Efficacy_model_different_years.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "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",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_validate, RandomizedSearchCV, RepeatedStratifiedKFold\n",
    "from sklearn.utils import resample\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.inspection import permutation_importance\n",
    "from scipy.stats import loguniform\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "## read data \n",
    "CIP_data_no_drop = pd.read_csv(\"CIP_data_encode_prev_not_dropped.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011\n",
      "2012\n",
      "2013\n",
      "2014\n",
      "2015\n",
      "2016\n",
      "2017\n",
      "2018\n"
     ]
    }
   ],
   "source": [
    "# so what is the marginal gain? e.g. at what point is the decrease in the proprotion of people receiving effective treatment worth the decrease in unnecessary cipro? \n",
    "\n",
    "#So what would these look like if we just used the 5% prevalence?\n",
    "\n",
    "##model\n",
    "model_nn = MLPClassifier(solver = 'lbfgs', activation = 'tanh', max_iter = 3000 ,hidden_layer_sizes= 13, alpha = 1.291549665014884, random_state=10, learning_rate = 'constant' )\n",
    "\n",
    "#loop setup\n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "\n",
    "#   train - from 2000 to 2010, dropped SW and Other\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','Northeast', 'Southeast',  'West', 'Midwest','PREV_REGION', 'PREV_CLINIC']]\n",
    "y_train = 1- train_data['Susceptible']\n",
    "\n",
    "oversample = RandomOverSampler(sampling_strategy = 0.5,random_state=42)\n",
    "X_train, y_train = oversample.fit_resample(X_train,y_train)\n",
    "\n",
    "years = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018]\n",
    "roc_auc_by_year = []\n",
    "for year in years:\n",
    "     print(year)\n",
    "     # test - vary by year dropped SW and Other\n",
    "     test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR']== year]\n",
    "     X_test = test_data[['MSM','MSMW', 'MSW', 'Northeast', 'Southeast', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC']]\n",
    "     y_test = 1 - test_data['Susceptible']\n",
    "     oversample = RandomOverSampler(sampling_strategy = 0.5,random_state=42)\n",
    "     X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "    # train model \n",
    "     model_fit_train = model_nn.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 ROC_AUC (implicit 0.5 threshold)\n",
    "     roc_auc = metrics.roc_auc_score(y_test, y_predict_test)\n",
    "     roc_auc_by_year.append(roc_auc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = CIP_data_no_drop.loc[CIP_data_no_drop['YEAR']== 2019]\n",
    "X_test = test_data[['MSM','MSMW', 'MSW', 'Northeast', 'Southeast', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC']]\n",
    "y_test = test_data['Susceptible']\n",
    "oversample = RandomOverSampler(sampling_strategy = 0.7,random_state=42)\n",
    "X_test, y_test = oversample.fit_resample(X_test,y_test)\n",
    "\n",
    "    # train model \n",
    "model_fit_train = model_nn.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 ROC_AUC (implicit 0.5 threshold)\n",
    "roc_auc = metrics.roc_auc_score(y_test, y_predict_test)\n",
    "roc_auc_by_year.append(roc_auc)\n",
    "years.append(2019)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(roc_auc_by_year)\n",
    "plt.plot(years, roc_auc_by_year, color = \"#2E4057\", linestyle = 'solid')\n",
    "\n",
    "plt.xlabel(\"Year\", fontsize=12)\n",
    "plt.ylabel(\"AUC ROC\", fontsize=12)\n",
    "plt.xticks(years, fontsize=12)\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.legend()\n",
    "plt.savefig('ROC_2000_2010_test_single_year_train_drop.png')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "GISP_init",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9 (main, Jan 11 2023, 09:18:18) [Clang 14.0.6 ]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "37b4cc77837642ee25c4eb6578aebe03e17eb3bb59efdde49edbbc888dbc418f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}