Workflow_for_paper_random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0.4', 'Unnamed: 0.3', 'Unnamed: 0.2', 'Unnamed: 0.1',\n",
      "       'Unnamed: 0', 'CLINIC', 'YEAR', 'GENDERSP', 'Susceptible', 'MSM',\n",
      "       'MSMW', 'MSW', 'Oth/Unk/Missing', 'REGION', 'Midwest', 'Northeast',\n",
      "       'Southeast', 'Southwest', 'West', 'PREV_REGION', 'PREV_CLINIC',\n",
      "       'DELTA_REGION', 'DELTA_CLINIC'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import numpy as np\n",
    "import eli5 \n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_validate, RandomizedSearchCV, RepeatedStratifiedKFold\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from scipy.stats import loguniform\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from eli5.sklearn import PermutationImportance\n",
    "from sklearn.inspection import permutation_importance\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "\n",
    "\n",
    "CIP_data_no_drop = pd.read_csv(\"CIP_data_encode_prev_not_dropped.csv\")\n",
    "print(CIP_data_no_drop.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.877938683132499\n",
      "Best Hyperparameters: {'n_estimators': 171, 'min_samples_split': 1, 'min_samples_leaf': 1, 'max_depth': 89}\n"
     ]
    }
   ],
   "source": [
    "######### Step 1: Hyperparameter tuning round 1 WITH OVERSAMPLING not \"balanced\" weights\n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators = 100, min_samples_leaf=5, max_features = 'sqrt', random_state = 10)\n",
    "oversample = RandomOverSampler(sampling_strategy = 0.5,random_state=42)\n",
    "\n",
    "## tuning just on training data\n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,2010])]\n",
    "\n",
    "X_train = train_data[['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "## what if tuned in all data?\n",
    "#X_train = CIP_data_no_drop[['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']]\n",
    "#y_train = 1 - CIP_data_no_drop['Susceptible']\n",
    "#X_train, y_train = oversample.fit_resample(X_train,y_train) ## need to resample even if I'm doing the entire dataset? Cos the model needs to be fit \n",
    "\n",
    "### parameters for search\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, random_state=1)\n",
    "space = dict()\n",
    "space['n_estimators'] = np.arange(100, 201, 1)\n",
    "space['max_depth'] = np.arange(1, 200, 1)\n",
    "space['min_samples_split'] = np.arange(1, 25, 1)\n",
    "space['min_samples_leaf'] = np.arange(1, 25, 1)\n",
    "\n",
    "\n",
    "## find hyperparameters\n",
    "search = RandomizedSearchCV(model_rf, space, scoring='roc_auc', n_jobs=-1, cv=cv, random_state=1)\n",
    "result = search.fit(X_train, y_train)\n",
    "# summarize result\n",
    "print('Best Score: %s' % result.best_score_)\n",
    "print('Best Hyperparameters: %s' % result.best_params_)\n",
    "\n",
    "#Best Hyperparameters:  {'n_estimators': 161, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_depth': 61}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC_AUC_rf: 0.6340716423354156\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <style>\n",
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       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0333\n",
       "                \n",
       "                    &plusmn; 0.0025\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PREV_CLINIC\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 88.11%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0159\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSM\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.09%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0073\n",
       "                \n",
       "                    &plusmn; 0.0023\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSW\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.88%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0035\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Southeast\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.19%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Oth/Unk/Missing\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.22%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSMW\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.51%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                DELTA_REGION\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.43%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0002\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                West\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.21%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0003\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Midwest\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 98.03%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0012\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Southwest\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 97.53%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0017\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Northeast\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 96.48%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0028\n",
       "                \n",
       "                    &plusmn; 0.0016\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PREV_REGION\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Step 2: FEATURE ENGINEERING\n",
    "## get better results using oversampling, not balanced dataset\n",
    "#Best Hyperparameters:  {'n_estimators': 161, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_depth': 61}\n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators = 171, min_samples_split = 1, min_samples_leaf=1, max_features = 'sqrt', max_depth = 89, random_state = 10)\n",
    "\n",
    "\n",
    "## Temporal split training data \n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,2010])]\n",
    "X_train = train_data[['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "model_fit = model_rf.fit(X_train, y_train)\n",
    "\n",
    "#test data: 2009 - 2019 \n",
    "test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])]\n",
    "X_test = test_data[['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']]\n",
    "y_test = 1 - test_data['Susceptible']\n",
    "X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "y_predict = model_rf.predict(X_test)\n",
    "\n",
    "ROC_AUC_random_forest= metrics.roc_auc_score(y_test, y_predict)\n",
    "\n",
    "print('ROC_AUC_rf:', ROC_AUC_random_forest) ## why is this higher than when I remove features?\n",
    "\n",
    "perm = PermutationImportance(model_fit, random_state=1).fit(X_test,y_test)\n",
    "eli5.show_weights(perm, feature_names = X_test.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prev. Clinic0.033 +/- 0.002\n",
      "MSM     0.015 +/- 0.001\n",
      "MSW     0.007 +/- 0.001\n",
      "Southeast0.003 +/- 0.000\n",
      "Midwest 0.001 +/- 0.001\n",
      "Oth/Unk/Missing0.000 +/- 0.000\n",
      "West    0.000 +/- 0.000\n",
      "MSMW    0.000 +/- 0.000\n",
      "Change  -0.000 +/- 0.001\n",
      "Southwest-0.001 +/- 0.000\n",
      "Northeast-0.001 +/- 0.000\n",
      "Prev. Region-0.002 +/- 0.001\n"
     ]
    }
   ],
   "source": [
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','Prev. Region', 'Prev. Clinic','Change']\n",
    "\n",
    "PI = permutation_importance(model_fit, X_test, y_test, n_repeats = 10, random_state = 42)\n",
    "#feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC','DELTA_REGION']\n",
    "for i in PI.importances_mean.argsort()[::-1]:\n",
    "    if PI.importances_mean[i] - 2 * PI.importances_std[i] > -1:\n",
    "            print(f\"{feature_names[i]:<8}\" #works cos they are in same order as the x columns\n",
    "            f\"{PI.importances_mean[i]:.3f}\"\n",
    "            f\" +/- {PI.importances_std[i]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " [Text(0, 0, 'Prev. Clinic'),\n",
       "  Text(0, 1, 'MSM'),\n",
       "  Text(0, 2, 'MSW'),\n",
       "  Text(0, 3, 'Southeast'),\n",
       "  Text(0, 4, 'Midwest'),\n",
       "  Text(0, 5, 'Oth/Unk/Missing'),\n",
       "  Text(0, 6, 'West'),\n",
       "  Text(0, 7, 'MSMW'),\n",
       "  Text(0, 8, 'Change'),\n",
       "  Text(0, 9, 'Southwest'),\n",
       "  Text(0, 10, 'Northeast'),\n",
       "  Text(0, 11, 'Prev. Region')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### graph feature engineering results \n",
    "feature_names = np.array(feature_names)\n",
    "feature_names_by_importance = feature_names[PI.importances_mean.argsort()[::-1]]\n",
    "mean_importance_by_importance = PI.importances_mean[PI.importances_mean.argsort()[::-1]]\n",
    "std_importance_by_importance = PI.importances_std[PI.importances_mean.argsort()[::-1]]\n",
    "\n",
    "plt.barh(y = range(mean_importance_by_importance.size), width =mean_importance_by_importance,\n",
    " xerr= std_importance_by_importance, alpha = 0.5)#c8b6ff\") # xmin = min(feature_names_by_importance), xmax = max(feature_names_by_importance))\n",
    "plt.yticks(range(feature_names_by_importance.size), feature_names_by_importance)\n",
    "\n",
    "#plt.savefig('Graphs/Features_by_importance_post_hyperparameter_random_forest.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.8779604949394924\n",
      "Best Hyperparameters: {'n_estimators': 171, 'min_samples_split': 1, 'min_samples_leaf': 1, 'max_depth': 89}\n"
     ]
    }
   ],
   "source": [
    "##### HYPERPARAMETER TUNING without  prev_region, southwest, northeast\n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators = 161, min_samples_split = 2, min_samples_leaf=1, max_features = 'sqrt', max_depth = 61, random_state = 10)\n",
    "#feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Southeast',  'West', 'Midwest','PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "\n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,2010])]\n",
    "X_train = train_data[['MSM','MSMW', 'MSW', 'Oth/Unk/Missing', 'Southeast',  'West', 'Midwest', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=1)\n",
    "\n",
    "space = dict()\n",
    "### parameters for search\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, random_state=1)\n",
    "space = dict()\n",
    "space['n_estimators'] = np.arange(100, 201, 1)\n",
    "space['max_depth'] = np.arange(1, 200, 1)\n",
    "space['min_samples_split'] = np.arange(1, 25, 1)\n",
    "space['min_samples_leaf'] = np.arange(1, 25, 1)\n",
    "search = RandomizedSearchCV(model_rf, space, scoring='roc_auc', n_jobs=-1, cv=cv, random_state=1)\n",
    "\n",
    "result = search.fit(X_train, y_train)\n",
    "# summarize result\n",
    "print('Best Score: %s' % result.best_score_)\n",
    "print('Best Hyperparameters: %s' % result.best_params_)\n",
    "#Best Hyperparameters: {'n_estimators': 161, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 61}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC_AUC_rf: 0.6203104695984067\n"
     ]
    }
   ],
   "source": [
    "###### Specificity and sensitivity of model on entire dataset\n",
    "###### Dropping \n",
    "model_rf = RandomForestClassifier(n_estimators = 171, min_samples_split = 1, min_samples_leaf=1, max_features = 'sqrt', max_depth = 89, random_state = 10)\n",
    "\n",
    "#loop setup\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Southeast',  'West', 'Midwest','PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "#feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "#   train \n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010])]\n",
    "X_train = train_data[feature_names]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "# test\n",
    "test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])]\n",
    "\n",
    "X_test = test_data[feature_names]\n",
    "y_test = 1 - test_data['Susceptible']\n",
    "X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    " #1. Create model using all data and get the apparent sensitivity and specificty \n",
    "\n",
    "model_fit_train = model_rf.fit(X_train, y_train)\n",
    "\n",
    "  #2. Test model on test data to get test specificity and sensitivity \n",
    "  \n",
    "y_predict = model_fit_train.predict(X_test)\n",
    "y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    "\n",
    "ROC_AUC_random_forest= metrics.roc_auc_score(y_test, y_predict)\n",
    "\n",
    "print('ROC_AUC_rf:', ROC_AUC_random_forest) \n",
    "#dataframes\n",
    "sensitivity_test_threshold = []\n",
    "specificity_test_threshold = [] #no bootstrapping, no 95% CI \n",
    "\n",
    "for threshold in threshold_seq:\n",
    "  #print(threshold)\n",
    " \n",
    " \n",
    "  y_predict_test = np.where(y_predict_proba[:, 1] > threshold, 1, 0)\n",
    "\n",
    "  tn_test , fp_test , fn_test , tp_test  = confusion_matrix(y_true=y_test, y_pred=y_predict_test).ravel()\n",
    "\n",
    "  sensitivity_test  = tp_test  / (tp_test   + fn_test )\n",
    "  specificity_test   = tn_test / (tn_test + fp_test )\n",
    "  \n",
    "  # Save results\n",
    "  sensitivity_test_threshold.append(sensitivity_test)\n",
    "  specificity_test_threshold.append(specificity_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x179c5b8e0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Graph of sensitivity and specificity \n",
    "hfont = {'fontname':'Helvetica'}\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "#plt.figure(figsize=(5.4,3.8))\n",
    "sensitivity_test_threshold = pd.DataFrame(sensitivity_test_threshold)\n",
    "specificity_test_threshold = pd.DataFrame(specificity_test_threshold)\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "plt.plot(threshold_seq, sensitivity_test_threshold, color = \"#c8b6ff\", label = \"Sensitivity\", linewidth = 2)\n",
    "plt.plot(threshold_seq, specificity_test_threshold, color = \"#99d98c\", label = \"Specificity\", linewidth = 2)\n",
    "#plt.errorbar(threshold_seq, specificity_test_table, yerr = y_err_sensitivity, ecolor=\"lightblue\")\n",
    "\n",
    "plt.xlabel(\"Threshold (p)\", fontsize=16, **hfont)\n",
    "plt.ylabel(\"Performance metric\", fontsize=16,**hfont)\n",
    "plt.xticks(np.linspace(0,1,11), fontsize=14,**hfont)\n",
    "plt.yticks(np.linspace(0,1,6), fontsize=14,**hfont)\n",
    "\n",
    "plt.legend()\n",
    "#plt.savefig('Graphs/Sensitivity_specificity_all_workflow_paper_random_forest.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23081886682486158\n"
     ]
    }
   ],
   "source": [
    "########### Now do proportion receiving effective and unnecessary treatment for overall testing data\n",
    "### can use model from the previous cells\n",
    "###### Cipro prevalence 2011-2019\n",
    "\n",
    "\n",
    "cipro_R_prev_all= ((1 - test_data['Susceptible']).sum())/len(test_data['Susceptible']) #get prevalence overall before radndom oversampling\n",
    "print(cipro_R_prev_all)\n",
    "### Loop over thresholds to see how classification threshold affects proportion receiving effective and unnecessary treatment ALL TEST DATA\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "get_effective_threshold = []\n",
    "incorrectly_get_X_threshold = [] #no bootstrapping, no 95% CI \n",
    "\n",
    "for threshold in threshold_seq:\n",
    "\n",
    "  y_predict_test = np.where(y_predict_proba[:, 1] > threshold, 1, 0)\n",
    "\n",
    "  tn_test , fp_test , fn_test , tp_test  = confusion_matrix(y_true=y_test, y_pred=y_predict_test).ravel()\n",
    "\n",
    "  sensitivity_test  = tp_test  / (tp_test   + fn_test )\n",
    "  specificity_test   = tn_test / (tn_test + fp_test )\n",
    "  \n",
    "  get_effective_threshold.append(sensitivity_test * cipro_R_prev_all + (1 -  cipro_R_prev_all)) #q_p\n",
    "  incorrectly_get_X_threshold.append((1 - cipro_R_prev_all) * (1 - specificity_test)) #c_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_effective_threshold' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m### Plotting overall proportion receiving effective and unnecessary treatment with all training data \u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m plt\u001b[39m.\u001b[39mplot(get_effective_threshold, incorrectly_get_X_threshold, color \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mblack\u001b[39m\u001b[39m\"\u001b[39m, linewidth \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mReceives effective treatment ($\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mtheta$(p))\u001b[39m\u001b[39m\"\u001b[39m, fontsize\u001b[39m=\u001b[39m\u001b[39m16\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mhfont)\n\u001b[1;32m      6\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m\"\u001b[39m\u001b[39mReceives unnecessary treatment ($\u001b[39m\u001b[39m\\\u001b[39m\u001b[39momega$(p))\u001b[39m\u001b[39m\"\u001b[39m, fontsize\u001b[39m=\u001b[39m\u001b[39m16\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mhfont)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'get_effective_threshold' is not defined"
     ]
    }
   ],
   "source": [
    "### Plotting overall proportion receiving effective and unnecessary treatment with all training data \n",
    "\n",
    "plt.plot(get_effective_threshold, incorrectly_get_X_threshold, color = \"black\", linewidth = 3)\n",
    "\n",
    "plt.xlabel(r\"Receives effective treatment ($\\theta$(p))\", fontsize=16, **hfont)\n",
    "plt.ylabel(\"Receives unnecessary treatment ($\\omega$(p))\", fontsize=16, **hfont)\n",
    "plt.xticks(np.linspace(0,1,21), fontsize=14, **hfont)\n",
    "plt.yticks(np.linspace(0,1,11), fontsize=16, **hfont)\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "\n",
    "plt.plot(1, 1 - cipro_R_prev_all, marker='s', ls='none', ms=16, color = \"black\", label = \"Dual\")\n",
    "plt.plot((1-cipro_R_prev_all), 0, marker='*', ls='none', ms=14, color = \"black\", label = \"Cipro\")\n",
    "plt.plot([1, 1-cipro_R_prev_all], [1-cipro_R_prev_all, 0], color = \"#808080\", linestyle=\"--\")\n",
    "plt.legend()\n",
    "text_for_graph = ['0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1']\n",
    "indices = [0,10,20,30,40,50,60,70,80,90,100]\n",
    "x = 0\n",
    "for index in indices:\n",
    "    plt.plot(get_effective_threshold[index], incorrectly_get_X_threshold[index], marker='.', ls='none', ms=11, color = \"#b56576\")\n",
    "    if get_effective_threshold[index] > 0.996:\n",
    "        plt.text(get_effective_threshold[index] + 0.001, incorrectly_get_X_threshold[index] - 0.001, text_for_graph[x], size = 9)\n",
    "    elif (get_effective_threshold[index] > 0.9) & (get_effective_threshold[index] < 0.996):\n",
    "        plt.text(get_effective_threshold[index] + 0.005, incorrectly_get_X_threshold[index] - 0.005, text_for_graph[x], size = 9)\n",
    "    else:\n",
    "        plt.text(get_effective_threshold[index], incorrectly_get_X_threshold[index] + 0.025, text_for_graph[x], size = 9)\n",
    "    x = x+1\n",
    "\n",
    "#plt.savefig('Graphs/Proportion_effective_unnecessary_all_workflow_paper_random_forest.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "########### Try functionalised RF w/hyperparameter tuning and feature engineering \n",
    "\n",
    "\n",
    "def effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R_prevalence):\n",
    "\n",
    "    get_effective_threshold = []\n",
    "    incorrectly_get_X_threshold = [] #no bootstrapping, no 95% CI \n",
    "\n",
    "    for threshold in threshold_seq:\n",
    "\n",
    "        y_predict_test = np.where(y_predict_proba[:, 1] > threshold, 1, 0)\n",
    "\n",
    "        tn_test , fp_test , fn_test , tp_test  = confusion_matrix(y_true=y_test, y_pred=y_predict_test).ravel()\n",
    "\n",
    "        sensitivity_test  = tp_test  / (tp_test   + fn_test )\n",
    "        specificity_test   = tn_test / (tn_test + fp_test )\n",
    "  \n",
    "        get_effective_threshold.append(sensitivity_test * cipro_R_prevalence*100 + (100 -  cipro_R_prevalence*100)) #q_p\n",
    "        incorrectly_get_X_threshold.append((100 - cipro_R_prevalence*100) * (1 - specificity_test)) #c_p\"\n",
    "    return(get_effective_threshold, incorrectly_get_X_threshold)\n",
    "\n",
    "def get_best_hyperparameters(model, cv, space, X_train, y_train):\n",
    "        search = RandomizedSearchCV(model, space, scoring='roc_auc', n_iter=1,  n_jobs=-1, cv=cv, random_state=1)\n",
    "        result = search.fit(X_train, y_train)\n",
    "        return(result.best_params_)\n",
    "\n",
    "def get_best_features(feature_names, model_fit, X_test, y_test):\n",
    "    PI = permutation_importance(model_fit, X_test, y_test, n_repeats = 10, random_state = 42)\n",
    "    important_features = []\n",
    "    for q in PI.importances_mean.argsort()[::-1]:\n",
    "        if PI.importances_mean[q] - PI.importances_std[q] > 0:\n",
    "          important_features.append(feature_names[q]) #works cos they are in same order as the x columns\n",
    "    return(important_features)\n",
    "\n",
    "\n",
    "oversample = RandomOverSampler(sampling_strategy = 0.5, random_state=42)\n",
    "\n",
    "def get_test_train_data_rf(CIP_data_no_drop, year, feature_names, oversample_size):\n",
    "    years_train = np.array(range(year - 5, year))\n",
    "\n",
    "    # first do for all clinics \n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin(years_train)]\n",
    "    X_train = train_data[feature_names] #need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data['Susceptible']\n",
    "    X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "    #test\n",
    "    test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([year])]\n",
    "    X_test = test_data[feature_names]\n",
    "    y_test = 1 - test_data['Susceptible']\n",
    "    cipro_R = y_test.sum()/len(y_test) \n",
    "    X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "    return(test_data, train_data, X_train, y_train, X_test, y_test, cipro_R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### Try this with LOOCV as \"error bars\" AND LOOCV... May be messy and long\n",
    "\n",
    "########### Now do proportion receiving effective and unnecessary treatment for years surrounding 2007 decision \n",
    "### now do a loop over different years to get the proportion receiving effective and unnecessary treatment \n",
    "### has LOOCV \n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators = 171, min_samples_split = 1, min_samples_leaf=1, max_features = 'sqrt', max_depth = 89, random_state = 10)\n",
    "\n",
    "#loop setup\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Southeast',  'West', 'Midwest','PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "#feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "#   train \n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010])]\n",
    "X_train = train_data[feature_names]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "# test\n",
    "test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])]\n",
    "\n",
    "X_test = test_data[feature_names]\n",
    "y_test = 1 - test_data['Susceptible']\n",
    "X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "### needed for hyperparameter tuning \n",
    "### parameters for search\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, random_state=1)\n",
    "space = dict()\n",
    "space['n_estimators'] = np.arange(100, 201, 1)\n",
    "space['max_depth'] = np.arange(1, 200, 1)\n",
    "space['min_samples_split'] = np.arange(1, 25, 1)\n",
    "space['min_samples_leaf'] = np.arange(1, 25, 1)\n",
    "\n",
    "## need for feature engineerig \n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "\n",
    "\n",
    "#### Loop set up \n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "test_years = [2005, 2006, 2007, 2008, 2009, 2010]\n",
    "\n",
    "labels = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\" ]\n",
    "text_for_graph = ['0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1']\n",
    "indices = [0,10,20,30,40,50,60,70,80,90,100]\n",
    "fig, axs = plt.subplots(2,3, figsize=(20, 10), facecolor='w', edgecolor='k', sharex = 'all', sharey = 'all')\n",
    "\n",
    "### hyperparameters by year \n",
    "best_hyperparameters_by_year = {}\n",
    "### best features by year\n",
    "best_features_by_year = {}\n",
    "### ROC by year \n",
    "ROC_by_year = {}\n",
    "\n",
    "fig.subplots_adjust(hspace = .15, wspace=.1)\n",
    "axs = axs.ravel()\n",
    "i = 0\n",
    "\n",
    "for year in test_years: \n",
    "    years_train = np.array(range(year - 5, year))\n",
    "\n",
    "    CIP_data_training_years = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin(years_train)]\n",
    "    CIP_data_testing_years = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([year])]\n",
    "    # first do for all clinics \n",
    " \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data_rf(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, oversample_size = 0.5)\n",
    "\n",
    "    ## before fitting the model, do hyperparameter tuning \n",
    "    best_hyperparameters = get_best_hyperparameters(model_rf, cv, space, X_train, y_train)\n",
    "    best_hyperparameters_by_year.__setitem__(year, best_hyperparameters) \n",
    "\n",
    "    model_rf = RandomForestClassifier(n_estimators = best_hyperparameters['n_estimators'], min_samples_split = best_hyperparameters['min_samples_split'], min_samples_leaf=best_hyperparameters['min_samples_leaf'], max_features = 'sqrt', max_depth = best_hyperparameters['max_depth'], random_state = 10)\n",
    "\n",
    "     \n",
    "    ## fit model w/hyperparameters \n",
    "    model_fit = model_rf.fit(X_train, y_train)\n",
    "    ## now also need to do feature engineering\n",
    "    important_features = get_best_features(feature_names, model_fit, X_test, y_test)\n",
    "    best_features_by_year.__setitem__(year, important_features) \n",
    "\n",
    "    # get new tets/train data and refit model \n",
    "\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data_rf(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = important_features, oversample_size = 0.5)\n",
    "\n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_fit_train = model_rf.fit(X_train, y_train)\n",
    "    y_predict_test = model_fit_train.predict(X_test)\n",
    "    y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    " \n",
    "    get_effective_threshold_all, incorrectly_get_X_threshold_all = effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R_prev)\n",
    "    ROC= metrics.roc_auc_score(y_test, y_predict_test)\n",
    "    ROC_by_year.__setitem__(year, ROC)\n",
    "\n",
    "    clinics = CIP_data_testing_years[\"CLINIC\"].unique()\n",
    "    for clinic in clinics: \n",
    "        train_data = CIP_data_training_years.loc[CIP_data_training_years['CLINIC'] != clinic]\n",
    "        oversample = RandomOverSampler(sampling_strategy = 0.5,random_state=42)\n",
    "\n",
    "          #train data - does not have clinic\n",
    "        X_train = train_data[important_features]\n",
    "        y_train = 1 - train_data['Susceptible']\n",
    "        X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "          #test data - has clinic \n",
    "        test_data = CIP_data_testing_years.loc[CIP_data_testing_years['CLINIC'] == clinic]\n",
    "        X_test = test_data[important_features]\n",
    "        y_test = 1 - test_data['Susceptible']\n",
    "        cipro_R = y_test.sum()/len(y_test)\n",
    "\n",
    "        if cipro_R > 0: \n",
    "          try:\n",
    "          # test \n",
    "              X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "              model_fit_train = model_rf.fit(X_train, y_train)\n",
    "              y_predict_test = model_fit_train.predict(X_test)\n",
    "              y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    "              get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic = effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R)\n",
    "\n",
    "              axs[i].plot(get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic,color = \"#e5e5e5\", linewidth = 3)\n",
    "              #axs[i].plot([1, 1-cipro_R], [1-cipro_R, 0], color = \"#e5e5e5\", linestyle=\"--\")\n",
    "\n",
    "              axs[i].plot(1, 1 - cipro_R, marker='.', ls='none', ms=18, color = \"#e5e5e5\")\n",
    "              axs[i].plot((1-cipro_R), 0, marker='*', ls='none', ms=14, color = \"#e5e5e5\")\n",
    "   \n",
    "\n",
    "          \n",
    "          except ValueError:\n",
    "            try:\n",
    "              oversample = RandomOverSampler(sampling_strategy = 0.85,random_state=42)\n",
    "          #train data - does not have clinic\n",
    "              X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "          #test data - has clinic \n",
    "\n",
    "              X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "            # test \n",
    "              model_fit_train = model_rf.fit(X_train, y_train)\n",
    "              y_predict_test = model_fit_train.predict(X_test)\n",
    "              y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    "              get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic = effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R)\n",
    "\n",
    "              axs[i].plot(get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic,color = \"#e5e5e5\", linewidth = 3)\n",
    "              \n",
    "              axs[i].plot(1, 1 - cipro_R, marker='.', ls='none', ms=18, color = \"#e5e5e5\")\n",
    "              axs[i].plot((1-cipro_R), 0, marker='*', ls='none', ms=14, color = \"#e5e5e5\")\n",
    "\n",
    "            except ValueError:\n",
    "              print('x')\n",
    "              pass\n",
    "    axs[i].plot(get_effective_threshold_all, incorrectly_get_X_threshold_all,color = \"black\", linewidth = 3)\n",
    "    axs[i].plot(100, 100 - cipro_R_prev*100, marker='s', ls='none', ms=10, color = \"#b56576\")\n",
    "    axs[i].plot((100-cipro_R_prev*100), 0, marker='*', ls='none', ms=14, color = \"#b56576\")\n",
    "\n",
    "    axs[i].set_ylim([0-0.05,1.01])\n",
    "    axs[i].set_xlim([0.4, 1.01])\n",
    "    axs[i].text(axs[i].get_xlim()[0] + 0.01 , axs[i].get_ylim()[1] - 0.5, f'auROC: {round(ROC, 3)}', fontsize = 16, **hfont)\n",
    "    axs[i].text(axs[i].get_xlim()[0] + 5 , axs[i].get_ylim()[1] - 25, f'CIP-R: {round(cipro_R_prev*100, 3)}', fontsize = 16, **hfont)\n",
    "    for index in indices:\n",
    "        axs[i].plot(get_effective_threshold_all[index], incorrectly_get_X_threshold_all[index], marker='.', ls='none', ms=11, color = \"#b56576\")\n",
    " \n",
    "    if (i == 0) | (i == 3):\n",
    "        axs[i].set_ylabel(\"Unnecessarily received dual therapy (%) ($\\omega$(p))\", fontsize=14, **hfont)\n",
    "        axs[i].tick_params(axis = 'both', labelsize = 13)\n",
    "\n",
    "    if(i == 3) | (i == 4) | (i == 5):\n",
    "        axs[i].set_xlabel(r\"Received effective treatment (%) ($\\theta$(p))\", fontsize=14, **hfont)\n",
    "        axs[i].tick_params(axis = 'both', labelsize = 13)\n",
    "\n",
    "    axs[i].text(axs[i].get_xlim()[0] , axs[i].get_ylim()[1] + 5, labels[i], fontsize = 16, **hfont)\n",
    "    axs[i].title.set_text(year)\n",
    "\n",
    "    i += 1   #\n",
    "#plt.savefig('Graphs/Proportion_effective_unnecessary_2006_2011_all_LOOCV_workflow_paper_random_forest.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### USE FUNCTIONS TO GET TRAIN SET PARAMETERS\n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators = 171, min_samples_split = 1, min_samples_leaf=1, max_features = 'sqrt', max_depth = 89, random_state = 10)\n",
    "\n",
    "#loop setup\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "### needed for hyperparameter tuning \n",
    "### parameters for search\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, random_state=1)\n",
    "space = dict()\n",
    "space['n_estimators'] = np.arange(100, 201, 1)\n",
    "space['max_depth'] = np.arange(1, 200, 1)\n",
    "space['min_samples_split'] = np.arange(1, 25, 1)\n",
    "space['min_samples_leaf'] = np.arange(1, 25, 1)\n",
    "\n",
    "## need for feature engineerig \n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "\n",
    "\n",
    "#### Loop set up \n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "test_years = [2005, 2006, 2007, 2008, 2009, 2010]\n",
    "\n",
    "### hyperparameters by year \n",
    "best_hyperparameters_by_year = {}\n",
    "### best features by year\n",
    "best_features_by_year = {}\n",
    "### ROC by year \n",
    "ROC_by_year = {}\n",
    "\n",
    "\n",
    "\n",
    "for year in test_years: \n",
    "    years_train = np.array(range(year - 5, year))\n",
    "\n",
    "    CIP_data_training_years = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin(years_train)]\n",
    "    CIP_data_testing_years = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([year])]\n",
    "    # first do for all clinics \n",
    " \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data_rf(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, oversample_size = 0.5)\n",
    "\n",
    "    ## before fitting the model, do hyperparameter tuning \n",
    "    best_hyperparameters = get_best_hyperparameters(model_rf, cv, space, X_train, y_train)\n",
    "\n",
    "    model_rf = RandomForestClassifier(n_estimators = best_hyperparameters['n_estimators'], min_samples_split = best_hyperparameters['min_samples_split'], min_samples_leaf=best_hyperparameters['min_samples_leaf'], max_features = 'sqrt', max_depth = best_hyperparameters['max_depth'], random_state = 10)\n",
    "\n",
    "     \n",
    "    ## fit model w/hyperparameters \n",
    "    model_fit = model_rf.fit(X_train, y_train)\n",
    "    ## now also need to do feature engineering\n",
    "    important_features = get_best_features(feature_names, model_fit, X_test, y_test)\n",
    "    best_features_by_year.__setitem__(year, important_features) \n",
    "\n",
    "    # get new tets/train data and redo hyperparameters\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data_rf(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = important_features, oversample_size = 0.5)\n",
    "    best_hyperparameters2 = get_best_hyperparameters(model_rf, cv, space, X_train, y_train)\n",
    "\n",
    "    best_hyperparameters_by_year.__setitem__(year, best_hyperparameters2) \n",
    "\n",
    "    \n",
    "    ## fit model w/hyperparameters \n",
    "    model_rf = RandomForestClassifier(n_estimators = best_hyperparameters2['n_estimators'], min_samples_split = best_hyperparameters2['min_samples_split'], min_samples_leaf=best_hyperparameters2['min_samples_leaf'], max_features = 'sqrt', max_depth = best_hyperparameters2['max_depth'], random_state = 10)\n",
    "\n",
    "    model_fit_train = model_rf.fit(X_train, y_train)\n",
    "    y_predict_test = model_fit_train.predict(X_test)\n",
    "    y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    " \n",
    "    ROC= metrics.roc_auc_score(y_test, y_predict_test)\n",
    "    ROC_by_year.__setitem__(year, ROC)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2005: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2006: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2007: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2008: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2009: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2010: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}}\n",
      "{2005: ['MSW', 'MSM', 'MSMW'], 2006: ['MSW', 'DELTA_CLINIC', 'MSM', 'MSMW', 'Oth/Unk/Missing'], 2007: ['MSW', 'PREV_CLINIC', 'MSM'], 2008: ['PREV_CLINIC', 'MSW', 'MSM', 'DELTA_CLINIC', 'Oth/Unk/Missing', 'Southeast'], 2009: ['MSW', 'MSM', 'PREV_CLINIC', 'DELTA_CLINIC', 'PREV_REGION', 'Midwest'], 2010: ['DELTA_CLINIC', 'PREV_CLINIC', 'MSW', 'PREV_REGION', 'West', 'DELTA_REGION', 'Midwest', 'Southeast', 'MSMW']}\n",
      "{2005: 0.7418091168091168, 2006: 0.6955865853135346, 2007: 0.6980265728800312, 2008: 0.7012934518997574, 2009: 0.6612742833389195, 2010: 0.6738955823293172}\n"
     ]
    }
   ],
   "source": [
    "### hyperparameters by year \n",
    "print(best_hyperparameters_by_year)\n",
    "### best features by year\n",
    "print(best_features_by_year)\n",
    "### ROC by year \n",
    "print(ROC_by_year)\n",
    "best_hyperparameters_by_year = {2005: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2006: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2007: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2008: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2009: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}, 2010: {'n_estimators': 163, 'min_samples_split': 5, 'min_samples_leaf': 14, 'max_depth': 81}}\n",
    "best_features_by_year = {2005: ['MSW', 'MSM', 'MSMW'], 2006: ['MSW', 'DELTA_CLINIC', 'MSM', 'MSMW', 'Oth/Unk/Missing'], 2007: ['MSW', 'PREV_CLINIC', 'MSM'], 2008: ['PREV_CLINIC', 'MSW', 'MSM', 'DELTA_CLINIC', 'Oth/Unk/Missing', 'Southeast'], 2009: ['MSW', 'MSM', 'PREV_CLINIC', 'DELTA_CLINIC', 'PREV_REGION', 'Midwest'], 2010: ['DELTA_CLINIC', 'PREV_CLINIC', 'MSW', 'PREV_REGION', 'West', 'DELTA_REGION', 'Midwest', 'Southeast', 'MSMW']}\n",
    "ROC_by_year = {2005: 0.7418091168091168, 2006: 0.6955865853135346, 2007: 0.6980265728800312, 2008: 0.7012934518997574, 2009: 0.6612742833389195, 2010: 0.6738955823293172}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x\n",
      "x\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####### ## Try do loop with saved features \n",
    "\n",
    "\n",
    "########### Now do proportion receiving effective and unnecessary treatment for years surrounding 2007 decision \n",
    "### now do a loop over different years to get the proportion receiving effective and unnecessary treatment \n",
    "### has LOOCV \n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators = 171, min_samples_split = 1, min_samples_leaf=1, max_features = 'sqrt', max_depth = 89, random_state = 10)\n",
    "\n",
    "#loop setup\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Southeast',  'West', 'Midwest','PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "#feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "#   train \n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010])]\n",
    "X_train = train_data[feature_names]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "# test\n",
    "test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])]\n",
    "\n",
    "X_test = test_data[feature_names]\n",
    "y_test = 1 - test_data['Susceptible']\n",
    "X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "\n",
    "#### Loop set up \n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "test_years = [2005, 2006, 2007, 2008, 2009, 2010]\n",
    "\n",
    "labels = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\" ]\n",
    "text_for_graph = ['0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1']\n",
    "indices = [0,10,20,30,40,50,60,70,80,90,100]\n",
    "fig, axs = plt.subplots(2,3, figsize=(20, 10), facecolor='w', edgecolor='k', sharex = 'all', sharey = 'all')\n",
    "\n",
    "fig.subplots_adjust(hspace = .15, wspace=.1)\n",
    "axs = axs.ravel()\n",
    "i = 0\n",
    "\n",
    "for year in test_years: \n",
    "    years_train = np.array(range(year - 5, year))\n",
    "\n",
    "    CIP_data_training_years = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin(years_train)]\n",
    "    CIP_data_testing_years = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([year])]\n",
    "    # first do for all clinics \n",
    " \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data_rf(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, oversample_size = 0.5)\n",
    "\n",
    "  \n",
    "    model_rf = RandomForestClassifier(n_estimators = best_hyperparameters_by_year[year]['n_estimators'], min_samples_split = best_hyperparameters_by_year[year]['min_samples_split'], min_samples_leaf=best_hyperparameters_by_year[year]['min_samples_leaf'], max_features = 'sqrt', max_depth = best_hyperparameters_by_year[year]['max_depth'], random_state = 10)\n",
    "\n",
    "     \n",
    "    ## fit model w/hyperparameters \n",
    "    model_fit = model_rf.fit(X_train, y_train)\n",
    " \n",
    "    # get new tets/train data and refit model \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data_rf(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = best_features_by_year[year], oversample_size = 0.5)\n",
    "\n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_fit_train = model_rf.fit(X_train, y_train)\n",
    "    y_predict_test = model_fit_train.predict(X_test)\n",
    "    y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    " \n",
    "    get_effective_threshold_all, incorrectly_get_X_threshold_all = effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R_prev)\n",
    "\n",
    "\n",
    "    clinics = CIP_data_testing_years[\"CLINIC\"].unique()\n",
    "    for clinic in clinics: \n",
    "        train_data = CIP_data_training_years.loc[CIP_data_training_years['CLINIC'] != clinic]\n",
    "        oversample = RandomOverSampler(sampling_strategy = 0.5,random_state=42)\n",
    "\n",
    "          #train data - does not have clinic\n",
    "        X_train = train_data[best_features_by_year[year]]\n",
    "        y_train = 1 - train_data['Susceptible']\n",
    "        X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "          #test data - has clinic \n",
    "        test_data = CIP_data_testing_years.loc[CIP_data_testing_years['CLINIC'] == clinic]\n",
    "        X_test = test_data[best_features_by_year[year]]\n",
    "        y_test = 1 - test_data['Susceptible']\n",
    "        cipro_R = y_test.sum()/len(y_test)\n",
    "\n",
    "        if cipro_R > 0: \n",
    "          try:\n",
    "          # test \n",
    "              X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "              model_fit_train = model_rf.fit(X_train, y_train)\n",
    "              y_predict_test = model_fit_train.predict(X_test)\n",
    "              y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    "              get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic = effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R)\n",
    "\n",
    "              axs[i].plot(get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic,color = \"#e5e5e5\", linewidth = 3)\n",
    "              #axs[i].plot([1, 1-cipro_R], [1-cipro_R, 0], color = \"#e5e5e5\", linestyle=\"--\")\n",
    "\n",
    "              axs[i].plot(1, 1 - cipro_R, marker='.', ls='none', ms=18, color = \"#e5e5e5\")\n",
    "              axs[i].plot((1-cipro_R), 0, marker='*', ls='none', ms=14, color = \"#e5e5e5\")\n",
    "   \n",
    "\n",
    "          \n",
    "          except ValueError:\n",
    "            try:\n",
    "              oversample = RandomOverSampler(sampling_strategy = 0.85,random_state=42)\n",
    "          #train data - does not have clinic\n",
    "              X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "          #test data - has clinic \n",
    "\n",
    "              X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "            # test \n",
    "              model_fit_train = model_rf.fit(X_train, y_train)\n",
    "              y_predict_test = model_fit_train.predict(X_test)\n",
    "              y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    "              get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic = effective_unnecessary_threshold(threshold_seq, y_predict_proba, y_test, cipro_R)\n",
    "\n",
    "              axs[i].plot(get_effective_threshold_clinic, incorrectly_get_X_threshold_clinic,color = \"#e5e5e5\", linewidth = 3)\n",
    "              \n",
    "              axs[i].plot(1, 1 - cipro_R, marker='.', ls='none', ms=18, color = \"#e5e5e5\")\n",
    "              axs[i].plot((1-cipro_R), 0, marker='*', ls='none', ms=14, color = \"#e5e5e5\")\n",
    "\n",
    "            except ValueError:\n",
    "              print('x')\n",
    "              pass\n",
    "    axs[i].plot(get_effective_threshold_all, incorrectly_get_X_threshold_all,color = \"black\", linewidth = 3)\n",
    "    axs[i].plot(100, 100 - cipro_R_prev*100, marker='s', ls='none', ms=10, color = \"#b56576\")\n",
    "    axs[i].plot((100-cipro_R_prev*100), 0, marker='*', ls='none', ms=14, color = \"#b56576\")\n",
    "\n",
    "    axs[i].set_ylim([0-5,101])\n",
    "    axs[i].set_xlim([50, 101])\n",
    "    axs[i].text(axs[i].get_xlim()[0] + 5 , axs[i].get_ylim()[1] - 15, f'auROC: {round(ROC_by_year[year], 3)}', fontsize = 16, **hfont)\n",
    "    axs[i].text(axs[i].get_xlim()[0] + 5 , axs[i].get_ylim()[1] - 25, f'CIP-R: {round(cipro_R_prev*100, 3)}', fontsize = 16, **hfont)\n",
    "    for index in indices:\n",
    "        axs[i].plot(get_effective_threshold_all[index], incorrectly_get_X_threshold_all[index], marker='.', ls='none', ms=11, color = \"#b56576\")\n",
    " \n",
    "    if (i == 0) | (i == 3):\n",
    "        axs[i].set_ylabel(\"Unnecessarily received dual therapy (%) ($\\omega$(p))\", fontsize=14, **hfont)\n",
    "        axs[i].tick_params(axis = 'both', labelsize = 13)\n",
    "\n",
    "    if(i == 3) | (i == 4) | (i == 5):\n",
    "        axs[i].set_xlabel(r\"Received effective treatment (%) ($\\theta$(p))\", fontsize=14, **hfont)\n",
    "        axs[i].tick_params(axis = 'both', labelsize = 13)\n",
    "\n",
    "    axs[i].text(axs[i].get_xlim()[0] , axs[i].get_ylim()[1] + 5, labels[i], fontsize = 16, **hfont)\n",
    "    axs[i].title.set_text(year)\n",
    "\n",
    "    i += 1   #\n",
    "#plt.savefig('Graphs/Proportion_effective_unnecessary_2006_2011_all_LOOCV_workflow_paper_random_forest.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC_AUC_rf: 0.6203104695984067\n",
      "F1_me:  0.5291763490833978\n",
      "F1 0.5291763490833978\n",
      "F1 macro:  0.5946493717707382\n",
      "MCC: 0.22687061194759015\n",
      "MCC: 0.22687061194759015\n",
      "Sensitivity:  0.6655661398571119\n",
      "Specificity:  0.5750547993397018\n"
     ]
    }
   ],
   "source": [
    "#performance metrics on training set of 2000-2010 and test set of 2011-2019 without west and southeast and with tuned hyperparameters\n",
    "model_rf = RandomForestClassifier(n_estimators = 171, min_samples_split = 1, min_samples_leaf=1, max_features = 'sqrt', max_depth = 89, random_state = 10)\n",
    "#feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "# using all featurs (above) gives better auROC score...\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Southeast',  'West', 'Midwest','PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC']\n",
    "\n",
    "train_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010])]\n",
    "X_train = train_data[feature_names]\n",
    "y_train = 1 - train_data['Susceptible']\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "# test\n",
    "test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR'].isin([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])]\n",
    "\n",
    "X_test = test_data[feature_names]\n",
    "y_test = 1 - test_data['Susceptible']\n",
    "X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "model_fit_train = model_rf.fit(X_train, y_train)\n",
    "\n",
    "  \n",
    "y_predict = model_fit_train.predict(X_test)\n",
    "y_predict_proba = model_fit_train.predict_proba(X_test)\n",
    "\n",
    "###ROC \n",
    "ROC_AUC_random_forest= metrics.roc_auc_score(y_test, y_predict)\n",
    "\n",
    "print('ROC_AUC_rf:', ROC_AUC_random_forest) \n",
    "\n",
    "###F1 score\n",
    "\n",
    "tn_test , fp_test , fn_test , tp_test  = confusion_matrix(y_true=y_test, y_pred=y_predict).ravel()\n",
    "\n",
    "precision = tp_test/(tp_test + fp_test)\n",
    "recall = tp_test/(tp_test + fn_test)\n",
    "f1_score = 2/(1/precision + 1/recall)\n",
    "print(\"F1_me: \", f1_score)\n",
    "###\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "print(\"F1\", f1_score(y_test, y_predict))\n",
    "print(\"F1 macro: \", f1_score(y_test, y_predict, average = \"macro\"))\n",
    "\n",
    "### MCC score\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "import math\n",
    "\n",
    "MCC_me = ((tp_test*tn_test) - (fp_test*fn_test))/math.sqrt((tp_test + fp_test)*(tp_test + fn_test)*(tn_test + fp_test)*(tn_test + fn_test))\n",
    "print(\"MCC:\", MCC_me)\n",
    "\n",
    "print(\"MCC:\", matthews_corrcoef(y_test, y_predict))\n",
    "\n",
    "\n",
    "## specificity and sensitivity for threshold = 0.5 \n",
    "\n",
    "sensitivity_test  = tp_test  / (tp_test   + fn_test )\n",
    "specificity_test   = tn_test / (tn_test + fp_test )\n",
    "\n",
    "print(\"Sensitivity: \", sensitivity_test)\n",
    "print(\"Specificity: \", specificity_test)"
   ]
  }
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