demo_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import StratifiedKFold, RepeatedStratifiedKFold\n",
    "from imblearn.combine import SMOTEENN\n",
    "import sys\n",
    "from ml_template.utils import *\n",
    "from ml_template.model.ml_model import *\n",
    "data_df = pd.read_csv('data/train.csv',sep='\\t',header=None)\n",
    "test_df = pd.read_csv('data/test.csv',sep='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_df.values[:,:-1]\n",
    "label=(data_df.values[:,-1]+1)//2\n",
    "qt = QuantileTransformer(n_quantiles=10)\n",
    "data = qt.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 10 0.1\n",
      "----- Model pre-training evaluation -----\n",
      "            model  eval_metric\n",
      "0   RF_depth_None     0.200000\n",
      "1           SVM_5     0.200000\n",
      "2            RF_3     0.266667\n",
      "3        XGB_31_3     0.266667\n",
      "4       XGB_310_3     0.266667\n",
      "5          XGB_31     0.266667\n",
      "6         XGB_310     0.266667\n",
      "7             SVM     0.266667\n",
      "8         SVM_lin     0.266667\n",
      "9         SVM_rbf     0.266667\n",
      "10   SVM_0.2_poly     0.266667\n",
      "11      SVM_5_lin     0.400000\n",
      "12     SVM_5_poly     0.400000\n",
      "13        SVM_0.2     0.466667\n",
      "14    SVM_0.2_lin     0.533333\n",
      "----- Stacking weights -----\n",
      "0      RF_3      : ['0.000', '0.000']\n",
      "1  RF_depth_None : ['0.000', '0.000']\n",
      "2    XGB_31_3    : ['0.281', '0.281']\n",
      "3    XGB_310_3   : ['0.281', '0.281']\n",
      "4     XGB_31     : ['0.202', '0.202']\n",
      "5     XGB_310    : ['0.236', '0.236']\n",
      "6       SVM      : ['0.000', '0.000']\n",
      "7     SVM_lin    : ['0.000', '0.000']\n",
      "8     SVM_rbf    : ['0.000', '0.000']\n",
      "9     SVM_0.2    : ['0.000', '0.000']\n",
      "10  SVM_0.2_lin  : ['0.000', '0.000']\n",
      "11 SVM_0.2_poly  : ['0.000', '0.000']\n",
      "12     SVM_5     : ['0.000', '0.000']\n",
      "13   SVM_5_lin   : ['0.000', '0.000']\n",
      "14  SVM_5_poly   : ['0.000', '0.000']\n",
      "11\n",
      "\n",
      "\n",
      "====== CV:0 ======\n",
      "             model  eval_metric\n",
      "0      SVM_0.2_lin     0.333333\n",
      "1          SVM_lin     0.611111\n",
      "2        SVM_5_lin     0.611111\n",
      "3       SVM_5_poly     0.666667\n",
      "4             RF_3     0.777778\n",
      "5    RF_depth_None     0.777778\n",
      "6          XGB_310     0.777778\n",
      "7          SVM_0.2     0.777778\n",
      "8         XGB_31_3     0.833333\n",
      "9        XGB_310_3     0.833333\n",
      "10  Ensemble Model     0.833333\n",
      "11          XGB_31     0.888889\n",
      "12             SVM     0.888889\n",
      "13         SVM_rbf     0.888889\n",
      "14    SVM_0.2_poly     0.888889\n",
      "15           SVM_5     0.888889\n",
      "----- Model pre-training evaluation -----\n",
      "            model  eval_metric\n",
      "0           SVM_5     0.066667\n",
      "1    SVM_0.2_poly     0.133333\n",
      "2             SVM     0.200000\n",
      "3         SVM_rbf     0.200000\n",
      "4      SVM_5_poly     0.266667\n",
      "5            RF_3     0.333333\n",
      "6   RF_depth_None     0.333333\n",
      "7        XGB_31_3     0.333333\n",
      "8       XGB_310_3     0.333333\n",
      "9          XGB_31     0.333333\n",
      "10        XGB_310     0.333333\n",
      "11      SVM_5_lin     0.333333\n",
      "12        SVM_lin     0.466667\n",
      "13        SVM_0.2     0.466667\n",
      "14    SVM_0.2_lin     0.466667\n",
      "----- Stacking weights -----\n",
      "0      RF_3      : ['0.000', '0.000']\n",
      "1  RF_depth_None : ['0.000', '0.000']\n",
      "2    XGB_31_3    : ['0.000', '0.000']\n",
      "3    XGB_310_3   : ['0.000', '0.000']\n",
      "4     XGB_31     : ['0.000', '0.000']\n",
      "5     XGB_310    : ['0.000', '0.000']\n",
      "6       SVM      : ['0.000', '0.000']\n",
      "7     SVM_lin    : ['0.000', '0.000']\n",
      "8     SVM_rbf    : ['0.000', '0.000']\n",
      "9     SVM_0.2    : ['0.000', '0.000']\n",
      "10  SVM_0.2_lin  : ['0.000', '0.000']\n",
      "11 SVM_0.2_poly  : ['0.000', '0.000']\n",
      "12     SVM_5     : ['1.000', '1.000']\n",
      "13   SVM_5_lin   : ['0.000', '0.000']\n",
      "14  SVM_5_poly   : ['0.000', '0.000']\n",
      "11\n",
      "\n",
      "\n",
      "====== CV:1 ======\n",
      "             model  eval_metric\n",
      "0      SVM_0.2_lin     0.500000\n",
      "1    RF_depth_None     0.555556\n",
      "2          SVM_0.2     0.555556\n",
      "3       SVM_5_poly     0.611111\n",
      "4           XGB_31     0.666667\n",
      "5          XGB_310     0.666667\n",
      "6              SVM     0.666667\n",
      "7     SVM_0.2_poly     0.666667\n",
      "8             RF_3     0.722222\n",
      "9         XGB_31_3     0.722222\n",
      "10       XGB_310_3     0.722222\n",
      "11         SVM_lin     0.722222\n",
      "12         SVM_rbf     0.722222\n",
      "13           SVM_5     0.722222\n",
      "14  Ensemble Model     0.722222\n",
      "15       SVM_5_lin     0.777778\n",
      "----- Model pre-training evaluation -----\n",
      "            model  eval_metric\n",
      "0        XGB_31_3     0.200000\n",
      "1       XGB_310_3     0.200000\n",
      "2             SVM     0.200000\n",
      "3         SVM_rbf     0.200000\n",
      "4    SVM_0.2_poly     0.200000\n",
      "5           SVM_5     0.200000\n",
      "6          XGB_31     0.266667\n",
      "7         XGB_310     0.266667\n",
      "8      SVM_5_poly     0.266667\n",
      "9   RF_depth_None     0.333333\n",
      "10           RF_3     0.400000\n",
      "11        SVM_0.2     0.466667\n",
      "12    SVM_0.2_lin     0.466667\n",
      "13      SVM_5_lin     0.466667\n",
      "14        SVM_lin     0.533333\n",
      "----- Stacking weights -----\n",
      "0      RF_3      : ['0.000', '0.000']\n",
      "1  RF_depth_None : ['0.000', '0.000']\n",
      "2    XGB_31_3    : ['0.173', '0.173']\n",
      "3    XGB_310_3   : ['0.173', '0.173']\n",
      "4     XGB_31     : ['0.000', '0.000']\n",
      "5     XGB_310    : ['0.000', '0.000']\n",
      "6       SVM      : ['0.214', '0.214']\n",
      "7     SVM_lin    : ['0.000', '0.000']\n",
      "8     SVM_rbf    : ['0.000', '0.000']\n",
      "9     SVM_0.2    : ['0.000', '0.000']\n",
      "10  SVM_0.2_lin  : ['0.000', '0.000']\n",
      "11 SVM_0.2_poly  : ['0.055', '0.055']\n",
      "12     SVM_5     : ['0.386', '0.386']\n",
      "13   SVM_5_lin   : ['0.000', '0.000']\n",
      "14  SVM_5_poly   : ['0.000', '0.000']\n",
      "11\n",
      "\n",
      "\n",
      "====== CV:2 ======\n",
      "             model  eval_metric\n",
      "0          SVM_0.2     0.444444\n",
      "1              SVM     0.500000\n",
      "2          SVM_rbf     0.555556\n",
      "3      SVM_0.2_lin     0.555556\n",
      "4             RF_3     0.611111\n",
      "5            SVM_5     0.611111\n",
      "6   Ensemble Model     0.611111\n",
      "7    RF_depth_None     0.666667\n",
      "8          XGB_310     0.666667\n",
      "9     SVM_0.2_poly     0.666667\n",
      "10       SVM_5_lin     0.666667\n",
      "11      SVM_5_poly     0.666667\n",
      "12        XGB_31_3     0.722222\n",
      "13       XGB_310_3     0.722222\n",
      "14          XGB_31     0.722222\n",
      "15         SVM_lin     0.722222\n",
      "----- Model pre-training evaluation -----\n",
      "            model  eval_metric\n",
      "0             SVM     0.333333\n",
      "1         SVM_rbf     0.333333\n",
      "2           SVM_5     0.333333\n",
      "3      SVM_5_poly     0.400000\n",
      "4   RF_depth_None     0.466667\n",
      "5         SVM_lin     0.466667\n",
      "6         SVM_0.2     0.466667\n",
      "7     SVM_0.2_lin     0.466667\n",
      "8    SVM_0.2_poly     0.466667\n",
      "9            RF_3     0.533333\n",
      "10       XGB_31_3     0.533333\n",
      "11      XGB_310_3     0.533333\n",
      "12         XGB_31     0.600000\n",
      "13        XGB_310     0.600000\n",
      "14      SVM_5_lin     0.600000\n",
      "----- Stacking weights -----\n",
      "0      RF_3      : ['0.000', '0.000']\n",
      "1  RF_depth_None : ['0.000', '0.000']\n",
      "2    XGB_31_3    : ['0.000', '0.000']\n",
      "3    XGB_310_3   : ['0.000', '0.000']\n",
      "4     XGB_31     : ['0.000', '0.000']\n",
      "5     XGB_310    : ['0.000', '0.000']\n",
      "6       SVM      : ['0.159', '0.159']\n",
      "7     SVM_lin    : ['0.036', '0.036']\n",
      "8     SVM_rbf    : ['0.088', '0.088']\n",
      "9     SVM_0.2    : ['0.165', '0.165']\n",
      "10  SVM_0.2_lin  : ['0.000', '0.000']\n",
      "11 SVM_0.2_poly  : ['0.000', '0.000']\n",
      "12     SVM_5     : ['0.481', '0.481']\n",
      "13   SVM_5_lin   : ['0.000', '0.000']\n",
      "14  SVM_5_poly   : ['0.071', '0.071']\n",
      "11\n",
      "\n",
      "\n",
      "====== CV:3 ======\n",
      "             model  eval_metric\n",
      "0      SVM_0.2_lin     0.388889\n",
      "1          SVM_lin     0.555556\n",
      "2          SVM_0.2     0.611111\n",
      "3              SVM     0.666667\n",
      "4        SVM_5_lin     0.666667\n",
      "5       SVM_5_poly     0.666667\n",
      "6          SVM_rbf     0.722222\n",
      "7   Ensemble Model     0.722222\n",
      "8             RF_3     0.777778\n",
      "9    RF_depth_None     0.777778\n",
      "10           SVM_5     0.777778\n",
      "11          XGB_31     0.833333\n",
      "12         XGB_310     0.833333\n",
      "13    SVM_0.2_poly     0.833333\n",
      "14        XGB_31_3     0.888889\n",
      "15       XGB_310_3     0.888889\n",
      "----- Model pre-training evaluation -----\n",
      "            model  eval_metric\n",
      "0           SVM_5     0.200000\n",
      "1          XGB_31     0.266667\n",
      "2         XGB_310     0.266667\n",
      "3      SVM_5_poly     0.266667\n",
      "4   RF_depth_None     0.333333\n",
      "5        XGB_31_3     0.333333\n",
      "6       XGB_310_3     0.333333\n",
      "7             SVM     0.333333\n",
      "8         SVM_rbf     0.333333\n",
      "9    SVM_0.2_poly     0.333333\n",
      "10           RF_3     0.400000\n",
      "11        SVM_lin     0.400000\n",
      "12      SVM_5_lin     0.400000\n",
      "13        SVM_0.2     0.466667\n",
      "14    SVM_0.2_lin     0.733333\n",
      "----- Stacking weights -----\n",
      "0      RF_3      : ['0.000', '0.000']\n",
      "1  RF_depth_None : ['0.000', '0.000']\n",
      "2    XGB_31_3    : ['0.144', '0.144']\n",
      "3    XGB_310_3   : ['0.144', '0.144']\n",
      "4     XGB_31     : ['0.426', '0.426']\n",
      "5     XGB_310    : ['0.000', '0.000']\n",
      "6       SVM      : ['0.000', '0.000']\n",
      "7     SVM_lin    : ['0.000', '0.000']\n",
      "8     SVM_rbf    : ['0.019', '0.019']\n",
      "9     SVM_0.2    : ['0.000', '0.000']\n",
      "10  SVM_0.2_lin  : ['0.000', '0.000']\n",
      "11 SVM_0.2_poly  : ['0.000', '0.000']\n",
      "12     SVM_5     : ['0.267', '0.267']\n",
      "13   SVM_5_lin   : ['0.000', '0.000']\n",
      "14  SVM_5_poly   : ['0.000', '0.000']\n",
      "11\n",
      "\n",
      "\n",
      "====== CV:4 ======\n",
      "             model  eval_metric\n",
      "0          SVM_0.2     0.500000\n",
      "1      SVM_0.2_lin     0.500000\n",
      "2          SVM_lin     0.555556\n",
      "3        SVM_5_lin     0.555556\n",
      "4             RF_3     0.611111\n",
      "5    RF_depth_None     0.611111\n",
      "6         XGB_31_3     0.666667\n",
      "7        XGB_310_3     0.666667\n",
      "8          XGB_310     0.666667\n",
      "9           XGB_31     0.722222\n",
      "10             SVM     0.722222\n",
      "11         SVM_rbf     0.722222\n",
      "12           SVM_5     0.722222\n",
      "13  Ensemble Model     0.722222\n",
      "14      SVM_5_poly     0.833333\n",
      "15    SVM_0.2_poly     0.888889\n",
      "\n",
      "====== CV Mean ======\n",
      "             model  eval_metric\n",
      "0      SVM_0.2_lin     0.455556\n",
      "1          SVM_0.2     0.577778\n",
      "2          SVM_lin     0.633333\n",
      "3        SVM_5_lin     0.655556\n",
      "4    RF_depth_None     0.677778\n",
      "5              SVM     0.688889\n",
      "6       SVM_5_poly     0.688889\n",
      "7             RF_3     0.700000\n",
      "8          XGB_310     0.722222\n",
      "9   Ensemble Model     0.722222\n",
      "10         SVM_rbf     0.722222\n",
      "11           SVM_5     0.744444\n",
      "12       XGB_310_3     0.766667\n",
      "13        XGB_31_3     0.766667\n",
      "14          XGB_31     0.766667\n",
      "15    SVM_0.2_poly     0.788889\n"
     ]
    }
   ],
   "source": [
    "# split 9:1 \n",
    "train_data, train_label, test_data, test_label = KFold_Sampler(data,label,n_splits=10).get_multi_fold(n_fold=1)\n",
    "print(len(train_data),len(test_data),len(test_data)/(len(train_data)+len(test_data)))\n",
    "\n",
    "# define model list\n",
    "model_lgb = lgb.LGBMClassifier(is_unbalance=True)\n",
    "\n",
    "model_dict = {'RF_3':RandomForestClassifier(n_estimators=310,max_depth=3),\n",
    "              'RF_depth_None':RandomForestClassifier(n_estimators=310),\n",
    "              'XGB_31_3':XGBClassifier(n_estimators=31,max_depth=3),\n",
    "              'XGB_310_3':XGBClassifier(n_estimators=31,max_depth=3),\n",
    "              'XGB_31':XGBClassifier(n_estimators=31),\n",
    "              'XGB_310':XGBClassifier(n_estimators=310),\n",
    "              'SVM':SVC(probability=True),\n",
    "              'SVM_lin':SVC(kernel='linear',probability=True),\n",
    "              'SVM_rbf':SVC(kernel='rbf',probability=True),\n",
    "              'SVM_0.2':SVC(C=0.2,probability=True),\n",
    "              'SVM_0.2_lin':SVC(C=0.2,kernel='linear',probability=True),\n",
    "              'SVM_0.2_poly':SVC(C=0.2,kernel='poly',probability=True),\n",
    "              'SVM_5':SVC(C=5,probability=True),\n",
    "              'SVM_5_lin':SVC(C=5,kernel='linear',probability=True),\n",
    "              'SVM_5_poly':SVC(C=5,kernel='poly',probability=True),\n",
    "              # 'KNN_Cls':KNeighborsClassifier(),\n",
    "              # 'LGB_Cls':model_lgb,\n",
    "              # 'Ridge_Cls':RidgeClassifier(),\n",
    "              # 'MLP_Cls':MLPClassifier(activation = \"relu\", alpha = 0.001, hidden_layer_sizes = (56,10),\n",
    "              #               learning_rate = \"constant\", max_iter = 3000, random_state = 3000,early_stopping=True),\n",
    "              # 'QDA':QuadraticDiscriminantAnalysis(),\n",
    "              }\n",
    "#stack_model=MLPClassifier(activation = \"relu\", alpha = 0.0001,validation_fraction=0.1, hidden_layer_sizes = (38),learning_rate = \"constant\", max_iter = 3000, random_state = 1000,early_stopping=True)\n",
    "# stack_model=SVC(C=1,kernel='linear', probability=True)\n",
    "#stack_model = DecisionTreeClassifier()\n",
    "num_model = len(list(model_dict.keys()))\n",
    "num_classes=2\n",
    "stack_model=ML_Weighted_Model(num_model,\n",
    "                              num_classes,\n",
    "                              epoch=1000,\n",
    "                              model_reg=0.00,\n",
    "                              classes_reg=0.00,\n",
    "                              l1_norm=0.00)\n",
    "model = Stack_Ensemble_Proba_Model(copy.deepcopy(model_dict),stack_model=stack_model,stack_training_split=0.2)\n",
    "# model = Mean_Ensemble_Proba_Model(copy.deepcopy(model_dict))\n",
    "cv_model, cv_df = model.cross_validation_evaluate(train_data, train_label, accuracy_score,n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== Model Classes Importance ============\n",
      "model\n",
      "SVM_5            0.4268\n",
      "XGB_31           0.1256\n",
      "XGB_310_3        0.1196\n",
      "XGB_31_3         0.1196\n",
      "SVM              0.0746\n",
      "XGB_310          0.0472\n",
      "SVM_0.2          0.0330\n",
      "SVM_rbf          0.0214\n",
      "SVM_5_poly       0.0142\n",
      "SVM_0.2_poly     0.0110\n",
      "SVM_lin          0.0072\n",
      "RF_3             0.0000\n",
      "RF_depth_None    0.0000\n",
      "SVM_0.2_lin      0.0000\n",
      "SVM_5_lin        0.0000\n",
      "Name: weights, dtype: float32\n",
      "                     weights\n",
      "class model                 \n",
      "0     SVM_5           0.4268\n",
      "      XGB_31          0.1256\n",
      "      XGB_310_3       0.1196\n",
      "      XGB_31_3        0.1196\n",
      "      SVM             0.0746\n",
      "      XGB_310         0.0472\n",
      "      SVM_0.2         0.0330\n",
      "      SVM_rbf         0.0214\n",
      "      SVM_5_poly      0.0142\n",
      "      SVM_0.2_poly    0.0110\n",
      "      SVM_lin         0.0072\n",
      "      RF_3            0.0000\n",
      "      RF_depth_None   0.0000\n",
      "      SVM_0.2_lin     0.0000\n",
      "      SVM_5_lin       0.0000\n",
      "1     SVM_5           0.4268\n",
      "      XGB_31          0.1256\n",
      "      XGB_310_3       0.1196\n",
      "      XGB_31_3        0.1196\n",
      "      SVM             0.0746\n",
      "      XGB_310         0.0472\n",
      "      SVM_0.2         0.0330\n",
      "      SVM_rbf         0.0214\n",
      "      SVM_5_poly      0.0142\n",
      "      SVM_0.2_poly    0.0110\n",
      "      SVM_lin         0.0072\n",
      "      RF_3            0.0000\n",
      "      RF_depth_None   0.0000\n",
      "      SVM_0.2_lin     0.0000\n",
      "      SVM_5_lin       0.0000\n",
      "=========== Feature Importance ============\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   class feature_name  importance  mean_importance\n",
      "0      0    feature_1    0.015407         0.015407\n",
      "3      0    feature_7    0.012811         0.012811\n",
      "5      0    feature_8    0.008026         0.008026\n",
      "6      0    feature_4    0.007868         0.007868\n",
      "8      0    feature_2    0.005424         0.005424\n",
      "10     0    feature_3    0.004752         0.004752\n",
      "12     0    feature_9    0.004247         0.004247\n",
      "14     0    feature_0    0.003895         0.003895\n",
      "16     0    feature_6    0.003319         0.003319\n",
      "18     0    feature_5    0.002917         0.002917\n",
      "1      1    feature_1    0.015407         0.015407\n",
      "2      1    feature_7    0.012811         0.012811\n",
      "4      1    feature_8    0.008026         0.008026\n",
      "7      1    feature_4    0.007868         0.007868\n",
      "9      1    feature_2    0.005424         0.005424\n",
      "11     1    feature_3    0.004752         0.004752\n",
      "13     1    feature_9    0.004247         0.004247\n",
      "15     1    feature_0    0.003895         0.003895\n",
      "17     1    feature_6    0.003319         0.003319\n",
      "19     1    feature_5    0.002917         0.002917\n"
     ]
    }
   ],
   "source": [
    "stacking_analysis_df = weighted_stacking_analysis(cv_model, [f'feature_{i}'for i in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== CV Ensemble Model ==========\n",
      "            model  eval_metric\n",
      "0         model_5          0.7\n",
      "1         model_1          0.8\n",
      "2         model_3          0.8\n",
      "3         model_4          0.9\n",
      "4         model_2          1.0\n",
      "5  Ensemble Model          1.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#stack_model = DecisionTreeClassifier()\n",
    "# cv_ensemble_model = Stack_Ensemble_Proba_Model(model_dict=cv_model, stack_model=copy.deepcopy(stack_model), stack_training_split=0.3)\n",
    "cv_ensemble_model = Mean_Ensemble_Proba_Model(cv_model)\n",
    "# cv_ensemble_model.fit(train_data,train_label)\n",
    "# # cv_ensemble_model.fit(train_data,train_label)\n",
    "# print('========== Ensemble Model ==========')\n",
    "# _=model.evaluate(test_data,test_label,evaluation_fn=accuracy_score,verbose=True)\n",
    "print('========== CV Ensemble Model ==========')\n",
    "_=cv_ensemble_model.evaluate(test_data,test_label,evaluation_fn=accuracy_score,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            model  eval_metric\n",
      "0         model_3     0.922222\n",
      "1         model_4     0.933333\n",
      "2         model_2     0.944444\n",
      "3         model_5     0.944444\n",
      "4         model_1     0.966667\n",
      "5  Ensemble Model     1.000000\n"
     ]
    }
   ],
   "source": [
    "_=cv_ensemble_model.evaluate(train_data,train_label,accuracy_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = test_df.values[:,1:]\n",
    "test_pred = cv_ensemble_model.predict(test_data)\n",
    "test_pred"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "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": "3.8.15"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}