdEHdKhUQGBCGgPNpQGIqCoaF4u7X+uH0sTi8/egnJvhpe2NLlK9RBsVLR+HKG5qnVwFnJfKXB0w1Pzfr3hhX9bkSYf/VdOLaeHe+eFv6viI+S3WgxFcCqGPHjiE1NRUxHA6zw983M8coE+PGjcOYMWOQ57ZtA0qU0PIs+QxrFZ0/l4AqDSo6JIgz9+j8mf8uZ1VuO0wI37luN2IqRaNodKS5LHbXEVM1vErDijiy5xjOnDqHqg0rmoCfQ3a/z1hspuw37doQV93W1tyGgc38GX/i7MlzSExIxu9fLEZAySAEFw9A1dDzKHG2KhLLFMHZV+NwqN1RnL7pOM4jEGfOnkPpY5Ww5+etKF60JCpUL43ujeqYWXc71u5G1QaVzN8MK1wQnW5ri8WzlmPdok2m9AHLHnCjSvUqoHztcuY2Rw8cx/6tsebyjPlODOra3dQq158LEZH8xKsDqMvB3qoRI0Y49ECVL18+d/8oZ9qx1lO3brn7d8TtOPV/66odeHfdqw7Jzp++8A02/rUZ7/07yeQJ2TseewIj2j+NoVMGocddncxlX074Dit++hsfbJ5iZuEtm73K/My8J4tZWsXu7JRVxl+Z/4wpB/D1pB9RMC0UMc3KoXzV4rjrup+wLHEPjgeHYfmo/bAttiHgRDBsySkY+Hx9XDOkC+6Y/Ad63tkxvYwBh+ZMGYNtUxFRrEj63+k6oIPZsipRwFl/Xfq1x9SlY/NVKQcRt1MSufhKEjmH8MLDw/H111/j+uuvT798wIABOHXqFL7//nvPJ5Fz2I55Tw0buv/YkutOHj5lhtJKli/uEBScPHLa5P5EVyhxUbCQkpyCI3uPI7JkEbNcyuPdnsfVd3cxRSs5e+308TNIPJd44baBATi6/zjuqPkAChYueKHitjmeDQWK2tBuaDjOHAjEuXOJqBt2EotOJ2LN4hhEx+7G+ZQAhEREYeyXzyGsYAGcOXYGj3R61gR0XHT3ken3m79nVflmCYKzp88hpkJJM5vO8sNbv2DOu7+ZYK1IUceClZztx1l94UXCTECXnV8/+sNUOR8/72mTvyXia0nk7Yv3d0sS+cLjHyuJ3A94dQ9UgQIF0KRJE8yfPz89gOIHPn8fOnSop5sHHDwIhIUBVap4uiVymYrGRGV++X9Dc8TK3gwyrh7c2QQmLClQpmpMetASu/Mw/lm4EUmJyY5nnwEBuKJzfRQtVRS2IuFArbMoXCEeTYLjkbazFg5FnMW/fx7ByUJnzSK/qefjcGxNUaRtPYvDx0PQsG1ts6Bu+UoxJgn9dOGCCAgMNEunJJ1PxpJZK3BFlwao16aW+XMMpDir77vXf0b1K6qYpV24xArrR7W5rhlCClw8g45DjKUqXXo2K5WtVgpX3tACoWGOCe4iIv7IqwMo4nAce5yaNm1qaj+xjMG5c+fSZ+V5zIEDF3qftDBwvnPm5FkznZ+1m+x7l3g56zFR4ahCF2bEAfh7/r/YuHQregy6ygRQTKo+deS06eg/efi0yRld8v1KLPlh5YUAKjAAAUEBKFA0DcFRt6BJ+8ZoXLAuztbdjjqRRXBznzXYHrke6w9F4Oe7o5F6phBsCUn4/VAAbLY0BAXFmQCu3U0tcd2QHuntY/BUrHRRM0OPrf7pvd9QKDI8PYAi9qaxt6ld75amHMKcd+ahS/8OZrkUV9VtU8tsIj5LSeTiK0N4ltdffz29kGajRo0wZcoUUxPKY12/DJzU85Rvjbt9Cnb8s9vUWrLPexo/8A0ztZ8efv9+dOnfPr3X00yMY2DE4pi7jmBQveGmfpMNNpgamdbbKCAAgZERqF68HKqP24USqyrgeOcdWJYagTMfnEfhleH4cN2rpkyAybZIC0gfShxY8wGcP5eIotERJn+K1cYD7RLY+VZlT5X9BzTbxF6kjPuwhhTzoaZvmISIEkUc9hHJb/JsCK/o7QgOcHEIz5aEhSc/1RCeH8gXAZRXvfGYML59O9C4sTuaJx6wa/1eM8OudsvqDj1QuzfsS192pGLd8ihe+kKez1ev/oCVc9fCdPuYquHJ2LR0y4XRupAg2EqXQFTrNJQqGYbyB6KwoeZJJJwJRcDf2xCYmoaI4hG4953hCDwahHM7482wWs+7OqFh+7omaGOg1vraptjAY6akmRynuq1rmuCOw28sL9Dx1itNaYGcit112Aw78jgcchTJzxRAiTfSJ6sztm4Fdu3SbLt8ignffy/410zTr1zvwlR+e5Xqlje1jjhkl5qckn75no37sXHZVlzRuQE2Hz2K2LOnENEmGKXOFUPRlAgElwqG7XwgtpfchbiyB3AyIRxnz4QgeGUgkBSAgLKhqBtRE6GlQnGs+AmM67vRlDNg8MZSAyEhwabCd6MO9S5q04V9wi5UEndC6coxZhMRJ5j+BA3hSc4ogHJm2I51nhQ8eT0ul5KcnILg4KD02WicOcdembG3TTaz1zrc0ibT2x4/eALP/e9VjHj3XtMzlJycClugDcXKRuLxd+7Dqz8tRsS8/Sje+CASyh9DYundqFvyFNaMDUf8CwVwOikVQUHnEBYYgJSkNAQGM+k8yCwyHBwajAAEmGApKCjIJH0/OeOhbO8Lc5ye+OxB5DbmdbFOFHu/VKJA/JaN4/FpbjiG+AMlRuQEyxSwwniGgp7infZu3o8+5e/Bstmr0y+bfP+7mHTP2/hs91um3EBOxB44iX6dxyPxxHlUftSG1R8/i+SoHxF/5yE8PvQhlPuzJk4NicT/KnyKl978AG8sH4eQAkEYNPY2vLHyJYSEFcDA5/vghR8fNzlTP7//O4qWisTH215Hz8EX6kd5ix+n/YoBNYaZ5HgRv8VxeXds4hfUA5WThYEPHwY6dvR0SySHomKizPpzzGOytL+pFRp3rIfI4kVM4vWJQ6fw+bhvLyRl/9dlb4spjjPBNkR3q40lp7fiwMRTOFYnAAWuKY2OzeujcugZLHx/HYI2x6Fw23B0ubEDGjWrj7DQKAQFBaJU5WDcP2mgmRVXqlJJ3DdxAOq2qmmSwZnLlJyQbJK52fP05zfLsG3NTvR7+n+mRyo+Lh4fj/kKzXs0NkOF9NcPK83sv/7P/A8FCrqWl3EpbDN7nzhcKCIil6YAKjubNgHR0VoYOJ9hkHTd/d0dLqtQuyzOnoo3CeQscHnmbDy+/nERApGMwrYglLMVRNHAhvi3egLQENhc6SD21DuG2DXxqFqhFppHNjBLnaQs2Q2sPW56+Rt1rGeG5vZu2m8SzlmI8tp7L5S14Aw9BiUsOJmWmopKdSuY2XCWgzsOY9PybRdm+LH8QFIqNizZnL7MCnG236ZlW5GakvtDAtUaVzabiF9TDpQ4QQFUVrgg8b59QO3anm6JuMG7j32KBTP/Mj8zp6h0lUoo1bM1gsqcQOWEFDTu+xt++DkcB/8qgchJGzGk7n2oW7smBvUYjkLFTuNo4RO4r+mjSElJdUjOZs7UkGaP4b5JA9ODJzpx+BSGNn8cg1/uh+uH9cDry8Y65Bbd/Egvs1mXRZYogilLHfe54YGeZlNOkkgeUQAlTlAAlVXC+NmzKpLpQ860r4iQIvFoUzAeS3YsxbH4ZThwMAX39LoS7VpXRkTIbWh+exgSbgxC0NMJKFG2OOKOxZnbfvjMTJSvWQavLx9nSguwLIBVGqB4mWJ4c9V4FCvtWNG8WKkovLHyZfM/ZQyCMguKcrKPiIh4BwVQGbFMQWQkULeup1siTjh9NA5/fPkXmnVvhLCykZizbRMC4oHw2clIbAgU+C0BSE3CpurbUeuK5qgVUxFV4iPQpEIVlC9cxhwjMsMKJakpKaZXac1v63Bo9xFUrFP+onICyQlJ+PfPTah3ZS2HBXwZYLEsgojkI6bzyNUeKDe1RbyeAih7x48D27ap58mL6zgxZ4hT++17Z+LjE7F7Wyxef/QTPHw8HEU7lMHSV/7B9lrncb4EcC45ESUCjiN841mcXxyODi3boG2TlkATx+OfP3seqclpKBR14fhRJSMxZPKdeOPB6Vi/ZLNVR9MB86o+ff5r3Plin0xrS7mK95cJ6KFhBbQGnUhu46SSAA3hSc4ogLIftuNyFwqevNb4ga/j+MGTmPLXi+mXpZxOwRujfsbxhaeQelNLTEn8G4dX/4LI33fhif7D0JYlC/iBeNN/n4s2m8PyLfbeePADbF6+DdP+nuBQvfveVweY2XpBIRffrmT54vh05xtZHtNVccfPYFCd4SZf6pZHXV/PTkRE3EMBFB07BgQFAbW0UKq3+eyFr7F1zU5mBJkyBMGlI/DUuNkosTkQ8SXSUCAhCEt2r0N4tA1PjLwNAUXSEJCagtCq8ajZtCoKWIFNDuKbXvd3M0umBPK1YIfBUVY3Z08VyxC4y68f/YG1f2zAg28ONr1OhSLC8cgHQ1CuRmm3/Q0RyVyAzYYAF8fgeAzxDwqgdu8GtmxRhXEvteHffVg9fwsikgugfkQ9BEUHYOWGjYhPjkdofCgqXVsDlQJKoFJoIXSqVw07/92DxPgA1L62qdNJ2FzixdNYFiHxXGL6MACDs5bXZBhrFJHcoVl44gT/DqA4bMeEcQVPHsdlRGwZlmM58slhRIRUQM2YUMQm7ce0lb8jIDUNAcdPITw+EdXqlcfr/XojcMD/B0ozxn6LA9ti8ebKlxEQlP9msV09uLPZRETEu/lvAJWQcKFUgWbbeYWX+k3FwT+PI6FScVQ8WAj7I08grngg7nm8B+q/1pXZ1GYYz5wdMtHTZjNrzGXsZHpo2t2m8CSrjYuIOEVJ5OIE/wygNm++UCSzSxdPt8QvHdpzFLPfnocAhCIoKQSFtofiwI44hISH4aqBjRCQmopqtsqwFQhB9ZqlUdyugvelFClaOFfbLiI+TEN44oRgv1zbjsuzKGE8zxw7eBI22FCyTDEcXH4IWz7ZjgVvL0G16OrYXekU0gqHAtEBqFYrBn0GtlMBSRHxDAVQ4gT/CqBSUi4sDKy17fJMWlIapvb7COGnQzFg+A2Y9v5nWLHiX6QUAoZ/2hlNOjgOoSp4EhF/88Ybb2DChAk4dOgQGjZsiKlTp6J58+aebpZcQrBfLQzM9e06dvR0S3waSw5MfeAD3HXNLQgqEYzZk+Zj74E9OB5wAvtf34deQ7qjz8vXMVJC+eqlFTCJiF/nQM2cORMjRozAtGnT0KJFC7z22mvo1q0btmzZgmiOlojX8p8AqkQJoKrnp6n7IpvNhrNrzyKocBBOfnAcJ1achO1JIKJqBAJXBqN2cvX/ksCB6o0r52iJEy7NsvynNWjcqT5KliueB/dCRPzd96c/RkREhEvHiIuLQyRnd+fQxIkTMXjwYAwcOND8zkBqzpw5mD59Oh5//HGX2iK5K9gfvtwpLjSUr2xPN8dnpJ5LRdKxJByfcxxFOxdF8tFkRLSIQHIPGw68dwTncB5VShXGvRP6ZPoBcynb/t2BcXe8htFfjkDTbo1y6V6ISH5gfWZYn+e5/XfccYyMxwoNDTWbvaSkJKxevRqjRo1KvywwMBCdO3fG0qVLXW6L5C6fD6DOnDlj/i9fXgu75pr/f++nu7LnV2459B83f++W44iIb3yeO9O7k1MFChRAqVKl3PY9Ubhw4YuO9cwzz+DZZ591uOzYsWNITU1FTEyMw+X8fTNni4tX8/kAqkyZMti3bx+KFCmS6/k2POPgm4Z/z9VuYG+l++gbdB99gz/dx40bN5rP89xQsGBB7Nq1y/QIuQN7yjJ+32TsfZL8z+cDKHaHlitXLk//Jj/IfPXDzKL76Bt0H32DP9zHsmXLms/z3MIgilteKlGiBIKCgnCYs8Pt8Hf2iIl3y71Xo4iIiGQ7dNikSRPMnz8//bK0tDTze6tWrTzaNrk0n++BEhER8VYsYTBgwAA0bdrU1H5iGYNz586lz8oT76UAyo04xs1EQV8e69Z99A26j75B9zH/u+WWW3D06FE8/fTTppBmo0aNMHfu3IsSy8X7BNhye16oiIiIiI9RDpSIiIiIkxRAiYiIiDhJAZSIiIiIkxRAiYiIiDhJAZSbvPHGG6hUqZIpxMYVtVesWAFfweUHWFXXfqtVqxbyu0WLFuHaa6811Y15n7777juH6zm/gjNjSpcujbCwMLM+1bZt2+BL9/GOO+646Lnt3r078otx48ahWbNmZqUBrlx//fXXm1Xs7SUkJGDIkCEoXry4WWKjd+/eFxUuzO/3sUOHDhc9j/feey/yi7feegsNGjRILwjKGkg///yzzzyH4psUQLnBzJkzTS0PTrVds2YNGjZsiG7duuHIkSPwFXXr1kVsbGz6tnjxYuR3rLXC54rBb2bGjx+PKVOmmNXRly9fjkKFCpnnlR/mvnIfiQGT/XP7+eefI79YuHCh+WJdtmwZ5s2bh+TkZHTt2tXcb8vw4cPx448/4quvvjL7Hzx4EDfeeCN86T7S4MGDHZ5Hvn7zC64W8dJLL5mFdVetWoWrrroK1113HTZs2OATz6H4KJYxENc0b97cNmTIkPTfU1NTbWXKlLGNGzfO5gueeeYZW8OGDW2+jG+FWbNmpf+elpZmK1WqlG3ChAnpl506dcoWGhpq+/zzz22+cB9pwIABtuuuu87mK44cOWLu58KFC9Ofs5CQENtXX32Vvs+mTZvMPkuXLrX5wn2k9u3b2x588EGbLylatKjtvffe88nnUHyDeqBcxMUnedbE4R0L12vi70uXLoWv4NAVh4GqVKmCvn37Yu/evfBlXFiURe3sn1euAs/hWV96XumPP/4wQ0M1a9bEfffdh+PHjyO/On36tPm/WLFi5n++N9ljY/88cvi5QoUK+fZ5zHgfLZ999plZW61evXoYNWoU4uPjkR+lpqbiiy++MD1sHMrzxedQfIMqkbvo2LFj5g2fsWosf9+8eTN8AYOGDz/80HzBcmhgzJgxaNu2LdavX2/yMnwRgyfK7Hm1rvMFHL7jUEjlypWxY8cOPPHEE+jRo4f5YuIip/kJ1xB76KGH0KZNGxNEEJ8rrjcWFRXlE89jZveRbrvtNlSsWNGc5Kxbtw6PPfaYyZP69ttvkV/8+++/JmDiEDnznGbNmoU6depg7dq1PvUciu9QACWXxC9UCxM9GVDxw/rLL7/EoEGDPNo2cc2tt96a/nP9+vXN81u1alXTK9WpUyfkJ8wTYlDvC/l5zt7Hu+++2+F55MQHPn8Mivl85gc8QWOwxB62r7/+2qwPx3wnEW+lITwXscucZ+oZZ4Tw91KlSsEX8UywRo0a2L59O3yV9dz50/NKHKLlazq/PbdDhw7F7NmzsWDBApOQbOFzxWH2U6dO5fvnMav7mBme5FB+eh7Zy1StWjU0adLEzDzk5IfJkyf71HMovkUBlBve9HzDz58/36Gbnb+zO9oXnT171pzZ8izXV3FIix/O9s9rXFycmY3nq88r7d+/3+RA5ZfnlrnxDCw43PP777+b580e35shISEOzyOHtpjDl1+ex0vdx8ywJ4fyy/OYGX6OJiYm+sRzKL5JQ3huwBIG7G5u2rQpmjdvjtdee80kQA4cOBC+4OGHHza1hDhsx+nDLNfAXrc+ffogvweC9mfoTBznFw+Tc5mgylyTF154AdWrVzdfWqNHjzY5JqzD4wv3kRvz2VhTh8Eig+JHH33U9AKwXEN+GdKaMWMGvv/+e5OPZ+XEMOGftbv4P4eZ+R7l/WWNoWHDhpkv3pYtW8IX7iOfN17fs2dPUyeJOVCc9t+uXTszJJsfMOmdqQJ83505c8bcHw4j//LLLz7xHIqP8vQ0QF8xdepUW4UKFWwFChQwZQ2WLVtm8xW33HKLrXTp0ua+lS1b1vy+fft2W363YMECMxU648ap/VYpg9GjR9tiYmJM+YJOnTrZtmzZYvOV+xgfH2/r2rWrrWTJkmaaeMWKFW2DBw+2HTp0yJZfZHbfuH3wwQfp+5w/f952//33m2nx4eHhthtuuMEWGxtr85X7uHfvXlu7du1sxYoVM6/TatWq2R555BHb6dOnbfnFnXfeaV5//Izh65HvtV9//dVnnkPxTQH8x9NBnIiIiEh+ohwoEREREScpgBIRERFxkgIoEREREScpgBIRERFxkgIoEREREScpgBIRERFxkgIoEREREScpgBIRERFxkgIo8Rt33HGHwzIsHTp0MMu15DUuUREQEHDR4qielvHxqFSpklmWyMI2f/fdd5d9/OyOt3v3bvO7tYZbXvrwww/NAtkiIs5QACUeD2r4xcnNWo39ueeeQ0pKSq7/7W+//RbPP/+8VwY9GYMNT1i5ciXuvvvuXDt+bGysWf9MRCQ/0mLC4nHdu3fHBx98YFZe/+mnn8ziqVx9nQuMZpSUlGQCLXfgwqSStZIlS+bq8bmAsYhIfqUeKPG40NBQ82VasWJF3HfffejcuTN++OEHh2G3F198EWXKlEHNmjXN5fv27cPNN99shl4YCF133XVmGMiSmppqVm/n9Vyh/tFHH+XC2dkOWTGAe+yxx1C+fHnTJvaGvf/+++a4HTt2NPsULVrU9ESxXZSWloZx48ahcuXKCAsLQ8OGDfH11187/B0GhTVq1DDX8zj27bxcb731FqpWrWqCST4mn3zyicP1mzdvxpVXXomCBQuiTp06+O2335wegrtUL9gzzzyD0qVLY926deb3xYsXo23btuZ+8jF84IEHcO7cuSxvn1l7du7caR6j8PBw81guXbrU4fpvvvkGdevWNc8P2/fqq686XH/y5En079/fPE88Bnu4tm3bdtGQXYUKFcz1N9xwA44fP57jx0RExKIASrwOv4DZ02SZP38+tmzZgnnz5mH27NlITk5Gt27dUKRIEfz5559YsmQJChcubHqyrNvxi5VflNOnTzdf7CdOnMCsWbOy/bv84v38888xZcoUbNq0CW+//bY5LoMBfnET28Ghp8mTJ5vfGTx9/PHHmDZtGjZs2IDhw4fj9ttvx8KFC9MDvRtvvBHXXnutye+566678Pjjj7v0+PB+PPjggxg5ciTWr1+Pe+65BwMHDsSCBQvSg0cGnQwQli9fjnfeeQdPPvkk3IWB6LBhw8z95uPfoEED7Nixwzz+vXv3NgHVzJkzzeM+dOhQp47Ndj788MPmsWLQ2adPn/Th3NWrV5ug+dZbb8W///6LZ599FqNHjzbPs4WB7apVq0wAzuCLbe3Zs6d5zRAfj0GDBpl28W8wWHvhhRfc9tiIiB+xiXjQgAEDbNddd535OS0tzTZv3jxbaGio7eGHH06/PiYmxpaYmJh+m08++cRWs2ZNs7+F14eFhdl++eUX83vp0qVt48ePT78+OTnZVq5cufS/Re3bt7c9+OCD5uctW7awe8r8/cwsWLDAXH/y5Mn0yxISEmzh4eG2v/76y2HfQYMG2fr06WN+HjVqlK1OnToO1z/22GMXHSujihUr2iZNmpTpda1bt7YNHjzY4bL//e9/tp49e5qff/75Z1twcLAtNjY2/XreL/7NWbNmZfk37R+PzNrA23/11Ve22267zVa7dm3b/v37He7z3Xff7XC8P//80xYYGGg7f/58lsez2rNr1y7z+3vvvZd+/YYNG8xlmzZtMr/z73bp0sXhbzzyyCPpj+/WrVvN/kuWLEm//tixY+Z18eWXX5rf+bxYj5PllltusUVGRmb5uIiIZEY9UOJx7FViTw+Hmzjkcsstt5jeBUv9+vUd8p7++ecfbN++3fRA8XbcOIyXkJBgekJOnz5teolatGiRfpvg4GA0bdo0yzawNyIoKAjt27fPcbvZhvj4eHTp0iW9HdzYM8N2EHuy7NtBrVq1git4zDZt2jhcxt95udVLxl4z+xyj5s2bwx3Yw8ZenEWLFqFs2bIOzwl7guwfB/YScohz165dOT4+e7MsHB6kI0eOZHu/OUTHXjdez+fZ/vHm8C2HOK3HJjeeDxHxT0oiF4/jMApzehgkMc+JX4L2ChUq5PD72bNn0aRJE3z22WduS3zmsKGz2A6aM2eOQzBBzNHxRQwWOcz5yy+/oG/fvg6PBYcSmfeUEfONcoqTB+xzpIhBmIiIt1EAJR7HAIkJ2zl1xRVXmByb6OhoREREZLoPey/YU9KuXTvzO/NomEPD22aGvVz8ombuEpPYM7J6wNjTYWFyNgOlvXv3ZtlzVbt27fSEeMuyZctyfF+zOibzvgYMGJB+GX9ne4g9Lsy9Onz4MGJiYtJLErhDr169TD7XbbfdZnrsmI9EfFw3btzo1PN4uffbHn9nrhTbwuv5PPN5b926tbmeCeLskbMeG+7D6935fIiIf9IQnuQ77PkoUaKEmXnHJGYOEbFOE3s/9u/fb/ZhkvVLL71kZnlxRtr999+fbQ0nzuhiQHLnnXea21jH/PLLL831nCHIHhEONx49etT0uHAIkQnPHNb66KOPzLDdmjVrMHXqVPM73XvvvWaI6ZFHHjFf5DNmzHBIes7OgQMHzNCi/cZZZjwWj8FeOx574sSJpqYV22L1EnGGHu8PE7oZZDz11FMOvTqu4Mw1zvpj4ro145CzF//666/05Gy26/vvv3c6iTw7TJrnhALW7tq6dat5jF9//fX0+129enXzmhg8eLBJYOewIhP62TvIy4mvkblz5+KVV14xbeTt+buIiNMyzYwS8UASuTPXM0G6f//+thIlSpik8ypVqpjE6tOnT6cnjTMhOiIiwhYVFWUbMWKE2T+rJHJisvPw4cNNAnqBAgVs1apVs02fPj39+ueee85WqlQpW0BAgGkXMZH9tddeM0ntISEhtpIlS9q6detmW7hwYfrtfvzxR3MstrNt27bmmDlJIuc+GTcm0NObb75p7jP/Zo0aNWwff/yxw+2ZeN2mTRtzP2rVqmXawNvPnTvXpSRy+yT0mTNn2goWLGj75ptvzO8rVqwwSd6FCxe2FSpUyNagQQPbiy++mKPjWUnkf//9d/r1fHx4GRP4LV9//bVJGuf9rlChgm3ChAkO9+HEiRO2fv36maRwJo/zuWByub3333/fTCjg9ddee63tlVdeURK5iDgtgP84H3aJSH7CXijWhWLiO3unRETENQqgRHwQa0VxJhyHtRg0cUiTxSU5tCUiIq5TErmIDzpz5ozJS2KCO/PFmBifsWq3iIhcPvVAiYiIiDhJs/BEREREnKQASkRERMRJCqBEREREnKQASkRERMRJCqBEREREnKQASkRERMRJCqBEREREnKQASkRERATO+T/kN+v0bMGhXQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mean: -13.264392156892503, std dev: 2.3630742536647333\n",
+ "=====\n",
+ "\n",
+ "\n",
+ "Testing Results!\n",
+ "[12.25331715 12.50975461 6.02831956 ... 16.69130161 16.54353682\n",
+ " 14.07405967]\n",
+ "Mean Absolute Error : 13.23815510143664\n",
+ "Line of best fit : y=0.29423081581879257x + -3.8983813438890977\n",
+ "Saving QQ to .pdf\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mean: -13.23815510143664, std dev: 2.361082992619628\n",
+ "=====\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "ename": "AttributeError",
+ "evalue": "'HistGradientBoostingRegressor' object has no attribute 'layers'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Now we need to test\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mml_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtest_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/software/MaCh3/MaCh3-PythonUtils/src/MaCh3PythonUtils/machine_learning/file_ml_interface.py:229\u001b[39m, in \u001b[36mFileMLInterface.test_model\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 226\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m=====\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 228\u001b[39m \u001b[38;5;66;03m# Check first layer weights\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m229\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m:\n\u001b[32m 230\u001b[39m \u001b[38;5;28mprint\u001b[39m(i.get_weights())\n",
+ "\u001b[31mAttributeError\u001b[39m: 'HistGradientBoostingRegressor' object has no attribute 'layers'"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we need to test\n",
+ "ml_model.test_model()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2fbaf177",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Saving to ../models/my_model.keras\n",
+ "Model saved to: ../models/my_model\n",
+ "Scaler saved to: ../models/my_scaler.pkl\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we can save it\n",
+ "ml_model.save_model(f\"{model_output}.keras\")\n",
+ "\n",
+ "\n",
+ "# We need to save the scaling information\n",
+ "ml_model.save_scaler(str(model_output.parent.joinpath(Path(\"my_scaler.pkl\"))))\n",
+ "print(\"Model saved to: \", model_output)\n",
+ "print(\"Scaler saved to: \", model_output.parent.joinpath(Path(\"my_scaler.pkl\")))\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/basic_sklearn.ipynb b/notebooks/basic_sklearn.ipynb
new file mode 100644
index 0000000..957e309
--- /dev/null
+++ b/notebooks/basic_sklearn.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2121eaca",
+ "metadata": {},
+ "source": [
+ "## Welcome to the basic neural network tutorial!\n",
+ "The following notebook is designed to walk through the process of building and training your first scikit based algorithm using the standalone MaCh3 python utilities!\n",
+ "\n",
+ "If you're writing scripts this is encapsulate in the objects in `MaCh3PythonUtils/config_reader` however this guide aims to break apart the process of writing the code for yourself!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81a4f83c",
+ "metadata": {},
+ "source": [
+ "The first step is to load in a MaCh3 MCMC fit as input file. This is done using the ChainHandler class!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea515328",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# MaCh3Python Deps\n",
+ "from MaCh3PythonUtils.file_handling.chain_handler import ChainHandler\n",
+ "from MaCh3PythonUtils.machine_learning.ml_factory import MLFactory\n",
+ "\n",
+ "# Other imports\n",
+ "from matplotlib import pyplot as plt\n",
+ "from pathlib import Path\n",
+ "import gdown"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57b30cc7",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7d7380ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Download file from google drive\n",
+ "\n",
+ "file_url=\"https://drive.google.com/file/d/1iE6xFhn3BH_HnLUfQ7KFGy2wfeH52Rwf/view?usp=sharing\"\n",
+ "\n",
+ "# download the file\n",
+ "input_file = Path(\"../models/demo_chain.root\")\n",
+ "\n",
+ "if not input_file.exists():\n",
+ " # download the file\n",
+ " input_file.parent.mkdir(parents=True, exist_ok=True)\n",
+ " gdown.download(file_url, str(input_file), quiet=False, fuzzy=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "3587f5d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attempting to open ../models/demo_chain.root\n",
+ "Succesfully opened ../models/demo_chain.root:posteriors\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set up file properties\n",
+ "chain_name = 'posteriors'\n",
+ "verbose=False\n",
+ "\n",
+ "chain_handler = ChainHandler(str(input_file), chain_name, verbose=verbose)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39d31a84",
+ "metadata": {},
+ "source": [
+ "Okay, now we've loaded in a file we need to do a bit of processing. This means we need to select the variables we care about and apply parameter cuts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0cd2f396",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Firstly we set things we want to ignore, these are parameters we really don't care about/want to learn!\n",
+ "chain_handler.ignore_plots([\"LogL_systematic_xsec_cov\", \"Log\", \"LogL_systematic_osc_cov\"])\n",
+ "\n",
+ "# Now we add parameters we care about. Note this only need to be a substring of the full name\n",
+ "chain_handler.add_additional_plots([\"sin2th\", \"delm2\", \"delta\", \"xsec\"])\n",
+ "\n",
+ "# The fitting label is special, it's the thing we want to train our network to predict\n",
+ "fitting_label = \"LogL\"\n",
+ "# We need to make sure the chain handler knows this exists, passing true means it is looking for some with that exact name\n",
+ "chain_handler.add_additional_plots(fitting_label, True)\n",
+ "\n",
+ "# Finally we can do some cuts to get rid of things like burn-in\n",
+ "chain_handler.add_new_cuts([\"LogL<30\", \"LogL_systematic_xsec_cov<1234\", \"step>10000\"])\n",
+ "\n",
+ "# Last step is convert the chain into a pandas dataframe\n",
+ "chain_handler.convert_ttree_to_array()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d259501",
+ "metadata": {},
+ "source": [
+ "Let's quickly use the chain handler to make a plot of the trace and posterior!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "bd6b44ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# We're just grab the first column of our pandas dataframe\n",
+ "parameter_vals = chain_handler.ttree_array.iloc[:,0]\n",
+ "# Grab the name from column label\n",
+ "param_label = chain_handler.ttree_array.columns[0]\n",
+ "\n",
+ "# Setup figure\n",
+ "fig, (trace_ax, post_ax) = plt.subplots(nrows=1, ncols=2)\n",
+ "\n",
+ "# Plot traces\n",
+ "trace_ax.plot(parameter_vals, linewidth=0.5, color='darkorange')\n",
+ "\n",
+ "# Plot posterior\n",
+ "post_ax.hist(parameter_vals, bins=50, color='darkorange', alpha=0.5, orientation='horizontal', density=True)\n",
+ "\n",
+ "# Merge the two axes\n",
+ "plt.setp(post_ax.get_yticklabels(), visible=False)\n",
+ "fig.subplots_adjust(wspace=.0)\n",
+ "\n",
+ "# Set the axis labels\n",
+ "trace_ax.set_xlabel(\"Step\")\n",
+ "post_ax.set_xlabel(\"Posterior Density\")\n",
+ "trace_ax.set_ylabel(param_label)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58068596",
+ "metadata": {},
+ "source": [
+ "## Machine learning\n",
+ "Okay now we've done some very basic file manipulation, it's time for some machine learning!\n",
+ "All algorithms use the same common `MLFactory` interface so let's go about configuring it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "23b769e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First step we need to initialise the factory\n",
+ "from pathlib import Path\n",
+ "\n",
+ "\n",
+ "model_output=Path(\"../models/my_model\")\n",
+ "# Make sure the model output directory exists\n",
+ "model_output.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# The factory produces ML models, we need to pass it the chain handler and the fitting label to get started\n",
+ "ml_factory = MLFactory(chain_handler, fitting_label, f\"{model_output}.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47842a01",
+ "metadata": {},
+ "source": [
+ "Now we need to define a model, for this demonstration we'll make a very simple BDT!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "80a10c4f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We now want the actual neural network properties\n",
+ "# Usually you set this using a YAML file (see configs for some examples) but we'll do this manually for now\n",
+ "build_settings = {\n",
+ " # Loss function\n",
+ " 'loss': 'mse',\n",
+ " # Metrics to measure\n",
+ " 'metrics': ['mae', 'mse'],\n",
+ " # Learning rate\n",
+ " 'learning_rate': 0.001,\n",
+ "}\n",
+ "\n",
+ "# Settings used when the model is fitting\n",
+ "fit_settings = {\n",
+ " 'batch_size': 4096,\n",
+ " 'epochs': 100,\n",
+ " 'validation_split': 0.2,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "85fa21f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now we can make the model\n",
+ "ml_model = ml_factory.make_interface('SciKit', 'histboost', loss='absolute_error', max_iter=10000, verbose=1, max_leaf_nodes=40)\n",
+ "\n",
+ "# Let's use 20% of the data for testing\n",
+ "ml_model.set_training_test_set(0.2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "04c8e8c9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Model\n",
+ "Binning 0.017 GB of training data: 0.167 s\n",
+ "Binning 0.002 GB of validation data: 0.005 s\n",
+ "Fitting gradient boosted rounds:\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we need to train our network\n",
+ "ml_model.train_model()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68ecf564",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Results!\n",
+ "[13.401329 12.655464 12.3677435 ... 13.951196 10.70321 13.456713 ]\n",
+ "Mean Absolute Error : 1.2414647037536466\n",
+ "Line of best fit : y=1.3195665735842563x + -4.253602859857818\n",
+ "Saving QQ to train_qq_plot.pdf\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mean: -0.00825973951703488, std dev: 1.5986446529958946\n",
+ "=====\n",
+ "\n",
+ "\n",
+ "Testing Results!\n",
+ "[12.680937 10.83576 12.229048 ... 15.073508 13.600174 13.867725]\n",
+ "Mean Absolute Error : 1.2464520602122047\n",
+ "Line of best fit : y=1.3222553769243799x + -4.279277615534686\n",
+ "Saving QQ to .pdf\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mean: -0.0016234549885157249, std dev: 1.6097761176684666\n",
+ "=====\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we need to test\n",
+ "ml_model.test_model()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2fbaf177",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model saved to: ../models/my_model\n",
+ "Scaler saved to: ../models/my_scaler.pkl\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now we can save it\n",
+ "ml_model.save_model(f\"{model_output}.keras\")\n",
+ "\n",
+ "\n",
+ "# We need to save the scaling information\n",
+ "ml_model.save_scaler(str(model_output.parent.joinpath(Path(\"my_scaler.pkl\"))))\n",
+ "print(\"Model saved to: \", model_output)\n",
+ "print(\"Scaler saved to: \", model_output.parent.joinpath(Path(\"my_scaler.pkl\")))\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/basic_torch.ipynb b/notebooks/basic_torch.ipynb
new file mode 100644
index 0000000..7483f43
--- /dev/null
+++ b/notebooks/basic_torch.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2121eaca",
+ "metadata": {},
+ "source": [
+ "## Welcome to the basic neural network tutorial!\n",
+ "The following notebook is designed to walk through the process of building and training your first neural network using the standalone MaCh3 python utilities!\n",
+ "\n",
+ "If you're writing scripts this is encapsulate in the objects in `MaCh3PythonUtils/config_reader` however this guide aims to break apart the process of writing the code for yourself!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81a4f83c",
+ "metadata": {},
+ "source": [
+ "The first step is to load in a MaCh3 MCMC fit as input file. This is done using the ChainHandler class!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ea515328",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Using mps device\n",
+ " \n"
+ ],
+ "text/plain": [
+ "Using mps device\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# MaCh3Python Deps\n",
+ "from MaCh3PythonUtils.file_handling.chain_handler import ChainHandler\n",
+ "from MaCh3PythonUtils.machine_learning.ml_factory import MLFactory\n",
+ "\n",
+ "\n",
+ "# Other imports\n",
+ "from matplotlib import pyplot as plt\n",
+ "from pathlib import Path\n",
+ "import gdown"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7d7380ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Download file from google drive\n",
+ "\n",
+ "file_url=\"https://drive.google.com/file/d/1iE6xFhn3BH_HnLUfQ7KFGy2wfeH52Rwf/view?usp=sharing\"\n",
+ "\n",
+ "# download the file\n",
+ "input_file = Path(\"../models/demo_chain.root\")\n",
+ "\n",
+ "if not input_file.exists():\n",
+ " # download the file\n",
+ " input_file.parent.mkdir(parents=True, exist_ok=True)\n",
+ " gdown.download(file_url, str(input_file), quiet=False, fuzzy=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "3587f5d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attempting to open ../models/demo_chain.root\n",
+ "Succesfully opened ../models/demo_chain.root:posteriors\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load in a file, for the purposes of this example we'll use a\n",
+ "# input_file = Path(\"../../../../T2K/AdaptiveTune/SummerProj/Adapt/NOvA/LongFit/Cut/mcmc_Adapt_NOvA_all_on.root\")\n",
+ "\n",
+ "# Set up file properties\n",
+ "chain_name = 'posteriors'\n",
+ "verbose=False\n",
+ "\n",
+ "chain_handler = ChainHandler(str(input_file), chain_name, verbose=verbose)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39d31a84",
+ "metadata": {},
+ "source": [
+ "Okay, now we've loaded in a file we need to do a bit of processing. This means we need to select the variables we care about and apply parameter cuts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0cd2f396",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Firstly we set things we want to ignore, these are parameters we really don't care about/want to learn!\n",
+ "# chain_handler.ignore_plots([\"LogL_systematic_xsec_cov\", \"Log\", \"LogL_systematic_osc_cov\"])\n",
+ "\n",
+ "chain_handler.ignore_plots([\"xsec_20\", \"xsec_19\"])\n",
+ "chain_handler.add_additional_plots([\"xsec_0\", \"xsec_1\", \"xsec_2\", \"xsec_3\", \"xsec_4\", \"xsec_5\", \"xsec_6\", \"xsec_7\", \"xsec_8\", \"xsec_9\", \"xsec_10\", \"xsec_11\", \"xsec_12\", \"xsec_13\",\n",
+ " \"xsec_14\", \"xsec_15\", \"xsec_16\", \"xsec_17\", \"xsec_18\", \"xsec_21\", \"xsec_22\"])\n",
+ "# Now we add parameters we care about. Note this only need to be a substring of the full name\n",
+ "# chain_handler.add_additional_plots([\"sin2th\", \"delm2\", \"delta\", \"xsec\"])\n",
+ "\n",
+ "# The fitting label is special, it's the thing we want to train our network to predict\n",
+ "fitting_label = \"LogL_systematic_xsec_cov\"\n",
+ "# We need to make sure the chain handler knows this exists, passing true means it is looking for some with that exact name\n",
+ "chain_handler.add_additional_plots(fitting_label, True)\n",
+ "\n",
+ "# Finally we can do some cuts to get rid of things like burn-in\n",
+ "# chain_handler.add_new_cuts([\"LogL<22.5\", \"step>10000\", \"delm2_23>0\"])\n",
+ "chain_handler.add_new_cuts([\"step>0\", \"LogL_systematic_xsec_cov<12345\"])\n",
+ "\n",
+ "# Last step is convert the chain into a pandas dataframe\n",
+ "chain_handler.convert_ttree_to_array()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d259501",
+ "metadata": {},
+ "source": [
+ "Let's quickly use the chain handler to make a plot of the trace and posterior!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "bd6b44ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from MaCh3PythonUtils.file_handling.chain_diagnostics import ChainDiagnostics\n",
+ "plotter=ChainDiagnostics(chain_handler)\n",
+ "\n",
+ "plotter(\"xsec_0\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58068596",
+ "metadata": {},
+ "source": [
+ "## Machine learning\n",
+ "Okay now we've done some very basic file manipulation, it's time for some machine learning!\n",
+ "All algorithms use the same common `MLFactory` interface so let's go about configuring it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "23b769e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First step we need to initialise the factory\n",
+ "\n",
+ "from pathlib import Path\n",
+ "\n",
+ "model_output=Path(\"../models/my_model\")\n",
+ "# Make sure the model output directory exists\n",
+ "model_output.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# The factory produces ML models, we need to pass it the chain handler and the fitting label to get started\n",
+ "ml_factory = MLFactory(chain_handler, fitting_label, f\"{model_output}.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47842a01",
+ "metadata": {},
+ "source": [
+ "Now we need to define a model, for this demonstration we'll make a very simple neural network!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "80a10c4f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We now want the actual neural network properties\n",
+ "# Usually you set this using a YAML file (see configs for some examples) but we'll do this manually for now\n",
+ "build_settings = {\n",
+ "}\n",
+ "\n",
+ "# Settings used when the model is fitting\n",
+ "fit_settings = {\n",
+ " 'num_epochs': 10000,\n",
+ " 'learning_rate': 0.01,\n",
+ " 'debug': True,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "85fa21f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now we can make the model\n",
+ "layers = []\n",
+ "layers.append({\"linear\": {\"in_features\": chain_handler.ndim-1, \"out_features\": 16}})\n",
+ "layers.append({\"leaky_relu\": {}})\n",
+ "layers.append({\"batch_norm\": {'num_features': 16}})\n",
+ "layers.append({\"linear\": {\"in_features\": 16, \"out_features\": 32}})\n",
+ "layers.append({\"leaky_relu\": {}})\n",
+ "layers.append({\"linear\": {\"in_features\": 32, \"out_features\": 16}})\n",
+ "layers.append({\"leaky_relu\": {}})\n",
+ "\n",
+ "layers.append({\"linear\": {\"in_features\": 16, \"out_features\": 1}})\n",
+ "\n",
+ "\n",
+ "ml_model = ml_factory.make_interface('Torch', 'sequential', Layers=layers, FitSettings=fit_settings, BuildSettings=build_settings)\n",
+ "\n",
+ "# Let's use 20% of the data for testing\n",
+ "ml_model.set_training_test_set(0.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "04c8e8c9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6ba3449c492e490eb37109b808eb4dc2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Training model: 0%| | 0/10000 [00:00, ?epoch/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now we need to train our network\n",
+ "ml_model.train_model()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68ecf564",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Training Results!\n",
+ " \n"
+ ],
+ "text/plain": [
+ "Training Results!\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Mean Absolute Error : 1.4847191753027202 \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1;38;5;160mMean Absolute Error :\u001b[0m \u001b[1;38;5;184m1.4847191753027202\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Line of best fit : y = 1. 5123302551231461x + -2.071078294225607 \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1;38;5;129mLine of best fit :\u001b[0m \u001b[38;5;33my\u001b[0m\u001b[38;5;33m=\u001b[0m\u001b[1;38;5;33m1\u001b[0m\u001b[1;38;5;33m.\u001b[0m\u001b[38;5;33m5123302551231461x + \u001b[0m\u001b[1;38;5;33m-2.071078294225607\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Saving QQ to train_qq_plot.pdf \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1;38;5;48mSaving QQ to\u001b[0m\u001b[38;5;33m train_qq_plot.pdf\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "mean: -1.369461655091277 , std dev: 0.9751198152328029 \n",
+ " \n"
+ ],
+ "text/plain": [
+ "mean: \u001b[1;36m-1.369461655091277\u001b[0m, std dev: \u001b[1;36m0.9751198152328029\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "=====\n",
+ " \n"
+ ],
+ "text/plain": [
+ "=====\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Testing Results!\n",
+ " \n"
+ ],
+ "text/plain": [
+ "Testing Results!\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Mean Absolute Error : 1.4800941535508254 \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1;38;5;160mMean Absolute Error :\u001b[0m \u001b[1;38;5;184m1.4800941535508254\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Line of best fit : y = 1. 4652746352543373x + -2.0105134458925833 \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1;38;5;129mLine of best fit :\u001b[0m \u001b[38;5;33my\u001b[0m\u001b[38;5;33m=\u001b[0m\u001b[1;38;5;33m1\u001b[0m\u001b[1;38;5;33m.\u001b[0m\u001b[38;5;33m4652746352543373x + \u001b[0m\u001b[1;38;5;33m-2.0105134458925833\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Saving QQ to .pdf \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1;38;5;48mSaving QQ to\u001b[0m\u001b[38;5;33m .pdf\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "mean: -1.3733435717482056 , std dev: 0.9634166089157359 \n",
+ " \n"
+ ],
+ "text/plain": [
+ "mean: \u001b[1;36m-1.3733435717482056\u001b[0m, std dev: \u001b[1;36m0.9634166089157359\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "=====\n",
+ " \n"
+ ],
+ "text/plain": [
+ "=====\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now we need to test\n",
+ "ml_model.test_model()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2fbaf177",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model Summary:\n",
+ " \n"
+ ],
+ "text/plain": [
+ "Model Summary:\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Model: Sequential ( \n",
+ " ( 0 ) : Linear ( in_features =21 , out_features =16 , bias =True ) \n",
+ " ( 1 ) : LeakyReLU ( negative_slope =0.01 ) \n",
+ " ( 2 ) : BatchNorm1d ( 16 , eps =1e-05 , momentum =0.1 , affine =True , track_running_stats =True ) \n",
+ " ( 3 ) : Linear ( in_features =16 , out_features =10 , bias =True ) \n",
+ " ( 4 ) : LeakyReLU ( negative_slope =0.01 ) \n",
+ " ( 5 ) : Linear ( in_features =10 , out_features =1 , bias =True ) \n",
+ ") \n",
+ " \n"
+ ],
+ "text/plain": [
+ "Model: \u001b[1;35mSequential\u001b[0m\u001b[1m(\u001b[0m\n",
+ " \u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m21\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m16\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
+ " \u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLeakyReLU\u001b[0m\u001b[1m(\u001b[0m\u001b[33mnegative_slope\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.01\u001b[0m\u001b[1m)\u001b[0m\n",
+ " \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m16\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
+ " \u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m16\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m10\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
+ " \u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLeakyReLU\u001b[0m\u001b[1m(\u001b[0m\u001b[33mnegative_slope\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.01\u001b[0m\u001b[1m)\u001b[0m\n",
+ " \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m10\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
+ "\u001b[1m)\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Device: mps\n",
+ " \n"
+ ],
+ "text/plain": [
+ "Device: mps\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now we can save it\n",
+ "ml_model.print_model_summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "118f3407",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de35d3b7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/MaCh3PythonUtils/file_handling/chain_diagnostics.py b/src/MaCh3PythonUtils/file_handling/chain_diagnostics.py
new file mode 100644
index 0000000..274336c
--- /dev/null
+++ b/src/MaCh3PythonUtils/file_handling/chain_diagnostics.py
@@ -0,0 +1,78 @@
+from MaCh3PythonUtils.file_handling.chain_handler import ChainHandler
+import matplotlib.pyplot as plt
+
+class ChainDiagnostics:
+ def __init__(self, config_reader: ChainHandler) -> None:
+ self._chain_handler = config_reader
+
+ def _extract_chain_information(self, parameter_name: str | int):
+ if isinstance(parameter_name, str):
+ parameter_id = self._chain_handler.ttree_array.columns.get_loc(parameter_name)
+ if isinstance(parameter_name, int):
+ parameter_id = parameter_name
+ parameter_name = self._chain_handler.ttree_array.columns[parameter_id]
+
+ return self._chain_handler.ttree_array.iloc[:,parameter_id], parameter_name
+
+ def __make_plot(self, fig, axs):
+ if fig is None:
+ fig, axs = plt.subplots(1, 1, figsize=(10, 5))
+ elif axs is None:
+ axs = fig.add_subplot(1, 1, 1)
+
+ return fig, axs
+
+ def make_trace_plot(self, parameter_name: str | int, axs=None, fig =None):
+ fig, axs = self.__make_plot(fig, axs)
+
+ chain, parameter_name = self._extract_chain_information(parameter_name)
+ axs.plot(chain, linewidth=0.5, color='darkorange')
+
+ return fig, axs
+
+ def make_autocorr_plot(self, parameter_name: str | int, axs=None, fig =None):
+ fig, axs = self.__make_plot(fig, axs)
+
+ if fig is None:
+ fig, axs = plt.subplots(1, 1, figsize=(10, 5))
+ elif axs is None:
+ axs = fig.add_subplot(1, 1, 1)
+
+ chain, parameter_name = self._extract_chain_information(parameter_name)
+ axs.acorr(chain, maxlags=1000, linewidth=0.5, color='darkorange')
+
+ return fig, axs
+
+ def make_posterior_hist_plot(self, parameter_name: str | int, axs=None, fig =None, is_horizontal=False):
+ fig, axs = self.__make_plot(fig, axs)
+
+ if fig is None:
+ fig, axs = plt.subplots(1, 1, figsize=(10, 5))
+ elif axs is None:
+ axs = fig.add_subplot(1, 1, 1)
+
+ orientation = 'vertical'
+
+ if is_horizontal:
+ orientation = 'horizontal'
+
+ chain, parameter_name = self._extract_chain_information(parameter_name)
+ axs.hist(chain, bins=50, density=True, linewidth=0.5, color='darkorange', alpha=0.5, orientation=orientation)
+
+ return fig, axs
+
+ def __call__(self, parameter_name: str):
+ fig, axs = plt.subplots(2, 2, figsize=(15, 5))
+ axs[1][1].remove()
+ axs[1][0].remove()
+
+ fig, axs[0][0] = self.make_trace_plot(parameter_name, axs=axs[0][0], fig=fig)
+ fig, axs[0][1] = self.make_posterior_hist_plot(parameter_name, axs=axs[0][1], fig=fig, is_horizontal=True)
+
+ # To share the same axis etc,
+ plt.setp(axs[0][1].get_yticklabels(), visible=False)
+ fig.subplots_adjust(wspace=.0)
+
+
+ # fig, axs[1][0] = self.make_autocorr_plot(parameter_name, axs=axs[1][0], fig=fig)
+ return fig, axs
\ No newline at end of file
diff --git a/src/MaCh3PythonUtils/machine_learning/file_ml_interface.py b/src/MaCh3PythonUtils/machine_learning/file_ml_interface.py
index 793ab3b..3724ce3 100644
--- a/src/MaCh3PythonUtils/machine_learning/file_ml_interface.py
+++ b/src/MaCh3PythonUtils/machine_learning/file_ml_interface.py
@@ -20,6 +20,8 @@
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
+from rich import print
+
class FileMLInterface(ABC):
white_viridis = LinearSegmentedColormap.from_list('white_viridis', [
(0, '#ffffff'),
@@ -59,7 +61,7 @@ def __init__(self, chain: ChainHandler, prediction_variable: str, fit_name: str)
self._scaler = StandardScaler()
# self._pca_matrix = PCA(n_components=0.95)
- self._label_scaler = MinMaxScaler(feature_range=(0, 1))
+ self._label_scaler = StandardScaler()
@@ -86,19 +88,19 @@ def set_training_test_set(self, test_size: float):
self._training_data, self._test_data, self._training_labels, self._test_labels = train_test_split(features, labels, test_size=test_size)
# Fit scaling pre-processors. These get applied properly when scale_data is called
- _= self._scaler.fit_transform(self._training_data)
- self._label_scaler.fit_transform(self._training_labels)
+ self._scaler.fit(self._training_data)
+ self._label_scaler.fit(self._training_labels)
# self._pca_matrix.fit(scaled_training)
def scale_data(self, input_data):
# Applies transformations to data set
scale_data = self._scaler.transform(input_data)
- # scale_data = self._pca_matrix.transform(scale_data)
return scale_data
def scale_labels(self, labels):
return self._label_scaler.transform(labels)
+ # return labels.values.reshape(-1, 1)
def invert_scaling(self, input_data):
# Inverts transform
@@ -193,7 +195,7 @@ def load_model(self, input_model: str):
:param input_file: Pickled Model
:type input_file: str
"""
- print(f"Attempting to load file from {input_file}")
+ print(f"[spring_green1]Attempting to load file from[/spring_green1][bold red3] {input_file}")
with open(input_model, 'r') as f:
self._model = pickle.load(f)
@@ -216,14 +218,18 @@ def test_model(self):
train_as_numpy = self.scale_labels(self._training_labels).T[0]
self.evaluate_model(train_prediction, train_as_numpy, "train_qq_plot.pdf")
- print("=====\n\n")
+ print("=====")
print("Testing Results!")
test_prediction = self.model_predict(self._test_data)
test_as_numpy = self.scale_labels(self._test_labels).T[0]
self.evaluate_model(test_prediction, test_as_numpy, outfile=f"{self._fit_name}")
- print("=====\n\n")
+ print("=====")
+
+
+ def print_model_summary(self):
+ print("Model Summary")
def model_predict_single_sample(self, sample):
sample_shaped = sample.reshape(1,-1)
@@ -232,7 +238,7 @@ def model_predict_single_sample(self, sample):
def get_maxlikelihood(self)->OptimizeResult:
init_vals = self.training_data.iloc[[1]].to_numpy()[0]
- print("Calculating max LLH")
+ print("[bold purple]Calculating max LLH")
maximal_likelihood = minimize(self.model_predict_single_sample, init_vals, bounds=zip(self._chain.lower_bounds[:-1], self._chain.upper_bounds[:-1]), method="L-BFGS-B", options={"disp": True})
return maximal_likelihood
@@ -245,9 +251,9 @@ def run_likelihood_scan(self, n_divisions: int = 500):
errors = np.sqrt(np.diag(maximal_likelihood.hess_inv(np.identity(self.chain.ndim-1))))
- print("Maximal Pars :")
+ print("[bold red3]Maximal Pars :")
for i in range(self.chain.ndim-1):
- print(f"Param : {self.chain.plot_branches[i]} : {maximal_likelihood.x[i]}±{errors[i]}")
+ print(f"[bold red3]Param :[/bold red3] [yellow3]{self.chain.plot_branches[i]} : {maximal_likelihood.x[i]}±{errors[i]}")
with PdfPages("llh_scan.pdf") as pdf:
@@ -285,13 +291,14 @@ def evaluate_model(self, predicted_values: Iterable, true_values: Iterable, outf
:type outfile: str, optional
"""
- print(predicted_values)
- print(f"Mean Absolute Error : {metrics.mean_absolute_error(predicted_values,true_values)}")
-
+ print(f"[bold red3]Mean Absolute Error :[/bold red3] [yellow3]{metrics.mean_absolute_error(predicted_values,true_values)}")
+ outfile_name = outfile.split(".")[0]
+ outfile = f"{outfile_name}.pdf"
+ warnings.filterwarnings("ignore", message="Polyfit may be poorly conditioned")
lobf = np.poly1d(np.polyfit(predicted_values, true_values, 1))
- print(f"Line of best fit : y={lobf.c[0]}x + {lobf.c[1]}")
+ print(f"[bold purple]Line of best fit :[/bold purple] [dodger_blue1]y={lobf.c[0]}x + {lobf.c[1]}")
fig = plt.figure()
@@ -322,11 +329,21 @@ def evaluate_model(self, predicted_values: Iterable, true_values: Iterable, outf
ax.set_ylabel("True Log Likelihood")
fig.legend()
+
if outfile=="": outfile = f"evaluated_model_qq_tf.pdf"
- print(f"Saving QQ to {outfile}")
+ print(f"[bold spring_green1]Saving QQ to[/bold spring_green1][dodger_blue1] {outfile}")
fig.savefig(outfile)
+
+ try:
+ is_notebook = self.is_notebook()
+ if is_notebook:
+ plt.show()
+ except Exception:
+ ...
+
+
plt.close()
# Gonna draw a hist
@@ -335,4 +352,18 @@ def evaluate_model(self, predicted_values: Iterable, true_values: Iterable, outf
plt.hist(difs, bins=100, density=True, range=(np.std(difs)*-5, np.std(difs)*5))
plt.xlabel("True - Pred")
plt.savefig(f"diffs_5sigma_range_{outfile}")
- plt.close()
\ No newline at end of file
+
+ plt.close()
+
+ @classmethod
+ def is_notebook(cls) -> bool:
+ try:
+ shell = get_ipython().__class__.__name__
+ if shell == 'ZMQInteractiveShell':
+ return True # Jupyter notebook or qtconsole
+ elif shell == 'TerminalInteractiveShell':
+ return False # Terminal running IPython
+ else:
+ return False # Other type (?)
+ except NameError:
+ return False # Probably standard Python interpreter
\ No newline at end of file
diff --git a/src/MaCh3PythonUtils/machine_learning/ml_factory.py b/src/MaCh3PythonUtils/machine_learning/ml_factory.py
index 39a0d61..4ce0860 100644
--- a/src/MaCh3PythonUtils/machine_learning/ml_factory.py
+++ b/src/MaCh3PythonUtils/machine_learning/ml_factory.py
@@ -11,8 +11,9 @@
from MaCh3PythonUtils.machine_learning.tensorflow.tf_manual_interface import TfManualLayeredInterface
from MaCh3PythonUtils.machine_learning.tensorflow.tf_interface import TfInterface
-from MaCh3PythonUtils.file_handling.chain_handler import ChainHandler
+from MaCh3PythonUtils.machine_learning.torch.torch_interface import TorchInterface
+from MaCh3PythonUtils.file_handling.chain_handler import ChainHandler
import sklearn.ensemble as ske
import tensorflow.keras as tfk
@@ -32,6 +33,9 @@ class MLFactory:
"normalizing_flow": TfNormalizingFlowModel,
"autotune": TfAutotuneInterface
},
+ "torch": {
+ "sequential": TorchInterface
+ }
}
def __init__(self, input_chain: ChainHandler, prediction_variable: str, plot_name: str):
@@ -92,7 +96,7 @@ def __make_scikit_model(self, algorithm: str, **kwargs)->SciKitInterface:
def __make_tensorflow_layered_model(self, interface: TfManualLayeredInterface, layers: dict)->TfManualLayeredInterface:
for layer in layers:
layer_id = list(layer.keys())[0]
- interface.add_layer(layer_id, layer[layer_id])
+ interface.add_layer(layer_id, layer[layer_id].copy())
return interface
@@ -106,7 +110,6 @@ def __make_tensorflow_model(self, algorithm: str, **kwargs)->TfInterface:
# Ugh
if algorithm=="sequential" or algorithm=="residual":
- print("HERE")
model = self.__make_tensorflow_layered_model(model, kwargs["Layers"])
model.set_training_settings(kwargs.get("FitSettings"))
@@ -114,6 +117,21 @@ def __make_tensorflow_model(self, algorithm: str, **kwargs)->TfInterface:
model.build_model(**kwargs["BuildSettings"])
return model
+
+ def __make_torch_model(self, algorithm: str, **kwargs)->TorchInterface:
+ model_func = self.__IMPLEMENTED_ALGORITHMS["torch"].get(algorithm.lower(), None)
+
+ if model_func is None:
+ raise Exception(f"Cannot find {algorithm}")
+
+ model: TorchInterface = model_func(self._chain, self._prediction_variable, self._plot_name)
+
+ for layer in kwargs["Layers"]:
+ layer_id = list(layer.keys())[0]
+ model.add_layer(layer_id=layer_id, layer_args=layer[layer_id].copy())
+
+ model.build_model(**kwargs["BuildSettings"], **kwargs["FitSettings"])
+ return model
def make_interface(self, interface_type: str, algorithm: str, **kwargs):
interface_type = interface_type.lower()
@@ -122,5 +140,9 @@ def make_interface(self, interface_type: str, algorithm: str, **kwargs):
return self.__make_scikit_model(algorithm, **kwargs)
case "tensorflow":
return self.__make_tensorflow_model(algorithm, **kwargs)
+ case "torch":
+ return self.__make_torch_model(algorithm, **kwargs)
+
case _:
- raise Exception(f"{interface_type} not implemented!")
\ No newline at end of file
+ raise Exception(f"{interface_type} not implemented!")
+
\ No newline at end of file
diff --git a/src/MaCh3PythonUtils/machine_learning/scikit/scikit_interface.py b/src/MaCh3PythonUtils/machine_learning/scikit/scikit_interface.py
index 9d565a9..a2e86a5 100644
--- a/src/MaCh3PythonUtils/machine_learning/scikit/scikit_interface.py
+++ b/src/MaCh3PythonUtils/machine_learning/scikit/scikit_interface.py
@@ -1,5 +1,6 @@
from pandas import DataFrame
from MaCh3PythonUtils.machine_learning.file_ml_interface import FileMLInterface
+from tqdm import tqdm
"""
TODO:
diff --git a/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_interface.py b/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_interface.py
index 06f4d04..bbe70ce 100644
--- a/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_interface.py
+++ b/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_interface.py
@@ -103,4 +103,5 @@ def evaluate_model(self, predicted_values: Iterable, true_values: Iterable, outf
# CODE TO DO TF SPECIFIC PLOTS GOES HERE
- return super().evaluate_model(predicted_values, true_values, outfile)
\ No newline at end of file
+ return super().evaluate_model(predicted_values, true_values, outfile)
+
diff --git a/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_manual_interface.py b/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_manual_interface.py
index 94bf203..d0e7a05 100644
--- a/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_manual_interface.py
+++ b/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_manual_interface.py
@@ -22,9 +22,8 @@ def train_model(self):
scaled_data = self.scale_data(self._training_data)
scaled_labels = self.scale_labels(self._training_labels)
- lr_schedule = tfk.callbacks.ReduceLROnPlateau(monitor="val_loss", patience=10, factor=0.5, min_lr=1e-8, verbose=1)
+ lr_schedule = tfk.callbacks.ReduceLROnPlateau(monitor="val_loss", patience=10, factor=0.1, min_lr=1e-9, verbose=1)
stop_early = tfk.callbacks.EarlyStopping(monitor='val_loss', patience=20)
-
self._model.fit(scaled_data, scaled_labels, **self._training_settings, callbacks=[lr_schedule, stop_early])
@@ -53,12 +52,6 @@ def add_layer(self, layer_id: str, layer_args: dict):
# Hacky, swaps string value of regularliser for proper one
layer_args["kernel_regularizer"] = tfk.regularizers.L2(layer_args["kernel_regularizer"])
+
self._layers.append(self.__TF_LAYER_IMPLEMENTATIONS[layer_id.lower()](**layer_args))
-
-
-
-
-
-
-
diff --git a/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_sequential_model.py b/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_sequential_model.py
index cbab8e2..46733f2 100644
--- a/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_sequential_model.py
+++ b/src/MaCh3PythonUtils/machine_learning/tensorflow/tf_sequential_model.py
@@ -16,14 +16,17 @@ def build_model(self, **kwargs: dict):
if self._model is None or not self._layers:
raise ValueError("No model can be built! Please setup model and layers")
+ # Add input layer
+ self._model.add(tfk.layers.InputLayer(input_shape=(self._chain.ndim-1,)))
+
for layer in self._layers:
self._model.add(layer)
self._model.build()
- optimizer = tfk.optimizers.AdamW(learning_rate=kwargs.get("learning_rate", 1e-5),
- weight_decay=1e-4, clipnorm=1.0)
+ optimizer = tfk.optimizers.Adam(learning_rate=kwargs.get("learning_rate", 1e-5), clipnorm=10.0)
kwargs.pop("learning_rate", None)
- self._model.compile(**kwargs, optimizer=optimizer)
\ No newline at end of file
+ self._model.compile(**kwargs, optimizer=optimizer)
+
\ No newline at end of file
diff --git a/src/MaCh3PythonUtils/machine_learning/torch/.DS_Store b/src/MaCh3PythonUtils/machine_learning/torch/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/src/MaCh3PythonUtils/machine_learning/torch/.DS_Store differ
diff --git a/src/MaCh3PythonUtils/machine_learning/torch/__init__.py b/src/MaCh3PythonUtils/machine_learning/torch/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/MaCh3PythonUtils/machine_learning/torch/torch_interface.py b/src/MaCh3PythonUtils/machine_learning/torch/torch_interface.py
new file mode 100644
index 0000000..d03412c
--- /dev/null
+++ b/src/MaCh3PythonUtils/machine_learning/torch/torch_interface.py
@@ -0,0 +1,224 @@
+from MaCh3PythonUtils.machine_learning.file_ml_interface import FileMLInterface
+import torch
+import pandas as pd
+import numpy as np
+from tqdm import tqdm_notebook
+from rich import print
+from matplotlib import pyplot as plt
+
+class TorchInterface(FileMLInterface):
+ __TORCH_LAYER_IMPLEMENTATIONS = {
+ "linear": torch.nn.Linear,
+ "relu": torch.nn.ReLU,
+ "sigmoid": torch.nn.Sigmoid,
+ "tanh": torch.nn.Tanh,
+ "softmax": torch.nn.Softmax,
+ "leaky_relu": torch.nn.LeakyReLU,
+ "dropout": torch.nn.Dropout,
+ "batch_norm": torch.nn.BatchNorm1d
+ }
+
+ _layers = []
+ _training_settings = {}
+ _loss_vals = []
+ _mean_weights_per_epoch = []
+ _mean_abs_weights_per_epoch = []
+
+ # Set device
+ device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
+ print(f"Using {device} device")
+
+
+ def add_layer(self, layer_id: str, layer_args: dict):
+ """Add new layer to PyTorch model
+
+ :param layer_id: Layer type [linear/relu/sigmoid/tanh/softmax/leaky_relu/dropout/batchnorm]
+ :type layer_id: str
+ :param layer_args: kwargs for layer
+ :type layer_args: dict
+ :raises ValueError: Layer type not implemented in __TORCH_LAYER_IMPLEMENTATIONS yet
+ """
+
+ if layer_id not in self.__TORCH_LAYER_IMPLEMENTATIONS.keys():
+ raise ValueError(f"{layer_id} not implemented yet!")
+
+ self._layers.append(self.__TORCH_LAYER_IMPLEMENTATIONS[layer_id.lower()](**layer_args))
+
+ def build_model(self, **kwargs):
+ """Build and compile PyTorch model
+
+ :param kwargs: Model arguments as dictionary
+ :type kwargs: dict
+ :raises ValueError: Model not set up yet
+ """
+ self._model = torch.nn.Sequential(*self._layers)
+
+ if self._model is None or not self._layers:
+ raise ValueError("No model can be built! Please setup model and layers")
+
+
+ self._model.to(self.device)
+ self._fit_settings = kwargs
+
+ self._model.compile()
+ self._model.to(self.device)
+
+
+
+ def train_model(self):
+ scaled_data = self.to_tensor(self.scale_data(self._training_data))
+ scaled_labels = self.to_tensor(self.scale_labels(self._training_labels))
+
+ loss = torch.nn.MSELoss()
+ self._learning_rate = self._fit_settings.get("learning_rate", 1e-5)
+ learning_rate_min = self._fit_settings.get("learning_rate_min", 1e-9)
+ learning_rate_decay = self._fit_settings.get("learning_rate_decay", 0.1)
+
+ num_epochs = self._fit_settings.get("num_epochs", 100)
+ debug = self._fit_settings.get("debug", False)
+
+ stop_on_plateau = self._fit_settings.get("stop_on_plateau", True)
+
+ # Debug
+ self._mean_weights_per_epoch = []*num_epochs # Store mean weights per epoch
+ self._mean_abs_weights_per_epoch = []*num_epochs
+
+ self._loss_vals = np.zeros(num_epochs)
+
+
+ for i in (pbar:=tqdm_notebook(range(num_epochs), desc="Training model", unit="epoch")):
+ self.model_training_iter(scaled_data, scaled_labels, loss, self._learning_rate, debug, i)
+ pbar.set_description(f"Epoch {i+1}/{num_epochs} - Loss: {self._loss_vals[i]:.5f}")
+
+ # Early stopping
+ if i < 10 or not stop_on_plateau:
+ continue
+
+ if self.early_stopping(loss, self._learning_rate, learning_rate_min, learning_rate_decay):
+ break
+
+
+ def early_stopping(self, loss, learning_rate, learning_rate_min, learning_rate_decay):
+ """Early stopping function to stop training if loss does not improve
+
+ :param loss: Loss function
+ :type loss: torch.nn.Module
+ :param learning_rate: Learning rate
+ :type learning_rate: float
+ :param learning_rate_min: Minimum learning rate
+ :type learning_rate_min: float
+ :param learning_rate_decay: Learning rate decay factor
+ :type learning_rate_decay: float
+ """
+
+ if all(abs(self._loss_vals[-10:] - self._loss_vals[-1]) < 1e-5):
+ if learning_rate > learning_rate_min:
+ learning_rate *= learning_rate_decay
+
+ else:
+ print("Stopping training early")
+ self._loss_vals = self._loss_vals[:i]
+ return True
+
+ return False
+
+ def model_training_iter(self, scaled_data, scaled_labels, loss, learning_rate, debug, epoch):
+ y_pred = self._model(scaled_data)
+ loss_value = loss(y_pred, scaled_labels)
+ self._model.zero_grad()
+ loss_value.backward()
+
+
+ with torch.no_grad():
+ epoch_weights = []
+ epoch_abs_weights = []
+
+ for param in self._model.parameters():
+ param -= learning_rate * param.grad
+ if not debug:
+ continue
+
+ # Store mean weights
+ if param.requires_grad and param.dim() > 1: # Only weights (not biases)
+ epoch_weights.append(param.data.mean().item())
+ epoch_abs_weights.append(param.data.abs().mean().item())
+
+ self._loss_vals[epoch] = loss_value.item()
+
+ if epoch_weights and debug: # Only if weights were found
+ self._mean_weights_per_epoch.append(np.mean(epoch_weights))
+ self._mean_abs_weights_per_epoch.append(np.mean(epoch_abs_weights))
+ elif debug:
+ self._mean_weights_per_epoch.append(0.0)
+ self._mean_abs_weights_per_epoch.append(0.0)
+
+
+ def to_tensor(self, data):
+ if isinstance(data, pd.DataFrame):
+ data = torch.tensor(data.values.astype(np.float32), device=self.device)
+ elif isinstance(data, np.ndarray):
+ data = torch.tensor(data.astype(np.float32), device=self.device)
+
+ return data
+
+
+ def model_predict(self, test_data):
+ """Predict using the model
+
+ :param test_data: Data to predict
+ :type test_data: np.ndarray
+ :return: Predictions
+ :rtype: np.ndarray
+ """
+
+ # Convert to tensor and move to device
+ if isinstance(test_data, pd.DataFrame):
+ test_data = torch.tensor(test_data.values.astype(np.float32), device=self.device)
+
+ self._model.eval()
+ with torch.no_grad():
+ predictions = self._model(torch.tensor(test_data, device=self.device))
+
+ return predictions.cpu().numpy().T[0]
+
+ def print_model_summary(self):
+ print("Model Summary:")
+ print(f"Model: {self._model}")
+ print(f"Device: {self.device}")
+
+ # Plot loss
+ plt.plot(self._loss_vals, color="orange")
+ plt.xlabel("Epochs")
+ plt.ylabel("Loss")
+ plt.title("Training Loss")
+ plt.show()
+
+ self.plot_weight_trends()
+
+
+ def plot_weight_trends(self):
+ """Plot mean weights and absolute weights over epochs."""
+ plt.figure(figsize=(12, 5))
+
+ plt.subplot(1, 2, 1)
+ plt.plot(self._mean_weights_per_epoch, color="blue")
+ plt.xlabel("Epochs")
+ plt.ylabel("Mean Weight")
+ plt.title("Mean Weight per Epoch")
+
+ plt.subplot(1, 2, 2)
+ plt.plot(self._mean_abs_weights_per_epoch, color="red")
+ plt.xlabel("Epochs")
+ plt.ylabel("Mean Absolute Weight")
+ plt.title("Mean Absolute Weight per Epoch")
+
+ plt.tight_layout()
+ plt.show()
+
+ def save_model(self, path: str) -> None:
+ """Save model weights"""
+ torch.save(self.model.state_dict(), path)
+
+ def load_model(self, path: str) -> None:
+ """Load model weights"""
+ self.model.load_state_dict(torch.load(path))