Tensorflow_trial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0.1', 'Unnamed: 0', 'CLINIC', 'YEAR', 'GENDERSP',\n",
      "       'Susceptible', 'REGION', 'MSM', 'MSMW', 'MSW', 'Oth/Unk/Missing',\n",
      "       'Midwest', 'Northeast', 'Southeast', 'Southwest', 'West', 'PREV_REGION',\n",
      "       'PREV_CLINIC', 'DELTA_REGION', 'DELTA_CLINIC', 'Count_Exceeds_75',\n",
      "       'Trend_N_greater_75'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#%reset\n",
    "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 os \n",
    "from matplotlib.patches import Polygon\n",
    "from sklearn import metrics\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_validate, RandomizedSearchCV, RepeatedStratifiedKFold\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",
    "from eli5.sklearn import PermutationImportance\n",
    "from sklearn.metrics import f1_score, matthews_corrcoef, roc_auc_score\n",
    "#from Functions_AMR_gonorrhea import effective_unnecessary_threshold, get_best_hyperparameters, get_best_features, get_test_train_data, get_feature_effects, f1_mcc_score_threshold\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "import pickle\n",
    "## read data \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": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_years' 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[4], line 73\u001b[0m\n\u001b[1;32m     71\u001b[0m space[\u001b[39m'\u001b[39m\u001b[39msolver\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mlbfgs\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msgd\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39madam\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m     72\u001b[0m i \u001b[39m=\u001b[39m \u001b[39m2\u001b[39m\n\u001b[0;32m---> 73\u001b[0m \u001b[39mfor\u001b[39;00m year \u001b[39min\u001b[39;00m test_years: \n\u001b[1;32m     74\u001b[0m     \u001b[39mprint\u001b[39m(year)\n\u001b[1;32m     75\u001b[0m     model_type \u001b[39m=\u001b[39m \u001b[39m2\u001b[39m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'test_years' is not defined"
     ]
    }
   ],
   "source": [
    "### Calculate quartiles using loaded models\n",
    "\n",
    "oversample = RandomOverSampler(sampling_strategy='minority', random_state=10)\n",
    "\n",
    "def get_test_train_data(CIP_data_no_drop, year, feature_names, years_train, model_type):\n",
    "\n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin(years_train)]\n",
    "    X_train = train_data[\n",
    "        feature_names\n",
    "    ]  # need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data[\"Susceptible\"]\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_prev = y_test.sum() / len(y_test)\n",
    "    if (model_type == 1) | (model_type == 2):\n",
    "        X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = oversample.fit_resample(X_test, y_test)\n",
    "        print(\"Oversample\")\n",
    "    return (test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev)\n",
    "\n",
    "def get_feature_effects(all_features, important_features, model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit,  X_train, y_train, n_repeats=10, random_state=0\n",
    "    )\n",
    "    feature_importances = []\n",
    "    for feature in all_features:\n",
    "        if feature in important_features:\n",
    "            feature_importances.append(PI.importances_mean[important_features.index(feature)])\n",
    "        else:\n",
    "            feature_importances.append(0)\n",
    "    return feature_importances\n",
    "\n",
    "def get_best_features(feature_names, model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit,  X_train, y_train, n_repeats=10, random_state=0\n",
    "    )\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(\n",
    "                feature_names[q]\n",
    "            )  # works cos they are in same order as the x columns\n",
    "    return important_features\n",
    "\n",
    "def get_feature_effects_sd(feature_names, important_features,model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=42\n",
    "    )\n",
    "\n",
    "    return PI.importances_std\n",
    "\n",
    "def get_best_hyperparameters(model, cv, space, X_train, y_train):\n",
    "    search = RandomizedSearchCV(\n",
    "        model, space, scoring=\"roc_auc\", n_iter=20, n_jobs=-1, cv=cv, random_state=1\n",
    "    )\n",
    "    result = search.fit(X_train, y_train)\n",
    "    return result.best_params_\n",
    "\n",
    "\n",
    "## Just NN\n",
    "model_nn = MLPClassifier(solver = 'lbfgs', activation = 'tanh', max_iter = 10000 ,hidden_layer_sizes= 12, alpha =1.291549665014884, random_state=10, learning_rate = 'adaptive' )\n",
    "\n",
    "space = dict()\n",
    "space['activation'] = ['tanh', 'relu']\n",
    "space['alpha'] = np.logspace(-1, 1, 1000)\n",
    "space['learning_rate'] = ['constant', 'adaptive']\n",
    "\n",
    "space['hidden_layer_sizes'] = [(8,), (9,), (10,), (11,), (12,), (13,), (14,)]\n",
    "space['solver'] = ['lbfgs', 'sgd', 'adam']\n",
    "i = 2\n",
    "for year in test_years: \n",
    "    print(year)\n",
    "    model_type = 2\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(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, years_train = years_train, model_type = model_type)\n",
    "\n",
    "    ## before fitting the model, do hyperparameter tuning \n",
    "    #if year == 2005: \n",
    "\n",
    "    best_hyperparameters1 = get_best_hyperparameters(model_nn, cv, space, X_train, y_train)\n",
    "    model_nn = MLPClassifier(solver = best_hyperparameters1['solver'], activation = best_hyperparameters1['activation'], max_iter = 10000 ,hidden_layer_sizes= best_hyperparameters1['hidden_layer_sizes'], alpha =  best_hyperparameters1['alpha'], random_state=10, learning_rate =best_hyperparameters1['learning_rate'])\n",
    "    \n",
    "     \n",
    "    ## fit model w/hyperparameters \n",
    "    model_fit = model_nn.fit(X_train, y_train)\n",
    "\n",
    "    ## now also need to do feature engineering\n",
    "    important_features = get_best_features(feature_names, model_fit, X_train, y_train)\n",
    "   \n",
    "    best_features_by_year[model_type].__setitem__(year, important_features) \n",
    "    imporances_all_models[i + indices_for_importance[model_type]] = get_feature_effects(feature_names, important_features, model_fit, X_train, y_train) #want it to be the correct block for each model\n",
    "    imporances_all_models_sd[i + indices_for_importance[model_type]] = get_feature_effects(feature_names, important_features, model_fit, X_train, y_train) #want it to be the correct block for each model\n",
    "\n",
    "    # get new test/train data and hyperparameter tuning round 2 \n",
    "\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = important_features, years_train = years_train, model_type = model_type)\n",
    "    \n",
    "    best_hyperparameters2 = get_best_hyperparameters(model_nn, cv, space, X_train, y_train)\n",
    "\n",
    "    best_hyperparameters_by_year[model_type].__setitem__(year, best_hyperparameters2) \n",
    "\n",
    "\n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn = MLPClassifier(solver = best_hyperparameters2['solver'], activation = best_hyperparameters2['activation'], max_iter = 5000 ,hidden_layer_sizes= best_hyperparameters2['hidden_layer_sizes'], alpha =  best_hyperparameters2['alpha'], random_state=10, learning_rate =best_hyperparameters2['learning_rate'])\n",
    "\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",
    "    ROC= metrics.roc_auc_score(y_test, y_predict_test)\n",
    "    ROC_by_year[model_type].__setitem__(year, ROC)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow.keras.wrappers'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodels\u001b[39;00m \u001b[39mimport\u001b[39;00m Sequential\n\u001b[1;32m      4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlayers\u001b[39;00m \u001b[39mimport\u001b[39;00m Dense\n\u001b[0;32m----> 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mwrappers\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mscikit_learn\u001b[39;00m \u001b[39mimport\u001b[39;00m KerasClassifier\n\u001b[1;32m      6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m class_weight\n\u001b[1;32m      7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m roc_auc_score\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.keras.wrappers'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# ... (Other parts of the code remain unchanged)\n",
    "\n",
    "def create_mlp_model(activation, alpha, learning_rate, hidden_layer_sizes):\n",
    "    model = Sequential()\n",
    "    for layer_size in hidden_layer_sizes:\n",
    "        model.add(Dense(layer_size, activation=activation))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "    return model\n",
    "\n",
    "space = dict()\n",
    "space['activation'] = ['tanh', 'relu']\n",
    "space['alpha'] = np.logspace(-1, 1, 1000)\n",
    "space['learning_rate'] = ['constant', 'adaptive']\n",
    "space['hidden_layer_sizes'] = [(8,), (9,), (10,), (11,), (12,), (13,), (14,)]\n",
    "\n",
    "i = 2\n",
    "for year in test_years: \n",
    "    print(year)\n",
    "    model_type = 2\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(CIP_data_no_drop=CIP_data_no_drop, year=year, feature_names=feature_names, years_train=years_train, model_type=model_type)\n",
    "\n",
    "    ## before fitting the model, do hyperparameter tuning\n",
    "    model_nn = KerasClassifier(build_fn=create_mlp_model, epochs=100, batch_size=32, verbose=0)\n",
    "\n",
    "    best_hyperparameters1 = get_best_hyperparameters(model_nn, cv, space, X_train, y_train)\n",
    "    model_nn = create_mlp_model(\n",
    "        activation=best_hyperparameters1['activation'],\n",
    "        alpha=best_hyperparameters1['alpha'],\n",
    "        learning_rate=best_hyperparameters1['learning_rate'],\n",
    "        hidden_layer_sizes=best_hyperparameters1['hidden_layer_sizes']\n",
    "    )\n",
    "\n",
    "    ## fit model w/hyperparameters\n",
    "    model_nn.fit(X_train, y_train)\n",
    "\n",
    "    ## now also need to do feature engineering\n",
    "    important_features = get_best_features(feature_names, model_nn, X_train, y_train)\n",
    "\n",
    "    best_features_by_year[model_type].__setitem__(year, important_features) \n",
    "    imporances_all_models[i + indices_for_importance[model_type]] = get_feature_effects(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "    imporances_all_models_sd[i + indices_for_importance[model_type]] = get_feature_effects(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "\n",
    "    # get new test/train data and hyperparameter tuning round 2\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev =  get_test_train_data(CIP_data_no_drop=CIP_data_no_drop, year=year, feature_names=important_features, years_train=years_train, model_type=model_type)\n",
    "\n",
    "    best_hyperparameters2 = get_best_hyperparameters(model_nn, cv, space, X_train, y_train)\n",
    "\n",
    "    best_hyperparameters_by_year[model_type].__setitem__(year, best_hyperparameters2) \n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn = create_mlp_model(\n",
    "        activation=best_hyperparameters2['activation'],\n",
    "        alpha=best_hyperparameters2['alpha'],\n",
    "        learning_rate=best_hyperparameters2['learning_rate'],\n",
    "        hidden_layer_sizes=best_hyperparameters2['hidden_layer_sizes']\n",
    "    )\n",
    "\n",
    "    model_nn.fit(X_train, y_train, verbose=0)\n",
    "    y_predict_test = model_nn.predict_classes(X_test)\n",
    "    y_predict_proba = model_nn.predict(X_test)\n",
    "\n",
    "    ROC = roc_auc_score(y_test, y_predict_proba)\n",
    "    ROC_by_year[model_type].__setitem__(year, ROC)\n",
    "    i += 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions for bootstrapping "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_percentiles(iterations, ROC_actual, test_data, model_type,y_test):\n",
    "        bootstrapped_stats = []\n",
    "        for j in range(iterations):\n",
    "            model_name = \"/Users/rem76/Documents/ML_Models/Bootstrap_models_GISP/CIP_bootstrap_no_dev_\" + str(model_type) + \"_\" + str(year) + \"_\" + str(j) + \".sav\" \n",
    "            X_data_name = \"/Users/rem76/Documents/ML_Models/Bootstrap_models_GISP/CIP_bootstrap_X_no_dev_\" + str(model_type) + \"_\" + str(year) + \"_\" + str(j) + \".csv\" \n",
    "            ## don't need to read in weights as the model has already been trained\n",
    "            X_train_bootstrap = pd.read_csv(X_data_name)\n",
    "            model_fit = pickle.load(open(model_name, 'rb'))\n",
    "            X_test_for_bootstrap = test_data[X_train_bootstrap.columns[1:len(X_train_bootstrap.columns)]]\n",
    "            y_bootstrap_predict = model_fit.predict(X_test_for_bootstrap)\n",
    "            ROC_AUC_bootstrap_test_performance = metrics.roc_auc_score(y_test, y_bootstrap_predict) \n",
    "        ### (D) Calculate estimate fo variance  by getting (B) - (D) \n",
    "\n",
    "            difference = ROC_AUC_bootstrap_test_performance - ROC_actual ## according to https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/resources/mit18_05s14_reading24/\n",
    "\n",
    "            bootstrapped_stats.append({'Difference': difference})\n",
    "\n",
    "\n",
    "        bootstrapped_stats = pd.DataFrame(bootstrapped_stats)\n",
    "    ## Step 3: Get average optimization\n",
    "        alpha = 0.05\n",
    "        #lower_quartile = np.percentile(bootstrapped_stats[\"Difference\"], 2.5)\n",
    "        #upper_quartile = np.percentile(bootstrapped_stats[\"Difference\"], 97.5)\n",
    "        upper_quartile, lower_quartile = ROC_actual - np.percentile(bootstrapped_stats[\"Difference\"], [100 * (1 - alpha / 2.0), 100 * alpha / 2.0])\n",
    "        #upper_quartile = np.percentile(bootstrapped_stats[\"Difference\"], 2.5)\n",
    "        #lower_quartile = np.percentile(bootstrapped_stats[\"Difference\"], 97.5)\n",
    " ## Step 4: Get optimization-corrected performance\n",
    "\n",
    "        return lower_quartile, upper_quartile\n",
    "\n",
    "### now try bootstrapping w/o feature selection\n",
    "iterations = 100\n",
    "## DO NOT SAMPLE THE TARGET DATA\n",
    "def bootstrap_auROC_no_dev(iterations, model, train_data, test_data, y_test, ROC_actual, important_features):\n",
    "      #1. Find apparent model performance\n",
    "    bootstrapped_stats = []\n",
    "    feature_names_with_weight = feature_names.copy()\n",
    "    feature_names_with_weight.extend(['weight'])\n",
    "    for i in range(iterations):\n",
    "        #2. (A) Sample all individuals from training data w/replacement\n",
    "\n",
    "          sample_train = train_data.sample(frac = 1, replace=True) ##(a) sample n individuals with replacement\n",
    "          \n",
    "          X_sample_train = sample_train[feature_names_with_weight]\n",
    "          y_sample_train = 1 - sample_train['Susceptible']\n",
    "\n",
    "          if model_type in [1,2]:\n",
    "            X_sample_train, y_sample_train = oversample.fit_resample(X_sample_train,y_sample_train)\n",
    "          weights_train = X_sample_train[\"weight\"]\n",
    "          X_sample_train  = X_sample_train.drop(\"weight\", axis = 1)\n",
    "        #  (B) Predictive model w/o feature selection \n",
    "          X_test_bootstrap = test_data[important_features]\n",
    "          model_fit = model.fit(X_sample_train, y_sample_train, sample_weight = weights_train)\n",
    "\n",
    "          model_name = \"/Users/rem76/Documents/ML_Models/Bootstrap_models_GISP/CIP_bootstrap_no_dev_\" + str(model_type) + \"_\" + str(year) + \"_\" + str(i) + \".sav\" \n",
    "          X_data_name = \"/Users/rem76/Documents/ML_Models/Bootstrap_models_GISP/CIP_bootstrap_X_no_dev_\" + str(model_type) + \"_\" + str(year) + \"_\" + str(i) + \".csv\" \n",
    "          y_data_name = \"/Users/rem76/Documents/ML_Models/Bootstrap_models_GISP/CIP_bootstrap_y_no_dev_\" + str(model_type) + \"_\" + str(year) + \"_\" + str(i) + \".csv\" \n",
    "          weights_data_name = \"/Users/rem76/Documents/ML_Models/Bootstrap_models_GISP/CIP_bootstrap_weights_\" + str(model_type) + \"_\" + str(year) + \"_\" + str(i) + \".csv\" \n",
    "          \n",
    "          weights_train.to_csv(weights_data_name)\n",
    "          X_sample_train.to_csv(X_data_name)\n",
    "          y_sample_train.to_csv(y_data_name)\n",
    "          pickle.dump(model_fit, open(model_name, 'wb'))\n",
    "        #  (C) Performance of predictive model on original sample (i.e. original training population, X_test, with new selected features)\n",
    "          y_bootstrap_predict = model_fit.predict(X_test_bootstrap)\n",
    "          ROC_AUC_bootstrap_test_performance = metrics.roc_auc_score(y_test, y_bootstrap_predict) \n",
    "        ### (D) Calculate estimate fo variance  by getting (B) - (D) \n",
    "\n",
    "          difference = ROC_AUC_bootstrap_test_performance - ROC_actual ## according to https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/resources/mit18_05s14_reading24/\n",
    "\n",
    "          bootstrapped_stats.append(\n",
    "          {\n",
    "\n",
    "              'Difference': difference#,\n",
    "          }\n",
    "        )\n",
    "\n",
    "\n",
    "    bootstrapped_stats = pd.DataFrame(bootstrapped_stats)\n",
    "    ## Step 3: Get average optimization\n",
    "\n",
    "    #lower_quartile = np.percentile(bootstrapped_stats[\"Difference\"], 2.5)\n",
    "    #upper_quartile = np.percentile(bootstrapped_stats[\"Difference\"], 97.5)\n",
    " ## Step 4: Get optimization-corrected performance\n",
    "    alpha = 0.05 \n",
    "    upper_quartile, lower_quartile = ROC_actual - np.percentile(bootstrapped_stats[\"Difference\"], [100 * (1 - alpha / 2.0), 100 * alpha / 2.0])\n",
    "\n",
    "    return lower_quartile, upper_quartile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import keras\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "################################ Get hyperparameters and best features for each model  ###########################\n",
    "#### Loop set up \n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "test_years = [2005, 2006, 2007, 2008, 2009, 2010]  #np.array(range(2005, 2011))\n",
    "oversample = RandomOverSampler(sampling_strategy = 'minority',random_state=42) #need for neural network and random forest\n",
    "model_types = [\"Logistic_regression\",  \"Random_forest\", \"Neural_network\"]\n",
    "i = 0\n",
    "\n",
    "# logistic regression - random initial parameters\n",
    "model_lr = LogisticRegression(class_weight = 'balanced', max_iter=4000, solver = \"lbfgs\", C = 0.27, penalty = 'l2')\n",
    "# random forest - random initial parameters\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",
    "# neural network - random parameters\n",
    "model_nn = MLPClassifier(solver = 'lbfgs', activation = 'tanh', max_iter = 3000 ,hidden_layer_sizes= 12, alpha =1.291549665014884, random_state=10, learning_rate = 'adaptive' )\n",
    "unfitted_models = [model_lr, model_rf, model_nn]\n",
    "\n",
    "### Hyperparameter tuning\n",
    "cv = RepeatedStratifiedKFold(n_splits=10,  n_repeats=10,random_state=1) ## 10-fold cross validations\n",
    "# logistic regression \n",
    "best_hyperparameters_by_year = {}\n",
    "\n",
    "space_lr = dict()\n",
    "space_lr['solver'] = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "space_lr['penalty'] = ['l1', 'l2']\n",
    "space_lr['C'] = np.arange(0, 100, .01)\n",
    "best_hyperparameters_by_year_lr = {}\n",
    "# random forest \n",
    "space_rf = dict()\n",
    "space_rf['n_estimators'] = np.arange(100, 201, 1)\n",
    "space_rf['max_depth'] = np.arange(1, 200, 1)\n",
    "space_rf['min_samples_split'] = np.arange(1, 25, 1)\n",
    "space_rf['min_samples_leaf'] = np.arange(1, 25, 1)\n",
    "best_hyperparameters_by_year_rf = {}\n",
    "\n",
    "# neural network \n",
    "space_nn = dict()\n",
    "space_nn['solver'] = ['lbfgs', 'sgd', 'adam']\n",
    "space_nn['activation'] = ['tanh', 'relu']\n",
    "space_nn['alpha'] = np.logspace(-1, 1, 10)\n",
    "space_nn['learning_rate'] = ['constant','adaptive']\n",
    "space_nn['hidden_layer_sizes'] = [(4,), (6,), (8,), (10,), (12,), (13,), (14,)]\n",
    "best_hyperparameters_by_year_nn = {}\n",
    "space = [space_lr, space_rf, space_nn]\n",
    "best_hyperparameters_by_year = [best_hyperparameters_by_year_lr, best_hyperparameters_by_year_rf,best_hyperparameters_by_year_nn]\n",
    "\n",
    "### Feature Engineering\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION',  'Count_Exceeds_75', 'Trend_N_greater_75']\n",
    "best_features_by_year_lr = {}\n",
    "best_features_by_year_rf = {}\n",
    "best_features_by_year_nn = {}\n",
    "best_features_by_year = [best_features_by_year_lr, best_features_by_year_rf, best_features_by_year_nn]\n",
    "\n",
    "imporances_all_models = pd.DataFrame(0, index = feature_names, columns=np.arange(len(test_years)*3))\n",
    "indices_for_importance = [6,12,0] ## need to be in correct order \n",
    "imporances_all_models_sd = pd.DataFrame(0, index = feature_names, columns=np.arange(len(test_years)*3))\n",
    "\n",
    "\n",
    "oversample = RandomOverSampler(sampling_strategy='minority', random_state=10)\n",
    "\n",
    "def get_test_train_data(CIP_data_no_drop, year, feature_names, years_train, model_type):\n",
    "\n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin(years_train)]\n",
    "    X_train = train_data[\n",
    "        feature_names\n",
    "    ]  # need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data[\"Susceptible\"]\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_prev = y_test.sum() / len(y_test)\n",
    "    if (model_type == 1) | (model_type == 2):\n",
    "        X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = oversample.fit_resample(X_test, y_test)\n",
    "        print(\"Oversample\")\n",
    "    return (test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev)\n",
    "\n",
    "def get_feature_effects(all_features, important_features, model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=0\n",
    "    )\n",
    "    feature_importances = []\n",
    "    for feature in all_features:\n",
    "        if feature in important_features:\n",
    "            feature_importances.append(PI.importances_mean[important_features.index(feature)])\n",
    "        else:\n",
    "            feature_importances.append(0)\n",
    "    return feature_importances\n",
    "\n",
    "def get_best_features(feature_names, model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=0\n",
    "    )\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(\n",
    "                feature_names[q]\n",
    "            )  # works cos they are in same order as the x columns\n",
    "    return important_features\n",
    "\n",
    "def get_feature_effects_sd(feature_names, important_features,model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=42\n",
    "    )\n",
    "\n",
    "    return PI.importances_std\n",
    "\n",
    "def get_best_hyperparameters(model, cv, space, X_train, y_train, sample_weights):\n",
    "    search = RandomizedSearchCV(\n",
    "        model, space, scoring=\"roc_auc\", n_iter=20, n_jobs=-1, cv=cv, random_state=1\n",
    "    )\n",
    "    result = search.fit(X_train, y_train, sample_weight = sample_weights)\n",
    "    return result.best_params_\n",
    "\n",
    "### ROC by year \n",
    "ROC_by_year_rf = {}\n",
    "ROC_by_year_lr = {}\n",
    "ROC_by_year_nn = {}\n",
    "\n",
    "ROC_by_year = [ROC_by_year_lr, ROC_by_year_rf, ROC_by_year_nn]\n",
    "\n",
    "\n",
    "def get_test_train_data(CIP_data_no_drop, year, feature_names, years_train, model_type):\n",
    "    feature_names_with_weight = feature_names.copy()\n",
    "    feature_names_with_weight.extend(['weight'])\n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin(years_train)]\n",
    "    X_train = train_data[feature_names_with_weight]  # need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data[\"Susceptible\"]\n",
    "    # test\n",
    "    test_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin([year])]\n",
    "    X_test = test_data[feature_names_with_weight]\n",
    "    y_test = 1 - test_data[\"Susceptible\"]\n",
    "    cipro_R_prev = y_test.sum() / len(y_test)\n",
    "\n",
    "    if (model_type == 1) | (model_type == 2):\n",
    "        X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = oversample.fit_resample(X_test, y_test)\n",
    "        print(\"Oversample\")\n",
    "    weights_train = X_train[\"weight\"]\n",
    "    X_train  = X_train.drop(\"weight\", axis = 1)\n",
    "    X_test = X_test.drop(\"weight\", axis = 1)\n",
    "\n",
    "    return (test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "CIP_data_no_drop = CIP_data_no_drop.drop('weight_y', axis = 1)\n",
    "CIP_data_no_drop = CIP_data_no_drop.drop('weight_x', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_train_data(CIP_data_no_drop, year, feature_names, years_train, model_type):\n",
    "    feature_names_with_weight = feature_names.copy()\n",
    "    feature_names_with_weight.extend(['weight'])\n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin(years_train)]\n",
    "    X_train = train_data[feature_names_with_weight]  # need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data[\"Susceptible\"]\n",
    "    # test\n",
    "    test_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin([year])]\n",
    "    X_test = test_data[feature_names_with_weight]\n",
    "    y_test = 1 - test_data[\"Susceptible\"]\n",
    "    cipro_R_prev = y_test.sum() / len(y_test)\n",
    "\n",
    "    if (model_type == 1) | (model_type == 2):\n",
    "        X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = oversample.fit_resample(X_test, y_test)\n",
    "        print(\"Oversample\")\n",
    "    weights_train = X_train[\"weight\"]\n",
    "    X_train  = X_train.drop(\"weight\", axis = 1)\n",
    "    X_test = X_test.drop(\"weight\", axis = 1)\n",
    "\n",
    "    return (test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversample\n"
     ]
    }
   ],
   "source": [
    "    feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION',  'Count_Exceeds_75', 'Trend_N_greater_75']\n",
    "\n",
    "    year = 2005\n",
    "    model_type = 2\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",
    "    counts = CIP_data_training_years.groupby(['YEAR', 'CLINIC']).size().reset_index(name='Count')\n",
    "    counts_by_year = CIP_data_training_years.groupby(['YEAR']).size().reset_index(name='Count')\n",
    "\n",
    "    merged_counts = counts.merge(counts_by_year, on='YEAR', suffixes=('_counts', '_counts_by_year'))\n",
    "\n",
    "    # Calculate the sum of all counts in the counts dataframe\n",
    "    total_counts = counts['Count'].sum()\n",
    "\n",
    "    # Calculate the 'weight' column as Count_counts / total_counts\n",
    "    merged_counts['weight'] = merged_counts['Count_counts'] / total_counts\n",
    "    # Calculate the 'weight' column as Count_counts / rolling sum of the previous 5 years\n",
    "\n",
    "    # Merge the 'weight' column back to the original DataFrame\n",
    "    CIP_data_no_drop = CIP_data_no_drop.merge(merged_counts[['YEAR', 'CLINIC', 'weight']], on=['YEAR', 'CLINIC'], how='left')\n",
    "\n",
    " \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, years_train = years_train, model_type = model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10169/10169 [==============================] - 9s 881us/step - loss: 0.0057 - auc: 0.4964\n",
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      "1130/1130 [==============================] - 1s 598us/steploss: 0.0057 - auc_1: 0.5150  \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
      "1 fits failed out of a total of 1000.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "1 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/pipeline.py\", line 406, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 1491, in fit\n",
      "    super().fit(X=X, y=y, sample_weight=sample_weight, **kwargs)\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 760, in fit\n",
      "    self._fit(\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 928, in _fit\n",
      "    self._fit_keras_model(\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 522, in _fit_keras_model\n",
      "    hist = self.model_.fit(x=X, y=y, **fit_args)\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n",
      "    raise e.with_traceback(filtered_tb) from None\n",
      "  File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 53, in quick_execute\n",
      "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
      "\n",
      "Detected at node 'div_no_nan_1' defined at (most recent call last):\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n",
      "      return _run_code(code, main_globals, None,\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/runpy.py\", line 86, in _run_code\n",
      "      exec(code, run_globals)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/joblib/externals/loky/backend/popen_loky_posix.py\", line 170, in <module>\n",
      "      exitcode = process_obj._bootstrap()\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "      self.run()\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "      self._target(*self._args, **self._kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 428, in _process_worker\n",
      "      r = call_item()\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 275, in __call__\n",
      "      return self.fn(*self.args, **self.kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 620, in __call__\n",
      "      return self.func(*args, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n",
      "      return [func(*args, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n",
      "      return [func(*args, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
      "      return self.function(*args, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "      estimator.fit(X_train, y_train, **fit_params)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/pipeline.py\", line 406, in fit\n",
      "      self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 1491, in fit\n",
      "      super().fit(X=X, y=y, sample_weight=sample_weight, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 760, in fit\n",
      "      self._fit(\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 928, in _fit\n",
      "      self._fit_keras_model(\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/scikeras/wrappers.py\", line 522, in _fit_keras_model\n",
      "      hist = self.model_.fit(x=X, y=y, **fit_args)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n",
      "      return fn(*args, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1742, in fit\n",
      "      tmp_logs = self.train_function(iterator)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1338, in train_function\n",
      "      return step_function(self, iterator)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1322, in step_function\n",
      "      outputs = model.distribute_strategy.run(run_step, args=(data,))\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1303, in run_step\n",
      "      outputs = model.train_step(data)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1085, in train_step\n",
      "      return self.compute_metrics(x, y, y_pred, sample_weight)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1180, in compute_metrics\n",
      "      return self.get_metrics_result()\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1196, in get_metrics_result\n",
      "      result = metric.result()\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/utils/metrics_utils.py\", line 137, in decorated\n",
      "      raw_result = result_fn(*args)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/metrics/base_metric.py\", line 159, in result_fn\n",
      "      return ag_result(*args, **kwargs)\n",
      "    File \"/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/metrics/confusion_metrics.py\", line 1602, in result\n",
      "      recall = tf.math.divide_no_nan(\n",
      "Node: 'div_no_nan_1'\n",
      "Incompatible shapes: [200] vs. [0]\n",
      "\t [[{{node div_no_nan_1}}]] [Op:__inference_train_function_2542557]\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/Users/rem76/miniconda3/envs/GISP_init/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.49993981 0.49999859 0.50000354 0.49993981 0.50000354 0.49993981\n",
      "        nan 0.49999859 0.50006018 0.50000354]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3767/3767 [==============================] - 1s 324us/step - loss: 0.0057 - auc: 0.4961\n",
      "Epoch 1/100\n",
      "3767/3767 [==============================] - 1s 312us/step - loss: 0.0035 - auc_1: 0.8758\n",
      "Epoch 2/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0032 - auc_1: 0.8914\n",
      "Epoch 3/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0032 - auc_1: 0.8960\n",
      "Epoch 4/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0031 - auc_1: 0.8990\n",
      "Epoch 5/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0031 - auc_1: 0.9015\n",
      "Epoch 6/100\n",
      "3767/3767 [==============================] - 1s 304us/step - loss: 0.0030 - auc_1: 0.9029\n",
      "Epoch 7/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0030 - auc_1: 0.9046\n",
      "Epoch 8/100\n",
      "3767/3767 [==============================] - 1s 319us/step - loss: 0.0030 - auc_1: 0.9057\n",
      "Epoch 9/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0030 - auc_1: 0.9061\n",
      "Epoch 10/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0030 - auc_1: 0.9077\n",
      "Epoch 11/100\n",
      "3767/3767 [==============================] - 1s 315us/step - loss: 0.0030 - auc_1: 0.9079\n",
      "Epoch 12/100\n",
      "3767/3767 [==============================] - 1s 301us/step - loss: 0.0029 - auc_1: 0.9087\n",
      "Epoch 13/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_1: 0.9086\n",
      "Epoch 14/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9086\n",
      "Epoch 15/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_1: 0.9090\n",
      "Epoch 16/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0029 - auc_1: 0.9091\n",
      "Epoch 17/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_1: 0.9094\n",
      "Epoch 18/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9093\n",
      "Epoch 19/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_1: 0.9093\n",
      "Epoch 20/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9095\n",
      "Epoch 21/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9101\n",
      "Epoch 22/100\n",
      "3767/3767 [==============================] - 1s 315us/step - loss: 0.0029 - auc_1: 0.9104\n",
      "Epoch 23/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9100\n",
      "Epoch 24/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_1: 0.9107\n",
      "Epoch 25/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9101\n",
      "Epoch 26/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_1: 0.9110\n",
      "Epoch 27/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9104\n",
      "Epoch 28/100\n",
      "3767/3767 [==============================] - 1s 355us/step - loss: 0.0029 - auc_1: 0.9109\n",
      "Epoch 29/100\n",
      "3767/3767 [==============================] - 1s 318us/step - loss: 0.0029 - auc_1: 0.9110\n",
      "Epoch 30/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9109\n",
      "Epoch 31/100\n",
      "3767/3767 [==============================] - 1s 302us/step - loss: 0.0029 - auc_1: 0.9108\n",
      "Epoch 32/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9109\n",
      "Epoch 33/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_1: 0.9111\n",
      "Epoch 34/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_1: 0.9113\n",
      "Epoch 35/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_1: 0.9114\n",
      "Epoch 36/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9109\n",
      "Epoch 37/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_1: 0.9113\n",
      "Epoch 38/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0029 - auc_1: 0.9117\n",
      "Epoch 39/100\n",
      "3767/3767 [==============================] - 1s 365us/step - loss: 0.0029 - auc_1: 0.9114\n",
      "Epoch 40/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_1: 0.9118\n",
      "Epoch 41/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9114\n",
      "Epoch 42/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_1: 0.9116\n",
      "Epoch 43/100\n",
      "3767/3767 [==============================] - 1s 312us/step - loss: 0.0029 - auc_1: 0.9118\n",
      "Epoch 44/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_1: 0.9118\n",
      "Epoch 45/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0029 - auc_1: 0.9118\n",
      "Epoch 46/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_1: 0.9122\n",
      "Epoch 47/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9121\n",
      "Epoch 48/100\n",
      "3767/3767 [==============================] - 1s 315us/step - loss: 0.0029 - auc_1: 0.9117\n",
      "Epoch 49/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9117\n",
      "Epoch 50/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_1: 0.9117\n",
      "Epoch 51/100\n",
      "3767/3767 [==============================] - 1s 346us/step - loss: 0.0029 - auc_1: 0.9123\n",
      "Epoch 52/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9122\n",
      "Epoch 53/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0029 - auc_1: 0.9123\n",
      "Epoch 54/100\n",
      "3767/3767 [==============================] - 1s 293us/step - loss: 0.0029 - auc_1: 0.9122\n",
      "Epoch 55/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_1: 0.9123\n",
      "Epoch 56/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 57/100\n",
      "3767/3767 [==============================] - 1s 303us/step - loss: 0.0029 - auc_1: 0.9124\n",
      "Epoch 58/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 59/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 60/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_1: 0.9122\n",
      "Epoch 61/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_1: 0.9120\n",
      "Epoch 62/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9122\n",
      "Epoch 63/100\n",
      "3767/3767 [==============================] - 1s 302us/step - loss: 0.0029 - auc_1: 0.9124\n",
      "Epoch 64/100\n",
      "3767/3767 [==============================] - 1s 307us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 65/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_1: 0.9128\n",
      "Epoch 66/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "Epoch 67/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 68/100\n",
      "3767/3767 [==============================] - 1s 311us/step - loss: 0.0029 - auc_1: 0.9121\n",
      "Epoch 69/100\n",
      "3767/3767 [==============================] - 1s 307us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 70/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_1: 0.9123\n",
      "Epoch 71/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 72/100\n",
      "3767/3767 [==============================] - 1s 309us/step - loss: 0.0029 - auc_1: 0.9123\n",
      "Epoch 73/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 74/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 75/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 76/100\n",
      "3767/3767 [==============================] - 1s 307us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "Epoch 77/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 78/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_1: 0.9124\n",
      "Epoch 79/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "Epoch 80/100\n",
      "3767/3767 [==============================] - 1s 380us/step - loss: 0.0029 - auc_1: 0.9127\n",
      "Epoch 81/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 82/100\n",
      "3767/3767 [==============================] - 1s 306us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 83/100\n",
      "3767/3767 [==============================] - 1s 318us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "Epoch 84/100\n",
      "3767/3767 [==============================] - 1s 335us/step - loss: 0.0029 - auc_1: 0.9130\n",
      "Epoch 85/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9125\n",
      "Epoch 86/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_1: 0.9134\n",
      "Epoch 87/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_1: 0.9130\n",
      "Epoch 88/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "Epoch 89/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_1: 0.9121\n",
      "Epoch 90/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_1: 0.9133\n",
      "Epoch 91/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_1: 0.9128\n",
      "Epoch 92/100\n",
      "3767/3767 [==============================] - 1s 353us/step - loss: 0.0029 - auc_1: 0.9130\n",
      "Epoch 93/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_1: 0.9126\n",
      "Epoch 94/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_1: 0.9128\n",
      "Epoch 95/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_1: 0.9127\n",
      "Epoch 96/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0029 - auc_1: 0.9132\n",
      "Epoch 97/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_1: 0.9128\n",
      "Epoch 98/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "Epoch 99/100\n",
      "3767/3767 [==============================] - 1s 302us/step - loss: 0.0029 - auc_1: 0.9132\n",
      "Epoch 100/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_1: 0.9129\n",
      "194/194 [==============================] - 0s 194us/step\n",
      "AUROC: 0.7654238\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import Sequential\n",
    "from tensorflow.keras.metrics import AUC\n",
    "np.random.seed(10)\n",
    "\n",
    "def create_model(optimizer=\"adam\", dropout=0.1, init='uniform', nbr_features=2500, dense_nparams=256):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(dense_nparams, activation='relu', input_shape=(len(feature_names),), kernel_initializer=init))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=[AUC()])\n",
    "    return model\n",
    "\n",
    "kears_estimator = KerasClassifier(model=create_model, verbose=1, init='zeros',dropout=0.1, dense_nparams=8, random_state = 10)\n",
    "\n",
    "estimator = Pipeline([\n",
    "    ('ss', StandardScaler()),  # Use StandardScaler for numerical features\n",
    "    (\"kc\", kears_estimator)\n",
    "])\n",
    "\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    'kc__dense_nparams': [2,4, 6, 8, 10, 12, 14, 16],\n",
    "    'kc__batch_size': [5, 10, 15, 20],\n",
    "    'kc__optimizer': [ 'Adamax'],\n",
    "    'kc__dropout': [0.075, 0.03, 0.05, 0.04, 0.025, 0]\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=10,  n_repeats=10,random_state=1) ## 10-fold cross validations\n",
    "#grid = RandomizedSearchCV(estimator=estimator,\n",
    "#                    n_jobs=-1,\n",
    "#                    verbose=1,\n",
    "#                    return_train_score=True,\n",
    "#                    cv=kfold_splits,\n",
    "#                    space=param_grid,\n",
    "#                    error_score='raise')\n",
    "grid = RandomizedSearchCV(estimator, param_grid, scoring=\"roc_auc\", n_iter=10, n_jobs=-1, cv=cv, random_state=1)\n",
    "\n",
    "\n",
    "\n",
    "grid_result = grid.fit(X_train, y_train, kc__sample_weight=weights_train);\n",
    "\n",
    "# summarize results\n",
    "\n",
    "params = grid_result.best_params_\n",
    "optimizer = params['kc__optimizer']\n",
    "\n",
    "dropout = params['kc__dropout']\n",
    "dense_nparams = params['kc__dense_nparams']\n",
    "batches = params['kc__batch_size']\n",
    "model = create_model(optimizer=params['kc__optimizer'], init='uniform', dropout=params['kc__dropout'], dense_nparams=params['kc__dense_nparams'])\n",
    "\n",
    "#return params \n",
    "# Train the model on your data\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=params['kc__batch_size'], sample_weight=weights_train)\n",
    "\n",
    "# Make predictions on test data\n",
    "y_predict_proba = model.predict(X_test)\n",
    "\n",
    "# Calculate AUROC on test data\n",
    "auc_metric = keras.metrics.AUC(num_thresholds=100)\n",
    "auc_metric.update_state(y_test, y_predict_proba)\n",
    "auroc = auc_metric.result().numpy()\n",
    "\n",
    "print(\"AUROC:\", auroc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10169/10169 [==============================] - 10s 836us/step - loss: 0.0057 - auc: 0.4941\n",
      "10169/10169 [==============================] - 10s 876us/step - loss: 0.0057 - auc: 0.4951\n",
      "10169/10169 [==============================] - 10s 884us/step - loss: 0.0057 - auc: 0.4936\n",
      "10169/10169 [==============================] - 11s 908us/step - loss: 0.0057 - auc: 0.4987\n",
      "10169/10169 [==============================] - 11s 900us/step - loss: 0.0057 - auc: 0.4945\n",
      "10169/10169 [==============================] - 11s 926us/step - loss: 0.0057 - auc: 0.5010\n",
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      "10169/10169 [==============================] - 11s 961us/step - loss: 0.0057 - auc: 0.4938\n",
      "1130/1130 [==============================] - 1s 680us/step\n",
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      "10169/10169 [==============================] - 10s 988us/step - loss: 0.0057 - auc_1: 0.5005\n",
      "10169/10169 [==============================] - 11s 1ms/step - loss: 0.0057 - auc_1: 0.4937\n",
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      "1130/1130 [==============================] - 1s 721us/steploss: 0.0056 - auc_2: 0.5000  \n",
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      "1130/1130 [==============================] - 1s 446us/steposs: 0.0058 - auc_3: 0.5000.5000\n",
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      "1130/1130 [==============================] - 1s 752us/steposs: 0.0056 - auc_4: 0.50400 \n",
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      "1130/1130 [==============================] - 0s 385us/steposs: 0.0056 - auc_4: 0.5084.\n",
      "1130/1130 [==============================] - 1s 548us/steposs: 0.0056 - auc_4: 0.50100  \n",
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      "1130/1130 [==============================] - 1s 423us/steposs: 0.0057 - auc_6: 0.5100    \n",
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      "1130/1130 [==============================] - 1s 628us/steposs: 0.0056 - auc_8: 0.50 0.500\n",
      "1130/1130 [==============================] - 1s 643us/steposs: 0.0056 - auc_8: 0.49858 \n",
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      "1130/1130 [==============================] - 1s 808us/steposs: 0.0057 - auc_10: 0.4926\n",
      "10169/10169 [==============================] - 10s 991us/step - loss: 0.0057 - auc_10: 0.4949\n",
      "10169/10169 [==============================] - 10s 989us/step - loss: 0.0057 - auc_10: 0.4944\n",
      "1130/1130 [==============================] - 1s 661us/steposs: 0.0057 - auc_10: 0.49\n",
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      "1130/1130 [==============================] - 1s 661us/step\n",
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      "1130/1130 [==============================] - 1s 636us/steposs: 0.0057 - auc_10: 0.49 0.50\n",
      "10169/10169 [==============================] - 11s 1ms/step - loss: 0.0057 - auc_10: 0.4961\n",
      "1130/1130 [==============================] - 1s 825us/steposs: 0.0057 - auc_11: 0.49\n",
      "10169/10169 [==============================] - 10s 975us/step - loss: 0.0057 - auc_11: 0.4962\n",
      "10169/10169 [==============================] - 10s 975us/step - loss: 0.0057 - auc_11: 0.4923\n",
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      "2543/2543 [==============================] - 3s 890us/step - loss: 0.0057 - auc_119: 0.4980\n",
      "2543/2543 [==============================] - 3s 907us/step - loss: 0.0057 - auc_121: 0.4982\n",
      "283/283 [==============================] - 0s 278us/step\n",
      "283/283 [==============================] - 0s 333us/step\n",
      "283/283 [==============================] - 0s 392us/step\n",
      "283/283 [==============================] - 0s 401us/step\n",
      "283/283 [==============================] - 0s 477us/step\n",
      "283/283 [==============================] - 0s 378us/step\n",
      "283/283 [==============================] - 0s 302us/step\n",
      "283/283 [==============================] - 0s 265us/step\n",
      "2543/2543 [==============================] - 3s 889us/step - loss: 0.0057 - auc_120: 0.4977\n",
      "2543/2543 [==============================] - 3s 919us/step - loss: 0.0057 - auc_122: 0.4963\n",
      "2543/2543 [==============================] - 3s 906us/step - loss: 0.0057 - auc_121: 0.4963\n",
      "2543/2543 [==============================] - 3s 902us/step - loss: 0.0057 - auc_124: 0.4988\n",
      "2543/2543 [==============================] - 3s 909us/step - loss: 0.0057 - auc_125: 0.4983\n",
      "2543/2543 [==============================] - 3s 910us/step - loss: 0.0057 - auc_120: 0.4979\n",
      "2543/2543 [==============================] - 3s 930us/step - loss: 0.0057 - auc_124: 0.4959\n",
      "2543/2543 [==============================] - 3s 879us/step - loss: 0.0057 - auc_122: 0.4973\n",
      "283/283 [==============================] - 0s 284us/step\n",
      "283/283 [==============================] - 0s 533us/step\n",
      "283/283 [==============================] - 0s 548us/step\n",
      "283/283 [==============================] - 0s 610us/step\n",
      "283/283 [==============================] - 0s 699us/step\n",
      "283/283 [==============================] - 0s 995us/step\n",
      "283/283 [==============================] - 0s 1ms/step\n",
      "283/283 [==============================] - 0s 825us/step\n",
      "2543/2543 [==============================] - 3s 988us/step - loss: 0.0057 - auc_125: 0.4983\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_121: 0.4967\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_122: 0.4978\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_121: 0.4969\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_126: 0.4956\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_123: 0.4976\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_125: 0.4983\n",
      "283/283 [==============================] - 0s 223us/steposs: 0.0057 - auc_123: 0.49\n",
      "2543/2543 [==============================] - 3s 1ms/step - loss: 0.0057 - auc_123: 0.4989\n",
      "283/283 [==============================] - 0s 571us/step\n",
      "283/283 [==============================] - 0s 574us/step\n",
      "283/283 [==============================] - 0s 411us/step\n",
      "283/283 [==============================] - 0s 384us/step\n",
      "283/283 [==============================] - 0s 287us/step\n",
      "283/283 [==============================] - 0s 596us/step\n",
      "283/283 [==============================] - 0s 645us/step\n",
      "2543/2543 [==============================] - 2s 716us/step - loss: 0.0057 - auc_123: 0.4979\n",
      "2543/2543 [==============================] - 2s 731us/step - loss: 0.0057 - auc_122: 0.4962\n",
      "2543/2543 [==============================] - 2s 732us/step - loss: 0.0057 - auc_126: 0.4971\n",
      "2543/2543 [==============================] - 2s 757us/step - loss: 0.0057 - auc_122: 0.4984\n",
      "2543/2543 [==============================] - 2s 732us/step - loss: 0.0057 - auc_124: 0.4969\n",
      "2543/2543 [==============================] - 2s 737us/step - loss: 0.0057 - auc_127: 0.4975\n",
      "283/283 [==============================] - 0s 229us/step\n",
      "283/283 [==============================] - 0s 426us/step\n",
      "283/283 [==============================] - 0s 419us/step\n",
      "283/283 [==============================] - 0s 396us/step\n",
      "283/283 [==============================] - 0s 437us/step\n",
      "283/283 [==============================] - 0s 431us/step\n",
      "3767/3767 [==============================] - 1s 331us/step - loss: 0.0057 - auc_3: 0.4961\n",
      "Epoch 1/100\n",
      "3767/3767 [==============================] - 1s 324us/step - loss: 0.0036 - auc_4: 0.8723\n",
      "Epoch 2/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0032 - auc_4: 0.8916\n",
      "Epoch 3/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0032 - auc_4: 0.8929\n",
      "Epoch 4/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0031 - auc_4: 0.8953\n",
      "Epoch 5/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0031 - auc_4: 0.8973\n",
      "Epoch 6/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0031 - auc_4: 0.9007\n",
      "Epoch 7/100\n",
      "3767/3767 [==============================] - 1s 362us/step - loss: 0.0030 - auc_4: 0.9036\n",
      "Epoch 8/100\n",
      "3767/3767 [==============================] - 1s 318us/step - loss: 0.0030 - auc_4: 0.9061\n",
      "Epoch 9/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0030 - auc_4: 0.9075\n",
      "Epoch 10/100\n",
      "3767/3767 [==============================] - 1s 293us/step - loss: 0.0030 - auc_4: 0.9087\n",
      "Epoch 11/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9093\n",
      "Epoch 12/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_4: 0.9095\n",
      "Epoch 13/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_4: 0.9095\n",
      "Epoch 14/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9095\n",
      "Epoch 15/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9099\n",
      "Epoch 16/100\n",
      "3767/3767 [==============================] - 1s 318us/step - loss: 0.0029 - auc_4: 0.9099\n",
      "Epoch 17/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_4: 0.9102\n",
      "Epoch 18/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_4: 0.9107\n",
      "Epoch 19/100\n",
      "3767/3767 [==============================] - 1s 351us/step - loss: 0.0029 - auc_4: 0.9107\n",
      "Epoch 20/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0029 - auc_4: 0.9104\n",
      "Epoch 21/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_4: 0.9107\n",
      "Epoch 22/100\n",
      "3767/3767 [==============================] - 1s 308us/step - loss: 0.0029 - auc_4: 0.9111\n",
      "Epoch 23/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9109\n",
      "Epoch 24/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_4: 0.9109\n",
      "Epoch 25/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9111\n",
      "Epoch 26/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_4: 0.9110\n",
      "Epoch 27/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_4: 0.9111\n",
      "Epoch 28/100\n",
      "3767/3767 [==============================] - 1s 321us/step - loss: 0.0029 - auc_4: 0.9114\n",
      "Epoch 29/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_4: 0.9115\n",
      "Epoch 30/100\n",
      "3767/3767 [==============================] - 1s 311us/step - loss: 0.0029 - auc_4: 0.9112\n",
      "Epoch 31/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9114\n",
      "Epoch 32/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9115\n",
      "Epoch 33/100\n",
      "3767/3767 [==============================] - 1s 315us/step - loss: 0.0029 - auc_4: 0.9117\n",
      "Epoch 34/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9114\n",
      "Epoch 35/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9117\n",
      "Epoch 36/100\n",
      "3767/3767 [==============================] - 1s 303us/step - loss: 0.0029 - auc_4: 0.9120\n",
      "Epoch 37/100\n",
      "3767/3767 [==============================] - 1s 293us/step - loss: 0.0029 - auc_4: 0.9116\n",
      "Epoch 38/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_4: 0.9120\n",
      "Epoch 39/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9120\n",
      "Epoch 40/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9119\n",
      "Epoch 41/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9119\n",
      "Epoch 42/100\n",
      "3767/3767 [==============================] - 1s 317us/step - loss: 0.0029 - auc_4: 0.9117\n",
      "Epoch 43/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9120\n",
      "Epoch 44/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9126\n",
      "Epoch 45/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_4: 0.9120\n",
      "Epoch 46/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_4: 0.9115\n",
      "Epoch 47/100\n",
      "3767/3767 [==============================] - 1s 328us/step - loss: 0.0029 - auc_4: 0.9122\n",
      "Epoch 48/100\n",
      "3767/3767 [==============================] - 1s 304us/step - loss: 0.0029 - auc_4: 0.9124\n",
      "Epoch 49/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_4: 0.9128\n",
      "Epoch 50/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9125\n",
      "Epoch 51/100\n",
      "3767/3767 [==============================] - 1s 313us/step - loss: 0.0029 - auc_4: 0.9126\n",
      "Epoch 52/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9123\n",
      "Epoch 53/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_4: 0.9127\n",
      "Epoch 54/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_4: 0.9128\n",
      "Epoch 55/100\n",
      "3767/3767 [==============================] - 1s 315us/step - loss: 0.0029 - auc_4: 0.9126\n",
      "Epoch 56/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_4: 0.9126\n",
      "Epoch 57/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_4: 0.9126\n",
      "Epoch 58/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9127\n",
      "Epoch 59/100\n",
      "3767/3767 [==============================] - 1s 309us/step - loss: 0.0029 - auc_4: 0.9127\n",
      "Epoch 60/100\n",
      "3767/3767 [==============================] - 1s 378us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 61/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_4: 0.9130\n",
      "Epoch 62/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 63/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 64/100\n",
      "3767/3767 [==============================] - 1s 354us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 65/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0029 - auc_4: 0.9130\n",
      "Epoch 66/100\n",
      "3767/3767 [==============================] - 1s 333us/step - loss: 0.0029 - auc_4: 0.9133\n",
      "Epoch 67/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 68/100\n",
      "3767/3767 [==============================] - 1s 321us/step - loss: 0.0029 - auc_4: 0.9135\n",
      "Epoch 69/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_4: 0.9129\n",
      "Epoch 70/100\n",
      "3767/3767 [==============================] - 1s 312us/step - loss: 0.0029 - auc_4: 0.9133\n",
      "Epoch 71/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 72/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9127\n",
      "Epoch 73/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_4: 0.9134\n",
      "Epoch 74/100\n",
      "3767/3767 [==============================] - 1s 307us/step - loss: 0.0029 - auc_4: 0.9130\n",
      "Epoch 75/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_4: 0.9128\n",
      "Epoch 76/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 77/100\n",
      "3767/3767 [==============================] - 1s 302us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 78/100\n",
      "3767/3767 [==============================] - 1s 307us/step - loss: 0.0029 - auc_4: 0.9130\n",
      "Epoch 79/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 80/100\n",
      "3767/3767 [==============================] - 1s 288us/step - loss: 0.0029 - auc_4: 0.9129\n",
      "Epoch 81/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 82/100\n",
      "3767/3767 [==============================] - 1s 313us/step - loss: 0.0029 - auc_4: 0.9128\n",
      "Epoch 83/100\n",
      "3767/3767 [==============================] - 1s 292us/step - loss: 0.0029 - auc_4: 0.9133\n",
      "Epoch 84/100\n",
      "3767/3767 [==============================] - 1s 287us/step - loss: 0.0029 - auc_4: 0.9130\n",
      "Epoch 85/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_4: 0.9133\n",
      "Epoch 86/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 87/100\n",
      "3767/3767 [==============================] - 1s 293us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 88/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 89/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 90/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_4: 0.9135\n",
      "Epoch 91/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_4: 0.9128\n",
      "Epoch 92/100\n",
      "3767/3767 [==============================] - 1s 287us/step - loss: 0.0029 - auc_4: 0.9133\n",
      "Epoch 93/100\n",
      "3767/3767 [==============================] - 1s 302us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 94/100\n",
      "3767/3767 [==============================] - 1s 309us/step - loss: 0.0029 - auc_4: 0.9130\n",
      "Epoch 95/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_4: 0.9132\n",
      "Epoch 96/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0028 - auc_4: 0.9133\n",
      "Epoch 97/100\n",
      "3767/3767 [==============================] - 1s 303us/step - loss: 0.0029 - auc_4: 0.9131\n",
      "Epoch 98/100\n",
      "3767/3767 [==============================] - 1s 306us/step - loss: 0.0028 - auc_4: 0.9133\n",
      "Epoch 99/100\n",
      "3767/3767 [==============================] - 1s 293us/step - loss: 0.0028 - auc_4: 0.9136\n",
      "Epoch 100/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0028 - auc_4: 0.9133\n",
      "194/194 [==============================] - 0s 198us/step\n",
      "AUROC: 0.74768347\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import Sequential\n",
    "from tensorflow.keras.metrics import AUC\n",
    "np.random.seed(10)\n",
    "\n",
    "def create_model(optimizer=\"adam\", dropout=0.1, init='uniform', nbr_features=2500, dense_nparams=256):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(dense_nparams, activation='relu', input_shape=(len(feature_names),), kernel_initializer=init))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=[AUC()])\n",
    "    return model\n",
    "\n",
    "kears_estimator = KerasClassifier(model=create_model, verbose=1, init='zeros',dropout=0.1, dense_nparams=8, random_state = 10)\n",
    "\n",
    "estimator = Pipeline([\n",
    "    ('ss', StandardScaler()),  # Use StandardScaler for numerical features\n",
    "    (\"kc\", kears_estimator)\n",
    "])\n",
    "\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    'kc__dense_nparams': [2,4, 6, 8, 10, 12, 14, 16],\n",
    "    'kc__batch_size': [5, 10, 15, 20],\n",
    "    'kc__optimizer': [ 'Adamax'],\n",
    "    'kc__dropout': [0.075, 0.03, 0.05, 0.04, 0.025, 0]\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=10,  n_repeats=10,random_state=1) ## 10-fold cross validations\n",
    "#grid = RandomizedSearchCV(estimator=estimator,\n",
    "#                    n_jobs=-1,\n",
    "#                    verbose=1,\n",
    "#                    return_train_score=True,\n",
    "#                    cv=kfold_splits,\n",
    "#                    space=param_grid,\n",
    "#                    error_score='raise')\n",
    "\n",
    "def get_best_hyperparameters_tensorflow(estimator, param_space, cv, X_train, y_train, sample_weights):\n",
    "    grid = RandomizedSearchCV(estimator, param_space, scoring=\"roc_auc\", n_iter=10, n_jobs=-1, cv=cv, random_state=1);\n",
    "    grid_result = grid.fit(X_train, y_train, kc__sample_weight=sample_weights);\n",
    "    params = grid_result.best_params_\n",
    "    return params \n",
    "best_hyperparameters1 = get_best_hyperparameters_tensorflow(estimator = estimator, param_space = param_grid, cv = cv, X_train = X_train, y_train = y_train, sample_weights = weights_train)\n",
    "model = create_model(optimizer=best_hyperparameters1['kc__optimizer'], init='uniform', dropout=best_hyperparameters1['kc__dropout'], dense_nparams=best_hyperparameters1['kc__dense_nparams'])\n",
    "\n",
    "#return params \n",
    "# Train the model on your data\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=best_hyperparameters1['kc__batch_size'], sample_weight=weights_train);\n",
    "\n",
    "# Make predictions on test data\n",
    "y_predict_proba = model.predict(X_test)\n",
    "\n",
    "# Calculate AUROC on test data\n",
    "auc_metric = keras.metrics.AUC(num_thresholds=100)\n",
    "auc_metric.update_state(y_test, y_predict_proba);\n",
    "auroc = auc_metric.result().numpy()\n",
    "\n",
    "print(\"AUROC:\", auroc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_years' 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[21], line 28\u001b[0m\n\u001b[1;32m     20\u001b[0m param_grid \u001b[39m=\u001b[39m {\n\u001b[1;32m     21\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mkc__dense_nparams\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m2\u001b[39m,\u001b[39m4\u001b[39m, \u001b[39m6\u001b[39m, \u001b[39m8\u001b[39m, \u001b[39m10\u001b[39m, \u001b[39m12\u001b[39m, \u001b[39m14\u001b[39m, \u001b[39m16\u001b[39m],\n\u001b[1;32m     22\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mkc__batch_size\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m5\u001b[39m, \u001b[39m10\u001b[39m, \u001b[39m15\u001b[39m, \u001b[39m20\u001b[39m],\n\u001b[1;32m     23\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mkc__optimizer\u001b[39m\u001b[39m'\u001b[39m: [ \u001b[39m'\u001b[39m\u001b[39mAdamax\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m     24\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mkc__dropout\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m0.075\u001b[39m, \u001b[39m0.03\u001b[39m, \u001b[39m0.05\u001b[39m, \u001b[39m0.04\u001b[39m, \u001b[39m0.025\u001b[39m, \u001b[39m0\u001b[39m]\n\u001b[1;32m     25\u001b[0m }\n\u001b[1;32m     27\u001b[0m i \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m---> 28\u001b[0m \u001b[39mfor\u001b[39;00m year \u001b[39min\u001b[39;00m test_years: \n\u001b[1;32m     29\u001b[0m     \u001b[39mprint\u001b[39m(year)\n\u001b[1;32m     30\u001b[0m     model_type \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'test_years' is not defined"
     ]
    }
   ],
   "source": [
    "# Just LR\n",
    "np.random.seed(10)\n",
    "\n",
    "def create_model(optimizer=\"adam\", dropout=0.1, init='uniform', nbr_features=2500, dense_nparams=256):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(dense_nparams, activation='relu', input_shape=(len(feature_names),), kernel_initializer=init))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=[AUC()])\n",
    "    return model\n",
    "\n",
    "kears_estimator = KerasClassifier(model=create_model, verbose=1, init='zeros',dropout=0.1, dense_nparams=8, random_state = 10)\n",
    "\n",
    "estimator = Pipeline([\n",
    "    ('ss', StandardScaler()),  # Use StandardScaler for numerical features\n",
    "    (\"kc\", kears_estimator)\n",
    "])\n",
    "\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    'kc__dense_nparams': [2,4, 6, 8, 10, 12, 14, 16],\n",
    "    'kc__batch_size': [5, 10, 15, 20],\n",
    "    'kc__optimizer': [ 'Adamax'],\n",
    "    'kc__dropout': [0.075, 0.03, 0.05, 0.04, 0.025, 0]\n",
    "}\n",
    "\n",
    "i = 0\n",
    "for year in test_years: \n",
    "    print(year)\n",
    "    model_type = 0\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",
    "    counts = CIP_data_training_years.groupby(['YEAR', 'CLINIC']).size().reset_index(name='Count')\n",
    "    counts_by_year = CIP_data_training_years.groupby(['YEAR']).size().reset_index(name='Count')\n",
    "\n",
    "    merged_counts = counts.merge(counts_by_year, on='YEAR', suffixes=('_counts', '_counts_by_year'))\n",
    "\n",
    "    # Calculate the sum of all counts in the counts dataframe\n",
    "    total_counts = counts['Count'].sum()\n",
    "\n",
    "    # Calculate the 'weight' column as Count_counts / total_counts\n",
    "    merged_counts['weight'] = merged_counts['Count_counts'] / total_counts\n",
    "    # Calculate the 'weight' column as Count_counts / rolling sum of the previous 5 years\n",
    "\n",
    "    # Merge the 'weight' column back to the original DataFrame\n",
    "    CIP_data_no_drop = CIP_data_no_drop.merge(merged_counts[['YEAR', 'CLINIC', 'weight']], on=['YEAR', 'CLINIC'], how='left')\n",
    "\n",
    " \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, years_train = years_train, model_type = model_type)\n",
    "    best_hyperparameters1 = get_best_hyperparameters_tensorflow(estimator = estimator, param_space = param_grid, cv = cv, X_train = X_train, y_train = y_train, sample_weights = weights_train)\n",
    "    model_nn = create_model(optimizer=best_hyperparameters1['kc__optimizer'], init='uniform', dropout=best_hyperparameters1['kc__dropout'], dense_nparams=best_hyperparameters1['kc__dense_nparams'])\n",
    "\n",
    "    print(\"Hyperparameters1\")\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn.fit(X_train, y_train, epochs=100, batch_size=best_hyperparameters1['kc__batch_size'], sample_weight=weights_train);\n",
    "    #model_fit = model_nn.copy()\n",
    "    ## now also need to do feature engineering\n",
    "    important_features = get_best_features(feature_names, model_nn, X_train, y_train)\n",
    "    best_features_by_year[model_type].__setitem__(year, important_features) \n",
    "    imporances_all_models[i + indices_for_importance[model_type]] = get_feature_effects(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "    imporances_all_models_sd[i + indices_for_importance[model_type]] = get_feature_effects(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "\n",
    "    # get new test/train data and hyperparameter tuning round 2 \n",
    "\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = important_features, years_train = years_train, model_type = model_type)\n",
    "    \n",
    "    best_hyperparameters2 = get_best_hyperparameters_tensorflow(estimator = estimator, param_space = param_grid, cv = cv, X_train = X_train, y_train = y_train, sample_weights = weights_train)\n",
    "    best_hyperparameters_by_year[model_type].__setitem__(year, best_hyperparameters2) \n",
    "\n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn.fit(X_train, y_train, epochs=100, batch_size=best_hyperparameters1['kc__batch_size'], sample_weight=weights_train);\n",
    "\n",
    "    # Make predictions on test data\n",
    "    y_predict_proba = model_nn.predict(X_test)\n",
    "\n",
    "    # Calculate AUROC on test data\n",
    "    auc_metric = keras.metrics.AUC(num_thresholds=100)\n",
    "    auc_metric.update_state(y_test, y_predict_proba);\n",
    "    auroc = auc_metric.result().numpy()\n",
    " \n",
    "    ROC_by_year[model_type].__setitem__(year, auroc)\n",
    "    CIP_data_no_drop = CIP_data_no_drop.drop('weight', axis = 1)\n",
    "    print(ROC)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2502/5650 [============>.................] - ETA: 0s - loss: 0.0037 - auc_6: 0.8659"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m model \u001b[39m=\u001b[39m create_model(optimizer\u001b[39m=\u001b[39moptimizer, init\u001b[39m=\u001b[39minit, dropout\u001b[39m=\u001b[39mdropout, dense_nparams\u001b[39m=\u001b[39mdense_nparams)\n\u001b[1;32m      8\u001b[0m \u001b[39m# Train the model on your data\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train, y_train, epochs\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m, batch_size\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m, sample_weight\u001b[39m=\u001b[39;49mweights_train)\n\u001b[1;32m     11\u001b[0m \u001b[39m# Make predictions on test data\u001b[39;00m\n\u001b[1;32m     12\u001b[0m y_predict_proba \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mpredict(X_test)\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/keras/src/engine/training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1734\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[1;32m   1735\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   1736\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1739\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m   1740\u001b[0m ):\n\u001b[1;32m   1741\u001b[0m     callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1742\u001b[0m     tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[1;32m   1743\u001b[0m     \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m   1744\u001b[0m         context\u001b[39m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    829\u001b[0m   execution_mode \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mnotTraced\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m without_tracing \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mtraced\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    830\u001b[0m   tm\u001b[39m.\u001b[39mset_metadata(tf_function_call\u001b[39m=\u001b[39mexecution_mode \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m-\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m compiler,\n\u001b[1;32m    831\u001b[0m                   tracing_count\u001b[39m=\u001b[39mnew_tracing_count)\n\u001b[0;32m--> 833\u001b[0m \u001b[39mif\u001b[39;00m context\u001b[39m.\u001b[39;49mexecuting_eagerly():\n\u001b[1;32m    834\u001b[0m   \u001b[39mif\u001b[39;00m without_tracing:\n\u001b[1;32m    835\u001b[0m     _frequent_tracing_detector_manager\u001b[39m.\u001b[39mcalled_without_tracing(\n\u001b[1;32m    836\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_key_for_call_stats)\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/tensorflow/python/eager/context.py:2308\u001b[0m, in \u001b[0;36mexecuting_eagerly\u001b[0;34m()\u001b[0m\n\u001b[1;32m   2305\u001b[0m \u001b[39mif\u001b[39;00m ctx \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   2306\u001b[0m   \u001b[39mreturn\u001b[39;00m default_execution_mode \u001b[39m==\u001b[39m EAGER_MODE\n\u001b[0;32m-> 2308\u001b[0m \u001b[39mreturn\u001b[39;00m ctx\u001b[39m.\u001b[39;49mexecuting_eagerly()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "optimizer = 'Adamax'\n",
    "init = 'uniform'\n",
    "dropout = 0.05\n",
    "dense_nparams = 10\n",
    "\n",
    "model = create_model(optimizer=optimizer, init=init, dropout=dropout, dense_nparams=dense_nparams)\n",
    "\n",
    "# Train the model on your data\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=10, sample_weight=weights_train)\n",
    "\n",
    "# Make predictions on test data\n",
    "y_predict_proba = model.predict(X_test)\n",
    "\n",
    "# Calculate AUROC on test data\n",
    "auc_metric = keras.metrics.AUC(num_thresholds=100)\n",
    "auc_metric.update_state(y_test, y_predict_proba)\n",
    "auroc = auc_metric.result().numpy()\n",
    "\n",
    "print(\"AUROC:\", auroc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "################################ Get hyperparameters and best features for each model  ###########################\n",
    "#### Loop set up \n",
    "threshold_seq = np.linspace(0,1,101)\n",
    "test_years = [2005, 2006, 2007, 2008, 2009, 2010]  #np.array(range(2005, 2011))\n",
    "oversample = RandomOverSampler(sampling_strategy = 'minority',random_state=42) #need for neural network and random forest\n",
    "model_types = [\"Logistic_regression\",  \"Random_forest\", \"Neural_network\"]\n",
    "i = 0\n",
    "\n",
    "# logistic regression - random initial parameters\n",
    "model_lr = LogisticRegression(class_weight = 'balanced', max_iter=4000, solver = \"lbfgs\", C = 0.27, penalty = 'l2')\n",
    "# random forest - random initial parameters\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",
    "# neural network - random parameters\n",
    "model_nn = MLPClassifier(solver = 'lbfgs', activation = 'tanh', max_iter = 3000 ,hidden_layer_sizes= 12, alpha =1.291549665014884, random_state=10, learning_rate = 'adaptive' )\n",
    "unfitted_models = [model_lr, model_rf, model_nn]\n",
    "\n",
    "### Hyperparameter tuning\n",
    "cv = RepeatedStratifiedKFold(n_splits=10,  n_repeats=10,random_state=1) ## 10-fold cross validations\n",
    "# logistic regression \n",
    "best_hyperparameters_by_year = {}\n",
    "\n",
    "space_lr = dict()\n",
    "space_lr['solver'] = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "space_lr['penalty'] = ['l1', 'l2']\n",
    "space_lr['C'] = np.arange(0, 100, .01)\n",
    "best_hyperparameters_by_year_lr = {}\n",
    "# random forest \n",
    "space_rf = dict()\n",
    "space_rf['n_estimators'] = np.arange(100, 201, 1)\n",
    "space_rf['max_depth'] = np.arange(1, 200, 1)\n",
    "space_rf['min_samples_split'] = np.arange(1, 25, 1)\n",
    "space_rf['min_samples_leaf'] = np.arange(1, 25, 1)\n",
    "best_hyperparameters_by_year_rf = {}\n",
    "\n",
    "# neural network \n",
    "space_nn = dict()\n",
    "space_nn['solver'] = ['lbfgs', 'sgd', 'adam']\n",
    "space_nn['activation'] = ['tanh', 'relu']\n",
    "space_nn['alpha'] = np.logspace(-1, 1, 10)\n",
    "space_nn['learning_rate'] = ['constant','adaptive']\n",
    "space_nn['hidden_layer_sizes'] = [(4,), (6,), (8,), (10,), (12,), (13,), (14,)]\n",
    "best_hyperparameters_by_year_nn = {}\n",
    "\n",
    "space = [space_lr, space_rf, space_nn]\n",
    "best_hyperparameters_by_year = [best_hyperparameters_by_year_lr, best_hyperparameters_by_year_rf,best_hyperparameters_by_year_nn]\n",
    "\n",
    "### Feature Engineering\n",
    "feature_names = ['MSM','MSMW', 'MSW', 'Oth/Unk/Missing','Northeast', 'Southeast', 'Southwest', 'West', 'Midwest','PREV_REGION', 'PREV_CLINIC', 'DELTA_REGION',  'Count_Exceeds_75', 'Trend_N_greater_75']\n",
    "best_features_by_year_lr = {}\n",
    "best_features_by_year_rf = {}\n",
    "best_features_by_year_nn = {}\n",
    "best_features_by_year = [best_features_by_year_lr, best_features_by_year_rf, best_features_by_year_nn]\n",
    "\n",
    "imporances_all_models = pd.DataFrame(0, index = feature_names, columns=np.arange(len(test_years)*3))\n",
    "indices_for_importance = [6,12,0] ## need to be in correct order \n",
    "imporances_all_models_sd = pd.DataFrame(0, index = feature_names, columns=np.arange(len(test_years)*3))\n",
    "\n",
    "\n",
    "oversample = RandomOverSampler(sampling_strategy='minority', random_state=10)\n",
    "\n",
    "def get_test_train_data(CIP_data_no_drop, year, feature_names, years_train, model_type):\n",
    "\n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin(years_train)]\n",
    "    X_train = train_data[\n",
    "        feature_names\n",
    "    ]  # need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data[\"Susceptible\"]\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_prev = y_test.sum() / len(y_test)\n",
    "    if (model_type == 1) | (model_type == 2):\n",
    "        X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = oversample.fit_resample(X_test, y_test)\n",
    "        print(\"Oversample\")\n",
    "    return (test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev)\n",
    "\n",
    "def get_feature_effects(all_features, important_features, model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=0\n",
    "    )\n",
    "    feature_importances = []\n",
    "    for feature in all_features:\n",
    "        if feature in important_features:\n",
    "            feature_importances.append(PI.importances_mean[important_features.index(feature)])\n",
    "        else:\n",
    "            feature_importances.append(0)\n",
    "    return feature_importances\n",
    "\n",
    "def get_best_features(feature_names, model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=0\n",
    "    )\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(\n",
    "                feature_names[q]\n",
    "            )  # works cos they are in same order as the x columns\n",
    "    return important_features\n",
    "\n",
    "def get_feature_effects_sd(feature_names, important_features,model_fit, X_train, y_train):\n",
    "    PI = permutation_importance(\n",
    "        model_fit, X_train, y_train, n_repeats=10, random_state=42\n",
    "    )\n",
    "\n",
    "    return PI.importances_std\n",
    "\n",
    "def get_best_hyperparameters(model, cv, space, X_train, y_train, sample_weights):\n",
    "    search = RandomizedSearchCV(\n",
    "        model, space, scoring=\"roc_auc\", n_iter=20, n_jobs=-1, cv=cv, random_state=1\n",
    "    )\n",
    "    result = search.fit(X_train, y_train, sample_weight = sample_weights)\n",
    "    return result.best_params_\n",
    "\n",
    "### ROC by year \n",
    "ROC_by_year_rf = {}\n",
    "ROC_by_year_lr = {}\n",
    "ROC_by_year_nn = {}\n",
    "\n",
    "ROC_by_year = [ROC_by_year_lr, ROC_by_year_rf, ROC_by_year_nn]\n",
    "\n",
    "\n",
    "def get_test_train_data(CIP_data_no_drop, year, feature_names, years_train, model_type):\n",
    "    feature_names_with_weight = feature_names.copy()\n",
    "    feature_names_with_weight.extend(['weight'])\n",
    "    train_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin(years_train)]\n",
    "    X_train = train_data[feature_names_with_weight]  # need to consider all columns BEFORE feature engineering\n",
    "    y_train = 1 - train_data[\"Susceptible\"]\n",
    "    # test\n",
    "    test_data = CIP_data_no_drop.loc[CIP_data_no_drop[\"YEAR\"].isin([year])]\n",
    "    X_test = test_data[feature_names_with_weight]\n",
    "    y_test = 1 - test_data[\"Susceptible\"]\n",
    "    cipro_R_prev = y_test.sum() / len(y_test)\n",
    "\n",
    "    if (model_type == 1) | (model_type == 2):\n",
    "        X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = oversample.fit_resample(X_test, y_test)\n",
    "        print(\"Oversample\")\n",
    "    weights_train = X_train[\"weight\"]\n",
    "    X_train  = X_train.drop(\"weight\", axis = 1)\n",
    "    X_test = X_test.drop(\"weight\", axis = 1)\n",
    "\n",
    "    return (test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_best_features_tensor_flow(feature_names, model_fit, X_train, y_train, n_repeats=10):\n",
    "    def calculate_score(X, y):\n",
    "        y_pred = model_fit.predict(X)\n",
    "        return roc_auc_score(y, y_pred)  # Replace 'roc_auc_score' with your desired metric\n",
    "\n",
    "    baseline_score = calculate_score(X_train, y_train)\n",
    "    important_features = []\n",
    "\n",
    "    for feature in feature_names:\n",
    "        X_permuted = X_train.copy()\n",
    "        X_permuted[feature] = np.random.permutation(X_permuted[feature])\n",
    "\n",
    "        permuted_score = np.mean([calculate_score(X_permuted, y_train) for _ in range(n_repeats)])\n",
    "        effect_sd = np.std(permuted_score)\n",
    "\n",
    "        importance = baseline_score - permuted_score\n",
    "\n",
    "        if importance - effect_sd> 0:  # Only consider features that improve the score\n",
    "            important_features.append(feature)\n",
    "\n",
    "    return important_features\n",
    "\n",
    "def get_feature_effects_sd_tensor_flow(feature_names, important_features, model_fit, X_train, y_train, n_repeats=10):\n",
    "    def calculate_score(X, y):\n",
    "        y_pred = model_fit.predict(X)\n",
    "        return roc_auc_score(y, y_pred)  # Replace 'roc_auc_score' with your desired metric\n",
    "\n",
    "    baseline_score = calculate_score(X_train, y_train)\n",
    "    feature_effects_sd = {}\n",
    "\n",
    "    for feature in important_features:\n",
    "        X_permuted = X_train.copy()\n",
    "        X_permuted[feature] = np.random.permutation(X_permuted[feature])\n",
    "\n",
    "        permuted_scores = [calculate_score(X_permuted, y_train) for _ in range(n_repeats)]\n",
    "        effect_sd = np.std(permuted_scores)\n",
    "\n",
    "        feature_effects_sd[feature] = effect_sd\n",
    "\n",
    "    return feature_effects_sd\n",
    "\n",
    "def get_feature_effects_tensor_flow(all_features, important_features, model_fit, X_train, y_train, n_repeats=10):\n",
    "    def calculate_score(X, y):\n",
    "        y_pred = model_fit.predict(X)\n",
    "        return roc_auc_score(y, y_pred)  # Replace 'roc_auc_score' with your desired metric\n",
    "\n",
    "    baseline_score = calculate_score(X_train, y_train)\n",
    "    feature_importances = []\n",
    "\n",
    "    for feature in all_features:\n",
    "        if feature in important_features:\n",
    "            X_permuted = X_train.copy()\n",
    "            X_permuted[feature] = np.random.permutation(X_permuted[feature])\n",
    "\n",
    "            permuted_scores = [calculate_score(X_permuted, y_train) for _ in range(n_repeats)]\n",
    "            effect = baseline_score - np.mean(permuted_scores)\n",
    "        else:\n",
    "            effect = 0\n",
    "\n",
    "        feature_importances.append(effect)\n",
    "\n",
    "    return feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005\n",
      "Oversample\n",
      "10169/10169 [==============================] - 10s 944us/step - loss: 0.0057 - auc: 0.4951\n",
      "10169/10169 [==============================] - 10s 953us/step - loss: 0.0057 - auc: 0.4964\n",
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      "1130/1130 [==============================] - 1s 970us/steposs: 0.0057 - auc_8: 0.\n",
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      "10169/10169 [==============================] - 9s 863us/step - loss: 0.0057 - auc_8: 0.4984\n",
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      "1130/1130 [==============================] - 1s 499us/steposs: 0.0057 - auc_9: 0.4969\n",
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      "1130/1130 [==============================] - 1s 617us/steposs: 0.0057 - auc_39: 0.49230 \n",
      "10169/10169 [==============================] - 8s 778us/step - loss: 0.0057 - auc_39: 0.4933\n",
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      "1130/1130 [==============================] - 1s 507us/steposs: 0.0057 - auc_41: 0.49.5000\n",
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      "10169/10169 [==============================] - 8s 771us/step - loss: 0.0057 - auc_42: 0.4940====================>.] - ETA: 0s - loss: 0.0057 - auc_39: 0.49\n",
      "10169/10169 [==============================] - 8s 789us/step - loss: 0.0057 - auc_39: 0.4988\n",
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      "283/283 [==============================] - 0s 456us/step\n",
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      "3767/3767 [==============================] - 2s 407us/step - loss: 0.0057 - auc_7: 0.4961\n",
      "Hyperparameters1\n",
      "Epoch 1/100\n",
      "3767/3767 [==============================] - 1s 325us/step - loss: 0.0037 - auc_8: 0.8634\n",
      "Epoch 2/100\n",
      "3767/3767 [==============================] - 1s 340us/step - loss: 0.0033 - auc_8: 0.8893\n",
      "Epoch 3/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0033 - auc_8: 0.8930\n",
      "Epoch 4/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0032 - auc_8: 0.8966\n",
      "Epoch 5/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0031 - auc_8: 0.8987\n",
      "Epoch 6/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0031 - auc_8: 0.9011\n",
      "Epoch 7/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0031 - auc_8: 0.9026\n",
      "Epoch 8/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0030 - auc_8: 0.9032\n",
      "Epoch 9/100\n",
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      "Epoch 10/100\n",
      "3767/3767 [==============================] - 1s 318us/step - loss: 0.0030 - auc_8: 0.9051\n",
      "Epoch 11/100\n",
      "3767/3767 [==============================] - 1s 288us/step - loss: 0.0030 - auc_8: 0.9053\n",
      "Epoch 12/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0030 - auc_8: 0.9055\n",
      "Epoch 13/100\n",
      "3767/3767 [==============================] - 1s 304us/step - loss: 0.0030 - auc_8: 0.9068\n",
      "Epoch 14/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0030 - auc_8: 0.9071\n",
      "Epoch 15/100\n",
      "3767/3767 [==============================] - 1s 288us/step - loss: 0.0030 - auc_8: 0.9078\n",
      "Epoch 16/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0030 - auc_8: 0.9077\n",
      "Epoch 17/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9087\n",
      "Epoch 18/100\n",
      "3767/3767 [==============================] - 1s 314us/step - loss: 0.0029 - auc_8: 0.9081\n",
      "Epoch 19/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9085\n",
      "Epoch 20/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_8: 0.9091\n",
      "Epoch 21/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9094\n",
      "Epoch 22/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9097\n",
      "Epoch 23/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9092\n",
      "Epoch 24/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9094\n",
      "Epoch 25/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_8: 0.9098\n",
      "Epoch 26/100\n",
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      "Epoch 27/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9094\n",
      "Epoch 28/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_8: 0.9102\n",
      "Epoch 29/100\n",
      "3767/3767 [==============================] - 1s 287us/step - loss: 0.0029 - auc_8: 0.9100\n",
      "Epoch 30/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_8: 0.9102\n",
      "Epoch 31/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_8: 0.9102\n",
      "Epoch 32/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9104\n",
      "Epoch 33/100\n",
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      "Epoch 34/100\n",
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      "Epoch 35/100\n",
      "3767/3767 [==============================] - 1s 357us/step - loss: 0.0029 - auc_8: 0.9110\n",
      "Epoch 36/100\n",
      "3767/3767 [==============================] - 1s 308us/step - loss: 0.0029 - auc_8: 0.9110\n",
      "Epoch 37/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9110\n",
      "Epoch 38/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9108\n",
      "Epoch 39/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9111\n",
      "Epoch 40/100\n",
      "3767/3767 [==============================] - 1s 314us/step - loss: 0.0029 - auc_8: 0.9110\n",
      "Epoch 41/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_8: 0.9108\n",
      "Epoch 42/100\n",
      "3767/3767 [==============================] - 1s 301us/step - loss: 0.0029 - auc_8: 0.9111\n",
      "Epoch 43/100\n",
      "3767/3767 [==============================] - 1s 290us/step - loss: 0.0029 - auc_8: 0.9106\n",
      "Epoch 44/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9112\n",
      "Epoch 45/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_8: 0.9113\n",
      "Epoch 46/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9115\n",
      "Epoch 47/100\n",
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      "Epoch 48/100\n",
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      "Epoch 49/100\n",
      "3767/3767 [==============================] - 1s 308us/step - loss: 0.0029 - auc_8: 0.9111\n",
      "Epoch 50/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_8: 0.9114\n",
      "Epoch 51/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9112\n",
      "Epoch 52/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_8: 0.9113\n",
      "Epoch 53/100\n",
      "3767/3767 [==============================] - 2s 406us/step - loss: 0.0029 - auc_8: 0.9112\n",
      "Epoch 54/100\n",
      "3767/3767 [==============================] - 1s 324us/step - loss: 0.0029 - auc_8: 0.9117\n",
      "Epoch 55/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9112\n",
      "Epoch 56/100\n",
      "3767/3767 [==============================] - 1s 308us/step - loss: 0.0029 - auc_8: 0.9116\n",
      "Epoch 57/100\n",
      "3767/3767 [==============================] - 1s 317us/step - loss: 0.0029 - auc_8: 0.9114\n",
      "Epoch 58/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_8: 0.9112\n",
      "Epoch 59/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9110\n",
      "Epoch 60/100\n",
      "3767/3767 [==============================] - 1s 300us/step - loss: 0.0029 - auc_8: 0.9111\n",
      "Epoch 61/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9114\n",
      "Epoch 62/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9115\n",
      "Epoch 63/100\n",
      "3767/3767 [==============================] - 1s 316us/step - loss: 0.0029 - auc_8: 0.9118\n",
      "Epoch 64/100\n",
      "3767/3767 [==============================] - 1s 298us/step - loss: 0.0029 - auc_8: 0.9114\n",
      "Epoch 65/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9122\n",
      "Epoch 66/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9123\n",
      "Epoch 67/100\n",
      "3767/3767 [==============================] - 1s 312us/step - loss: 0.0029 - auc_8: 0.9118\n",
      "Epoch 68/100\n",
      "3767/3767 [==============================] - 1s 294us/step - loss: 0.0029 - auc_8: 0.9117\n",
      "Epoch 69/100\n",
      "3767/3767 [==============================] - 1s 309us/step - loss: 0.0029 - auc_8: 0.9120\n",
      "Epoch 70/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9119\n",
      "Epoch 71/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9121\n",
      "Epoch 72/100\n",
      "3767/3767 [==============================] - 1s 315us/step - loss: 0.0029 - auc_8: 0.9119\n",
      "Epoch 73/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9120\n",
      "Epoch 74/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9117\n",
      "Epoch 75/100\n",
      "3767/3767 [==============================] - 1s 287us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 76/100\n",
      "3767/3767 [==============================] - 1s 321us/step - loss: 0.0029 - auc_8: 0.9122\n",
      "Epoch 77/100\n",
      "3767/3767 [==============================] - 1s 289us/step - loss: 0.0029 - auc_8: 0.9121\n",
      "Epoch 78/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 79/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9122\n",
      "Epoch 80/100\n",
      "3767/3767 [==============================] - 1s 317us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 81/100\n",
      "3767/3767 [==============================] - 1s 296us/step - loss: 0.0029 - auc_8: 0.9123\n",
      "Epoch 82/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9119\n",
      "Epoch 83/100\n",
      "3767/3767 [==============================] - 1s 305us/step - loss: 0.0029 - auc_8: 0.9120\n",
      "Epoch 84/100\n",
      "3767/3767 [==============================] - 1s 303us/step - loss: 0.0029 - auc_8: 0.9121\n",
      "Epoch 85/100\n",
      "3767/3767 [==============================] - 1s 299us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 86/100\n",
      "3767/3767 [==============================] - 1s 293us/step - loss: 0.0029 - auc_8: 0.9122\n",
      "Epoch 87/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 88/100\n",
      "3767/3767 [==============================] - 1s 310us/step - loss: 0.0029 - auc_8: 0.9125\n",
      "Epoch 89/100\n",
      "3767/3767 [==============================] - 1s 309us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 90/100\n",
      "3767/3767 [==============================] - 1s 297us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 91/100\n",
      "3767/3767 [==============================] - 1s 291us/step - loss: 0.0029 - auc_8: 0.9124\n",
      "Epoch 92/100\n",
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      "Epoch 93/100\n",
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      "Epoch 94/100\n",
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      "Epoch 95/100\n",
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      "Epoch 96/100\n",
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      "Epoch 97/100\n",
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      "Epoch 98/100\n",
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      "Epoch 99/100\n",
      "3767/3767 [==============================] - 1s 295us/step - loss: 0.0029 - auc_8: 0.9127\n",
      "Epoch 100/100\n",
      "3767/3767 [==============================] - 1s 306us/step - loss: 0.0029 - auc_8: 0.9122\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model_fit' 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[28], line 76\u001b[0m\n\u001b[1;32m     73\u001b[0m model_nn\u001b[39m.\u001b[39mfit(X_train, y_train, epochs\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m, batch_size\u001b[39m=\u001b[39mbest_hyperparameters1[\u001b[39m'\u001b[39m\u001b[39mkc__batch_size\u001b[39m\u001b[39m'\u001b[39m], sample_weight\u001b[39m=\u001b[39mweights_train);\n\u001b[1;32m     74\u001b[0m \u001b[39m#model_fit = model_nn.copy()\u001b[39;00m\n\u001b[1;32m     75\u001b[0m \u001b[39m## now also need to do feature engineering\u001b[39;00m\n\u001b[0;32m---> 76\u001b[0m important_features \u001b[39m=\u001b[39m get_best_features_tensor_flow(feature_names, model_fit, X_train, y_train, n_repeats\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n\u001b[1;32m     77\u001b[0m best_features_by_year[model_type]\u001b[39m.\u001b[39m\u001b[39m__setitem__\u001b[39m(year, important_features) \n\u001b[1;32m     78\u001b[0m imporances_all_models[i \u001b[39m+\u001b[39m indices_for_importance[model_type]] \u001b[39m=\u001b[39m get_feature_effects_tensor_flow(feature_names, important_features, model_nn, X_train, y_train) \u001b[39m#want it to be the correct block for each model\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model_fit' is not defined"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import Sequential\n",
    "from tensorflow.keras.metrics import AUC\n",
    "np.random.seed(10)\n",
    "def get_best_hyperparameters_tensorflow(estimator, param_space, cv, X_train, y_train, sample_weights):\n",
    "    grid = RandomizedSearchCV(estimator, param_space, scoring=\"roc_auc\", n_iter=10, n_jobs=-1, cv=cv, random_state=1);\n",
    "    grid_result = grid.fit(X_train, y_train, kc__sample_weight=sample_weights);\n",
    "    params = grid_result.best_params_\n",
    "    return params \n",
    "\n",
    "np.random.seed(10)\n",
    "\n",
    "def create_model(optimizer=\"adam\", dropout=0.1, init='uniform', nbr_features=2500, dense_nparams=256):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(dense_nparams, activation='relu', input_shape=(len(feature_names),), kernel_initializer=init))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=[AUC()])\n",
    "    return model\n",
    "\n",
    "kears_estimator = KerasClassifier(model=create_model, verbose=1, init='zeros',dropout=0.1, dense_nparams=8, random_state = 10)\n",
    "\n",
    "estimator = Pipeline([\n",
    "    ('ss', StandardScaler()),  # Use StandardScaler for numerical features\n",
    "    (\"kc\", kears_estimator)\n",
    "])\n",
    "\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    'kc__dense_nparams': [2,4, 6, 8, 10, 12, 14, 16],\n",
    "    'kc__batch_size': [5, 10, 15, 20],\n",
    "    'kc__optimizer': [ 'Adamax'],\n",
    "    'kc__dropout': [0.075, 0.03, 0.05, 0.04, 0.025, 0]\n",
    "}\n",
    "\n",
    "i = 0\n",
    "for year in test_years: \n",
    "    print(year)\n",
    "    model_type = 2\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",
    "    counts = CIP_data_training_years.groupby(['YEAR', 'CLINIC']).size().reset_index(name='Count')\n",
    "    counts_by_year = CIP_data_training_years.groupby(['YEAR']).size().reset_index(name='Count')\n",
    "\n",
    "    merged_counts = counts.merge(counts_by_year, on='YEAR', suffixes=('_counts', '_counts_by_year'))\n",
    "\n",
    "    # Calculate the sum of all counts in the counts dataframe\n",
    "    total_counts = counts['Count'].sum()\n",
    "\n",
    "    # Calculate the 'weight' column as Count_counts / total_counts\n",
    "    merged_counts['weight'] = merged_counts['Count_counts'] / total_counts\n",
    "    # Calculate the 'weight' column as Count_counts / rolling sum of the previous 5 years\n",
    "\n",
    "    # Merge the 'weight' column back to the original DataFrame\n",
    "    CIP_data_no_drop = CIP_data_no_drop.merge(merged_counts[['YEAR', 'CLINIC', 'weight']], on=['YEAR', 'CLINIC'], how='left')\n",
    "\n",
    " \n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = feature_names, years_train = years_train, model_type = model_type)\n",
    "    best_hyperparameters1 = get_best_hyperparameters_tensorflow(estimator = estimator, param_space = param_grid, cv = cv, X_train = X_train, y_train = y_train, sample_weights = weights_train)\n",
    "    model_nn = create_model(optimizer=best_hyperparameters1['kc__optimizer'], init='uniform', dropout=best_hyperparameters1['kc__dropout'], dense_nparams=best_hyperparameters1['kc__dense_nparams'])\n",
    "\n",
    "    print(\"Hyperparameters1\")\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn.fit(X_train, y_train, epochs=100, batch_size=best_hyperparameters1['kc__batch_size'], sample_weight=weights_train);\n",
    "    #model_fit = model_nn.copy()\n",
    "    ## now also need to do feature engineering\n",
    "    important_features = get_best_features_tensor_flow(feature_names, model_nn, X_train, y_train, n_repeats=10)\n",
    "    best_features_by_year[model_type].__setitem__(year, important_features) \n",
    "    imporances_all_models[i + indices_for_importance[model_type]] = get_feature_effects_tensor_flow(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "    imporances_all_models_sd[i + indices_for_importance[model_type]] = get_feature_effects_sd_tensor_flow(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "\n",
    "    # get new test/train data and hyperparameter tuning round 2 \n",
    "\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = important_features, years_train = years_train, model_type = model_type)\n",
    "    \n",
    "    best_hyperparameters2 = get_best_hyperparameters_tensorflow(estimator = estimator, param_space = param_grid, cv = cv, X_train = X_train, y_train = y_train, sample_weights = weights_train)\n",
    "    best_hyperparameters_by_year[model_type].__setitem__(year, best_hyperparameters2) \n",
    "    model_nn = create_model(optimizer=best_hyperparameters2['kc__optimizer'], init='uniform', dropout=best_hyperparameters2['kc__dropout'], dense_nparams=best_hyperparameters2['kc__dense_nparams'])\n",
    "\n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn.fit(X_train, y_train, epochs=100, batch_size=best_hyperparameters2['kc__batch_size'], sample_weight=weights_train);\n",
    "\n",
    "    # Make predictions on test data\n",
    "    y_predict_proba = model_nn.predict(X_test)\n",
    "\n",
    "    # Calculate AUROC on test data\n",
    "    auc_metric = keras.metrics.AUC(num_thresholds=100)\n",
    "    auc_metric.update_state(y_test, y_predict_proba);\n",
    "    auroc = auc_metric.result().numpy()\n",
    " \n",
    "    ROC_by_year[model_type].__setitem__(year, auroc)\n",
    "    CIP_data_no_drop = CIP_data_no_drop.drop('weight', axis = 1)\n",
    "    print(auroc)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "important_features = get_best_features_tensor_flow(feature_names, model_nn, X_train, y_train, n_repeats=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"['weight'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 8\u001b[0m\n\u001b[1;32m      4\u001b[0m imporances_all_models_sd[i \u001b[39m+\u001b[39m indices_for_importance[model_type]] \u001b[39m=\u001b[39m get_feature_effects_sd_tensor_flow(feature_names, important_features, model_nn, X_train, y_train) \u001b[39m#want it to be the correct block for each model\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[39m# get new test/train data and hyperparameter tuning round 2 \u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train \u001b[39m=\u001b[39m  get_test_train_data(CIP_data_no_drop \u001b[39m=\u001b[39;49m CIP_data_no_drop, year \u001b[39m=\u001b[39;49m year, feature_names \u001b[39m=\u001b[39;49m important_features, years_train \u001b[39m=\u001b[39;49m years_train, model_type \u001b[39m=\u001b[39;49m model_type)\n\u001b[1;32m     10\u001b[0m best_hyperparameters2 \u001b[39m=\u001b[39m get_best_hyperparameters_tensorflow(estimator \u001b[39m=\u001b[39m estimator, param_space \u001b[39m=\u001b[39m param_grid, cv \u001b[39m=\u001b[39m cv, X_train \u001b[39m=\u001b[39m X_train, y_train \u001b[39m=\u001b[39m y_train, sample_weights \u001b[39m=\u001b[39m weights_train)\n\u001b[1;32m     11\u001b[0m best_hyperparameters_by_year[model_type]\u001b[39m.\u001b[39m\u001b[39m__setitem__\u001b[39m(year, best_hyperparameters2) \n",
      "Cell \u001b[0;32mIn[23], line 129\u001b[0m, in \u001b[0;36mget_test_train_data\u001b[0;34m(CIP_data_no_drop, year, feature_names, years_train, model_type)\u001b[0m\n\u001b[1;32m    127\u001b[0m feature_names_with_weight\u001b[39m.\u001b[39mextend([\u001b[39m'\u001b[39m\u001b[39mweight\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m    128\u001b[0m train_data \u001b[39m=\u001b[39m CIP_data_no_drop\u001b[39m.\u001b[39mloc[CIP_data_no_drop[\u001b[39m\"\u001b[39m\u001b[39mYEAR\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39misin(years_train)]\n\u001b[0;32m--> 129\u001b[0m X_train \u001b[39m=\u001b[39m train_data[feature_names_with_weight]  \u001b[39m# need to consider all columns BEFORE feature engineering\u001b[39;00m\n\u001b[1;32m    130\u001b[0m y_train \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m train_data[\u001b[39m\"\u001b[39m\u001b[39mSusceptible\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m    131\u001b[0m \u001b[39m# test\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/pandas/core/frame.py:3811\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3809\u001b[0m     \u001b[39mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   3810\u001b[0m         key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> 3811\u001b[0m     indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49m_get_indexer_strict(key, \u001b[39m\"\u001b[39;49m\u001b[39mcolumns\u001b[39;49m\u001b[39m\"\u001b[39;49m)[\u001b[39m1\u001b[39m]\n\u001b[1;32m   3813\u001b[0m \u001b[39m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   3814\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(indexer, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m \u001b[39mbool\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/pandas/core/indexes/base.py:6113\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6110\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   6111\u001b[0m     keyarr, indexer, new_indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6113\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[1;32m   6115\u001b[0m keyarr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtake(indexer)\n\u001b[1;32m   6116\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, Index):\n\u001b[1;32m   6117\u001b[0m     \u001b[39m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/GISP_init/lib/python3.10/site-packages/pandas/core/indexes/base.py:6176\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6173\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNone of [\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m] are in the [\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   6175\u001b[0m not_found \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[39m.\u001b[39mnonzero()[\u001b[39m0\u001b[39m]]\u001b[39m.\u001b[39munique())\n\u001b[0;32m-> 6176\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mnot_found\u001b[39m}\u001b[39;00m\u001b[39m not in index\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['weight'] not in index\""
     ]
    }
   ],
   "source": [
    "    #important_features = get_best_features_tensor_flow(feature_names, model_nn, X_train, y_train, n_repeats=10)\n",
    "    #best_features_by_year[model_type].__setitem__(year, important_features) \n",
    "    #imporances_all_models[i + indices_for_importance[model_type]] = get_feature_effects_tensor_flow(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "    #imporances_all_models_sd[i + indices_for_importance[model_type]] = get_feature_effects_sd_tensor_flow(feature_names, important_features, model_nn, X_train, y_train) #want it to be the correct block for each model\n",
    "\n",
    "    # get new test/train data and hyperparameter tuning round 2 \n",
    "\n",
    "    test_data, train_data, X_train, y_train, X_test, y_test, cipro_R_prev, weights_train =  get_test_train_data(CIP_data_no_drop = CIP_data_no_drop, year = year, feature_names = important_features, years_train = years_train, model_type = model_type)\n",
    "    \n",
    "    best_hyperparameters2 = get_best_hyperparameters_tensorflow(estimator = estimator, param_space = param_grid, cv = cv, X_train = X_train, y_train = y_train, sample_weights = weights_train)\n",
    "    best_hyperparameters_by_year[model_type].__setitem__(year, best_hyperparameters2) \n",
    "    model_nn = create_model(optimizer=best_hyperparameters2['kc__optimizer'], init='uniform', dropout=best_hyperparameters2['kc__dropout'], dense_nparams=best_hyperparameters2['kc__dense_nparams'])\n",
    "\n",
    "\n",
    "    ## fit model w/hyperparameters \n",
    "    model_nn.fit(X_train, y_train, epochs=100, batch_size=best_hyperparameters2['kc__batch_size'], sample_weight=weights_train);\n",
    "\n",
    "    # Make predictions on test data\n",
    "    y_predict_proba = model_nn.predict(X_test)\n",
    "\n",
    "    # Calculate AUROC on test data\n",
    "    auc_metric = keras.metrics.AUC(num_thresholds=100)\n",
    "    auc_metric.update_state(y_test, y_predict_proba);\n",
    "    auroc = auc_metric.result().numpy()\n",
    " \n",
    "    ROC_by_year[model_type].__setitem__(year, auroc)\n",
    "    CIP_data_no_drop = CIP_data_no_drop.drop('weight', axis = 1)\n",
    "    print(auroc)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "    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",
    "    counts = CIP_data_training_years.groupby(['YEAR', 'CLINIC']).size().reset_index(name='Count')\n",
    "    counts_by_year = CIP_data_training_years.groupby(['YEAR']).size().reset_index(name='Count')\n",
    "\n",
    "    merged_counts = counts.merge(counts_by_year, on='YEAR', suffixes=('_counts', '_counts_by_year'))\n",
    "\n",
    "    # Calculate the sum of all counts in the counts dataframe\n",
    "    total_counts = counts['Count'].sum()\n",
    "\n",
    "    # Calculate the 'weight' column as Count_counts / total_counts\n",
    "    merged_counts['weight'] = merged_counts['Count_counts'] / total_counts\n",
    "    # Calculate the 'weight' column as Count_counts / rolling sum of the previous 5 years\n",
    "\n",
    "    # Merge the 'weight' column back to the original DataFrame\n",
    "    CIP_data_no_drop = CIP_data_no_drop.merge(merged_counts[['YEAR', 'CLINIC', 'weight']], on=['YEAR', 'CLINIC'], how='left')\n"
   ]
  }
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