{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Today we will need a new env, python3.10 is highly recommended (use pyenv if needed)\n", "\n", "```\n", "python3.10 -m venv env_adv\n", "source env_fairness/bin/activate\n", "pip install --upgrade pip\n", "pip install numpy==1.26 fairlearn==0.9.0 plotly==5.24.1 nbformat==5.10.4 aif360['AdversarialDebiasing']==0.6.1 aif360['inFairness']==0.6.1 ipykernel==6.29.5 BlackBoxAuditing==0.1.54 cvxpy==1.6.0 dice-ml==0.11 lime==0.2.0.1\n", "cd env_adv/lib/python3.9/site-packages/aif360/data/raw/meps\n", "Rscipt generate_data.R\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2 new packages in comparison to TD4\n", "`pip install dice-ml==0.11 lime==0.2.0.1`" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "HRe6S30b9Ng7" }, "source": [ "# TD 5: Audit de modèles\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this TD the aim is to analyse the decision made by a model.\n", "You will use 3 different methods:\n", "- feature importances with LIME\n", "- black box auditing that consider the features by couple\n", "- counter factual examples with dice-ml" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "9c9ITNS89NhA" }, "source": [ "## Import and load the dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 17, "status": "ok", "timestamp": 1707145084689, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "88mMZIic9NhB", "outputId": "46750ce4-6670-4c2c-bdb7-b4b9ab7c4af1" }, "outputs": [], "source": [ "# imports\n", "import numpy as np\n", "import pandas as pd\n", "import plotly.express as px\n", "import warnings\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", "warnings.simplefilter(action=\"ignore\", append=True, category=UserWarning)\n", "# Datasets\n", "from aif360.datasets import MEPSDataset19\n", "\n", "# Fairness metrics\n", "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "MEPSDataset19_data = MEPSDataset19()\n", "(dataset_orig_panel19_train, dataset_orig_panel19_val, dataset_orig_panel19_test) = (\n", " MEPSDataset19().split([0.5, 0.8], shuffle=True)\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "executionInfo": { "elapsed": 22874, "status": "ok", "timestamp": 1707145107555, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "LYbdfIPs9NhE" }, "outputs": [ { "data": { "text/plain": [ "(7915, 4749, 3166)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataset_orig_panel19_train.instance_weights), len(\n", " dataset_orig_panel19_val.instance_weights\n", "), len(dataset_orig_panel19_test.instance_weights)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-03-24 22:25:16.209714: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2025-03-24 22:25:16.220061: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1742851516.231002 159003 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1742851516.234134 159003 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2025-03-24 22:25:16.246206: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } ], "source": [ "from aif360.sklearn.metrics import *\n", "from sklearn.metrics import balanced_accuracy_score\n", "\n", " \n", "# This method takes lists\n", "def get_metrics(\n", " y_true, # list or np.array of truth values\n", " y_pred=None, # list or np.array of predictions\n", " prot_attr=None, # list or np.array of protected/sensitive attribute values\n", " priv_group=1, # value taken by the privileged group\n", " pos_label=1, # value taken by the positive truth/prediction\n", " sample_weight=None # list or np.array of weights value,\n", "):\n", " group_metrics = {}\n", " group_metrics[\"base_rate_truth\"] = base_rate(\n", " y_true=y_true, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " group_metrics[\"statistical_parity_difference\"] = statistical_parity_difference(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, priv_group=priv_group, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " group_metrics[\"disparate_impact_ratio\"] = disparate_impact_ratio(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, priv_group=priv_group, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " if not y_pred is None:\n", " group_metrics[\"base_rate_preds\"] = base_rate(\n", " y_true=y_pred, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " group_metrics[\"equal_opportunity_difference\"] = equal_opportunity_difference(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, priv_group=priv_group, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " group_metrics[\"average_odds_difference\"] = average_odds_difference(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, priv_group=priv_group, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " if len(set(y_pred))>1:\n", " group_metrics[\"conditional_demographic_disparity\"] = conditional_demographic_disparity(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " else:\n", " group_metrics[\"conditional_demographic_disparity\"] =None\n", " group_metrics[\"smoothed_edf\"] = smoothed_edf(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " group_metrics[\"df_bias_amplification\"] = df_bias_amplification(\n", " y_true=y_true, y_pred=y_pred, prot_attr=prot_attr, pos_label=pos_label, sample_weight=sample_weight\n", " )\n", " group_metrics[\"balanced_accuracy_score\"] = balanced_accuracy_score(\n", " y_true=y_true, y_pred=y_pred, sample_weight=sample_weight\n", " )\n", " return group_metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utilisation de LIME\n", "### Question 1.1 - apprendre une regression logistique qui prédit l'UTILIZATION (comme dans le TD3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8383099526592211" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.pipeline import make_pipeline\n", "\n", "X_train = dataset_orig_panel19_train.features\n", "y_train = dataset_orig_panel19_train.labels[:,0]\n", "X_val = dataset_orig_panel19_val.features\n", "y_val = dataset_orig_panel19_val.labels[:,0]\n", "\n", "\n", "model = make_pipeline(StandardScaler(), LogisticRegression(solver='liblinear', random_state=42))\n", "\n", "model = model.fit(\n", " X_train,\n", " y_train,\n", " **{\"logisticregression__sample_weight\": dataset_orig_panel19_train.instance_weights}\n", ")\n", "\n", "preds = model.predict_proba(X_val)\n", "\n", "model.score(X_val, y_val, sample_weight=dataset_orig_panel19_val.instance_weights)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['RACE']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_orig_panel19_train.protected_attribute_names" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4749, 2)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preds.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1.2 (optionelle) - Observer l'impact du threshold sur les performances de la regression logistique (balanced accuracy et disparate impact)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "thresh_arr = np.linspace(0.01, 0.5, 50)\n", "metrics_list=[]\n", "for thr in thresh_arr:\n", " y_val_pred = (preds[:, -1] > thr).astype(np.float64)\n", " metrics = get_metrics(y_true=dataset_orig_panel19_val.labels[:,0], y_pred=y_val_pred, prot_attr=dataset_orig_panel19_val.protected_attributes[:,0], sample_weight=dataset_orig_panel19_val.instance_weights)\n", " metrics['threshold'] = thr\n", " metrics_list.append(metrics)\n", "df_metrics = pd.DataFrame.from_records(metrics_list)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "dimensions": [ { "label": "threshold", "values": [ 0.01, 0.02, 0.03, 0.04, 0.05, 0.060000000000000005, 0.06999999999999999, 0.08, 0.09, 0.09999999999999999, 0.11, 0.12, 0.13, 0.14, 0.15000000000000002, 0.16, 0.17, 0.18000000000000002, 0.19, 0.2, 0.21000000000000002, 0.22, 0.23, 0.24000000000000002, 0.25, 0.26, 0.27, 0.28, 0.29000000000000004, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36000000000000004, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(df_metrics, x='balanced_accuracy_score', y='disparate_impact_ratio', color='threshold', hover_data=[\"threshold\"])\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1.3 : apprendre un LimeEncoder (nomer l'objet lime_data) sur le dataset AIF360 de train, puis transformer avec ce LimeEncoder le dataset de train et celui de test en s_train et s_test" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from aif360.datasets.lime_encoder import LimeEncoder" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "\n", "lime_data = LimeEncoder().fit(dataset_orig_panel19_train)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "s_train = lime_data.transform(dataset_orig_panel19_train.features)\n", "s_test = lime_data.transform(dataset_orig_panel19_test.features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1.4 use LimeTabularExplainer to explain the decision made on several instances of the test dataset.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from lime.lime_tabular import LimeTabularExplainer" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "explainer = LimeTabularExplainer(\n", " s_train, \n", " class_names=lime_data.s_class_names, \n", " feature_names=lime_data.s_feature_names,\n", " categorical_features=lime_data.s_categorical_features, \n", " categorical_names=lime_data.s_categorical_names, \n", " kernel_width=3, verbose=False, discretize_continuous=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def s_predict_fn(x):\n", " return model.predict_proba(lime_data.inverse_transform(x))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def show_explanation(ind):\n", " exp = explainer.explain_instance(s_test[ind], s_predict_fn, num_features=10)\n", " print(\"Actual label: \" + str(dataset_orig_panel19_test.labels[ind]))\n", " exp.as_pyplot_figure()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Actual label: [0.]\n" ] }, { "data": { "image/png": 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", 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nokuXLnIdNzc3uLm54auvvsLRo0fRpk0bLFu2DN9++22h/bl48SLWrFmDn376Cffv34eTkxNmzJgBPz8/+YMwrwoNDUVwcHCh7drZ2RW6Sl1U77zzDnR0dHD69Gn4+PjI5enp6YiJidEoK8igQYOwbNkybN68GdevX4ckSRgwYECRjq1fvz4mTZqESZMmIS4uDk2bNsWCBQuwbt26POsX9t/Hm7xnVLq40kdUjrS1tdG5c2fs2LFD44/G/fv3sWHDBrRt21Z+bEOfPn3w119/Yfv27bnayVkVylk1enWV6MSJEwU+CLYgPj4+uHv3LlauXJlr3/PnzzUuFU6dOhW3bt3C2rVrsXDhQtjb22PYsGF5XqZasWIFMjIy5NdLly5FZmamxh/315X22PJqDwAWLVqUq25OUCrKN3J07doVJ0+e1OjX06dPsWLFCtjb28PFxaVE/S0Ka2trNG3aFGvXrtXo6/nz57Fv374CQ3VhivLz97qiznFWVlauS341a9aEjY2N/POjVquRmZmpUcfNzQ1aWlp5/oy9Kjo6Gu+//z5cXV2xZMkSdO7cGYcOHcLly5cxderUPAMfUD739JmamqJjx45Yt24dUlNT5fKffvoJT548KfD5fq9q06YN7O3tsW7dOmzevBnt27dHnTp1Cjzm2bNnePHihUZZ/fr1YWxsXOAc5/ffx5u8Z1Q2uNJH9BasXr1avqT1qgkTJuDbb79FZGQk2rZti9GjR0NHRwfLly9HWloa5s2bJ9edPHmy/FBXf39/NG/eHElJSdi5cyeWLVuGJk2aoHv37ti2bRs+/PBDdOvWDdevX8eyZcvg4uJS6Fc45WXIkCGIiIhAQEAAoqKi0KZNG2RlZeHSpUuIiIjAb7/9hhYtWuDgwYP44YcfEBgYiHfffRcAsGbNGnh4eODrr7/WGAfwctWiQ4cO8PHxweXLl/HDDz+gbdu26NmzZ759Ke2xmZiYyPcsZWRkoHbt2ti3bx+uX7+eq27z5s0BAF9++SV8fX2hq6uLHj165LlqNm3aNGzcuBFdunTB+PHjYW5ujrVr1+L69ev4+eefy/zbO+bPn48uXbqgdevWGDFihPzIFlNT0zf6ruWi/Py9rqhznJqaijp16qBv375o0qQJjIyMsH//fpw6dQoLFiwA8PLZg2PHjkW/fv3g5OSEzMxM/PTTT9DW1kafPn0K7PuhQ4eQkZGBH374AQMHDizyo0IcHBw0PmD1pnJWti5cuADgZZD7/fffAQBfffWVXG/mzJlwd3dH+/btMWrUKNy5cwcLFixA586d4e3tXaRzSZKEgQMHYtasWQCAkJCQQo+5cuWK/N+li4sLdHR0sH37dty/fx++vr75Hle/fn2YmZlh2bJlMDY2hqGhIVq1aoW//vqrxO8ZlZHy++AwUdWX84iS/Lbbt28LIYQ4c+aM8PLyEkZGRqJatWrC09NTHD16NFd7iYmJYuzYsaJ27dpCT09P1KlTRwwbNkw8evRICPHy0RmzZs0SdnZ2QqVSiWbNmoldu3aJYcOG5XqsBIrwyBYhhEhPTxdz584Vrq6uQqVSierVq4vmzZuL4OBgkZKSItRqtbCzsxPvvvuuyMjI0Dj2008/FVpaWuLYsWMa83Ho0CExatQoUb16dWFkZCQGDRqk8YgRIXI/FqKoY8t5DEVej514fcx37twRH374oTAzMxOmpqaiX79+4t69e3nOzYwZM0Tt2rWFlpaWxuNbXn9kixBC/PPPP6Jv377CzMxM6Ovri5YtW4pdu3Zp1MnvkSt5PfYkL/kdL4QQ+/fvF23atBEGBgbCxMRE9OjRQ1y8eFGjTs7jSB4+fFjgeV5V2M9fXn0vyhynpaWJyZMniyZNmghjY2NhaGgomjRpIn744Qe5nWvXrgl/f39Rv359oa+vL8zNzYWnp6fYv39/of1+8uRJkcdYlgr6XfC6I0eOCHd3d6Gvry8sLS3FmDFjhFqtLtb5Lly4IAAIlUolHj9+nGv/6+/Xo0ePxJgxY4Szs7MwNDQUpqamolWrViIiIkLjuNf/2xTi5SORXFxchI6Ojtzmm7xnVDYkId7S3cJEpHjh4eEYPnw4Tp06levhs0REVLZ4Tx8RERGRAjD0ERERESkAQx8RERGRAvCePiIiIiIF4EofERERkQIw9BEREREpAB/OTLLs7Gzcu3cPxsbGxfraKSIiIio/QgikpqbCxsamwAfAM/SR7N69e/L3vBIREVHlcvv27QK/bo+hj2TGxsYAXv7Q5HzfKxEREVVsarUatra28t/x/DD0kSznkq6JiQlDHxERUSVT2K1Z/CAHERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQLolHcHiIhKkxQslXcXiIjyJAJFuZ6fK31ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1EREREClChQ19CQgLGjRsHBwcHqFQq2NraokePHjhw4AAAwN7eHosWLcp1XFBQEJo2barxWpIkeTM1NUW7du1w6NAhjePs7e3lOtWqVYObmxt+/PHHXO1nZWUhLCwMbm5u0NfXR/Xq1dGlSxf88ccfGvXCw8MhSRK8vb01ypOTkyFJEqKjo0s2MXm4cOEC+vTpI48hr3khIiIi5aqwoe/GjRto3rw5Dh48iPnz5+PcuXPYu3cvPD09MWbMmGK35+rqivj4eMTHx+PYsWNo0KABunfvjpSUFI16ISEhiI+Px/nz5zF48GCMHDkSe/bskfcLIeDr64uQkBBMmDABsbGxiI6Ohq2tLTw8PPDLL79otKejo4P9+/cjKiqqRPNQVM+ePYODgwPmzJkDKyurMj0XERERVT4V9rt3R48eDUmScPLkSRgaGsrlrq6u8Pf3L3Z7Ojo6chiysrJCSEgI1qxZgytXruC9996T6xkbG8v1pk6dinnz5iEyMhJdunQBAERERGDr1q3YuXMnevToIR+3YsUKJCYm4uOPP0anTp3kPhsaGsLHxwfTpk3DiRMnij8RRfTee+/J45g2bVqZnYeIiIgqpwq50peUlIS9e/dizJgxGoEvh5mZ2Ru1n5aWhjVr1sDMzAwNGzbMs052djZ+/vlnPH78GHp6enL5hg0b4OTkpBH4ckyaNAmJiYmIjIzUKA8KCsK5c+ewdevWfPvk6uoKIyOjfLec0Fma0tLSoFarNTYiIiKqmirkSt/Vq1chhICzs3OhdadOnYqvvvpKoyw9PR0uLi4aZefOnYORkRGAl5dCjY2NsXnzZpiYmOTZXlpaGjIzM2Fubo6PP/5Y3n/lyhU0atQoz77klF+5ckWj3MbGBhMmTMCXX36J3r1753ns7t27kZGRke84DQwM8t1XUrNnz0ZwcHCpt0tEREQVT4Vc6RNCFLnu5MmTERMTo7EFBATkqtewYUN5/59//olPPvkE/fr1w+nTp/Ns7+DBg2jVqhXCwsLg6OhY4v7lmDp1Kh4+fIjVq1fnud/Ozg6Ojo75brVr1wYA3Lp1S2MFcNasWcXuS47p06cjJSVF3m7fvl3itoiIiKhiq5ArfQ0aNIAkSbh06VKhdWvUqJErlJmbm+eqp6enp1GvWbNm+OWXX7Bo0SKsW7cuV3uOjo7YsmUL3Nzc0KJFC3nl0MnJCbGxsXn2Jafcyckp1z4zMzNMnz4dwcHB6N69e679rq6uuHnzZr7jbNeuHfbs2QMbGxvExMQUONaiUqlUUKlUJT6eiIiIKo8KGfrMzc3h5eWFJUuWYPz48bnu60tOTn7j+/oAQFtbG8+fP893v62tLfr374/p06djx44dAABfX18MHDgQ//vf/3Ld17dgwQJYWFigU6dOebY3btw4/Oc//8HixYtz7Svq5V0dHZ1cIZeIiIioMBUy9AHAkiVL0KZNG7Rs2RIhISFo3LgxMjMzERkZiaVLl+a72pafzMxMJCQkAABSU1OxefNmXLx4EVOnTi3wuAkTJuCdd97B6dOn0aJFC/j6+mLLli0YNmwY5s+fjw4dOkCtVmPJkiXYuXMntmzZkueHTwBAX18fwcHBeT5yxs7OrljjeV16ejouXrwo//vu3buIiYmBkZERQyIRERFVzHv6AMDBwQFnzpyBp6cnJk2ahHfeeQedOnXCgQMHsHTp0mK3d+HCBVhbW8Pa2hpNmzZFREQEli5diqFDhxZ4nIuLCzp37oxvvvkGACBJEiIiIvDFF18gLCwMDRs2RLt27XDz5k1ER0fn+0GNHMOGDYODg0Ox+1+Ye/fuoVmzZmjWrBni4+MRGhqKZs2aaXwIhYiIiJRLEiX5VAJVSWq1GqampkhJScn1qWaiykIKlsq7C0REeRKBZRO5ivr3u8Ku9BERERFR6WHoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBaiw38hBRFQSZfUcLCKiyo4rfUREREQKwNBHREREpAAMfUREREQKwNBHREREpAAMfUREREQKwNBHREREpAB8ZAsRVSlSsFTeXSCiKqKqPQKKK31ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1EREREClDpQl9CQgLGjRsHBwcHqFQq2NraokePHjhw4IBc5+jRo+jatSuqV68OfX19uLm5YeHChcjKysrVXlRUFLp37w5LS0vo6+ujfv366N+/Pw4fPizXiY6OhiRJSE5OzrNPQUFBaNq0qcZrSZLg7e2dq+78+fMhSRI8PDxKPAd5mTlzJtzd3VGtWjWYmZmVattERERU+VWq0Hfjxg00b94cBw8exPz583Hu3Dns3bsXnp6eGDNmDABg+/btaN++PerUqYOoqChcunQJEyZMwLfffgtfX18I8X9fqfLDDz+gQ4cOsLCwwObNm3H58mVs374d7u7u+PTTT9+or9bW1oiKisKdO3c0ylevXo26deu+Udt5SU9PR79+/fDJJ5+UettERERU+VWq794dPXo0JEnCyZMnYWhoKJe7urrC398fT58+xciRI9GzZ0+sWLFC3v/xxx+jVq1a6NmzJyIiItC/f3/cunULEydOxMSJE7Fw4UKN8zRu3Bjjx49/o77WrFkTzZs3x9q1a/Hll18CeLkC+ejRI/Tr1w8XL158o/ZfFxwcDAAIDw8v8jFpaWlIS0uTX6vV6lLtExEREVUclWalLykpCXv37sWYMWM0Al8OMzMz7Nu3D4mJifj8889z7e/RowecnJywceNGAMDPP/+MjIwMTJkyJc/zSdKbf2m7v7+/RghbvXo1Bg0aBD09PY1669evh5GRUYHbkSNH3rg/r5s9ezZMTU3lzdbWttTPQURERBVDpQl9V69ehRACzs7O+da5cuUKAKBRo0Z57nd2dpbrXLlyBSYmJrCyspL3//zzzxpB69y5c2/U5+7du0OtVuPw4cN4+vQpIiIi4O/vn6tez549ERMTU+DWokWLN+pLXqZPn46UlBR5u337dqmfg4iIiCqGSnN599V78Uqr7uureV5eXoiJicHdu3fh4eGR5wc/ikNXVxeDBw/GmjVrcO3aNTg5OaFx48a56hkbG8PY2LhIbQYEBGDdunXy6ydPnpS4fyqVCiqVqsTHExERUeVRaVb6GjRoAEmScOnSpXzrODk5AQBiY2Pz3B8bGyvXadCgAVJSUpCQkCDvNzIygqOjI+zs7Eqt3/7+/tiyZQuWLFmS5yofULzLuyEhIRorgERERERFUWlW+szNzeHl5YUlS5Zg/Pjxue7rS05ORufOnWFubo4FCxbA3d1dY//OnTsRFxeHGTNmAAD69u2LadOmYe7cuQgLCyuzfru6usLV1RV///03Bg4cmGednj17olWrVgW2U7t2bQAvPyBSs2bNUu8nERERVW2VJvQBwJIlS9CmTRu0bNkSISEhaNy4MTIzMxEZGYmlS5ciNjYWy5cvh6+vL0aNGoWxY8fCxMQEBw4cwOTJk9G3b1/4+PgAAOrWrYsFCxZgwoQJSEpKgp+fH+rVq4ekpCT58qm2trbG+c+dO6dxGVaSJDRp0qTQfh88eBAZGRn5Pj+vOJd383Pr1i0kJSXh1q1byMrKklcBHR0dYWRk9EZtExERUeVXqUKfg4MDzpw5g5kzZ2LSpEmIj4+HpaUlmjdvjqVLlwJ4uYIXFRWFmTNnol27dnjx4gUaNGiAL7/8EhMnTtS4j2/cuHFo1KgRFi5ciL59+0KtVsPCwgKtW7fG3r174ebmpnH+Dz74QOO1trY2MjMzC+13Xp82Lm3ffPMN1q5dK79u1qwZgJcPny7tB0ETERFR5SOJ4nxCgqo0tVoNU1NTpKSkwMTEpLy7Q1QiUvCbP26JiAgARGDliEhF/ftdaT7IQUREREQlx9BHREREpAAMfUREREQKwNBHREREpAAMfUREREQKwNBHREREpACV6jl9RESFqSyPWCAietu40kdERESkAAx9RERERArA0EdERESkAAx9RERERArA0EdERESkAAx9RERERArAR7YQUZUiBUvl3QUixeGjkioHrvQRERERKQBDHxEREZECMPQRERERKQBDHxEREZECMPQRERERKQBDHxEREZECMPQRERERKQBDHxEREZECMPQRERERKUCVDX1+fn7o3bs3JEkqcAsKCsKNGzc0yiwsLNC5c2ecPXtWo81jx45BW1sb3bp1y3W+nDZiYmI0XtesWROpqakadZs2bYqgoKBSG+uLFy/g5+cHNzc36OjooHfv3qXWNhEREVUNVTb05YiPj5e3RYsWwcTERKPs888/l+vu378f8fHx+O233/DkyRN06dIFycnJ8v5Vq1Zh3LhxOHz4MO7du1ek86empiI0NLS0h6UhKysLBgYGGD9+PDp27Fim5yIiIqLKqcqHPisrK3kzNTWFJEkaZUZGRnJdCwsLWFlZoUWLFggNDcX9+/dx4sQJAMCTJ0+wefNmfPLJJ+jWrRvCw8OLdP5x48Zh4cKFePDgQVkMDwBgaGiIpUuXYuTIkbCysiqz8xAREVHlVeVDX0kZGBgAANLT0wEAERERcHZ2RsOGDTF48GCsXr0aQhT+BdMDBgyAo6MjQkJC8q0TEBAAIyOjAreykJaWBrVarbERERFR1aRT3h2oiJKTkzFjxgwYGRmhZcuWAF5e2h08eDAAwNvbGykpKTh06BA8PDwKbEuSJMyZMwc9evTAp59+ivr16+eqExISonGZ+W2ZPXs2goOD3/p5iYiI6O3jSt8r3N3dYWRkhOrVq+Ovv/7C5s2bUatWLVy+fBknT57EgAEDAAA6Ojro378/Vq1aVaR2vby80LZtW3z99dd57q9ZsyYcHR0L3HK4urrKq39dunR5o/FOnz4dKSkp8nb79u03ao+IiIgqLq70vWLz5s1wcXGBhYUFzMzM5PJVq1YhMzMTNjY2cpkQAiqVCt9//z1MTU0LbXvOnDlo3bo1Jk+enGtfQEAA1q1bV+DxT548AQDs3r0bGRkZAP7vEnRJqVQqqFSqN2qDiIiIKgeGvlfY2trmuvyamZmJ//73v1iwYAE6d+6ssa93797YuHEjAgICCm27ZcuW+OijjzBt2rRc+4pzedfOzq5I9YiIiIhexdBXiF27duHx48cYMWJErhW9Pn36YNWqVUUKfQAwc+ZMuLq6QkdHc9pr1qyJmjVrvlE/L168iPT0dCQlJSE1NVV+XmDTpk3fqF0iIiKqGqps6MvOzs4Vrkpi1apV6NixY56XcPv06YN58+bh77//homJSaFtOTk5wd/fHytWrHjjfr2ua9euuHnzpvy6WbNmAFCkTxgTERFR1SeJKpoKvL294ejoiO+//768u1JpqNVqmJqaIiUlpUghlqgikoKl8u4CkeKIwCoZJSqNov79rnKf3n38+DF27dqF6OhofjsFERER0f9X5S7v+vv749SpU5g0aRJ69epV3t0hIiIiqhCqXOjbvn17eXeBiIiIqMKpcpd3iYiIiCg3hj4iIiIiBWDoIyIiIlKAKndPHxEpGx8dQUSUN670ERERESkAQx8RERGRAjD0ERERESkAQx8RERGRAjD0ERERESkAQx8RERGRAvCRLURUpUjBUnl3gajU8BFEVJq40kdERESkAAx9RERERArA0EdERESkAAx9RERERArA0EdERESkAAx9RERERArA0EdERESkAAx9RERERArA0EdERESkAMUKfX5+fpAkCZIkQU9PD46OjggJCUFmZqZcRwiBFStWoFWrVjAyMoKZmRlatGiBRYsW4dmzZwCAZ8+eYfr06ahfvz709fVhaWmJ9u3bY8eOHXmeNyAgAJIkYdGiRXLZjRs3MGLECNSrVw8GBgaoX78+AgMDkZ6eXoJpeHvi4+MxcOBAODk5QUtLCxMnTizScbdu3UK3bt1QrVo11KxZE5MnT9aYdwCIjo7Gu+++C5VKBUdHR4SHh5f+AIiIiKhSKvbXsHl7e2PNmjVIS0vD7t27MWbMGOjq6mL69OkAgCFDhmDbtm346quv8P3338PS0hJ//fUXFi1aBHt7e/Tu3RsBAQE4ceIEvvvuO7i4uCAxMRFHjx5FYmJirvNt374dx48fh42NjUb5pUuXkJ2djeXLl8PR0RHnz5/HyJEj8fTpU4SGhpZwOl6Gq7p165b4+MKkpaXB0tISX331FcLCwop0TFZWFrp16wYrKyscPXoU8fHxGDp0KHR1dTFr1iwAwPXr19GtWzcEBARg/fr1OHDgAD7++GNYW1vDy8urzMZDRERElYMkhCjyF/v5+fkhOTkZv/zyi1zWuXNnpKam4tixY4iIiED//v3xyy+/oFevXhrHCiGgVqthamoKMzMzLF68GMOGDSvwfHfv3kWrVq3w22+/oVu3bpg4cWKBK2Pz58/H0qVLce3ataIOCQCgVquxZcsWrF27FufPn0dSUlKxji8pDw8PNG3aVGMFMy979uxB9+7dce/ePdSqVQsAsGzZMkydOhUPHz6Enp4epk6dil9//RXnz5+Xj/P19UVycjL27t1bpP7kvD8pKSkwMTEp8biIyhO/e5eqEn73LhVFUf9+v/E9fQYGBvIl1fXr16Nhw4a5Ah8ASJIEU1NTAICVlRV2796N1NTUfNvNzs7GkCFDMHnyZLi6uhapLykpKTA3Ny9S3ezsbERGRmLw4MGwsrLCnDlz0KFDB5w+fVquc+vWLRgZGRW45ay0laVjx47Bzc1NDnwA4OXlBbVajQsXLsh1OnbsqHGcl5cXjh07lm+7aWlpUKvVGhsRERFVTcW+vJtDCIEDBw7gt99+w7hx4wAAcXFxaNiwYaHHrlixAoMGDYKFhQWaNGmCtm3bom/fvmjTpo1cZ+7cudDR0cH48eOL1J+rV6/iu+++K/TS7pUrVxAeHo6ffvoJT58+hY+PD/bv3w93d/dcdW1sbBATE1Nge0UNmW8iISFBI/ABkF8nJCQUWEetVuP58+cwMDDI1e7s2bMRHBxcRr0mIiKiiqTYoW/Xrl0wMjJCRkYGsrOzMXDgQAQFBQF4GQSL4oMPPsC1a9dw/PhxHD16FAcOHMDixYsRHByMr7/+Gn/++ScWL16MM2fOQJIKv1Rz9+5deHt7o1+/fhg5cmSBdUeNGoVDhw4hICAACxcuzDMM5dDR0YGjo2ORxpQXIyMj+d+DBw/GsmXLStxWWZg+fTo+++wz+bVarYatrW059oiIiIjKSrEv73p6eiImJgZxcXF4/vw51q5dC0NDQwCAk5MTLl26VKR2dHV10a5dO0ydOhX79u1DSEgIZsyYgfT0dBw5cgQPHjxA3bp1oaOjAx0dHdy8eROTJk2Cvb29Rjv37t2Dp6cn3N3dsWLFikLPu2jRIowdOxZbtmyBk5MTpk2bJl8ifd2bXt6NiYmRt5CQkCLNS16srKxw//59jbKc11ZWVgXWMTExyTfYqlQqmJiYaGxERERUNRV7pc/Q0DDf1a+BAwfC19cXO3bsKPCDHHlxcXFBZmYmXrx4gSFDhuR5f9qQIUMwfPhwuezu3bvw9PRE8+bNsWbNGmhpFZ5hmzZtiu+++w4LFizArl27sHbtWjRr1gzvvPMOhgwZggEDBshB6k0v777JKuGrWrdujZkzZ+LBgweoWbMmACAyMhImJiZwcXGR6+zevVvjuMjISLRu3bpU+kBERESVW4nv6cuLj48Ptm/fjgEDBuCrr75C586dYWlpiXPnziEsLAzjxo1D79694eHhgQEDBqBFixawsLDAxYsX8cUXX8DT01NebbKwsNBoW1dXF1ZWVvI9g3fv3oWHhwfs7OwQGhqKhw8fynVzQltB9PT08NFHH+Gjjz7Cw4cPsX79eqxduxbffvut/OiYN728m5+cIPnkyRM8fPgQMTEx0NPTkwPc9u3bMX36dHnVtHPnznBxccGQIUMwb948JCQk4KuvvsKYMWOgUqkAvHyW4ffff48pU6bA398fBw8eREREBH799ddS7z8RERFVPqUa+iRJwoYNG7BixQqsXr0aM2fOhI6ODho0aIChQ4fKz4vz8vLC2rVr8cUXX+DZs2ewsbFB9+7d8c033xT5XJGRkbh69SquXr2KOnXqaOwrxlNoAACWlpby42CKenn6TTRr1kz+959//okNGzbAzs4ON27cAPDyU8iXL1+W62hra2PXrl345JNP0Lp1axgaGmLYsGEal4zr1auHX3/9FZ9++ikWL16MOnXq4Mcff+Qz+oiIiAhAMZ/TR1Ubn9NHVQGf00dVCZ/TR0Xx1p7TR0REREQVH0MfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpQKk+nJmIqLzxuWZERHnjSh8RERGRAjD0ERERESkAQx8RERGRAjD0ERERESkAQx8RERGRAjD0ERERESkAH9lCb4cklXcPSCkEH9lCRJQXrvQRERERKQBDHxEREZECMPQRERERKQBDHxEREZECMPQRERERKQBDHxEREZECMPQRERERKQBDHxEREZEClEnoO3bsGLS1tdGtWzeN8hs3bkCSJHkzNjaGq6srxowZg7i4OI26WVlZmDNnDpydnWFgYABzc3O0atUKP/74o1zHz88PkiQhICAgVx/GjBkDSZLg5+enUX779m34+/vDxsYGenp6sLOzw4QJE5CYmKhRz8PDAxMnTtQoW7x4MVQqFTZt2pTrfAEBAZAkCYsWLdIY74gRI1CvXj0YGBigfv36CAwMRHp6ekHTV2zbtm1D586dYWFhAUmSEBMTU6rtExERUeVXJqFv1apVGDduHA4fPox79+7l2r9//37Ex8fjr7/+wqxZsxAbG4smTZrgwIEDcp3g4GCEhYVhxowZuHjxIqKiojBq1CgkJydrtGVra4tNmzbh+fPnctmLFy+wYcMG1K1bV6PutWvX0KJFC8TFxWHjxo24evUqli1bhgMHDqB169ZISkrKd0yBgYH44osvsGPHDvj6+mrs2759O44fPw4bGxuN8kuXLiE7OxvLly/HhQsXEBYWhmXLluGLL74odA6L4+nTp2jbti3mzp1bqu0SERFRFSJKWWpqqjAyMhKXLl0S/fv3FzNnzpT3Xb9+XQAQZ8+e1TgmKytLeHh4CDs7O5GZmSmEEKJJkyYiKCiowHMNGzZM9OrVS7zzzjti3bp1cvn69etF48aNRa9evcSwYcPkcm9vb1GnTh3x7NkzjXbi4+NFtWrVREBAgFzWvn17MWHCBJGdnS3Gjh0rzMzMxB9//JGrD3fu3BG1a9cW58+fF3Z2diIsLKzAPs+bN0/Uq1evwDolld/8FlVKSooAIFJSUkq3Y0II8fLLsbhxK/uNiEhhivr3u9RX+iIiIuDs7IyGDRti8ODBWL16NYQQBR6jpaWFCRMm4ObNm/jzzz8BAFZWVjh48CAePnxY6Dn9/f2xZs0a+fXq1asxfPhwjTpJSUn47bffMHr0aBgYGGjss7KywqBBg7B582aNvmZmZmLw4MHYunUrDh06BHd3d43jsrOzMWTIEEyePBmurq6F9hMAUlJSYG5uLr++desWjIyMCtxmzZpVpLaLKy0tDWq1WmMjIiKiqkmntBtctWoVBg8eDADw9vZGSkoKDh06BA8PjwKPc3Z2BvDyPriWLVti4cKF6Nu3L6ysrODq6gp3d3f06tULXbp0yXXs4MGDMX36dNy8eRMA8Mcff2DTpk2Ijo6W68TFxUEIgUaNGuV5/kaNGuHx48d4+PAhatasCQBYuXIlAOCvv/6S+/equXPnQkdHB+PHjy94Uv6/q1ev4rvvvkNoaKhcZmNjU+g9eK+GxNI0e/ZsBAcHl0nbREREVLGU6krf5cuXcfLkSQwYMAAAoKOjg/79+2PVqlWFHpuzwiZJEgDAxcUF58+fx/Hjx+Hv748HDx6gR48e+Pjjj3Mda2lpiW7duiE8PBxr1qxBt27dUKNGjQLPUxRt27aFkZERvv76a2RmZmrs+/PPP7F48WKEh4fLfS7I3bt34e3tjX79+mHkyJFyuY6ODhwdHQvcckLf+vXrNVYAjxw5UuSx5GX69OlISUmRt9u3b79Re0RERFRxlWroW7VqFTIzM2FjYwMdHR3o6Ohg6dKl+Pnnn5GSklLgsbGxsQCAevXq/V/ntLTw3nvvYeLEidi2bRvCw8OxatUqXL9+Pdfx/v7+CA8Px9q1a+Hv759rv6OjIyRJks+T1/mrV68OS0tLuczNzQ0HDhxAVFQU+vfvrxH8jhw5ggcPHqBu3bryWG/evIlJkybB3t5eo+179+7B09MT7u7uWLFihca+4lze7dmzJ2JiYuStRYsWBc5pYVQqFUxMTDQ2IiIiqppK7fJuZmYm/vvf/2LBggXo3Lmzxr7evXtj48aN8Pb2zvPY7Oxs/Oc//0G9evXQrFmzfM/h4uIC4OWnVV/n7e2N9PR0SJIELy+vXPstLCzQqVMn/PDDD/j000817utLSEjA+vXrMXTo0Fyrdk2bNsWBAwfQsWNH+Pj4YPPmzdDV1cWQIUPQsWNHjbpeXl4YMmSIxv2Ed+/ehaenJ5o3b441a9ZAS0szZxfn8q6xsTGMjY0LrEtERESUl1ILfbt27cLjx48xYsQImJqaauzr06cPVq1aJYe+xMREJCQk4NmzZzh//jwWLVqEkydP4tdff4W2tjYAoG/fvmjTpg3c3d1hZWWF69evY/r06XBycsrz/jptbW15FS+njdd9//33cHd3h5eXF7799lvUq1cPFy5cwOTJk1G7dm3MnDkzz+OaNGmCgwcPokOHDvDx8UFERAQsLCxgYWGhUU9XVxdWVlZo2LAhgJeBz8PDA3Z2dggNDdX4UIqVlRWA/7u8+yaSkpJw69Yt+fE4ly9fls+Rcx4iIiJStlK7vLtq1Sp07NgxV+ADXoa+06dPy58O7dixI6ytreHm5oZp06ahUaNG+Pvvv+Hp6Skf4+Xlhf/973/o0aMHnJycMGzYMDg7O2Pfvn3Q0ck7qxZ2ibJBgwY4ffo0HBwc4OPjg/r162PUqFHw9PTEsWPHCvzAhJubGw4ePIijR4+iX79+RXrAcmRkJK5evYoDBw6gTp06sLa2lrfStHPnTjRr1kx+GLavry+aNWuGZcuWlep5iIiIqPKSRHE+2UBVmlqthqmpKVJSUkr//r4ifNiFqFTwVxoRKUxR/37zu3eJiIiIFIChj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFKDUvpGDqEB8dhoREVG54kofERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAB/ZQuVDksq7B1RV8fFARER54kofERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQJUmtDn5+cHSZIgSRL09PTg6OiIkJAQZGZmIjo6GpIkITk5Oddx9vb2WLRokUbZ0aNH0bVrV1SvXh36+vpwc3PDwoULkZWVJde5ceMGRowYgXr16sHAwAD169dHYGAg0tPT8+zf1atXYWxsDDMzM43ylStXol27dqhevTqqV6+Ojh074uTJk286HRpevHgBPz8/uLm5QUdHB7179y7V9omIiKjyqzShDwC8vb0RHx+PuLg4TJo0CUFBQZg/f36x2ti+fTvat2+POnXqICoqCpcuXcKECRPw7bffwtfXF+L/f4XTpUuXkJ2djeXLl+PChQsICwvDsmXL8MUXX+RqMyMjAwMGDEC7du1y7YuOjsaAAQMQFRWFY8eOwdbWFp07d8bdu3dLNgl5yMrKgoGBAcaPH4+OHTuWWrtERERUhYhKYtiwYaJXr14aZZ06dRLvv/++iIqKEgDE48ePcx1nZ2cnwsLChBBCPHnyRFhYWIiPPvooV72dO3cKAGLTpk359mHevHmiXr16ucqnTJkiBg8eLNasWSNMTU0LHEdmZqYwNjYWa9euLbBeSeU1T0WVkpIiAIiUlJTS7VReXn5DKjdupb8RESlMUf9+V6qVvtcZGBjke7k1L/v27UNiYiI+//zzXPt69OgBJycnbNy4Md/jU1JSYG5urlF28OBBbNmyBUuWLClSH549e4aMjAyNdgICAmBkZFTgVhbS0tKgVqs1NiIiIqqadMq7AyUhhMCBAwfw22+/Ydy4cXJ5nTp1ctV99uyZ/O8rV64AABo1apRnu87OznKd1129ehXfffcdQkND5bLExET4+flh3bp1MDExKVLfp06dChsbG43LsCEhIXkG0bI2e/ZsBAcHv/XzEhER0dtXqULfrl27YGRkhIyMDGRnZ2PgwIEICgrCqVOnAABHjhyBsbGxxjEeHh652hFC5HsOPT29XGV3796Ft7c3+vXrh5EjR8rlI0eOxMCBA/HBBx8Uqf9z5szBpk2bEB0dDX19fbm8Zs2aqFmzZpHacHV1xc2bNwEA7dq1w549e4p0XF6mT5+Ozz77TH6tVqtha2tb4vaIiIio4qpUoc/T0xNLly6Fnp4ebGxsoKOj2f169erl+vTsq3UaNGgAAIiNjYW7u3uu9mNjY9G0aVONsnv37sHT0xPu7u5YsWKFxr6DBw9i586d8uqfEALZ2dnQ0dHBihUr4O/vL9cNDQ3FnDlzsH//fjRu3FijnYCAAKxbt67AsT958gQAsHv3bmRkZAB4eXn7TahUKqhUqjdqg4iIiCqHShX6DA0N4ejoWOLjvby8YG5ujgULFuQKfTt37kRcXJzG413u3r0LT09PNG/eHGvWrIGWluYtkMeOHdN4zMuOHTswd+5cHD16FLVr15bL582bh5kzZ+K3335DixYtcvWrOJd37ezsilSPiIiI6FWVKvS9KUNDQyxfvhy+vr4YNWoUxo4dCxMTExw4cACTJ0/GyJEj0bVrVwAvA5+Hhwfs7OwQGhqKhw8fyu1YWVkByH1v4OnTp6GlpYV33nlHLps7dy6++eYbbNiwAfb29khISAAAjQ9oFOfybn4uXryI9PR0JCUlITU1FTExMQCQa+WSiIiIlElRoQ8A+vbti6ioKMycORPt2rWTP7E6d+5cTJkyRa4XGRmJq1ev4urVq7k+IFLQPYGvW7p0KdLT09G3b1+N8sDAQAQFBZV8IK/p2rWrfK8fADRr1gxA8fpKREREVZckFJ4KXrx4gV69euH27ds4dOgQLC0ty7tL5UatVsPU1BQpKSlF/jRyiUlS2bZPyqXsX2lEpEBF/ftdqZ/TVxr09fWxY8cODB06FIcPHy7v7hARERGVCcWv9NH/4UofVQn8lUZECsOVPiIiIiKSMfQRERERKQBDHxEREZECMPQRERERKYDintNHFQRvticiInqruNJHREREpAAMfUREREQKwNBHREREpAAMfUREREQKwNBHREREpAAMfUREREQKwEe2UNXA7/KlHHwcEBFRnrjSR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQAlT70LVu2DMbGxsjMzJTLnjx5Al1dXXh4eGjUjY6OhiRJ+Oeff2Bvb49Fixbl2eaNGzcgSRJiYmLkstTUVHh6esLFxQV37tzJs86rwsPDYWZmpvFakiQ0atQoV90tW7ZAkiTY29sXcdSaMjIyMHXqVLi5ucHQ0BA2NjYYOnQo7t27V6L2iIiIqOqp9KHP09MTT548wenTp+WyI0eOwMrKCidOnMCLFy/k8qioKNStWxf169cv1jkePnwIT09PPH36FEeOHEGdOnVK1FdDQ0M8ePAAx44d0yhftWoV6tatW6I2AeDZs2c4c+YMvv76a5w5cwbbtm3D5cuX0bNnzxK3SURERFVLpQ99DRs2hLW1NaKjo+Wy6Oho9OrVC/Xq1cPx48c1yj09PYvV/u3bt9GuXTuYmpri4MGDsLCwKHFfdXR0MHDgQKxevVouu3PnDqKjozFw4MASt2tqaorIyEj4+PigYcOGeP/99/H999/jzz//xK1bt0rcLhEREVUdlT70AS9X+6KiouTXUVFR8PDwQPv27eXy58+f48SJE8UKfZcvX0abNm3g4uKC3bt3w8jI6I376u/vj4iICDx79gzAy8u+3t7eqFWrlka9I0eOwMjIqMBt/fr1+Z4nJSUFkiRpXGJ+XVpaGtRqtcZGREREVZNOeXegNHh6emLixInIzMzE8+fPcfbsWbRv3x4ZGRlYtmwZAODYsWNIS0srVugbOnQo2rRpgy1btkBbW7tU+tqsWTM4ODhg69atGDJkCMLDw7Fw4UJcu3ZNo16LFi3yvV8wx+tBMceLFy8wdepUDBgwACYmJvkeP3v2bAQHBxd7DERERFT5VInQ5+HhgadPn+LUqVN4/PgxnJycYGlpifbt22P48OF48eIFoqOj4eDgUKx753r27IlffvkF27ZtQ79+/Uqtv/7+/lizZg3q1q2Lp0+fomvXrvj+++816hgYGMDR0bHYbWdkZMDHxwdCCCxdurTAutOnT8dnn30mv1ar1bC1tS32OYmIiKjiqxKhz9HREXXq1EFUVBQeP36M9u3bAwBsbGxga2uLo0ePIioqCv/617+K1e6XX36Jxo0bY+DAgRBCwMfHp1T6O2jQIEyZMgVBQUEYMmQIdHRyvw1HjhxBly5dCmxn+fLlGDRokPw6J/DdvHkTBw8eLHCVDwBUKhVUKlXJBkFERESVSpUIfcDLS7zR0dF4/PgxJk+eLJd/8MEH2LNnD06ePIlPPvmk2O1+/fXX0NLSwqBBgyCEQP/+/d+4r+bm5ujZsyciIiLky8+vK+7l3ZzAFxcXh6ioqDf6wAkRERFVPVUq9I0ZMwYZGRnySh8AtG/fHmPHjkV6enqu+/nu3r2bK1jZ2dnlavvLL7+EtrY2Bg0ahOzsbAwYMEDed/ny5Vz1XV1dC+1veHg4fvjhh3zDWXEu72ZkZKBv3744c+YMdu3ahaysLCQkJAB4GTD19PSK1A4RERFVXVUq9D1//hzOzs4aK2Dt27dHamqq/GiXV4WGhiI0NFSj7KeffkLbtm1ztT9t2jRoaWlhyJAhEELA3d0dAODr65ur7u3btwvtr4GBAQwMDIo0tsLcvXsXO3fuBAA0bdpUY1/OJ5mJiIhI2SQhhCjvTlDFoFarYWpqipSUlELvB6xwJKm8e0AVBX+lEZHCFPXvd5V4Th8RERERFYyhj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFKDKPJyZFI7PZiMiIioQV/qIiIiIFIChj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFICf3iWiKkUKlsq7C1TGRCA/rU9UElzpIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlKAMg99fn5+kCQJkiRBV1cXtWrVQqdOnbB69WpkZ2fL9ezt7eV6OVudOnU09i9atChX+0FBQWjatKnG69fbkSQJzs7OuY7duHEjtLW1MWbMmFz7oqOjNY63tLRE165dce7cuXzHOmfOHEiShIkTJ8plSUlJGDduHBo2bAgDAwPUrVsX48ePR0pKSiEzVzyHDx9Gjx49YGNjA0mS8Msvv5Rq+0RERFS5vZWVPm9vb8THx+PGjRvYs2cPPD09MWHCBHTv3h2ZmZlyvZCQEMTHx8vb2bNnS3Q+V1dXjXbi4+Px+++/56q3atUqTJkyBRs3bsSLFy/ybOvy5cuIj4/Hb7/9hrS0NHTr1g3p6em56p06dQrLly9H48aNNcrv3buHe/fuITQ0FOfPn0d4eDj27t2LESNGlGhs+Xn69CmaNGmCJUuWlGq7REREVDW8le/eValUsLKyAgDUrl0b7777Lt5//3106NAB4eHh+PjjjwEAxsbGcr03oaOjU2g7169fx9GjR/Hzzz8jKioK27Ztw8CBA3PVq1mzJszMzGBlZYWJEyeiZ8+euHTpkka4e/LkCQYNGoSVK1fi22+/1Tj+nXfewc8//yy/rl+/PmbOnInBgwcjMzMTOjql8xZ06dIFXbp0KZW2iIiIqOopt3v6/vWvf6FJkybYtm1buZx/zZo16NatG0xNTTF48GCsWrWqwPopKSnYtGkTAEBPT09j35gxY9CtWzd07NixSOdOSUmBiYmJRuAzMjIqcAsICCjmCAuXlpYGtVqtsREREVHV9FZW+vLj7OyMv//+W349depUfPXVV/LrWbNmYfz48fnuB4D09HS4uLholJ07dw5GRkYaZYMHD8ayZcsAANnZ2QgPD8d3330HAPD19cWkSZNw/fp11KtXT+O4nPsKnz59CgDo2bOnxv2BmzZtwpkzZ3Dq1KkijfnRo0eYMWMGRo0apVEeExNT4HEmJiZFar84Zs+ejeDg4FJvl4iIiCqecg19QghIkiS/njx5Mvz8/OTXNWrU0Kj/+n4A+M9//oPDhw9rlDVs2BA7d+7UKHs1NEVGRuLp06fo2rWrfJ6cD5fMmDFD47gjR46gWrVqOH78OGbNmiUHRwC4ffs2JkyYgMjISOjr6xc6XrVajW7dusHFxQVBQUEa+xwdHQs9Pqc/r17GXb58OQYNGlSkY183ffp0fPbZZxr9s7W1LVFbREREVLGVa+iLjY3VWFmrUaNGgeEnr/3m5ua56unp6RXYzqpVq5CUlAQDAwO5LDs7G3///TeCg4OhpfV/V73r1asHMzMzNGzYEA8ePED//v3lkPnnn3/iwYMHePfdd+X6WVlZOHz4ML7//nukpaVBW1sbAJCamgpvb28YGxtj+/bt0NXV1ejT6yuTr8tZqWzRooXGqmCtWrUKPK4gKpUKKpWqxMcTERFR5VFuoe/gwYM4d+4cPv3007d63sTEROzYsQObNm2Cq6urXJ6VlYW2bdti37598Pb2zvPYMWPGYPbs2di+fTs+/PBDdOjQIdcjXIYPHw5nZ2dMnTpVDnxqtRpeXl5QqVTYuXNnnquCRb28a2BgUORVQSIiIqIcbyX0paWlISEhAVlZWbh//z727t2L2bNno3v37hg6dGipny8zMxMJCQkaZZIkoVatWvjpp59gYWEBHx8fjUvLANC1a1esWrUq39BXrVo1jBw5EoGBgejduzeMjY3xzjvvaNQxNDSEhYWFXK5Wq9G5c2c8e/YM69at0/jAhKWlpRwM3zTIPXnyBFevXpVfX79+HTExMTA3N0fdunXfqG0iIiKq/N5K6Nu7dy+sra2ho6OD6tWro0mTJvjPf/6DYcOGaVxKLS0XLlyAtbW1RplKpcKLFy+wevVqfPjhh7kCHwD06dMHQ4YMwaNHj/Jte+zYsVi4cCG2bNkCHx+fQvty5swZnDhxAkDuYHf9+nXY29sXYUSFO336NDw9PeXXOffqDRs2DOHh4aVyDiIiIqq8JCGEKO9OUMWgVqthamoqP1KGqDKSgnP/HzqqWkQg/2wRvaqof7/53btERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1EREREClCu371LRFTa+Aw3IqK8caWPiIiISAEY+oiIiIgUgKGPiIiISAEY+oiIiIgUgKGPiIiISAEY+oiIiIgUgI9sISotklTePSAAEHxkCxFRXrjSR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQADH1ERERECsDQR0RERKQAVSb0PXz4EJ988gnq1q0LlUoFKysreHl54Y8//pDrHD16FF27dkX16tWhr68PNzc3LFy4EFlZWbnai4qKQteuXWFhYYFq1arBxcUFkyZNwt27dwEA0dHRkCQJycnJefYnKCgITZs21XgtSRK8vb1z1Z0/fz4kSYKHh0eJxp6UlIRx48ahYcOGMDAwQN26dTF+/HikpKSUqD0iIiKqeqpM6OvTpw/Onj2LtWvX4sqVK9i5cyc8PDyQmJgIANi+fTvat2+POnXqICoqCpcuXcKECRPw7bffwtfXF+KVr25avnw5OnbsCCsrK/z888+4ePEili1bhpSUFCxYsKDEfbS2tkZUVBTu3LmjUb569WrUrVu3xO3eu3cP9+7dQ2hoKM6fP4/w8HDs3bsXI0aMKHGbREREVLVUie/eTU5OxpEjRxAdHY327dsDAOzs7NCyZUsAwNOnTzFy5Ej07NkTK1askI/7+OOPUatWLfTs2RMRERHo378/7ty5g/Hjx2P8+PEICwuT69rb2+ODDz7Id2WvKGrWrInmzZtj7dq1+PLLLwG8XH189OgR+vXrh4sXL5ao3XfeeQc///yz/Lp+/fqYOXMmBg8ejMzMTOjo5P02p6WlIS0tTX6tVqtLdH4iIiKq+KrESp+RkRGMjIzwyy+/aISYHPv27UNiYiI+//zzXPt69OgBJycnbNy4EQCwZcsWpKenY8qUKXmey8zM7I366u/vj/DwcPn16tWrMWjQIOjp6WnUW79+vTyu/LYjR47ke56UlBSYmJjkG/gAYPbs2TA1NZU3W1vbNxobERERVVxVIvTp6OggPDwca9euhZmZGdq0aYMvvvgCf//9NwDgypUrAIBGjRrlebyzs7NcJy4uDiYmJrC2ti6Tvnbv3h1qtRqHDx/G06dPERERAX9//1z1evbsiZiYmAK3Fi1a5HmOR48eYcaMGRg1alSBfZk+fTpSUlLk7fbt26UyRiIiIqp4qsTlXeDlPX3dunXDkSNHcPz4cezZswfz5s3Djz/+KNd59b69/AghIElSmfVTV1cXgwcPxpo1a3Dt2jU4OTmhcePGueoZGxvD2Ni42O2r1Wp069YNLi4uCAoKKrCuSqWCSqUq9jmIiIio8qkSK3059PX10alTJ3z99dc4evQo/Pz8EBgYCCcnJwBAbGxsnsfFxsbKdZycnJCSkoL4+Pgy66e/vz+2bNmCJUuW5LnKB5Ts8m5qaiq8vb1hbGyM7du3Q1dXt8zGQERERJVLlQp9r3NxccHTp0/RuXNnmJub5/nJ2507dyIuLg4DBgwAAPTt2xd6enqYN29enm2+yQc5cri6usLV1RXnz5/HwIED86xT3Mu7arUanTt3hp6eHnbu3Al9ff037icRERFVHVXi8m5iYiL69esHf39/NG7cGMbGxjh9+jTmzZuHXr16wdDQEMuXL4evry9GjRqFsWPHwsTEBAcOHMDkyZPRt29f+Pj4AABsbW0RFhaGsWPHQq1WY+jQobC3t8edO3fw3//+F0ZGRhrh8dy5cxqXYSVJQpMmTQrt88GDB5GRkZHvB0OKc3k3J/A9e/YM69atg1qtlj+Ja2lpCW1t7SK1Q0RERFVXlQh9RkZGaNWqFcLCwvDPP/8gIyMDtra2GDlyJL744gsAL1fwoqKiMHPmTLRr1w4vXrxAgwYN8OWXX2LixIka9/GNHj0aTk5OCA0NxYcffojnz5/D3t4e3bt3x2effaZx7g8++EDjtba2NjIzMwvts6GhYSmM/KUzZ87gxIkTAABHR0eNfdevX4e9vX2pnYuIiIgqJ0kU5dMNpAhqtRqmpqby416omMrwA0BUDPyVRkQKU9S/31X6nj4iIiIieomhj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFIChj4iIiEgBqsRz+ogqBD4qhIiIKjCu9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQIw9BEREREpAEMfERERkQLwkS1EpU2SyrsHysZH5xAR5YkrfUREREQKwNBHREREpAAMfUREREQKwNBHREREpAAMfUREREQKwNBHREREpAAMfUREREQKwNBHREREpAAMfUREREQKUClCn5+fH3r37p2rPDo6GpIkITk5GQCQlZWFsLAwuLm5QV9fH9WrV0eXLl3wxx9/aBwXHh4OSZLg7e2tUZ6cnAxJkhAdHS2XSZIkbyYmJnjvvfewY8eOPNuTJAna2tqoXr06WrVqhZCQEKSkpOTqd0JCAsaNGwcHBweoVCrY2tqiR48eOHDgQInm5/Lly/D09EStWrWgr68PBwcHfPXVV8jIyChRe0RERFT1VIrQVxRCCPj6+iIkJAQTJkxAbGwsoqOjYWtrCw8PD/zyyy8a9XV0dLB//35ERUUV2vaaNWsQHx+P06dPo02bNujbty/OnTunUcfExATx8fG4c+cOjh49ilGjRuG///0vmjZtinv37sn1bty4gebNm+PgwYOYP38+zp07h71798LT0xNjxowp0dh1dXUxdOhQ7Nu3D5cvX8aiRYuwcuVKBAYGlqg9IiIiqnqqzHfvRkREYOvWrdi5cyd69Oghl69YsQKJiYn4+OOP0alTJxgaGgIADA0N4ePjg2nTpuHEiRMFtm1mZgYrKytYWVlhxowZWLx4MaKiouDm5ibXkSQJVlZWAABra2s0atQIPXr0gKurK6ZMmYJ169YBAEaPHg1JknDy5Em5LwDg6uoKf3//Eo3dwcEBDg4O8ms7OztER0fjyJEjJWqPiIiIqp4qs9K3YcMGODk5aQS+HJMmTUJiYiIiIyM1yoOCgnDu3Dls3bq1SOfIzMzEqlWrAAB6enqF1q9ZsyYGDRqEnTt3IisrC0lJSdi7dy/GjBmjEfhymJmZyf/u0qULjIyM8t1cXV3zPe/Vq1exd+9etG/fvsD+paWlQa1Wa2xERERUNVWalb5du3bByMhIoywrK0v+95UrV9CoUaM8j80pv3Llika5jY0NJkyYgC+//DLPewZzDBgwANra2nj+/Dmys7Nhb28PHx+fIvXb2dkZqampSExMxI0bNyCEgLOzc6HH/fjjj3j+/Hm++3V1dXOVubu748yZM0hLS8OoUaMQEhJS4Dlmz56N4ODgwgdBRERElV6lWenz9PRETEyMxvbjjz9q1BFCFLvdqVOn4uHDh1i9enW+dcLCwhATE4M9e/bAxcUFP/74I8zNzYvUfk6fJEkqVv9q164NR0fHfDc7O7tcx2zevBlnzpzBhg0b8OuvvyI0NLTAc0yfPh0pKSnydvv27SL3j4iIiCqXSrPSZ2hoCEdHR42yO3fuyP92cnJCbGxsnsfmlDs5OeXaZ2ZmhunTpyM4OBjdu3fP83grKys5bK1ZswZdu3bFxYsXUbNmzUL7HRsbCxMTE1hYWEBbWxuSJOHSpUuFHtelS5cC78mzs7PDhQsXNMpsbW0BAC4uLsjKysKoUaMwadIkaGtr59mGSqWCSqUqtC9ERERU+VWalb7C+Pr6Ii4uDv/73/9y7VuwYAEsLCzQqVOnPI8dN24ctLS0sHjx4kLP07JlSzRv3hwzZ84stO6DBw+wYcMG9O7dG1paWjA3N4eXlxeWLFmCp0+f5qqf8+gZ4OXl3ddXNl/ddu/eXeC5s7OzkZGRgezs7EL7SURERFVfpVnpK4yvry+2bNmCYcOGYf78+ejQoQPUajWWLFmCnTt3YsuWLXl+eAIA9PX1ERwcXORHpkycOBEffvghpkyZgtq1awN4eRk3ISEBQggkJyfj2LFjmDVrFkxNTTFnzhz52CVLlqBNmzZo2bIlQkJC0LhxY2RmZiIyMhJLly6VVyVz2i2K9evXQ1dXF25ublCpVDh9+jSmT5+O/v3753nvHxERESlPlQl9kiQhIiICixYtQlhYGEaPHg19fX20bt0a0dHRaNOmTYHHDxs2DAsWLMDFixcLPZe3tzfq1auHmTNn4ocffgAAqNVqWFtbyw9xbtiwIYYNG4YJEybAxMREPtbBwQFnzpzBzJkzMWnSJMTHx8PS0hLNmzfH0qVLSzR2HR0dzJ07F1euXIEQAnZ2dhg7diw+/fTTErVHREREVY8kSvLpB6qS1Go1TE1NkZKSohFUqZgkqbx7oGz8lUZEClPUv99V5p4+IiIiIsofQx8RERGRAjD0ERERESkAQx8RERGRAjD0ERERESkAQx8RERGRAlSZ5/QRVRh8ZAgREVVAXOkjIiIiUgCGPiIiIiIFYOgjIiIiUgCGPiIiIiIFYOgjIiIiUgCGPiIiIiIFYOgjIiIiUgCGPiIiIiIFYOgjIiIiUgCGPiIiIiIFYOgjIiIiUgCGPiIiIiIFYOgjIiIiUgCGPiIiIiIFYOgjIiIiUgCd8u4AVRxCCACAWq0u554QERFRUeX83c75O54fhj6SpaamAgBsbW3LuSdERERUXKmpqTA1Nc13vyQKi4WkGNnZ2bh37x6MjY0hSdJbO69arYatrS1u374NExOTt3beyobzVDjOUeE4R4XjHBWOc1S4tzlHQgikpqbCxsYGWlr537nHlT6SaWlpoU6dOuV2fhMTE/7yKALOU+E4R4XjHBWOc1Q4zlHh3tYcFbTCl4Mf5CAiIiJSAIY+IiIiIgVg6KNyp1KpEBgYCJVKVd5dqdA4T4XjHBWOc1Q4zlHhOEeFq4hzxA9yEBERESkAV/qIiIiIFIChj4iIiEgBGPqIiIiIFIChj4iIiEgBGPqIiIiIFIChj96KpKQkDBo0CCYmJjAzM8OIESPw5MmTAo9ZsWIFPDw8YGJiAkmSkJycXCrtVlQlGcuLFy8wZswYWFhYwMjICH369MH9+/c16kiSlGvbtGlTWQ6l1CxZsgT29vbQ19dHq1atcPLkyQLrb9myBc7OztDX14ebmxt2796tsV8IgW+++QbW1tYwMDBAx44dERcXV5ZDKHOlPUd+fn65fl68vb3LcghlrjhzdOHCBfTp0wf29vaQJAmLFi164zYrg9Keo6CgoFw/R87OzmU4grJXnDlauXIl2rVrh+rVq6N69ero2LFjrvrl8vtIEL0F3t7eokmTJuL48ePiyJEjwtHRUQwYMKDAY8LCwsTs2bPF7NmzBQDx+PHjUmm3oirJWAICAoStra04cOCAOH36tHj//feFu7u7Rh0AYs2aNSI+Pl7enj9/XpZDKRWbNm0Senp6YvXq1eLChQti5MiRwszMTNy/fz/P+n/88YfQ1tYW8+bNExcvXhRfffWV0NXVFefOnZPrzJkzR5iamopffvlF/PXXX6Jnz56iXr16lWI+8lIWczRs2DDh7e2t8fOSlJT0toZU6oo7RydPnhSff/652Lhxo7CyshJhYWFv3GZFVxZzFBgYKFxdXTV+jh4+fFjGIyk7xZ2jgQMHiiVLloizZ8+K2NhY4efnJ0xNTcWdO3fkOuXx+4ihj8rcxYsXBQBx6tQpuWzPnj1CkiRx9+7dQo+PiorKM/S9absVSUnGkpycLHR1dcWWLVvkstjYWAFAHDt2TC4DILZv315mfS8rLVu2FGPGjJFfZ2VlCRsbGzF79uw86/v4+Ihu3bpplLVq1Ur8+9//FkIIkZ2dLaysrMT8+fPl/cnJyUKlUomNGzeWwQjKXmnPkRAvQ1+vXr3KpL/lobhz9Co7O7s8A82btFkRlcUcBQYGiiZNmpRiL8vXm77nmZmZwtjYWKxdu1YIUX6/j3h5l8rcsWPHYGZmhhYtWshlHTt2hJaWFk6cOFHh2i0PJRnLn3/+iYyMDHTs2FEuc3Z2Rt26dXHs2DGNumPGjEGNGjXQsmVLrF69GqKCP5M9PT0df/75p8bYtLS00LFjx1xjy3Hs2DGN+gDg5eUl179+/ToSEhI06piamqJVq1b5tlmRlcUc5YiOjkbNmjXRsGFDfPLJJ0hMTCz9AbwFJZmj8mizPJXleOLi4mBjYwMHBwcMGjQIt27detPulovSmKNnz54hIyMD5ubmAMrv9xFDH5W5hIQE1KxZU6NMR0cH5ubmSEhIqHDtloeSjCUhIQF6enowMzPTKK9Vq5bGMSEhIYiIiEBkZCT69OmD0aNH47vvviv1MZSmR48eISsrC7Vq1dIof31sr0pISCiwfs7/FqfNiqws5ggAvL298d///hcHDhzA3LlzcejQIXTp0gVZWVmlP4gyVpI5Ko82y1NZjadVq1YIDw/H3r17sXTpUly/fh3t2rVDamrqm3b5rSuNOZo6dSpsbGzkkFdev490yqxlqvKmTZuGuXPnFlgnNjb2LfWmYqoIc/T111/L/27WrBmePn2K+fPnY/z48WV6XqqcfH195X+7ubmhcePGqF+/PqKjo9GhQ4dy7BlVJl26dJH/3bhxY7Rq1Qp2dnaIiIjAiBEjyrFnb9+cOXOwadMmREdHQ19fv1z7wtBHJTZp0iT4+fkVWMfBwQFWVlZ48OCBRnlmZiaSkpJgZWVV4vOXVbulqSznyMrKCunp6UhOTtZY7bt//36B42/VqhVmzJiBtLS0CvVF4K+qUaMGtLW1c30SuaCxWVlZFVg/53/v378Pa2trjTpNmzYtxd6/HWUxR3lxcHBAjRo1cPXq1UoX+koyR+XRZnl6W+MxMzODk5MTrl69Wmptvi1vMkehoaGYM2cO9u/fj8aNG8vl5fX7iJd3qcQsLS3h7Oxc4Kanp4fWrVsjOTkZf/75p3zswYMHkZ2djVatWpX4/GXVbmkqyzlq3rw5dHV1ceDAAbns8uXLuHXrFlq3bp1vn2JiYlC9evUKG/gAQE9PD82bN9cYW3Z2Ng4cOJDv2Fq3bq1RHwAiIyPl+vXq1YOVlZVGHbVajRMnThQ4XxVVWcxRXu7cuYPExESNP0yVRUnmqDzaLE9vazxPnjzBP//8o6ifo3nz5mHGjBnYu3evxv3aQDn+Piqzj4gQvcLb21s0a9ZMnDhxQvz++++iQYMGGo8juXPnjmjYsKE4ceKEXBYfHy/Onj0rVq5cKQCIw4cPi7Nnz4rExMQit1uZlGSOAgICRN26dcXBgwfF6dOnRevWrUXr1q3l/Tt37hQrV64U586dE3FxceKHH34Q1apVE998881bHVtJbNq0SahUKhEeHi4uXrwoRo0aJczMzERCQoIQQoghQ4aIadOmyfX/+OMPoaOjI0JDQ0VsbKwIDAzM85EtZmZmYseOHeLvv/8WvXr1qvSPbCnNOUpNTRWff/65OHbsmLh+/brYv3+/ePfdd0WDBg3EixcvymWMb6q4c5SWlibOnj0rzp49K6ytrcXnn38uzp49K+Li4orcZmVTFnM0adIkER0dLa5fvy7++OMP0bFjR1GjRg3x4MGDtz6+0lDcOZozZ47Q09MTW7du1XhsTWpqqkadt/37iKGP3orExEQxYMAAYWRkJExMTMTw4cM1fvivX78uAIioqCi5LDAwUADIta1Zs6bI7VYmJZmj58+fi9GjR4vq1auLatWqiQ8//FDEx8fL+/fs2SOaNm0qjIyMhKGhoWjSpIlYtmyZyMrKeptDK7HvvvtO1K1bV+jp6YmWLVuK48ePy/vat28vhg0bplE/IiJCODk5CT09PeHq6ip+/fVXjf3Z2dni66+/FrVq1RIqlUp06NBBXL58+W0MpcyU5hw9e/ZMdO7cWVhaWgpdXV1hZ2cnRo4cWWnDTI7izFHOf2evb+3bty9ym5VRac9R//79hbW1tdDT0xO1a9cW/fv3F1evXn2LIyp9xZkjOzu7POcoMDBQrlMev48kISr4sxuIiIiI6I3xnj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBWDoIyIiIlIAhj4iIiIiBfh/ItW7gPqNHIQAAAAASUVORK5CYII=", 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", 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", 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HH3yAGTNmoE2bNuo6NWrUQI0aNfDll1/iyJEjaNSoEZYtW4ZvvvmmwP5cvHgRK1euxOrVq3Hv3j24u7vj66+/RmBgoPqDgK+aP38+pk2bVmC7Tk5OBf6VorCqV68OPT09nDp1CgEBAeryjIwMxMTEaJTlp0+fPli2bBk2bNiAa9euQZIk9OrVq1DHVq1aFWPHjsXYsWORkJCA2rVrY8GCBVizZk2u9Qv6+XiT54zeLs70ElGp0dXVRatWrbBt2zaNN8179+5h3bp1aNy4sfq2RV27dsW5c+ewdetWrXZyZgVzZg1fnSU8fvx4vjfCz09AQADu3LmDn3/+WWvfs2fPNP5UPmHCBNy8eROrVq3CwoUL4ezsjAEDBuT6Z9qffvoJmZmZ6sdLly5FVlaWRrh53dseW27tAcDixYu16uYExcJ8I9snn3yCEydOaPTryZMn+Omnn+Ds7AwvL69i9bcwbG1tUbt2baxatUqjrxcuXMDu3bvz/aWiIIV5/b2usNf4xYsXWn/ytra2hp2dnfr1o1KpkJWVpVGnRo0a0NHRyfU19qro6Gh89NFH8Pb2xpIlS9CqVSscOHAA8fHxmDBhQq6BFyidNb1mZmbw8/PDmjVrkJqaqi5fvXo10tLS8r2/76saNWoEZ2dnrFmzBhs2bEDTpk1hb2+f7zFPnz7F8+fPNcqqVq0KExOTfK9xXj8fb/KcUcngTC8RlbgVK1ao/6T7qlGjRuGbb77Bnj170LhxYwwbNgx6enpYvnw50tPTMXfuXHXdcePGqW9qHxQUhLp16+Lx48fYvn07li1bhlq1aqFdu3aIjIxE586d0bZtW1y7dg3Lli2Dl5dXgV9hmpt+/fph48aNGDJkCKKiotCoUSO8ePECly5dwsaNG7Fr1y7Uq1cP+/fvx48//ojQ0FB88MEHAICVK1eiWbNmmDJlisY4gJezVi1atEBAQADi4+Px448/onHjxujQoUOefXnbYzM1NVWvWczMzETlypWxe/duXLt2Tatu3bp1AQCTJ09Gz549Ua5cObRv3z7XWdOJEyciIiICbdq0wciRI2FhYYFVq1bh2rVr2LJlS4l/e9u8efPQpk0bNGzYEIMGDVLfsszMzAxTp04tdruFef29rrDXODU1Ffb29ujWrRtq1aoFY2Nj7N27FydPnsSCBQsAvLz38PDhw9G9e3e4u7sjKysLq1evhq6uLrp27Zpv3w8cOIDMzEz8+OOP6N27d6FvleXi4qLxAdM3lTOzGRsbC+BlkP37778BAF9++aW63owZM+Dj44OmTZti8ODBuH37NhYsWIBWrVrB39+/UOeSJAm9e/fGzJkzAQDTp08v8JjLly+rfy69vLygp6eHrVu34t69e+jZs2eex1WtWhXm5uZYtmwZTExMYGRkhAYNGuDcuXPFfs6ohJTejSOISO5ybtGV13br1i0hhBBnzpwRrVu3FsbGxqJ8+fLC19dXHDlyRKu9R48eieHDh4vKlSsLfX19YW9vLwYMGCAePnwohHh566iZM2cKJycnoVAoRJ06dcSOHTvEgAEDtG6rhELcskwIITIyMsScOXOEt7e3UCgUokKFCqJu3bpi2rRpQqlUCpVKJZycnMQHH3wgMjMzNY79/PPPhY6Ojjh69KjG9Thw4IAYPHiwqFChgjA2NhZ9+vTRuMWWENq3RSrs2HJuw5TbbZdeH/Pt27dF586dhbm5uTAzMxPdu3cXd+/ezfXafP3116Jy5cpCR0dH4/Zlr9+yTAghrly5Irp16ybMzc2FgYGBqF+/vtixY4dGnbxuOZbbbb9yk9fxQgixd+9e0ahRI2FoaChMTU1F+/btxcWLFzXq5NyO68GDB/me51UFvf5y63thrnF6eroYN26cqFWrljAxMRFGRkaiVq1a4scff1S3c/XqVREUFCSqVq0qDAwMhIWFhfD19RV79+4tsN9paWmFHmNJyu//Ba87dOiQ8PHxEQYGBsLKykoEBwcLlUpVpPPFxsYKAEKhUIjk5GSt/a8/Xw8fPhTBwcHC09NTGBkZCTMzM9GgQQOxceNGjeNe/9kU4uUtAb28vISenp66zTd5zqhkSEL8R58WICJ6z4WHh2PgwIE4efKk1s33iYioZHFNLxERERHJHkMvEREREckeQy8RERERyR7X9BIRERGR7HGml4iIiIhkj6GXiIiIiGSPX05BBCA7Oxt3796FiYlJkb5ylYiIiEqPEAKpqamws7Mr8MtvGHqJANy9excODg6l3Q0iIiIqhlu3bhX4VdMMvUQATExMALz8oTE1NS3l3hAREVFhqFQqODg4qN/H88PQSwSolzSYmpoy9BIREZUxhVmayA+yEREREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHs6ZV2B4jeC5JU2j0goveREKXdA6J3Bmd6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6ZSApKQkjRoyAi4sLFAoFHBwc0L59e+zbt09d58iRI/jkk09QoUIFGBgYoEaNGli4cCFevHih1V5UVBTatWsHKysrGBgYoGrVqujRowcOHjyorhMdHQ1JkpCSkpJrn6ZOnYratWtrPJYkCf7+/lp1582bB0mS0KxZs2KN//HjxxgxYgQ8PDxgaGgIR0dHjBw5EkqlsljtERERkfww9JZx169fR926dbF//37MmzcP58+fx86dO+Hr64vg4GAAwNatW9G0aVPY29sjKioKly5dwqhRo/DNN9+gZ8+eEK98TeWPP/6IFi1awNLSEhs2bEB8fDy2bt0KHx8ffP7552/UV1tbW0RFReH27dsa5StWrICjo2Ox27179y7u3r2L+fPn48KFCwgPD8fOnTsxaNCgN+ovERERyYigMq1NmzaicuXKIi0tTWtfcnKySEtLE5aWlqJLly5a+7dv3y4AiPXr1wshhLhx44YoV66c+Pzzz3M9V3Z2tvrfUVFRAoBITk7OtW5oaKioVauW1uN27dqJb775Rl1++PBhUbFiRTF06FDRtGnTQoy4cDZu3Cj09fVFZmZmoeorlUoBQCiVyrfWBw0AN27cuP33G5HMFeX9mzO9Zdjjx4+xc+dOBAcHw8jISGu/ubk5du/ejUePHuGLL77Q2t++fXu4u7sjIiICALBlyxZkZmZi/PjxuZ5PkqQ37nNQUBDCw8PVj1esWIE+ffpAX19fo97atWthbGyc73bo0KE8z6NUKmFqago9Pb1c96enp0OlUmlsREREJF8MvWXYv//+CyEEPD0986xz+fJlAEC1atVy3e/p6amuc/nyZZiamsLGxka9f8uWLRpB8/z582/U53bt2kGlUuHgwYN48uQJNm7ciKCgIK16HTp0QExMTL5bvXr1cj3Hw4cP8fXXX2Pw4MF59mPWrFkwMzNTbw4ODm80LiIiInq35T4NRmWCEOKt1319Nrd169aIiYnBnTt30KxZs1w/+FYU5cqVQ9++fbFy5UpcvXoV7u7uqFmzplY9ExMTmJiYFLl9lUqFtm3bwsvLC1OnTs2zXkhICMaMGaNxHIMvERGRfDH0lmFubm6QJAmXLl3Ks467uzsAIC4uDj4+Plr74+Li4OXlpW5PqVQiKSlJPdtrbGwMV1fXPJcJFEdQUBAaNGiACxcu5DrLC7xc3vDZZ5/l285ff/2FJk2aqB+npqbC398fJiYm2Lp1K8qVK5fnsQqFAgqFongDICIiojKHyxvKMAsLC7Ru3RpLlizBkydPtPanpKSgVatWsLCwwIIFC7T2b9++HQkJCejVqxcAoFu3bihXrhzmzJlTov329vaGt7c3Lly4gN69e+dap6jLG1QqFVq1agV9fX1s374dBgYGJToGIiIiKls401vGLVmyBI0aNUL9+vUxffp01KxZE1lZWdizZw+WLl2KuLg4LF++HD179sTgwYMxfPhwmJqaYt++fRg3bhy6deuGgIAAAICjoyMWLFiAUaNG4fHjxwgMDESVKlXw+PFjrFmzBgCgq6urcf7z589rLEOQJAm1atUqsN/79+9HZmYmzM3Nc91flOUNOYH36dOnWLNmjcYH06ysrLT6TERERO8fht4yzsXFBWfOnMGMGTMwduxYJCYmwsrKCnXr1sXSpUsBvJzBjYqKwowZM9CkSRM8f/4cbm5umDx5MkaPHq2xjnfEiBGoVq0aFi5ciG7dukGlUsHS0hINGzbEzp07UaNGDY3zf/zxxxqPdXV1kZWVVWC/c7vbRHGdOXMGx48fBwC4urpq7Lt27RqcnZ3f2rmIiIiobJJEUT4NRSRTKpUKZmZm6ludvXVv4XZvRERFxrd4krmivH9zTS8RERERyR5DLxERERHJHkMvEREREckeQy8RERERyR5DLxERERHJHkMvEREREckeQy8RERERyR6/nILov8B7ZRIREZUqzvQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHs8ZZlRO8KSSrtHhCR3PB2iURqnOklIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+gtQ5KSkjBixAi4uLhAoVDAwcEB7du3x759+wAAzs7OWLx4sdZxU6dORe3atTUeS5Kk3szMzNCkSRMcOHBA4zhnZ2d1nfLly6NGjRr45ZdftNp/8eIFFi1ahBo1asDAwAAVKlRAmzZtcPjwYY164eHhkCQJ/v7+GuUpKSmQJAnR0dHFui6RkZFo2bIlrKysYGpqioYNG2LXrl3FaouIiIjkiaG3jLh+/Trq1q2L/fv3Y968eTh//jx27twJX19fBAcHF7k9b29vJCYmIjExEUePHoWbmxvatWsHpVKpUW/69OlITEzEhQsX0LdvX3z66af466+/1PuFEOjZsyemT5+OUaNGIS4uDtHR0XBwcECzZs3w22+/abSnp6eHvXv3IioqqljXITcHDx5Ey5Yt8eeff+L06dPw9fVF+/btcfbs2bd2DiIiIirb9Eq7A1Q4w4YNgyRJOHHiBIyMjNTl3t7eCAoKKnJ7enp6sLGxAQDY2Nhg+vTpWLlyJS5fvowPP/xQXc/ExERdb8KECZg7dy727NmDNm3aAAA2btyIzZs3Y/v27Wjfvr36uJ9++gmPHj3C//73P7Rs2VLdZyMjIwQEBGDixIk4fvx40S9ELl6f3Z45cya2bduG33//HXXq1Mn1mPT0dKSnp6sfq1Sqt9IXIiIiejdxprcMePz4MXbu3Ing4GCNwJvD3Nz8jdpPT0/HypUrYW5uDg8Pj1zrZGdnY8uWLUhOToa+vr66fN26dXB3d9cIvDnGjh2LR48eYc+ePRrlU6dOxfnz57F58+Y8++Tt7Q1jY+M8t5zQnVdfU1NTYWFhkWedWbNmwczMTL05ODjkWZeIiIjKPs70lgH//vsvhBDw9PQssO6ECRPw5ZdfapRlZGTAy8tLo+z8+fMwNjYGADx9+hQmJibYsGEDTE1Nc20vPT0dWVlZsLCwwP/+9z/1/suXL6NatWq59iWn/PLlyxrldnZ2GDVqFCZPnoxOnTrleuyff/6JzMzMPMdpaGiY57758+cjLS0NAQEBedYJCQnBmDFj1I9VKhWDLxERkYwx9JYBQohC1x03bhwCAwM1yr777jscPHhQo8zDwwPbt28HAKSmpmLDhg3o3r07oqKiUK9ePa32EhMTMW7cOAwbNgyurq7F7l+OCRMmYPny5VixYkWu4dTJyanIbQIvZ56nTZuGbdu2wdraOs96CoUCCoWiWOcgIiKisoehtwxwc3ODJEm4dOlSgXUrVqyoFUpz+zO/vr6+Rr06dergt99+w+LFi7FmzRqt9lxdXbFp0ybUqFED9erVU88cu7u7Iy4uLte+5JS7u7tr7TM3N0dISAimTZuGdu3aae339vbGjRs38hxnkyZNND5QBwDr16/H//73P2zatAl+fn55HktERETvH67pLQMsLCzQunVrLFmyBE+ePNHan5KS8lbOo6uri2fPnuW538HBAT169EBISIi6rGfPnkhISMDvv/+uVX/BggWwtLREy5Ytc21vxIgR0NHRwbfffqu1788//0RMTEye2+u3TouIiMDAgQMRERGBtm3bFnbIRERE9J7gTG8ZsWTJEjRq1Aj169fH9OnTUbNmTWRlZWHPnj1YunRpnrOtecnKykJSUhKA/1vecPHiRUyYMCHf40aNGoXq1avj1KlTqFevHnr27IlNmzZhwIABmDdvHlq0aAGVSoUlS5Zg+/bt2LRpU64fvgMAAwMDTJs2LddbrhVlecO6deswYMAAfPvtt2jQoIF6XIaGhjAzMyt0O0RERCRfnOktI1xcXHDmzBn4+vpi7NixqF69Olq2bIl9+/Zh6dKlRW4vNjYWtra2sLW1Re3atbFx40YsXboU/fv3z/c4Ly8vtGrVCl999RUAQJIkbNy4EZMmTcKiRYvg4eGBJk2a4MaNG4iOjs7zg2o5BgwYABcXlyL3/1U//fQTsrKyEBwcrB6Tra0tRo0a9UbtEhERkXxIojifQiKSGZVKBTMzMyiVSq07WPxnJKl0zktE8sW3eJK5orx/c6aXiIiIiGSPoZeIiIiIZI+hl4iIiIhkj6GXiIiIiGSPoZeIiIiIZI+hl4iIiIhkj19OQfSu4K2FiIiISgxneomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPZ4yzIiouKSpNLuAVH+eCtEIjXO9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9MpEYGAgOnXqpP63JEmYPXu2Rp3ffvsN0mvfIPXzzz+jVq1aMDY2hrm5OerUqYNZs2Zp1FGpVJg8eTI8PT1hYGAAGxsb+Pn5ITIyEuKVb/uJjY1FQEAArKysoFAo4O7ujq+++gpPnz4FAJw+fRqSJOHYsWO5jqFFixbo0qVLscbfoUMHODo6wsDAALa2tujXrx/u3r1brLaIiIhIfhh6ZcrAwABz5sxBcnJynnVWrFiB0aNHY+TIkYiJicHhw4cxfvx4pKWlqeukpKTAx8cHv/76K0JCQnDmzBkcPHgQPXr0wPjx46FUKgEAx44dQ4MGDZCRkYE//vgDly9fxowZMxAeHo6WLVsiIyMDdevWRa1atbBixQqtvly/fh1RUVEYNGhQscbr6+uLjRs3Ij4+Hlu2bMGVK1fQrVu3YrVFREREMiRIFgYMGCA6duyo/ne7du2Ep6enGDdunLrO1q1bxatPeceOHUVgYGC+7Q4dOlQYGRmJO3fuaO1LTU0VmZmZIjs7W3h5eYl69eqJFy9eaNSJiYkRkiSJ2bNnCyGE+O6774Spqal48uSJRr3Q0FBhZ2cnsrKyijTuvGzbtk1IkiQyMjIKVV+pVAoAQqlUvpXz03sC4Mbt3d6IZK4o79+c6ZUpXV1dzJw5E99//z1u376dax0bGxscO3YMN27cyHV/dnY21q9fjz59+sDOzk5rv7GxMfT09BATE4OLFy9izJgx0NHRfEnVqlULfn5+iIiIAAD06dMH6enp2Lx5s7qOEAKrVq1CYGAgdHV1AQAzZ86EsbFxvtvNmzdz7ffjx4+xdu1a+Pj4oFy5crnWSU9Ph0ql0tiIiIhIvhh6Zaxz586oXbs2QkNDc90fGhoKc3NzODs7w8PDA4GBgdi4cSOys7MBAA8fPkRycjI8PT3zPc/ly5cBANWqVct1f7Vq1dR1LCws0LlzZ40lDlFRUbh+/ToGDhyoLhsyZAhiYmLy3V4P4hMmTICRkREsLS1x8+ZNbNu2Lc8+z5o1C2ZmZurNwcEh3zESERFR2cbQK3Nz5szBqlWrEBcXp7XP1tYWR48exfnz5zFq1ChkZWVhwIAB8Pf3R3Z2NoQQRTpXYesHBQXh4MGDuHLlCoCXa4ubNm0KV1dXdR0LCwu4urrmu+np6Wm0O27cOJw9exa7d++Grq4u+vfvn2efQkJCoFQq1dutW7eKNFYiIiIqWxh6Ze7jjz9G69atERISkmed6tWrY9iwYVizZg327NmDPXv24MCBA7CysoK5uTkuXbqU7znc3d0BINdgnVOeUwd4eZcGR0dHhIeHQ6VSITIyUusDbMVZ3lCxYkW4u7ujZcuWWL9+Pf7888887xShUChgamqqsREREZF86RVchcq62bNno3bt2vDw8CiwrpeXFwDgyZMn0NHRQc+ePbF69WqEhoZqLSdIS0uDgYEBateuDU9PTyxatAg9e/bUWNd77tw57N27V+M2aDo6Ohg4cCDCwsJQuXJl6Ovra91pYciQIQgICMi3r7mtM86Rs0QjPT29wDETERGR/EmiqH/DpndSYGAgUlJS8Ntvv2n8O0f//v2xadMmPH/+XP0n/6FDh8LOzg7NmzeHvb09EhMT8c033+DEiROIi4uDpaUlHj9+jEaNGiEtLQ0zZsxAvXr1UK5cORw6dAizZs3CyZMnYW5ujiNHjqBly5Zo1aoVQkJCYGNjg+PHj2Ps2LFwcHDA/v37oVAo1P25efMmqlSpAjMzM/To0QNLly4t9tiPHz+OkydPonHjxqhQoQKuXLmCKVOm4N69e4iNjdU4b15UKhXMzMygVCo560uF99p9r4neOXyLJ5kryvs3lze8J6ZPn66e/czh5+eHY8eOoXv37nB3d0fXrl1hYGCAffv2wdLSEsDLtbXHjh1D37598c0336BOnTpo0qQJIiIiMG/ePJiZmQEAfHx8cOzYMejq6qJNmzZwdXVFSEgIBgwYgD179mgFT0dHR/j5+SE5ORlBQUFvNLby5csjMjISLVq0gIeHBwYNGoSaNWviwIEDhQq8REREJH+c6SUCZ3qpmDjTS+86vsWTzHGml4iIiIjoFQy9RERERCR7DL1EREREJHsMvUREREQkewy9RERERCR7DL1EREREJHv8RjYiouLi7aCIiMoMzvQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHs8ZZlREREMiVNk0q7C0RqIrR0b/PImV4iIiIikj2GXiIiIiKSPYZeIiIiIpI9hl4iIiIikj2GXiIiIiKSPYZeIiIiIpI9hl4iIiIikj2GXiIiIiKSPYZeIiIiIpI9ht7/yNGjR6Grq4u2bdtqlF+/fh2SJKk3ExMTeHt7Izg4GAkJCRp1X7x4gdmzZ8PT0xOGhoawsLBAgwYN8Msvv6jrBAYGQpIkDBkyRKsPwcHBkCQJgYGBGuW3bt1CUFAQ7OzsoK+vDycnJ4waNQqPHj3SqNesWTOMHj1ao+zbb7+FQqHA+vXrtc43ZMgQSJKExYsXa4x30KBBqFKlCgwNDVG1alWEhoYiIyMjv8uXr1mzZuHDDz+EiYkJrK2t0alTJ8THxxe7PSIiIpIfht7/SFhYGEaMGIGDBw/i7t27Wvv37t2LxMREnDt3DjNnzkRcXBxq1aqFffv2qetMmzYNixYtwtdff42LFy8iKioKgwcPRkpKikZbDg4OWL9+PZ49e6Yue/78OdatWwdHR0eNulevXkW9evWQkJCAiIgI/Pvvv1i2bBn27duHhg0b4vHjx3mOKTQ0FJMmTcK2bdvQs2dPjX1bt27FsWPHYGdnp1F+6dIlZGdnY/ny5YiNjcWiRYuwbNkyTJo0qcBrmJcDBw4gODgYx44dw549e5CZmYlWrVrhyZMnxW6TiIiI5EWvtDvwPkhLS8OGDRtw6tQpJCUlITw8XCvkWVpawsbGBgDg4uKC9u3bo0WLFhg0aBCuXLkCXV1dbN++HcOGDUP37t3Vx9WqVUvrfB988AGuXLmCyMhI9OnTBwAQGRkJR0dHVKlSRaNucHAw9PX1sXv3bhgaGgIAHB0dUadOHVStWhWTJ0/G0qVLNY4RQmDkyJFYs2YN9uzZAx8fH439d+7cwYgRI7Br1y6tmW1/f3/4+/urH7u4uCA+Ph5Lly7F/PnzC3U9X7dz506Nx+Hh4bC2tsbp06fx8ccfF6tNIiIikhfO9P4HNm7cCE9PT3h4eKBv375YsWIFhBD5HqOjo4NRo0bhxo0bOH36NADAxsYG+/fvx4MHDwo8Z1BQEFauXKl+vGLFCgwcOFCjzuPHj7Fr1y4MGzZMHXhz2NjYoE+fPtiwYYNGX7OystC3b19s3rwZBw4c0Aq82dnZ6NevH8aNGwdvb+8C+wkASqUSFhYW6sc3b96EsbFxvtvMmTPzbQ+ARpuvS09Ph0ql0tiIiIhIvjjT+x8ICwtD3759Abyc6VQqlThw4ACaNWuW73Genp4AXq6DrV+/PhYuXIhu3brBxsYG3t7e8PHxQceOHdGmTRutY/v27YuQkBDcuHEDAHD48GGsX78e0dHR6joJCQkQQqBatWq5nr9atWpITk7GgwcPYG1tDQD4+eefAQDnzp1T9+9Vc+bMgZ6eHkaOHJn/Rfn//v33X3z//fcas7x2dnaIiYnJ97i8Am12djZGjx6NRo0aoXr16nkeP2vWLEybNq1QfSQiIqKyj6G3hMXHx+PEiRPYunUrAEBPTw89evRAWFhYgaE3Z4ZVkiQAgJeXFy5cuIDTp0/j8OHDOHjwINq3b4/AwECND7MBgJWVFdq2bYvw8HAIIdC2bVtUrFgx3/MURuPGjRETE4MpU6YgIiICenr/9xI6ffo0vv32W5w5c0bd5/zcuXMH/v7+6N69Oz799FN1uZ6eHlxdXQvdp1cFBwfjwoUL+Pvvv/OtFxISgjFjxqgfq1QqODg4FOucRERE9O7j8oYSFhYWhqysLNjZ2UFPTw96enpYunQptmzZov4zfF7i4uIAQGMdro6ODj788EOMHj0akZGRCA8PR1hYGK5du6Z1fFBQEMLDw7Fq1SoEBQVp7Xd1dYUkSerz5Hb+ChUqwMrKSl1Wo0YN7Nu3D1FRUejRoweysrLU+w4dOoT79+/D0dFRPdYbN25g7NixcHZ21mj77t278PX1hY+PD3766SeNfcVd3jB8+HDs2LEDUVFRsLe3z3VMORQKBUxNTTU2IiIiki/O9JagrKws/Prrr1iwYAFatWqlsa9Tp06IiIjQ+FDXq7Kzs/Hdd9+hSpUqqFOnTp7n8PLyAoBc71Tg7++PjIwMSJKE1q1ba+23tLREy5Yt8eOPP+Lzzz/XWNeblJSEtWvXon///lqztrVr18a+ffvg5+eHgIAAbNiwAeXKlUO/fv3g5+enUbd169bo16+fxnriO3fuwNfXF3Xr1sXKlSuho6P5u1dRlzcIITBixAhs3boV0dHRWh/WIyIiImLoLUE7duxAcnIyBg0aBDMzM419Xbt2RVhYmDr0Pnr0CElJSXj69CkuXLiAxYsX48SJE/jjjz+gq6sLAOjWrRsaNWoEHx8f2NjY4Nq1awgJCYG7u3uu62t1dXXVs7g5bbzuhx9+gI+PD1q3bo1vvvkGVapUQWxsLMaNG4fKlStjxowZuR5Xq1Yt7N+/Hy1atEBAQAA2btwIS0tLWFpaatQrV64cbGxs4OHhAeBl4G3WrBmcnJwwf/58jQ/l5dy9oqjLG4KDg7Fu3Tps27YNJiYmSEpKAgCYmZlpfUCPiIiI3k9c3lCCwsLC4OfnpxV4gZeh99SpU+q7Bvj5+cHW1hY1atTAxIkTUa1aNfzzzz/w9fVVH9O6dWv8/vvvaN++Pdzd3TFgwAB4enpi9+7dGmtrX1XQn+7d3Nxw6tQpuLi4ICAgAFWrVsXgwYPh6+uLo0eP5nsHhBo1amD//v04cuQIunfvXqgvmNizZw/+/fdf7Nu3D/b29rC1tVVvxbV06VIolUo0a9ZMo70NGzYUu00iIiKSF0kU5VNMRDKlUqlgZmYGpVLJ9b1EJBvStII/VEz0XxGhbz9yFuX9mzO9RERERCR7DL1EREREJHsMvUREREQkewy9RERERCR7DL1EREREJHsMvUREREQkewy9RERERCR7/EY2IiIimSqJ+6ISlVWc6SUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItnjLcuIiP5LklTaPaD3ieAty4hycKaXiIiIiGSPoZeIiIiIZI+hl4iIiIhkj6GXiIiIiGSPoZeIiIiIZI+hl4iIiIhkj6GXiIiIiGSPoZeIiIiIZI+h9z0SGBiITp06QZKkfLepU6fi+vXrGmWWlpZo1aoVzp49q9Hm0aNHoauri7Zt22qdL6eNmJgYjcfW1tZITU3VqFu7dm1MnTq1WOM6d+4cevXqBQcHBxgaGqJatWr49ttvi9UWERERyRND73soMTFRvS1evBimpqYaZV988YW67t69e5GYmIhdu3YhLS0Nbdq0QUpKinp/WFgYRowYgYMHD+Lu3buFOn9qairmz5//1sZz+vRpWFtbY82aNYiNjcXkyZMREhKCH3744a2dg4iIiMo2fg3xe8jGxkb9bzMzM0iSpFEGAA8fPgQAWFpawsbGBjY2Npg/fz4aNWqE48ePo3Xr1khLS8OGDRtw6tQpJCUlITw8HJMmTSrw/CNGjMDChQsRHBwMa2vrNx5PUFCQxmMXFxccPXoUkZGRGD58+Bu3T0RERGUfZ3qp0AwNDQEAGRkZAICNGzfC09MTHh4e6Nu3L1asWAFRiO9579WrF1xdXTF9+vQ86wwZMgTGxsb5bvlRKpWwsLDIc396ejpUKpXGRkRERPLFmV4qlJSUFHz99dcwNjZG/fr1Abxc2tC3b18AgL+/P5RKJQ4cOIBmzZrl25YkSZg9ezbat2+Pzz//HFWrVtWqM336dI1lFkVx5MgRbNiwAX/88UeedWbNmoVp06YVq30iIiIqexh6KV8+Pj7Q0dHBkydP4OLigg0bNqBSpUqIj4/HiRMnsHXrVgCAnp4eevTogbCwsAJDLwC0bt0ajRs3xpQpU7Bu3Tqt/dbW1sVa+nDhwgV07NgRoaGhaNWqVZ71QkJCMGbMGPVjlUoFBweHIp+PiIiIygaGXsrXhg0b4OXlBUtLS5ibm6vLw8LCkJWVBTs7O3WZEAIKhQI//PADzMzMCmx79uzZaNiwIcaNG6e1b8iQIVizZk2+x6elpWk8vnjxIlq0aIHBgwfjyy+/zPdYhUIBhUJRYB+JiIhIHhh6KV8ODg5ayw+ysrLw66+/YsGCBVqzqZ06dUJERASGDBlSYNv169dHly5dMHHiRK19RV3eEBsbi+bNm2PAgAGYMWNGoY8jIiKi9wNDLxXZjh07kJycjEGDBmnN6Hbt2hVhYWGFCr0AMGPGDHh7e0NPT/OlWJTlDRcuXEDz5s3RunVrjBkzBklJSQAAXV1dWFlZFaoNIiIikjfeveE9kp2drRUuiyMsLAx+fn65LmHo2rUrTp06hX/++adQbbm7uyMoKAjPnz8vdn82b96MBw8eYM2aNbC1tVVvH374YbHbJCIiInmRRGHuMUWy4O/vD1dXV35pQy5UKhXMzMygVCphampa2t0hOZOk0u4BvU/4Fk8yV5T3b870vgeSk5OxY8cOREdHw8/Pr7S7Q0RERPSf45re90BQUBBOnjyJsWPHomPHjqXdHSIiIqL/HEPveyDnXrpERERE7ysubyAiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2eMH2YiI/ku8byoRUangTC8RERERyR5DLxERERHJHkMvEREREckeQy8RERERyR5DLxERERHJHkMvEREREckeb1lGRERvjySVdg/oVbxFHpEaZ3qJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHrLsGXLlsHExARZWVnqsrS0NJQrVw7NmjXTqBsdHQ1JknDlyhU4Oztj8eLFubZ5/fp1SJKEmJgYdVlqaip8fX3h5eWF27dv51rnVeHh4TA3N9d4LEkSqlWrplV306ZNkCQJzs7OhRy1pszMTEyYMAE1atSAkZER7Ozs0L9/f9y9e7dY7REREZE8MfSWYb6+vkhLS8OpU6fUZYcOHYKNjQ2OHz+O58+fq8ujoqLg6OiIqlWrFukcDx48gK+vL548eYJDhw7B3t6+WH01MjLC/fv3cfToUY3ysLAwODo6FqtNAHj69CnOnDmDKVOm4MyZM4iMjER8fDw6dOhQ7DaJiIhIfhh6yzAPDw/Y2toiOjpaXRYdHY2OHTuiSpUqOHbsmEa5r69vkdq/desWmjRpAjMzM+zfvx+WlpbF7quenh569+6NFStWqMtu376N6Oho9O7du9jtmpmZYc+ePQgICICHhwc++ugj/PDDDzh9+jRu3rxZ7HaJiIhIXhh6yzhfX19ERUWpH0dFRaFZs2Zo2rSpuvzZs2c4fvx4kUJvfHw8GjVqBC8vL/z5558wNjZ+474GBQVh48aNePr0KYCXyx78/f1RqVIljXqHDh2CsbFxvtvatWvzPI9SqYQkSRpLLF6Xnp4OlUqlsREREZF86ZV2B+jN+Pr6YvTo0cjKysKzZ89w9uxZNG3aFJmZmVi2bBkA4OjRo0hPTy9S6O3fvz8aNWqETZs2QVdX9630tU6dOnBxccHmzZvRr18/hIeHY+HChbh69apGvXr16uW5XjjH60E5x/PnzzFhwgT06tULpqameR4/a9YsTJs2rchjICIiorKJobeMa9asGZ48eYKTJ08iOTkZ7u7usLKyQtOmTTFw4EA8f/4c0dHRcHFxKdLa2Q4dOuC3335DZGQkunfv/tb6GxQUhJUrV8LR0RFPnjzBJ598gh9++EGjjqGhIVxdXYvcdmZmJgICAiCEwNKlS/OtGxISgjFjxqgfq1QqODg4FPmcREREVDYw9JZxrq6usLe3R1RUFJKTk9G0aVMAgJ2dHRwcHHDkyBFERUWhefPmRWp38uTJqFmzJnr37g0hBAICAt5Kf/v06YPx48dj6tSp6NevH/T0tF+Chw4dQps2bfJtZ/ny5ejTp4/6cU7gvXHjBvbv35/vLC8AKBQKKBSK4g2CiIiIyhyGXhnw9fVFdHQ0kpOTMW7cOHX5xx9/jL/++gsnTpzA0KFDi9zulClToKOjgz59+kAIgR49erxxXy0sLNChQwds3LhRvfzidUVd3pATeBMSEhAVFfVGH7gjIiIieWLolQFfX18EBwcjMzNTPdMLAE2bNsXw4cORkZGhtZ73zp07WsHSyclJq+3JkydDV1cXffr0QXZ2Nnr16qXeFx8fr1Xf29u7wP6Gh4fjxx9/zDOcFmV5Q2ZmJrp164YzZ85gx44dePHiBZKSkgC8DNj6+vqFaoeIiIjkjaFXBnx9ffHs2TN4enpqzIA2bdoUqamp6lubvWr+/PmYP3++Rtnq1avRuHFjrfYnTpwIHR0d9OvXD0II+Pj4AAB69uypVffWrVsF9tfQ0BCGhoaFGltB7ty5g+3btwMAateurbEv504WRERERJIQQpR2J4hKm0qlgpmZGZRKZYHrgYkoH5JU2j2gV/EtnmSuKO/fvE8vEREREckeQy8RERERyR5DLxERERHJHkMvEREREckeQy8RERERyR5DLxERERHJHu/TS0REbw9vkUVE7yjO9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkezxlmVERPR2SVJp94By8BZyRGqc6SUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItkrduidPXs2JEnC6NGjNcqfP3+O4OBgWFpawtjYGF27dsW9e/fybCczMxMTJkxAjRo1YGRkBDs7O/Tv3x93795V17l+/ToGDRqEKlWqwNDQEFWrVkVoaCgyMjKK2/1CcXZ2hiRJkCQJRkZG+OCDD7Bp0yaNOiqVCpMnT4anpycMDAxgY2MDPz8/REZGQvz/b8K5du0aevfuDTs7OxgYGMDe3h4dO3bEpUuXtM6Znp6O2rVrQ5IkxMTEqMujo6PRsWNH2NrawsjICLVr18batWtLdPxFFR4err5eOZuBgUGe9YcMGQJJkrB48eJ8233x4gWmTJmi8fx//fXX6usLAEIIfPXVV7C1tYWhoSH8/PyQkJDwtoZGREREZVyxQu/JkyexfPly1KxZU2vf559/jt9//x2bNm3CgQMHcPfuXXTp0iXPtp4+fYozZ85gypQpOHPmDCIjIxEfH48OHTqo61y6dAnZ2dlYvnw5YmNjsWjRIixbtgyTJk0qTvfVbt68WWCd6dOnIzExEWfPnsWHH36IHj164MiRIwCAlJQU+Pj44Ndff0VISAjOnDmDgwcPokePHhg/fjyUSiUyMzPRsmVLKJVK9dg2bNiAGjVqICUlRet848ePh52dnVb5kSNHULNmTWzZsgX//PMPBg4ciP79+2PHjh1vdA1u3br1Rse/ztTUFImJiertxo0budbbunUrjh07lutYXzdnzhwsXboUP/zwA+Li4jBnzhzMnTsX33//vbrO3Llz8d1332HZsmU4fvw4jIyM0Lp1azx//vytjY2IiIjKMFFEqampws3NTezZs0c0bdpUjBo1Sr0vJSVFlCtXTmzatEldFhcXJwCIo0ePFvocJ06cEADEjRs38qwzd+5cUaVKlaJ2XyiVSvHLL7+IJk2aiAoVKuRb18nJSSxatEj9ODMzU5QvX15MnDhRCCHE0KFDhZGRkbhz547WsampqSIzM1OcPXtWABDXr18vsG9//vmn8PT0FLGxsQKAOHv2bL71P/nkEzFw4MAC233djRs3xDfffCPc3NxEp06dinx8XlauXCnMzMwKrHf79m1RuXJlceHCBa1rnJu2bduKoKAgjbIuXbqIPn36CCGEyM7OFjY2NmLevHnq/SkpKUKhUIiIiIhC9V2pVAoAQqlUFqo+EeUD4PaubEQyV5T37yLP9AYHB6Nt27bw8/PT2nf69GlkZmZq7PP09ISjoyOOHj1a6HMolUpIkgRzc/N861hYWBSqvezsbOzZswd9+/aFjY0NZs+ejRYtWuDUqVOF7hMA6OnpoVy5csjIyEB2djbWr1+PPn365DpbaWxsDD09PVhZWUFHRwebN2/Gixcv8mz73r17+PTTT7F69WqUL1++UP0pyjV48uQJVq9eDT8/P1SpUgV//vknxo4dixUrVqjrrF27FsbGxvluhw4dyvc8aWlpcHJygoODAzp27IjY2FiN/dnZ2ejXrx/GjRsHb2/vQvXdx8cH+/btw+XLlwEA586dw99//402bdoAeLl8JCkpSeN1Z2ZmhgYNGuT5uktPT4dKpdLYiIiISL70ilJ5/fr1OHPmDE6ePJnr/qSkJOjr62uF1UqVKiEpKalQ53j+/DkmTJiAXr16wdTUNNc6//77L77//nvMnz8/37YuX76M8PBwrF69Gk+ePEFAQAD27t0LHx+fQvXlVRkZGViwYAGUSiWaN2+Ohw8fIjk5GZ6envkeV7lyZXz33XcYP348pk2bhnr16sHX1xd9+vSBi4sLgJfrUQMDAzFkyBDUq1cP169fL7A/GzduVC8zyc+BAwewatUqbNq0CdbW1ujbty+WL1+OqlWratXt0KEDGjRoUOB48uLh4YEVK1agZs2aUCqVmD9/Pnx8fBAbGwt7e3sAL5cq6OnpYeTIkQWOMcfEiROhUqng6ekJXV1dvHjxAjNmzECfPn0AQP3aqlSpksZx+b3uZs2ahWnTphW6D0RERFTGFXb6+ObNm8La2lqcO3dOXfb68oa1a9cKfX19rWM//PBDMX78+ALPkZGRIdq3by/q1KmT5zT17du3RdWqVcWgQYMKbK9p06YCgBgyZIh4+vRpgfVf5+TkJPT19YWRkZHQ1dUVZmZmYvbs2UIIIZKSkgQAsXDhwkK1pVKpxPbt28WkSZNEjRo1hIGBgdi9e7cQQohvv/1WNGrUSGRlZQkhhLh27Vq+yxv2798vypcvL1atWlXgeQEIQ0NDsWzZskL1823KyMgQVatWFV9++aUQQohTp06JSpUqaSwHKczyhoiICGFvby8iIiLEP//8I3799VdhYWEhwsPDhRBCHD58WAAQd+/e1Tiue/fuIiAgINc2nz9/LpRKpXq7desWlzcQvS2l/Sd9blzeQO+NoixvKPRPxNatWwUAoaurq94ACEmShK6ursjKyhL79u0TAERycrLGsY6OjgWGw4yMDNGpUydRs2ZN8fDhw1zr3LlzR7i5uYl+/fqJFy9eFNjns2fPiuHDhwtLS0thb28vJkyYIC5cuFDYIQsnJycxefJkkZCQIBITE0V2drZ634sXL4S5ubkYPHhwodvLkZ2dLVq2bCk+/vhjIYQQHTt2FDo6OlrXVldXV/Tv31/j2OjoaGFkZCSWL19eqHP9/vvvolu3bkKhUIg6deqIhQsXisTExFzrrlmzRhgZGeW7HTx4sEhj7datm+jZs6cQQohFixapXy+vjlNHR0c4OTnl2Ya9vb344YcfNMq+/vpr4eHhIYQQ4sqVK7n+kvDxxx+LkSNHFqqfXNNL9BaVdtDjxtBL740SCb0qlUqcP39eY6tXr57o27evOH/+vBDi/z7ItnnzZvVxly5dEkD+H2TLCbze3t7i/v37uda5ffu2cHNzEz179lTPiBZWenq62LJli+jQoYMoV65cgeEvR0GzkEOGDCnwg2x5GTFihKhbt64Q4uUHy169rrt27RIAxObNm8WtW7fUx0RFRQkjIyOtAFgYjx8/FkuWLBH169cXurq6wt/fX6xdu1Y8efJEXUelUomEhIR8t6LMmGdlZQkPDw/x+eefCyGEePjwodZryM7OTkyYMEFcunQpz3YsLCzEjz/+qFE2c+ZM4ebmJoT4vw+yzZ8/X71fqVTyg2xEpaW0gx43hl56b5RI6M3N68sbhHgZBB0dHcX+/fvFqVOnRMOGDUXDhg016nh4eIjIyEghxMvA26FDB2Fvby9iYmJEYmKiektPTxdCvAy8rq6uokWLFuL27dsadYrq/v37YtGiRaJ27drCwsIi37oFhd5Hjx4JT09PYW9vL1atWiViY2PF5cuXRVhYmHB1dRXJycni7NmzokOHDmLTpk0iNjZWJCQkiF9++UUYGRmJ6dOn59pubssbcpY0hISEaIz/0aNHRb4GcXFxYsKECaJy5cqiS5cuRT4+L9OmTRO7du0SV65cEadPnxY9e/YUBgYGIjY2Ns9jcrvGzZs3F99//7368YABA0TlypXFjh07xLVr10RkZKSoWLGixpKZ2bNnC3Nzc7Ft2zbxzz//iI4dO4oqVaqIZ8+eFarvDL1Eb1FpBz1uDL303ijV0Pvs2TMxbNgwUaFCBVG+fHnRuXNnrXAKQKxcuVII8X8BL7ctKipKCPHyVlh51XkTcXFx+e4vzHrTlJQUMXHiROHm5ib09fVFpUqVhJ+fn9i6davIzs4WDx48ECNHjhTVq1cXxsbGwsTERNSoUUPMnz8/zyUauYXeAQMG5Dr+pk2bFnHU/+fFixciPj6+2Me/bvTo0cLR0VF9HT755BNx5syZfI/J7Ro7OTmJ0NBQ9WOVSiVGjRolHB0dhYGBgXBxcRGTJ09W/1IkxMvZ3ilTpohKlSoJhUIhWrRoUaSxMfQSvUWlHfS4MfTSe6Mo79+SEEKU0GfkiMoMlUoFMzMzKJXKPO8aQkSFJEml3QPKwbd4krmivH8X+2uIiYiIiIjKCoZeIiIiIpI9hl4iIiIikj2GXiIiIiKSPYZeIiIiIpI9hl4iIiIikj2GXiIiIiKSPb3S7gAREckM7w1LRO8gzvQSERERkewx9BIRERGR7DH0EhEREZHsMfQSERERkewx9BIRERGR7PHuDURERDIlTZNKuwuyJUJ5l5KyhjO9RERERCR7DL1EREREJHsMvUREREQkewy9RERERCR7DL1EREREJHsMvUREREQkewy9RERERCR7DL1EREREJHsMvUREREQkewy9MnD06FHo6uqibdu2GuXXr1+HJEmwtrZGamqqxr7atWtj6tSpGmX//vsvgoKC4OjoCIVCgcqVK6NFixZYu3YtsrKy1PUkScJvv/2m8ThnMzIygpubGwIDA3H69GmN9pcuXQpzc3PcunVLo3zEiBFwd3fH06dPizz2c+fOoVevXnBwcIChoSGqVauGb7/9tsjtEBERkbwx9MpAWFgYRowYgYMHD+Lu3bta+1NTUzF//vx82zhx4gQ++OADxMXFYcmSJbhw4QKio6Pxv//9D0uXLkVsbGy+x69cuRKJiYmIjY3FkiVLkJaWhgYNGuDXX39V1xkyZAjq16+PQYMGqcv27duHpUuXIjw8HOXLly/iyIHTp0/D2toaa9asQWxsLCZPnoyQkBD88MMPRW6LiIiI5EuvtDtAbyYtLQ0bNmzAqVOnkJSUhPDwcEyaNEmjzogRI7Bw4UIEBwfD2tpaqw0hBAIDA+Hu7o7Dhw9DR+f/fhdyc3NDr169IET+3zFubm4OGxsbAICzszNatWqFAQMGYPjw4Wjfvj0qVKgASZIQFhaG6tWrY9myZejduzeCgoIwZswY+Pj4FGv8QUFBGo9dXFxw9OhRREZGYvjw4cVqk4iIiOSHM71l3MaNG+Hp6QkPDw/07dsXK1as0AqovXr1gqurK6ZPn55rGzExMYiLi8MXX3yhEXhfJUlSkfv2+eefIzU1FXv27FGXOTg4YPHixRg3bhz69u0LY2NjfP311xrHDRkyBMbGxvlu+VEqlbCwsMi3Tnp6OlQqlcZGRERE8sXQW8aFhYWhb9++AAB/f38olUocOHBAo44kSZg9ezZ++uknXLlyRauNy5cvAwA8PDzUZffv39cImT/++GOR++bp6Qng5driVw0cOBDVq1fH77//jpUrV0KhUGjsnz59OmJiYvLd8nLkyBFs2LABgwcPzrdvs2bNgpmZmXpzcHAo8viIiIio7GDoLcPi4+Nx4sQJ9OrVCwCgp6eHHj16ICwsTKtu69at0bhxY0yZMqVQbVtaWqoDprm5OTIyMorcv5wZ59dnic+dO4czZ86gfPnyOHTokNZx1tbWcHV1zXfLzYULF9CxY0eEhoaiVatW+fYtJCQESqVSvb3+4ToiIiKSF67pLcPCwsKQlZUFOzs7dZkQAgqFItcPcs2ePRsNGzbEuHHjNMrd3NwAvAzRderUAQDo6uqqw6WeXvFeJnFxcQCAKlWqqMsyMjLQv39/9OnTB02bNsWQIUPQrl07jVnmIUOGYM2aNfm2nZaWpvH44sWLaNGiBQYPHowvv/yywL4pFAqtGWYiIiKSL4beMiorKwu//vorFixYoDWr2alTJ0RERMDf31+jvH79+ujSpQsmTpyoUV6nTh14enpi/vz5CAgIyHNdb1EtXrwYpqam8PPzU5dNnz4djx8/xqJFi2BmZoYtW7Zg4MCB+Pvvv9XnnT59Or744otCnyc2NhbNmzfHgAEDMGPGjLfSdyIiIpIXht4yaseOHUhOTsagQYNgZmamsa9r164ICwvTCr0AMGPGDHh7e2vM3kqShJUrV6Jly5Zo1KgRQkJCUK1aNWRmZuLgwYN48OABdHV18+1PSkoKkpKSkJ6ejsuXL2P58uX47bff8Ouvv8Lc3BwAcPLkScyZMwd//PGHus/Lly9H9erVsWjRIowdOxbAy+UNud1lIjcXLlxA8+bN0bp1a4wZMwZJSUkAXs5UW1lZFaoNIiIikj+u6S2jwsLC4OfnpxV4gZeh99SpU7nekcDd3R1BQUF4/vy5RvlHH32E06dPw8PDA8HBwfDy8oKPjw8iIiKwaNEiDB06NN/+DBw4ELa2tvD09MTQoUNhbGyMEydOoHfv3gBe3i1hwIABGDhwoMbMtK2tLb7//nt8+eWXiI+PL/J12Lx5Mx48eIA1a9bA1tZWvX344YdFbouIiIjkSxIF3YCV6D2gUqlgZmYGpVIJU1PT0u4OEdFbIU0r+u0mqXBEKOPTu6Ao79+c6SUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2eM3shEREckU7yVL9H8400tEREREssfQS0RERESyx9BLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLHW5YREdG7Q5JKuwfyInjLMqIcnOklIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+iViQcPHmDo0KFwdHSEQqGAjY0NWrdujcOHD6vrHDlyBJ988gkqVKgAAwMD1KhRAwsXLsSLFy+02ouKisInn3wCS0tLlC9fHl5eXhg7dizu3LkDAIiOjoYkSUhJScm1P1OnTkXt2rU1HkuSBH9/f6268+bNgyRJaNasWbHG/vjxY4wYMQIeHh4wNDSEo6MjRo4cCaVSWaz2iIiISH4YemWia9euOHv2LFatWoXLly9j+/btaNasGR49egQA2Lp1K5o2bQp7e3tERUXh0qVLGDVqFL755hv07NkT4pWvqly+fDn8/PxgY2ODLVu24OLFi1i2bBmUSiUWLFhQ7D7a2toiKioKt2/f1ihfsWIFHB0di93u3bt3cffuXcyfPx8XLlxAeHg4du7ciUGDBhW7TSIiIpIXvdLuAL25lJQUHDp0CNHR0WjatCkAwMnJCfXr1wcAPHnyBJ9++ik6dOiAn376SX3c//73P1SqVAkdOnTAxo0b0aNHD9y+fRsjR47EyJEjsWjRInVdZ2dnfPzxx3nO7BaGtbU16tati1WrVmHy5MkAXs4+P3z4EN27d8fFixeL1W716tWxZcsW9eOqVatixowZ6Nu3L7KysqCnp/0yT09PR3p6uvqxSqUq1rmJiIiobOBMrwwYGxvD2NgYv/32m0aQy7F79248evQIX3zxhda+9u3bw93dHREREQCATZs2ISMjA+PHj8/1XObm5m/U16CgIISHh6sfr1ixAn369IG+vr5GvbVr16rHldd26NChPM+jVCphamqaa+AFgFmzZsHMzEy9OTg4vNG4iIiI6N3G0CsDenp6CA8Px6pVq2Bubo5GjRph0qRJ+OeffwAAly9fBgBUq1Yt1+M9PT3VdRISEmBqagpbW9sS6Wu7du2gUqlw8OBBPHnyBBs3bkRQUJBWvQ4dOiAmJibfrV69erme4+HDh/j6668xePDgPPsREhICpVKp3m7duvXWxkhERETvHi5vkImuXbuibdu2OHToEI4dO4a//voLc+fOxS+//KKu8+q63bwIISBJUon1s1y5cujbty9WrlyJq1evwt3dHTVr1tSqZ2JiAhMTkyK3r1Kp0LZtW3h5eWHq1Kl51lMoFFAoFEVun4iIiMomzvTKiIGBAVq2bIkpU6bgyJEjCAwMRGhoKNzd3QEAcXFxuR4XFxenruPu7g6lUonExMQS62dQUBA2bdqEJUuW5DrLCxRveUNqair8/f1hYmKCrVu3oly5ciU2BiIiIipbGHplzMvLC0+ePEGrVq1gYWGR650Xtm/fjoSEBPTq1QsA0K1bN+jr62Pu3Lm5tvkmH2TL4e3tDW9vb1y4cAG9e/fOtU5RlzeoVCq0atUK+vr62L59OwwMDN64n0RERCQfXN4gA48ePUL37t0RFBSEmjVrwsTEBKdOncLcuXPRsWNHGBkZYfny5ejZsycGDx6M4cOHw9TUFPv27cO4cePQrVs3BAQEAAAcHBywaNEiDB8+HCqVCv3794ezszNu376NX3/9FcbGxhrh+fz58xrLECRJQq1atQrs8/79+5GZmZnnB+OKsrwhJ/A+ffoUa9asgUqlUt+NwcrKCrq6uoVqh4iIiOSLoVcGjI2N0aBBAyxatAhXrlxBZmYmHBwc8Omnn2LSpEkAXs7gRkVFYcaMGWjSpAmeP38ONzc3TJ48GaNHj9ZYxzts2DC4u7tj/vz56Ny5M549ewZnZ2e0a9cOY8aM0Tj3xx9/rPFYV1cXWVlZBfbZyMjoLYz8pTNnzuD48eMAAFdXV419165dg7Oz81s7FxEREZVNkijMp5uIZE6lUsHMzEx9qzMiKiUl+EHa9xLf4knmivL+zTW9RERERCR7DL1EREREJHsMvUREREQkewy9RERERCR7DL1EREREJHsMvUREREQke7xPLxERvTt4iy0iKiGc6SUiIiIi2WPoJSIiIiLZY+glIiIiItlj6CUiIiIi2WPoJSIiIiLZY+glIiIiItnjLcuIiOjdIkml3QP54C3giNQ400tEREREssfQS0RERESyx9BLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLH0FsGBAYGQpIkSJIEfX19uLq6Yvr06cjKykJ0dLR6nyRJsLKywieffILz58/n2carm7+/v0a9s2fPokePHrC1tYVCoYCTkxPatWuH33//HeL/f7PP9evXIUkSrK2tkZqaqnF87dq1MXXqVHWd/Lbw8PC3cn0iIyPRqlUrWFpaQpIkxMTEvJV2iYiISD4YessIf39/JCYmIiEhAWPHjsXUqVMxb9489f74+HgkJiZi165dSE9PR9u2bZGRkZFrG69uERER6v3btm3DRx99hLS0NKxatQpxcXHYuXMnOnfujC+//BJKpVKjvdTUVMyfPz/X/jo4OGicZ+zYsfD29tYo69Gjx1u5Nk+ePEHjxo0xZ86ct9IeERERyY9eaXeACkehUMDGxgYAMHToUGzduhXbt29Hw4YNAQDW1tYwNzeHjY0NRo8ejQ4dOuDSpUuoWbNmrm287smTJxg0aBDatm2LyMhIjX3VqlXDoEGD1DO9OUaMGIGFCxciODgY1tbWGvt0dXU1zmVsbAw9Pb08z/8m+vXrB+DlDDQRERFRbjjTW0YZGhpqzeQCgFKpxPr16wEA+vr6hW5v9+7dePToEcaPH59nHUmSNB736tVLvdSiuG7evAljY+N8t5kzZxa7/bykp6dDpVJpbERERCRfnOktY4QQ2LdvH3bt2oURI0aoy+3t7QG8nLEFgA4dOsDT01Pj2B07dsDY2FijbNKkSZg0aRIuX74MAPDw8FDvO3nyJHx9fdWP169fj3bt2qkfS5KE2bNno3379vj8889RtWrVIo/Hzs6uwDW4FhYWRW63ILNmzcK0adPeertERET0bmLoLSNyAmtmZiays7PRu3dvTJ06FSdPngQAHDp0COXLl8exY8cwc+ZMLFu2TKsNX19fLF26VKMsv0BZs2ZNdSB1c3NDVlaWVp3WrVujcePGmDJlCtatW1fkcenp6cHV1bVQddeuXYvPPvtM/fivv/5CkyZNinxOAAgJCcGYMWPUj1UqFRwcHIrVFhEREb37GHrLiJzAqq+vDzs7O+jpaT51VapUgbm5OTw8PHD//n306NEDBw8e1KhjZGSUZ8B0c3MD8PIDcR999BGAl2uACxNIZ8+ejYYNG2LcuHFFHtfNmzfh5eWVb52c2egOHTqgQYMG6vLKlSsX+Xw5FAoFFApFsY8nIiKisoWht4zIL7C+Ljg4GLNmzcLWrVvRuXPnQh3TqlUrWFhYYM6cOdi6dWuR+la/fn106dIFEydOLNJxQNGWN5iYmMDExKTI5yAiIiJi6JWh8uXL49NPP0VoaCg6deqk/gBaeno6kpKSNOrq6emhYsWKMDY2xi+//IIePXqgbdu2GDlyJNzc3JCWloadO3cCeHlHhrzMmDED3t7eWjPQBSnK8oa8PH78GDdv3sTdu3cBvJytBgAbG5sSuVsEERERlT28e4NMDR8+HHFxcdi0aZO6bOfOnbC1tdXYGjdurN7fuXNnHDlyBOXLl0f//v3h4eGB5s2bY//+/VofYnudu7s7goKC8Pz58xIdV262b9+OOnXqoG3btgCAnj17ok6dOrmuayYiIqL3kyRev/kq0XtIpVLBzMwMSqUSpqampd0dovfba7dHpDfAt3iSuaK8f3Oml4iIiIhkj6GXiIiIiGSPoZeIiIiIZI+hl4iIiIhkj6GXiIiIiGSPoZeIiIiIZI9fTkFERO8W3maLiEoAZ3qJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj2GHqJiIiISPYYeomIiIhI9hh6iYiIiEj29Eq7A0TvAiEEAEClUpVyT4iIiKiwct63c97H88PQSwQgNTUVAODg4FDKPSEiIqKiSk1NhZmZWb51JFGYaEwkc9nZ2bh79y5MTEwgSVJpd+edolKp4ODggFu3bsHU1LS0u1Om8Nq9GV6/N8PrV3y8dm/mv7x+QgikpqbCzs4OOjr5r9rlTC8RAB0dHdjb25d2N95ppqam/J9/MfHavRlevzfD61d8vHZv5r+6fgXN8ObgB9mIiIiISPYYeomIiIhI9hh6iShfCoUCoaGhUCgUpd2VMofX7s3w+r0ZXr/i47V7M+/q9eMH2YiIiIhI9jjTS0RERESyx9BLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLH0Ev0nnv8+DH69OkDU1NTmJubY9CgQUhLS8v3mJ9++gnNmjWDqakpJElCSkqKVh1nZ2dIkqSxzZ49u4RGUXpK6voVp92ypjhjfP78OYKDg2FpaQljY2N07doV9+7d06jz+utOkiSsX7++JIfyn1iyZAmcnZ1hYGCABg0a4MSJE/nW37RpEzw9PWFgYIAaNWrgzz//1NgvhMBXX30FW1tbGBoaws/PDwkJCSU5hFL1tq9fYGCg1uvM39+/JIdQaopy7WJjY9G1a1f1e8DixYvfuM23RhDRe83f31/UqlVLHDt2TBw6dEi4urqKXr165XvMokWLxKxZs8SsWbMEAJGcnKxVx8nJSUyfPl0kJiaqt7S0tBIaRekpqetXnHbLmuKMcciQIcLBwUHs27dPnDp1Snz00UfCx8dHow4AsXLlSo3X3rNnz0pyKCVu/fr1Ql9fX6xYsULExsaKTz/9VJibm4t79+7lWv/w4cNCV1dXzJ07V1y8eFF8+eWXoly5cuL8+fPqOrNnzxZmZmbit99+E+fOnRMdOnQQVapUKfPXKjclcf0GDBgg/P39NV5njx8//q+G9J8p6rU7ceKE+OKLL0RERISwsbERixYteuM23xaGXqL32MWLFwUAcfLkSXXZX3/9JSRJEnfu3Cnw+KioqHxDb27/s5OTkrp+b9puWVCcMaakpIhy5cqJTZs2qcvi4uIEAHH06FF1GQCxdevWEut7aahfv74IDg5WP37x4oWws7MTs2bNyrV+QECAaNu2rUZZgwYNxGeffSaEECI7O1vY2NiIefPmqfenpKQIhUIhIiIiSmAEpettXz8hXobejh07lkh/3yVFvXavyut94E3afBNc3kD0Hjt69CjMzc1Rr149dZmfnx90dHRw/PjxN25/9uzZsLS0RJ06dTBv3jxkZWW9cZvvkpK6fiX9vLwLijPG06dPIzMzE35+fuoyT09PODo64ujRoxp1g4ODUbFiRdSvXx8rVqyAKMPfw5SRkYHTp09rjFtHRwd+fn5a485x9OhRjfoA0Lp1a3X9a9euISkpSaOOmZkZGjRokGebZVVJXL8c0dHRsLa2hoeHB4YOHYpHjx69/QGUouJcu9Jos7D0SrR1InqnJSUlwdraWqNMT08PFhYWSEpKeqO2R44ciQ8++AAWFhY4cuQIQkJCkJiYiIULF75Ru++Skrp+Jfm8vCuKM8akpCTo6+vD3Nxco7xSpUoax0yfPh3NmzdH+fLlsXv3bgwbNgxpaWkYOXLkWx/Hf+Hhw4d48eIFKlWqpFFeqVIlXLp0KddjkpKScq2fc51y/ptfHbkoiesHAP7+/ujSpQuqVKmCK1euYNKkSWjTpg2OHj0KXV3dtz+QUlCca1cabRYWQy+RDE2cOBFz5szJt05cXFyJ9mHMmDHqf9esWRP6+vr47LPPMGvWrHfu+9hf9y5cv7LqXbh2U6ZMUf+7Tp06ePLkCebNm1dmQy+9m3r27Kn+d40aNVCzZk1UrVoV0dHRaNGiRSn2jPLC0EskQ2PHjkVgYGC+dVxcXGBjY4P79+9rlGdlZeHx48ewsbF5q31q0KABsrKycP36dXh4eLzVtt+20r5+/+Xz8raV5LWzsbFBRkYGUlJSNGZ77927l+91adCgAb7++mukp6e/879w5aZixYrQ1dXVuktFfuO2sbHJt37Of+/duwdbW1uNOrVr136LvS99JXH9cuPi4oKKFSvi33//lU3oLc61K402C4treolkyMrKCp6envlu+vr6aNiwIVJSUnD69Gn1sfv370d2djYaNGjwVvsUExMDHR0drT9pv4tK+/r9l8/L21aS165u3booV64c9u3bpy6Lj4/HzZs30bBhwzz7FBMTgwoVKpTJwAsA+vr6qFu3rsa4s7OzsW/fvjzH3bBhQ436ALBnzx51/SpVqsDGxkajjkqlwvHjx/O9lmVRSVy/3Ny+fRuPHj3S+CWirCvOtSuNNgutRD8mR0TvPH9/f1GnTh1x/Phx8ffffws3NzeN20bdvn1beHh4iOPHj6vLEhMTxdmzZ8XPP/8sAIiDBw+Ks2fPikePHgkhhDhy5IhYtGiRiImJEVeuXBFr1qwRVlZWon///v/5+EpaSVy/wrQrB8W5dkOGDBGOjo5i//794tSpU6Jhw4aiYcOG6v3bt28XP//8szh//rxISEgQP/74oyhfvrz46quv/tOxvW3r168XCoVChIeHi4sXL4rBgwcLc3NzkZSUJIQQol+/fmLixInq+ocPHxZ6enpi/vz5Ii4uToSGhuZ6yzJzc3Oxbds28c8//4iOHTvK+pZlb/P6paamii+++EIcPXpUXLt2Tezdu1d88MEHws3NTTx//rxUxlhSinrt0tPTxdmzZ8XZs2eFra2t+OKLL8TZs2dFQkJCodssKQy9RO+5R48eiV69egljY2NhamoqBg4cKFJTU9X7r127JgCIqKgodVloaKgAoLWtXLlSCCHE6dOnRYMGDYSZmZkwMDAQ1apVEzNnzpTdm4EQJXP9CtOuHBTn2j179kwMGzZMVKhQQZQvX1507txZJCYmqvf/9ddfonbt2sLY2FgYGRmJWrVqiWXLlokXL178l0MrEd9//71wdHQU+vr6on79+uLYsWPqfU2bNhUDBgzQqL9x40bh7u4u9PX1hbe3t/jjjz809mdnZ4spU6aISpUqCYVCIVq0aCHi4+P/i6GUird5/Z4+fSpatWolrKysRLly5YSTk5P49NNPSzy0lZaiXLucn9vXt6ZNmxa6zZIiCVGG7+NCRERERFQIXNNLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLH0EtEREREssfQS0RERESyx9BLRERERLLH0EtEREREsvf/AOJY3WetVJtDAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_explanation(0)\n", "show_explanation(1)\n", "show_explanation(2)\n", "show_explanation(3)\n", "show_explanation(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1.5: Redo the with a regression logistic trained on the Rewieghted dataset" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "from aif360.algorithms.preprocessing import Reweighing\n", "\n", "RW = Reweighing(\n", " unprivileged_groups=[{'RACE': 0.0}], privileged_groups=[{'RACE': 1.0}]\n", ")\n", "RW.fit(dataset_orig_panel19_train)\n", "dataset_rw_train = RW.transform(dataset_orig_panel19_train)\n", "dataset_rw_val = RW.transform(dataset_orig_panel19_val)\n", "dataset_rw_test = RW.transform(dataset_orig_panel19_test)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8396979651939311" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_rw = make_pipeline(StandardScaler(), LogisticRegression(solver='liblinear', random_state=42))\n", "\n", "model_rw = model.fit(\n", " dataset_rw_train.features,\n", " dataset_rw_train.labels[:,0],\n", " **{\"logisticregression__sample_weight\": dataset_rw_train.instance_weights}\n", ")\n", "\n", "preds_rw = model_rw.predict_proba(dataset_rw_val.features)\n", "\n", "model_rw.score(dataset_rw_val.features, dataset_rw_val.labels[:,0], sample_weight=dataset_rw_val.instance_weights)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "thresh_arr = np.linspace(0.01, 0.5, 50)\n", "metrics_list=[]\n", "for thr in thresh_arr:\n", " y_val_pred = (preds_rw[:, -1] > thr).astype(np.float64)\n", " metrics = get_metrics(y_true=dataset_rw_val.labels[:,0], y_pred=y_val_pred, prot_attr=dataset_rw_val.protected_attributes[:,0], sample_weight=dataset_rw_val.instance_weights)\n", " metrics['threshold'] = thr\n", " metrics_list.append(metrics)\n", "df_metrics = pd.DataFrame.from_records(metrics_list)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ 0.01 ], [ 0.02 ], [ 0.03 ], [ 0.04 ], [ 0.05 ], [ 0.060000000000000005 ], [ 0.06999999999999999 ], [ 0.08 ], [ 0.09 ], [ 0.09999999999999999 ], [ 0.11 ], [ 0.12 ], [ 0.13 ], [ 0.14 ], [ 0.15000000000000002 ], [ 0.16 ], [ 0.17 ], [ 0.18000000000000002 ], [ 0.19 ], [ 0.2 ], [ 0.21000000000000002 ], [ 0.22 ], [ 0.23 ], [ 0.24000000000000002 ], [ 0.25 ], [ 0.26 ], [ 0.27 ], [ 0.28 ], [ 0.29000000000000004 ], [ 0.3 ], [ 0.31 ], [ 0.32 ], [ 0.33 ], [ 0.34 ], [ 0.35000000000000003 ], [ 0.36000000000000004 ], [ 0.37 ], [ 0.38 ], [ 0.39 ], [ 0.4 ], [ 0.41000000000000003 ], [ 0.42000000000000004 ], [ 0.43 ], [ 0.44 ], [ 0.45 ], [ 0.46 ], [ 0.47000000000000003 ], [ 0.48000000000000004 ], [ 0.49 ], [ 0.5 ] ], "hovertemplate": "balanced_accuracy_score=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(df_metrics, x='balanced_accuracy_score', y='disparate_impact_ratio', color='threshold', hover_data=[\"threshold\"])\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "lime_data_rw = LimeEncoder().fit(dataset_rw_train)\n", "s_train_rw = lime_data_rw.transform(dataset_rw_train.features)\n", "s_test_rw = lime_data_rw.transform(dataset_rw_test.features)\n", "explainer_rw = LimeTabularExplainer(\n", " s_train_rw, class_names=lime_data_rw.s_class_names, \n", " feature_names=lime_data_rw.s_feature_names,\n", " categorical_features=lime_data_rw.s_categorical_features, \n", " categorical_names=lime_data_rw.s_categorical_names, \n", " kernel_width=3, verbose=False, discretize_continuous=True)\n", "def s_predict_fn_rw(x):\n", " return model_rw.predict_proba(lime_data_rw.inverse_transform(x))\n", "def show_explanation_rw(ind):\n", " exp = explainer_rw.explain_instance(s_test_rw[ind], s_predict_fn_rw, num_features=10)\n", " print(\"Actual label: \" + str(dataset_rw_test.labels[ind]))\n", " exp.as_pyplot_figure()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Actual label: [0.]\n" ] }, { "data": { "image/png": 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", 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", 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_explanation(0)\n", "show_explanation(1)\n", "show_explanation(2)\n", "show_explanation(3)\n", "show_explanation(4)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "xNnaNQKu9NhF" }, "source": [ "## Question2 Utilisation de BlackBoxAuditing\n", "\n", "Attention cette fois, nous nous intéressons aux influences indirectes, cette méthode considères les features par couple.\n", "\n", "Aussi transformer les attributs catégoriels en \"one hot encoding\", n'est cette fois pas une bonne approche car ces colonnes seront par construction très liées entre elles.\n", "\n", "Nous allons du coup utiliser un ordinal encoding puis uniquement les classifieurs de sklearn compatible avec les attributs catégoriels ( HistGradientBoostingClassifier).\n", "\n", "Il faut dans un premier temps transformer le dataset AIF en dataframe et regrouper les colonnes qui ont déjà été one_hot_encodé (tout cela a déja été fait dans le TD3) puis appliqué un ordinal encoding aux colonnes catégorielles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2.1 preprocesser la donnée\n", "\n", "Afin que vous puissiez passer plus de temps à manipuler les explications, nous vous fournissons le code pour bien formatter le dataframe\n", "vous pouvez passer à la 2.2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['REGION', 'SEX', 'MARRY', 'FTSTU', 'ACTDTY', 'HONRDC', 'RTHLTH', 'MNHLTH', 'HIBPDX', 'CHDDX', 'ANGIDX', 'MIDX', 'OHRTDX', 'STRKDX', 'EMPHDX', 'CHBRON', 'CHOLDX', 'CANCERDX', 'DIABDX', 'JTPAIN', 'ARTHDX', 'ARTHTYPE', 'ASTHDX', 'ADHDADDX', 'PREGNT', 'WLKLIM', 'ACTLIM', 'SOCLIM', 'COGLIM', 'DFHEAR42', 'DFSEE42', 'ADSMOK42', 'PHQ242', 'EMPST', 'POVCAT', 'INSCOV'])\n", "REGION\n", "0 ['1']\n", "1 ['2']\n", "2 ['3']\n", "3 ['4']\n", "SEX\n", "0 ['1']\n", "1 ['2']\n", "MARRY\n", "0 ['1']\n", "1 ['10']\n", "2 ['2']\n", "3 ['3']\n", "4 ['4']\n", "5 ['5']\n", "6 ['6']\n", "7 ['7']\n", "8 ['8']\n", "9 ['9']\n", "FTSTU\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "3 ['3']\n", "ACTDTY\n", "0 ['1']\n", "1 ['2']\n", "2 ['3']\n", "3 ['4']\n", "HONRDC\n", "0 ['1']\n", "1 ['2']\n", "2 ['3']\n", "3 ['4']\n", "RTHLTH\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "3 ['3']\n", "4 ['4']\n", "5 ['5']\n", "MNHLTH\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "3 ['3']\n", "4 ['4']\n", "5 ['5']\n", "HIBPDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "CHDDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "ANGIDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "MIDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "OHRTDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "STRKDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "EMPHDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "CHBRON\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "CHOLDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "CANCERDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "DIABDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "JTPAIN\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "ARTHDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "ARTHTYPE\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "3 ['3']\n", "ASTHDX\n", "0 ['1']\n", "1 ['2']\n", "ADHDADDX\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "PREGNT\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "WLKLIM\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "ACTLIM\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "SOCLIM\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "COGLIM\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "DFHEAR42\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "DFSEE42\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "ADSMOK42\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "PHQ242\n", "0 ['-1']\n", "1 ['0']\n", "2 ['1']\n", "3 ['2']\n", "4 ['3']\n", "5 ['4']\n", "6 ['5']\n", "7 ['6']\n", "EMPST\n", "0 ['-1']\n", "1 ['1']\n", "2 ['2']\n", "3 ['3']\n", "4 ['4']\n", "POVCAT\n", "0 ['1']\n", "1 ['2']\n", "2 ['3']\n", "3 ['4']\n", "4 ['5']\n", "INSCOV\n", "0 ['1']\n", "1 ['2']\n", "2 ['3']\n" ] } ], "source": [ "from sklearn import preprocessing\n", "\n", "\n", "def get_df(MepsDataset):\n", " data = MepsDataset.convert_to_dataframe()\n", " # data_train est un tuple, avec le data_frame et un dictionnaire avec toutes les infos (poids, attributs sensibles etc)\n", " df = data[0]\n", " df[\"WEIGHT\"] = data[1][\"instance_weights\"]\n", " # Get categorical column from one hot encoding (specitic to MEPSdataset)\n", " # Here we create a dictionnary that links each categorical column name\n", " # to the list of corresponding one hot encoded columns\n", " categorical_columns_dic = {}\n", " for col in df.columns:\n", " col_split = col.split(\"=\")\n", " if len(col_split) > 1:\n", " cat_col = col_split[0]\n", " if not (cat_col in categorical_columns_dic.keys()):\n", " categorical_columns_dic[cat_col] = []\n", " categorical_columns_dic[cat_col].append(col)\n", " categorical_features = categorical_columns_dic.keys()\n", " print(categorical_features)\n", "\n", " def categorical_transform(df, onehotencoded, cat_col):\n", " if len(onehotencoded) > 1:\n", " return df[onehotencoded].apply(\n", " lambda x: onehotencoded[np.argmax(x)][len(cat_col) + 1 :], axis=1\n", " )\n", " else:\n", " return df[onehotencoded]\n", "\n", "\n", " # Reverse the categorical one hot encoded\n", " for cat_col, onehotencoded in categorical_columns_dic.items():\n", " df[cat_col] = categorical_transform(df, onehotencoded, cat_col)\n", " df.drop(columns=onehotencoded, inplace=True)\n", "\n", " encoders = {cat_col:preprocessing.LabelEncoder() for cat_col in categorical_features}\n", "\n", " for cat_col in categorical_features:\n", " df[cat_col] = encoders[cat_col].fit_transform(df[cat_col])\n", " print(cat_col)\n", " for idx in sorted(df[cat_col].unique()):\n", " print(idx, encoders[cat_col].inverse_transform([idx]))\n", " return df, encoders\n", "\n", "\n", "df, encoders = get_df(MEPSDataset19_data)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE RACE PCS42 MCS42 K6SUM42 UTILIZATION WEIGHT REGION \\\n", "0 53.0 1.0 25.93 58.47 3.0 1.0 21854.981705 1 \n", "1 56.0 1.0 20.42 26.57 17.0 1.0 18169.604822 1 \n", "3 23.0 1.0 53.12 50.33 7.0 0.0 17191.832515 1 \n", "4 3.0 1.0 -1.00 -1.00 -1.0 0.0 20261.485463 1 \n", "5 27.0 0.0 -1.00 -1.00 -1.0 0.0 0.000000 2 \n", "... ... ... ... ... ... ... ... ... \n", "16573 25.0 0.0 56.71 62.39 0.0 0.0 4111.315754 2 \n", "16574 25.0 0.0 56.71 62.39 0.0 0.0 5415.228173 2 \n", "16575 2.0 1.0 -1.00 -1.00 -1.0 0.0 3896.116219 2 \n", "16576 54.0 0.0 43.97 42.45 24.0 0.0 4883.851005 0 \n", "16577 73.0 0.0 42.68 43.46 0.0 0.0 6630.588948 0 \n", "\n", " SEX MARRY ... ACTLIM SOCLIM COGLIM DFHEAR42 DFSEE42 ADSMOK42 \\\n", "0 0 5 ... 1 2 2 2 2 2 \n", "1 1 3 ... 1 2 2 2 2 2 \n", "3 1 5 ... 2 2 2 2 2 2 \n", "4 0 6 ... 0 2 0 2 2 0 \n", "5 0 0 ... 2 2 2 2 2 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "16573 0 0 ... 2 2 2 2 2 2 \n", "16574 1 0 ... 2 2 2 2 2 2 \n", "16575 1 6 ... 0 2 0 2 2 0 \n", "16576 1 3 ... 2 2 2 2 2 2 \n", "16577 1 2 ... 2 2 2 2 2 2 \n", "\n", " PHQ242 EMPST POVCAT INSCOV \n", "0 1 4 0 1 \n", "1 7 4 2 1 \n", "3 1 1 1 1 \n", "4 0 0 1 1 \n", "5 0 1 2 0 \n", "... ... ... ... ... \n", "16573 1 1 0 0 \n", "16574 1 4 0 0 \n", "16575 0 0 0 1 \n", "16576 1 1 2 1 \n", "16577 1 4 2 1 \n", "\n", "[15830 rows x 43 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "nI0ZWDi_85Oi" }, "source": [ "### Question 2.2 Separation train/test du dataframe transformé pour BlackBoxAudit" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 439 }, "executionInfo": { "elapsed": 271, "status": "ok", "timestamp": 1707147531367, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "0THW5WH238Xn", "outputId": "7d81820a-0764-4a12-9f1c-c51e1db3b712" }, "outputs": [ { "data": { "text/html": [ "
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15830 rows × 42 columns

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" ], "text/plain": [ " AGE RACE PCS42 MCS42 K6SUM42 WEIGHT REGION SEX MARRY \\\n", "0 53.0 1.0 25.93 58.47 3.0 21854.981705 1 0 5 \n", "1 56.0 1.0 20.42 26.57 17.0 18169.604822 1 1 3 \n", "3 23.0 1.0 53.12 50.33 7.0 17191.832515 1 1 5 \n", "4 3.0 1.0 -1.00 -1.00 -1.0 20261.485463 1 0 6 \n", "5 27.0 0.0 -1.00 -1.00 -1.0 0.000000 2 0 0 \n", "... ... ... ... ... ... ... ... ... ... \n", "16573 25.0 0.0 56.71 62.39 0.0 4111.315754 2 0 0 \n", "16574 25.0 0.0 56.71 62.39 0.0 5415.228173 2 1 0 \n", "16575 2.0 1.0 -1.00 -1.00 -1.0 3896.116219 2 1 6 \n", "16576 54.0 0.0 43.97 42.45 24.0 4883.851005 0 1 3 \n", "16577 73.0 0.0 42.68 43.46 0.0 6630.588948 0 1 2 \n", "\n", " FTSTU ... ACTLIM SOCLIM COGLIM DFHEAR42 DFSEE42 ADSMOK42 \\\n", "0 0 ... 1 2 2 2 2 2 \n", "1 0 ... 1 2 2 2 2 2 \n", "3 3 ... 2 2 2 2 2 2 \n", "4 0 ... 0 2 0 2 2 0 \n", "5 0 ... 2 2 2 2 2 0 \n", "... ... ... ... ... ... ... ... ... \n", "16573 0 ... 2 2 2 2 2 2 \n", "16574 0 ... 2 2 2 2 2 2 \n", "16575 0 ... 0 2 0 2 2 0 \n", "16576 0 ... 2 2 2 2 2 2 \n", "16577 0 ... 2 2 2 2 2 2 \n", "\n", " PHQ242 EMPST POVCAT INSCOV \n", "0 1 4 0 1 \n", "1 7 4 2 1 \n", "3 1 1 1 1 \n", "4 0 0 1 1 \n", "5 0 1 2 0 \n", "... ... ... ... ... \n", "16573 1 1 0 0 \n", "16574 1 4 0 0 \n", "16575 0 0 0 1 \n", "16576 1 1 2 1 \n", "16577 1 4 2 1 \n", "\n", "[15830 rows x 42 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label = \"UTILIZATION\"\n", "df_X = df[[col for col in df.columns if col!=label]]\n", "df_X" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "executionInfo": { "elapsed": 315, "status": "ok", "timestamp": 1707147539967, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "afm7AT502z_j" }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(df_X, df[label], test_size=0.33, random_state=42)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qwVAJmpO8-yV" }, "source": [ "### Question 2.3: Apprentissage d'un modèle HistGradientBoostingClassifier" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1487, "status": "ok", "timestamp": 1707147589219, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "_Te7t7i59NhI", "outputId": "1e81deee-47e5-4393-bd59-d40f117d1852" }, "outputs": [ { "data": { "text/plain": [ "(0.858728943338438, 0.9137280784461626)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import ensemble\n", "cat_mask = np.array([ col_name in encoders.keys() for col_name in df_X.columns])\n", "clf = ensemble.HistGradientBoostingClassifier(random_state=42, categorical_features=cat_mask)\n", "clf = clf.fit(X_train, y_train)\n", "\n", "preds = clf.predict(X_test)\n", "\n", "clf.score(X_test, y_test), clf.score(X_train, y_train)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "B3Zbw-U4mTKf" }, "source": [ "### Question 2.4 utiliser la librairie BlackBoxAuditing pour \"auditer\" le modèle par l'analyse des influences indirectes de l'age (le calcul prend du temps mais n'hesitez pas à faires d'autres attributs)\n", "\n", "Le code est de nouveau fournit, vous avez juste à adapter avec vos notations\n", "\n", "Voici la documentaiton de la librairie utilisée\n", "https://github.com/algofairness/BlackBoxAuditing/tree/master" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "executionInfo": { "elapsed": 330, "status": "ok", "timestamp": 1707147598049, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "TqqF108qfkO9" }, "outputs": [], "source": [ "import pickle\n", "\n", "# Save your data and model (named clf here) on disk\n", "\n", "data_test = X_test.copy(deep=True)\n", "data_test[\"Y\"] = y_test\n", "\n", "data_test.to_csv(\"TD5_test_data.csv\",\n", " index=False)\n", "\n", "data_train = X_train.copy(deep=True)\n", "data_train[\"Y\"] = y_train\n", "\n", "data_train.to_csv(\"TD5_train_data.csv\",\n", " index=False)\n", "\n", "with open( 'TD5_clf.pickle', 'wb' ) as f:\n", " pickle.dump(clf, f )" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "executionInfo": { "elapsed": 75340, "status": "ok", "timestamp": 1707147682075, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "ham07j36mm16" }, "outputs": [], "source": [ "from BlackBoxAuditing.data import load_from_file\n", "from BlackBoxAuditing.model_factories.AbstractModelFactory import AbstractModelFactory\n", "from BlackBoxAuditing.model_factories.AbstractModelVisitor import AbstractModelVisitor\n", "\n", "import BlackBoxAuditing as BBA\n", "\n", "\n", "(_, train_BBA, _, _, _, _) = load_from_file(\"TD5_train_data.csv\",\n", " correct_types = [int if col_type==\"int\" else float for col_type in data_train.dtypes],\n", " response_header = 'Y',\n", " train_percentage = 1.0)\n", "(headers, _, test_BBA, response_header, features_to_ignore, correct_types) = load_from_file(\"TD5_test_data.csv\",\n", " correct_types = [int if col_type==\"int\" else float for col_type in data_test.dtypes],\n", " response_header = 'Y',\n", " train_percentage = 0.0)\n", "BBA_data = (headers, train_BBA, test_BBA, response_header, features_to_ignore, correct_types)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "executionInfo": { "elapsed": 19, "status": "ok", "timestamp": 1707147682077, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "C8wbMPKrnAYy" }, "outputs": [], "source": [ "class HirePredictorBuilder(AbstractModelFactory):\n", " def __init__(self, *args, **kwargs):\n", " AbstractModelFactory.__init__(self, *args, **kwargs)\n", " self.verbose_factory_name = \"HirePredictor\"\n", " def build(self, train_set):\n", " return HirePredictor()\n", "\n", "class HirePredictor(AbstractModelVisitor):\n", " def __init__(self):\n", " with open( 'TD5_clf.pickle', 'rb' ) as f:\n", " self.clf = pickle.load(f)\n", "\n", " def test(self, test_set, test_name=\"\"):\n", " return [[v[-1], self.clf.predict(np.expand_dims(np.array(v[:-1]), axis = 0))] for v in test_set]\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "executionInfo": { "elapsed": 18, "status": "ok", "timestamp": 1707147682079, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "n7am-Snruh0t" }, "outputs": [], "source": [ "features_to_audit = [\n", " \"AGE\",\n", " \"SEX\",\n", " \"RACE\",\n", " \"REGION\"\n", " ]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1410995, "status": "ok", "timestamp": 1707149093060, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "1j5lPGOinDL8", "outputId": "447d78a3-4877-4b75-c166-6cb05ad714e7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training initial model. (22:26:00)\n", "Calculating original model statistics on test data:\n", "\tTraining Set:\n", "\t\tConf-Matrix: {0.0: {0.0: 8619, 1.0: 165}, 1.0: {0.0: 750, 1.0: 1072}}\n", "\t\taccuracy: 0.9137280784461626\n", "\t\tBCR: 0.7847901408408462\n", "\tTesting Set:\n", "\t\tConf-Matrix {0.0: {0.0: 4104, 1.0: 224}, 1.0: {1.0: 382, 0.0: 514}}\n", "\t\taccuracy: 0.858728943338438\n", "\t\tBCR: 0.6872916391602852\n", "Auditing: 'AGE' (1/4). (22:27:21)\n", "Auditing: 'SEX' (2/4). (22:32:19)\n", "Auditing: 'RACE' (3/4). (22:37:15)\n", "Auditing: 'REGION' (4/4). (22:42:06)\n", "Audit file dump set to False: Only mininal audit files have been saved.\n", "Audit files dumped to: audit-output.\n", "\n", "Ranking audit files by accuracy. (22:47:00)\n", "\t[('AGE', 0.6872128637059725), ('RACE', 0.0003828483920367276), ('SEX', -0.0007656967840735662), ('REGION', -0.0026799387442573153)] (22:47:00)\n", "Ranking audit files by BCR. (22:47:00)\n", "\t[('AGE', 0.18729163916028524), ('REGION', 0.02316312384473207), ('RACE', 0.01262130314232901), ('SEX', -0.0013471250330076012)] (22:47:00)\n", "Audit Start Time: 2025-03-24 22:26:00.438093\n", "Audit End Time: 2025-03-24 22:47:00.537171\n", "Retrained Per Repair: False\n", "Model Factory ID: 1742851560.4382565\n", "Model Type: HirePredictor\n", "Non-standard Model Options: {}\n", "Train Size: 10606\n", "Test Size: 5224\n", "Non-standard Ignored Features: []\n", "Features: ['AGE', 'RACE', 'PCS42', 'MCS42', 'K6SUM42', 'WEIGHT', 'REGION', 'SEX', 'MARRY', 'FTSTU', 'ACTDTY', 'HONRDC', 'RTHLTH', 'MNHLTH', 'HIBPDX', 'CHDDX', 'ANGIDX', 'MIDX', 'OHRTDX', 'STRKDX', 'EMPHDX', 'CHBRON', 'CHOLDX', 'CANCERDX', 'DIABDX', 'JTPAIN', 'ARTHDX', 'ARTHTYPE', 'ASTHDX', 'ADHDADDX', 'PREGNT', 'WLKLIM', 'ACTLIM', 'SOCLIM', 'COGLIM', 'DFHEAR42', 'DFSEE42', 'ADSMOK42', 'PHQ242', 'EMPST', 'POVCAT', 'INSCOV', 'Y']\n", "\n", "Ranked Features by accuracy: [('AGE', 0.6872128637059725), ('RACE', 0.0003828483920367276), ('SEX', -0.0007656967840735662), ('REGION', -0.0026799387442573153)]\n", "\tApprox. Trend Groups: [['AGE'], ['SEX', 'RACE', 'REGION']]\n", "\n", "Ranked Features by BCR: [('AGE', 0.18729163916028524), ('REGION', 0.02316312384473207), ('RACE', 0.01262130314232901), ('SEX', -0.0013471250330076012)]\n", "\tApprox. Trend Groups: [['AGE'], ['SEX', 'RACE', 'REGION']]\n", "\n", "Summary file written to: audit-output/summary.txt\n", " (22:47:00)\n" ] } ], "source": [ "def warn(*args, **kwargs):\n", " pass\n", "import warnings\n", "warnings.warn = warn\n", "\n", "auditor = BBA.Auditor()\n", "auditor.ModelFactory = HirePredictorBuilder\n", "auditor(BBA_data, output_dir = \"audit-output\", features_to_audit=features_to_audit)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "zEvStY5L9NhZ" }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2.5: If you are curious redo the auditing of a model with bias mitigation approach (for example Reweighing)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "mADer-oDCHHn" }, "source": [ "## Question 3: Generer des exemples contrefactuels en utilisant dice-ml\n", "\n", "Voici la documentation de la librairie utilisée\n", "https://github.com/interpretml/DiCE?tab=readme-ov-file\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "executionInfo": { "elapsed": 735, "status": "ok", "timestamp": 1707149615501, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "524Dt_0dC9N_" }, "outputs": [], "source": [ "import dice_ml\n", "from dice_ml.utils import helpers" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "executionInfo": { "elapsed": 1354, "status": "ok", "timestamp": 1707149621904, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "RwXSW1zkCsF3" }, "outputs": [], "source": [ "# provide the trained ML model to DiCE's model object\n", "# use the HistGradientBoostingClassifier from the BlackBoxAuditiing\n", "backend = 'sklearn'\n", "m = dice_ml.Model(model=clf, backend=backend)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3.1 : Create a list with all continuous features" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['AGE', 'RACE', 'PCS42', 'MCS42', 'K6SUM42', 'UTILIZATION', 'WEIGHT',\n", " 'REGION', 'SEX', 'MARRY', 'FTSTU', 'ACTDTY', 'HONRDC', 'RTHLTH',\n", " 'MNHLTH', 'HIBPDX', 'CHDDX', 'ANGIDX', 'MIDX', 'OHRTDX', 'STRKDX',\n", " 'EMPHDX', 'CHBRON', 'CHOLDX', 'CANCERDX', 'DIABDX', 'JTPAIN', 'ARTHDX',\n", " 'ARTHTYPE', 'ASTHDX', 'ADHDADDX', 'PREGNT', 'WLKLIM', 'ACTLIM',\n", " 'SOCLIM', 'COGLIM', 'DFHEAR42', 'DFSEE42', 'ADSMOK42', 'PHQ242',\n", " 'EMPST', 'POVCAT', 'INSCOV'],\n", " dtype='object')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['UTILIZATION', 'RACE', 'PCS42', 'AGE', 'WEIGHT', 'MCS42', 'K6SUM42']" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numerical_features = list(set(df.columns)-set(encoders.keys()))\n", "numerical_features\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "numerical_features.remove(\"RACE\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 341, "status": "ok", "timestamp": 1707150836287, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "IN37LAMIDk6s", "outputId": "d39582a1-da54-4c2b-b9a1-63d7423869e3" }, "outputs": [ { "data": { "text/plain": [ "(Index(['AGE', 'RACE', 'PCS42', 'MCS42', 'K6SUM42', 'WEIGHT', 'REGION', 'SEX',\n", " 'MARRY', 'FTSTU', 'ACTDTY', 'HONRDC', 'RTHLTH', 'MNHLTH', 'HIBPDX',\n", " 'CHDDX', 'ANGIDX', 'MIDX', 'OHRTDX', 'STRKDX', 'EMPHDX', 'CHBRON',\n", " 'CHOLDX', 'CANCERDX', 'DIABDX', 'JTPAIN', 'ARTHDX', 'ARTHTYPE',\n", " 'ASTHDX', 'ADHDADDX', 'PREGNT', 'WLKLIM', 'ACTLIM', 'SOCLIM', 'COGLIM',\n", " 'DFHEAR42', 'DFSEE42', 'ADSMOK42', 'PHQ242', 'EMPST', 'POVCAT',\n", " 'INSCOV', 'Y'],\n", " dtype='object'),\n", " ['PCS42', 'AGE', 'WEIGHT', 'MCS42', 'K6SUM42'])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "continuous_features = [col for col in numerical_features if col!=label]\n", "data_train.columns, continuous_features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3.2 ceate a dice_ml Data with the dataframe." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "d = dice_ml.Data(dataframe=data_train,continuous_features=continuous_features,outcome_name='Y')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# DiCE explanation instance\n", "exp = dice_ml.Dice(d,m, method=\"random\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3.3 use dice to create counterfactual example 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" ], "text/plain": [ " AGE RACE PCS42 MCS42 K6SUM42 WEIGHT REGION SEX MARRY FTSTU \\\n", "0 6.0 1.0 -1.0 -1.0 -1.0 19452.455078 1 1 6 0 \n", "\n", " ... SOCLIM COGLIM DFHEAR42 DFSEE42 ADSMOK42 PHQ242 EMPST POVCAT \\\n", "0 ... 2 0 2 2 0 0 0 0 \n", "\n", " INSCOV Y \n", "0 1 0.0 \n", "\n", "[1 rows x 43 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "No counterfactuals found!\n" ] } ], "source": [ "dice_exp.visualize_as_dataframe(show_only_changes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3.4 Redo the counterfactuals creation using only data statistics not the data itself" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# Data privacy, provides only stats of the data, not the data itself\n", "features={} \n", "for c in data_train.columns:\n", " if c in continuous_features:\n", " features[c]=[data_train[c].min(), data_train[c].max()]\n", " elif c==\"Y\":\n", " continue\n", " else:\n", " features[c]=data_train[c].unique().tolist()\n", " features[c].sort()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'AGE': [0.0, 85.0],\n", " 'RACE': [0.0, 1.0],\n", " 'PCS42': [-9.0, 72.07],\n", " 'MCS42': [-9.0, 74.41],\n", " 'K6SUM42': [-9.0, 24.0],\n", " 'WEIGHT': [0.0, 94264.071559],\n", " 'REGION': [0, 1, 2, 3],\n", " 'SEX': [0, 1],\n", " 'MARRY': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n", " 'FTSTU': [0, 1, 2, 3],\n", " 'ACTDTY': [0, 1, 2, 3],\n", " 'HONRDC': [0, 1, 2, 3],\n", " 'RTHLTH': [0, 1, 2, 3, 4, 5],\n", " 'MNHLTH': [0, 1, 2, 3, 4, 5],\n", " 'HIBPDX': [0, 1, 2],\n", " 'CHDDX': [0, 1, 2],\n", " 'ANGIDX': [0, 1, 2],\n", " 'MIDX': [0, 1, 2],\n", " 'OHRTDX': [0, 1, 2],\n", " 'STRKDX': [0, 1, 2],\n", " 'EMPHDX': [0, 1, 2],\n", " 'CHBRON': [0, 1, 2],\n", " 'CHOLDX': [0, 1, 2],\n", " 'CANCERDX': [0, 1, 2],\n", " 'DIABDX': [0, 1, 2],\n", " 'JTPAIN': [0, 1, 2],\n", " 'ARTHDX': [0, 1, 2],\n", " 'ARTHTYPE': [0, 1, 2, 3],\n", " 'ASTHDX': [0, 1],\n", " 'ADHDADDX': [0, 1, 2],\n", " 'PREGNT': [0, 1, 2],\n", " 'WLKLIM': [0, 1, 2],\n", " 'ACTLIM': [0, 1, 2],\n", " 'SOCLIM': [0, 1, 2],\n", " 'COGLIM': [0, 1, 2],\n", " 'DFHEAR42': [0, 1, 2],\n", " 'DFSEE42': [0, 1, 2],\n", " 'ADSMOK42': [0, 1, 2],\n", " 'PHQ242': [0, 1, 2, 3, 4, 5, 6, 7],\n", " 'EMPST': [0, 1, 2, 3, 4],\n", " 'POVCAT': [0, 1, 2, 3, 4],\n", " 'INSCOV': [0, 1, 2]}" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 306, "status": "ok", "timestamp": 1707150841052, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "5v7toF4HC6c0" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "d = dice_ml.Data(features=features,\n", " outcome_name='Y')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "executionInfo": { "elapsed": 1184, "status": "ok", "timestamp": 1707153845627, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "JLiE2C64Dgzs" }, "outputs": [], "source": [ "# DiCE explanation instance\n", "exp = dice_ml.Dice(d,m, method=\"random\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 470 }, "executionInfo": { "elapsed": 1869, "status": "error", "timestamp": 1707153928688, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "nlYyhIq_Hs0B", "outputId": "380a57eb-afc6-431f-f2a2-ee8d0f7aed07" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 10/10 [00:01<00:00, 5.65it/s]\n" ] } ], "source": [ "# Generate counterfactual examples\n", "query_instance = data_test.drop(columns=\"Y\")[0:10]\n", "dice_exp = exp.generate_counterfactuals(\n", " query_instance,\n", " total_CFs=1,\n", " desired_class=\"opposite\")#, sparsity_weight=0.1)\n", "# Visualize counterfactual explanation\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "4UTY35uJH4RV" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Query instance (original outcome : 0.0)\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE RACE PCS42 MCS42 K6SUM42 WEIGHT REGION SEX MARRY FTSTU \\\n", "0 6.0 1.0 -1.0 -1.0 -1.0 19452.455901 1 1 6 0 \n", "\n", " ... SOCLIM COGLIM DFHEAR42 DFSEE42 ADSMOK42 PHQ242 EMPST POVCAT \\\n", "0 ... 2 0 2 2 0 0 0 0 \n", "\n", " INSCOV Y \n", "0 1 0.0 \n", "\n", "[1 rows x 43 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Diverse Counterfactual set without sparsity correction since only metadata about each feature is available (new outcome: 1.0\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE RACE PCS42 MCS42 K6SUM42 WEIGHT REGION SEX MARRY FTSTU ... SOCLIM \\\n", "0 - - - - - - - - - - ... 1.0 \n", "\n", " COGLIM DFHEAR42 DFSEE42 ADSMOK42 PHQ242 EMPST POVCAT INSCOV Y \n", "0 - - - - - - 1.0 - 1.0 \n", "\n", "[1 rows x 43 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dice_exp.visualize_as_dataframe(show_only_changes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3.5: If you are curious redo the counter factual example creation with a model with bias mitigation approach (for example Reweighing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [ { "file_id": "14Gtb6_jB6U1zunonEcL1zmiTvzsljksu", "timestamp": 1707144797600 }, { "file_id": "1reWieE41k1RU9_T08Chmufh9WOwBoIKZ", "timestamp": 1706013971768 }, { "file_id": "1fWzH-WkCZ9xcagC-8OY71_b_aNcXfdpM", "timestamp": 1705973751107 } ] }, "kernelspec": { "display_name": "env_adv", "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": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 0 }