4_LTC_Logistic_regression_optimal_cutoff.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc28175d",
   "metadata": {},
   "source": [
    "# In this file LR is used for both balanced and imbalanced dataset.\n",
    "\n",
    "* resource :https://www.geeksforgeeks.org/logistic-regression-using-statsmodels/\n",
    "\n",
    "\n",
    "### The sensitivity with balanced class is improved to a great extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9c3dd0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Import the liberaries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# visualization \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm \n",
    "from sklearn.metrics import roc_curve,confusion_matrix , accuracy_score, classification_report , roc_auc_score, f1_score,precision_score, recall_score\n",
    "from plot_metric.functions import BinaryClassification\n",
    "from imblearn.over_sampling import RandomOverSampler,SMOTE\n",
    "from sklearn.preprocessing import PowerTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ae8cc9",
   "metadata": {},
   "source": [
    "# Read the data after cleaning and pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e1e1ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final=pd.read_hdf(\"data_final.h5\", key='data_final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c24802a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# these are the features that we have finilized to give the model \n",
    "\n",
    "feature_list_for_training=[\n",
    "'spec_pat_num_age',\n",
    "# Gender-male\n",
    "'patient_gender_M',\n",
    "# Specimen Collection location\n",
    "'collected_in_ed', 'collected_in_hospital', 'collected_in_dsl',\n",
    "# Resident During Collection\n",
    "'dsl_resident_during_collection',\n",
    "# Symptomatic During Collection\n",
    "'symptomatic_during_collection_U', 'symptomatic_during_collection_Y',\n",
    "# Result of the covid test\n",
    "'interp_result_Positive',\n",
    "# Specimen year-month collection\n",
    "'y_m__2020-10',\n",
    "'y_m__2020-11', 'y_m__2020-12', 'y_m__2020-3', 'y_m__2020-4',\n",
    "'y_m__2020-5', 'y_m__2021-3', 'y_m__2020-7', 'y_m__2020-8',\n",
    "'y_m__2020-9', 'y_m__2021-1', 'y_m__2021-2', \n",
    "# Num of Elixhauser \n",
    "'chf_2_years_full_1', 'htn_unc_2_years_full_1',\n",
    "'cancer_mets_2_years_full_1', 'fluid_elec_dis_2_years_full_1',\n",
    "'card_arrh_2_years_full_1', 'valv_dis_2_years_full_1',\n",
    "'pcd_2_years_full_1', 'pvd_2_years_full_1', 'htn_c_2_years_full_1',\n",
    "'paral_2_years_full_1', 'oth_neur_dis_2_years_full_1',\n",
    "'cpd_2_years_full_1', 'diab_2_years_full_1',\n",
    "'hypothyroidism_2_years_full_1', 'ren_fail_2_years_full_1',\n",
    "'liver_dis_2_years_full_1', 'pep_ulc_exc_bld_2_years_full_1',\n",
    "'aids_hiv_2_years_full_1', 'lymph_2_years_full_1',\n",
    "'tumour_no_mets_2_years_full_1', 'rheum_col_vasc_dis_2_years_full_1',\n",
    "'coag_2_years_full_1', 'obes_2_years_full_1', 'wt_loss_2_years_full_1',\n",
    "'anemia_2_years_full_1', 'alc_abuse_2_years_full_1',\n",
    "'drug_abuse_2_years_full_1', 'psych_2_years_full_1',\n",
    "'depress_2_years_full_1',\n",
    "#Num of Admits for 1 year\n",
    "'num_admits_1_year', 'num_scu_admits_1_year',\n",
    "#Num Procedures for 1 year\n",
    "'num_procs_dad_1_year', 'num_procs_nacrs_1_year',\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27300d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Find_Optimal_Cutoff(target, predicted):    \n",
    "    '''Find data-driven cut-off for classification\n",
    "    \n",
    "    Cut-off is determied using Youden's index defined as sensitivity + specificity - 1.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    \n",
    "    y_true : array, shape = [n_samples]\n",
    "        True binary labels.\n",
    "        \n",
    "    y_score : array, shape = [n_samples]\n",
    "        Target scores, can either be probability estimates of the positive class,\n",
    "        confidence values, or non-thresholded measure of decisions (as returned by\n",
    "        “decision_function” on some classifiers).\n",
    "        \n",
    "    References\n",
    "    ----------\n",
    "    \n",
    "    Ewald, B. (2006). Post hoc choice of cut points introduced bias to diagnostic research.\n",
    "    Journal of clinical epidemiology, 59(8), 798-801.\n",
    "    \n",
    "    Steyerberg, E.W., Van Calster, B., & Pencina, M.J. (2011). Performance measures for\n",
    "    prediction models and markers: evaluation of predictions and classifications.\n",
    "    Revista Espanola de Cardiologia (English Edition), 64(9), 788-794.\n",
    "    \n",
    "    Jiménez-Valverde, A., & Lobo, J.M. (2007). Threshold criteria for conversion of probability\n",
    "    of species presence to either–or presence–absence. Acta oecologica, 31(3), 361-369.\n",
    "    '''\n",
    "    fpr, tpr, thresholds = roc_curve(target, predicted)\n",
    "    idx = np.argmax(tpr - fpr)\n",
    "    return thresholds[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496dd648",
   "metadata": {},
   "source": [
    "# trian the model by Imbalanced data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca145677",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are imbalanceness:\n",
      "0    89.857528\n",
      "1    10.142472\n",
      "Name: died_within_60_days, dtype: float64\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.273800\n",
      "         Iterations 8\n"
     ]
    }
   ],
   "source": [
    "# separate the features for training \n",
    "X=data_final[feature_list_for_training]\n",
    "# separate the labels for the training \n",
    "y=data_final['died_within_60_days']\n",
    "\n",
    "# add the constant to the x features\n",
    "X=sm.add_constant(X)\n",
    "# split the dataset into train and tes set \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86b08d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08317237543971503\n"
     ]
    }
   ],
   "source": [
    "# Find optimal probability threshold by using the yhat as a continuous measure\n",
    "threshold = Find_Optimal_Cutoff(y_test, yhat)\n",
    "print(threshold)\n",
    "\n",
    "# Find prediction to the dataframe applying threshold :\n",
    "# choose the cutoff so that the outcome is more balanced, using either the youden index \n",
    "prediction = pd.Series(yhat).map(lambda x: 1 if x > threshold else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfc4e69d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      " [[1509  794]\n",
      " [  59  196]]\n",
      "Test accuracy =  0.6665363565285379\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.66      0.78      2303\n",
      "           1       0.20      0.77      0.31       255\n",
      "\n",
      "    accuracy                           0.67      2558\n",
      "   macro avg       0.58      0.71      0.55      2558\n",
      "weighted avg       0.89      0.67      0.73      2558\n",
      "\n",
      "F1 Score: 0.314859437751004\n",
      "AUC: 0.71%\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix for the test dataset\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1257d795",
   "metadata": {},
   "source": [
    "# summary of the model trained by imbalanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51da1fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Logit Regression Results                           \n",
      "===============================================================================\n",
      "Dep. Variable:     died_within_60_days   No. Observations:                23022\n",
      "Model:                           Logit   Df Residuals:                    22967\n",
      "Method:                            MLE   Df Model:                           54\n",
      "Date:                 Thu, 26 Jan 2023   Pseudo R-squ.:                  0.1658\n",
      "Time:                         14:14:52   Log-Likelihood:                -6303.4\n",
      "converged:                        True   LL-Null:                       -7555.9\n",
      "Covariance Type:             nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "const                                -7.3378      0.251    -29.222      0.000      -7.830      -6.846\n",
      "spec_pat_num_age                      0.0465      0.003     17.339      0.000       0.041       0.052\n",
      "patient_gender_M                      0.4518      0.050      9.050      0.000       0.354       0.550\n",
      "collected_in_ed                       1.5123      0.123     12.308      0.000       1.271       1.753\n",
      "collected_in_hospital                 1.2802      0.135      9.507      0.000       1.016       1.544\n",
      "collected_in_dsl                     -0.0331      0.150     -0.221      0.825      -0.327       0.260\n",
      "dsl_resident_during_collection       -0.6216      0.139     -4.474      0.000      -0.894      -0.349\n",
      "symptomatic_during_collection_U       0.3685      0.066      5.576      0.000       0.239       0.498\n",
      "symptomatic_during_collection_Y       0.7049      0.069     10.218      0.000       0.570       0.840\n",
      "interp_result_Positive                1.2830      0.071     18.055      0.000       1.144       1.422\n",
      "y_m__2020-10                          0.7667      0.112      6.855      0.000       0.547       0.986\n",
      "y_m__2020-11                          0.8569      0.106      8.074      0.000       0.649       1.065\n",
      "y_m__2020-12                          0.7813      0.105      7.432      0.000       0.575       0.987\n",
      "y_m__2020-3                           1.5306      0.112     13.721      0.000       1.312       1.749\n",
      "y_m__2020-4                           1.0853      0.082     13.185      0.000       0.924       1.247\n",
      "y_m__2020-5                           0.7339      0.100      7.330      0.000       0.538       0.930\n",
      "y_m__2021-3                           0.3826      0.366      1.046      0.295      -0.334       1.099\n",
      "y_m__2020-7                           0.7661      0.127      6.019      0.000       0.517       1.016\n",
      "y_m__2020-8                           0.5552      0.172      3.230      0.001       0.218       0.892\n",
      "y_m__2020-9                           0.5347      0.164      3.251      0.001       0.212       0.857\n",
      "y_m__2021-1                           0.7068      0.123      5.743      0.000       0.466       0.948\n",
      "y_m__2021-2                           0.7133      0.221      3.228      0.001       0.280       1.146\n",
      "chf_2_years_full_1                    0.1817      0.061      2.958      0.003       0.061       0.302\n",
      "htn_unc_2_years_full_1               -0.2345      0.052     -4.467      0.000      -0.337      -0.132\n",
      "cancer_mets_2_years_full_1            0.4581      0.165      2.774      0.006       0.134       0.782\n",
      "fluid_elec_dis_2_years_full_1         0.1784      0.058      3.086      0.002       0.065       0.292\n",
      "card_arrh_2_years_full_1              0.0388      0.058      0.666      0.506      -0.076       0.153\n",
      "valv_dis_2_years_full_1               0.1064      0.129      0.827      0.408      -0.146       0.358\n",
      "pcd_2_years_full_1                    0.1569      0.113      1.389      0.165      -0.064       0.378\n",
      "pvd_2_years_full_1                    0.1028      0.077      1.335      0.182      -0.048       0.254\n",
      "htn_c_2_years_full_1                 -0.2289      0.239     -0.958      0.338      -0.697       0.239\n",
      "paral_2_years_full_1                 -0.2457      0.127     -1.934      0.053      -0.495       0.003\n",
      "oth_neur_dis_2_years_full_1          -0.0366      0.049     -0.746      0.456      -0.133       0.060\n",
      "cpd_2_years_full_1                    0.1349      0.058      2.323      0.020       0.021       0.249\n",
      "diab_2_years_full_1                   0.1020      0.054      1.881      0.060      -0.004       0.208\n",
      "hypothyroidism_2_years_full_1         0.0372      0.073      0.513      0.608      -0.105       0.179\n",
      "ren_fail_2_years_full_1               0.0885      0.069      1.282      0.200      -0.047       0.224\n",
      "liver_dis_2_years_full_1              0.4144      0.140      2.970      0.003       0.141       0.688\n",
      "pep_ulc_exc_bld_2_years_full_1        0.2526      0.172      1.465      0.143      -0.085       0.591\n",
      "aids_hiv_2_years_full_1              -0.7965      1.042     -0.764      0.445      -2.839       1.246\n",
      "lymph_2_years_full_1                  0.0381      0.222      0.171      0.864      -0.398       0.474\n",
      "tumour_no_mets_2_years_full_1         0.1920      0.078      2.452      0.014       0.038       0.346\n",
      "rheum_col_vasc_dis_2_years_full_1    -0.1958      0.055     -3.538      0.000      -0.304      -0.087\n",
      "coag_2_years_full_1                   0.0936      0.120      0.781      0.435      -0.141       0.328\n",
      "obes_2_years_full_1                  -0.2036      0.144     -1.417      0.156      -0.485       0.078\n",
      "wt_loss_2_years_full_1                0.0123      0.099      0.124      0.902      -0.182       0.207\n",
      "anemia_2_years_full_1                 0.0723      0.073      0.985      0.325      -0.072       0.216\n",
      "alc_abuse_2_years_full_1             -0.3102      0.125     -2.473      0.013      -0.556      -0.064\n",
      "drug_abuse_2_years_full_1             0.0179      0.132      0.135      0.893      -0.242       0.278\n",
      "psych_2_years_full_1                  0.0467      0.056      0.834      0.404      -0.063       0.156\n",
      "depress_2_years_full_1               -0.0568      0.050     -1.130      0.259      -0.155       0.042\n",
      "num_admits_1_year                     0.0218      0.028      0.769      0.442      -0.034       0.077\n",
      "num_scu_admits_1_year                 0.1386      0.168      0.824      0.410      -0.191       0.468\n",
      "num_procs_dad_1_year                  0.0117      0.022      0.538      0.590      -0.031       0.054\n",
      "num_procs_nacrs_1_year               -0.0007      0.001     -0.645      0.519      -0.003       0.002\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# print summary of the model trained with Imbalanced data\n",
    "print(log_reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0b4f2b",
   "metadata": {},
   "source": [
    "# train the model by balanced  classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c0795e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are no longer any imbalanceness:\n",
      "0    50.0\n",
      "1    50.0\n",
      "Name: died_within_60_days, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.544549\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "# only balnce the classes with out normalization \n",
    "\n",
    "X=data_final[feature_list_for_training]\n",
    "\n",
    "\n",
    "X=sm.add_constant(X)\n",
    "#split to test and training \n",
    "# x_train : features for the training the model\n",
    "# x_test :features for testing the model \n",
    "\n",
    "# y_train: the labels for the training the model\n",
    "# y_test: the labels for the testing  the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "\n",
    "#only oversample for the trian sets\n",
    "\n",
    "scaler_PT = PowerTransformer() \n",
    "over_sampler = RandomOverSampler(random_state=42)\n",
    "X_train, y_train = over_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are no longer any imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cfd0e74e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42626588779865554\n"
     ]
    }
   ],
   "source": [
    "# Find optimal probability threshold by using the yhat as a continuous measure\n",
    "threshold = Find_Optimal_Cutoff(y_test, yhat)\n",
    "print(threshold)\n",
    "# Find prediction to the dataframe applying threshold :\n",
    "# choose the cutoff so that the outcome is more balanced, using either the youden index \n",
    "prediction = pd.Series(yhat).map(lambda x: 1 if x > threshold else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a926053a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      " [[1486  817]\n",
      " [  55  200]]\n",
      "Test accuracy =  0.6591086786551994\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.65      0.77      2303\n",
      "           1       0.20      0.78      0.31       255\n",
      "\n",
      "    accuracy                           0.66      2558\n",
      "   macro avg       0.58      0.71      0.54      2558\n",
      "weighted avg       0.89      0.66      0.73      2558\n",
      "\n",
      "F1 Score: 0.31446540880503143\n",
      "AUC: 0.71%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96f4bbb",
   "metadata": {},
   "source": [
    "# summary of the model trained by balanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9e12cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Logit Regression Results                           \n",
      "===============================================================================\n",
      "Dep. Variable:     died_within_60_days   No. Observations:                41374\n",
      "Model:                           Logit   Df Residuals:                    41319\n",
      "Method:                            MLE   Df Model:                           54\n",
      "Date:                 Thu, 26 Jan 2023   Pseudo R-squ.:                  0.2144\n",
      "Time:                         14:14:53   Log-Likelihood:                -22530.\n",
      "converged:                        True   LL-Null:                       -28678.\n",
      "Covariance Type:             nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "const                                -5.1568      0.114    -45.066      0.000      -5.381      -4.933\n",
      "spec_pat_num_age                      0.0461      0.001     37.035      0.000       0.044       0.048\n",
      "patient_gender_M                      0.4172      0.025     16.916      0.000       0.369       0.466\n",
      "collected_in_ed                       1.6114      0.074     21.834      0.000       1.467       1.756\n",
      "collected_in_hospital                 1.2875      0.082     15.677      0.000       1.127       1.448\n",
      "collected_in_dsl                     -0.1683      0.087     -1.933      0.053      -0.339       0.002\n",
      "dsl_resident_during_collection       -0.5694      0.083     -6.825      0.000      -0.733      -0.406\n",
      "symptomatic_during_collection_U       0.4445      0.034     13.023      0.000       0.378       0.511\n",
      "symptomatic_during_collection_Y       0.8360      0.036     23.113      0.000       0.765       0.907\n",
      "interp_result_Positive                1.4262      0.038     37.631      0.000       1.352       1.501\n",
      "y_m__2020-10                          0.7613      0.052     14.505      0.000       0.658       0.864\n",
      "y_m__2020-11                          0.6942      0.052     13.360      0.000       0.592       0.796\n",
      "y_m__2020-12                          0.7162      0.051     14.080      0.000       0.616       0.816\n",
      "y_m__2020-3                           1.5990      0.056     28.520      0.000       1.489       1.709\n",
      "y_m__2020-4                           1.1875      0.037     32.532      0.000       1.116       1.259\n",
      "y_m__2020-5                           0.6944      0.046     15.046      0.000       0.604       0.785\n",
      "y_m__2021-3                           0.3485      0.160      2.182      0.029       0.035       0.662\n",
      "y_m__2020-7                           0.7690      0.060     12.737      0.000       0.651       0.887\n",
      "y_m__2020-8                           0.4408      0.081      5.470      0.000       0.283       0.599\n",
      "y_m__2020-9                           0.4412      0.078      5.691      0.000       0.289       0.593\n",
      "y_m__2021-1                           0.6326      0.062     10.197      0.000       0.511       0.754\n",
      "y_m__2021-2                           0.6074      0.107      5.690      0.000       0.398       0.817\n",
      "chf_2_years_full_1                    0.1945      0.031      6.296      0.000       0.134       0.255\n",
      "htn_unc_2_years_full_1               -0.2425      0.026     -9.391      0.000      -0.293      -0.192\n",
      "cancer_mets_2_years_full_1            0.5203      0.088      5.922      0.000       0.348       0.693\n",
      "fluid_elec_dis_2_years_full_1         0.2064      0.029      7.161      0.000       0.150       0.263\n",
      "card_arrh_2_years_full_1              0.0727      0.029      2.487      0.013       0.015       0.130\n",
      "valv_dis_2_years_full_1               0.0545      0.067      0.808      0.419      -0.078       0.187\n",
      "pcd_2_years_full_1                    0.0984      0.060      1.645      0.100      -0.019       0.216\n",
      "pvd_2_years_full_1                    0.0958      0.039      2.452      0.014       0.019       0.172\n",
      "htn_c_2_years_full_1                 -0.1056      0.119     -0.887      0.375      -0.339       0.128\n",
      "paral_2_years_full_1                 -0.3669      0.061     -6.057      0.000      -0.486      -0.248\n",
      "oth_neur_dis_2_years_full_1          -0.0419      0.024     -1.732      0.083      -0.089       0.006\n",
      "cpd_2_years_full_1                    0.1967      0.029      6.724      0.000       0.139       0.254\n",
      "diab_2_years_full_1                   0.1186      0.027      4.413      0.000       0.066       0.171\n",
      "hypothyroidism_2_years_full_1         0.0444      0.035      1.265      0.206      -0.024       0.113\n",
      "ren_fail_2_years_full_1               0.1480      0.035      4.267      0.000       0.080       0.216\n",
      "liver_dis_2_years_full_1              0.3203      0.073      4.392      0.000       0.177       0.463\n",
      "pep_ulc_exc_bld_2_years_full_1        0.2391      0.093      2.571      0.010       0.057       0.421\n",
      "aids_hiv_2_years_full_1              -0.5191      0.435     -1.194      0.233      -1.372       0.333\n",
      "lymph_2_years_full_1                  0.0310      0.112      0.276      0.783      -0.189       0.251\n",
      "tumour_no_mets_2_years_full_1         0.2359      0.039      5.997      0.000       0.159       0.313\n",
      "rheum_col_vasc_dis_2_years_full_1    -0.2606      0.027     -9.588      0.000      -0.314      -0.207\n",
      "coag_2_years_full_1                  -0.0374      0.062     -0.606      0.544      -0.158       0.084\n",
      "obes_2_years_full_1                  -0.1525      0.065     -2.340      0.019      -0.280      -0.025\n",
      "wt_loss_2_years_full_1               -0.0493      0.051     -0.969      0.333      -0.149       0.050\n",
      "anemia_2_years_full_1                 0.1203      0.037      3.231      0.001       0.047       0.193\n",
      "alc_abuse_2_years_full_1             -0.2995      0.059     -5.056      0.000      -0.416      -0.183\n",
      "drug_abuse_2_years_full_1            -0.0053      0.063     -0.085      0.932      -0.128       0.118\n",
      "psych_2_years_full_1                  0.0083      0.028      0.299      0.765      -0.046       0.063\n",
      "depress_2_years_full_1               -0.0897      0.025     -3.649      0.000      -0.138      -0.042\n",
      "num_admits_1_year                     0.0409      0.014      2.888      0.004       0.013       0.069\n",
      "num_scu_admits_1_year                -0.0218      0.088     -0.248      0.805      -0.194       0.151\n",
      "num_procs_dad_1_year                  0.0452      0.011      4.191      0.000       0.024       0.066\n",
      "num_procs_nacrs_1_year               -0.0014      0.001     -2.572      0.010      -0.002      -0.000\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# print summary of the model trained with balanced data\n",
    "print(log_reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714a2d42",
   "metadata": {},
   "source": [
    "# train the model by normalization and balanced  classes \n",
    "### 1-Random over sampling method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef93178e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are no longer any imbalanceness:\n",
      "0    50.0\n",
      "1    50.0\n",
      "Name: died_within_60_days, dtype: float64\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.545353\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "# only balnce the classes with out normalization \n",
    "X=data_final[feature_list_for_training]\n",
    "\n",
    "scaler_PT = PowerTransformer() \n",
    "# normalize the x features \n",
    "X = scaler_PT.fit_transform(X)\n",
    "X=sm.add_constant(X)\n",
    "\n",
    "#split to test and training \n",
    "# x_train : features for the training the model\n",
    "# x_test :features for testing the model \n",
    "\n",
    "# y_train: the labels for the training the model\n",
    "# y_test: the labels for the testing  the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "\n",
    "#only oversample for the trian sets\n",
    "\n",
    "over_sampler = RandomOverSampler(random_state=42)\n",
    "X_train, y_train = over_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are no longer any imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e25365d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42264550434893283\n"
     ]
    }
   ],
   "source": [
    "# Find optimal probability threshold\n",
    "threshold = Find_Optimal_Cutoff(y_test, yhat)\n",
    "print(threshold)\n",
    "# [0.31762762459360921]\n",
    "# Find prediction to the dataframe applying threshold\n",
    "prediction = pd.Series(yhat).map(lambda x: 1 if x > threshold else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c1a5225",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      " [[1479  824]\n",
      " [  55  200]]\n",
      "Test accuracy =  0.6563721657544958\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.64      0.77      2303\n",
      "           1       0.20      0.78      0.31       255\n",
      "\n",
      "    accuracy                           0.66      2558\n",
      "   macro avg       0.58      0.71      0.54      2558\n",
      "weighted avg       0.89      0.66      0.73      2558\n",
      "\n",
      "F1 Score: 0.31274433150899145\n",
      "AUC: 0.71%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d253433d",
   "metadata": {},
   "source": [
    "### 2-SMOTE Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96aaf491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are no longer any imbalanceness:\n",
      "0    50.0\n",
      "1    50.0\n",
      "Name: died_within_60_days, dtype: float64\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.540758\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "# only balnce the classes with out normalization \n",
    "X=data_final[feature_list_for_training]\n",
    "\n",
    "scaler_PT = PowerTransformer() \n",
    "# normalize the x features \n",
    "X = scaler_PT.fit_transform(X)\n",
    "X=sm.add_constant(X)\n",
    "\n",
    "#split to test and training \n",
    "# x_train : features for the training the model\n",
    "# x_test :features for testing the model \n",
    "\n",
    "# y_train: the labels for the training the model\n",
    "# y_test: the labels for the testing  the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "\n",
    "#only oversample for the trian sets\n",
    "\n",
    "smote_over_sampler = SMOTE(random_state=42)\n",
    "X_train, y_train = smote_over_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are no longer any imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "39109aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.46227295774799937\n"
     ]
    }
   ],
   "source": [
    "# Find optimal probability threshold\n",
    "threshold = Find_Optimal_Cutoff(y_test, yhat)\n",
    "print(threshold)\n",
    "# [0.31762762459360921]\n",
    "# Find prediction to the dataframe applying threshold\n",
    "prediction = pd.Series(yhat).map(lambda x: 1 if x > threshold else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd2f8f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      " [[1579  724]\n",
      " [  65  190]]\n",
      "Test accuracy =  0.6915559030492572\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.69      0.80      2303\n",
      "           1       0.21      0.75      0.33       255\n",
      "\n",
      "    accuracy                           0.69      2558\n",
      "   macro avg       0.58      0.72      0.56      2558\n",
      "weighted avg       0.89      0.69      0.75      2558\n",
      "\n",
      "F1 Score: 0.32506415739948674\n",
      "AUC: 0.72%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3380474a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}