2_model_development.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models Development\n",
    "by: Kaike W. Reis\n",
    "\n",
    "## Steps\n",
    "- Importing Data\n",
    "- Train Test Split \n",
    "- Oversampling\n",
    "- Transforming data\n",
    "- Auxiliar functions\n",
    "- Hyper parameters optimization\n",
    "- Dummy Classifier\n",
    "- Backtest creation\n",
    "- Exporting all results/models from here\n",
    "\n",
    "\n",
    "## Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard modules\n",
    "from time import sleep\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Graphical modules\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Preparation\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Workaround for skopt with sklearn==0.23.1\n",
    "from numpy.ma import MaskedArray\n",
    "import sklearn.utils.fixes\n",
    "sklearn.utils.fixes.MaskedArray = MaskedArray\n",
    "\n",
    "# Development\n",
    "from skopt import BayesSearchCV\n",
    "from skopt.space import Real, Categorical, Integer\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, StratifiedKFold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import xgboost as XGBoost\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Evaluation\n",
    "from scipy import stats\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, recall_score, precision_score\n",
    "\n",
    "# Export\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset\n",
    "df = pd.read_csv('results_EDA/paper_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define X and Y\n",
    "features = ['Patient age quantile','Basophils', 'Eosinophils','Hemoglobin', 'Leukocytes', 'Lymphocytes','MCHC','MCV','MPV',\n",
    "            'Monocytes', 'Platelets', 'RDW']\n",
    "# Get X and Y\n",
    "x = df[features]\n",
    "y = df['COVID-19 Exam result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.15, random_state = 101, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.864173\n",
       "1    0.135827\n",
       "Name: COVID-19 Exam result, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of target class in train set\n",
    "pd.Series(y_train).value_counts()/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    439\n",
       "1     69\n",
       "Name: COVID-19 Exam result, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target class in train set\n",
    "pd.Series(y_train).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.866667\n",
       "1    0.133333\n",
       "Name: COVID-19 Exam result, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of target class in test set\n",
    "y_test.value_counts()/len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    78\n",
       "1    12\n",
       "Name: COVID-19 Exam result, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target class in test set\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling\n",
    "Basically balance our data creating new synth samples for positive cases, using **default parameters**, i.e. `k` equals to 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create SMOTE object\n",
    "smt = SMOTE(random_state=1206)\n",
    "\n",
    "# Do the process\n",
    "x_train_smt, y_train_smt = smt.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Distribution for synthetic Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get copy of created samples\n",
    "synth_samples_y = y_train_smt.copy()\n",
    "# Create a new category for synth samples\n",
    "synth_samples_y[len(y_train):] = 2\n",
    "# Create a dataframe using oversampled data\n",
    "df_synth = x_train_smt.copy()\n",
    "df_synth['target'] = synth_samples_y\n",
    "# Get slices for positive cases and synth positive cases\n",
    "target_1 = df_synth[df_synth['target'] == 1]\n",
    "target_2 = df_synth[df_synth['target'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT - KDE Dist plots\n",
    "fig, axes = plt.subplots(nrows=6, ncols=2, figsize=(15,10))\n",
    "r = 0      # Index row\n",
    "c = 0      # Index col\n",
    "\n",
    "num_features = ['Basophils', 'Basophils', 'Eosinophils','Hemoglobin', 'Leukocytes', 'Lymphocytes','MCHC','MCV','MPV',\n",
    "                'Monocytes', 'Platelets', 'RDW']\n",
    "\n",
    "for f in num_features:\n",
    "    # Plot distribuição\n",
    "    sns.kdeplot(list(target_1[f]), shade=True, color='#F06C61', ax=axes[r][c], label='Positive')\n",
    "    sns.kdeplot(list(target_2[f]), shade=True, color='Green', ax=axes[r][c], label='Synth Positive')\n",
    "    # Plot configs\n",
    "    axes[r][c].legend(title='COVID-19 Exams', loc='upper right')\n",
    "    axes[r][c].set_title('Variable - ' + f)\n",
    "    # Index control\n",
    "    c += 1\n",
    "    if c > 1:\n",
    "        c = 0\n",
    "        r += 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New correlation heatmap with synth features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1280 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation calculation\n",
    "spearman_corr = df_synth[num_features].corr('spearman')\n",
    "matrix = np.triu(spearman_corr)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "sns.heatmap(spearman_corr, annot = True, mask=matrix)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Commentary\n",
    "SMOTE increases some correlations, but not high enough ( > 0.8) to give any danger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(pd.unique(x_train_smt['Patient age quantile'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming data\n",
    "\n",
    "Apply `MinMaxScaler` for **Patient age quantile** to reduce the scale difference between this variable and the numerical features\n",
    "\n",
    "## See expected results\n",
    "\n",
    "It's known that maximum is 19 and minimum is 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -> 0.0\n",
      "1 -> 0.052632\n",
      "2 -> 0.105263\n",
      "3 -> 0.157895\n",
      "4 -> 0.210526\n",
      "5 -> 0.263158\n",
      "6 -> 0.315789\n",
      "7 -> 0.368421\n",
      "8 -> 0.421053\n",
      "9 -> 0.473684\n",
      "10 -> 0.526316\n",
      "11 -> 0.578947\n",
      "12 -> 0.631579\n",
      "13 -> 0.684211\n",
      "14 -> 0.736842\n",
      "15 -> 0.789474\n",
      "16 -> 0.842105\n",
      "17 -> 0.894737\n",
      "18 -> 0.947368\n",
      "19 -> 1.0\n"
     ]
    }
   ],
   "source": [
    "age_origin = sorted(list(pd.unique(x_train['Patient age quantile'])))\n",
    "age_transf = sorted(list(pd.unique(x_train['Patient age quantile'])/19))\n",
    "for origin, transf in zip(age_origin, age_transf):\n",
    "    print(origin, '->', round(transf,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define instance and fit in train set\n",
    "mms = MinMaxScaler().fit(np.array(x_train['Patient age quantile']).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply transformation x train\n",
    "x_train['Patient age quantile'] = mms.transform(np.array(x_train['Patient age quantile']).reshape(-1, 1)).round(6)\n",
    "# Apply transformation x test\n",
    "x_test['Patient age quantile'] = mms.transform(np.array(x_test['Patient age quantile']).reshape(-1, 1)).round(6)\n",
    "# Apply transformation x train with SMOTE\n",
    "x_train_smt['Patient age quantile'] = mms.transform(np.array(x_train_smt['Patient age quantile']).reshape(-1, 1)).round(6)\n",
    "# Apply transformation df\n",
    "df['Patient age quantile'] = mms.transform(np.array(df['Patient age quantile']).reshape(-1, 1)).round(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auxiliar Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printModelGeneralInfo(model, hp, score, metric_evaluated):\n",
    "    print('#####################################################')\n",
    "    print('Model:', type(model).__name__)\n",
    "    print('Metric: ', metric_evaluated, ' | Score (%): ', 100*round(score,5))\n",
    "    print('Best Parameters: ', hp)\n",
    "    print('#####################################################\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimizationGridCV(model_estimator, params, metric_to_eval, cv_generator, features, target):\n",
    "    \"\"\"\n",
    "    Function to evaluate the best set of HP given a specific metric using Grid Search CV.\n",
    "    \n",
    "    Arguments:\n",
    "        model_estimator: Model to be evaluated\n",
    "        params: hyper parameters grid\n",
    "        metric_to_eval: metric to be evaluated in the search (model evaluation)\n",
    "        cv_generator: Stratified K-Fold Generator to guarantee reproductibility\n",
    "        features: x train set to be used\n",
    "        target: y train set to be used\n",
    "    \n",
    "    Returns:\n",
    "        (best_model, best_hiperparameters, best_metric_score)\n",
    "    \"\"\"\n",
    "    # Define Random Search CV object\n",
    "    hp_search = GridSearchCV(estimator=model_estimator,param_grid=params,cv=cv_generator,\n",
    "                             scoring=metric_to_eval,verbose=2,n_jobs=-1)\n",
    "    # Search\n",
    "    hp_search.fit(features, target)\n",
    "    # Finish\n",
    "    return (hp_search.best_estimator_, hp_search.best_params_, hp_search.best_score_, hp_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimizationRandomCV(model_estimator, params, metric_to_eval, cv_generator, n_iter, features, target):\n",
    "    \"\"\"\n",
    "    Function to evaluate the best set of HP given a specific metric using Random Search CV.\n",
    "    \n",
    "    Arguments:\n",
    "        model_estimator: Model to be evaluated\n",
    "        params: hyper parameters grid\n",
    "        metric_to_eval: metric to be evaluated in the search (model evaluation)\n",
    "        cv_generator: Stratified K-Fold Generator to guarantee reproductibility\n",
    "        n_iter: iterations in RandomizedSearchCV\n",
    "        features: x train set to be used\n",
    "        target: y train set to be used\n",
    "    \n",
    "    Returns:\n",
    "        (best_model, best_hiperparameters, best_metric_score)\n",
    "    \"\"\"\n",
    "    # Define Random Search CV object\n",
    "    hp_search = RandomizedSearchCV(estimator=model_estimator,\n",
    "                                   param_distributions=params, \n",
    "                                   n_iter=n_iter,\n",
    "                                   cv=cv_generator,\n",
    "                                   scoring=metric_to_eval,\n",
    "                                   verbose=2,\n",
    "                                   random_state=101,\n",
    "                                   n_jobs=-1)\n",
    "    # Search\n",
    "    hp_search.fit(features, target)\n",
    "    # Finish\n",
    "    return (hp_search.best_estimator_, hp_search.best_params_, hp_search.best_score_, hp_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimizationBayesCV(model_estimator, params, metric_to_eval, cv_generator, n_iter, features, target):\n",
    "    \"\"\"\n",
    "    Function to evaluate the best set of HP given a specific metric using Bayes Optimization Search CV.\n",
    "    \n",
    "    Arguments:\n",
    "        model_estimator: Model to be evaluated\n",
    "        params: hyper parameters grid\n",
    "        metric_to_eval: metric to be evaluated in the search (model evaluation)\n",
    "        cv_generator: Stratified K-Fold Generator to guarantee reproductibility\n",
    "        n_iter: iterations in BayesSearchCV\n",
    "        features: x train set to be used\n",
    "        target: y train set to be used\n",
    "    \n",
    "    Returns:\n",
    "        (best_model, best_hiperparameters, best_metric_score)\n",
    "    \"\"\"\n",
    "    # Define Search CV object\n",
    "    hp_search = BayesSearchCV(estimator=model_estimator,\n",
    "                              search_spaces=params,\n",
    "                              n_iter=n_iter,\n",
    "                              cv=cv_generator,\n",
    "                              scoring=metric_to_eval,\n",
    "                              verbose=0,\n",
    "                              random_state=101,\n",
    "                              n_jobs=5,\n",
    "                              n_points=5)\n",
    "    # Search\n",
    "    hp_search.fit(features, target)\n",
    "    # Finish\n",
    "    return (hp_search.best_estimator_, hp_search.best_params_, hp_search.best_score_, hp_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimizationPipeline(estimator,params,x_train,y_train,metric_eval,cv_generator,opt_option='grid',n_iter=None):\n",
    "    \"\"\"\n",
    "    Function to apply a specific HP optimization and present results.\n",
    "    Arguments:\n",
    "        estimator: Model to be evaluated\n",
    "        params: hyper parameters grid\n",
    "        x_train: self explanatory\n",
    "        y_train: self explanatory\n",
    "        metric_to_eval: metric to be evaluated in the search (model evaluation)\n",
    "        opt_option: define the methodology to HP Optimization. Can be 'grid','random' or 'bayes'\n",
    "        cv_generator: Stratified K-Fold Generator to guarantee reproductibility\n",
    "        n_iter: iterations in RandomSearchCV or BayesOptimization. It's ignored if opt_option is 'grid'.\n",
    "    Returns:\n",
    "        dictionary with overall results\n",
    "    \"\"\"  \n",
    "    # 1 - SEARCH\n",
    "    if opt_option == 'grid':\n",
    "        best_model,best_params,best_score,cv_results = optimizationGridCV(estimator,params,metric_eval,cv_generator,\n",
    "                                                                          x_train,y_train)\n",
    "    elif opt_option == 'random':\n",
    "        best_model,best_params,best_score,cv_results = optimizationRandomCV(estimator,params,metric_eval,cv_generator,\n",
    "                                                                            n_iter,x_train,y_train)\n",
    "    elif opt_option == 'bayes':\n",
    "        best_model,best_params,best_score,cv_results = optimizationBayesCV(estimator,params,metric_eval,cv_generator,\n",
    "                                                                           n_iter,x_train,y_train)\n",
    "    else:\n",
    "        print('ERROR - Wrong opt_option selected')\n",
    "        return\n",
    "    \n",
    "    # Wait for results\n",
    "    sleep(1.5)    \n",
    "    # 2 - Show Model Results\n",
    "    printModelGeneralInfo(best_model, best_params, best_score, metric_eval)\n",
    "    # 3 - Returns\n",
    "    returned_dict = {'best_model':best_model,'best_params':best_params,'best_score':best_score,\n",
    "                     'cv_res':pd.DataFrame(cv_results),'metric_used':metric_eval}\n",
    "    return returned_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameters optimization\n",
    "\n",
    "Details:\n",
    "- **Metrics**: `f1` which represents F1-Score\n",
    "- **Models**: Logistic Regression, SVM, Random Forest, Gradient Boosting and XGBoost\n",
    "- **Process to optimize**: CV 5-Fold Strafied. Optimization algorithm is Bayes Optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metric used\n",
    "metric_optimize = 'f1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stratified K-Fold generator - Generate a general split to be able to compare all models with CV results\n",
    "strat_cv_5folds = StratifiedKFold(n_splits=5,random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HPO Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################\n",
      "Model: LogisticRegression\n",
      "Metric:  f1  | Score (%):  83.44200000000001\n",
      "Best Parameters:  OrderedDict([('C', 0.03615307550295243), ('class_weight', 'balanced'), ('l1_ratio', 0.2615294799095248)])\n",
      "#####################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Define Estimator\n",
    "estimator = LogisticRegression(random_state=1206,penalty='elasticnet', solver='saga', max_iter=5000)\n",
    "## Define Parameters\n",
    "params = {'C': Real(1e-4, 10, prior='log-uniform'),\n",
    "          'l1_ratio': Real(0, 1, prior='uniform'),\n",
    "          'class_weight': Categorical(['balanced',None], transform='onehot')}\n",
    "\n",
    "## HP Optimization\n",
    "lre_res_ove = optimizationPipeline(estimator,params,x_train_smt,y_train_smt,metric_optimize, \n",
    "                                   strat_cv_5folds,opt_option='bayes',n_iter=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################\n",
      "Model: SVC\n",
      "Metric:  f1  | Score (%):  94.785\n",
      "Best Parameters:  OrderedDict([('C', 9.717159027484037), ('class_weight', None), ('gamma', 'scale'), ('kernel', 'rbf'), ('shrinking', False)])\n",
      "#####################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Define Estimator\n",
    "estimator = SVC(random_state=1206, probability=True)\n",
    "## Define Parameters\n",
    "params = {'C': Real(1e-4, 10, prior='log-uniform'),\n",
    "          'shrinking': Categorical([True, False], transform='onehot'),\n",
    "          'kernel': Categorical(['linear','rbf','poly','sigmoid'], transform='onehot'),\n",
    "          'gamma': Categorical(['scale', 'auto'], transform='onehot'),\n",
    "          'class_weight': Categorical(['balanced',None], transform='onehot')}\n",
    "\n",
    "## HP Optimization\n",
    "svm_res_ove = optimizationPipeline(estimator,params,x_train_smt,y_train_smt,metric_optimize,strat_cv_5folds,\n",
    "                                   opt_option='bayes',n_iter=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################\n",
      "Model: RandomForestClassifier\n",
      "Metric:  f1  | Score (%):  94.56099999999999\n",
      "Best Parameters:  OrderedDict([('class_weight', 'balanced_subsample'), ('max_depth', 21), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 1000)])\n",
      "#####################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Define Estimator\n",
    "estimator = RandomForestClassifier(random_state=1206, criterion='gini', warm_start=True)\n",
    "## Define Parameters Grid\n",
    "params = {'n_estimators': Integer(100, 1000, prior='uniform'),\n",
    "          'max_depth': Integer(2, 32, prior='uniform'),\n",
    "          'min_samples_split': Integer(2, 16, prior='uniform'),\n",
    "          'min_samples_leaf': Integer(1, 8, prior='uniform'),\n",
    "          'class_weight': Categorical(['balanced','balanced_subsample',None], transform='onehot'),\n",
    "          'max_features': Categorical(['sqrt','log2'], transform='onehot')}\n",
    "## HP Optimization\n",
    "rfc_res_ove = optimizationPipeline(estimator,params,x_train_smt,y_train_smt,metric_optimize,strat_cv_5folds,\n",
    "                                   opt_option='bayes',n_iter=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################\n",
      "Model: GradientBoostingClassifier\n",
      "Metric:  f1  | Score (%):  96.024\n",
      "Best Parameters:  OrderedDict([('learning_rate', 0.08683528363758376), ('max_depth', 32), ('max_features', 'sqrt'), ('min_samples_leaf', 8), ('min_samples_split', 2), ('n_estimators', 516), ('subsample', 0.75)])\n",
      "#####################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Define Estimator\n",
    "estimator = GradientBoostingClassifier(random_state=1206, loss='deviance', criterion='friedman_mse', warm_start=True)\n",
    "## Define Parameters\n",
    "params = {'n_estimators': Integer(100, 1000, prior='uniform'),\n",
    "          'min_samples_leaf': Integer(1, 8, prior='uniform'),\n",
    "          'min_samples_split': Integer(2, 16, prior='uniform'),\n",
    "          'max_depth': Integer(2, 32, prior='uniform'),\n",
    "          'subsample': Real(0.75, 1, prior='uniform'),\n",
    "          'learning_rate': Real(1e-5, 10, prior='log-uniform'),\n",
    "          'max_features': Categorical(['sqrt','log2'], transform='onehot')}\n",
    "## HP Optimization\n",
    "bst_res_ove = optimizationPipeline(estimator,params,x_train_smt,y_train_smt,metric_optimize,strat_cv_5folds,\n",
    "                                   opt_option='bayes',n_iter=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################\n",
      "Model: XGBClassifier\n",
      "Metric:  f1  | Score (%):  93.683\n",
      "Best Parameters:  OrderedDict([('colsample_bytree', 0.75), ('gamma', 0.0001), ('learning_rate', 0.023954630075641786), ('max_depth', 32), ('min_child_weight', 1), ('n_estimators', 1000), ('reg_alpha', 0.0029462438238392467), ('reg_lambda', 0.00223462634609437), ('scale_pos_weight', 0.9029486207298593), ('subsample', 0.8696705487627351)])\n",
      "#####################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Define Estimator\n",
    "estimator = XGBClassifier(objective='binary:logistic',random_state=1206,booster='gbtree', eval_metric='logloss')\n",
    "## Define Parameters\n",
    "params = {'n_estimators': Integer(100, 1000, prior='uniform'),\n",
    "          'min_child_weight': Integer(1, 10, prior='uniform'),\n",
    "          'max_depth': Integer(2, 32, prior='uniform'),\n",
    "          'subsample': Real(0.75, 1, prior='uniform'),\n",
    "          'colsample_bytree': Real(0.75, 1, prior='uniform'),\n",
    "          'scale_pos_weight': Real(0.75, 1, prior='uniform'),\n",
    "          'learning_rate': Real(1e-6, 10, prior='log-uniform'),\n",
    "          'gamma': Real(1e-4, 10, prior='log-uniform'),\n",
    "          'reg_alpha': Real(1e-3, 10, prior='log-uniform'),\n",
    "          'reg_lambda': Real(1e-3, 10, prior='log-uniform')}\n",
    "## HP Optimization\n",
    "xgb_res_ove = optimizationPipeline(estimator,params,x_train_smt,y_train_smt,metric_optimize,strat_cv_5folds,\n",
    "                                   opt_option='bayes',n_iter=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving BO5-CV results in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns to save for overall results\n",
    "cols_cv = ['split0_test_score', 'split1_test_score', 'split2_test_score','split3_test_score', 'split4_test_score', \n",
    "           'mean_test_score','std_test_score','rank_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.837696</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.775281</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.834418</td>\n",
       "      <td>0.030798</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.939227</td>\n",
       "      <td>0.967033</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.961326</td>\n",
       "      <td>0.947847</td>\n",
       "      <td>0.013516</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.960894</td>\n",
       "      <td>0.949721</td>\n",
       "      <td>0.927374</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.945608</td>\n",
       "      <td>0.020210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.960894</td>\n",
       "      <td>0.949721</td>\n",
       "      <td>0.927374</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.945608</td>\n",
       "      <td>0.020210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.929730</td>\n",
       "      <td>0.982857</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.960238</td>\n",
       "      <td>0.019869</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.943820</td>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.966292</td>\n",
       "      <td>0.936826</td>\n",
       "      <td>0.024477</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0           0.847826           0.837696           0.864865           0.775281   \n",
       "1           0.936170           0.939227           0.967033           0.935484   \n",
       "2           0.918033           0.960894           0.949721           0.927374   \n",
       "3           0.918033           0.960894           0.949721           0.927374   \n",
       "4           0.929730           0.982857           0.972067           0.944444   \n",
       "5           0.900000           0.956044           0.943820           0.918033   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0           0.846154         0.834418        0.030798                1  \n",
       "1           0.961326         0.947847        0.013516                1  \n",
       "2           0.972067         0.945608        0.020210                1  \n",
       "3           0.972067         0.945608        0.020210                1  \n",
       "4           0.972067         0.960238        0.019869                1  \n",
       "5           0.966292         0.936826        0.024477                1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating df with B05CV results for all models\n",
    "df_bo5cv_bests = lre_res_ove['cv_res'].loc[lre_res_ove['cv_res']['rank_test_score'] == 1][cols_cv]\n",
    "df_bo5cv_bests = df_bo5cv_bests.append(svm_res_ove['cv_res'].loc[svm_res_ove['cv_res']['rank_test_score'] == 1][cols_cv])\n",
    "df_bo5cv_bests = df_bo5cv_bests.append(rfc_res_ove['cv_res'].loc[rfc_res_ove['cv_res']['rank_test_score'] == 1][cols_cv])\n",
    "df_bo5cv_bests = df_bo5cv_bests.append(bst_res_ove['cv_res'].loc[bst_res_ove['cv_res']['rank_test_score'] == 1][cols_cv])\n",
    "df_bo5cv_bests = df_bo5cv_bests.append(xgb_res_ove['cv_res'].loc[xgb_res_ove['cv_res']['rank_test_score'] == 1][cols_cv])\n",
    "df_bo5cv_bests.reset_index(drop=True, inplace=True)\n",
    "df_bo5cv_bests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.837696</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.775281</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.834418</td>\n",
       "      <td>0.030798</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.939227</td>\n",
       "      <td>0.967033</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.961326</td>\n",
       "      <td>0.947847</td>\n",
       "      <td>0.013516</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.960894</td>\n",
       "      <td>0.949721</td>\n",
       "      <td>0.927374</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.945608</td>\n",
       "      <td>0.020210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929730</td>\n",
       "      <td>0.982857</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.960238</td>\n",
       "      <td>0.019869</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.943820</td>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.966292</td>\n",
       "      <td>0.936826</td>\n",
       "      <td>0.024477</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0           0.847826           0.837696           0.864865           0.775281   \n",
       "1           0.936170           0.939227           0.967033           0.935484   \n",
       "2           0.918033           0.960894           0.949721           0.927374   \n",
       "3           0.929730           0.982857           0.972067           0.944444   \n",
       "4           0.900000           0.956044           0.943820           0.918033   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0           0.846154         0.834418        0.030798                1  \n",
       "1           0.961326         0.947847        0.013516                1  \n",
       "2           0.972067         0.945608        0.020210                1  \n",
       "3           0.972067         0.960238        0.019869                1  \n",
       "4           0.966292         0.936826        0.024477                1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Erase sample 3 because is another RF, but more complex and with the same metrics from sample 2 (another rf)\n",
    "df_bo5cv_bests = df_bo5cv_bests.loc[[0,1,2,4,5],:]\n",
    "df_bo5cv_bests.reset_index(drop=True, inplace=True)\n",
    "df_bo5cv_bests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set name columns\n",
    "df_bo5cv_bests['ai_algorithm'] = ['lre','svm','rfc','bst','xgb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>ai_algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.837696</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.775281</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.834418</td>\n",
       "      <td>0.030798</td>\n",
       "      <td>1</td>\n",
       "      <td>lre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.939227</td>\n",
       "      <td>0.967033</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.961326</td>\n",
       "      <td>0.947847</td>\n",
       "      <td>0.013516</td>\n",
       "      <td>1</td>\n",
       "      <td>svm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.960894</td>\n",
       "      <td>0.949721</td>\n",
       "      <td>0.927374</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.945608</td>\n",
       "      <td>0.020210</td>\n",
       "      <td>1</td>\n",
       "      <td>rfc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929730</td>\n",
       "      <td>0.982857</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.972067</td>\n",
       "      <td>0.960238</td>\n",
       "      <td>0.019869</td>\n",
       "      <td>1</td>\n",
       "      <td>bst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.943820</td>\n",
       "      <td>0.918033</td>\n",
       "      <td>0.966292</td>\n",
       "      <td>0.936826</td>\n",
       "      <td>0.024477</td>\n",
       "      <td>1</td>\n",
       "      <td>xgb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0           0.847826           0.837696           0.864865           0.775281   \n",
       "1           0.936170           0.939227           0.967033           0.935484   \n",
       "2           0.918033           0.960894           0.949721           0.927374   \n",
       "3           0.929730           0.982857           0.972067           0.944444   \n",
       "4           0.900000           0.956044           0.943820           0.918033   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0           0.846154         0.834418        0.030798                1   \n",
       "1           0.961326         0.947847        0.013516                1   \n",
       "2           0.972067         0.945608        0.020210                1   \n",
       "3           0.972067         0.960238        0.019869                1   \n",
       "4           0.966292         0.936826        0.024477                1   \n",
       "\n",
       "  ai_algorithm  \n",
       "0          lre  \n",
       "1          svm  \n",
       "2          rfc  \n",
       "3          bst  \n",
       "4          xgb  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall dataset\n",
    "df_bo5cv_bests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most Frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DummyClassifier(strategy='most_frequent')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create\n",
    "model_dummy_mf_ove = DummyClassifier(strategy=\"most_frequent\")\n",
    "# Fit\n",
    "model_dummy_mf_ove.fit(x_train_smt,y_train_smt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DummyClassifier(strategy='stratified')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create\n",
    "model_dummy_st_ove = DummyClassifier(strategy=\"stratified\")\n",
    "# Fit\n",
    "model_dummy_st_ove.fit(x_train_smt,y_train_smt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtest - Generate dataset\n",
    "Samples from test set where **COVID is negative** and **Any respiratory disease is positive**\n",
    "\n",
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMatchIdx(idx1, idx2):\n",
    "    return list(set(idx1) & set(idx2))\n",
    "\n",
    "def backtestReport(backtestIdx, label, trainIdx, testIdx):\n",
    "    print('###################',label)\n",
    "    print('All Match Condition  -',len(backtestIdx),'samples.')\n",
    "    print('Train Match Conditon -',len(getMatchIdx(trainIdx, backtestIdx)),'samples.')\n",
    "    print('Test Match Conditon  -',len(getMatchIdx(testIdx, backtestIdx)),'samples.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_resp=df[(df['COVID-19 Exam result'] == 0)&(df['Respiratory Disease detection'] == 1)][features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a report about how those samples are distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################### Any Respiratory disease\n",
      "All Match Condition  - 172 samples.\n",
      "Train Match Conditon - 146 samples.\n",
      "Test Match Conditon  - 26 samples.\n"
     ]
    }
   ],
   "source": [
    "backtestReport(x_resp.index, 'Any Respiratory disease', x_train.index, x_test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get backtest samples only present in test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_resp = x_resp.loc[getMatchIdx(x_test.index, x_resp.index), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validate\n",
    "len(x_resp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to export best models\n",
    "def exportModels(best_models_names, best_models_objects,type_of_dataset):\n",
    "    for model_name, model_object in zip(best_models_names, best_models_objects):\n",
    "        joblib.dump(model_object, 'results_modelsDevelopment/model_'+model_name+'_'+type_of_dataset+'.sav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Name of the models\n",
    "models_name_list = ['lre','svm','rfc','bst','xgb','dummy_mf','dummy_st']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Datasets..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# XY trainset before SMOTE\n",
    "x_train.to_csv('results_modelsDevelopment/x_train.csv',index=False)\n",
    "y_train.to_csv('results_modelsDevelopment/y_train.csv',index=False)\n",
    "# XY trainset with SMOTE\n",
    "x_train_smt.to_csv('results_modelsDevelopment/x_train_SMOTE.csv',index=False)\n",
    "y_train_smt.to_csv('results_modelsDevelopment/y_train_SMOTE.csv',index=False)\n",
    "# XY testset\n",
    "x_test.to_csv('results_modelsDevelopment/x_test.csv',index=False)\n",
    "y_test.to_csv('results_modelsDevelopment/y_test.csv',index=False)\n",
    "# Backtest datasets - Only X because Y is a zero array\n",
    "x_resp.to_csv('results_modelsDevelopment/x_resp.csv',index=False)\n",
    "# Dataset with age transformation\n",
    "df.to_csv('results_modelsDevelopment/paper_dataset_age_transformed.csv',index=False)\n",
    "# BO5CV best results for all models\n",
    "df_bo5cv_bests.to_csv('results_modelsDevelopment/BO5CV_best_results.csv',index=False)\n",
    "# BO5CV all results for all models\n",
    "lre_res_ove['cv_res'].to_csv('results_modelsDevelopment/BO5CV_lre_results.csv',index=False)\n",
    "svm_res_ove['cv_res'].to_csv('results_modelsDevelopment/BO5CV_svm_results.csv',index=False)\n",
    "rfc_res_ove['cv_res'].to_csv('results_modelsDevelopment/BO5CV_rfc_results.csv',index=False)\n",
    "bst_res_ove['cv_res'].to_csv('results_modelsDevelopment/BO5CV_bst_results.csv',index=False)\n",
    "xgb_res_ove['cv_res'].to_csv('results_modelsDevelopment/BO5CV_xgb_results.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving models ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define objects\n",
    "models_objects_list = [lre_res_ove['best_model'],svm_res_ove['best_model'],rfc_res_ove['best_model'],bst_res_ove['best_model'],\n",
    "                       xgb_res_ove['best_model'],model_dummy_mf_ove,model_dummy_st_ove]\n",
    "# Use the function\n",
    "exportModels(models_name_list, models_objects_list,'oversampled')"
   ]
  }
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