Modelling_temporal_cat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfe993d6-361a-416b-bb80-aa2f8b7d7960",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 11.7,8.27 # figure size in inches\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.max_rows', 500) \n",
    "pd.set_option('display.max_columns', 30) \n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "%config Completer.use_jedi = False\n",
    "\n",
    "import joblib\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import *\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f483d21-0642-47c9-86a3-6378c1f988ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temporal_cat = pd.read_csv('CSV/df_temporal_cat.csv')\n",
    "\n",
    "X = df_temporal_cat.iloc[:,1:-1]\n",
    "y = df_temporal_cat['Stage_Progress']\n",
    "\n",
    "X_train_temporal, X_test_temporal, y_train_temporal, y_test_temporal = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22e63097-bb54-437f-bc8b-87df65478c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temporal_cat_drug = pd.read_csv('CSV/df_temporal_cat_drug.csv')\n",
    "\n",
    "X = df_temporal_cat_drug.iloc[:,1:]\n",
    "X.drop('Stage_Progress', axis=1, inplace=True)\n",
    "y = df_temporal_cat_drug['Stage_Progress']\n",
    "\n",
    "X_train_temporal_drug, X_test_temporal_drug, y_train_temporal_drug, y_test_temporal_drug = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "715dd615-a7f8-45a2-9658-483c2217392a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For ensemble\n",
    "def get_predictions(model, preds, X_test, algoname):\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_proba = model.predict_proba(X_test)[:,1] # only get probability for 1\n",
    "\n",
    "    preds[algoname] = {\n",
    "    'pred': y_pred,\n",
    "    'pred_proba': y_pred_proba\n",
    "    }\n",
    "    \n",
    "    return preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232a9749-0157-4bda-8f6a-e5552db348eb",
   "metadata": {},
   "source": [
    "# Build models\n",
    "## Generate result and store in dict\n",
    "* Set weight to prevent bias, 4 to CKD (1), 1 to non-CKD (0)\n",
    "* Implement GridSearchCV to find the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6555fb72-bb06-4353-807a-003e29d9fbc7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_model(model, parameters, algoname, X_train, X_test, y_train, preds, months_name):\n",
    "\n",
    "    print('========================================')\n",
    "    print('Training %s ' % algoname)\n",
    "    print()\n",
    "    \n",
    "    scores = ['f1']\n",
    "    \n",
    "    # retain for loop in case want to try different scores\n",
    "    for score in scores:\n",
    "\n",
    "        print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "        print()\n",
    "\n",
    "        pipe = Pipeline(steps=[('model', model)])\n",
    "\n",
    "        search = GridSearchCV(pipe, parameters, n_jobs=-1)\n",
    "        search.fit(X_train, y_train)\n",
    "        \n",
    "        print(\"Best parameter (CV score=%0.3f):\" % search.best_score_)\n",
    "        print()\n",
    "        print(\"Best parameters set found on development set:\")\n",
    "        print()\n",
    "        print(search.best_params_)\n",
    "        print()\n",
    "        print(\"Grid scores on development set:\")\n",
    "        print()\n",
    "        means = search.cv_results_['mean_test_score']\n",
    "        stds = search.cv_results_['std_test_score']\n",
    "        for mean, std, params in zip(means, stds, search.cv_results_['params']):\n",
    "            print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "                  % (mean, std * 2, params))\n",
    "       \n",
    "        # Obtained best model\n",
    "        optimized_model = search.best_estimator_\n",
    "        \n",
    "        # Save model\n",
    "        if 'drug' in months_name:\n",
    "            months_name = months_name.split('_')[0]\n",
    "            joblib.dump(optimized_model, 'models/' + months_name + '/'+ algoname +'_cat_drug.joblib')\n",
    "        else:\n",
    "            joblib.dump(optimized_model, 'models/' + months_name + '/'+ algoname +'_cat.joblib')\n",
    "        \n",
    "        \n",
    "        # Get predictions\n",
    "        y_pred = optimized_model.predict(X_test)\n",
    "        y_pred_proba = optimized_model.predict_proba(X_test)[:,1] # only get probability for 1\n",
    "        \n",
    "        preds[algoname] = {\n",
    "        'pred': y_pred,\n",
    "        'pred_proba': y_pred_proba\n",
    "    }        \n",
    "        \n",
    "        return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c97a2169-015f-4d22-87db-a260f39d8345",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression\n",
    "model_lr = LogisticRegression(random_state=0, max_iter=10000, class_weight={0: 1, 1: 4})\n",
    "parameters_lr = {'model__C': [1e-05, 1e-04, 1e-03, 1e-02, 1e-01, 1, 10, 100,1000, 1e4, 1e5, 1e6]}\n",
    "\n",
    "# Decision tree\n",
    "model_dt = DecisionTreeClassifier(random_state=0, class_weight={0: 1, 1: 4}, min_samples_leaf = 30)    \n",
    "parameters_dt = {'model__max_depth': np.linspace(10, 100, 10)}\n",
    "\n",
    "# lightGBM\n",
    "model_lgbm = LGBMClassifier(n_estimators=1000, objective='binary',scale_pos_weight=4)\n",
    "parameters_lgbm = {'model__learning_rate': [1e-05, 1e-04, 1e-03, 1e-02, 1e-01, 1, 10, 100]}\n",
    "\n",
    "# Random forest\n",
    "model_rf = RandomForestClassifier(class_weight={0: 1, 1: 4}, n_estimators=800, n_jobs=-1, verbose=1)\n",
    "parameters_rf = {'model__max_depth': list(range(1,31))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e3f50b4-beb4-4a8e-b99f-ef326398d423",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Training LogisticRegression \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n",
      "Best parameter (CV score=0.621):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__C': 100000.0}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.333 (+/-0.000) for {'model__C': 1e-05}\n",
      "0.333 (+/-0.000) for {'model__C': 0.0001}\n",
      "0.333 (+/-0.000) for {'model__C': 0.001}\n",
      "0.396 (+/-0.046) for {'model__C': 0.01}\n",
      "0.571 (+/-0.101) for {'model__C': 0.1}\n",
      "0.579 (+/-0.119) for {'model__C': 1}\n",
      "0.608 (+/-0.135) for {'model__C': 10}\n",
      "0.583 (+/-0.121) for {'model__C': 100}\n",
      "0.592 (+/-0.125) for {'model__C': 1000}\n",
      "0.604 (+/-0.137) for {'model__C': 10000.0}\n",
      "0.621 (+/-0.103) for {'model__C': 100000.0}\n",
      "0.617 (+/-0.117) for {'model__C': 1000000.0}\n",
      "========================================\n",
      "Training DecisionTreeClassifier \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n",
      "Best parameter (CV score=0.513):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__max_depth': 10.0}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.513 (+/-0.136) for {'model__max_depth': 10.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 20.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 30.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 40.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 50.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 60.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 70.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 80.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 90.0}\n",
      "0.513 (+/-0.136) for {'model__max_depth': 100.0}\n",
      "========================================\n",
      "Training LGBMClassifier \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n",
      "Best parameter (CV score=0.667):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__learning_rate': 1e-05}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.667 (+/-0.000) for {'model__learning_rate': 1e-05}\n",
      "0.667 (+/-0.000) for {'model__learning_rate': 0.0001}\n",
      "0.567 (+/-0.081) for {'model__learning_rate': 0.001}\n",
      "0.554 (+/-0.082) for {'model__learning_rate': 0.01}\n",
      "0.588 (+/-0.072) for {'model__learning_rate': 0.1}\n",
      "0.604 (+/-0.083) for {'model__learning_rate': 1}\n",
      "0.537 (+/-0.317) for {'model__learning_rate': 10}\n",
      "0.408 (+/-0.068) for {'model__learning_rate': 100}\n",
      "========================================\n",
      "Training RandomForestClassifier \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 800 out of 800 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=0.683):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__max_depth': 20}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.333 (+/-0.000) for {'model__max_depth': 1}\n",
      "0.337 (+/-0.017) for {'model__max_depth': 2}\n",
      "0.392 (+/-0.067) for {'model__max_depth': 3}\n",
      "0.504 (+/-0.113) for {'model__max_depth': 4}\n",
      "0.542 (+/-0.139) for {'model__max_depth': 5}\n",
      "0.571 (+/-0.086) for {'model__max_depth': 6}\n",
      "0.571 (+/-0.077) for {'model__max_depth': 7}\n",
      "0.575 (+/-0.057) for {'model__max_depth': 8}\n",
      "0.600 (+/-0.061) for {'model__max_depth': 9}\n",
      "0.625 (+/-0.087) for {'model__max_depth': 10}\n",
      "0.633 (+/-0.062) for {'model__max_depth': 11}\n",
      "0.654 (+/-0.086) for {'model__max_depth': 12}\n",
      "0.650 (+/-0.031) for {'model__max_depth': 13}\n",
      "0.667 (+/-0.087) for {'model__max_depth': 14}\n",
      "0.658 (+/-0.077) for {'model__max_depth': 15}\n",
      "0.675 (+/-0.057) for {'model__max_depth': 16}\n",
      "0.663 (+/-0.049) for {'model__max_depth': 17}\n",
      "0.679 (+/-0.068) for {'model__max_depth': 18}\n",
      "0.675 (+/-0.062) for {'model__max_depth': 19}\n",
      "0.683 (+/-0.055) for {'model__max_depth': 20}\n",
      "0.679 (+/-0.050) for {'model__max_depth': 21}\n",
      "0.675 (+/-0.042) for {'model__max_depth': 22}\n",
      "0.662 (+/-0.055) for {'model__max_depth': 23}\n",
      "0.667 (+/-0.053) for {'model__max_depth': 24}\n",
      "0.662 (+/-0.072) for {'model__max_depth': 25}\n",
      "0.679 (+/-0.042) for {'model__max_depth': 26}\n",
      "0.675 (+/-0.057) for {'model__max_depth': 27}\n",
      "0.675 (+/-0.050) for {'model__max_depth': 28}\n",
      "0.679 (+/-0.033) for {'model__max_depth': 29}\n",
      "0.683 (+/-0.031) for {'model__max_depth': 30}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['predictions/predictions_temporal_cat.joblib']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_temporal = {'y': y_test_temporal}\n",
    "\n",
    "preds_temporal = train_model(model_lr, parameters_lr, 'LogisticRegression', X_train_temporal, X_test_temporal, y_train_temporal, preds_temporal, 'temporal')\n",
    "preds_temporal = train_model(model_dt, parameters_dt, 'DecisionTreeClassifier', X_train_temporal, X_test_temporal, y_train_temporal, preds_temporal, 'temporal')\n",
    "preds_temporal = train_model(model_lgbm, parameters_lgbm, 'LGBMClassifier', X_train_temporal, X_test_temporal, y_train_temporal, preds_temporal, 'temporal')\n",
    "preds_temporal = train_model(model_rf, parameters_rf, 'RandomForestClassifier', X_train_temporal, X_test_temporal, y_train_temporal, preds_temporal, 'temporal')\n",
    "\n",
    "joblib.dump(preds_temporal, 'predictions/predictions_temporal_cat.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "049cd672-5d05-477a-8a68-3338f740e729",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 328 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 800 out of 800 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 312 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 312 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 312 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['predictions/preds_temporal_ensemble_cat.joblib']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_name = 'temporal'\n",
    "    \n",
    "# Load models\n",
    "clf1 = joblib.load('models/'+ folder_name +'/LogisticRegression_cat.joblib')\n",
    "clf2 = joblib.load('models/'+ folder_name +'/RandomForestClassifier_cat.joblib')\n",
    "clf3 = joblib.load('models/'+ folder_name +'/DecisionTreeClassifier_cat.joblib')\n",
    "clf4 = joblib.load('models/'+ folder_name +'/LGBMClassifier_cat.joblib')\n",
    "\n",
    "# Build ensemble\n",
    "eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], voting='soft').fit(X_train_temporal, y_train_temporal)\n",
    "\n",
    "# Stacked\n",
    "model_stack1 = StackingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], final_estimator=LogisticRegression(), cv=5).fit(X_train_temporal, y_train_temporal)\n",
    "model_stack2 = StackingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], final_estimator=RandomForestClassifier(), cv=5).fit(X_train_temporal, y_train_temporal)\n",
    "model_stack3 = StackingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], final_estimator=LGBMClassifier(), cv=5).fit(X_train_temporal, y_train_temporal)\n",
    "\n",
    "# Get predictions\n",
    "preds_temporal_ensemble = {'y': y_test_temporal}\n",
    "preds_temporal_ensemble = get_predictions(eclf, preds_temporal_ensemble, X_test_temporal, 'VotingClassifier')\n",
    "preds_temporal_ensemble = get_predictions(model_stack1, preds_temporal_ensemble, X_test_temporal, 'StackingClassifier_logistic')\n",
    "preds_temporal_ensemble = get_predictions(model_stack2, preds_temporal_ensemble,X_test_temporal, 'StackingClassifier_rf')\n",
    "preds_temporal_ensemble = get_predictions(model_stack3, preds_temporal_ensemble,X_test_temporal, 'StackingClassifier_lgbm')\n",
    "\n",
    "joblib.dump(preds_temporal_ensemble, 'predictions/preds_temporal_ensemble_cat.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0eb64e5a-a248-4734-987e-a824cceea607",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Training LogisticRegression \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n",
      "Best parameter (CV score=0.567):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__C': 1}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.333 (+/-0.000) for {'model__C': 1e-05}\n",
      "0.333 (+/-0.000) for {'model__C': 0.0001}\n",
      "0.333 (+/-0.000) for {'model__C': 0.001}\n",
      "0.387 (+/-0.057) for {'model__C': 0.01}\n",
      "0.525 (+/-0.096) for {'model__C': 0.1}\n",
      "0.567 (+/-0.096) for {'model__C': 1}\n",
      "0.562 (+/-0.087) for {'model__C': 10}\n",
      "0.562 (+/-0.087) for {'model__C': 100}\n",
      "0.562 (+/-0.087) for {'model__C': 1000}\n",
      "0.562 (+/-0.087) for {'model__C': 10000.0}\n",
      "0.562 (+/-0.087) for {'model__C': 100000.0}\n",
      "0.562 (+/-0.087) for {'model__C': 1000000.0}\n",
      "========================================\n",
      "Training DecisionTreeClassifier \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n",
      "Best parameter (CV score=0.500):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__max_depth': 10.0}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.500 (+/-0.134) for {'model__max_depth': 10.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 20.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 30.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 40.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 50.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 60.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 70.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 80.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 90.0}\n",
      "0.500 (+/-0.134) for {'model__max_depth': 100.0}\n",
      "========================================\n",
      "Training LGBMClassifier \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n",
      "Best parameter (CV score=0.667):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__learning_rate': 1e-05}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.667 (+/-0.000) for {'model__learning_rate': 1e-05}\n",
      "0.667 (+/-0.000) for {'model__learning_rate': 0.0001}\n",
      "0.579 (+/-0.049) for {'model__learning_rate': 0.001}\n",
      "0.567 (+/-0.072) for {'model__learning_rate': 0.01}\n",
      "0.617 (+/-0.101) for {'model__learning_rate': 0.1}\n",
      "0.596 (+/-0.062) for {'model__learning_rate': 1}\n",
      "0.567 (+/-0.278) for {'model__learning_rate': 10}\n",
      "0.388 (+/-0.033) for {'model__learning_rate': 100}\n",
      "========================================\n",
      "Training RandomForestClassifier \n",
      "\n",
      "# Tuning hyper-parameters for f1\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=-1)]: Done 800 out of 800 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=0.688):\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'model__max_depth': 9}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.333 (+/-0.000) for {'model__max_depth': 1}\n",
      "0.362 (+/-0.042) for {'model__max_depth': 2}\n",
      "0.467 (+/-0.062) for {'model__max_depth': 3}\n",
      "0.550 (+/-0.090) for {'model__max_depth': 4}\n",
      "0.575 (+/-0.082) for {'model__max_depth': 5}\n",
      "0.596 (+/-0.077) for {'model__max_depth': 6}\n",
      "0.621 (+/-0.107) for {'model__max_depth': 7}\n",
      "0.646 (+/-0.134) for {'model__max_depth': 8}\n",
      "0.688 (+/-0.065) for {'model__max_depth': 9}\n",
      "0.662 (+/-0.067) for {'model__max_depth': 10}\n",
      "0.683 (+/-0.096) for {'model__max_depth': 11}\n",
      "0.667 (+/-0.059) for {'model__max_depth': 12}\n",
      "0.667 (+/-0.070) for {'model__max_depth': 13}\n",
      "0.662 (+/-0.055) for {'model__max_depth': 14}\n",
      "0.671 (+/-0.049) for {'model__max_depth': 15}\n",
      "0.675 (+/-0.057) for {'model__max_depth': 16}\n",
      "0.671 (+/-0.055) for {'model__max_depth': 17}\n",
      "0.671 (+/-0.061) for {'model__max_depth': 18}\n",
      "0.679 (+/-0.057) for {'model__max_depth': 19}\n",
      "0.662 (+/-0.067) for {'model__max_depth': 20}\n",
      "0.662 (+/-0.055) for {'model__max_depth': 21}\n",
      "0.671 (+/-0.055) for {'model__max_depth': 22}\n",
      "0.667 (+/-0.059) for {'model__max_depth': 23}\n",
      "0.679 (+/-0.068) for {'model__max_depth': 24}\n",
      "0.675 (+/-0.042) for {'model__max_depth': 25}\n",
      "0.671 (+/-0.049) for {'model__max_depth': 26}\n",
      "0.675 (+/-0.062) for {'model__max_depth': 27}\n",
      "0.671 (+/-0.067) for {'model__max_depth': 28}\n",
      "0.662 (+/-0.055) for {'model__max_depth': 29}\n",
      "0.662 (+/-0.067) for {'model__max_depth': 30}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['predictions/predictions_temporal_cat_drug.joblib']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_temporal_drug = {'y': y_test_temporal_drug}\n",
    "\n",
    "preds_temporal_drug = train_model(model_lr, parameters_lr, 'LogisticRegression', X_train_temporal_drug, X_test_temporal_drug, y_train_temporal_drug, preds_temporal_drug, 'temporal_drug')\n",
    "preds_temporal_drug = train_model(model_dt, parameters_dt, 'DecisionTreeClassifier', X_train_temporal_drug, X_test_temporal_drug, y_train_temporal_drug, preds_temporal_drug, 'temporal_drug')\n",
    "preds_temporal_drug = train_model(model_lgbm, parameters_lgbm, 'LGBMClassifier', X_train_temporal_drug, X_test_temporal_drug, y_train_temporal_drug, preds_temporal_drug, 'temporal_drug')\n",
    "preds_temporal_drug = train_model(model_rf, parameters_rf, 'RandomForestClassifier', X_train_temporal_drug, X_test_temporal_drug, y_train_temporal_drug, preds_temporal_drug, 'temporal_drug')\n",
    "\n",
    "joblib.dump(preds_temporal_drug, 'predictions/predictions_temporal_cat_drug.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c49494a-cd4f-4603-b703-f0dbb98b0d4f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 800 out of 800 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['predictions/preds_temporal_ensemble_cat_drug.joblib']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_name = 'temporal'\n",
    "    \n",
    "# Load models\n",
    "clf1 = joblib.load('models/'+ folder_name +'/LogisticRegression_cat_drug.joblib')\n",
    "clf2 = joblib.load('models/'+ folder_name +'/RandomForestClassifier_cat_drug.joblib')\n",
    "clf3 = joblib.load('models/'+ folder_name +'/DecisionTreeClassifier_cat_drug.joblib')\n",
    "clf4 = joblib.load('models/'+ folder_name +'/LGBMClassifier_cat_drug.joblib')\n",
    "\n",
    "# Build ensemble\n",
    "eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], voting='soft').fit(X_train_temporal_drug, y_train_temporal_drug)\n",
    "\n",
    "# Stacked\n",
    "model_stack1 = StackingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], final_estimator=LogisticRegression(), cv=5).fit(X_train_temporal_drug, y_train_temporal_drug)\n",
    "model_stack2 = StackingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], final_estimator=RandomForestClassifier(), cv=5).fit(X_train_temporal_drug, y_train_temporal_drug)\n",
    "model_stack3 = StackingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3), ('lgbm', clf4)], final_estimator=LGBMClassifier(), cv=5).fit(X_train_temporal_drug, y_train_temporal_drug)\n",
    "\n",
    "# Get predictions\n",
    "preds_temporal_drug_ensemble = {'y': y_test_temporal_drug}\n",
    "preds_temporal_drug_ensemble = get_predictions(eclf, preds_temporal_drug_ensemble, X_test_temporal_drug, 'VotingClassifier')\n",
    "preds_temporal_drug_ensemble = get_predictions(model_stack1, preds_temporal_drug_ensemble, X_test_temporal_drug, 'StackingClassifier_logistic')\n",
    "preds_temporal_drug_ensemble = get_predictions(model_stack2, preds_temporal_drug_ensemble,X_test_temporal_drug, 'StackingClassifier_rf')\n",
    "preds_temporal_drug_ensemble = get_predictions(model_stack3, preds_temporal_drug_ensemble,X_test_temporal_drug, 'StackingClassifier_lgbm')\n",
    "\n",
    "joblib.dump(preds_temporal_drug_ensemble, 'predictions/preds_temporal_ensemble_cat_drug.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b3e1e6-f6f1-453a-86ac-3672416cd39d",
   "metadata": {},
   "source": [
    "# Compare models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0583cd1-ad32-461b-a2c3-5a561e4ef716",
   "metadata": {},
   "outputs": [],
   "source": [
    "def specificity(y_valid, y_pred):\n",
    "    tn, fp, fn, tp = confusion_matrix(y_valid, y_pred).ravel()\n",
    "    \n",
    "    specificity = tn / (tn+fp)\n",
    "    return specificity\n",
    "\n",
    "# Performance of a dichotomous diagnostic test\n",
    "def youden_index(y, pred_proba):\n",
    "    fpr, tpr, thresh = roc_curve(y, pred_proba)\n",
    "    optimal_idx = np.argmax(tpr - fpr)\n",
    "    optimal_threshold = thresh[optimal_idx]\n",
    "    return optimal_threshold\n",
    "\n",
    "# Threshold used\n",
    "def get_threshold_table(preds):\n",
    "    thresh = [[algo, val['threshold']] for algo, val in preds.items() if algo != 'y']\n",
    "    return pd.DataFrame(thresh, columns=['Algo', \"Threshold\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59dc8a5a-2587-455a-ad48-7c87caf24e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_matrics(predications, days_bin):\n",
    "    # Calculate probability thresholds\n",
    "    for algo, preds in predications.items():\n",
    "        if algo == 'y':\n",
    "            continue\n",
    "        threshold = youden_index(predications['y'], preds['pred_proba'])\n",
    "        preds['threshold'] = threshold\n",
    "        preds['pred'] = preds['pred_proba'] >= threshold\n",
    "\n",
    "    # Get threshold to categorize as 1 (CKD)\n",
    "    threshold = pd.concat([get_threshold_table(preds) for preds in [predications]])\n",
    "    pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "    display(threshold)\n",
    "\n",
    "    # Compute matrics\n",
    "    metrics_list = []\n",
    "\n",
    "    y_truth = predications['y']\n",
    "    for algo, preds in predications.items():\n",
    "        if algo == 'y':\n",
    "            continue\n",
    "\n",
    "        y_pred = preds['pred']\n",
    "        y_pred_proba = preds['pred_proba']\n",
    "\n",
    "        acc = accuracy_score(y_truth, y_pred)\n",
    "        f1 = f1_score(y_truth, y_pred)\n",
    "        prec = precision_score(y_truth, y_pred)\n",
    "        rec = recall_score(y_truth, y_pred)\n",
    "        spec = specificity(y_truth, y_pred)\n",
    "        roc = roc_auc_score(y_truth, y_pred_proba)\n",
    "\n",
    "        row = [algo, acc, f1, prec, rec, spec, roc]\n",
    "        metrics_list.append(row)\n",
    "\n",
    "    cols = ['Algorithm', 'Accuracy', 'F1', 'Precision', 'Recall', 'Specificiy', 'ROC_AUC']\n",
    "    metrics = pd.DataFrame(metrics_list, columns=cols)\n",
    "    display(metrics.sort_values('ROC_AUC'))\n",
    "\n",
    "\n",
    "    # Plot ROC\n",
    "    fig = plt.figure(figsize=(6,6))\n",
    "    lw = 2\n",
    "\n",
    "    rocs = []\n",
    "    for algo, model in predications.items():\n",
    "        if algo == 'y':\n",
    "            continue\n",
    "        fpr, tpr, thresh = roc_curve(predications['y'], predications[algo]['pred_proba'])\n",
    "        auc = roc_auc_score(predications['y'], predications[algo]['pred_proba'])\n",
    "        auc = round(auc, 3)\n",
    "        rocs.append((algo, auc, [fpr, tpr]))\n",
    "    # sort legend by AUC\n",
    "    rocs = sorted(rocs, key=lambda x: x[1], reverse=True)\n",
    "    for algo, auc, [fpr, tpr] in rocs:    \n",
    "        plt.plot(fpr,tpr, label=algo+f' ({auc})', lw=lw)\n",
    "\n",
    "    # chance line\n",
    "    plt.plot([0, 1], [0, 1], color='navy', label='chance line (0.5)', linestyle='--', lw=lw)\n",
    "    plt.xlabel('1 - Specificity')\n",
    "    plt.ylabel('Sensitivity')\n",
    "    # plt.title('ROC curve for 6 months', size=12)\n",
    "    plt.text(0.7,0.3, days_bin, fontsize=12)\n",
    "    plt.grid(linewidth=1.2, color='lightgray')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.savefig('figures/' + days_bin + '.jpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "067c4114-fb17-441a-ac97-fab4c782ce23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algo</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.98624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.55172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.33515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.43280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Algo  Threshold\n",
       "0      LogisticRegression    0.98624\n",
       "1  DecisionTreeClassifier    0.55172\n",
       "2          LGBMClassifier    0.33515\n",
       "3  RandomForestClassifier    0.43280"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Specificiy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.71667</td>\n",
       "      <td>0.41379</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>0.92500</td>\n",
       "      <td>0.53125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.55000</td>\n",
       "      <td>0.58462</td>\n",
       "      <td>0.42222</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>0.62813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.57143</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.64437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.85000</td>\n",
       "      <td>0.72727</td>\n",
       "      <td>0.92308</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.97500</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Algorithm  Accuracy      F1  Precision  Recall  Specificiy  \\\n",
       "0      LogisticRegression   0.71667 0.41379    0.66667 0.30000     0.92500   \n",
       "1  DecisionTreeClassifier   0.55000 0.58462    0.42222 0.95000     0.35000   \n",
       "2          LGBMClassifier   0.50000 0.57143    0.40000 1.00000     0.25000   \n",
       "3  RandomForestClassifier   0.85000 0.72727    0.92308 0.60000     0.97500   \n",
       "\n",
       "   ROC_AUC  \n",
       "0  0.53125  \n",
       "1  0.62813  \n",
       "2  0.64437  \n",
       "3  0.68125  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_matrics(joblib.load('predictions/predictions_temporal_cat.joblib'), 'Temporal_cat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45f377ad-bac7-48d0-bd5d-209a6dc71614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algo</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VotingClassifier</td>\n",
       "      <td>0.40686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StackingClassifier_logistic</td>\n",
       "      <td>0.33334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StackingClassifier_rf</td>\n",
       "      <td>0.21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>StackingClassifier_lgbm</td>\n",
       "      <td>0.36838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Algo  Threshold\n",
       "0             VotingClassifier    0.40686\n",
       "1  StackingClassifier_logistic    0.33334\n",
       "2        StackingClassifier_rf    0.21000\n",
       "3      StackingClassifier_lgbm    0.36838"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Specificiy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>StackingClassifier_lgbm</td>\n",
       "      <td>0.65000</td>\n",
       "      <td>0.46154</td>\n",
       "      <td>0.47368</td>\n",
       "      <td>0.45000</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.54875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StackingClassifier_rf</td>\n",
       "      <td>0.61667</td>\n",
       "      <td>0.56604</td>\n",
       "      <td>0.45455</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.55000</td>\n",
       "      <td>0.59500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VotingClassifier</td>\n",
       "      <td>0.68333</td>\n",
       "      <td>0.55814</td>\n",
       "      <td>0.52174</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.72500</td>\n",
       "      <td>0.67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StackingClassifier_logistic</td>\n",
       "      <td>0.68333</td>\n",
       "      <td>0.57778</td>\n",
       "      <td>0.52000</td>\n",
       "      <td>0.65000</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.69125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Algorithm  Accuracy      F1  Precision  Recall  \\\n",
       "3      StackingClassifier_lgbm   0.65000 0.46154    0.47368 0.45000   \n",
       "2        StackingClassifier_rf   0.61667 0.56604    0.45455 0.75000   \n",
       "0             VotingClassifier   0.68333 0.55814    0.52174 0.60000   \n",
       "1  StackingClassifier_logistic   0.68333 0.57778    0.52000 0.65000   \n",
       "\n",
       "   Specificiy  ROC_AUC  \n",
       "3     0.75000  0.54875  \n",
       "2     0.55000  0.59500  \n",
       "0     0.72500  0.67000  \n",
       "1     0.70000  0.69125  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_matrics(joblib.load('predictions/preds_temporal_ensemble_cat.joblib'), 'Temporal_CE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c3dfc3e-008e-484c-925c-41eedcd7c7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algo</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.57257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.53731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.33539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.47295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Algo  Threshold\n",
       "0      LogisticRegression    0.57257\n",
       "1  DecisionTreeClassifier    0.53731\n",
       "2          LGBMClassifier    0.33539\n",
       "3  RandomForestClassifier    0.47295"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Specificiy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.48333</td>\n",
       "      <td>0.55072</td>\n",
       "      <td>0.38776</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.59125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.55000</td>\n",
       "      <td>0.58462</td>\n",
       "      <td>0.42222</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>0.62375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.57143</td>\n",
       "      <td>0.54545</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.78333</td>\n",
       "      <td>0.72340</td>\n",
       "      <td>0.62963</td>\n",
       "      <td>0.85000</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.81875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Algorithm  Accuracy      F1  Precision  Recall  Specificiy  \\\n",
       "1  DecisionTreeClassifier   0.48333 0.55072    0.38776 0.95000     0.25000   \n",
       "2          LGBMClassifier   0.55000 0.58462    0.42222 0.95000     0.35000   \n",
       "3  RandomForestClassifier   0.70000 0.57143    0.54545 0.60000     0.75000   \n",
       "0      LogisticRegression   0.78333 0.72340    0.62963 0.85000     0.75000   \n",
       "\n",
       "   ROC_AUC  \n",
       "1  0.59125  \n",
       "2  0.62375  \n",
       "3  0.67000  \n",
       "0  0.81875  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_matrics(joblib.load('predictions/predictions_temporal_cat_drug.joblib'), 'Temporal_CD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36acb140-2a85-474e-b162-82f6ba9718bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algo</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VotingClassifier</td>\n",
       "      <td>0.59260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StackingClassifier_logistic</td>\n",
       "      <td>0.30326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StackingClassifier_rf</td>\n",
       "      <td>0.35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>StackingClassifier_lgbm</td>\n",
       "      <td>0.34517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Algo  Threshold\n",
       "0             VotingClassifier    0.59260\n",
       "1  StackingClassifier_logistic    0.30326\n",
       "2        StackingClassifier_rf    0.35000\n",
       "3      StackingClassifier_lgbm    0.34517"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Specificiy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StackingClassifier_rf</td>\n",
       "      <td>0.68333</td>\n",
       "      <td>0.57778</td>\n",
       "      <td>0.52000</td>\n",
       "      <td>0.65000</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VotingClassifier</td>\n",
       "      <td>0.78333</td>\n",
       "      <td>0.62857</td>\n",
       "      <td>0.73333</td>\n",
       "      <td>0.55000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>0.76375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StackingClassifier_logistic</td>\n",
       "      <td>0.68333</td>\n",
       "      <td>0.65455</td>\n",
       "      <td>0.51429</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>0.57500</td>\n",
       "      <td>0.78250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>StackingClassifier_lgbm</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.78750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Algorithm  Accuracy      F1  Precision  Recall  \\\n",
       "2        StackingClassifier_rf   0.68333 0.57778    0.52000 0.65000   \n",
       "0             VotingClassifier   0.78333 0.62857    0.73333 0.55000   \n",
       "1  StackingClassifier_logistic   0.68333 0.65455    0.51429 0.90000   \n",
       "3      StackingClassifier_lgbm   0.75000 0.66667    0.60000 0.75000   \n",
       "\n",
       "   Specificiy  ROC_AUC  \n",
       "2     0.70000  0.71000  \n",
       "0     0.90000  0.76375  \n",
       "1     0.57500  0.78250  \n",
       "3     0.75000  0.78750  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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js70KnJdSNgI6Av8TQqiTeoqSC/qEBMS6dQB4TJqMsLKMA/2jRyPo2zeA6Ogkhg1rQEDAM9jZWWsdVpFkzuPH5sBlKeVVACFEADAAOJ9uHgm4CCEE4AzcByx3dAilyJJSUvWOxCEJ4o4fL9BtRwUGIR48wL6eFy7duhbotrWya9c1Jk7cT0JCCs8915CFCwdgbW0ZCdAczJkIKgK30j0PBVo8Ns8PwEYgHHABhkop9dmtVEpJcnJyroPR6XQZfloCS2xzWpm52h/uzHa+vPwNvfbna+wL35fptPZn9Xy52fCne2PZqFyvOz+UnDABXUoKpKRosv2CEh4ezeDBa0hISGHMGB9+/rk3en0Ken3xbrc5/5/NmQhEJq/Jx573AE4BnYGawB9CiL1SykcZViTEeGA8gKenJ8HBwXkO6sqVK3letqiypDY3MGEevwqOefobyioJ2OgkQ/cYksA/FZwo51o51+t+WrJmLW5XqMDtp/jfKEreftuHS5eiePPN2ly+HKJ1OAXKHP/P5kwEoUD6/4hKGL75pzcOmCWllMBlIcQ1wAs4kn4mKeVcYC5A06ZNZd26dXMdjE6n48qVK9SsWRMbC+lRYYltTnPh48753+bUMz4nRpzI8HJUQAARjz7HrlYt2qxbW+Dn6C3lc46NTTLeF/DuuzUtos3pmfNzNuc7eBSoLYSoDoQBw4DHyyDeBLoAe4UQ5YC6wNXsViqEyLTOvKlsbGyeavmiSLU5f6Vfrz4hgQdzfwWgzGuTsdOwpENx/pxXrDjL1Kk72bUr49jCxbnNWTFHm8321UVKqQMmATuAC8BqKeU5IcQEIcSE1Nk+AVoLIc4Cu4FpUsrIzNeoKIXPg4AAdBER2Nevh0u3blqHUywtXnyK555bz+3bMWzefEnrcIolsx5TSSm3Alsfe+3ndL+HA93NGYOimIs+NpZ7xqOB1zB0flPy07x5Jxg/fhNSwiefdOL//q+N1iEVS5Zxck1RzOD+8hWk3L+PQ6OGOHfooHU4xc6cOUeYNGkbAF980ZW331ZJwFxUIlCUPEiJjube/PkAlJ0yRR0N5LPZsw/xxhs7Un/vwZQpLTWOqHhTiUBR8uD+4iXoo6Jw8vPDqVUrrcMpdpyd7bCyEsyZ05sJE/y0DqfYU4lAUXIp5eFD7i9aBECZKeragDm8+GIT2ratgpeXh9ahWAR1T7ai5NK9BQvRx8RQonVrnJo10zqcYkFKyWef7eHMmTvG11QSKDgqEShKLrjGSu4vWwYYjgaUpyel5O23/+CDD4Lo2XMZcXG5L/+hPB11akhRcmHAIT0yLg7njh1xbNRI63CKPCklU6Zs5/vvj2Bra8UPP/TGycmybhArDFQiUBQTlYqW9DhhKJdV5rXJGkdT9On1kokTt/DLL8exs7Nm3bpn6du3jtZhWSSVCBSLErN3L8lhYXla9sUdeux04NKtGw71Hx9aQ8mNlBQ9L720iYULT+HgYMOGDUPp0aOW1mFZLJUIlCyNW3iEoOAIrcN4wgLbL+hsfSrXyyVevcqtl8bnebvNAD3gMXlSntehGOzff4tFi07h6GjDpk3D6dKlhtYhWTSVCJQsFcYkAOSYBI7bNqVhJq+nPIwCwLp0aVy65n4AlzWXVnOxkuDHOur0xdNq374q8+b1p1Ytd9q3r6p1OBZPJQIlR9dn9dE6hIymp/2MemJScnIy9jnU5LerXBnPGdOznSczvy5el+tllH8lJaVw/fpD6tQpDcDzzzfWOCIljeo+qiiK2SUk6Bg8eBWtW8/n7Nk7OS+gFCiVCBRFMau4uGQGDAhgy5YQpASdLtvRaBUNqFNDiqKYTWxsEv36rSQo6Dplyjixe/dofHzKaR2W8hiVCBRFMYvo6ER6917Bvn03KV/emd27M44uphQeKhEoipLvUlL09Oq1nP37b1GxoguBgWOMF4mVwkclAkVR8p21tRUvvtiEsLBodu16jpo13bUOScmGSgSKouQbKaWxLPfYsb4MHeqNo6OqHVTYqV5DiqLkizt3YujQYRGnTv1jfE0lgaJBJQJFUZ5aeHg0HTsuZu/em0yZsh0ppdYhKbmgTg0pivJUbt2KonPnJVy+fB8fn7KsWTNEjdpWxKhEoChKnl279oDOnZdw/fpDmjTxZOfOUZQu7aR1WEouqURQjL249AR/XYoEbhT4tifumsjesL3mWXn1Koafi32emORzTU/zS5JdmSzmFgMtgFMRp+iXybJK7ly+fJ/OnRdz69YjmjevyI4do3Bzc9A6LCUPVCIoxgxJ4Ol0qpu3G4DMlgSyUSJe8tZ6PU6J2c8X45D30xbtKrbL87LFzcmTtwkNfUSbNpXZunUkrq72Woek5JFKBBYg5JPu2Npq03vj7Jiz+b/S6SVTf2asPnr3m9ncS/wFWbs2ZYYMwdr6yb4QwsqKoR07MsrTM//jsjBDhnjj5GRLhw7VcHa20zoc5SmoRKAUC7r797m/dCkA+nFjcevXT7PkV5ydPv0POp2epk0rANCnjxqboThQ3UeVYuHer/OQcXE4tW8PauAYszh+PJxOnRbTrdtSgoOf/rSjUnioRKAUecl37/JgxQoA3F+dqHE0xdOhQ6F06bKEBw8SaN++KtWquWkdkpKPVCJQirx7c39FJibi0q2rGlTeDPbtu0m3bkuJikrE378+a9YMwd5enVUuTlQiUIq05PBwHq5aBULgMWmy1uEUO0FB1+jRYxkxMUkMH96AlSufwdbWWuuwlHymEoFSpEX+/AsyORnXXr1wqKuuDeSnyMg4+vcPIC4umTFjGrF06SBsbNQuozhSx3dKkZV06xYPf/sNrKzwmDRJ63CKHQ8PJ378sTf799/ixx/7YGWlykYUVyoRKEVW5JwfQaej5MCB2NeornU4xUZ0dCIuLoabw557rhHPPddI44gUc1PHeUqRlPjImqiNG8HGBg/VUyjfrFlzjho1vuPEidtah6IUIJUIlCIp8m8X0OtxGzwYu8qVtQ6nWFi+/AzDhq0jMjKOrVtDtA5HKUAqEShFTsJDGx7ddETY2uLxygStwykWFi06xXPPrUevl0yf3oH331c1lSyJukagFDmRf7sAArdnn8VW1Qx6anPnHufllzcD8NlnnXnvPZUELI1KBEXcuIVHCAqO0DqMDPTx8bz2ewru0ZLr82rk78olxEc6IqwlpV8en7/rtkBz5hxh0qRtAHz1VTfeequ1xhEpWlCJoIjLKQn4VXAsoEj+FX/qFG3PG4YqjMc8pYndW5TBtmxZs6zbkri7O2JtLfjmmx5MntxC63AUjahEUExcn9XnideSk5MJDg4u8Fhkih6Ay+Why5dL8n39ws4OB2/vfF+vJRo+3IdmzSpSq5a71qEoGlKJQDGbWAeBU7NmWoehpCOlZNasfXTpUoPmzSsCqCSgqF5DimIppJS8995u3nsvkN69lxMVlaB1SEohoY4IFMUCSCl5662dfPPNIaytBXPm9KZkSTW+sGKgEoGiFHN6veS117YxZ85RbG2tWLXKn0GD6mkdllKIqESgKMWYXi+ZMGEzv/56Ajs7a9ate5a+fVWVViUjlQgUpRg7diycBQtO4uBgw4YNQ+nRo5bWISmFkEoEilKMNW9ekWXLBlO2bAk6d1YVWpXMqUSgKMVMcnIKISH3qV+/DADDhjXQOCKlsFPdRxWlGElM1PHss2tp1Wo+R46EaR2OUkSoRKAoxURCgo7Bg1ezYcNFrK2FGlFMMZlZE4EQoqcQIlgIcVkI8U4W83QUQpwSQpwTQvxlzngUpbiKj9cxePAatm4NwcPDicDAMfj5VdA6LKWIMNs1AiGENTAH6AaEAkeFEBullOfTzeMG/Aj0lFLeFEKoKmKKkksxMUm8+uoBjh6NoFy5EuzaNZoGDdS/kmI6c14sbg5cllJeBRBCBAADgPPp5hkB/CalvAkgpbyb00qllCQnJ+c6GJ1Ol+FnYWEdMAyrK7vyvPz1tJtDpz85zRbI7jLhxHJl2OuU/9VJG17V80Hq73n5rJ5GYf2czUVKyaBBqzl6NILy5Uuwc+dI6tYtVeDve0GztM8ZzNtmcyaCisCtdM9Dgcfr3NYBbIUQfwIuwLdSyifKVQohxgPjATw9PZ+qouaVK1fyvKw5NHiKJPC0zJEE0nNDaFL9FArf52xO/ft7cvlyJHPntkWIewQH39M6pAJjSZ9zGnO02ZyJILMrVTKT7TcFugCOwEEhxCEp5aUMC0k5F5gL0LRpU1m3bt1cB6PT6bhy5Qo1a9bExqbw9ZpNfj8yT8vV/nAnACGfdH9iWo5tXtEEgBMjTuRp21mJO3CA8FUT8PJsQcU8fFZPo7B/zvlFSokQhn+xmjVr0qGDJ/Xr1ynWbU7PUj7n9MzZZnO+g6FA+lHFKwHhmcwTKaWMBWKFEHuARsAlsiCEwNbWNs9B2djYPNXy5vK0MWW3fE5tzu/3w9ra8Gdl9ZSf1dMorJ9zfoiIiGXQoFV8+WU3WrUy/IvZ21sX6zZnRbU5f5iz19BRoLYQoroQwg4YBmx8bJ7fgXZCCBshhBOGU0cXzBiTohRp//wTQ8eOi9m//xZvvLEDKR8/yFaU3DPbEYGUUieEmATsAKyBBVLKc0KICanTf5ZSXhBCbAfOAHpgnpTyb3PFpChFWVjYIzp3XsKlS/fw9i7D778PM54eUpSnYdaTa1LKrcDWx177+bHnXwJfmjMORSnqbt6MonPnxVy58oBGjcrxxx/PUaZMCa3DUooJk04NCSHWCSH6CCHUnciKUsCuXn1A+/YLuXLlAU2behIYOEYlASVfmbpj/wlDn/8QIcQsIYSXGWNSFCWd8+cjCA19RMuWldi1azTu7ubt9qtYHpNODUkpdwG7hBAlgeHAH0KIW8CvwDIpZfG+e0VRNNS3bx22bRtJixaVcHW11zocpRgy+VSPEKI0MBZ4ETgJfAs0Af4wS2SKYsH+/vsu+/ffND7v1q2mSgKK2Zh0RCCE+A3wApYC/aSUt1MnrRJCHDNXcIpiiU6evE23bktJSkph//7n8fEpp3VISjFnaq+heak9gIyEEPZSykQppZ8Z4lIUi3T0aBjduy/j4cMEeveuTe3apbUOSbEApp4a+jST1w7mZyCKYukOHrxF165LefgwgQED6vLbb8/i4GAZ5RMUbWX7VyaEKI+heJyjEKIx/9YPcgWczByborGUhw/RJybmfrkHD8wQTfG2Z88N+vRZQUxMEkOG1Gf58sHY2lprHZZiIXL6utEDwwXiSsDX6V6PBt4zU0zFzsRdE9kbtjfzidWrGH4u9snTul3qGX76LM503B+D47lf76Nt2wh74808xaTkjuEIIICYmCRGjPBh8eKB2NioW3aUgpNtIpBSLgYWCyGekVKuK6CYip0sk0Ah0K5iu0xfj9qyBQDrkiUR9nnorWJjjWvfvk8TmsVwc3NgwYL+bN0aws8/98XaWiUBpWDldGpolJRyGVBNCPHE10Mp5deZLKZk4eyYs0++OL1k6s+oPK2z2juGHfb1WX2emJacnExwcDB169bNVbVCmZJC3JGjhvWvW4tdpUp5ik3JXlRUAiVLGkYWGjSoHoMG1dM4IsVS5fTVI+0+dmcMA8c8/lCKoYSLF9E/eoRtxYoqCZjJ+vUXqF79W/btu5nzzIpiZjmdGvol9dcfpZQRBRCPUgjEHToMgFPLxweUU/LDqlV/M3Lkb6SkSHbsuEzbtlW0DkmxcKaejDwghNgphHhBCFHKrBEpmos9fAiAEi1UIshvy5adYcQIQxJ47722zJzZSeuQFMW0RCClrA18AHgDx4UQm4UQo8wamaIJmZxM/DFDNyMnlQjy1YIFJxk9ej16vWTGjI58+mlnNZ6AUiiY3D1BSnlESvkm0By4Dyw2W1SKZuL//ht9XBx21atjW06VNsgvc+ce54UXNiIlfP55Zz76qINKAkqhYep4BK5CiDFCiG3AAeA2hoSgFDNxh1OvD7RQH29+8vR0xtbWiv/9rzvvvpt5l11F0Yqp96+fBjYAM6WUqrREMRabmghKtGypcSTFS79+dbl4cRI1aqhLbErhY2oiqCHVKNnFnj4xkfgTJwFwaq6OCJ7WF1/sp3nzinTsWA1AJQGl0MrphrLZUsrXgY1CiCcSgZSyv7kCUwpe/KnTyMRE7OvWxcbdXetwiiwpJdOn/8nMmXtwdbXn6tXXKF1aleZSCq+cjgiWpv78ytyBKNpT1weenpSSd9/dzX//ux8rK8GPP/ZWSUAp9HK6oSytXJmvlPLb9NOEEFOAv8wVmFLw1PWBpyOl5M03dzB79mFsbKxYsWIwQ4Z4ax2WouTI1O6jYzJ5bWw+xqFoTB8XR/yZM2BlhZOfGmsot/R6yaRJW5k9+zC2tlasXTtEJQGlyMjpGsFwYARQXQixMd0kF+CeOQMrltIKzOXCuIVHCAo2f3WPuBMnITkZhwYNsHZ1Nfv2ipu//77LvHknsbe35rffhtK7d22tQ1IUk+V0jSDtngEP4H/pXo8GzpgrKItTu3uWk0xJAp3qlnnqEOLSykqo+kJ50rBhOdasGYKDgw3du9fUOhxFyZWcrhHcAG4ArQomnGIuj6WmIfMy0/kpNq3QXAt1fcBUOp2ec+fu0qhReQD696+rcUSKkjfZXiMQQuxL/RkthHiU7hEthHhUMCEq5pYSHU3CuXNgY4NT0yZah1MkJCWlMGzYWlq1ms9ff13XOhxFeSo5HRG0Tf2pxh4oxuKOHgO9HkdfX6ycVFfHnCQm6hgyZA2bNl2iZEl7NcC8UuSZWmuophDCPvX3jkKI14QQbmaNTCkw6vqA6eLjkxk4cBWbNl3C3d2R3btH06KFGrxHKdpM7T66DkgRQtQC5gPVgRVmi0opULGHjwDq+kBO4uKS6ddvJdu3X8bDw4nAwNE0bVpB67AU5amZmgj0UkodMAiYLaV8A/A0X1hKQdE9eEDixYsIOzscfRtpHU6hJaVk8OBV7N59jXLlSvDnn2OMF4kVpagzNREkp95TMAbYnPqa6aOhK4VWXOrRgGOTJljZ22scTeElhOC111pQvbobf/01Fm/vslqHpCj5xtSrXOOACcBnUsprQojqwDLzhaUUlLgjaWUl1PWBzEgpjQPI9O5dm4sXJ2FnZ61xVIqSv0wdqvK8lPI1KeXK1OfXpJSzzBuaUhD+vX9AJYLHRUbG0bbtQgIDrxlfU0lAKY5M7TXURgjxhxDikhDiqhDimhDiqrmDU8wr+c5dkq5eRTg54diggdbhFCp378bSufNiDhy4xZtv7kCvV8NxKMWXqaeG5gNvAMeBFPOFoxSkuCOpvYX8miJs1SWfNLdvR9OlyxIuXIikbt3SbN06EisrNb6wUnyZmgiipJTbzBqJUuBi0+4fUN1GjUJDH9G582JCQu7j7V2G3btHU66cs9ZhKYpZmZoIgoQQXwK/AYlpL0opT5glKqVAxKnrAxncuPGQzp2XcPXqAxo1KscffzxHmTIltA5LUczO1ESQtqdIX6heAp3zN5zirdo7W7QOwSj+7FmSQ0OxcnXFoZ6X1uEUCpcv3yc09BF+fhXYsWMU7u6OWoekKAXCpEQgpexk7kCUrOVHmenHRXz3PQClnh2CsFY9YQC6dKnBzp2jaNSoPG5uDlqHoygFxqREIIQoB3wOVJBS9hJC1AdaSSnnmzW6YsbcpaRNFXfiBLF792JVogTuL7ygdTiaunAhgrCwaLp2rQFAhw7VtA1IUTRg6p3Fi4AdQFphlUvA62aIRykAEd9+B4D7mNHYlCqlcTTaOXv2Dh06LKJfv5UcPRqmdTiKohlTE4GHlHI1oAdIrTukupEWQbGHDhF3+DBWrq64jx2rdTiaOXnyNp06LSYiIo4OHarSoIEqGaFYLlMTQawQojSGC8QIIVoCeR9uS9GElNJ4NFD6+XEWOzbx0aNhdO68hHv34unbtw4bNgzD0VHdR6FYLlN7Db0JbARqCiH2A2UAf7NFpZhF7L59xJ88ibWbG6VGPad1OJo4cOAWvXot59GjRAYN8iIgwF+VjVAsXk5DVTYTQpRPvV+gA/AehvsIdgKhBRCfkk8yHA289BLWzpbXPz42NomBAwN49CiRoUO9WbVKJQFFgZxPDf0CJKX+3hp4H5gDPADmmjEuJZ/FBAaS8PffWJfxoNSI4VqHo4kSJexYunQQL7zQmGXLBmNrq5KAokDOp4aspZT3U38fCsyVUq4D1gkhTpk1MiXfSL3eeDTgMf5lrBwt60apBw/iKVXK0OYePWrRo0ctjSNSlMIlpyMCayFEWrLoAgSmm6ZG7C4iYnb+QeKlS9iUL4/bs0O0DqdAbdoUTLVq37Jz5xWtQ1GUQiunRLAS+EsI8TsQD+wFSB27WPUaKgpSUrj/448AeEyYYFGjkK1bd57Bg1fz6FEiu3apqumKkpVsE4GU8jPgLQw3lLWVUqYVZbcCJue0ciFETyFEsBDishDinWzmayaESBFCqJ5I+Uzs20fytWvYVqqE2+BBWodTYAICzjF06Fp0Oj1vv92a//63q9YhKUqhlePpHSnloUxeu5TTckIIawwXlrth6GF0VAixUUp5PpP5/ovhzmUlH8nkZMSaNQB4TJyIsLPTOKKCsXHjDT788Dh6veSDD9oxc2Yn43CTiqI8yZzn+ZsDl6WUVwGEEAHAAOD8Y/NNBtYBzUxZqZSS5OTkXAej0+ky/Myt1/58jX3h+/K0bJq8xJ0dfVwc936Ygz7qYabTdZH3EP/cwaZqVZx69cz37RdGCxac5IMPjiElfPxxe95/v22eP/Oi4mn/tosi1eb8Zc5EUBG4le55KP+WswZACFERGIShnHWWiUAIMR4YD+Dp6UlwcHCeg7pyJW8XDZ82CdSPtX2quDMjVgZgtW5djvMlDRrEpTy2u6ixto7Bzs6KV16pj79/mXx/zwuzvP5tF2WqzfnDnIkgs2Pxxwd+nQ1Mk1KmZHfoLqWcS+p9C02bNpV169bNdTA6nY4rV65Qs2ZNbGzy0Ozjhh8nRjw5Fo/tZx4AJL8fmemitT/cyWEgL3FnJeXBA65v24YESr/5Btbu7k/Mo0/RcycxgRr+/thayFCUNWvWpEqVErRr1zBvn3MR9NR/20WQanP+ttmc72AoUDnd80pA+GPz+AEBqUnAA+gthNBJKTdktVIhxFPt1GxsbJ5q+eyWzWm9+bkzvr94MTIujhLt21F2/PhM50lOTuZOcDC2trbFOhH8738HqF+/DL161QagQoUST/05F0WqzZbBHG02ZyI4CtQWQlQHwoBhwIj0M0gpq6f9LoRYBGzOLgkoBrqICB4sXwFAmcmvaRyNtj755C8++uhPHB1tuHp1CqVLW073WEXJL2ZLBFJKnRBiEobeQNbAAinlOSHEhNTpP5tr28Vd5K+/IhMScO7SBUefBlqHowkpJR99FMSnn+7Fykrw0099KF/e2SIuiCtKfjPryTUp5VZg62OvZZoApJRjzRlLcZH8zz88XBkAQJnXcryVo1iSUjJt2i6+/PIA1taCpUsHMXy4j9ZhKUqRZRlXWYqRyJ9/RiYn49KrJw75ePG5qJBS8sYbO/j228PY2FgREPAMzzxTX+uwFKVIU4mgCEkKDeXh2nVgZUWZSZO0DkcTly7dY+7c49jaWrF27bP07295yVBR8ptKBEVI5I8/gU5HyQH9sa9ZU+twNFG3rgebNg0nMTGF3r1rax2OohQLKhEUEYnXrhH1++9gbY3HxIlah1OgdDo9p079g59fBQC6dKmhcUSKUryYOmaxorHIOT9CSgpugwdhV7Wq1uEUmOTkFEaN+o02bRawbVuI1uEoSrGkEkERkBgSwqMtW8DWFo8JE7QOp8AkJaUwdOhaVq06h729Na6u6h4BRTEHdWqoCIj4/geQklJD/LGtWFHrcApEYqIOf/81bN58iZIl7dmxYxQtWlTSOixFKZZUIijkEi5cIHrnToS9PaVftoyjgfj4ZAYNWsWOHVdwd3fkjz+eo0kTT63DUpRiSyWCVBN3TWRv2F6tw3hCxA9zACg1bBi25cpqHE3BGD58HTt2XKFMGSd27RpNw4bltA5JUYo1dY0glSlJoF3FdgUQSUbxxw1lT0s991yBb1srr7/ekho1SvHnn2NVElCUAqCOCB5zdsxZrUPIKHV0UKsSThoHYl5SSuMoYh07VuPixVextbXWOCpFsQzqiEDR3IMH8bRrt5DNm/8dAVUlAUUpOCoRKJqKjIyjc+cl7N9/i//7vz/Q6fRah6QoFkedGlI0c/duLF27LuHs2bvUru3Ozp3PYWOjvpsoSkFTiUDRxO3b0XTpsoQLFyLx8vIgMHA0np4uWoelKBZJff1SClxo6CM6dFjEhQuRNGhQlj//HKOSgFJoCCG4fPmy1mEUKJUIlAJ361YUYWHR+PqWJyhoDOXKOWsdkpJHzs7OxoeVlRWOjo7G58uXL9c6PLM6cuQIvXv3xs3NDXd3d5o3b87ChQsB+PPPP7Gyssrw/jg7O3Pw4EEAOnbsiIODAy4uLri6utK0aVNmzZpFYmKiJm1RiUApcK1aVWbXrucIDByNh0fx7hZb3MXExBgfVapUYdOmTcbnI0eO1Dq8J+h0unxZz8GDB+ncuTMdOnTg8uXL3Lt3j59++olt27YZ56lQoUKG9ycmJoZWrVoZp//www9ER0dz+/Zt/ve//xEQEEDv3r2RqV3GC5JKBEqBCA6OzNA9tFWrypQq5ahhRIo56fV6Zs2aRc2aNSldujTPPvss9+/fB+D69esIIVi4cCGVK1emVKlS/Pzzzxw9epSGDRvi5ubGpHQDLy1atIg2bdowefJkSpYsiZeXF4GBgcbp4eHh9O/fH3d3d2rVqsWvv/5qnDZ9+nT8/f0ZNWoUrq6uLFq0iCNHjtCqVSvc3Nzw9PRk0qRJJCUl5ap9b7/9NmPGjGHatGl4eHgghKBp06asXr061+9ViRIl6NixIxs3buTgwYNs2bIl1+t4WioRKGZ37txdOnRYxODBq9iz54bW4SgF4LvvvmPDhg389ddfhIeHU6pUKV599dUM8xw+fJiQkBBWrVrF66+/zmeffcauXbs4d+4cq1ev5q+//sowb40aNYiMjGTGjBk8++yzREVFATB8+HAqVapEeHg4a9eu5b333mP37t3GZX///Xf8/f15+PAhI0eOxNramm+++YbIyEgOHjzI7t27+fHHH01uW1xcHAcPHsTf3/8p36WMqlSpgp+fH3v3FnypG5UIFLM6ffofOnZczJ07sbRvX5WmTVXxOEvwyy+/8Nlnn1GpUiXs7e2ZPn06a9euzXBq5sMPP8TBwYHu3btTokQJhg8fTtmyZalYsSLt2rXj5MmTxnnLli3L66+/jq2tLUOHDqVOnTrs2bOHW7dusW/fPv773//i4OCAr68vL774IkuXLjUu26pVKwYOHGi8htG0aVNatmyJjY0N1apV4+WXX86QdHLy4MED9Ho9np7Z/y2Hh4fj5uaW4REbG5vtMhUqVDAeORUk1X1UMZvjx8Pp1m0pDx4k0LNnLX777VkcHW21DkspADdu3GDQoEFYWf37XdPa2po7d+4Yn5cr928dKUdHxyeex8TEGJ9XrFjRWIIEDN+e7969y+3bt3F3d8fF5d9eZ1WrVuXYsWPG55UrV84Q26VLl3jzzTc5duwYcXFx6HQ6mjZtanLbSpUqhZWVFbdv38bLyyvL+SpUqEBoaKjJ6wUICwujdevWuVomP6gjAsUsDh8OpUuXJTx4kEC/fnXYsGGoSgIWpHLlymzbto2HDx8aHwkJCVTM43gaYWFhGS6i3rp1i7Jly+Lp6cn9+/eJjo42Trt582aG7aRPIACvvPIKXl5ehISE8OjRIz7//PNcXaB1cnKiVatWrFu3Lk9tycqtW7c4fvw47doVfHFLdURggsSQEM4OHYVdfOaHdbbCcIiYvKpBptM3pv68sOXd3G88OTn3y2gsIUHH4MGriYpK5Jln6rFixTPY2anaQZZkwoQJvP/++yxevJiqVasSERHBgQMHGDBgQJ7Wd/fuXb777jsmTpzIhg0buHjxIu3ataNy5cq0bt2ad999l6+++opLly4xf/58li1bluW6oqOjcXV1xdnZmYsXL/LTTz9RpkyZXMXzxRdf0L17d6pWrcrzzz9P6dKlOX36NP/5z38ICAjI1bri4uI4evQob7zxBs2bN6d37965Wj4/qCMCE0Rt3kKJuEfYypRMH+gF6EWW043zJSfn/gE4eHtj7eqq8btgOgcHGwICnuH5530JCPBXScACTZkyhf79+9O9e3dcXFxo2bIlhw8fzvP6WrRoQUhICB4eHrz//vsEBATg5uYGwMqVK7l+/ToVKlRg0KBBzJgxg27dumW5rq+++ooVK1bg4uLCSy+9xNChQ3MdT+vWrQkMDCQwMJAaNWrg7u7O+PHjM+zEw8PDn7iPIP1RxKRJk3BxcaFcuXK8/vrrPPPMM2zfvj3D6bSCIrTos/o0/Pz8ZPrzf6ZKTk4mODiYunXrYmv75CkKn8U+QOZlqK8PHUb86dNMbzGOgF9ff3Lln6R+m/gwItdxmcTW9onDW1Pk1Ob8du9eHKVLa3tfQEG3uTAo7m1etGgR8+bNY9++fcbXinubM/O0bRZCHJdS+mU2TR0R5CAlJob4v/8mRVhxxqMmws7uyYc1hkdm0/LjkYckUNC2bg2hWrVv2bDhotahKIqSSyoR5CDu2DFISeGSW2XibR20DqdQ+v33iwwcGEBMTBJBQde0DkdR8oW3t/cTp3aKa+kMdbE4B3GHjwBwukwtjSMpnNasOceIEb+h0+mZMqUF33zTQ+uQlGJm7NixjB07tsC3e+7cuQLfplbUEUEOYg8fAlQiyMyKFWcZNmwdOp2e//u/1nzzTY8icRpLUZSMVCLIRsrDhyReuIiwteW8ezWtwylUli07w6hRv6HXSz74oB2zZnVVSUBRiih1aigbsUePgpQ4+vqSZG0ZPRNMVaNGKUqUsOP//q81H37YQetwFEV5CioRZCPukKHfs1PLFnBL42AKmdatK3PhwqtUqlR07m9QFCVz6tRQNtKuD5Ro0ULjSAqHb789xNq1543PVRJQlOJBJYIs6CIiSLp8BeHggGPDhlqHo7n//ncfr7++gxEj1nHjxkOtw1EUJR+pRJCF2COGbqNOTZsi7Ow0jkZbn3zyF++8sxsh4Kef+lC1qpvWISmKko/UNYIsZLg+YKGklHz4YRCffbYXKyvBokUDeO65RlqHpShKPlOJIAuxqQWyLPX6gJSS//u/P/jqq4NYWwuWLx/M0KGZV1dVFKVoU4ngMdXe2UKZuAcsuXmTWBsHvJddR291iwW2X9DZ+hRM1zrCgnHjRhS//HIcW1srAgL8GTy4ntYhKYpiJioRZKJR5GUAznrUQG9lKKHc2fpU9gvV7m7mqApWtWpubN8+ivv34+nbt47W4SiKYkYqETzm+qw+hE/bS9QJ6D+2H+PG9jFMmJ46w/QorUIzu5QUPUePhtOyZSXAcK+AoijFn+o19Bgp5b/XB1q21DiagqPT6RkzZgNt2y7gt98uaB2OoigFSB0RPCb55k10//yDtZsb9nUs45RIcnIKo0atZ/Xqczg72+Hhoe3gMo9LTk4mNDSUhISEHOeVUpKSkkJISIjF1D5SbVZtTs/BwYFKlSrlavAalQgeE5vWbbRFC4QGQ8YVtKSkFIYNW8v69RdxdbVn27aRhe6UUGhoKC4uLlSrVi3Hf3q9Xk9iYiL29vaaDPmnBdVm1eY0Ukru3btHaGgo1atXN3ndlvEO5kJcalkJpxbNNY7E/AyDzK9i/fqLuLk5sGvXc4UuCQAkJCRQunRpi/nmpyh5JYSgdOnSJh09p6eOCNKTktjUgWgs4frAmDEb2LIlhNKlHfnjj+do3NhT65CypJKAopgmL/8r6oggnUqRkHLvHjZlymCXi8OqourNN1tSq5Y7QUFjCnUSUBTFvNQRQToNbkgg9fpAMf0GqtdLrKwMbWvRohIXLryKjY36PqAolkztAdLxTk0EJYppfaGHDxNo334hq1b9bXxNJQHTfPbZZ3h7e9OwYUN8fX05nNrFePbs2cTFxeVpnYsWLWLSpElPvP7zzz+zZMmSPMcaExPDyy+/TM2aNfH29qZ9+/bGeJ2dnfO83selj/PixYv4+vrSuHFjrly5QuvWrfNlG9WqVSMyMjLbeTp27MixY8fyZXsAr7/+Onv27AHg2rVrtGjRgtq1azN06FCSkpKemD8oKAhfX1/jw8HBgQ0bNgCwe/dumjRpgq+vL23btuXyZcPNqlFRUfTr149GjRrh7e3NwoULAUhKSqJ9+/bodLp8a48p1F4gldBLvG+mHhEUw+sD9+/H07XrEvbvv8UHHwSRmFiwf2hF2cGDB9m8eTMnTpzgzJkz7Nq1i8qVDRfVnyYRZGXChAmMHj06z8u/+OKLuLu7ExISwrlz51i0aFGOO9O8SB/nhg0bGDBgACdPnqRmzZocOHDA5PVIKdHr9fkeX17cv3+fQ4cO0b59ewCmTZvGG2+8QUhICKVKlWL+/PlPLNOpUydOnTrFqVOnCAwMxMnJie7dDZUGXnnlFZYvX86pU6cYMWIEn376KQBz5syhfv36nD59mj///JO33nqLpKQk7Ozs6NKlC6tWrSq4RmPmU0NCiJ7At4A1ME9KOeux6SOBaalPY4BXpJSnzRlTVqrdBecEsK1QAbtKlbQIwWwiI+Po1Wslp0/foWbNUuzePRp7+6J5VrDaO1vMst7rs/pkOe327dt4eHhgb28PgIeHBwDfffcd4eHhdOrUCQ8PD4KCgnjllVc4evQo8fHx+Pv7M2PGDACOHj3KlClTiI2Nxd7ent27d2fYxpYtW/j000/ZtGkTP/zwA87OzkydOpWOHTvSokULgoKCePjwIfPnz6ddu3bExcUxduxYLl68iJeXF9euXWPOnDmULl2aw4cPs3z5cmMXwxo1alCjRo0M24uJiWHAgAE8ePCA5ORkPv30UwYMGEBsbCzPPvssoaGhpKSk8OGHHzJ06FDeeecdNm7ciI2NDd27d+err75i+vTpODs7U79+fWbPno21tTV79uwhKCgIZ2dnYmJiAPjyyy9ZvXo1iYmJDBo0iBkzZnD9+nV69epFp06dOHjwIBs2bKBq1arZfkaffPIJy5cvp3LlypQuXZpGjRoxbZph97Fs2TJee+01Hj16xIIFC2jevDnTp0/n2rVr3L59m0uXLvH1119z6NAhtm3bRsWKFdm0adMTfe3Xrl1Lz549AUOCCgwMZMWKFQCMGTOG6dOn88orr2QZ49q1a+nVqxdOToZ7cYQQPHr0CDAcBVSoUMH4enR0NFJKYmJicHd3x8bG8D85cOBA3n33XUaOHJnt+5GfzLY3EEJYA3OAbkAocFQIsVFKeT7dbNeADlLKB0KIXsBcQJPzMsbrA8XsaCAyMoGhQ5dx/nwkdeqUJjBwNBUrqpHFcqN79+7MnDmTOnXq0LVrV4YOHUqHDh147bXX+PrrrwkKCjImh88++wx3d3dSUlLo0qULZ86cwcvLi6FDh7Jq1SqaNWvGo0ePcHR0NK5//fr1fP3112zdupVSpUo9sX2dTseRI0fYunUrM2bMYNeuXfz444+UKlWKM2fOcObMGZo0aQLAuXPn8PX1xdraOts2OTg4sH79elxdXYmMjKRly5b079+f7du3U6FCBbZsMSTcqKgo7t+/z/r167l48SJCCB4+fJhhXb1792bChAnG5JXezp07CQkJ4ciRI0gp6d+/P3v27KFKlSoEBwezcOFCfvzxxxw/g2PHjrFu3TpOnjyJTqejSZMmNGr0b0n02NhYDhw4wJ49e3j++ef5+2/D6c8rV64QFBTE+fPnadWqFevWreOLL75g0KBBbNmyhYEDB2bYzv79+/H39wfg3r17uLm5GXfQlSpVIiwsLNs4AwICePPNN43P582bR+/evXF0dMTV1ZVDhwzd0ydNmkT//v2pUKEC0dHRrFq1ypi4GzRowNGjR3N8T/KTOb8WNgcuSymvAgghAoABgDERSCnTHz8eAsz2VVyfmAjHjxNz4wbW1k82u8VFw6Fpcbo+EB4ezfPP7+HatWjq1y/D7t2jKV8+/84RayG7b+5gnhuNnJ2dOX78OHv37iUoKIihQ4cya9Ysxo4d+8S8q1evZu7cueh0Om7fvs358+cRQuDp6UmzZs0AcHX9NxEHBQVx7Ngxdu7cmeH19AYPHgxA06ZNuX79OgD79u1jypQpgGHH0aBB7kqESyl577332LNnD1ZWVoSFhXHnzh18fHyYOnUq06ZNo2/fvrRr1w6dToeDgwMvvvgiffr0oW/fviZvZ+fOnezcuZPGjRsDhiORkJAQqlSpQtWqVWlp4hevffv2MWDAAGMCfTyG4cOHA9C+fXsePXpkTFa9evXC1tYWHx8fUlJSjN/2fXx8jO9lerdv36ZMmTLG9+hx2XUiuX37NmfPnqVHjx7G17755hu2bt1KixYt+PLLL3nzzTeZN28eO3bswNfXl8DAQK5cuUK3bt1o164drq6uWFtbY2dnR3R0NC4uLia9P0/LnImgIhmHfA8l+2/7LwDbclqplJLk5ORcB3N/3nysf/6Zf7KYnlZMwmnfGDiR9fnKvGxbK+Hhj4iMTMDHpwzbt4+gdGn7IhV/mryeQ87P885CCNq3b0/79u3x9vZmyZIlxvPjer0evV7PtWvX+Oqrrzh8+DClSpVi3LhxxMXFkZKSghDiiXj0ej3Vq1fn2rVrXLx4ET8/P8DQ3vRttrW1Ra/XI4RAp9MZt5f2SK9evXqcPn0anU6XZSLU6/UsXbqUu3fvcvToUWxtbalRowZxcXHUqlWLo0ePsnXrVt599126devGhx9+yKFDh9i9ezerVq3ihx9+YNeuXRnifDzm9O/LtGnTePnllzPEcP36dUqUKGHSZ5S2nuz+Dh6flhaPnZ1dhvcx7XUhBMnJyU+sz8HBgbi4OPR6Pe7u7jx8+JCkpCRsbGy4efMmnp6eWcawatUqBg4ciLW1NXq9noiICE6fPk2zZs3Q6/UMGTKE3r17o9frWbBgAdOmTUNKSY0aNahevTrnz5+neXPDjayJiYkZYs/sPclObveT5kwEmaXOJ1MsIITohCERtM1i+nhgPICnpyfBwcG5D+baNawAWa0qlC//xPTDD45wsZLgv05Zv8HR5VtxIw/b1oqLC8yb144KFZy4fz+U+/e1jihvUlJSSExMzNUyuZ0/O5cuXcLKyopatWoBcPz4cSpVqkRiYiLOzs7cu3cPFxcXIiMjcXJywsHBgZs3b7J9+3batGlD9erVCQsLY//+/fj5+REdHY2joyM6nY5KlSrx2WefMWzYMJYtW0b9+vXR6XTodDoSExPR6/UkJSWRmJhIYmIiUkoSExNp2bIlAQEBtG7dmgsXLnDu3DmSkpKoVKkSjRs35oMPPuCjjz5CCMHly5e5cOEC/fr1M7439+/fp3Tp0uj1enbu3MmNGzdISkri2rVruLu7M2TIEOzt7Vm2bBn37t0jLi6OLl260LhxY3x8fEhMTMwQZ/rf038GnTp1YubMmfj7++Ps7ExYWBi2trYkJSUZ25KdtHmaNWvG5MmTeeONN9DpdGzZsoVx48YZ36OVK1fSunVrDhw4gKurKw4ODlnGBGQ6DaBOnTpcvHiRVq1aAYYjjICAAIYMGcLChQvp3bt3ljGvWLGCmTNnGqc7OTkRFRXF33//Te3atdm2bRt16tQhMTGRihUrsnPnTpo3b86dO3e4ePEiFStWJDExkXv37uHh4WE8un2cKX/bycnJudpPmjMRhALp6xVUAsIfn0kI0RCYB/SSUt7LbEVSyrkYrh/QtGlTWbdu3VwHc6ekK9FA6WHDcM/kIszQFYZzrJ+OyLp3hQOQ+y0XrMuX73Pq1B38/euldkG7Qs2aNY3nOYuikJAQ44VaU6SdGsovSUlJTJkyhYcPH2JjY0PNmjX55ZdfsLe3Z/z48QwaNAhPT092795N48aN8fPzo3r16rRu3RobGxtcXFwICAhgypQpxMfH4+joyM6dO7GxscHa2hofHx+WLVvGc889x++//46NjQ02NjbG01t2dnbY29tjb2+PEAJ7e3smT57M2LFjadGiBb6+vjRo0IAyZcpgb2/P/PnzmTp1Kj4+Pjg5OVG6dGn++9//Gt8Te3t7Ro8ezYABA2jXrh2NGjXCy8sLOzs7goODmTZtGlZWVtja2jJnzhySkpIYMmQICQkJSCn53//+h729fYY40/+ext7enj59+nDlyhU6d+4MGE6zLVmyBDs7O2NbspM2T5s2bRgwYAAtW7akSpUq+Pn54erqanyPPDw86NKlC48ePWLevHnZxgRkOg2gX79+zJ07lwkTJgDwxRdfMGLECGbOnImvry8vv/wy9vb2HDt2jF9++YVff/0VMBzhhIWF0bVrV+ORmL29Pb/88gsjR47EysqKUqVKGWP7+OOPGTduHM2bN0dKyaxZs6hYsSIABw4coHfv3pm+N6b+bdva2lK7du0c5zNKO1TK7weGJHMVqA7YAacB78fmqQJcBlqbut6mTZvKvAj76GN5vq6XvLt4SabTGyxqIBssapCndRcWFy5ESE/Pr6SV1Qy5fXuITEpKkmfPnpVJSUlah/ZUzp8/b/K8KSkpMi4uTqakpJgxIu3pdDoZHx8vpZTy0qVLskqVKsbnxVV0dLSUUsrY2FjZtGlTuX//frN8zm3atJEPHjzI9/WaatCgQfLixYtPvJ6bv+3M/meAYzKL/arZviZKKXVCiEnADgzdRxdIKc8JISakTv8Z+AgoDfyYehFGJ6X0M1dMxdnff9+lS5cl3L0bS4cOVWnTporWISlmFBcXR6dOnUhOTkZKybfffoudnZ3WYZnV+PHjOX/+PAkJCYwePdp4ATq//e9//+PmzZu4ubmZZf3ZSUpKYuDAgeTlrMfTMOv5AinlVmDrY6/9nO73F4EXzRmDJTh16h+6dl3CvXvxdO1ag99/H4aTk22RvDCsmMbFxcV4N21W55KLkhYtWjzRhqVLl+Lj42N8ntafH8zb5hYttOs5aGdn91Q3E+ZV0T1xrABw7Fg43bsv5cGDBHr1qsVvvw3FwUF9rErRklYCQ9GGKjFRhCUnp/Dss2t48CCB/v3rsn69SgKKouSeSgRFmK2tNatXD2HcOF/WrBlSZMtGKIqiLZUIiqCIiFjj735+FViwYAB2dtmXFFAURcmKSgRFzB9/XKF69W9ZvvyM1qFYFFWG+kmFpQx1Vj766CN27dqV5fQNGzZw/vx5k+d/3O3btzOUuvjPf/5DrVq1qFu3Ljt27Mh0maFDhxrLVVerVg1fX1/AcAPYmDFj8PHxoV69evznP/8BDL3D+vTpg5eXFz4+Pnz44YfGdf3www/G8tVPLat+pYX1Ycn3EWzZckna238iYbqcMGFTjvOr+wjyx4EDB2TLli1lQkKClFLKiIgIGRYWJqWUsmrVqjIiIiJP6124cKF89dVXnzq+x9s8dOhQ+c477xifX7lyRW7evFlKKWWJEiWeenuZ+c9//iM/+uijPC2r1+uz/Lyyen/z43MeM2aMXLNmTZ6Xnzp1qtywYYOUUspz587Jhg0byoSEBHn16lVZo0YNqdPpsl3+zTfflDNmzJBSSrl8+XI5dOhQKaXhPomqVavKa9euydjYWBkYGCillDI+Pl62bt3a+FnGxsZKX1/fTNddaO4jUPLXhg0XefbZNSQn65k40Y/vv++tdUjamF4y28lWgGO2c2S13qgsJ6ky1IWjDPXXX3/NggULAHjhhReMd/+mL0/t4eFB06ZNmTp1KmPHjqVv3774+/s/Ef/gwYPZuHEjf/31F59++inr1q3jk08+Mc6f2ef1eAG4devWGccX+P333xk2bBj29vZUr16dWrVqceTIEWOpisdJKVm9ejWBgYGA4Q7q2NhYdDod8fHx2NnZ4erqipOTE506dQIMXUt9fX0JDQ0FDCUsqlWrxpEjR4w1ivJKJYIiYM2ac4wY8Rs6nZ433mjJ//7XvdgOpVkYqTLU2pehPn78OAsXLuTw4cNIKWnRogUtW7bE2tr6ifLUTZs2zbBsZvG7ubnRv39/444/vaSkpGw/LzCMXFaqVCnjl4OwsLAMlVRzKlm9d+9eypUrZywD4e/vz++//46npydxcXF88803uLu7Z1jm4cOHbN26lTfeeMP4mp+fH3v37lWJwFSBlyJoCszYfIHN558c3MSlXsHHZIrVq88xfPg69HrJO++04fPPu1h2EsjmmzuoMtSmkkWwDPWgQYMoUaIEAIMGDeLAgQNYWVllKE+dVlgvvbQidKbGHxwcnOXnlSZ9uWrIfcnqlStXGktnAxw5cgRra2vCw8N58OAB7dq1o2vXrsYjOZ1Ox4gRI5g4cWKGo7uyZcty8eLFbNtjCou5WBz+MEHrEPKkdm13XF3t+fjjDioJaMja2pqOHTsyY8YMfvjhB9atW/fEPGllqHfv3s2ZM2fo06ePsVBbVp9bjRo1iI6O5tKlS1luO+1bp7W1tXEs28x2PADe3t6cPn06xzLFy5cvJyIiguPHj3Pq1CnKlStHQkICderU4fjx4/j4+PDuu+8yc+ZMbGxsOHLkCM888wwbNmww1vQ3hZSSd9991ziU4+XLl3nhhRcAjDt1U9eTm9fTy2382X1eaRwdHUlI+HefUqlSJW7d+rfqfmhoqHE0ssfpdDp+++03hg4danxtxYoV9OzZE1tbW8qWLUubNm0yjMM8fvx4ateu/UTngoSEhCeOVvLCYhJBmo/71uP6rD5PPAqrxo09OX9+ItOnd1RJQCPBwcGEhIQYn586dcp4PtvFxYXo6GgAHj16RIkSJShZsiR37txh2zbD8BpeXl6Eh4cbR52Kjo427tCrVq3Kb7/9xujRozl37pzJMbVt25bVq1cDcP78eeOyNWvWxM/Pj48//ti4kwwJCeH333/PsHxUVBRly5bF1taWoKAgbty4AUB4eDhOTk6MGjWKqVOncuLECWJiYoiKiqJ3797Mnj2bU6dOmRxnjx49WLBggfF6QVhYGHfv3jV5+TTt27dnw4YNxMXFERsby4YNG2jdujVt2rRh06ZNJCQkEBMTYzyllV5W8af/7NLL7vNKU6dOnQwD2/Tv35+AgAASExO5du0aISEhWZ6u2bVrF15eXlRKNyRulSpVCAwMREpJbGwshw4dwsvLC4APPviAqKgovvnmmyfWdenSpVwfDWbGYk4NFSXff38YV1d7xozxBcDTs2BGKVIyFxMTw+TJk41lqGvVqsXcuXMBwze1Xr164enpSVBQEI0bN8bb25saNWrQpk0bwHCRb9WqVUyePNlYhjp9N8W6deuyfPlyhgwZwqZNm0yKaeLEiYwZM8bYnbVBgwaULGm4kD5v3jzeeustatWqZSxD/eWXX2ZYfuTIkfTr1w8/Pz98fX2NO52zZ8/y9ttvG8tQ//TTT0RHRzNgwADj0U1mO6SsdO/enQsXLhgvmjo7O7Ns2bIcr2E8rkmTJowdO9a4c33hhRfw9fXF3t6e/v3706hRI6pWrYqfn5/xfUiTVfzDhg3jpZde4rvvvmPt2rXG+bP6vNJ3vS1RogQ1a9bk8uXL1KpVC29vb5599lnq16+PjY0Nc+bMMbbxxRdfZMKECcaBhwICAjKcFgJ49dVXGTduHA0aNEBKybhx42jYsCGhoaF89tlneHl54efnh16vZ9KkSYwfPx4wDK358ccf5+q9zFRW3YkK6yOv3Uf/2/P5ItF99Kuv9kuYLq2tZ8hLlyKfal2q+2jxZYllqNNL/zk/Xp76+PHjBRLDb7/9Jt9///0C2ZaUT/5tnzhxQo4aNSrTeVX30SLs88/38v77hu5kc+b0pnbt0hpHpBRWlliGOivpy1OPGTPG2IPK3AYNGsS9e5mOpVUgIiMj+eSTT/JlXSoRFAJSSmbM+IsZM/5CCJg/vz/jxpmn1rpSPFhiGeqspC9PXdBefFG7KvrdunXLt3WpRKAxKSXvvbebWbP2Y2UlWLx4IKNGNdQ6LEUpUKoMtbZUItDY7dsxzJ17AmtrwYoVz/Dss95ah6QoioVRiUBjFSq48Mcfz3HrVhQDBnhpHY6iKBbI4u4jKAz0esnevTeMz5s08VRJQFEUzahEUMBSUvQ8//zvdOiwiCVLTmsdjmKCjh07PlFWePbs2UycODHLZT7//PMMz5+2LPO2bdvw8/OjXr16eHl5GWv6TJ8+nf/9739Pte700sf59ttv4+3tzdtvv/3UpbEBTp48aby4KqXktddeo1atWjRs2JATJ05kuky7du2MZZsrVKjAwIEDjdP27NlDkyZN8Pb2pkOHDhmWS0lJoXHjxhnKSUydOtVY5E3JSJ0aKkA6nZ4xYzawYsVZnJxsqVQp89oySuEyfPhwAgIC6NGjh/G1gICAJ27SSu/zzz/nvffeMz4/cOBAnrf/999/M2nSJLZs2YKXlxc6nc54Q1t+Sx/nL7/8QkREhLHERW7odDpsbDLuXj7//HM++OADwJDYQkJCCAkJ4fDhw7zyyiuZXjDeu3ev8fdnnnmGAQMGAIYCbK+//jrbtm2jWrVqT9yt/O2331KvXj0ePXpkfG3y5Mm89NJLdO7cOdftKe7UEUEBSU5OYcSIdaxYcRZnZzu2bx9J587VtQ5LMYG/vz+bN282dm+8fv064eHhtG3blpUrV+Lj40ODBg2YNm0aAO+88w7x8fH4+voycuRI4N8BYf788086duyIv78/Xl5ejBw50lgKYuvWrXh5edG2bVtee+0147fZL774gvfff99496+NjU2mRyO//vorzZo1o1GjRjzzzDPGAXPWrFlDgwYNaNSoEe3btwcMVUqbN2+Or68vDRs2NJbQSIuzf//+xMbG0qJFC1atWsX06dP56quvALhy5Qo9e/akadOmtGvXzlj0bOzYsbz55pt06tTJ+F6kiY6O5syZMzRq1AgwlG0ePXo0QghatmzJw4cPuX37dpafQXR0NIGBgcYjgpUrV9K/f3+qVKkCGIqvpQkNDWXLli1PdO2sWrUq9+7d459//slyO5ZKHREUgMREHUOHruX334NxdbVn+/aRtGpVWeuwiiSfxTn3K8+Ls2POZjmtdOnSNG/enO3btzNgwAACAgIYOnQot2/fZtq0aRw/fpxSpUrRvXt3NmzYwKxZs/jhhx+yrMlz8uRJzp07R4UKFWjTpg379+/Hz8+Pl19+mT179lC9evUMJQj+/vtv3nrrrRzbMHjwYF5++WXAUJ9m/vz5TJ48mZkzZ7Jjxw4qVqxoLCH9888/M2XKFEaOHElSUhIpKSkZ1rVx40acnZ2NbZg+fbpx2vjx4/n555+pXbs2hw8fZuLEicZTLpcuXWLXrl1PlJA4duxYhpo4YWFhVK787/9AWtlmT0/PTNu2fv16unTpYqwEeunSJRISEujcuTPR0dFMmTKF0aNHA/D666/zxRdfZFpHqEmTJuzfv59nnnkmp7fToqgjggIwfvxmfv89GDc3B3btek4lgSIo7fQQ/Fsr5ujRo3Ts2JEyZcpgY2PDyJEj2bNnT47rat68OZUqVcLKygpfX1+uX7/OxYsXqVGjBtWrVzduL7f+/vtv2rVrh4+PD8uXLzcWomvTpg1jx47l119/Ne7wW7Vqxeeff85///tfbty4YXIFy5iYGA4cOMCQIUPw9fXl5ZdfzvBNfsiQIZnWEcrvss06nY6TJ0+yadMmduzYwSeffMKlS5fYvHkzZcuWfWJMgjRly5YlPDzcpLZaEnVEUADefLMlhw+HEhDgj69vea3DKdKy++YO5hmPAGDgwIG8+eabnDhxgvj4eJo0acLNmzfztK7059zTSktntmNM4+3tzfHjx42nVbLy/PPPs2HDBho1asSiRYv4888/AcO3/8OHD7NlyxZ8fX05deoUI0aMoEWLFmzZsoUePXowb948k86d6/V63Nzcsjzayaq09NOUbb537x5Hjhxh/fr1GZZ3c3OjRIkSuLi40L59e06fPs2JEyfYuHEjW7duJSEhgUePHjFq1CiWLVsG5F/Z5uJGHRGYSUrKv/XgGzUqz7lzE1USKMKcnZ3p2LEjzz//vPGbaYsWLfjrr7+IjIwkJSWFlStXGnuv2NrakpycbPL6vby8uHr1qrG08apVq4zT3n77bT7//HPjmAV6vZ6vv/76iXVER0fj6elJcnIyy5cvN75+5coVWrRowcyZM/Hw8ODWrVtcvXqVGjVq8Nprr9G/f3/OnDljUpyurq5Ur16dNWvWAIZv9qdP59z7rV69ely+fNn4vH///ixZsgQpJYcOHaJkyZJZnhZas2YNffv2xcHBIcPyBw4cQKfTERcXx+HDh42DvoeGhnL9+nUCAgLo3LmzMQlA/pVtLm5UIjCDR48S6dx5CfPn/9slztpavdVF3fDhwzl9+jTDhg0DwNPTk//85z906tSJRo0a0aRJE2OvlvHjx9OwYUPjxeKcODo68uOPP9KzZ0/atm1LuXLljOWUGzZsyOzZsxk+fDj16tWjQYMGmV5YnTFjBi1atKBbt27GC8tgSCRpF7Tbt29Po0aNWLVqFQ0aNMDX15eLFy8az6+bYvny5cyfP59GjRrh7e39xFgHmfHy8iIqKsp43r53797UqFGDWrVq8dJLL2UYrrJ3794ZTt9kVra5Xr16dOvWDV9fX5o3b86LL76Y4w4+OTmZy5cvG8tBK/8S2R2SFkZ+fn4y/cg9pvqi1wv0u3YAj/feo8zo556YnnYRMqdTDzl5+DCBnj2XcfhwGFWrluT8+VdxcrJ9qnXmVXJyMsHBwdStWxdbW21iyA8XLlygXj3TxhI116mhghATE4OzszNSSl599VVq166dYXzarBSVNn/zzTe4uLjkS6G2vLR5/fr1nDhxIt8qdha03LQ5s/8ZIcRxKWWmWbDw/tUUQffvx9O16xIOHw6jWjU3goLGaJYElKLn119/xdfXF29vb6Kioow9gIqLV155JU/3JOQXnU5nUu8rS6QuFueTiIhYunZdypkzd6hZsxSBgWOoUqVkzgsqSqo33njDpCOAosrBwYHnnnvyaLygDBkyRLNtF3YqEeSDf/6JoUuXJZw/H0HduqXZvXs0FSuqu4YVRSkaVCLIBw8exHP3biz165dh9+7RlC/vnPNCiqIohYRKBPmgXr0yBAWNoVy5EpQpk3k/akVRlMJKXSzOo2vXHrB06b/9pxs0KKuSgKIoRZI6IsiDkJB7dO68hNDQR7i42DNwoBpLQFGUoksdEeTShQsRdOiwiNDQR7RtW0VVELUQn332Gd7e3jRs2BBfX19jyeTZs2cbq3zm1qJFi5g0adITrz9t7f+YmBhefvllatasibe3N+3btzfGm1ZdND+kj/PixYv4+vrSuHFjrly58tTjL2Rl7969eHt706RJE+Lj4zNMi4+Pp0OHDsZ6SosXL6Z27drUrl2bxYsXZ7q+N954wzjeQZ06dXBzczNO69mzJ25ubhnGNAAYNmyYsVprcaGOCHLh77/v0qXLEu7ejaVTp2ps3DgcZ2c7rcNSzOzgwYNs3ryZEydOYG9vT2RkJElJSYAhEYwaNQonJ6d8296ECROeavkXX3yR6tWrExISgpWVFVevXuXChQv5FN2/0se5YcMGBgwYwIwZM4Dcjb8gpURKmeNNUikpKSxfvpypU6cyZswYY1nwNAsWLGDw4MFYW1tz//59ZsyYwbFjxxBC0LRpU/r370+pUqUyLPPNN98Yf//+++85efKk8fnbb79NXFwcv/zyS4ZlXnnlFb744gt+/fVXk9tY2KlEYKJTp/6ha9cl3LsXT7duNdiwYZi6WUwDF7xMu8M4t+pdzHpHefv2bTw8PIw3Q3l4eADw3XffER4eTqdOnfDw8CAoKIhXXnmFo0ePEh8fj7+/v3HHePToUaZMmUJsbCz29vbs3r07wza2bNnCp59+yqZNm/jhhx9wdnZm6tSpdOzYkRYtWhAUFMTDhw+ZP38+7dq1Iy4ujrFjx3Lx4kW8vLy4du0ac+bMoXTp0hw+fJjly5cbd6w1atSgRo0aGbYXExPDgAEDePDgAcnJyXz66acMGDCA2NhYnn32WUJDQ0lJSeHDDz9k6NChvPPOO2zcuBEbGxu6d+/OV199xfTp03F2dqZ+/frMnj0ba2tr9uzZQ1BQEM7OzsTExADw5Zdfsnr1ahITExk0aBAzZszg+vXr9OrVi06dOnHw4EE2bNhA1apVn3jvnZ2defPNN9mxYwf9+vVj9erV7Nixgz/++IP58+dnmHf58uWsWLECgB07dtCtWzfc3d0B6NatG9u3b8+2quvKlSuNnxdAly5djIX70mvXrh1jx47NdPCdoqp4tMIEna0Mmd56x//B1ScPx6leJctlU1L0jBixjnv34unduzbr1j2Lg4PFvHUWr3v37sycOZM6derQtWtXhg4dSocOHXjttdf4+uuvCQoKMiaHzz77DHd3d1JSUujSpQtnzpzBy8uLoUOHsmrVKpo1a8ajR48yVMBcv349X3/9NVu3bn3iGysY7og9cuQIW7duZcaMGezatYsff/yRUqVKcebMGc6cOUOTJk0Aw4Azvr6+mZaCTs/BwYH169fj6upKZGQkLVu2pH///mzfvp0KFSqwZcsWAKKiorh//z7r16/n4sWLCCGMYxqk6d27NxMmTDAmr/R27txJSEgIR44cQUpJ//792bNnD1WqVCE4OJiFCxdmqDP0uNjYWBo0aMDMmTMBuHz5Mn379mXw4MEZjgiSkpK4evUq1apVA7Ie7yArN27c4Nq1ayZVYLWysqJWrVqcPn06y3LXRY3F7M0qiUgekLdePdbWVqxePYRvvjnIjz/2wd7eYt62Qie7b+5gnro7zs7OHD9+nL179xIUFMTQoUOZNWsWY8eOfWLe1atXM3fuXHQ6Hbdv3+b8+fMIIfD09KRZs2YAxsFVAIKCgjh27Bg7d+7M8Hp6gwcPBqBp06bG6qT79u1jypQpADRo0CDXFTWllLz33nvs2bMHKysrwsLCuHPnDj4+PkydOpVp06bRt29f2rVrh06nw8HBgRdffJE+ffo8cc48Ozt37mTnzp00btwYMByJhISEUKVKFapWrUrLli2zXd7a2tqkQWQiIyMznN/P7XgHAQEB+Pv755hA06SNa1BcEoHFXSxO6fEFTI968pGJf/6JMf7eoEFZ5s8foJKAhbK2tqZjx47MmDGDH374gXXr1j0xz7Vr1/jqq6/YvXs3Z86coU+fPiQkJCClzHInVKNGDaKjo40lpjOTdkoqbewCyHxHB4axC06fPo1er890eprly5cTERHB8ePHOXXqFOXKlSMhIYE6depw/PhxfHx8ePfdd5k5cyY2NjYcOXKEZ555hg0bNtCzZ89s152elJJ3332XU6dOcerUKS5fvswLL7wAZD12QXoODg4m7ZyfZrwDyLzCaXaK27gGFpcITBUYeI1atb5j7tzjWoeiaCw4ODhDL5FTp04Zz2e7uLgYSys/evSIEiVKULJkSe7cucO2bdsAQwnm8PBwjh49ChjGDUjboVetWpXffvuN0aNHG0cUM0Xbtm1ZvXo1AOfPnzcuW7NmTfz8/Pj444+NySIkJOSJUtFRUVGULVsWW1tbgoKCuHHjBgDh4eE4OTkxatQopk6dyokTJ4iJiSEqKorevXsze/bsLAelyUyPHj1YsGCB8XpBWFjYEwPN54dSpUqRkpJiTAY9evRg586dPHjwgAcPHrBz50569OiR6bLBwcE8ePCAVq1amby9S5cu4e3tnS+xFwbq620mduy4zMCBq0hI0HHkSBgvvdQk28NKpXiLiYlh8uTJPHz4EBsbG2rVqsXcuXMBw7gDvXr1wtPTk6CgIBo3boy3tzc1atSgTZs2ANjZ2bFq1SomT55MfHw8jo6O7Nq1y7j+unXrsnz5coYMGcKmTZtMimnixImMGTPG2J21QYMGxvEL5s2bx1tvvUWtWrVwcnKidOnSfPnllxmWHzlyJP369cPPzw9fX1/j+AVnz57l7bffxsrKCltbW3766Seio6MZMGCA8egmfU+bnHTv3p0LFy4Yd7LOzs4sW7bM5FMwudG9e3f27dtH165dcXd358MPPzSejvvoo4+MF44/+ugj/Pz86N+/P2C4SDxs2LAn/sfbtWvHxYsXiYmJoVKlSsyfP58ePXpw584dHB0dsxxIp0hK67pVVB5NmzaVeXG7T2V5vq6XvLt4SabTGyxqIBssaiA3bQqWdnafSJguX355k0xJ0edpe4VBUlKSPHv2rExKStI6lKdy/vx5k+dNSUmRcXFxMiUlxYwRaU+n08n4+HgppZSXLl2SVapUMT63BJl9zidOnJCjRo0y+7a//vprOW/ePLNv53G5+dvO7H8GOCaz2K+qI4J0Hh2vwOCfV5GcrGfy5OZ8+21PdSSgFEpxcXF06tSJ5ORkpJR8++232NlZ9j0tjRs3plOnTqSkpJjliCONm5ubpuW0zUElglRRxypw68eWoNczdWorvviim0oCSqHl4uJC2kh9aT2lirIWLVo80YalS5fi4+OTq/U8//zz+RlWpsaNG2f2bRQ0lQhS2VeIxto5iXcmd+OTTzqpJKAoBSitBIaiDdVrKJVDhWhqf/oHn37aWSUBRVEsikUngp9+OsqcOUeMz21ci/bhtaIoSl5Y7Kmhb789xOuv7wCgffsna5woiqJYCos8Ivjyy/3GJPD9973w8SmncUSKoijaMWsiEEL0FEIECyEuCyHeyWS6EEJ8lzr9jBCiiTnjAfj69wf83/8Zbub55Ze+TJrU3NybVIqpsWPHsnbtWq3DoEaNGkRGRgLk6zgAs2fPNo43cP/+fbp160bt2rXp1q0bDx48yHSZatWq4ePjg6+vL35+fsbXp06dSmBgYL7FpuQvsyUCIYQ1MAfoBdQHhgsh6j82Wy+gdupjPPCTueKREr6PbMCs3x4gBCxY0J/x44tHwShFSZObcQCyo9PpWLBgASNGjABg1qxZdOnShZCQELp06cKsWbOyXDYoKIhTp04Zu7cCTJ48OdtlFG2Z8xpBc+CylPIqgBAiABgAnE83zwBgSepdb4eEEG5CCE8p5e2sViqlJDk5OdfBzLcvzZqHNRFCT8WXjvK11Vq+zmTQorysu7BKq2eT9rOoklJmKKJmbf1JlvP+9FMfxowx1ID5+edjvPLKliznTUn50OQYlixZwtdff40QAh8fH5YsWYKUkr/++ouvv/6af/75h1mzZuHv709MTAyDBg0y1vqfOXMmAwYM4Pr16/Tp04c2bdpw8OBBKlSowIYNG3B0dOTy5ctMnDiRiIgIrK2tWbVqFTVr1uSrr75izZo1JCYmMnDgQKZPn55tnM7Ozjx69Ig///yTmTNnUrp0ac6dO0eTJk1YunQpQgiOHz/O1KlTiYmJoXTp0ixcuPCJcgm7du2icePGWFlZodfr+f333wkMDESv1/Pcc8/RuXNn/vOf/2Qag16vf6LoXeXKlbl37x7h4eGUL1/e5PfdFDkV2CuOcmpzbveT5kwEFYFb6Z6HAi1MmKcikCERCCHGYzhiwNPTk+Dg4FwH4+iUyK/VgphftyTXW2eeZ3xdffO07sLuypUrWofwVFJSUky+YUqnM/zxJyYmGn/PiqnrPH/+PJ9//jm7d+/Gw8OD+/fvk5iYiF6vJywsjD/++IPg4GCGDBlCv379EEKwYsUKY63/jh070qNHD5KSkggJCWHhwoV8//33jBo1ilWrVjF8+HBGjhzJW2+9Zazpo9fr2bJlCxcvXuSvv/5CSom/vz+7d++mbdu2GeKTqcXl0tqTmJhIcnIyJ0+e5NixY1SoUIHOnTvz559/0qxZMyZPnszq1aspU6YMa9eu5d13331iFK49e/bQqFEj4zrv3LmDu7s7iYmJuLu7c/fu3Szfv+7duyOE4IUXXjBWGgVo2LAhf/31FwMHDjTpfTdFUb+RLi9MaXNycnKu9mXmTASZdcZ/vHauKfMgpZwLzAVo2rSprFu3bq6Dqfnrca5cucLqmjWLzahCOdHpdFy5coWaRbzNISEhxlLMkPM3+bTxCCZObMHEiY9/98i9/fv34+/vT8WKFQGM356trKwYNGgQjo6O+Pr6cvfuXeM4CO+88w579+7FysqK8PBwHj58iJ2dHdWrV6d5c8N1qWbNmhEWFkZSUhK3b9/m2WefBf4tO/3nn38SGBhoPO8fExPD9evX6dKlS4b40u57SVvO3t4eW1tbmjVrRs2aNQFD+YWwsDDKlCnD+fPnjQXXUlJSKF++fIb3FyAiIoIGDRpkeP3xeR5/DoZxEipUqMDdu3fp0aMHDRo0oH379sb3LSIiItPl8iLtc7YkprbZ1taW2rVrm7xec+4dQoHK6Z5XAsLzME8GQghsbfM+RKSNjc1TLV8UFfU2CyFMHmQm/SFzfg1Mk7aux9cnhMDR0dH4ukwdd3flypVERkZy/PhxbG1tqVatGklJSVhZWWUYMMfGxoaEhATjjjyzeN99911efvnlPMXq4OCQYVt6vR4hBN7e3hw8eDDbdTg5ORljBihXrhx37tzB09OT27dvU7Zs2UzjrVSpEgDly5dn0KBBHDt2jI4dOwKGnZiTk1O+fC7m+pwLs9y0Obf7SXO+g0eB2kKI6kIIO2AYsPGxeTYCo1N7D7UEorK7PqAoWujSpQurV6/m3r17gKEHTXayqvWfFVdXVypVqsSGDRsAww4zLi7OLLX869atS0REhDERJCcnZzoOQr169bh8+bLxef/+/Vm82HBRbfHixQwYMOCJZWJjY41jM8TGxrJz584MI6ddunQp1yOpKQXDbIlASqkDJgE7gAvAainlOSHEBCHEhNTZtgJXgcvAr8BEc8WjKHnl7e3N+++/T4cOHWjUqBFvvvlmtvOPHDmSY8eO4efnx/Lly421/rOzdOlSvvvuOxo2bEjr1q35559/6N69OyNGjKBVq1b4+Pjg7+9v3NHmlZ2dHWvXrmXatGk0atQIX1/fTHsa9erViz179hifv/POO/zxxx/Url2bP/74g3feMfQGDw8Pp3fv3oDhOkLbtm1p1KgRzZs3p0+fPsbRzJKTk7l8+XKGLqVK4SHSLjQVFX5+fjJ9tzRTpV08qVu3bpE+TZIbxaXNFy5coF69eibNa44xiws7c7V50KBBfPHFF7k615yV9evXc+LECT75JOseX7mhPufs25zZ/4wQ4riUMtNMbBnvoKIouTZr1ixu386fM7U6nY633norX9al5L+i25VEURSzqlu3LnnpoZeZIUOG5Mt6FPNQRwRKkVDUTmEqilby8r+iEoFS6Dk4OHDv3j2VDBQlB1JK7t27h4ODQ66WU6eGlEKvUqVKhIaGEhERkeO8abfW29raWswAQ6rNqs3pOTg4GO/nMJVKBEqhZ2trS/Xq1U2aN62nVO3atYt0T6ncUG1WbX5a6tSQoiiKhVOJQFEUxcKpRKAoimLhitydxUKICCD74i1Z8wAi8zGcokC12TKoNluGp2lzVSllmcwmFLlE8DSEEMeyusW6uFJttgyqzZbBXG1Wp4YURVEsnEoEiqIoFs7SEsFcrQPQgGqzZVBttgxmabNFXSNQFEVRnmRpRwSKoijKY4plIhBC9BRCBAshLgsh3slkuhBCfJc6/YwQookWceYnE9o8MrWtZ4QQB4QQjbSIMz/l1OZ08zUTQqQIIfwLMj5zMKXNQoiOQohTQohzQoi/CjrG/GbC33ZJIcQmIcTp1DaP0yLO/CKEWCCEuCuE+DuL6fm//5JSFqsHYA1cAWoAdsBpoP5j8/QGtgECaAkc1jruAmhza6BU6u+9LKHN6eYLxDAsqr/WcRfA5+wGnAeqpD4vq3XcBdDm94D/pv5eBrgP2Gkd+1O0uT3QBPg7i+n5vv8qjkcEzYHLUsqrUsokIAB4fKTtAcASaXAIcBNCeBZ0oPkoxzZLKQ9IKR+kPj0E5K48YeFjyucMMBlYBzzdqO+FgyltHgH8JqW8CSClLOrtNqXNEnARhpKczhgSga5gw8w/Uso9GNqQlXzffxXHRFARuJXueWjqa7mdpyjJbXtewPCNoijLsc1CiIrAIODnAozLnEz5nOsApYQQfwohjgshRhdYdOZhSpt/AOoB4cBZYIqUUl8w4Wki3/dfxbEMdWaFuh/vGmXKPEWJye0RQnTCkAjamjUi8zOlzbOBaVLKlGJSs96UNtsATYEugCNwUAhxSEp5ydzBmYkpbe4BnAI6AzWBP4QQe6WUj8wcm1byff9VHBNBKFA53fNKGL4p5HaeosSk9gghGgLzgF5SynsFFJu5mNJmPyAgNQl4AL2FEDop5YYCiTD/mfq3HSmljAVihRB7gEZAUU0EprR5HDBLGk6gXxZCXAO8gCMFE2KBy/f9V3E8NXQUqC2EqC6EsAOGARsfm2cjMDr16ntLIEpKebugA81HObZZCFEF+A14rgh/O0wvxzZLKatLKatJKasBa4GJRTgJgGl/278D7YQQNkIIJ6AFcKGA48xPprT5JoYjIIQQ5YC6wNUCjbJg5fv+q9gdEUgpdUKIScAODD0OFkgpzwkhJqRO/xlDD5LewGUgDsM3iiLLxDZ/BJQGfkz9hqyTRbhgl4ltLlZMabOU8oIQYjtwBtAD86SUmXZDLApM/Jw/ARYJIc5iOG0yTUpZZKuSCiFWAh0BDyFEKPAxYAvm23+pO4sVRVEsXHE8NaQoiqLkgkoEiqIoFk4lAkVRFAunEoGiKIqFU4lAURTFwqlEoBQbOVVtNGH5vkKIk6lVLM8LIV7O5/hmCiG6pv7eLrVS5ikhREUhxNoclp0nhKif+vt7+RmXoqjuo0qxIYRoD8RgKMjVIJfL2gI3gOZSylAhhD1QTUoZbIZQEUL8jKFq5MI8LBsjpXQ2Q1iKhVJHBEqxYULVxuy4YLjB8l7quhLTkoAQYpEQ4mchxF4hxCUhRN/U162FEF8KIY6m1oU3HkEIIf5PCHE29ehiVrr1+AshXgSeBT4SQiwXQlRLO4pJXedXqcueEUJMTn39TyGEX+q6HFOPJJYLIT4RQkxJt93PhBCv5fE9UCxUsbuzWFHyQkp5XwixEbghhNgNbAZWpqtiWQ3ogKGoWZAQohYwGsPt/c1SjyD2CyF2YqhzMxBoIaWME0K4P7ateUKItsBmKeVaIUS1dJPHA9WBxql31T6+7DtCiElSSl+A1GV/A74VQlhhKMHQPH/eFcVSqESgKKmklC8KIXyArsBUoBswNnXy6tSkECKEuIphZ98daCj+HfmsJFA7dfmFUsq41PXm5iilK/CzlFJnyrJSyutCiHtCiMZAOeBkMSgoqBQwlQgUiyGEsAaOpz7dKKX86PF5pJRngbNCiKXANf5NBI9fTJMY6tpMllLueGw7PTOZ3+Qw87DsPAxxlgcW5HG7igVT1wgUiyGlTJFS+qY+MiQBIYSzEKJjupd8MVw8TjNECGElhKiJYdjEYAyF0F5JvdCMEKKOEKIEsBN4PrX6J4+f3snBTmCCEMImm2WT07aZaj3QE2iWGpOi5Io6IlCKjcyqNkop55u6OPB/QohfgHggln+PBsCw4/8Lw+mXCVLKBCHEPAzXDk4IQ0nXCGCglHK7EMIXOCaESMJQLdLULp/zMIwydkYIkQz8imEErvTmpk4/IaUcKaVMEkIEAQ+llCkmbkdRjFT3UUXJgRBiEakXdrWOJTOpF4lPAEOklCFax6MUPerUkKIUYak3mV0GdqskoOSVOiJQFEWxcOqIQFEUxcKpRKAoimLhVCJQFEWxcCoRKIqiWDiVCBRFUSycSgSKoigW7v8BKXt0oSqE+owAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_matrics(joblib.load('predictions/preds_temporal_ensemble_cat_drug.joblib'), 'Temporal_CED')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}