credit-part3-modeling-2-selectmodels-pipe1-part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ed0f1c7",
   "metadata": {
    "papermill": {
     "duration": 0.047714,
     "end_time": "2021-12-27T14:36:20.439567",
     "exception": false,
     "start_time": "2021-12-27T14:36:20.391853",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Feature engineering and feature selection is an iterative process that starts with engineering new features, training a model, and then testing the model predictions against the target labels. The goal is to determine which features improve the model’s prediction accuracy. You repeat this process, along with hyperparameter tuning, until you are satisfied with the model’s accuracy. \n",
    "\n",
    "# Load Lib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d6d7ec7",
   "metadata": {
    "_cell_guid": "ddedf45c-cdd2-4013-973f-6ccf6c78347a",
    "_uuid": "20c9b28e-4c93-447d-8210-cad89f404354",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:20.538821Z",
     "iopub.status.busy": "2021-12-27T14:36:20.536877Z",
     "iopub.status.idle": "2021-12-27T14:36:23.817534Z",
     "shell.execute_reply": "2021-12-27T14:36:23.818137Z",
     "shell.execute_reply.started": "2021-12-27T14:25:46.814332Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 3.332125,
     "end_time": "2021-12-27T14:36:23.818498",
     "exception": false,
     "start_time": "2021-12-27T14:36:20.486373",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
       ".datatable .bool    { background: #DDDD99; }\n",
       ".datatable .object  { background: #565656; }\n",
       ".datatable .int     { background: #5D9E5D; }\n",
       ".datatable .float   { background: #4040CC; }\n",
       ".datatable .str     { background: #CC4040; }\n",
       ".datatable .time    { background: #40CC40; }\n",
       ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
       ".datatable .frame tbody td { text-align: left; }\n",
       ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
       ".datatable th:nth-child(2) { padding-left: 12px; }\n",
       ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n",
       ".datatable .sp {  opacity: 0.25;}\n",
       ".datatable .footer { font-size: 9px; }\n",
       ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
       ".datatable .frame thead tr.colnames {  background-image: url('data:image/png;base64,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');  background-repeat: repeat-x;  background-size: 14px;  height: 28px;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load the librarys\n",
    "import pandas as pd #To work with dataset\n",
    "import numpy as np #Math library\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns #Graph library that use matplot in background\n",
    "import matplotlib.pyplot as plt #to plot some parameters in seaborn\n",
    "import warnings\n",
    "# Preparation  \n",
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import PowerTransformer, StandardScaler,Normalizer,RobustScaler,MaxAbsScaler,MinMaxScaler,QuantileTransformer\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "# Import StandardScaler from scikit-learn\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.impute import KNNImputer,IterativeImputer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.compose import make_column_transformer,ColumnTransformer\n",
    "from sklearn.pipeline import make_pipeline, Pipeline,FeatureUnion\n",
    "from sklearn.manifold import TSNE\n",
    "# Import train_test_split()\n",
    "# Metrics\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score,recall_score\n",
    "from sklearn.metrics import make_scorer,mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error,classification_report,f1_score\n",
    "from sklearn.metrics import roc_curve,confusion_matrix\n",
    "from datetime import datetime, date\n",
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.linear_model import LinearRegression, RidgeCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#import tensorflow as tf \n",
    "#from tensorflow.keras import layers\n",
    "#from tensorflow.keras.callbacks import EarlyStopping\n",
    "#from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "#import smogn\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "# For training random forest model\n",
    "import lightgbm as lgb\n",
    "from scipy import sparse\n",
    "from sklearn.neighbors import KNeighborsRegressor \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "# Model selection\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression,f_classif,chi2\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.feature_selection import mutual_info_classif,VarianceThreshold\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from lightgbm import LGBMClassifier\n",
    "import lightgbm as lgbm\n",
    "from catboost import CatBoostRegressor, CatBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import svm\n",
    "from xgboost import XGBClassifier,XGBRegressor\n",
    "from sklearn import set_config\n",
    "from itertools import combinations\n",
    "# Cluster :\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "#from yellowbrick.cluster import KElbowVisualizer\n",
    "#import smong \n",
    "import category_encoders as ce\n",
    "import warnings\n",
    "#import optuna \n",
    "from joblib import Parallel, delayed\n",
    "import joblib \n",
    "from sklearn import set_config\n",
    "from typing import List, Optional, Union\n",
    "set_config(display='diagram')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5437cdc8",
   "metadata": {
    "_cell_guid": "38c6befb-892d-4ef3-9c63-13be5959b671",
    "_uuid": "2953f9f3-02f8-4fa9-a7fc-cb93565c2183",
    "papermill": {
     "duration": 0.048282,
     "end_time": "2021-12-27T14:36:23.914910",
     "exception": false,
     "start_time": "2021-12-27T14:36:23.866628",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25b8bf18",
   "metadata": {
    "_cell_guid": "98a5b4a6-90e9-449d-96cc-0d85c9ff1c77",
    "_uuid": "e086592f-9f6a-4592-80b5-c1e7ad227e02",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:24.012977Z",
     "iopub.status.busy": "2021-12-27T14:36:24.012206Z",
     "iopub.status.idle": "2021-12-27T14:36:24.547975Z",
     "shell.execute_reply": "2021-12-27T14:36:24.547444Z",
     "shell.execute_reply.started": "2021-12-27T14:05:24.762591Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.58725,
     "end_time": "2021-12-27T14:36:24.548127",
     "exception": false,
     "start_time": "2021-12-27T14:36:23.960877",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 272 ms, sys: 69.4 ms, total: 341 ms\n",
      "Wall time: 516 ms\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0           1                 1                              0.766127   45   \n",
       "1           2                 0                              0.957151   40   \n",
       "2           3                 0                              0.658180   38   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "train = pd.read_csv('../input/GiveMeSomeCredit/cs-training.csv')\n",
    "test = pd.read_csv('../input/GiveMeSomeCredit/cs-test.csv')\n",
    "train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb11043a",
   "metadata": {
    "papermill": {
     "duration": 0.046799,
     "end_time": "2021-12-27T14:36:24.641442",
     "exception": false,
     "start_time": "2021-12-27T14:36:24.594643",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1-Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bee04e4",
   "metadata": {
    "papermill": {
     "duration": 0.046869,
     "end_time": "2021-12-27T14:36:24.734739",
     "exception": false,
     "start_time": "2021-12-27T14:36:24.687870",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Convert Dtypes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "519deeaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:24.861461Z",
     "iopub.status.busy": "2021-12-27T14:36:24.840592Z",
     "iopub.status.idle": "2021-12-27T14:36:24.930146Z",
     "shell.execute_reply": "2021-12-27T14:36:24.929533Z",
     "shell.execute_reply.started": "2021-12-27T14:05:25.293251Z"
    },
    "papermill": {
     "duration": 0.148389,
     "end_time": "2021-12-27T14:36:24.930322",
     "exception": false,
     "start_time": "2021-12-27T14:36:24.781933",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert Dtypes :\n",
    "train[train.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns] = train[train.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns].apply(pd.to_numeric)\n",
    "train[train.select_dtypes(['object','category']).columns] = train.select_dtypes(['object','category']).apply(lambda x: x.astype('category'))\n",
    "# Convert Dtypes :\n",
    "test[test.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns] = test[test.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns].apply(pd.to_numeric)\n",
    "test[test.select_dtypes(['object','category']).columns] = test.select_dtypes(['object','category']).apply(lambda x: x.astype('category'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03834104",
   "metadata": {
    "papermill": {
     "duration": 0.046051,
     "end_time": "2021-12-27T14:36:25.023494",
     "exception": false,
     "start_time": "2021-12-27T14:36:24.977443",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Reduce Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8764b75d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:25.128413Z",
     "iopub.status.busy": "2021-12-27T14:36:25.127636Z",
     "iopub.status.idle": "2021-12-27T14:36:25.199152Z",
     "shell.execute_reply": "2021-12-27T14:36:25.199763Z",
     "shell.execute_reply.started": "2021-12-27T14:05:25.398214Z"
    },
    "papermill": {
     "duration": 0.129076,
     "end_time": "2021-12-27T14:36:25.199962",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.070886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 13.73 MB\n",
      "Memory usage after optimization is: 3.29 MB\n",
      "Decreased by 76.0%\n",
      "Memory usage of dataframe is 9.29 MB\n",
      "Memory usage after optimization is: 2.90 MB\n",
      "Decreased by 68.7%\n"
     ]
    }
   ],
   "source": [
    "# Author : https://www.kaggle.com/gemartin/load-data-reduce-memory-usage\n",
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        name =df[col].dtype.name \n",
    "        \n",
    "        if col_type != object and col_type.name != 'category':\n",
    "        #if name != \"category\":    \n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df\n",
    "train= reduce_mem_usage(train)\n",
    "test= reduce_mem_usage(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0a3367",
   "metadata": {
    "papermill": {
     "duration": 0.046347,
     "end_time": "2021-12-27T14:36:25.294823",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.248476",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e780643",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:25.396472Z",
     "iopub.status.busy": "2021-12-27T14:36:25.395464Z",
     "iopub.status.idle": "2021-12-27T14:36:25.398444Z",
     "shell.execute_reply": "2021-12-27T14:36:25.397884Z",
     "shell.execute_reply.started": "2021-12-27T14:05:25.480075Z"
    },
    "papermill": {
     "duration": 0.056867,
     "end_time": "2021-12-27T14:36:25.398605",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.341738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cardinality : \n",
    "# - RevolvingUtilizationOfUnsecuredLines :125728, high Outlier\n",
    "# - DebtRatio :114194 , high Outlier \n",
    "# deal with outlier + bin \n",
    "PERCENTAGE = [\"RevolvingUtilizationOfUnsecuredLines\", \"DebtRatio\"]\n",
    "# MonthlyIncome:13594 , high outlier +bin \n",
    "REAL= [\"MonthlyIncome\"]\n",
    "# Can be considred as cat \n",
    "NUMERIC_DISCRET_low = [\"NumberOfDependents\",\n",
    "                       \"NumberOfTime60-89DaysPastDueNotWorse\",\n",
    "                       \"NumberRealEstateLoansOrLines\",\n",
    "                       \"NumberOfTimes90DaysLate\",\n",
    "                       \"NumberOfOpenCreditLinesAndLoans\",\n",
    "                       \"NumberOfTime30-59DaysPastDueNotWorse\",\n",
    "                       \"age\"]\n",
    "Late_Pay_Cols = ['NumberOfTime30-59DaysPastDueNotWorse',\n",
    "                 'NumberOfTimes90DaysLate',\n",
    "                 'NumberOfTime60-89DaysPastDueNotWorse']\n",
    "TARGET = [\"SeriousDlqin2yrs\"]\n",
    "\n",
    "#also change the type for TARGET to categorical\n",
    "#df[TARGET] = df[TARGET].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6616557f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:25.498772Z",
     "iopub.status.busy": "2021-12-27T14:36:25.498060Z",
     "iopub.status.idle": "2021-12-27T14:36:25.501193Z",
     "shell.execute_reply": "2021-12-27T14:36:25.501668Z",
     "shell.execute_reply.started": "2021-12-27T14:05:25.488987Z"
    },
    "papermill": {
     "duration": 0.053931,
     "end_time": "2021-12-27T14:36:25.501867",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.447936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = train['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8993a6b",
   "metadata": {
    "papermill": {
     "duration": 0.049333,
     "end_time": "2021-12-27T14:36:25.599214",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.549881",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Imput before Descritaziation : \n",
    "\n",
    "### IterativeImputer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5cc4f04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:25.702246Z",
     "iopub.status.busy": "2021-12-27T14:36:25.701106Z",
     "iopub.status.idle": "2021-12-27T14:36:25.714313Z",
     "shell.execute_reply": "2021-12-27T14:36:25.713288Z",
     "shell.execute_reply.started": "2021-12-27T14:05:25.510234Z"
    },
    "papermill": {
     "duration": 0.065773,
     "end_time": "2021-12-27T14:36:25.714519",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.648746",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 324 µs, sys: 39 µs, total: 363 µs\n",
      "Wall time: 369 µs\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "param =  {   \"verbosity\": 0,\n",
    "            #\"objective\": \"binary:logistic\",\n",
    "            #\"eval_metric\": \"auc\",\n",
    "            'random_state': 42,\n",
    "            # regression\n",
    "            'objective':'reg:squarederror', \n",
    "             'eval_metric': 'mae',\n",
    "            #early_stopping_rounds=100 ,\n",
    "            #'gpu_id':0, \n",
    "            #'predictor':\"gpu_predictor\",\n",
    "            # use exact for small dataset.\n",
    "            #\"tree_method\": \"exact\",\n",
    "            # big data :\n",
    "            # 'tree_method': 'gpu_hist',\n",
    "            # defines booster, gblinear for linear functions.\n",
    "             'booster': 'gbtree', \n",
    "            'lambda': 8.544792472633987e-07,\n",
    "            'alpha': 0.31141671752487043,\n",
    "            'subsample': 0.8779467596981366, \n",
    "            'colsample_bytree': 0.9759532762677546,\n",
    "            'learning_rate': 0.008686087328805853, \n",
    "            'n_estimators': 6988,\n",
    "            'max_depth': 9,\n",
    "            'min_child_weight': 2, \n",
    "            'eta': 3.7603213457541647e-06,\n",
    "            'gamma': 2.1478058456847449e-07,\n",
    "            'grow_policy': 'lossguide'}\n",
    "                \n",
    "\n",
    "#model_xgb = XGBRegressor(\n",
    "       #objective=\"mae\",\n",
    " #   **xgb_params2)\n",
    "\n",
    "numeric_transformer1 = Pipeline(\n",
    "                            steps=[\n",
    "                            ('imputer', SimpleImputer(strategy='median'\n",
    "                                                      ,add_indicator=True)),\n",
    "                            ('scaler', PowerTransformer()),#(Numerical Input, Numerical Output)\n",
    "                            # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                            #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                           # ('reducedim',  SelectPercentile(f_classif,percentile=90))\n",
    "                            ]\n",
    "                            )\n",
    "numeric_transformer2 = Pipeline(\n",
    "                            steps=[\n",
    "                            #('imputer', SimpleImputer(strategy='median'\n",
    "                             #                         ,add_indicator=True)),\n",
    "                            ('scaler', PowerTransformer()),#(Numerical Input, Numerical Output)\n",
    "                            # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                            #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                           # ('reducedim',  SelectPercentile(f_classif,percentile=90))\n",
    "                            ]\n",
    "                            )\n",
    "\n",
    "pipe_xgbr1 = Pipeline(\n",
    "                    steps=[\n",
    "                        ('preprocessor', numeric_transformer1),\n",
    "                        ('classifier', XGBRegressor(\n",
    "                      #objective=\"mae\",\n",
    "                       **param))\n",
    "                    ]\n",
    "                )\n",
    "pipe_xgbr2 = Pipeline(\n",
    "                    steps=[\n",
    "                        ('preprocessor', numeric_transformer2),\n",
    "                        ('classifier', XGBRegressor(\n",
    "                      #objective=\"mae\",\n",
    "                       **param))\n",
    "                    ]\n",
    "                )\n",
    "\n",
    "#imputer=IterativeImputer(estimator=XGBRegressor(\n",
    "#                      #objective=\"mae\",\n",
    " #                     **param),max_iter=2,\n",
    "  #                       add_indicator=True,\n",
    "   #                      random_state=42)\n",
    "#train=train.drop(['Unnamed: 0','SeriousDlqin2yrs'], axis=1)\n",
    "#test=test.drop(['Unnamed: 0','SeriousDlqin2yrs'], axis=1)\n",
    "#imputer.fit(train)\n",
    "#train = imputer.transform(train)\n",
    "#test = imputer.transform(test)\n",
    "#features=['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
    "#      'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
    " #      'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
    " #     'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
    " #     'NumberOfDependents']\n",
    "#train=pd.DataFrame(train,columns=features)\n",
    "#test=pd.DataFrame(test,columns=features)\n",
    "#train= reduce_mem_usage(train)\n",
    "#test= reduce_mem_usage(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2f352e",
   "metadata": {
    "papermill": {
     "duration": 0.046862,
     "end_time": "2021-12-27T14:36:25.810214",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.763352",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Rgression Imputer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fa5f914",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:36:25.914520Z",
     "iopub.status.busy": "2021-12-27T14:36:25.913751Z",
     "iopub.status.idle": "2021-12-27T14:56:17.784192Z",
     "shell.execute_reply": "2021-12-27T14:56:17.784807Z",
     "shell.execute_reply.started": "2021-12-27T14:05:25.534127Z"
    },
    "papermill": {
     "duration": 1191.927732,
     "end_time": "2021-12-27T14:56:17.785006",
     "exception": false,
     "start_time": "2021-12-27T14:36:25.857274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 17min 15s, sys: 7.77 s, total: 1h 17min 22s\n",
      "Wall time: 19min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "train=train.drop(['Unnamed: 0','SeriousDlqin2yrs'], axis=1)\n",
    "test=test.drop(['Unnamed: 0','SeriousDlqin2yrs'], axis=1)\n",
    "train_final= pd.concat( [train, test], axis=0) \n",
    "#testdf_income= train_final[train_final['MonthlyIncome'].isnull()==True]\n",
    "traindf_income = train_final[train_final['MonthlyIncome'].isnull()==False]\n",
    "y_income = traindf_income['MonthlyIncome']\n",
    "X_income=traindf_income.drop([\"MonthlyIncome\"],axis=1)\n",
    "pipe_xgbr1.fit(X_income, y_income)\n",
    "train_income_missing=train[train['MonthlyIncome'].isnull()==True].drop([\"MonthlyIncome\"],axis=1)\n",
    "test_income_missing=test[test['MonthlyIncome'].isnull()==True].drop([\"MonthlyIncome\"],axis=1)\n",
    "train_predicted = pipe_xgbr1.predict(train_income_missing)\n",
    "test_predicted = pipe_xgbr1.predict(test_income_missing)\n",
    "train.loc[(train.MonthlyIncome.isnull()), 'MonthlyIncome'] = train_predicted\n",
    "test.loc[(test.MonthlyIncome.isnull()), 'MonthlyIncome'] = test_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a23a816",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T14:56:17.890803Z",
     "iopub.status.busy": "2021-12-27T14:56:17.889570Z",
     "iopub.status.idle": "2021-12-27T15:19:37.217127Z",
     "shell.execute_reply": "2021-12-27T15:19:37.217721Z",
     "shell.execute_reply.started": "2021-12-27T14:24:19.308661Z"
    },
    "papermill": {
     "duration": 1399.385357,
     "end_time": "2021-12-27T15:19:37.217906",
     "exception": false,
     "start_time": "2021-12-27T14:56:17.832549",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 31min, sys: 6.46 s, total: 1h 31min 7s\n",
      "Wall time: 23min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "traindf_NumberOfDependents = train_final[train_final['NumberOfDependents'].isnull()==False]\n",
    "y_NumberOfDependents = traindf_NumberOfDependents['NumberOfDependents']\n",
    "X_NumberOfDependents=traindf_NumberOfDependents.drop([\"NumberOfDependents\"],axis=1)\n",
    "pipe_xgbr2.fit(X_NumberOfDependents, y_NumberOfDependents)\n",
    "train_NumberOfDependents_missing=train[train['NumberOfDependents'].isnull()==True].drop([\"NumberOfDependents\"],axis=1)\n",
    "test_NumberOfDependents_missing=test[test['NumberOfDependents'].isnull()==True].drop([\"NumberOfDependents\"],axis=1)\n",
    "train_predicted = pipe_xgbr2.predict(train_NumberOfDependents_missing)\n",
    "test_predicted = pipe_xgbr2.predict(test_NumberOfDependents_missing)\n",
    "train.loc[(train.NumberOfDependents.isnull()), 'NumberOfDependents'] = train_predicted\n",
    "test.loc[(test.NumberOfDependents.isnull()), 'NumberOfDependents'] = test_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d0d1b5",
   "metadata": {
    "papermill": {
     "duration": 0.047541,
     "end_time": "2021-12-27T15:19:37.313923",
     "exception": false,
     "start_time": "2021-12-27T15:19:37.266382",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Features Engineer\n",
    "### Add Bin \n",
    "It's always good to bin data when we have lot of outlier and skweness "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad6e685d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:37.427289Z",
     "iopub.status.busy": "2021-12-27T15:19:37.426560Z",
     "iopub.status.idle": "2021-12-27T15:19:38.117621Z",
     "shell.execute_reply": "2021-12-27T15:19:38.118318Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.084335Z"
    },
    "papermill": {
     "duration": 0.756891,
     "end_time": "2021-12-27T15:19:38.118500",
     "exception": false,
     "start_time": "2021-12-27T15:19:37.361609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add bin data \n",
    "# initializing append_str\n",
    "append_str = 'cat_'\n",
    "# Append suffix / prefix to strings in list\n",
    "num_features1=[\"RevolvingUtilizationOfUnsecuredLines\", \"DebtRatio\",\"MonthlyIncome\"]\n",
    "num_features2=[\"NumberOfDependents\",\n",
    "                       \"NumberOfTime60-89DaysPastDueNotWorse\",\n",
    "                       \"NumberRealEstateLoansOrLines\",\n",
    "                       \"NumberOfTimes90DaysLate\",\n",
    "                       \"NumberOfOpenCreditLinesAndLoans\",\n",
    "                       \"NumberOfTime30-59DaysPastDueNotWorse\",\n",
    "                       \"age\"]\n",
    "cat_features1 = [append_str + sub for sub in num_features1]\n",
    "cat_features2 = [append_str + sub for sub in num_features2]\n",
    "\n",
    "# create the discretizer object with strategy quantile and 1000 bins\n",
    "discretizer1 = KBinsDiscretizer(n_bins=40, encode='ordinal',strategy='quantile')\n",
    "discretizer2 = KBinsDiscretizer(n_bins=4, encode='ordinal',strategy='quantile')\n",
    "\n",
    "pipeline1 = Pipeline([\n",
    "        ('imputer', SimpleImputer( strategy='median')),\n",
    "        ('bin', discretizer1)\n",
    "    ])\n",
    "# fit the discretizer to the train set\n",
    "pipeline1.fit(train.loc[:,num_features1])\n",
    "# apply the discretisation\n",
    "train_cat1 = pipeline1.transform(train.loc[:,num_features1])\n",
    "test_cat1 = pipeline1.transform(test.loc[:,num_features1])\n",
    "train_df1=pd.DataFrame(train_cat1,columns=cat_features1).astype('category')\n",
    "test_df1=pd.DataFrame(test_cat1,columns=cat_features1).astype('category')\n",
    "train_final1= pd.concat( [train.loc[:,num_features1], train_df1], axis=1) \n",
    "test_final1= pd.concat( [test.loc[:,num_features1], test_df1], axis=1) \n",
    "\n",
    "pipeline2 = Pipeline([\n",
    "        ('imputer', SimpleImputer( strategy='median')),\n",
    "        ('bin', discretizer2)\n",
    "    ])\n",
    "# fit the discretizer to the train set\n",
    "pipeline2.fit(train.loc[:,num_features2])\n",
    "# apply the discretisation\n",
    "train_cat2 = pipeline2.transform(train.loc[:,num_features2])\n",
    "test_cat2 = pipeline2.transform(test.loc[:,num_features2])\n",
    "train_df2=pd.DataFrame(train_cat2,columns=cat_features2).astype('category')\n",
    "test_df2=pd.DataFrame(test_cat2,columns=cat_features2).astype('category')\n",
    "train_final2= pd.concat( [train.loc[:,num_features2], train_df2], axis=1) \n",
    "test_final2= pd.concat( [test.loc[:,num_features2], test_df2], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb4aa907",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:38.220596Z",
     "iopub.status.busy": "2021-12-27T15:19:38.219881Z",
     "iopub.status.idle": "2021-12-27T15:19:38.226354Z",
     "shell.execute_reply": "2021-12-27T15:19:38.225791Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.870413Z"
    },
    "papermill": {
     "duration": 0.059849,
     "end_time": "2021-12-27T15:19:38.226522",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.166673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_final= pd.concat( [train_final1, train_final2], axis=1) \n",
    "test_final= pd.concat( [test_final1, test_final2], axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1687ef2a",
   "metadata": {
    "papermill": {
     "duration": 0.048446,
     "end_time": "2021-12-27T15:19:38.322344",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.273898",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Final X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec31c86b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:38.423414Z",
     "iopub.status.busy": "2021-12-27T15:19:38.422757Z",
     "iopub.status.idle": "2021-12-27T15:19:38.425140Z",
     "shell.execute_reply": "2021-12-27T15:19:38.425819Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.884807Z"
    },
    "papermill": {
     "duration": 0.055887,
     "end_time": "2021-12-27T15:19:38.425992",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.370105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Pour le train test\n",
    "target= \"SeriousDlqin2yrs\"\n",
    "X = train_final# axis=1\n",
    "X_test_final =test_final# axis=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17e17680",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:38.529504Z",
     "iopub.status.busy": "2021-12-27T15:19:38.528809Z",
     "iopub.status.idle": "2021-12-27T15:19:38.560334Z",
     "shell.execute_reply": "2021-12-27T15:19:38.559778Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.893004Z"
    },
    "papermill": {
     "duration": 0.085158,
     "end_time": "2021-12-27T15:19:38.560481",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.475323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101503 entries, 0 to 101502\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                    Non-Null Count   Dtype   \n",
      "---  ------                                    --------------   -----   \n",
      " 0   RevolvingUtilizationOfUnsecuredLines      101503 non-null  float16 \n",
      " 1   DebtRatio                                 101503 non-null  float32 \n",
      " 2   MonthlyIncome                             101503 non-null  float32 \n",
      " 3   cat_RevolvingUtilizationOfUnsecuredLines  101503 non-null  category\n",
      " 4   cat_DebtRatio                             101503 non-null  category\n",
      " 5   cat_MonthlyIncome                         101503 non-null  category\n",
      " 6   NumberOfDependents                        101503 non-null  float16 \n",
      " 7   NumberOfTime60-89DaysPastDueNotWorse      101503 non-null  int8    \n",
      " 8   NumberRealEstateLoansOrLines              101503 non-null  int8    \n",
      " 9   NumberOfTimes90DaysLate                   101503 non-null  int8    \n",
      " 10  NumberOfOpenCreditLinesAndLoans           101503 non-null  int8    \n",
      " 11  NumberOfTime30-59DaysPastDueNotWorse      101503 non-null  int8    \n",
      " 12  age                                       101503 non-null  int8    \n",
      " 13  cat_NumberOfDependents                    101503 non-null  category\n",
      " 14  cat_NumberOfTime60-89DaysPastDueNotWorse  101503 non-null  category\n",
      " 15  cat_NumberRealEstateLoansOrLines          101503 non-null  category\n",
      " 16  cat_NumberOfTimes90DaysLate               101503 non-null  category\n",
      " 17  cat_NumberOfOpenCreditLinesAndLoans       101503 non-null  category\n",
      " 18  cat_NumberOfTime30-59DaysPastDueNotWorse  101503 non-null  category\n",
      " 19  cat_age                                   101503 non-null  category\n",
      "dtypes: category(10), float16(2), float32(2), int8(6)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "X_test_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46e0f88e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:38.662478Z",
     "iopub.status.busy": "2021-12-27T15:19:38.661053Z",
     "iopub.status.idle": "2021-12-27T15:19:38.690325Z",
     "shell.execute_reply": "2021-12-27T15:19:38.689719Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.939956Z"
    },
    "papermill": {
     "duration": 0.081055,
     "end_time": "2021-12-27T15:19:38.690481",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.609426",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                    Non-Null Count   Dtype   \n",
      "---  ------                                    --------------   -----   \n",
      " 0   RevolvingUtilizationOfUnsecuredLines      150000 non-null  float16 \n",
      " 1   DebtRatio                                 150000 non-null  float32 \n",
      " 2   MonthlyIncome                             150000 non-null  float32 \n",
      " 3   cat_RevolvingUtilizationOfUnsecuredLines  150000 non-null  category\n",
      " 4   cat_DebtRatio                             150000 non-null  category\n",
      " 5   cat_MonthlyIncome                         150000 non-null  category\n",
      " 6   NumberOfDependents                        150000 non-null  float16 \n",
      " 7   NumberOfTime60-89DaysPastDueNotWorse      150000 non-null  int8    \n",
      " 8   NumberRealEstateLoansOrLines              150000 non-null  int8    \n",
      " 9   NumberOfTimes90DaysLate                   150000 non-null  int8    \n",
      " 10  NumberOfOpenCreditLinesAndLoans           150000 non-null  int8    \n",
      " 11  NumberOfTime30-59DaysPastDueNotWorse      150000 non-null  int8    \n",
      " 12  age                                       150000 non-null  int8    \n",
      " 13  cat_NumberOfDependents                    150000 non-null  category\n",
      " 14  cat_NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  category\n",
      " 15  cat_NumberRealEstateLoansOrLines          150000 non-null  category\n",
      " 16  cat_NumberOfTimes90DaysLate               150000 non-null  category\n",
      " 17  cat_NumberOfOpenCreditLinesAndLoans       150000 non-null  category\n",
      " 18  cat_NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  category\n",
      " 19  cat_age                                   150000 non-null  category\n",
      "dtypes: category(10), float16(2), float32(2), int8(6)\n",
      "memory usage: 4.0 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ed907e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:38.792391Z",
     "iopub.status.busy": "2021-12-27T15:19:38.791702Z",
     "iopub.status.idle": "2021-12-27T15:19:38.795342Z",
     "shell.execute_reply": "2021-12-27T15:19:38.795881Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.972344Z"
    },
    "papermill": {
     "duration": 0.056087,
     "end_time": "2021-12-27T15:19:38.796047",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.739960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del train\n",
    "del test \n",
    "del train_final\n",
    "del test_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d515eae5",
   "metadata": {
    "papermill": {
     "duration": 0.049288,
     "end_time": "2021-12-27T15:19:38.894311",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.845023",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extract final cat  and num Features \n",
    "**What should we do for each colmun**\n",
    "\n",
    "Separate features by dtype:\n",
    "\n",
    "Next we’ll separate the features in the dataframe by their datatype. There are a few different ways to achieve this. I’ve used the select_dtypes() function to obtain specific data types by passing in np.number to obtain the numeric data and exclude=['np.number'] to return the categorical data. Appending .columns to the end returns an Index list containing the column names. For the categorical features, we don’t want to include the target  column, so I’ve dropped that.\n",
    "### Cat Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65350a49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:38.998735Z",
     "iopub.status.busy": "2021-12-27T15:19:38.998086Z",
     "iopub.status.idle": "2021-12-27T15:19:39.010664Z",
     "shell.execute_reply": "2021-12-27T15:19:39.011290Z",
     "shell.execute_reply.started": "2021-12-27T14:24:34.978886Z"
    },
    "papermill": {
     "duration": 0.066028,
     "end_time": "2021-12-27T15:19:39.011472",
     "exception": false,
     "start_time": "2021-12-27T15:19:38.945444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cat_RevolvingUtilizationOfUnsecuredLines', 'cat_DebtRatio',\n",
       "       'cat_MonthlyIncome', 'cat_NumberOfDependents',\n",
       "       'cat_NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'cat_NumberRealEstateLoansOrLines', 'cat_NumberOfTimes90DaysLate',\n",
       "       'cat_NumberOfOpenCreditLinesAndLoans',\n",
       "       'cat_NumberOfTime30-59DaysPastDueNotWorse', 'cat_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select non-numeric columns\n",
    "cat_columns = X.select_dtypes(exclude=['int64','int16','float16','float32','float64','int8']).columns\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efdf6d87",
   "metadata": {
    "papermill": {
     "duration": 0.050232,
     "end_time": "2021-12-27T15:19:39.112349",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.062117",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Num Features\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16994a49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:39.221977Z",
     "iopub.status.busy": "2021-12-27T15:19:39.221311Z",
     "iopub.status.idle": "2021-12-27T15:19:39.228596Z",
     "shell.execute_reply": "2021-12-27T15:19:39.227944Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.001033Z"
    },
    "papermill": {
     "duration": 0.066707,
     "end_time": "2021-12-27T15:19:39.228744",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.162037",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the float columns\n",
    "num_columns = X.select_dtypes(include=['int64','int16','float16','float32','float64','int8']).columns\n",
    "num_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87835e09",
   "metadata": {
    "papermill": {
     "duration": 0.048916,
     "end_time": "2021-12-27T15:19:39.327282",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.278366",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extracted features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f7c21fb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:39.439984Z",
     "iopub.status.busy": "2021-12-27T15:19:39.433403Z",
     "iopub.status.idle": "2021-12-27T15:19:39.443135Z",
     "shell.execute_reply": "2021-12-27T15:19:39.443621Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.029716Z"
    },
    "papermill": {
     "duration": 0.067438,
     "end_time": "2021-12-27T15:19:39.443797",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.376359",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FeaturesEngineer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        X=X.copy()\n",
    "        # Calculate some metrics across rows\n",
    "        X[\"num_missing\"]  = X.isnull().sum(axis=1)\n",
    "        X[\"std_row\"] = X.std(axis=1)\n",
    "        X[\"sem_row\"] = X.sem(axis=1)\n",
    "        X[\"abs_sum_row\"] = X.abs().sum(axis=1)\n",
    "        X[\"mean_row\"] = X.mean(axis=1)\n",
    "        X[\"max_row\"]= X.max(axis=1)\n",
    "        X[\"min_row\"]= X.min(axis=1)\n",
    "        X['Weighted_Sum_PastDue'] = 2 * X['NumberOfTime30-59DaysPastDueNotWorse'] + 3 * X['NumberOfTime60-89DaysPastDueNotWorse'] + 6 * X['NumberOfTimes90DaysLate']\n",
    "        X['90days_out_of_TotalPastDue'] = X['NumberOfTimes90DaysLate'] / (X['Weighted_Sum_PastDue'])\n",
    "        X.loc[X['Weighted_Sum_PastDue']==0, '90days_out_of_TotalPastDue'] = 0\n",
    "        X['RemainingLines'] = X['NumberOfOpenCreditLinesAndLoans'] - X['NumberRealEstateLoansOrLines']\n",
    "        X['Loans_vs_Other_Lines'] = X['RemainingLines'] / (1 + X['NumberRealEstateLoansOrLines'])\n",
    "        X['Debt_per_Real_Estate_Loan'] = X['DebtRatio'] / X['NumberRealEstateLoansOrLines']\n",
    "        X.loc[X['NumberRealEstateLoansOrLines']==0, 'Debt_per_Real_Estate_Loan'] = 0\n",
    "        X['Disposable_Income_per_person'] = (X['MonthlyIncome'] - X['DebtRatio']) / (X['NumberOfDependents'] + 1)\n",
    "        X.loc[X['NumberOfDependents']==0, 'Disposable_Income_per_person'] = 0\n",
    "        X['RemainingLines_per_person'] = X['RemainingLines'] / (X['NumberOfDependents'] + 1)\n",
    "        X['NumberRE_X_DebtRatio_X_age'] = X['NumberRealEstateLoansOrLines'] * X['DebtRatio'] / X['age']\n",
    "        X['RevolvingUtilization_per_age'] = X['RevolvingUtilizationOfUnsecuredLines'] / X['age']\n",
    "        X[X==np.inf]=np.nan\n",
    "        X=reduce_mem_usage(X)\n",
    "        return X  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e72981",
   "metadata": {
    "papermill": {
     "duration": 0.049149,
     "end_time": "2021-12-27T15:19:39.542059",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.492910",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Kmeans + encoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1cbf1c4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:39.649369Z",
     "iopub.status.busy": "2021-12-27T15:19:39.648689Z",
     "iopub.status.idle": "2021-12-27T15:19:39.652126Z",
     "shell.execute_reply": "2021-12-27T15:19:39.652752Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.047071Z"
    },
    "papermill": {
     "duration": 0.061701,
     "end_time": "2021-12-27T15:19:39.652924",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.591223",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MiniKmeansTransformerEncoder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, num_clusters = 11, encoder=ce.woe.WOEEncoder()):\n",
    "        self.num_clusters = num_clusters\n",
    "        self.encoder= encoder\n",
    "        if self.num_clusters > 0:\n",
    "            self.kmeans = MiniBatchKMeans(n_clusters=self.num_clusters, random_state=0)\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        if self.num_clusters > 0:\n",
    "            self.kmeans.fit(X)\n",
    "            preds=self.kmeans.predict(X)\n",
    "            preds=pd.DataFrame(preds, columns=['kmeans']).astype('category')\n",
    "            self.encoder.fit(preds,y)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        pred_classes = self.kmeans.predict(X)\n",
    "        pred_classes=pd.DataFrame(pred_classes, columns=['kmeans']).astype('category')\n",
    "        pred_encoded = self.encoder.transform(pred_classes)\n",
    "        return np.hstack((X, pred_encoded))\n",
    "        #return pred_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3cd8de19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:39.754039Z",
     "iopub.status.busy": "2021-12-27T15:19:39.753426Z",
     "iopub.status.idle": "2021-12-27T15:19:39.765062Z",
     "shell.execute_reply": "2021-12-27T15:19:39.765593Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.079690Z"
    },
    "papermill": {
     "duration": 0.063821,
     "end_time": "2021-12-27T15:19:39.765769",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.701948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SparseInteractions(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, degree=2, feature_name_separator=\"_\"):\n",
    "        self.degree = degree\n",
    "        self.feature_name_separator = feature_name_separator\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "        \n",
    "    def transform(self, X):\n",
    "        if not sparse.isspmatrix_csc(X):\n",
    "            X = sparse.csc_matrix(X)\n",
    "            \n",
    "        if hasattr(X, \"columns\"):\n",
    "            self.orig_col_names = X.columns\n",
    "        else:\n",
    "            self.orig_col_names = np.array([str(i) for i in range(X.shape[1])])\n",
    "            \n",
    "        spi = self._create_sparse_interactions(X)\n",
    "        return spi\n",
    "    \n",
    "    \n",
    "    def get_feature_names(self):\n",
    "        return self.feature_names\n",
    "    \n",
    "    def _create_sparse_interactions(self, X):\n",
    "        out_mat = []\n",
    "        self.feature_names = self.orig_col_names.tolist()\n",
    "        \n",
    "        for sub_degree in range(2, self.degree + 1):\n",
    "            for col_ixs in combinations(range(X.shape[1]), sub_degree):\n",
    "                # add name for new column\n",
    "                name = self.feature_name_separator.join(self.orig_col_names[list(col_ixs)])\n",
    "                self.feature_names.append(name)\n",
    "                \n",
    "                # get column multiplications value\n",
    "                out = X[:, col_ixs[0]]    \n",
    "                for j in col_ixs[1:]:\n",
    "                    out = out.multiply(X[:, j])\n",
    "\n",
    "                out_mat.append(out)\n",
    "\n",
    "        return sparse.hstack([X] + out_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed66ae6",
   "metadata": {
    "papermill": {
     "duration": 0.048743,
     "end_time": "2021-12-27T15:19:39.863766",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.815023",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Complete Pipe \n",
    "\n",
    "1. Features Engineer\n",
    "\n",
    "1. Kmeans\n",
    "\n",
    "1. Sparse\n",
    "\n",
    "1. Poly\n",
    "\n",
    "1. Scaler/Transformer\n",
    "\n",
    "1. imput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1acdda8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:39.970847Z",
     "iopub.status.busy": "2021-12-27T15:19:39.970127Z",
     "iopub.status.idle": "2021-12-27T15:19:39.978327Z",
     "shell.execute_reply": "2021-12-27T15:19:39.978853Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.104181Z"
    },
    "papermill": {
     "duration": 0.063848,
     "end_time": "2021-12-27T15:19:39.979043",
     "exception": false,
     "start_time": "2021-12-27T15:19:39.915195",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cat pipeline\n",
    "categorical_transformer = Pipeline(\n",
    "                    steps=[\n",
    "                        ('imputer', SimpleImputer(strategy='most_frequent',\n",
    "                                                  fill_value='missing',\n",
    "                                                  add_indicator=True)),\n",
    "                        ('encoder',  ce.woe.WOEEncoder()),#(Numerical Input, Categorical Output)\n",
    "                        #('sparse_features', SparseInteractions(degree=2)),\n",
    "                        #('reducedim',  SelectPercentile( mutual_info_classif, percentile=90))\n",
    "\n",
    "                    ]\n",
    "                    ) \n",
    "#Define vnum pipeline\n",
    "numeric_transformer = Pipeline(\n",
    "                            steps=[\n",
    "                            ('imputer', SimpleImputer(strategy='median'\n",
    "                                                      ,add_indicator=True)),\n",
    "                            #('general_features',FeaturesEngineer()),\n",
    "                            ('scaler', PowerTransformer()),#(Numerical Input, Numerical Output)\n",
    "                            # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                            #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                            ('kmeans',MiniKmeansTransformerEncoder()),\n",
    "                            ('polynominal_features', PolynomialFeatures(degree=2)),\n",
    "                            #('reducedim',  SelectPercentile(f_classif,percentile=90))\n",
    "                            ]\n",
    "                            )\n",
    "# Features union cat + num \n",
    "# WOE+PowerTransformer\n",
    "preprocessor_woe_powertransformer = ColumnTransformer(\n",
    "            transformers=[\n",
    "                ('numerical', numeric_transformer, num_columns),\n",
    "               # ('categorical', categorical_transformer, cat_columns)\n",
    "            ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c948cee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:40.082981Z",
     "iopub.status.busy": "2021-12-27T15:19:40.082316Z",
     "iopub.status.idle": "2021-12-27T15:19:40.160874Z",
     "shell.execute_reply": "2021-12-27T15:19:40.161407Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.130409Z"
    },
    "papermill": {
     "duration": 0.133323,
     "end_time": "2021-12-27T15:19:40.161599",
     "exception": false,
     "start_time": "2021-12-27T15:19:40.028276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9cdf3e10-49d1-4e54-a4b5-1ff3b1a94952\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9cdf3e10-49d1-4e54-a4b5-1ff3b1a94952\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1a91446e-5335-499f-9799-70b4e341ac65\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"1a91446e-5335-499f-9799-70b4e341ac65\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a584e763-87f2-4042-bca1-36bab15e79b2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a584e763-87f2-4042-bca1-36bab15e79b2\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"538be318-a8b4-4015-9780-74f0ea63069e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"538be318-a8b4-4015-9780-74f0ea63069e\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3277423f-85a6-468b-97fb-c3ae29fb7192\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3277423f-85a6-468b-97fb-c3ae29fb7192\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"07c4e6ab-9c4f-42d0-aa5f-0f6667564778\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"07c4e6ab-9c4f-42d0-aa5f-0f6667564778\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ab639ec7-31d3-4421-981e-4b8d6a14f00a\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ab639ec7-31d3-4421-981e-4b8d6a14f00a\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc51df8",
   "metadata": {
    "papermill": {
     "duration": 0.049657,
     "end_time": "2021-12-27T15:19:40.260947",
     "exception": false,
     "start_time": "2021-12-27T15:19:40.211290",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Modeling\n",
    "Modeling is the part of the Cross-Industry Standard Process for Data Mining (CRISP-DM) process model that i like best. Our data is already in good shape, and now we can search for useful patterns in our data.\n",
    "\n",
    "Tasks\n",
    "\n",
    "    Select modeling technique Select technique\n",
    "\n",
    "    Generate test design\n",
    "\n",
    "    Build model\n",
    "\n",
    "    Assess model\n",
    "\n",
    "Define CrossValidation Strategie :\n",
    "\n",
    "                skf = StratifiedKFold(n_splits=FOLDS, \n",
    "                shuffle=True, \n",
    "                random_state=SEED)\n",
    "\n",
    "We will use StratifiedCV in order to keep track of our imblanced data .\n",
    "\n",
    "\n",
    "\n",
    "## Chose Best model\n",
    "\n",
    "We can combine CV and hyper param tuning like this :\n",
    "\n",
    "    RF_MODEL = {}\n",
    "    # Définir la pipe\n",
    "    RF_MODEL['pipeline']=Pipeline([\n",
    "                                   ('data_cleaning', data_cleaning),\n",
    "                                   ('rf', RandomForest(random_state=7))])\n",
    "    # Définir la grille d'hyperparams\n",
    "    RF_MODEL['hyperparams'] = {}\n",
    "    RF_MODEL['hyperparams']['rf__n_estimators'] = [10, 30, 50, 100, 150]\n",
    "    RF_MODEL['hyperparams']['rf__max_features'] = [None, 'sqrt', 'log2', .1, .25, .50, .75, .85]\n",
    "    RF_MODEL['hyperparams']['rf__max_depth'] = [None, 4, 7, 10, 20]\n",
    "    # Recherche d'HP\n",
    "    RF_MODEL['gridsearch'] = GridSearchCV(\n",
    "        estimator=RF_MODEL['pipeline'],\n",
    "        param_grid=RF_MODEL['hyperparams'],\n",
    "        scoring='accuracy',\n",
    "        cv=cross_validation_design\n",
    "    )\n",
    "    RF_MODEL['gridsearch'].fit(X, Y)\n",
    "\n",
    "\n",
    "But this is so computing expensive.\n",
    "\n",
    "For this reason i used optuna to tune param and CV separatly .\n",
    "\n",
    "## List of models tuned for pipe1:\n",
    "**1-HGBC:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90c6b04f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:40.390821Z",
     "iopub.status.busy": "2021-12-27T15:19:40.385636Z",
     "iopub.status.idle": "2021-12-27T15:19:40.773063Z",
     "shell.execute_reply": "2021-12-27T15:19:40.772478Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.225616Z"
    },
    "papermill": {
     "duration": 0.461791,
     "end_time": "2021-12-27T15:19:40.773228",
     "exception": false,
     "start_time": "2021-12-27T15:19:40.311437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"914f8c6d-12f3-4438-8ba4-61d36af95ebb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"914f8c6d-12f3-4438-8ba4-61d36af95ebb\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 HistGradientBoostingClassifier(early_stopping='False',\n",
       "                                                l2_regularization=2.940296779699346e-10,\n",
       "                                                learning_rate=0.015318591316397998,\n",
       "                                                max_bins=137, max_depth=19,\n",
       "                                                max_iter=1537,\n",
       "                                                max_leaf_nodes=23,\n",
       "                                                min_samples_leaf=140))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c7ecd4a6-250d-40bc-aea1-54fb547370a0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c7ecd4a6-250d-40bc-aea1-54fb547370a0\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fc12f3dc-4a5b-4e5e-b18d-6fdfdb581db5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fc12f3dc-4a5b-4e5e-b18d-6fdfdb581db5\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"67f9db57-b9d4-4dfd-bc3b-e3c5c299d441\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"67f9db57-b9d4-4dfd-bc3b-e3c5c299d441\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"32e43e09-7935-4e68-a23e-81ed234f0aac\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"32e43e09-7935-4e68-a23e-81ed234f0aac\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bed2ef62-0260-4e64-979f-0592fedb3491\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bed2ef62-0260-4e64-979f-0592fedb3491\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3222041a-1a28-47ef-9524-03f6faf0886f\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3222041a-1a28-47ef-9524-03f6faf0886f\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6a3f7f51-0208-4cde-8150-db857547b8f0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6a3f7f51-0208-4cde-8150-db857547b8f0\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"20f7d736-afee-4baf-824e-657961643d41\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"20f7d736-afee-4baf-824e-657961643d41\">HistGradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingClassifier(early_stopping='False',\n",
       "                               l2_regularization=2.940296779699346e-10,\n",
       "                               learning_rate=0.015318591316397998, max_bins=137,\n",
       "                               max_depth=19, max_iter=1537, max_leaf_nodes=23,\n",
       "                               min_samples_leaf=140)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 HistGradientBoostingClassifier(early_stopping='False',\n",
       "                                                l2_regularization=2.940296779699346e-10,\n",
       "                                                learning_rate=0.015318591316397998,\n",
       "                                                max_bins=137, max_depth=19,\n",
       "                                                max_iter=1537,\n",
       "                                                max_leaf_nodes=23,\n",
       "                                                min_samples_leaf=140))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_hgbc = {'l2_regularization': 2.940296779699346e-10, \n",
    "         'early_stopping': 'False',\n",
    "         'learning_rate': 0.015318591316397998, \n",
    "         'max_iter': 1537, \n",
    "         'max_depth': 19, \n",
    "         'max_bins': 137, \n",
    "         'min_samples_leaf': 140,\n",
    "         'max_leaf_nodes': 23}\n",
    "model_hgbc = HistGradientBoostingClassifier(**params_hgbc)\n",
    "pipe1_model_hgbc_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_hgbc)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_hgbc_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3b79b0",
   "metadata": {
    "papermill": {
     "duration": 0.050724,
     "end_time": "2021-12-27T15:19:40.875643",
     "exception": false,
     "start_time": "2021-12-27T15:19:40.824919",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**2-CatBoost**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cabd6084",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:40.990929Z",
     "iopub.status.busy": "2021-12-27T15:19:40.989888Z",
     "iopub.status.idle": "2021-12-27T15:19:41.374740Z",
     "shell.execute_reply": "2021-12-27T15:19:41.374192Z",
     "shell.execute_reply.started": "2021-12-27T14:24:35.642459Z"
    },
    "papermill": {
     "duration": 0.4484,
     "end_time": "2021-12-27T15:19:41.374896",
     "exception": false,
     "start_time": "2021-12-27T15:19:40.926496",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"33913901-828b-4c19-aa3a-de8d90280814\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"33913901-828b-4c19-aa3a-de8d90280814\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 <catboost.core.CatBoostClassifier object at 0x7ff415a905d0>)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8c43ae66-938f-4eb4-a79e-71a572d08ff2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8c43ae66-938f-4eb4-a79e-71a572d08ff2\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2c32bdc6-9989-475f-a0ba-8ea566e479d7\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"2c32bdc6-9989-475f-a0ba-8ea566e479d7\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"10652b4a-8358-4559-8de6-aa322b23b834\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"10652b4a-8358-4559-8de6-aa322b23b834\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c2dd0ef1-26a6-40bd-adc0-64b8e9090e62\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c2dd0ef1-26a6-40bd-adc0-64b8e9090e62\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"38e11fbf-1010-44df-a040-d9c9ef4974d6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"38e11fbf-1010-44df-a040-d9c9ef4974d6\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b0cdf74c-e99d-4d1b-a8cb-d3018f6bb7e6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b0cdf74c-e99d-4d1b-a8cb-d3018f6bb7e6\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a941696c-4906-4ac7-95af-44f5de21441f\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a941696c-4906-4ac7-95af-44f5de21441f\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5bea1ee2-f903-4322-a770-85ed9674b190\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5bea1ee2-f903-4322-a770-85ed9674b190\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre><catboost.core.CatBoostClassifier object at 0x7ff415a905d0></pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 <catboost.core.CatBoostClassifier object at 0x7ff415a905d0>)])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_catboost = { \"random_state\": 42,\n",
    "                    \"loss_function\":\"Logloss\",\n",
    "                    \"eval_metric\":\"AUC\",\n",
    "                    #\"task_type\" : \"GPU\",\n",
    "                   'learning_rate': 0.03174251209279996,\n",
    "                   'iterations': 1957,\n",
    "                   'objective': 'CrossEntropy',\n",
    "                   'colsample_bylevel': 0.05457006237382004,\n",
    "                   'depth': 4,\n",
    "                   'boosting_type': 'Ordered',\n",
    "                   'bootstrap_type': 'MVS'}\n",
    "model_catboost = CatBoostClassifier(\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             **params_catboost)\n",
    "pipe1_model_catboost_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_catboost)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_catboost_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e951e1de",
   "metadata": {
    "papermill": {
     "duration": 0.051577,
     "end_time": "2021-12-27T15:19:41.477623",
     "exception": false,
     "start_time": "2021-12-27T15:19:41.426046",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**-3 XGBR**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46129f11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:41.600393Z",
     "iopub.status.busy": "2021-12-27T15:19:41.589603Z",
     "iopub.status.idle": "2021-12-27T15:19:41.994371Z",
     "shell.execute_reply": "2021-12-27T15:19:41.994856Z",
     "shell.execute_reply.started": "2021-12-27T14:24:36.058013Z"
    },
    "papermill": {
     "duration": 0.465989,
     "end_time": "2021-12-27T15:19:41.995037",
     "exception": false,
     "start_time": "2021-12-27T15:19:41.529048",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ecf4fcc8-93bd-4b7e-b4da-99acc8626e96\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ecf4fcc8-93bd-4b7e-b4da-99acc8626e96\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "                               interaction_constraints=None,\n",
       "                               lambda=0.05718879852112006,\n",
       "                               learning_rate=0.04028462871213848,\n",
       "                               max_delta_step=None, max_depth=3,\n",
       "                               min_child_weight=9, missing=nan,\n",
       "                               monotone_constraints=None, n_estimators=6258,\n",
       "                               n_jobs=None, num_parallel_tree=None,\n",
       "                               predictor=None, random_state=42, reg_alpha=None,\n",
       "                               reg_lambda=None, scale_pos_weight=None,\n",
       "                               subsample=0.4600950043573827, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"56b5e75c-2228-47e8-91f2-1a19a475e147\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"56b5e75c-2228-47e8-91f2-1a19a475e147\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f6c4985b-03b3-4fd6-9c05-0c7007ede8b6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f6c4985b-03b3-4fd6-9c05-0c7007ede8b6\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"64d00c50-e974-4d3f-b856-09ff60ca1cd1\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"64d00c50-e974-4d3f-b856-09ff60ca1cd1\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ec0f5d5b-2b64-41fb-a825-3b20a5ea1f13\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ec0f5d5b-2b64-41fb-a825-3b20a5ea1f13\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"93ed4939-ba7a-489d-89d7-2a3edac9fae1\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"93ed4939-ba7a-489d-89d7-2a3edac9fae1\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"47e2bdd6-9cfe-4770-9fbe-0b19e51771a6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"47e2bdd6-9cfe-4770-9fbe-0b19e51771a6\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"df665835-0181-473f-8df6-67d89bd38bc8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"df665835-0181-473f-8df6-67d89bd38bc8\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f937f641-5b0c-455b-9234-65cbcc5da79a\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f937f641-5b0c-455b-9234-65cbcc5da79a\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(alpha=0.3744455966091751, base_score=None, booster='gbtree',\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4826795171274075, enable_categorical=False,\n",
       "              eta=1.0243140309668855e-07, eval_metric='auc',\n",
       "              gamma=0.0002882867942209271, gpu_id=None, grow_policy='lossguide',\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              lambda=0.05718879852112006, learning_rate=0.04028462871213848,\n",
       "              max_delta_step=None, max_depth=3, min_child_weight=9, missing=nan,\n",
       "              monotone_constraints=None, n_estimators=6258, n_jobs=None,\n",
       "              num_parallel_tree=None, predictor=None, random_state=42,\n",
       "              reg_alpha=None, reg_lambda=None, scale_pos_weight=None,\n",
       "              subsample=0.4600950043573827, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "                               interaction_constraints=None,\n",
       "                               lambda=0.05718879852112006,\n",
       "                               learning_rate=0.04028462871213848,\n",
       "                               max_delta_step=None, max_depth=3,\n",
       "                               min_child_weight=9, missing=nan,\n",
       "                               monotone_constraints=None, n_estimators=6258,\n",
       "                               n_jobs=None, num_parallel_tree=None,\n",
       "                               predictor=None, random_state=42, reg_alpha=None,\n",
       "                               reg_lambda=None, scale_pos_weight=None,\n",
       "                               subsample=0.4600950043573827, ...))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_xgbr = { \"verbosity\": 0,\n",
    "                \"objective\": \"binary:logistic\",\n",
    "                \"eval_metric\": \"auc\",\n",
    "                'random_state': 42,\n",
    "                # regression\n",
    "                #'objective':'reg:squarederror', \n",
    "                # 'eval_metric': 'mae',\n",
    "                #early_stopping_rounds=100 ,\n",
    "               # 'gpu_id':0, \n",
    "               # 'predictor':\"gpu_predictor\",\n",
    "                # use exact for small dataset.\n",
    "                #\"tree_method\": \"exact\",\n",
    "                # big data :\n",
    "                # 'tree_method': 'gpu_hist',\n",
    "                # defines booster, gblinear for linear functions.\n",
    "               'booster': 'gbtree',\n",
    "               'lambda': 0.05718879852112006,\n",
    "               'alpha': 0.3744455966091751, \n",
    "               'subsample': 0.4600950043573827, \n",
    "               'colsample_bytree': 0.4826795171274075, \n",
    "               'learning_rate': 0.04028462871213848, \n",
    "               'n_estimators': 6258, \n",
    "               'max_depth': 3,\n",
    "               'min_child_weight': 9,\n",
    "               'eta': 1.0243140309668855e-07,\n",
    "               'gamma': 0.0002882867942209271, \n",
    "               'grow_policy': 'lossguide'\n",
    "              }\n",
    "model_xgbr = XGBClassifier(\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             **params_xgbr)\n",
    "pipe1_model_xgbr_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_xgbr)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_xgbr_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745eedaa",
   "metadata": {
    "papermill": {
     "duration": 0.051645,
     "end_time": "2021-12-27T15:19:42.099227",
     "exception": false,
     "start_time": "2021-12-27T15:19:42.047582",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**4-LR:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "265e6631",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:42.205711Z",
     "iopub.status.busy": "2021-12-27T15:19:42.205067Z",
     "iopub.status.idle": "2021-12-27T15:19:42.602057Z",
     "shell.execute_reply": "2021-12-27T15:19:42.601552Z",
     "shell.execute_reply.started": "2021-12-27T14:24:36.479834Z"
    },
    "papermill": {
     "duration": 0.451389,
     "end_time": "2021-12-27T15:19:42.602221",
     "exception": false,
     "start_time": "2021-12-27T15:19:42.150832",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"747b337a-bc3c-4047-943c-ee54ea0bec4c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"747b337a-bc3c-4047-943c-ee54ea0bec4c\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LogisticRegression(C=6805.270095210636, random_state=42,\n",
       "                                    solver='liblinear'))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c2dde96b-a3ec-487d-ad85-d71b52fbdc2c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c2dde96b-a3ec-487d-ad85-d71b52fbdc2c\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"505b9732-d00a-4c37-8079-1f5eb015e0ed\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"505b9732-d00a-4c37-8079-1f5eb015e0ed\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"15fbf6d0-95eb-477c-b8c6-9d31e4b5e27f\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"15fbf6d0-95eb-477c-b8c6-9d31e4b5e27f\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d71a8975-b455-479f-b301-b1f6f53f2834\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d71a8975-b455-479f-b301-b1f6f53f2834\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fc13c3e2-ae4f-44e9-bdac-682a38b5cdd4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fc13c3e2-ae4f-44e9-bdac-682a38b5cdd4\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7cd067be-b4cf-427d-896a-25c013524e04\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7cd067be-b4cf-427d-896a-25c013524e04\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a5278c7c-e91e-4e42-89a7-16ef5f641327\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a5278c7c-e91e-4e42-89a7-16ef5f641327\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"08655829-4200-4f6d-982f-c96d36eda10d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"08655829-4200-4f6d-982f-c96d36eda10d\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=6805.270095210636, random_state=42, solver='liblinear')</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LogisticRegression(C=6805.270095210636, random_state=42,\n",
       "                                    solver='liblinear'))])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_lr = {\n",
    "             \"random_state\": 42,\n",
    "             'solver':'liblinear',\n",
    "             'C': 6805.270095210636,\n",
    "             'penalty': 'l2'\n",
    "             }\n",
    "model_lr = LogisticRegression(**params_lr,\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             )\n",
    "pipe1_model_lr_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_lr)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_lr_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57ce49f",
   "metadata": {
    "papermill": {
     "duration": 0.054486,
     "end_time": "2021-12-27T15:19:42.709270",
     "exception": false,
     "start_time": "2021-12-27T15:19:42.654784",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**5-SVM:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e29a403",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:42.853392Z",
     "iopub.status.busy": "2021-12-27T15:19:42.852685Z",
     "iopub.status.idle": "2021-12-27T15:19:43.220092Z",
     "shell.execute_reply": "2021-12-27T15:19:43.220630Z",
     "shell.execute_reply.started": "2021-12-27T14:24:36.901117Z"
    },
    "papermill": {
     "duration": 0.457288,
     "end_time": "2021-12-27T15:19:43.220806",
     "exception": false,
     "start_time": "2021-12-27T15:19:42.763518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f2f8dc09-a975-4aa7-b1e6-930df472d822\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f2f8dc09-a975-4aa7-b1e6-930df472d822\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier', SVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"38631cff-7aa6-468f-bbb4-87251817731d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"38631cff-7aa6-468f-bbb4-87251817731d\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"cd40535f-687e-451a-a1aa-e2cd84624335\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"cd40535f-687e-451a-a1aa-e2cd84624335\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bbb5fdfc-9cb1-40dc-b65b-9047babe6af8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bbb5fdfc-9cb1-40dc-b65b-9047babe6af8\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"629ade60-8eb3-4186-b8c3-d5ed2d4600f4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"629ade60-8eb3-4186-b8c3-d5ed2d4600f4\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6ad3e338-dfcd-4f1a-a14a-0df505288b13\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6ad3e338-dfcd-4f1a-a14a-0df505288b13\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3d38ad7a-af82-4096-8cd1-3a5b2ac59c17\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3d38ad7a-af82-4096-8cd1-3a5b2ac59c17\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fcf4fe54-277b-4b7b-9869-ddb46e275e64\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fcf4fe54-277b-4b7b-9869-ddb46e275e64\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e605d7ee-361a-42cf-a078-232262ec7539\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e605d7ee-361a-42cf-a078-232262ec7539\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier', SVC())])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_svm = {\"random_state\": 42,\n",
    "             'C': 244.5231198228094, \n",
    "             'penalty': 'l2'\n",
    "            }\n",
    "model_svm = svm.SVC(\n",
    "     #**params_svm,\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             )\n",
    "pipe1_model_svm_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_svm)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_svm_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788700a4",
   "metadata": {
    "papermill": {
     "duration": 0.053324,
     "end_time": "2021-12-27T15:19:43.327203",
     "exception": false,
     "start_time": "2021-12-27T15:19:43.273879",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**6-RF:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5626a49d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:43.483362Z",
     "iopub.status.busy": "2021-12-27T15:19:43.447217Z",
     "iopub.status.idle": "2021-12-27T15:19:43.838316Z",
     "shell.execute_reply": "2021-12-27T15:19:43.837696Z",
     "shell.execute_reply.started": "2021-12-27T14:24:37.326127Z"
    },
    "papermill": {
     "duration": 0.458069,
     "end_time": "2021-12-27T15:19:43.838469",
     "exception": false,
     "start_time": "2021-12-27T15:19:43.380400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e2eefb70-27f1-4644-beb4-2727483a5841\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e2eefb70-27f1-4644-beb4-2727483a5841\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(max_depth=12, min_samples_leaf=4,\n",
       "                                        min_samples_split=3, n_estimators=2975,\n",
       "                                        random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"00159f8f-56c1-406b-87b7-48359fc04298\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"00159f8f-56c1-406b-87b7-48359fc04298\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8d4243ff-b9dd-4a79-80b0-0a9f9bf2a7ff\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8d4243ff-b9dd-4a79-80b0-0a9f9bf2a7ff\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a7a0db96-80f5-44b8-b351-bd56dfb59613\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a7a0db96-80f5-44b8-b351-bd56dfb59613\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"75d0e855-1e57-464c-aa85-5d72d7a7b7e1\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"75d0e855-1e57-464c-aa85-5d72d7a7b7e1\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9882edb8-203c-4eed-95da-114f02fc2e17\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9882edb8-203c-4eed-95da-114f02fc2e17\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"42ad0632-baad-42a4-bfd3-7b0e31fe8ee5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"42ad0632-baad-42a4-bfd3-7b0e31fe8ee5\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f419224b-adc5-4143-8921-38b5e289cd10\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f419224b-adc5-4143-8921-38b5e289cd10\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"dccb6444-2f68-4a15-9cbf-b7c1b514481e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"dccb6444-2f68-4a15-9cbf-b7c1b514481e\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=12, min_samples_leaf=4, min_samples_split=3,\n",
       "                       n_estimators=2975, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(max_depth=12, min_samples_leaf=4,\n",
       "                                        min_samples_split=3, n_estimators=2975,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_rf =  {\"random_state\": 42,\n",
    "              'n_estimators': 2975,\n",
    "              'max_features': 'auto', \n",
    "              'max_depth': 12,\n",
    "              'min_samples_split': 3, \n",
    "              'min_samples_leaf': 4, \n",
    "              'bootstrap': True\n",
    "             }\n",
    "model_rf = RandomForestClassifier(**params_rf,\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             )\n",
    "pipe1_model_rf_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_rf)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_rf_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69326aeb",
   "metadata": {
    "papermill": {
     "duration": 0.054798,
     "end_time": "2021-12-27T15:19:43.947481",
     "exception": false,
     "start_time": "2021-12-27T15:19:43.892683",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**7-lgbm:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1f26c5a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:44.061799Z",
     "iopub.status.busy": "2021-12-27T15:19:44.060746Z",
     "iopub.status.idle": "2021-12-27T15:19:44.463997Z",
     "shell.execute_reply": "2021-12-27T15:19:44.463393Z",
     "shell.execute_reply.started": "2021-12-27T14:24:37.733208Z"
    },
    "papermill": {
     "duration": 0.460407,
     "end_time": "2021-12-27T15:19:44.464132",
     "exception": false,
     "start_time": "2021-12-27T15:19:44.003725",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f59c2be8-8e21-46f5-9842-769da31e0653\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f59c2be8-8e21-46f5-9842-769da31e0653\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(bagging_fraction=0.8225459856082522,\n",
       "                                bagging_freq=3,\n",
       "                                feature_fraction=0.7099515479738928,\n",
       "                                lambda_l1=2.5825901359648176e-05,\n",
       "                                lambda_l2=4.490094872871969,\n",
       "                                learning_rate=0.005282912028625591,\n",
       "                                metric='auc', min_child_samples=35,\n",
       "                                n_estimators=1491, num_leaves=76,\n",
       "                                objective='binary', random_state=42,\n",
       "                                verbosity=-1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ea1d0203-c859-478e-b306-dacea4f088ed\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ea1d0203-c859-478e-b306-dacea4f088ed\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3fe206d3-4a54-4fd7-9cfd-ac2d172138ff\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3fe206d3-4a54-4fd7-9cfd-ac2d172138ff\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"824866ee-a3ac-4e6b-8d42-dc566ae61478\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"824866ee-a3ac-4e6b-8d42-dc566ae61478\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"22d19b4d-2c99-440c-9fec-f1f4795e05d0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"22d19b4d-2c99-440c-9fec-f1f4795e05d0\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4183bf0c-0d65-481c-ad56-9ec69c3a1097\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4183bf0c-0d65-481c-ad56-9ec69c3a1097\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e129b026-2ae0-48d9-89e9-b10667795109\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e129b026-2ae0-48d9-89e9-b10667795109\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d076d36b-712e-42f1-aa6f-7f16c1809d26\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d076d36b-712e-42f1-aa6f-7f16c1809d26\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9242b7e5-02bc-4482-8cee-c393aac18205\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9242b7e5-02bc-4482-8cee-c393aac18205\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(bagging_fraction=0.8225459856082522, bagging_freq=3,\n",
       "               feature_fraction=0.7099515479738928,\n",
       "               lambda_l1=2.5825901359648176e-05, lambda_l2=4.490094872871969,\n",
       "               learning_rate=0.005282912028625591, metric='auc',\n",
       "               min_child_samples=35, n_estimators=1491, num_leaves=76,\n",
       "               objective='binary', random_state=42, verbosity=-1)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(bagging_fraction=0.8225459856082522,\n",
       "                                bagging_freq=3,\n",
       "                                feature_fraction=0.7099515479738928,\n",
       "                                lambda_l1=2.5825901359648176e-05,\n",
       "                                lambda_l2=4.490094872871969,\n",
       "                                learning_rate=0.005282912028625591,\n",
       "                                metric='auc', min_child_samples=35,\n",
       "                                n_estimators=1491, num_leaves=76,\n",
       "                                objective='binary', random_state=42,\n",
       "                                verbosity=-1))])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_lgbm =  {\n",
    "               \"objective\": \"binary\",\n",
    "                'random_state': 42,\n",
    "                'metric': 'auc',\n",
    "                \"verbosity\": -1,\n",
    "                \"boosting_type\": \"gbdt\",\n",
    "                #\"device\": \"gpu\",\n",
    "                #\"early_stopping_rounds\":100,\n",
    "                'learning_rate': 0.005282912028625591, \n",
    "                'n_estimators': 1491,\n",
    "                'lambda_l1': 2.5825901359648176e-05, \n",
    "                'lambda_l2': 4.490094872871969,\n",
    "                'num_leaves': 76,\n",
    "                'feature_fraction': 0.7099515479738928,\n",
    "                'bagging_fraction': 0.8225459856082522,\n",
    "                'bagging_freq': 3,\n",
    "                'min_child_samples': 35\n",
    "               }\n",
    "model_lgbm = lgbm.LGBMClassifier(**params_lgbm,\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             )\n",
    "pipe1_model_lgbm_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_lgbm)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_lgbm_woe_powertransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "20658fe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:44.599321Z",
     "iopub.status.busy": "2021-12-27T15:19:44.594367Z",
     "iopub.status.idle": "2021-12-27T15:19:44.993020Z",
     "shell.execute_reply": "2021-12-27T15:19:44.992462Z",
     "shell.execute_reply.started": "2021-12-27T14:24:38.158141Z"
    },
    "papermill": {
     "duration": 0.473403,
     "end_time": "2021-12-27T15:19:44.993186",
     "exception": false,
     "start_time": "2021-12-27T15:19:44.519783",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"132a2029-75a9-4736-810e-ee66abd95a5a\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"132a2029-75a9-4736-810e-ee66abd95a5a\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(bagging_fraction=0.8225459856082522,\n",
       "                                bagging_freq=3,\n",
       "                                feature_fraction=0.7099515479738928,\n",
       "                                lambda_l1=2.5825901359648176e-05,\n",
       "                                lambda_l2=4.490094872871969,\n",
       "                                learning_rate=0.005282912028625591,\n",
       "                                metric='auc', min_child_samples=35,\n",
       "                                n_estimators=1491, num_leaves=76,\n",
       "                                objective='binary', random_state=42,\n",
       "                                verbosity=-1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f15869b8-b93b-4043-b202-a298b6d00f6e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f15869b8-b93b-4043-b202-a298b6d00f6e\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bc904c22-6e00-4383-af3e-396b824de9f2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bc904c22-6e00-4383-af3e-396b824de9f2\">numerical</label><div class=\"sk-toggleable__content\"><pre>Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"18e027a4-0286-4ff7-bf88-0c4f3ca0801e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"18e027a4-0286-4ff7-bf88-0c4f3ca0801e\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(add_indicator=True, strategy='median')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"99530243-d3ec-449b-8a76-b6fae731627d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"99530243-d3ec-449b-8a76-b6fae731627d\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d8b28b2c-7aa4-43eb-8a6d-d59b747eeb33\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d8b28b2c-7aa4-43eb-8a6d-d59b747eeb33\">kmeans: MiniKmeansTransformerEncoder</label><div class=\"sk-toggleable__content\"><pre>MiniKmeansTransformerEncoder()</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"38f30dbf-98c5-4f83-b875-1d8abf13ec2e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"38f30dbf-98c5-4f83-b875-1d8abf13ec2e\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"725c2fbb-124a-402b-8217-b94ffc9d7366\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"725c2fbb-124a-402b-8217-b94ffc9d7366\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"cc391630-00e5-487b-860a-f5213b90b4dd\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"cc391630-00e5-487b-860a-f5213b90b4dd\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(bagging_fraction=0.8225459856082522, bagging_freq=3,\n",
       "               feature_fraction=0.7099515479738928,\n",
       "               lambda_l1=2.5825901359648176e-05, lambda_l2=4.490094872871969,\n",
       "               learning_rate=0.005282912028625591, metric='auc',\n",
       "               min_child_samples=35, n_estimators=1491, num_leaves=76,\n",
       "               objective='binary', random_state=42, verbosity=-1)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=True,\n",
       "                                                                                 strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder()),\n",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome'...\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(bagging_fraction=0.8225459856082522,\n",
       "                                bagging_freq=3,\n",
       "                                feature_fraction=0.7099515479738928,\n",
       "                                lambda_l1=2.5825901359648176e-05,\n",
       "                                lambda_l2=4.490094872871969,\n",
       "                                learning_rate=0.005282912028625591,\n",
       "                                metric='auc', min_child_samples=35,\n",
       "                                n_estimators=1491, num_leaves=76,\n",
       "                                objective='binary', random_state=42,\n",
       "                                verbosity=-1))])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_param2={'learning_rate': 0.0018069834369607075,\n",
    "                             'max_depth': 8,\n",
    "                             #'max_features': 4,\n",
    "                             'min_samples_leaf': 47,\n",
    "                             #'min_samples_split': 389,\n",
    "                             'subsample': 0.8573598985000007,\n",
    "                             #'n_iter_no_change': 300,\n",
    "                             'n_estimators': 5000,\n",
    "                            # 'verbose': 0,\n",
    "                             'random_state': 42,\n",
    "                             'metric': 'auc',\n",
    "                            #\"device_type\" : \"gpu\",\n",
    "                            'boosting_type': 'gbdt',\n",
    "                           # 'tree_method': \"gpu_hist\"\n",
    "                           }\n",
    "model_lgbm2 = lgbm.LGBMClassifier(**lgbm_param2,\n",
    "                              # use it  only on gpu\n",
    "                            # rsm=np.nan\n",
    "                              #cat_features=cat_features\n",
    "                             )\n",
    "pipe1_model_lgbm_woe_powertransformer2 = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        # ('reducedim',  SelectPercentile( f_classif, percentile=95)),\n",
    "        # ('dim_red', SelectKBest(f_regression, k=20)),\n",
    "        #('Sparseinteractions', SparseInteractions(degree=2)),\n",
    "         #('PolynomialFeatures', PolynomialFeatures(degree=2)),\n",
    "        #('PCAreducer', PCA(n_components=0.8)),\n",
    "        ('classifier',model_lgbm)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_lgbm_woe_powertransformer2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5085d3",
   "metadata": {
    "papermill": {
     "duration": 0.054866,
     "end_time": "2021-12-27T15:19:45.103658",
     "exception": false,
     "start_time": "2021-12-27T15:19:45.048792",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Test models pipe1  with CV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e9590880",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:45.217695Z",
     "iopub.status.busy": "2021-12-27T15:19:45.216963Z",
     "iopub.status.idle": "2021-12-27T15:19:45.223256Z",
     "shell.execute_reply": "2021-12-27T15:19:45.223807Z",
     "shell.execute_reply.started": "2021-12-27T14:24:38.585282Z"
    },
    "papermill": {
     "duration": 0.065101,
     "end_time": "2021-12-27T15:19:45.223990",
     "exception": false,
     "start_time": "2021-12-27T15:19:45.158889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_roc(y,y_pred):\n",
    "    from sklearn.metrics import auc\n",
    "    fpr, tpr, thresholds = roc_curve(y_true=y, y_score=y_pred)\n",
    "    auc = auc(fpr, tpr)\n",
    "    plt.figure(1)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.plot(fpr, tpr, label='Model(area = {:.3f})'.format(auc))\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.title('ROC curve')\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "27776c31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:19:45.339146Z",
     "iopub.status.busy": "2021-12-27T15:19:45.338496Z",
     "iopub.status.idle": "2021-12-27T15:31:37.913733Z",
     "shell.execute_reply": "2021-12-27T15:31:37.912875Z"
    },
    "papermill": {
     "duration": 712.633425,
     "end_time": "2021-12-27T15:31:37.913917",
     "exception": false,
     "start_time": "2021-12-27T15:19:45.280492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0 | AUC: 0.8661355719937422\n",
      "Fold 0 | Avergae_precesion: 0.4002936145762436\n",
      "Fold 0 | recall: 0.18843469591226322\n",
      "Fold 0 | F1: 0.2876712328767123\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97     13997\n",
      "           1       0.61      0.19      0.29      1003\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.78      0.59      0.63     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13875   122]\n",
      " [  814   189]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 | AUC: 0.8608987640208616\n",
      "Fold 1 | Avergae_precesion: 0.3831851052656008\n",
      "Fold 1 | recall: 0.19242273180458624\n",
      "Fold 1 | F1: 0.28805970149253735\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97     13997\n",
      "           1       0.57      0.19      0.29      1003\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.76      0.59      0.63     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13853   144]\n",
      " [  810   193]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 2 | AUC: 0.864747901042176\n",
      "Fold 2 | Avergae_precesion: 0.39894283748611464\n",
      "Fold 2 | recall: 0.2053838484546361\n",
      "Fold 2 | F1: 0.3029411764705882\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97     13997\n",
      "           1       0.58      0.21      0.30      1003\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.76      0.60      0.63     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13846   151]\n",
      " [  797   206]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 3 | AUC: 0.8634007956839634\n",
      "Fold 3 | Avergae_precesion: 0.3936949195524541\n",
      "Fold 3 | recall: 0.19940179461615154\n",
      "Fold 3 | F1: 0.29739776951672864\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97     13997\n",
      "           1       0.58      0.20      0.30      1003\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.77      0.59      0.63     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13855   142]\n",
      " [  803   200]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFoCAYAAABHW8L2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABbRUlEQVR4nO3deXxM1//H8ddk32OPqNhqF0UttSaE1BJr7VtV6YKgShUlLbUVtUar+WkppZvag1Jq3yr2fQ0JMkQi+zpzf3+E+TYNEmRyZ5LP8/HoozN37tx55yTyybn33HM0iqIoCCGEEMLsWKgdQAghhBAvRoq4EEIIYaakiAshhBBmSoq4EEIIYaakiAshhBBmSoq4EEIIYaakiAshhBBmykrtAEKI5+Pj40NkZCSWlpY4ODjQrFkzJk2ahKOjo2Gf48ePM3/+fM6cOYOFhQX169dnzJgxVKxY0bBPfHw8CxYsYMeOHcTExFC0aFFatGjBkCFDKFKkiBpfmhDiOUlPXAgztGTJEk6cOMH69es5f/48QUFBhtdOnDjBoEGDaNmyJfv27WPnzp1UqVKF3r17ExYWBkBqaioDBgzg6tWrLF26lJCQEH799VcKFSrEmTNnjJY7PT3daMcWoiCSIi6EGStevDhNmzblwoULhm2zZ8+mU6dODBgwACcnJwoVKsSoUaOoVasWixYtAmDDhg3cvXuXwMBAKlasiIWFBUWLFmXYsGF4e3s/8bOuXLnCwIEDadCgAY0bN2bJkiUAjBs3jnnz5hn2O3LkCF5eXobnPj4+BAUF0aFDB2rXrk1QUBAjRozIdOypU6cydepUAOLi4pgwYQJNmzalWbNmzJs3D51OlzsNJkQ+I0VcCDMWERHBvn37KFOmDABJSUmcOHGCNm3aZNm3bdu2HDx4EICDBw/SrFmzTKfgnyU+Pp6BAwfSrFkz9u3bx/bt22nUqFGOcwYHBxMUFMSxY8fw8/Njz549xMfHA6DT6di2bRvt27cHMv4osLKyYvv27axfv54DBw7w+++/5/izhChIpIgLYYaGDRtGnTp18Pb2pkiRIoaebUxMDHq9nuLFi2d5T/HixYmOjgbg4cOHT9znaXbv3k2xYsV49913sbW1xcnJiVq1auX4/f3798fd3R07OzteeeUVqlevzl9//QXA4cOHsbOzo3bt2kRGRrJnzx4mTJiAg4MDRYsW5Z133iE4ODjHnyVEQSID24QwQ4sXL6Zx48YcPXqU0aNHEx0djYuLCy4uLlhYWHD//n1effXVTO+5f/8+hQsXBqBQoULcv38/x5939+5dQ2//Rbi7u2d63r59ezZv3kznzp3ZvHmzoRd+584d0tPTadq0qWFfvV6f5f1CiAzSExfCjDVo0IC33nqLr776CgAHBwdq167Ntm3bsuy7detWGjZsCEDjxo3Zv38/iYmJOfocd3d3w6C4/7K3tyc5OdnwPDIyMss+Go0m0/O2bdty9OhRIiIi2LFjBx06dACgZMmS2NjYcPjwYY4dO8axY8c4fvy49MSFeAop4kKYuQEDBnDw4EEuXrwIwOjRo1m/fj0rVqwgPj6emJgY5s2bx8mTJ/H39wegU6dOlCxZkuHDh3Pt2jX0ej3R0dEsWbKEPXv2ZPmM5s2bc//+fZYvX05qairx8fGcOnUKgGrVqrFnzx4ePnzI/fv3+fHHH7PNXKRIERo0aMD48eMpXbq04axBiRIlaNKkCTNnziQ+Ph69Xs+tW7c4evRobjWXEPmKFHEhzFyRIkXo1KkTixcvBqBevXosXbqUHTt20KxZM1q0aMGFCxdYvXo15cqVA8DGxobly5dToUIF3n33XerWrUv37t2Jjo7mtddey/IZTk5O/PDDD/z99980adKE1q1bc+TIESDjD4KqVavi4+PDu+++S7t27XKUu3379hw8eNBwKv2xWbNmkZaWRrt27ahfvz4jRox4rlP/QhQkGkVRFLVDCCGEEOL5SU9cCCGEMFNSxIUQQggzJUVcCCGEMFNSxIUQQggzZVaTvej1ehISErC2ts5y36kQQgiR3yiKQlpaGo6OjlhYZO13m1URT0hI4PLly2rHEEIIIfJU5cqVcXZ2zrLdrIq4tbU1kPHF2NjY5Moxz549i6enZ64cqyCTdnx50oYvT9rw5UkbvrzcbMPU1FQuX75sqH//ZVZF/PEpdBsbG2xtbXPtuLl5rIJM2vHlSRu+PGnDlydt+PJyuw2fdglZBrYJIYQQZkqKuBBCCGGmpIgLIYQQZkqKuBBCCGGmpIgLIYQQZkqKuBBCCGGmpIgLIYQQZspoRXz8+PE0atSI9u3bP/F1RVGYOnUqvr6+dOjQgXPnzhkrSp6oUqUKY8aMMTxPT0+nYcOGfPDBB891HB8fH6KionK8T3JyMv369UOn0z1/6Dzy3Xff4evrS+vWrdm3b98T9zl06BBdunShU6dO9O7dm5s3bxpe27JlC+3atcPPz4/Ro0dnel98fDxeXl5MmTLFsO2dd94hJibGOF+MEEKYEKMV8bfeeoulS5c+9fW9e/cSGhrK9u3b+fLLL/niiy+MFSVPODg4cOXKFZKTkwE4cOAAbm5uRv/cP/74A19fXywtLXO0v6Io6PV6I6f6n6tXrxIcHExwcDBLly5l8uTJT/yD44svvmDOnDls2LCB9u3b8+233wIQGhpKUFAQP//8M8HBwUyYMCHT++bPn0/9+vUzbevUqROrV6823hclhBAmwmgzttWvX5/w8PCnvr5z5046d+6MRqOhdu3axMbGcu/ePUqUKPFSnzt2UwhrTt3MfsdHUlNTsdn67P271SrLrA51sz2Wt7c3u3fvpk2bNgQHB+Pn50dISAgADx8+ZMKECYSFhWFvb8+UKVOoWrUq0dHRjB49Gq1WS+3atVEUxXC8DRs2sHLlStLS0qhVqxaff/55lmK9adMm5syZA2TMLT906FBiY2NJT09n5MiRtGrVivDwcAYNGkStWrU4d+4cQUFBbN26la1bt5Kamoqvry8jRowAYOjQoURERJCSksLbb79Nz549c9yWT7Jz5078/PywsbHBw8ODsmXLcvr0aerUqZNl3/j4eMP/H/8c/Pbbb/Tt2xdXV1cAihYtatj/7NmzPHjwgGbNmnH27FnDdh8fH/r27cuQIUNeKrsQIv/Q6fWcufsQ/b9+x+amE7ejiIxPITY2lmIWqdTNvmTkCtWmXdVqtZQsWdLwvGTJkmi12hwV8X//wn7ScVNTU58rS3b7a7VaQzF+Gp1OR/ny5Vm1ahWurq6cPHmSmjVrEhMTQ0hICMuXL6dQoUIMGjSIc+fOMWLECGbMmMGPP/6Iu7s7w4YN48SJE6xZs4ZTp04RFxfHzz//zCeffIKVlRU//PADCxcuxMvLi5SUFE6dOoWDgwPXr19Hq9Wi1WrR6XQMHjwYBwcHYmNj+fzzzylUqBCRkZHcvHmTgQMH0rNnT7Zv386xY8cYP348iqLw9ddfU6RIEapVq0bPnj1xcnIiNTWViRMn4ubmlmXS/ZUrV3L+/PksbdCoUSM6duyYaduZM2eoVKmSof2srKw4dOhQlrMBb7/9Nu+++y42NjbY29szefJkQkJCOHnyJFqtlhUrVqDX6+natSu1atVCr9czbdo0hg4dytmzZ7l3716m71FsbCy7d+9+4oIBpiy7nzORPWnDl5fXbfgwOZ0U/b+KqwLH7yWQps++4EYn6wi5l4AGOHQ3ARcbS540Q2lMSt5dcrSzUGhS6hiWFsZfbdOs5k5/zNPT86nz0v74nH/9hISEUDcX/mSytLSkS5curFy5kjt37tCmTRsqV67M/v37qVu3Ll9++SWLFi3Cw8ODunXr8v3331OlShVu3rxJYGCgYXtQUBC1atViy5YthIeHM336dCDj2nfVqlWpW7cutra21KpVi7S0NIoUKWLIn5aWxowZM/jnn3+wsLDg4cOHlC1blpIlS1KqVCl69eoFwF9//cXly5f58ssvAUhMTMTW1pa6deuyaNEiduzYAWScPShUqBC1a9fO9LU+qb2e1o7BwcGUK1fO8FrRokWpUKFCln2XLVvGDz/8QK1atVi6dCnbtm1j2rRpODs7k5yczNq1a4mIiKBfv35s2rSJjRs34ufnx5tvvkl8fDzx8fGZjlmqVCnc3d2pUqXKi3w7VZFbP4sFmbThy3vRNkxJz1wkz0fEcDs28Yn7/nnxjqHALdp38flDPkNsqo4aJV2zbC8N3IiKZ9AblbDM5aWs79+/x85du4iKjKSU7iHv9ehMg/r1cuXYKSkpz+y4qlbE3dzciIiIMDyPiIjIk2vIxubj48OsWbNYsWIFDx8+fOHjKIpCly5dsgzk+jc7O7tMZxE2bdpEVFQUa9euxdraGh8fH1JSUoCMa/b/Pvb7779vKOqPHTlyhIMHD/Lrr79ib29P//79De//t+nTp3PkyJFM25KSkujWrRvvv/9+pu3//T5rtdos3+eoqCguXrxIrVq1AGjXrh2DBw82vL9WrVpYW1vj4eFBuXLlCA0N5cSJE4SEhPDzzz+TkJBAWloaDg4OhsGFqamp2NnZPbXthBBZaeOSOBuZSPrN+6Tq9IzecAx7ayuy61DuvX7vpT+7SbnilC3iZHiemJZOdTdXKhV3yfa9FhoNXhXccLC2pJhT3v27T01NZfbs2ayYPx+dTsfQgQP54otFebpktmpF3MfHh59++gk/Pz9OnTqFs7PzS18PNwXdunXDxcWFKlWqZCp09erVY+PGjQwbNowjR45QuHBhnJycqF+/Pps2bWLo0KHs2bPHMKq6UaNGDB06lHfeeYeiRYvy8OFDEhISeOWVVwzHdHV1RafTkZKSgq2tLXFxcRQtWhRra2sOHz7M7du3n5ixadOmLFiwgA4dOuDo6IhWq8XKyoq4uDhcXV2xt7fn2rVrnDx58onv/+/gMnj6X+8+Pj6MHj2agQMHotVqCQ0N5bXXXsu0j4uLC3Fxcdy4cYPy5ctz4MABXn31VQBatWpFcHAwXbt2JSoqitDQUDw8PPj6668N71+7di1nz541FHBFUbh//36mthKiILkdk0jqv3rGVyPjuB4Vz39r8cnb0Xx36DJOthmlID4lPeOF7aEv9Lm+ld0Nj3V6BQcbK7wqZP29rgClCzlQzS2jx1ymkCOFHcxv5bRbt24RGBiIu7s7CxcupHnz5nmewWhF/OOPP+bo0aNER0fj5eXF8OHDSU/P+AHp3bs33t7e7NmzB19fX+zt7Q2njc1dyZIlefvtt7Ns9/f3Z8KECXTo0AF7e3tmzpwJwLBhwxg9ejR+fn7UqVOHUqVKAVCxYkU++ugj3n33XfR6PdbW1gQEBGQpTE2aNCEkJITGjRvToUMHhgwZQocOHfD09KRChQpPzNi0aVOuXbtm6Ik7ODgwe/ZsvLy8+OWXX2jbti3ly5fPchr9RVSqVIm2bdvSrl07LC0tCQgIMAzOe++995g6dSpubm5MnTqVESNGoNFocHV1Nfw8NGvWjAMHDhjeP3bsWAoXLvzMzzx79iy1a9fGysosrxYJ8Uwp6TrSdHoUBY7ciiQuJY0zd6JZuO8ihR1suP4g/rmPGZ+STu1ShdErEJeYSLfXKwKQkJrOgPqvUs+jaDZHKDhSU1O5d+8epUuXpmLFiqxatYp69erh4pL9GQNj0CiKkYbqGcHjawPPuib+vMz9Gtq5c+dYvnw5s2fPVjWHKbXj1KlTadmyJY0aNVI7ynMxpTY0V+bQhmk6PSHhD9DpFcIfJvJPWCS/ngilbGGnbN974nYUyenPHqBVysWeO7FJtK9emqKO//s9mZKuw6966Sz7u9hZ06ZKKawsM+44Noc2VMvZs2cZOnQoer2eXbt2YWNj88T9crMNs6t70lUxczVq1OCNN95Ap9Pl+F7x/K5y5cpmV8BF/hCTlMrvp26Smv7kuRg2Xwjnz4t3nvjandgkrC2fPXXH49ujyhVxpLpbIRJT06lcwoVqJVyxtrKg3+sVcLazfrkvQmSRlpbGvHnzmDNnDunp6fTv35+0tLSnFvG8JEU8H+jWrZvaEUxKjx491I4g8jFFUYhNTuO8NoawhwnM23Oey/fjcLa1Iuzhk0djP8knLWpgZaEhKU3HW6+VoUGZYtkWcZH3zp8/z9ChQzl9+jTu7u4sWLCAVq1aqR3LQIq4EEI8QZpOz9FbkaTpMnrVekVh9IYQTt+NfuL+D5NSKVfEkcRUHVPb1cbF7sm9tIpFnXmtVCEsLaRgmzqdTkf//v25ceMGffr0Ydq0aYaJp0yFFHEhRIEQGZ+MNj5jWuSL92LYf/0eR29FUsj+ycV221NOez/WvnpprCw1tKrkzqvFnHmzSqlczyzUkZiYiIODA5aWlsybN4+UlBR8fX3VjvVEUsSFEGZt7zUtYQ8T2HUlgnuRkRS7nJzp9RO3ozhz9+ELH/9TnxrYWmWMN0lJ19HR04OGZYu/TGRhotLT01m0aBHfffcdf//9N+7u7nh5eakd65mkiAshzEJqug7vxX+SnKY3TKt56s4TTm3fePoKdlWKu+BTKWO6Z52i0KVmGRqXK47FU2bwcrCRX5EFxcWLF/H39+f48eOULFmSsLAw3N3ds3+jyuQnVAhhEvR6hW8OXHrqLVSfbj5ueOz6aAS2i501sclpNC1fgn71KhCvvU2XZlmnu3S2tc50u5UQj6Wnp7N48WJmzJhBamoqPXr0YMaMGdnOR2EqpIgLIVSRkq7j15Oh6PXw+baThMfkbGT3uoHN6ejp8cTXQkJiKVck+/uthXhs4sSJBAUF4ebmxty5c2nbtq3akZ6LFHEhRJ5K1+lZcew67/126Imvj/KuRouKJZ/4Wj2Porg52xsznigAFEVB8+gSyocffkh8fDxTpkyhSJEiKid7flLEhRBGdTsmkcM377PnqpbFBy5leX1q29q4OdvzRtli1ChZKO8DigLl6tWrjBgxgoCAABo2bEi5cuUIDAxUO9YLkyIuRAGWrtNzO5vT2AdC77P86NWnLlCx5tRNXO2sDT2bf0tITTfcZ/1vdV4pQsOyxVj0VoMnvk+I3KbT6ViyZAnTpk0jOTmZbdu20bBhQ7VjvTQp4kIUQFGJKTSYt4UbUc+/WMaTxCSn8Zr7kwcCpen12FlZ0rBsMXrWKUfjcsVlohORp65du4a/vz9HjhyhWLFiLFmyhI4dO6odK1dIERcinwuLTiDsYQIAP4Xc4LtDWdc67l/vySvePZau0zOrQ12snrKwdFFHWynMwiTt37+fnj17kpSURKdOnZg9ezbFihVTO1aukSIuRD4UEvaA306GMmf3+afuY2NpwbYPWuH9qlseJhMib9WpU4caNWowZMgQunTponacXCdFXAgzlJymI+U/91P/deUuQYeu8Nflu1n2b1CmKD6VMiauKO5oy0fe1fMkpxB5Ta/Xs3TpUhwdHenbty+Ojo78+eef+XbshRRxIcxA+MMEjoU9YN/1e8zfeyFH76lR0pUpbWrTrtor2FjJMrUi/wsNDWX48OEcOHCAMmXK0KNHD6ytnzzoMr+QIi6EidLrFb47fBn/P44+8fVSLvbU9SiaaVtpVwc+9fHEo7BjXkQUwiTo9Xp++OEHJk+eTEJCAn5+fsyZMwdr6/y/troUcSFMSGJqOu/9dghFgV9PhmZ6rb5HUZpVcKNN1VI0Kldc5vUWAoiNjaVfv37s37+fQoUKERQURNeuXfN17/vf5LeAECq7GRXP1cg4NpyLZPHqn7O8PqVNLT5sXEXm/hbiCZydnbG1taVNmzbMnTuXkiWfPNtffiVFXAiV7LuuZeymEI7eepDltW+7vUGbqq/gUcihwPQohMipsLAw/vzzTwYPHoxGo2H58uU4OBTMfytSxIXIA+k6PT8cvcqo9cewt7ZEo4GoxNRM+3xQszivlvWga62ysoiHEE+gKAo//vgjAQEBxMfHU79+fWrVqoWjY8EdAyJFXAgj0ekzphtN1elxGve/0+TJ6TpKuzpQtJgtaXo9W99vReXiLoSEhFC3bg214gph0sLDwxkxYgS7d+/GxcWFb775htdee03tWKqTIi5ELkvX6Zm49SSz/z6X5bX+9SoQ+FYDnGzz/6hZIXLLqlWrGD9+PPHx8fj6+jJv3jxKlSqldiyTIEVciFxyLTKO2X+f4/8OXzFsK+pgS61Shbn1MIGVfZvSoEz+me5RiLxy7do1NBoNixYtok+fPgXy2vfTSBEX4gXp9HqiElMpP3UtAElpmWdQ+6J1LT5rVROLp8w3LoR4MkVR2LJlC23atMHS0pJPP/2UQYMG8corr6gdzeRIERfiBfx+6ia9VuzNtM3J1gorCwt+6NWY1lVKYWcts6QJ8bzu3LnDRx99xF9//cX06dP58MMPsbW1lQL+FFLEhcih2ORU+q86QHRiCgdC7xu2V3dz5Ze3vahRspB64YQwc4qi8MsvvzB+/HhiY2Np3rw57du3VzuWyZMiLsRTJKWls+PSXU7ejmLy9tNZXi/pbM+NiV1kXnIhXtLdu3cZNWoU27dvx8nJiblz5zJgwAC59p0DUsSF+I/Y5FSaLtrGuYiYJ76+/t3meL/qhoudTR4nEyJ/OnToENu3b8fb25uFCxfi4eGhdiSzIUVciEc2nA1j8K8Hs0zC8vmbr1HC2Z4PGlWSnoEQuUSr1WJnZ4erqytdunTB2dmZVq1ayb+x5yRFXBR4er3CurO36PFj5oFqB0e04Y2yxVVKJUT+pCgKa9as4dNPP6VNmzZ88803aDQafH191Y5mlqSIiwIrKS2dgK2nmLvnfKbtiV/1wVaucwuR6+7du8fo0aMJDg7GwcGB119/HUVRpPf9EqSIiwInKjGF4pN+y7K9Y43S/DbAG2tLCxVSCZF/KYrC2rVr+fTTT4mKiqJJkyYsWrSIcuXKqR3N7EkRF/learqOA6H36bB0F442VkQmpBheK+lsz5yOdfGr/ooMVBPCSMLCwhg6dCjW1tbMnDmTwYMHY2EhfyznBiniIl/bcuE2HZbuMjxPStPhUciBwva2rOrXlOpyb7cQRhMbG4uLiwtlypQhMDCQevXqUb58ebVj5StSxEW+1en7v9l8PtzwvHutskx68zWZlEUII4uMjOSTTz4hNDSU7du3Y21tTffu3dWOlS9JERf5ym8nQ+m9ch9FHGwMt4rV9yjKoZFtZfCMEHlg48aNjBkzhsjISN544w0ePnxI8eJyl4exSBEXZk9RFG7HJNJt+R7+CXsAQFRiKtXcXKlbuig/9mmickIh8r8HDx4wduxY1q1bh52dHV9++SUffvghlpZyp4cxSREXZiv8YQJLDl5mxs6zWV5LmdUXKxllLkSeUBSFLl26cPbsWerVq8fixYupVKmS2rEKBCniwuwkpaXjNO7nLNuru7myok9T6pQuokIqIQqex/d4azQaPvvsMy5fvszQoUOl952HpIgLs/LfAl6luAsTfGvS9bUy2FvLj7MQeWXLli1Mnz6ddevWUbx4cVq3bk3r1q3VjlXgyG89YdKuRsaijUvmt5OhBO6/lOm1P97xpnPNMiolE6Jgio6OZvz48fz222/Y2Njwzz//0K5dO7VjFVhSxIXJSU3XsfualgnBJzhxOyrL643KFqdH7bJSwIXIY3/++SejRo0iIiKCOnXqEBgYSLVq1dSOVaBJERcm425sIrVmb+ZBYkqm7dXdXOnk6UEJJzuGN6sqt4oJoYL58+czZcoUrK2tmTRpEsOHD8fKSkqI2uQ7IEzCG/O3cOzR7WEA1pYWdK9Vlo+9q8tANSFMgJ+fH9u3b2fOnDlUr15d7TjiEaMW8b179zJt2jT0ej3du3fn/fffz/T6nTt3+PTTT4mLi0On0zFmzBi8vb2NGUmYoI83/GMo4E3KFSew6xu8VqqwyqmEKNhiYmKYNGkSAwcOpE6dOlSqVIktW7aoHUv8h9GKuE6nY8qUKSxbtgw3Nze6deuGj48PFStWNOzz7bff0rZtW/r06cPVq1d5//332bVr1zOOKvKblt9sZ/c1LQATWnnyZds6KicSQvz111+MHDmSu3fvkpycTFBQkNqRxFMYrYifPn2asmXL4uHhAWScitm5c2emIq7RaIiPjwcgLi6OEiVKGCuOMEF15mzm9N1oAAY2eFUKuBAqi42N5euvv2bbtm1YWVkxbtw4Ro0apXYs8QxGK+JarZaSJUsanru5uXH69OlM+/j7+zNo0CB++uknkpKSWLZsmbHiCBMybcdpAradMjzvUKM0S3s2VjGREOLcuXP06tWL27dvU7NmTRYvXoynp6fasUQ2VB3YFhwcTJcuXXj33Xc5ceIEY8eOZfPmzdmuM3v2bNZpNl9GSEhIrh6voMpJO56LTCJg+w3D8zF1S9Kjiot8Dx6Rdnh50oYvJiEhAUVR6N+/P7179yYlJUXa8iXkVdsZrYi7ubkRERFheK7VanFzc8u0z5o1a1i6dCkAderUISUlhejoaIoWLfrMY3t6emJra5srOUNCQqhbt26uHKsgy0k7nrwdxcDVwYbnabP7YWEht4s9Jj+LL0/a8Pns3r2b2NhYOnbsCMCxY8c4d+6ctOFLys2fw5SUlGd2XI22QkTNmjUJDQ0lLCyM1NRUgoOD8fHxybSPu7s7hw4dAuDatWukpKRQpIjcTpRf6PUK68/covGCrbzyxRrqzv1fAb8yobMUcCFUEhcXx+jRo3nrrbcYPXo0iYmJANjZ2amcTDwvo/XEraysCAgIYPDgweh0Orp27UqlSpVYsGABnp6etGzZknHjxjFx4kSWL1+ORqNh5syZMpFHPhAWncC0v87wf4evZHnNQqMhelpPnGytVUgmhNi7dy/Dhw8nLCyMatWqsXjxYhwcHNSOJV6QUa+Je3t7Z7nve+TIkYbHFStW5JdffjFmBJHH0nV6yk1dm2nboDcqMqFVTcoWdpQ/0oRQSUpKChMnTuT777/H0tKS0aNHM2bMmFy7NCnUITO2iVxzLTKOyjPWG55vGNSCNlVKybreQpgAa2trLl++TJUqVVi8eDGvv/662pFELpAiLl7ag4QUAg7eZtvq84Ztawc2p3310uqFEkKQkJDA33//Tfv27bGwsGDp0qU4OzvLte98RIq4eCkbz4bRZdnuTNsiJnenuJP8khBCTQcPHsTf35+bN2+ydetWGjRoQPHixdWOJXKZFHHxwkasPcriA/9b4/uPd7zpWMNDRp0LoaLExES+/PJLgoKC0Gg0DB8+nNdee03tWMJIpIiLF3JBG2Mo4O4u9qz3K0c9Wd9bCFUdPnwYf39/rl+/TqVKlQgMDKR+/fpqxxJGJEVcPJe45DRKTf6dxFSdYVv4591kZichTMDGjRu5ceMGw4YNY8KECdjb26sdSRiZFHHxXF6bs8lQwMsVceT0mA4qJxKiYDt37hzVqlXDwsKCiRMn0qVLF+l9FyBy74/IscG/HuRWdAIAu4e9ybXP3sJRJm0RQhVJSUkEBATg7e3N8uXLAXBwcJACXsBIT1xkSxuXRLflezgYeh+ACkWdaFbBLZt3CSGM5Z9//sHf358rV65Qvnx5qlWrpnYkoRIp4uKJYpNT+WrXOZLTdMzfe8GwvaZ7IU7KKXQhVJGcnMzMmTMJDAxEr9fz/vvvM2nSJBwdHdWOJlQiRVxkoigKNWdv4oI2Jstrl8Z3omIxFxVSCSEAtm3bxsKFCylXrhyLFi2iSZMmakcSKpMiLjKpNSdzAV/SvSF1SxfBs2QhbKwsVUwmRMGUkpJCeno6jo6OdOrUia+//poePXpI71sAUsTFI3uuafH5Zrvh+SctajCzvcytLISaTpw4wbBhw3jjjTeYN28eGo2GgQMHqh1LmBAZnS4AMhXwL1rXkgIuhIpSUlKYNm0ab775JhcvXsTKygq9Xq92LGGCpCcuuB2TaHicNrufTJsqhIpOnTrF0KFDuXDhAh4eHixatAgvLy+1YwkTJUW8gLuojaHGrI0A2FhaSAEXQkX37t2jTZs2pKSkMHDgQL744gucnZ3VjiVMmBTxAmzt6Vt0/3GP4fmdL7qpmEaIgkun02FpaUmJEiUICAigWrVqNG/eXO1YwgxIES+AktN0LD18hZHr//nftll9sbaUIRJC5KW0tDTmzp3Lvn372LBhA5aWlgwZMkTtWMKMSBEvYH785xrv/nIw07b0Of3QaOQ0uhB56ezZswwbNowzZ85QqlQpwsLCKFeunNqxhJmRrlcBEpWYkqmAz+lYl4vjOkkBFyIPpaWlMXv2bFq2bMmZM2fo168fBw8elAIuXoj0xAuIiNgkXpm8xvBcet9CqKNPnz7s3LkTd3d35s+fj6+vr9qRhBmTIl5A/LuAnxrTXgq4ECrp27cvJUqUYPr06bi6uqodR5g5OZ1eAOy7rjU8Dv+8K57uhVVMI0TBcv78eXr16kV0dDQAnTt3ZvHixVLARa6QIp6PRcYnYzl6Jc0XZ8zG9na9Cri7OKicSoiCIT09nfnz5+Pj48P27dvZsGGD2pFEPiSn0/MpbVwSpb743yn0Oq8UIfCtBiomEqLguHTpEsOGDeP48eO4ubkxb9482rRpo3YskQ9JEc+n5u4+b3h8ZUJnKhSVWZ+EyAu///47I0aMICUlhR49ejBjxgwKF5ZLWMI4pIjnQ0sPX2HOoyL+fc/GUsCFyEM1atSgePHizJw5k3bt2qkdR+RzUsTzmQ9/P8z/Hb5ieN6rTjn1wghRAOh0Or799ltatGhBjRo1qF69OiEhIVhbW6sdTRQAUsTzkfCHCYYC3rN2OVb1ayq3kglhRFevXsXf35+jR4/SokUL/vjjDwAp4CLPSBHPB5LS0nEa93Ombav7N1MpjRD5n06n47vvvmPq1KkkJyfTpUsXZs2apXYsUQBle4tZaGgovXv3xsfHB4Bz586xaNEiowcTOVd/3hbDY49CDlwc10nFNELkb+Hh4bRv356JEyfi6OjIsmXL+P777ylatKja0UQBlG0R/+KLLxgyZIhhTdtq1aqxbds2owcT2bsdk0iXH/7mgjYGgC3vtSR0UlcqFXdROZkQ+ZeTkxO3bt2iU6dOHDp0iE6d5I9moZ5si3hcXBxeXl6Ga6sWFhZyvccE3I1NpMyUP9h4LhyAt14rQ+uqpVROJUT+dOPGDXbv3g1AoUKF2LVrF8uWLaNYsWLqBhMFXrbXxC0tLUlLSzMUca1Wi4WFTPSmJkVRKD35D8PzX9/2olutsiomEiJ/0uv1LF26lClTpmBra0tISAiFChXCzc1N7WhCADko4n369MHf35/o6GgWLVrE+vXrGTVqVF5kE0/x84lQw+Pbn3ejpIu9emGEyKdCQ0MZPnw4Bw4coHDhwsyaNUvmOxcmJ9si3rlzZ0qXLs3ff/9NUlISX331FfXq1cuLbOIp+q/aD8D7jSpJARcil+n1epYtW8YXX3xBQkIC7dq14+uvv5betzBJ2RbxDRs20KlTp0yF+/E2kfeaL/7T8HiG3+sqJhEif1IUhTVr1mBtbc13331Ht27dZL4FYbKyvbi9fPnyHG0TxqfT69l3/R4AfeuWp5C9jcqJhMgfFEXh2LFjQMY4oO+++44DBw7QvXt3KeDCpD21J37mzBlOnz5NdHQ0q1atMmyPj48nLS0tT8KJzNacumV4vKJPUxWTCJF/hIWFMWLECPbt28eff/5J3bp1KVOmjNqxhMiRpxZxrVbL2bNnSUpK4uzZs4btjo6OzJgxI0/Cif8Z+PMBVhy7DsDYFjVUTiOE+VMUhRUrVjBp0iTi4+Px9fXF3d1d7VhCPJenFvFWrVrRqlUr9u/fT9Om0utTS0JKGi4Tfsm0bUKrmiqlESJ/CA8PZ+TIkfz999+4uLgQGBhI79695dS5MDvZDmxr2rQp169f5+LFi6Smphq2d+7c2Zi5xCP/LuCD3qhIUI9GKqYRIn9YvHgxf//9Ny1btmT+/Pm88sorakcS4oVkW8RXrFjBr7/+yv3796lZsybHjh2jfv36UsTzgDYuyfD41Jj2eLoXVjGNEObt/v37FCtWDI1Gw4QJE6hTp44MXBNmL9vR6b/99hu///477u7ufP/99/z+++84OjrmRbYCr9QXawCwsbSQAi7EC1IUhdWrV1O/fn3DIF1nZ2d69OghBVyYvWx74jY2Njg4OKDX61EUhcqVKxMaGpoH0Qq2BvOCDY9vTHxLxSRCmK87d+7w8ccfs337dpycnLCyktWXRf6S7U+0vb09aWlpVK1aldmzZ+Pu7o5er8+LbAXWujO3CAmPAmBww4oyK5sQz0lRFH799VfGjx9PTEwM3t7eLFq0iNKlS6sdTYhcle3p9M8//5y0tDTGjRtHTEwM//zzD7NmzcrRwffu3Uvr1q3x9fUlKCjoifts2bKFdu3a4efnx+jRo58vfT7VbfkeAJxsrfiuuwxkE+J57dixg6FDh6LT6Zg7dy5r166VAi7ypWf2xHU6Hdu2bWPEiBE4ODgwbdq0HB9Yp9MxZcoUli1bhpubG926dcPHx4eKFSsa9gkNDSUoKIiff/4ZV1dXHjx48OJfST5x5X6s4fHDab1UTCKEeVEUhfT0dAB8fX0ZNWoUAwYMkIlbRL72zJ64paUle/fufaEDnz59mrJly+Lh4YGNjQ1+fn7s3Lkz0z6//fYbffv2NawMVLRo0Rf6rPxCp9dTdeYGAGqUdJVBN0LkkFarpX///nz33XcAaDQaJk2aJAVc5HvZXhNv3rw533//PZ07d8bBwcGw3d7+2ddptVotJUuWNDx3c3Pj9OnTmfZ5PECuV69e6PV6/P398fLyyjb0v2eQyw0hISG5erwXNfufu4bHUxsUN5lcOWVueU2RtOHzURSFv//+m8DAQOLi4qhduzZHjhyRAWwvSX4OX15etWG2P+mBgYEAzJ49G41Gg6IoaDQaLly48NIfrtPpuHnzJitXriQiIoJ+/fqxadMmXFxcnvk+T09PbG1tX/rzIaOh69atmyvHelm/r14JwGetatLRu7a6YZ6TKbWjuZI2fD737t1jzJgxbN68GQcHB2bNmkWtWrWoX7++2tHMmvwcvrzcbMOUlJRndlyzLeIXL158oQ92c3MjIiLC8Fyr1WZZj9fNzY1atWphbW2Nh4cH5cqVIzQ0lNdee+2FPtOczd51zvB40psF7+sX4nnExsbSrFkz7t+/T+PGjVm0aBHly5eXHqQocLIdnf6iatasSWhoKGFhYaSmphIcHIyPj0+mfVq1asXRo0cBiIqKIjQ0FA8PD2NFMllJaemMCz4OwBtlimFtabRvixD5gouLC2+//TYzZsxg48aNlC9fXu1IQqjCaBeOrKysCAgIYPDgweh0Orp27UqlSpVYsGABnp6etGzZkmbNmnHgwAHatWuHpaUlY8eOpXDhgjczWctvdhgeHxjRRsUkQpiu9evXs379en744QcsLCz47LPP1I4khOqMOvrD29sbb2/vTNtGjhxpeKzRaBg/fjzjx483ZgyTdvjmfY7cigRgy3stZUS6EP/x4MEDPvnkE9avX4+dnR3nzp2jZk1ZyU8IMOLpdJG95DQdTRZuA8DBxpLWVUupnEgI07Jp0yYaN27M+vXradCgAXv37pUCLsS/ZFvEHzx4wJgxY+jbty+QMdDt559/NnqwgsDv//533/zdL7qrmEQI0zNmzBgGDBhAXFwcX375JcHBwZkmixJC5KCIT5w4kbp16xIbmzGTWIUKFVi9erXRg+V32rgkdl/TAjCvUz2cbK1VTiSEaalfvz716tVjz549DBs2DEtLS7UjCWFysi3iWq2W3r17G/4B2djYYGEhZ+FfRmq6jnJfrjU8H+FVTcU0QpiG6Ohoxo0bZ+gw9OjRg61bt1KpUiWVkwlhurKtxv+d+Sg2NhZFUYwWqCDov/oAqbqMleBOjmmvchoh1Ld161YaN25MUFCQYbEkjUYjvW8hspHt6HRfX18CAgJISEhg7dq1rF69mq5du+ZFtnwpMTWdNaduAvBV+9ep6V7wbqkT4rGHDx8yfvx4fv31V2xsbAgICMDf31/tWEKYjWyL+HvvvcfGjRuJjY1lz5499O/fn06dOuVFtnzJefz/BgWObl5dxSRCqGvfvn188MEHREREUKdOHQIDA6lWTS4tCfE8si3it2/fpmPHjnTs2DEv8uRrj3vgACdGt5d7wkWB5uLiQmxsLBMnTmTEiBGyaIkQLyDbfzW9evXi1Vdf5a233qJ169a5tvBIQXMnJpGeKzKWdfUo5MBrpeQ0uih4duzYQZkyZahSpQq1atXi9OnTFClSRO1YQpitbAe27d69mwEDBvDXX3/h7e3NpEmTOHHiRF5kyzcURcFjyh+G51cmdFExjRB5LzY2luHDh9OzZ09GjRplGBwrBVyIl5NtEbe0tKRFixYsXLiQbdu2odFo6NOnT15kyzcO34w0PL4yobMscCIKlJ07d9K4cWNWrVpFzZo1DcsaCyFeXo4uQj18+JDNmzezbt064uPjGTFihLFz5Rt6vULTRRlTq7at9goVijqrnEiIvBEbG8ukSZNYuXIlVlZWjBs3jlGjRmFtLRMbCZFbsi3i/v7+hISE0KpVKyZMmCCLxT+n8Y+WGAVY0aeJikmEyFtpaWls27YNT09PFi9eLHOeC2EE2RbxN998kzlz5mBnZ5cXefKdObvPAzDDrw5FHGRQoMjf4uLiuHbtGrVr16Zo0aKsX7+eV199FRsbG7WjCZEvPfXibGpqKpAx2YuiKCQlJWX6T2Tvt5Ohhscjmsn9ryJ/27NnD02bNqVnz55ERUUBUK1aNSngQhjRU3viPXv2ZN26ddSpUweNRoOiKJn+f+HChbzMaZZ6r9wHQJuqpbCzlukjRf4UHx/P5MmT+f7777G0tOSjjz7CyclJ7VhCFAhPLeLr1q0DMpYeFc/vbmyi4fHagc1VyyGEMe3fv5/hw4dz8+ZNqlatyuLFi6lTp47asYQoMLK912natGk52iYyKz05477whmWLYWslvXCR/yiKwhdffEFYWBijRo3i77//lgIuRB7LdmDbsWPHsmz7559/jBImv7h0L8bweEn3hiomESL33b59m1deeQWNRsPixYuJj4+Xu1aEUMlTi/jWrVvZunUrt2/fZuTIkYbt8fHxMlL9GcIfJlD9q42G57JKmcgvEhIS+PLLL1m+fDk7duygZs2aVKlSRe1YQhRoTy3i5cuXp3nz5pw5c4bmzZsbtjs5OdGoUaO8yGaW/rocYXgcPa2nikmEyD2HDh3C39+fGzduUKlSJfR6vdqRhBA8o4hXrVqVqlWr4uPjQ6FChfIwknn7JyxjitUxzavjYie31gjzlpiYyNSpU/nuu+8AGD58OOPGjcPe3l7lZEIIeEYR//HHHxkwYABBQUFPfH3s2LFGC2XONpwNA+CNssVVTiLEy5s9ezZLliyhYsWKBAYG0qBBA7UjCSH+5alF/PGSow4ODnkWJj+4G5sxEU6HGqVVTiLEi0lJScHGxgaNRsNHH32EjY0NH330kfS+hTBBTy3ivXr1AjLmThc5cz7ioeGxrFQmzNHRo0fx9/dn7NixdOvWDVdXV8aPH692LCHEU2RbaZYtW0ZcXBwAn3zyCW3atGH//v1GD2aONp0LB6BVZXeVkwjxfJKTk/n8889p164d165d48qVK2pHEkLkQLZFfO3atTg7O3P48GGioqKYPn06c+fOzYtsZudx77t3nfIqJxEi544dO4a3tzeLFi2ibNmybN68WXrfQpiJbCd7sbTMmG3syJEjdOjQgddffx1FUYwezBwFn8/oiZcuJOMIhHk4fPgw7du3R6/X8/777zNp0iQcHR3VjiWEyKFsi7idnR1BQUEEBwezatUqFEUhLS0tL7KZnd3XtABULeGichIhcqZBgwb06NGDvn370qSJrHcvhLnJ9nT6jBkzuH//PmPGjKF48eKEhYXRoUOHvMhmVjY/6oUDlC4kPRlhmlJSUvjyyy/58ssvAbCwsOCbb76RAi6Emcq2J16+fHk+++wzEhMTSUxMpEyZMnzwwQd5kc2szN9zHoBSLnIbjjBNJ06cYNiwYVy8eJHy5cszevRouYVUCDOXbU/81q1b9OjRgzfeeIOGDRvSq1cvwsLC8iKb2YhLTuPvqxmn0ncPa61yGiEyS0lJYdq0abz55ptcvHiRgQMHsnv3bingQuQD2fbEP//8c3r06EHXrl2BjNHqAQEBLFu2zOjhzMXjqVYBXi3mrGISITJLSkrizTff5Ny5c5QuXZqFCxdmWgtBCGHesu2JR0VF0a1bNzQaDRqNhq5duxIVFZUX2cxG0KGMe2rHtqihchIhMrO3t6dx48YMGDCA/fv3SwEXIp/JtohbWFhw/fp1w/MbN24YbjsToNcr/H7qJgC+VWSSF6G+s2fPMm7cOMOtoDNmzGDevHm4uMhdE0LkN9meTh81ahR9+/alWrVqKIrCpUuXmDVrVl5kMwsTtpwwPPZ+1U3FJKKgS0tLY+7cuXz99dekp6fToUMHmjRpgoWFTAEsRH6VbRH38vIiODiYU6dOAVCrVi2KFCli9GDmYvbf5wAY6VUVS/llKVRy7tw5hg0bxunTp3F3d2fBggVy25gQBYBUnZdwNzbR8PjrjvVUTCIKssWLF+Pj48Pp06fp06cPBw8epFWrVmrHEkLkgWyL+Pbt22nbti0//fQTK1euxM/Pj7/++isvspm8Q6EZo9ItLTIG/QmhBicnJ4oVK8avv/5KYGAgrq6uakcSQuSRbE+nz5s3j19++YXy5TMW9QgNDWXIkCHylz4Q/jABgM/ffE3lJKIgSU9P54cffqBPnz44OTnx9ttv06VLFxm4JkQBlG1P3NbW1lDAAcqVK4ednZ1RQ5mLLRduA1DEwVblJKKguHjxIm3atGHcuHF8/fXXAGg0GingQhRQ2Rbxli1b8u2333L//n3u3bvHkiVLaNmyJcnJySQlJeVFRpMUl5zGjst3AXizSimV04j8Lj09nQULFtC8eXOOHz9Oz549GTlypNqxhBAqy/Z0+uLFiwFYsGBBpu2BgYFoNBouXLhgnGQmbuO5/009K7O0CWO6cuUKQ4YM4fjx47i5uTF37lzatm2rdiwhhAnItohfvHgxL3KYnfHBGfeHT2tXW90gIt97+PAhJ0+epHv37sycOZPChQurHUkIYSKyLeIiK0VRuB2TcXvZew0rq5xG5EdXrlzB2tqacuXKUb9+ffbv30/VqlXVjiWEMDFGvU987969tG7dGl9fX4KCgp66359//kmVKlU4c+aMMePkmgvaGMPjoo4yqE3kHp1OR2BgIN7e3gwbNgy9Xg8gBVwI8URGK+I6nY4pU6awdOlSgoOD2bx5M1evXs2yX3x8PCtWrKBWrVrGipLr9l6/B0Dl4jIiWOSe8PBw/Pz8CAgIwMnJiQ8++ECmTBVCPJPRfkOcPn2asmXL4uHhgY2NDX5+fuzcuTPLfgsWLOC9997D1tZ8erT345MBeL9RJZWTiPxAp9PxzTff8MEHH3D06FE6d+7MwYMH6dixo9rRhBAmLttr4g8ePGDGjBncvXuXVatWcfHiRU6cOEHv3r2f+T6tVkvJkiUNz93c3Dh9+nSmfc6dO0dERATNmzfn+++/z3Hos2fP5njfnAgJCXmu/afvyBiRnxgZQUhIwb3N7r+etx1FhujoaL766ivs7e0ZO3Ys3t7e3Lx5k5s3b6odzSzJz+HLkzZ8eXnVhtkW8YkTJ+Ll5cXq1asBqFChAp988km2RTw7er2emTNnMmPGjOd+r6enZ6713ENCQqhbt26O91cUhdTV5wH4uKMX9tYyNhCevx0LOr1eT3h4OGXKlAFg9erVJCUlyUyIL0l+Dl+etOHLy802TElJeWbHNdvT6Vqtlt69exvWELexscnRdTo3NzciIiIyHcfN7X9LdSYkJHD58mXefvttfHx8OHnyJEOGDDH5wW2PZ2kDpICLFxIaGkrHjh1p27YtMTEZgySbNGkit44JIZ5bttXYyipzoYqNjUVRlGwPXLNmTUJDQwkLCyM1NZXg4GB8fHwMrzs7O3PkyBF27drFrl27qF27Nt9++y01a9Z8gS8j7+y9pgWgWYUSKicR5kav17N06VKaNm3KwYMHqVu3Lunp6WrHEkKYsWy7kr6+vgQEBJCQkMDatWtZvXo1Xbt2zf7AVlYEBAQwePBgdDodXbt2pVKlSixYsABPT09atmyZK19AXkpN1zFnd8ap9AmtTPuPDWFabt68yfDhw9m/fz+FCxdm/vz5dO3aVVa/E0K8lGyL+HvvvcfGjRuJjY1lz5499O/fn06dOuXo4N7e3nh7e2fa9rT5nleuXJmjY6pp0b7/zV7n/arbM/YUIrMPP/yQI0eO0K5dO77++utMl5aEEOJF5eiibseOHeV2F+DCvYzrl9Pb1cHWylLlNMLUJSYm4uDgAMCsWbO4cOEC3bt3l963ECLXZFvER4wY8cRfOv9dEKUg2Hw+HIDXSskAJPF0iqLw448/Mm3aNDZt2kTVqlWpWbOmyY/3EEKYn2yLeIsWLQyPU1JS+PPPP3n11VeNGspU3Y9PAaBNVVl6VDxZeHg4I0aMYPfu3bi4uHDr1i2ZMlUIYTTZFvEuXbpkev7WW28xaNAgowUyVf8ekS+nQ8V/KYrCihUrmDRpEvHx8fj6+jJv3jxKlZI/+IQQxvPc065qNBq0Wq0xspi0i/diAXjNXU6li6wWLVrEqFGj0Gg0LFq0iF9++UUKuBDC6J7rmriiKFy6dInGjRsbPZip2XHpDgAW0gkXjyiKYvi30a9fP86dO8ekSZMoXbq0ysmEEAXFc10Tt7S0ZNCgQWa14lhueTyobVhTub4p4M6dO4waNYq+ffvSsWNHihQpwnfffad2LCFEAfPMIq7T6QgJCWHq1Kl5lcckpabr2HklYwrZN6u4q5xGqElRFH7++WcmTJhAbGwshQoVktsvhRCqeWYRt7S05NKlS3mVxWQFHbpieFy6kKOKSYSa7t69y6hRo9i+fTtOTk7MmzePt99+W+1YQogCLNvT6Q0bNmTKlCl07tzZMHEFQMWKFY0azBTN9Htd7QhCJefPn8fPz4+YmBi8vb1ZuHAhHh4eascSQhRwTy3iEyZMYPr06QQHBwOwe/duw2sajYadO3caPZypOH47CoDqJV1VTiLUUrlyZWrXrk3Hjh1555135DZDIYRJeGoRv3DhAgC7du3KszCm6kR4RhF3tJGlRwsKRVFYs2YNd+/eZcSIEVhZWbF27Vop3kIIkyJVKQdO340GoFG54ionEXnh3r17jB49muDgYFxcXBgwYACurq5SwIUQJuepRfzy5cs0atQoy/bH98YeOnTIqMFMRUxSquGxLHqSvymKwtq1a/n000+JioqiSZMmLFq0CFdXuYwihDBNTy3i5cqVIygoKC+zmKS91zNmp3Oxs1Y5iTCm9PR0Bg8ezMaNG3FwcOCrr75i0KBBWFg896SGQgiRZ55axG1sbHjllVfyMotJ+inkBgCTfF9TOYkwJisrK1xcXGjUqBGBgYGUL19e7UhCCJGtpxZxa2vpeQKsOXUTAE/3QuoGEbkuMjKS1atXM3z4cDQaDV999RW2trbS+xZCmI2nFvHffvstL3OYpAcJKYbHvpVlprb8ZMOGDXzyySdERkZSpUoVWrdujb29vdqxhBDiucjo9Gc4FvYAyLgeLiOT84cHDx4wduxY1q1bh52dHVOnTqVVq1ZqxxJCiBciRfwZrj+IA+C9hpVUTiJyw9atW/noo4+4f/8+9evXJzAwkEqV5HsrhDBfUsSf4WZ0AgAehRyy2VOYg/DwcGJjY5k8eTJDhw7F0lJuGRRCmDcp4s9wMzoegFeLuaicRLyoHTt20LRpU+zt7Rk0aBC+vr6UK1dO7VhCCJErZBjuM2jjkgGo80phlZOI5xUdHc0HH3xAz549mTFjBgAWFhZSwIUQ+Yr0xJ9hz7WMiV6cbOR2O3Oybds2Ro0ahVar5fXXX6dPnz5qRxJCCKOQnngOOMtsbWbh4cOHDB06lD59+hAdHU1AQADbtm2jatWqakcTQgijkJ74Uzx8NGd6pWLOKicROXXhwgV++eUXateuzeLFi6lWrZrakYQQwqikiD/F/D0ZS7HKnOmmLSYmhqSkJEqWLEmjRo1Ys2YNXl5eWFnJj7YQIv+T0+lPsfZMxnSrA9+oqHIS8TQ7duygcePGDBkyBEVRAPDx8ZECLoQoMKSIP0Fquo5zETEADKj3qsppxH/FxsYyfPhwevbsyf3792ncuDE6nU7tWEIIkeeky/IEOy7fNTx2sJEmMiW7du1ixIgR3Llzh5o1a7J48WI8PT3VjiWEEKqQCvUEP/5zDYBPWtRQOYn4t5iYGN59910SExMZO3YsH3/8MTY2NmrHEkII1UgRf4I/Tt8C4F25Hm4SYmNjcXFxwdXVlW+++YbSpUvz2muyvrsQQsg18f9ISf/ftdXKxWW6VTXFxcXx8ccf4+XlRVxcxmI07dq1kwIuhBCPSBH/j+jEjPvD63kUVTlJwbZ3716aNm3K8uXLcXR0JDIyUu1IQghhcqSI/0fYw4yVy1zl/nBVxMfH88knn9C5c2fu3LnD6NGj2bVrF+XLl1c7mhBCmBy5Jv4fa05l3B9etrCTykkKpkGDBrFjxw6qVKnC4sWLef3119WOJIQQJkuK+H8cuZVx2ta7opvKSQoORVHQaDQAjB07lho1ajB27Fjs7OxUTiaEEKZNTqf/R1FHWwBaVymlcpKC4eDBgzRr1oxr1zJu66tbty4BAQFSwIUQIgekiP/HRW3GTG3WltI0xpSYmMj48ePp0KEDFy9eZN++fWpHEkIIsyOn0/+jmKMdEIuDtaXaUfKtw4cP4+/vz/Xr16lUqRKBgYHUr19f7VhCCGF2pLv5H/tv3MPKQoONlRRxY/jll1/w8/Pjxo0b+Pv7s3v3bingQgjxgqQn/i/pOn3G//WKyknyLx8fH+rXr8/kyZN544031I4jhBBmTXri/xIaHQ9AjZKuKifJP5KSkpg0aRLbt28HoESJEmzbtk0KuBBC5ALpif9LaFTGRC8Vi8l0q7nhn3/+wd/fnytXrnDy5EnefPNNtSMJIUS+Ij3xfzn66B7xsoUdVU5i3pKTk/niiy9o27YtV69e5YMPPuDXX39VO5YQQuQ7Ru2J7927l2nTpqHX6+nevTvvv/9+pteXLVvG77//jqWlJUWKFGH69Om88sorxoz0TJpH/29Z2V21DOYuPDycbt26cfnyZcqVK0dgYCCNGzdWO5YQQuRLRuuJ63Q6pkyZwtKlSwkODmbz5s1cvXo10z7VqlXjjz/+YNOmTbRu3ZrZs2cbK06OnLwTDYCjjVxleFFubm7Y29vz/vvvs2/fPingQghhREYr4qdPn6Zs2bJ4eHhgY2ODn58fO3fuzLRPw4YNsbe3B6B27dpEREQYK06OOD0q3k5SxJ/L8ePH2bZtGwDW1tZs3bqVmTNn4ugolyWEEMKYjFbEtVotJUuWNDx3c3NDq9U+df81a9bg5eVlrDg5EhL+AIDShRxUzWEuUlJSmDp1Kq1bt2bhwoWGP8JkylQhhMgbJtHl3LBhA2fPnuWnn37K0f5nz57N1c8PCQkBIC05GYAbF89xR6ZdfaYrV64wa9YsQkNDcXNzY/To0dy+fZvbt2+rHc2sPf5ZFC9O2vDlSRu+vLxqQ6MVcTc3t0ynx7VaLW5uWVcGO3jwIEuWLOGnn37CxsYmR8f29PTE1tY2V3KGhIRQt25dAGx338U6NpXGDWQGsadRFIUZM2Ywb948dDodAwcO5IsvvuDy5cuGdhQv5t8/i+LFSBu+PGnDl5ebbZiSkvLMjqvRups1a9YkNDSUsLAwUlNTCQ4OxsfHJ9M+58+fJyAggG+//ZaiRYsaK0qOnboTTSF7a7VjmDSNRsPNmzdxd3dn7dq1fP311zg7O6sdSwghCiSj9cStrKwICAhg8ODB6HQ6unbtSqVKlViwYAGenp60bNmSWbNmkZiYyMiRIwFwd3dnyZIlxoqUreJOttyPT1Ht803V4z/CunTpAsCsWbPQaDS4uMikOEIIoSajXhP39vbG29s707bHBRtg+fLlxvz455KaruN+fAp1SxdRO4pJOXv2LMOGDePMmTNYWVnRoUMHXF1lWlohhDAFMnrrkTuxSQDEJqepnMQ0pKWlMXv2bHx8fDhz5gz9+vXL8geZEEIIdZnE6HRTcC8+Y2R6xeJyivj8+fMMGzaMU6dO4e7uzvz58/H19VU7lhBCiP+QnvgjR27eB6C6m5wq3rlzJ6dOnaJ3794cPHhQCrgQQpgo6Yk/oo3L6Ik72xbM0emXLl2iXLly2NraMnToUF5//XWaNGmidiwhhBDPID3xR2IeXQuv/UphlZPkrfT0dObNm4e3t7dh7npLS0sp4EIIYQakJ/7I42VIyxdxUjlJ3rl48SL+/v4cP34cNzc36tWrp3YkIYQQz0F64o+42mWcRi9XAIp4eno6CxYsoHnz5hw/fpwePXpw8OBB2rRpo3Y0IYQQz0F64o8cD48CwME6/zfJsWPHmDx5MiVKlGDu3Lm0a9dO7UhCCCFeQP6vWDkUnZQKgIWFRuUkxqHT6UhISMDFxYWGDRuyaNEi2rZtS5EiMrmNEEKYKzmdDuj1CgA2+XTlsqtXr9KuXTvef/99FCXja+3bt68UcCGEMHP5s2o9p4fJGb3wV1zz1zriOp2Ob775Bi8vL/755x8cHR1JfrTcqhBCCPMnp9OBpDQdAG+ULaZyktxz7do1/P39OXLkCMWKFWPJkiV07NhR7VhCCCFykRRx4N6jiV4eProubu6SkpJo27YtkZGRdOrUidmzZ1OsWP75A0UIIUQGKeLA9ag4ACqb+bzpOp0OS0tL7O3tmTx5MnZ2doblQ4UQQuQ/ck0ceDSuDXtrS3WDvCC9Xk9QUBAtWrQgMTERgN69e0sBF0KIfE6KOHDu7kMAyprhRC+hoaF06tSJcePGcfv2bS5duqR2JCGEEHlEijiQrtcDYKkxn3vE9Xo9S5cupVmzZhw4cAA/Pz8OHjxInTp11I4mhBAij8g1cSAiLgmAki72KifJuY8++oiffvqJQoUKERQURNeuXdGY0R8hQgghXp4UccDRJqMZSpvRfeJ9+vQhOjqa2bNnU7JkSbXjCCGEUIGcTgduRMUDpj2wLSwsjH79+nHz5k0AGjZsyMqVK6WACyFEASZFHIiMTwHA2dZa5SRZKYrC8uXLadKkCVu2bGHVqlVqRxJCCGEi5HQ6YPeoB17S2bSuiYeHhzNixAh2796Ni4sLgYGB9O7dW+1YQgghTIQUceDIzUhsrSxMagWznTt3MnDgQOLj42nVqhXz58+nVKlSascSQghhQqSIAxoNJKfp1Y6RSbVq1ShcuDAzZsygT58+MvJcCCFEFgW+iCuKQlKaTvVT6YqisHr1ajw8PPDy8qJUqVIcO3YMa2vTu04vhBDCNBT4Ih6TmrGCmZOtek1x584dRo0axY4dO6hWrRr79+9Ho9FIARdCCPFMBX50+v7bGbeXPb5XPC8pisLPP/9M48aN2bFjB97e3vz6669y6lwIIUSOFPie+O34jOVHe9cpn6efGx0dzdChQ/nzzz9xcnJi7ty5DBgwQAq4EEKIHCvwRfzQnYyeeLWSrnn6uQ4ODty8eRMvLy8WLlxImTJl8vTzhRBCmL8CX8SdbTLuEa/hZvwirtVqOX78OG3btsXW1pb169dTrFgxLCwK/FUNIYQQL6DAV49HS4lTupCj8T5DUVizZg2NGzdm0KBBhqlTS5QoIQVcCCHECyvwPfGrD5MBsDLSRC/37t1jzJgxbN68GQcHByZPnoyHh4dRPksIIUTBUuCLuP5RVzy3B5QpisK6desYO3YsUVFRNG7cmEWLFlG+fN4OoBNCCJF/FfgibmWhwcXOOPdjb9y4kaSkJGbMmMF7770np86FEELkqgJfxCOT0qlawiXXjhcSEkLdunXRaDTMmTOH2NhYKlSokGvHF0IIIR6TriGgjUt+6WNERkby7rvv4uvry7Zt2wAoVqyYFHAhhBBGU6B74oqScUG8bOGXG5m+ceNGxowZQ2RkJA0aNKBixYq5EU8IIYR4pgJdxHWPRrUVcbB9ofdHRUUxduxY1q5di52dHV9++SUffvghlpaWuRlTCCGEeKICXcTTHxVxyxe8veynn35i7dq11KtXj8WLF1OpUqXcjCeEEEI8U4Eu4jp9xhriz1PEo6OjcXJywtramiFDhlC0aFF69eolvW8hhBB5rkAPbItPTQcg7GFCjvbfunUrjRs3Zv78+QBYW1vTt29fKeBCCCFUUaCLeOKjIl7nlaLP3C86OpoPP/yQvn37Eh0djYODQ17EE0IIIZ6pQJ9O18Zn3FqWkq576j5//vkno0aNIiIigtdff53AwECqVq2aVxGFEEKIpyrQRTz8YSIApQs9uWd95swZevfujbW1NZMmTWL48OFYWRXoJhNCCGFCpCIBNpaZryqkpaVhbW1NzZo1+eyzz2jbti3Vq1dXKZ0QQgjxZAW6iCc/Oo1evqgzADExMXz22WfExcWxfPlyNBoNo0ePVjOiEEII8VRGHdi2d+9eWrduja+vL0FBQVleT01N5aOPPsLX15fu3bsTHh5uzDhZXL0fB2QsgvLXX3/RuHFjVq9ezc2bN4mLi8vTLEIIIcTzMloR1+l0TJkyhaVLlxIcHMzmzZu5evVqpn1+//13XFxc2LFjB++88w5z5swxVpwncrbNOBHx86pV9OjRg/v37zNu3Dh27NiBi0vuLYoihBBCGIPRivjp06cpW7YsHh4e2NjY4Ofnx86dOzPts2vXLrp06QJA69atOXTokGE+87wQn5IKwKGd2/D09GTnzp2MHTsWa2vjLE0qhBBC5CajXRPXarWULFnS8NzNzY3Tp09n2cfd3T0jiJUVzs7OREdHU6RIkWce++zZs7mS0So+Biv0dGvZlA96dyU1NZWQkJBcOXZBJG338qQNX5604cuTNnx5edWGZjmwzdPTE1vbF1u05N/q1oVWZY/xRv0BuZCqYHu8jrp4cdKGL0/a8OVJG7683GzDlJSUZ3ZcjXY63c3NjYiICMNzrVaLm5tbln3u3r0LQHp6OnFxcRQuXNhYkZ7I6gUXPxFCCCHUZrQiXrNmTUJDQwkLCyM1NZXg4GB8fHwy7ePj48O6deuAjJnRGjZsiEYjRVUIIYTICaOdTreysiIgIIDBgwej0+no2rUrlSpVYsGCBXh6etKyZUu6devGJ598gq+vL66ursybN89YcYQQQoh8x6jXxL29vfH29s60beTIkYbHtra2LFy40JgRhBBCiHyrQK9iJoQQQpgzKeJCCCGEmZIiLoQQQpgpKeJCCCGEmZIiLoQQQpgpKeJCCCGEmZIiLoQQQpgps5o7/fEKZ6mpqbl63JSUlFw9XkEl7fjypA1fnrThy5M2fHm51YaP693TVvjUKHm59udLiouL4/Lly2rHEEIIIfJU5cqVcXZ2zrLdrIq4Xq8nISEBa2trmWNdCCFEvqcoCmlpaTg6OmJhkfUKuFkVcSGEEEL8jwxsE0IIIcyUFHEhhBDCTEkRF0IIIcyUFHEhhBDCTBWYIr53715at26Nr68vQUFBWV5PTU3lo48+wtfXl+7duxMeHq5CStOWXRsuW7aMdu3a0aFDBwYMGMDt27dVSGnasmvDx/7880+qVKnCmTNn8jCd+chJO27ZsoV27drh5+fH6NGj8zih6cuuDe/cuUP//v3p3LkzHTp0YM+ePSqkNF3jx4+nUaNGtG/f/omvK4rC1KlT8fX1pUOHDpw7d844QZQCID09XWnZsqVy69YtJSUlRenQoYNy5cqVTPv89NNPyqRJkxRFUZTNmzcrI0eOVCGp6cpJGx46dEhJTExUFEVRVq1aJW34HzlpQ0VRlLi4OKVPnz5K9+7dldOnT6uQ1LTlpB1v3LihdOrUSXn48KGiKIoSGRmpRlSTlZM2nDhxorJq1SpFURTlypUrSosWLdSIarKOHj2qnD17VvHz83vi67t371YGDRqk6PV65cSJE0q3bt2MkqNA9MRPnz5N2bJl8fDwwMbGBj8/P3bu3Jlpn127dtGlSxcAWrduzaFDh546Q05BlJM2bNiwIfb29gDUrl2biIgINaKarJy0IcCCBQt47733sLW1VSGl6ctJO/7222/07dsXV1dXAIoWLapGVJOVkzbUaDTEx8cDGRNtlShRQo2oJqt+/fqGn68n2blzJ507d0aj0VC7dm1iY2O5d+9erucoEEVcq9VSsmRJw3M3Nze0Wm2Wfdzd3QGwsrLC2dmZ6OjoPM1pynLShv+2Zs0avLy88iKa2chJG547d46IiAiaN2+ex+nMR07aMTQ0lBs3btCrVy969OjB3r178zqmSctJG/r7+7Np0ya8vLx4//33mThxYl7HNGv/beOSJUs+83fmiyoQRVzkrQ0bNnD27FkGDx6sdhSzotfrmTlzJp9++qnaUcyeTqfj5s2brFy5kq+//ppJkyYRGxurdiyzEhwcTJcuXdi7dy9BQUGMHTsWvV6vdizxHwWiiLu5uWU6tavVanFzc8uyz927dwFIT08nLi6OwoUL52lOU5aTNgQ4ePAgS5Ys4dtvv8XGxiYvI5q87NowISGBy5cv8/bbb+Pj48PJkycZMmSIDG77j5z+e/bx8cHa2hoPDw/KlStHaGhoHic1XTlpwzVr1tC2bVsA6tSpQ0pKipydfA7/beOIiIgn/s58WQWiiNesWZPQ0FDCwsJITU0lODgYHx+fTPv4+Piwbt06IGNkcMOGDWV+9n/JSRueP3+egIAAvv32W7kG+QTZtaGzszNHjhxh165d7Nq1i9q1a/Ptt99Ss2ZNFVObnpz8LLZq1YqjR48CEBUVRWhoKB4eHmrENUk5aUN3d3cOHToEwLVr10hJSaFIkSJqxDVLPj4+rF+/HkVROHnyJM7OzkYZV2BWS5G+KCsrKwICAhg8eDA6nY6uXbtSqVIlFixYgKenJy1btqRbt2588skn+Pr64urqyrx589SObVJy0oazZs0iMTGRkSNHAhm/BJYsWaJyctORkzYU2ctJOzZr1owDBw7Qrl07LC0tGTt2rJxZ+5ectOG4ceOYOHEiy5cvR6PRMHPmTOnY/MvHH3/M0aNHiY6OxsvLi+HDh5Oeng5A79698fb2Zs+ePfj6+mJvb8/06dONkkMWQBFCCCHMVIE4nS6EEELkR1LEhRBCCDMlRVwIIYQwU1LEhRBCCDMlRVwIIYQwU1LEhcgDPj4+tGnThk6dOtGpU6dsbzfx8fHh8uXLeZQu57RaLf379zc8X7RoEampqYbnCxYsYMuWLWpEIzw8nF9//VWVzxZCLXKLmRB5wMfHhyVLllC5cmWj7K+WKlWqcPz4cRwdHY3+Wenp6VhZPX1qiyNHjvDVV1+xdu1ao2cRwlQUiMlehDBFmzZtYsWKFaSlpQHw6aef0qhRoyz7BQYGsnnzZmxtbdFoNKxYsQIXFxdOnTrFnDlzSEhIAGDEiBFPXDhl3LhxWFlZcfXqVaKjo6lfvz4BAQHY2NgQGRnJ559/zq1btwAYNGgQnTt3Rq/XM2XKFA4fPoyNjQ0ODg788ssvhIeH07VrV44cOcLkyZMB6NWrFxYWFqxcuZLp06fj6elJ165dad68OVu3bjXM8vXVV1/h6OiIv7//c2W3tLTkxo0bJCQksGHDBkaPHs2NGzdIS0ujTJkyTJ8+HVdXV6ZMmUJ4eDidOnWibNmyLFy4kOvXrzN9+nSio6NJS0tjwIABdO3a9aW/d0KYDKMscCqEyKRFixZK69atlY4dOyodO3ZU9u7dq0RFRSl6vV5RFEW5du2a0qxZs0z7X7p0SYmOjlbq1q2rJCUlKYqSsdZ4WlqaEhMTo3Tq1EnRarWKoiiKVqtVmjVrpsTExGT57E8//VRp3769Eh8fr6SlpSkDBw5UVq5cqSiKoowcOVKZN2+e4RhNmjRRLl26pJw7d05p06aNotPpFEVRDOtyh4WFKQ0aNDAcu3Llykp8fHymz3p87AkTJig//vijoiiKkpaWpjRp0kQJCwt77uxdunRREhISDNsePHhgeDx37lxl9uzZiqIoyuHDh5UuXboYXktLS1O6dOmiXL161dB2b775puG5EPmB9MSFyCMLFy7MdHr89OnTjB49Gq1Wi5WVFZGRkdy/f5/ixYsb9nF2dqZMmTKMHTuWpk2b0rx5c5ycnDhx4gTh4eG89957hn01Gg03b9584lzr7dq1M5zy7ty5M9u3b6dfv34cOnSIcePGAVCiRAm8vb05cuQInTt3Jj09nc8++4w33niDFi1aPPfX26VLF6ZNm8bbb7/N3r17qVChAqVLl2bPnj3Plb1NmzY4ODgYnm/YsIFNmzaRlpZGYmIi5cqVe+Lnh4aGcu3aNT7++GPDtrS0NK5fv86rr7763F+PEKZIirgQKvn4448ZN24crVq1Qq/XU6tWLVJSUjLtY2lpyW+//cbx48c5fPgwb731FkuXLkVRFKpUqcKqVauMks3Z2Zng4GCOHDnCwYMHmTNnjmGBoJyqV68eCQkJXLp0iXXr1vHWW28BPHf2fxfwY8eO8fPPP/PLL79QpEgRNm3axG+//fbE9ymKQuHChdmwYcNz5RbCnMjodCFUEhcXR+nSpQH4448/Mo3yfiw+Pp6oqCgaNGjAiBEjqFy5MleuXKFOnTrcvHmTw4cPG/Y9ffo0ylPGqW7bto3ExETS09PZsGEDDRs2BKBRo0aGInj//n327NlDw4YNiYqKIikpiWbNmjFmzBicnZ0JCwvLclxHR0fi4+Of+jV27tyZZcuW8c8//9C6dWuA587+b7GxsTg5OVGoUCFSU1P5448/DK85OTllylK+fHns7OxYv369Ydu1a9eemVcIcyM9cSFUMn78eIYOHYqrqyvNmjWjUKFCWfaJj49n+PDhJCcnoygK1atX580338TW1pZvvvmG2bNnM336dNLS0vDw8GDJkiVPXGmqZs2avPvuu4Y/CHr06AHAxIkTCQgIoEOHDgCMGTOGSpUqce7cOSZNmkR6ejo6nQ4vLy9q167NnTt3Mh333Xff5e2338bOzo6VK1dm+dzOnTvTsmVL3nrrLezt7QFwdXV9ruz/1qxZMzZu3Ejr1q0pXLgw9erVM6y3XqVKFcqXL0/79u2pUKECCxcuZMmSJUyfPp3vv/8evV5P0aJFmT9/frbfGyHMhdxiJkQ+N27cODw9PenXr5/aUYQQuUxOpwshhBBmSnriQgghhJmSnrgQQghhpqSICyGEEGZKirgQQghhpqSICyGEEGZKirgQQghhpqSICyGEEGbq/wEMQ3Z+/FDDRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4 | AUC: 0.8644803248324612\n",
      "Fold 4 | Avergae_precesion: 0.39853717751007056\n",
      "Fold 4 | recall: 0.2073778664007976\n",
      "Fold 4 | F1: 0.3083765752409192\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97     13997\n",
      "           1       0.60      0.21      0.31      1003\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.77      0.60      0.64     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13859   138]\n",
      " [  795   208]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 5 | AUC: 0.8739007668001211\n",
      "Fold 5 | Avergae_precesion: 0.43916282497530695\n",
      "Fold 5 | recall: 0.20837487537387836\n",
      "Fold 5 | F1: 0.31859756097560976\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97     13997\n",
      "           1       0.68      0.21      0.32      1003\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.81      0.60      0.64     15000\n",
      "weighted avg       0.93      0.94      0.93     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13897   100]\n",
      " [  794   209]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 6 | AUC: 0.8580936783384224\n",
      "Fold 6 | Avergae_precesion: 0.3630849846395366\n",
      "Fold 6 | recall: 0.17265469061876249\n",
      "Fold 6 | F1: 0.2629179331306991\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97     13998\n",
      "           1       0.55      0.17      0.26      1002\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.75      0.58      0.61     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13857   141]\n",
      " [  829   173]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 7 | AUC: 0.8579977136739522\n",
      "Fold 7 | Avergae_precesion: 0.40655367087025357\n",
      "Fold 7 | recall: 0.20758483033932135\n",
      "Fold 7 | F1: 0.31067961165048547\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97     13998\n",
      "           1       0.62      0.21      0.31      1002\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.78      0.60      0.64     15000\n",
      "weighted avg       0.92      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13869   129]\n",
      " [  794   208]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 8 | AUC: 0.8742110007731358\n",
      "Fold 8 | Avergae_precesion: 0.444278890676069\n",
      "Fold 8 | recall: 0.2025948103792415\n",
      "Fold 8 | F1: 0.3096872616323417\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97     13998\n",
      "           1       0.66      0.20      0.31      1002\n",
      "\n",
      "    accuracy                           0.94     15000\n",
      "   macro avg       0.80      0.60      0.64     15000\n",
      "weighted avg       0.93      0.94      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13892   106]\n",
      " [  799   203]]\n",
      "[LightGBM] [Warning] lambda_l1 is set=2.5825901359648176e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5825901359648176e-05\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8225459856082522, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8225459856082522\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] feature_fraction is set=0.7099515479738928, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099515479738928\n",
      "[LightGBM] [Warning] lambda_l2 is set=4.490094872871969, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.490094872871969\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 9 | AUC: 0.8641739952014816\n",
      "Fold 9 | Avergae_precesion: 0.3773115678714637\n",
      "Fold 9 | recall: 0.17065868263473055\n",
      "Fold 9 | F1: 0.2585034013605442\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97     13998\n",
      "           1       0.53      0.17      0.26      1002\n",
      "\n",
      "    accuracy                           0.93     15000\n",
      "   macro avg       0.74      0.58      0.61     15000\n",
      "weighted avg       0.92      0.93      0.92     15000\n",
      "\n",
      "Confusion_matrix:\n",
      " [[13848   150]\n",
      " [  831   171]]\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Overall mean AUC score : 0.8648040512360318\n",
      "\n",
      "Overall mean F1 score : 0.2944832224347166\n",
      "\n",
      "Overall mean average precision score : 0.40050455934231144\n",
      "\n",
      "Overall mean recall score : 0.1954888826534369\n",
      "CPU times: user 44min 26s, sys: 15.1 s, total: 44min 41s\n",
      "Wall time: 11min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# evaluate each strategy on the dataset\n",
    "results = list()\n",
    "# Setting a 10-fold stratified cross-validation (note: shuffle=True)\n",
    "SEED = 42\n",
    "FOLDS = 10\n",
    "skf = StratifiedKFold(n_splits=FOLDS, shuffle=True, random_state=SEED)\n",
    "# CV interations\n",
    "# Create arrays for the features and the response variable\n",
    "roc_auc = list()\n",
    "average_precision = list()\n",
    "#X1=X.iloc[0:2000,:].copy()\n",
    "#y1=y[0:2000].copy()\n",
    "oof = np.empty((X.shape[0],))\n",
    "#oof_bin = np.empty((X.shape[0],))\n",
    "predictions=[]\n",
    "mean_auc = 0\n",
    "mean_ap=0\n",
    "F1 = list()\n",
    "Recall=list()\n",
    "best_iteration = list()\n",
    "for fold, (train_idx, test_idx) in enumerate(skf.split(X, y)):    \n",
    "    X_train, y_train = X.iloc[list(train_idx), :], y.iloc[list(train_idx)]\n",
    "    X_test, y_test = X.iloc[list(test_idx), :],y.iloc[list(test_idx)]\n",
    "    pipe1_model_lgbm_woe_powertransformer2.fit(X_train,y_train,\n",
    "                                                  #classifier__verbose=False\n",
    "                                                  #,verbose=False\n",
    "                                                 )\n",
    "    preds = pipe1_model_lgbm_woe_powertransformer2.predict_proba(X_test)[:,1]\n",
    "    oof[test_idx] = preds\n",
    "    auc_score= roc_auc_score(y_true=y_test, y_score=preds)\n",
    "    roc_auc.append(auc_score)\n",
    "    ap=average_precision_score(y_true=y_test, y_score=preds)\n",
    "    plot_roc(y_test,preds)\n",
    "    mean_auc += auc_score / FOLDS\n",
    "    mean_ap+=ap/FOLDS\n",
    "    predictions.append(pipe1_model_lgbm_woe_powertransformer2.predict_proba(X_test_final)[:,1]) \n",
    "    y_predicted = pipe1_model_lgbm_woe_powertransformer2.predict(X_test)\n",
    "    recall = recall_score(y_test, y_predicted)\n",
    "    f1= f1_score(y_test, y_predicted)\n",
    "    print(f\"Fold {fold} | AUC: {auc_score}\")\n",
    "    print(f\"Fold {fold} | Avergae_precesion: {ap}\")\n",
    "    print(f\"Fold {fold} | recall: {recall}\")\n",
    "    print(f\"Fold {fold} | F1: {f1}\")\n",
    "    print('Classification report:\\n',classification_report(y_test,y_predicted))\n",
    "    print('Confusion_matrix:\\n',confusion_matrix(y_test,y_predicted))\n",
    "    roc_auc.append(auc_score)\n",
    "    F1.append(f1)\n",
    "    Recall.append(recall)\n",
    "    #Mean of the predictions\n",
    "print('-' * 80)  \n",
    "print(f\"\\nOverall mean AUC score : {mean_auc}\")\n",
    "print(f\"\\nOverall mean F1 score : {np.mean(F1)}\")\n",
    "print(f\"\\nOverall mean average precision score : {mean_ap}\")\n",
    "print(f\"\\nOverall mean recall score : {np.mean(Recall)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4bbace",
   "metadata": {
    "papermill": {
     "duration": 0.079953,
     "end_time": "2021-12-27T15:31:38.071328",
     "exception": false,
     "start_time": "2021-12-27T15:31:37.991375",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**m1_hgbc:**  \n",
    "\n",
    "        CV : 'auc' :0.8654007907881465\n",
    "             'average_precision_score' :0.402\n",
    "             'f1_score' : 0.2805834930186596\n",
    "\n",
    "\n",
    "\n",
    "---------------------------------------------\n",
    "\n",
    "**m2_catboost:**\n",
    "\n",
    "       CV : 'auc' : 0.8662925048383615\n",
    "             'average_precision_score' : 0.4007558117925276\n",
    "             'f1_score' : 0.28917295975957824\n",
    "      \n",
    "      \n",
    "-----------------------------------------------\n",
    "\n",
    "**m3_xgbr:**  \n",
    "\n",
    "      CV : 'auc' :0.855\n",
    "             'average_precision_score' :0.372\n",
    "             'f1_score' : 0.294\n",
    "      \n",
    "----------------------------------------------\n",
    "\n",
    "**m4_LR**\n",
    "\n",
    "            CV : 'auc' :0.862\n",
    "                 'average_precision_score' :0.381\n",
    "                 'f1_score' : 0.259\n",
    "      \n",
    "\n",
    "\n",
    "-----------------------------------------------\n",
    "**m5_svm**   \n",
    "\n",
    "           CV : 'auc' :\n",
    "                 'average_precision_score' :!\n",
    "                 'f1_score' : !\n",
    "      \n",
    "-----------------------------------------------\n",
    "**m6_RF**    \n",
    "\n",
    "        CV : 'auc' :0.863\n",
    "             'average_precision_score' :0.396\n",
    "             'f1_score' : 0.276\n",
    "      \n",
    "---------------------------------------------------\n",
    "**m7_Lgbm**\n",
    "\n",
    "          CV : 'auc' :0.8649\n",
    "             'average_precision_score' :0.400\n",
    "             'f1_score' :0.291\n",
    "  ---------------------------------------------------\n",
    "**m8_Lgbm2**\n",
    "\n",
    "          CV : 'auc' :0.8648\n",
    "             'average_precision_score' :0.400\n",
    "             'f1_score' :0.294     \n",
    "             \n",
    "             \n",
    "             \n",
    "**Final Results :**             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "02df7f7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:38.236513Z",
     "iopub.status.busy": "2021-12-27T15:31:38.235393Z",
     "iopub.status.idle": "2021-12-27T15:31:38.324791Z",
     "shell.execute_reply": "2021-12-27T15:31:38.325331Z",
     "shell.execute_reply.started": "2021-12-27T14:27:12.005749Z"
    },
    "papermill": {
     "duration": 0.176115,
     "end_time": "2021-12-27T15:31:38.325542",
     "exception": false,
     "start_time": "2021-12-27T15:31:38.149427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Modified Stlying DataFrame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_35a96_row0_col0, #T_35a96_row2_col1, #T_35a96_row2_col3, #T_35a96_row3_col2 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_35a96_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >model</th>\n",
       "      <th class=\"col_heading level0 col1\" >auc</th>\n",
       "      <th class=\"col_heading level0 col2\" >f1_score</th>\n",
       "      <th class=\"col_heading level0 col3\" >average_precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_35a96_row0_col0\" class=\"data row0 col0\" >m1_hgbc</td>\n",
       "      <td id=\"T_35a96_row0_col1\" class=\"data row0 col1\" >0.865434</td>\n",
       "      <td id=\"T_35a96_row0_col2\" class=\"data row0 col2\" >0.279313</td>\n",
       "      <td id=\"T_35a96_row0_col3\" class=\"data row0 col3\" >0.403261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_35a96_row1_col0\" class=\"data row1 col0\" >m2_catboost</td>\n",
       "      <td id=\"T_35a96_row1_col1\" class=\"data row1 col1\" >0.866293</td>\n",
       "      <td id=\"T_35a96_row1_col2\" class=\"data row1 col2\" >0.289173</td>\n",
       "      <td id=\"T_35a96_row1_col3\" class=\"data row1 col3\" >0.400756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_35a96_row2_col0\" class=\"data row2 col0\" >m3_xgbr</td>\n",
       "      <td id=\"T_35a96_row2_col1\" class=\"data row2 col1\" >0.855243</td>\n",
       "      <td id=\"T_35a96_row2_col2\" class=\"data row2 col2\" >0.294449</td>\n",
       "      <td id=\"T_35a96_row2_col3\" class=\"data row2 col3\" >0.372796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_35a96_row3_col0\" class=\"data row3 col0\" >m4_LR</td>\n",
       "      <td id=\"T_35a96_row3_col1\" class=\"data row3 col1\" >0.862013</td>\n",
       "      <td id=\"T_35a96_row3_col2\" class=\"data row3 col2\" >0.259481</td>\n",
       "      <td id=\"T_35a96_row3_col3\" class=\"data row3 col3\" >0.381870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_35a96_row4_col0\" class=\"data row4 col0\" >m5_RF</td>\n",
       "      <td id=\"T_35a96_row4_col1\" class=\"data row4 col1\" >0.863872</td>\n",
       "      <td id=\"T_35a96_row4_col2\" class=\"data row4 col2\" >0.276701</td>\n",
       "      <td id=\"T_35a96_row4_col3\" class=\"data row4 col3\" >0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_35a96_row5_col0\" class=\"data row5 col0\" >m6_lgbm1</td>\n",
       "      <td id=\"T_35a96_row5_col1\" class=\"data row5 col1\" >0.864935</td>\n",
       "      <td id=\"T_35a96_row5_col2\" class=\"data row5 col2\" >0.292513</td>\n",
       "      <td id=\"T_35a96_row5_col3\" class=\"data row5 col3\" >0.400006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35a96_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_35a96_row6_col0\" class=\"data row6 col0\" >m7_lgbm2</td>\n",
       "      <td id=\"T_35a96_row6_col1\" class=\"data row6 col1\" >0.864804</td>\n",
       "      <td id=\"T_35a96_row6_col2\" class=\"data row6 col2\" >0.294483</td>\n",
       "      <td id=\"T_35a96_row6_col3\" class=\"data row6 col3\" >0.400505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff415a85110>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_results={'model':['m1_hgbc','m2_catboost',\n",
    "               'm3_xgbr','m4_LR',\n",
    "               'm5_RF','m6_lgbm1',\n",
    "              'm7_lgbm2'],\n",
    "'auc' :[ 0.865434044881213,0.8662925048383615, \n",
    "        0.8552429800216585,0.8620132487013733,\n",
    "        0.8638723261807801,0.8649350128689158,\n",
    "       0.8648040512360318],\n",
    "\n",
    "'f1_score' : [0.27931324050967715,0.28917295975957824,\n",
    "              0.2944488401724624,0.25948063132387106,\n",
    "              0.2767006882442805,0.2925125077182382,\n",
    "             0.2944832224347166],\n",
    "\n",
    "'average_precision_score' :[0.4032613815557755,0.4007558117925276,\n",
    "                            0.3727962618723695,0.3818703865043032,\n",
    "                            0.3960003199342976,0.40000554322071746,\n",
    "                           0.40050455934231144]    \n",
    "     }\n",
    "\n",
    "df_results= pd.DataFrame.from_dict(data=data_results\n",
    "                         #,columns=['model','auc','f1_score', 'average_precision_score']\n",
    "                       )\n",
    "# Highlight the Min values in each column\n",
    "print(\"\\nModified Stlying DataFrame:\")\n",
    "df_results.style.highlight_min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "633a915c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:38.496310Z",
     "iopub.status.busy": "2021-12-27T15:31:38.495225Z",
     "iopub.status.idle": "2021-12-27T15:31:38.500650Z",
     "shell.execute_reply": "2021-12-27T15:31:38.500050Z",
     "shell.execute_reply.started": "2021-12-27T14:28:00.361263Z"
    },
    "papermill": {
     "duration": 0.098208,
     "end_time": "2021-12-27T15:31:38.500822",
     "exception": false,
     "start_time": "2021-12-27T15:31:38.402614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4d64e_row0_col3, #T_4d64e_row1_col1, #T_4d64e_row6_col0, #T_4d64e_row6_col2 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4d64e_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >model</th>\n",
       "      <th class=\"col_heading level0 col1\" >auc</th>\n",
       "      <th class=\"col_heading level0 col2\" >f1_score</th>\n",
       "      <th class=\"col_heading level0 col3\" >average_precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4d64e_row0_col0\" class=\"data row0 col0\" >m1_hgbc</td>\n",
       "      <td id=\"T_4d64e_row0_col1\" class=\"data row0 col1\" >0.865434</td>\n",
       "      <td id=\"T_4d64e_row0_col2\" class=\"data row0 col2\" >0.279313</td>\n",
       "      <td id=\"T_4d64e_row0_col3\" class=\"data row0 col3\" >0.403261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_4d64e_row1_col0\" class=\"data row1 col0\" >m2_catboost</td>\n",
       "      <td id=\"T_4d64e_row1_col1\" class=\"data row1 col1\" >0.866293</td>\n",
       "      <td id=\"T_4d64e_row1_col2\" class=\"data row1 col2\" >0.289173</td>\n",
       "      <td id=\"T_4d64e_row1_col3\" class=\"data row1 col3\" >0.400756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_4d64e_row2_col0\" class=\"data row2 col0\" >m3_xgbr</td>\n",
       "      <td id=\"T_4d64e_row2_col1\" class=\"data row2 col1\" >0.855243</td>\n",
       "      <td id=\"T_4d64e_row2_col2\" class=\"data row2 col2\" >0.294449</td>\n",
       "      <td id=\"T_4d64e_row2_col3\" class=\"data row2 col3\" >0.372796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_4d64e_row3_col0\" class=\"data row3 col0\" >m4_LR</td>\n",
       "      <td id=\"T_4d64e_row3_col1\" class=\"data row3 col1\" >0.862013</td>\n",
       "      <td id=\"T_4d64e_row3_col2\" class=\"data row3 col2\" >0.259481</td>\n",
       "      <td id=\"T_4d64e_row3_col3\" class=\"data row3 col3\" >0.381870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_4d64e_row4_col0\" class=\"data row4 col0\" >m5_RF</td>\n",
       "      <td id=\"T_4d64e_row4_col1\" class=\"data row4 col1\" >0.863872</td>\n",
       "      <td id=\"T_4d64e_row4_col2\" class=\"data row4 col2\" >0.276701</td>\n",
       "      <td id=\"T_4d64e_row4_col3\" class=\"data row4 col3\" >0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_4d64e_row5_col0\" class=\"data row5 col0\" >m6_lgbm1</td>\n",
       "      <td id=\"T_4d64e_row5_col1\" class=\"data row5 col1\" >0.864935</td>\n",
       "      <td id=\"T_4d64e_row5_col2\" class=\"data row5 col2\" >0.292513</td>\n",
       "      <td id=\"T_4d64e_row5_col3\" class=\"data row5 col3\" >0.400006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d64e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_4d64e_row6_col0\" class=\"data row6 col0\" >m7_lgbm2</td>\n",
       "      <td id=\"T_4d64e_row6_col1\" class=\"data row6 col1\" >0.864804</td>\n",
       "      <td id=\"T_4d64e_row6_col2\" class=\"data row6 col2\" >0.294483</td>\n",
       "      <td id=\"T_4d64e_row6_col3\" class=\"data row6 col3\" >0.400505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff416362710>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.style.highlight_max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ac4bc8e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:38.685048Z",
     "iopub.status.busy": "2021-12-27T15:31:38.663238Z",
     "iopub.status.idle": "2021-12-27T15:31:38.689458Z",
     "shell.execute_reply": "2021-12-27T15:31:38.690043Z",
     "shell.execute_reply.started": "2021-12-27T14:34:54.053677Z"
    },
    "papermill": {
     "duration": 0.111802,
     "end_time": "2021-12-27T15:31:38.690296",
     "exception": false,
     "start_time": "2021-12-27T15:31:38.578494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_46492_row0_col1, #T_46492_row1_col3, #T_46492_row3_col2 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "  background-color: red;\n",
       "}\n",
       "#T_46492_row0_col2 {\n",
       "  background-color: #2424fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row0_col3 {\n",
       "  background-color: #1414fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row1_col1 {\n",
       "  background-color: #1212fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row1_col2 {\n",
       "  background-color: #6868fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row2_col1 {\n",
       "  background-color: #1d1dfe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row2_col2 {\n",
       "  background-color: #0d0dfe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row2_col3 {\n",
       "  background-color: #1919fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row3_col0 {\n",
       "  background-color: red;\n",
       "}\n",
       "#T_46492_row3_col1 {\n",
       "  background-color: #2020fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row3_col3 {\n",
       "  background-color: #1616fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row4_col1 {\n",
       "  background-color: #3535fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row4_col2 {\n",
       "  background-color: #7b7bf9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row4_col3 {\n",
       "  background-color: #3a3afc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row5_col1 {\n",
       "  background-color: #5d5dfa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_46492_row5_col2, #T_46492_row6_col1, #T_46492_row6_col3 {\n",
       "  background-color: #f0f0f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_46492_row5_col3 {\n",
       "  background-color: #a9a9f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_46492_row6_col2 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_46492_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >model</th>\n",
       "      <th class=\"col_heading level0 col1\" >auc</th>\n",
       "      <th class=\"col_heading level0 col2\" >f1_score</th>\n",
       "      <th class=\"col_heading level0 col3\" >average_precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_46492_row0_col0\" class=\"data row0 col0\" >m2_catboost</td>\n",
       "      <td id=\"T_46492_row0_col1\" class=\"data row0 col1\" >0.866293</td>\n",
       "      <td id=\"T_46492_row0_col2\" class=\"data row0 col2\" >0.289173</td>\n",
       "      <td id=\"T_46492_row0_col3\" class=\"data row0 col3\" >0.400756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
       "      <td id=\"T_46492_row1_col0\" class=\"data row1 col0\" >m1_hgbc</td>\n",
       "      <td id=\"T_46492_row1_col1\" class=\"data row1 col1\" >0.865434</td>\n",
       "      <td id=\"T_46492_row1_col2\" class=\"data row1 col2\" >0.279313</td>\n",
       "      <td id=\"T_46492_row1_col3\" class=\"data row1 col3\" >0.403261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
       "      <td id=\"T_46492_row2_col0\" class=\"data row2 col0\" >m6_lgbm1</td>\n",
       "      <td id=\"T_46492_row2_col1\" class=\"data row2 col1\" >0.864935</td>\n",
       "      <td id=\"T_46492_row2_col2\" class=\"data row2 col2\" >0.292513</td>\n",
       "      <td id=\"T_46492_row2_col3\" class=\"data row2 col3\" >0.400006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
       "      <td id=\"T_46492_row3_col0\" class=\"data row3 col0\" >m7_lgbm2</td>\n",
       "      <td id=\"T_46492_row3_col1\" class=\"data row3 col1\" >0.864804</td>\n",
       "      <td id=\"T_46492_row3_col2\" class=\"data row3 col2\" >0.294483</td>\n",
       "      <td id=\"T_46492_row3_col3\" class=\"data row3 col3\" >0.400505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_46492_row4_col0\" class=\"data row4 col0\" >m5_RF</td>\n",
       "      <td id=\"T_46492_row4_col1\" class=\"data row4 col1\" >0.863872</td>\n",
       "      <td id=\"T_46492_row4_col2\" class=\"data row4 col2\" >0.276701</td>\n",
       "      <td id=\"T_46492_row4_col3\" class=\"data row4 col3\" >0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row5\" class=\"row_heading level0 row5\" >3</th>\n",
       "      <td id=\"T_46492_row5_col0\" class=\"data row5 col0\" >m4_LR</td>\n",
       "      <td id=\"T_46492_row5_col1\" class=\"data row5 col1\" >0.862013</td>\n",
       "      <td id=\"T_46492_row5_col2\" class=\"data row5 col2\" >0.259481</td>\n",
       "      <td id=\"T_46492_row5_col3\" class=\"data row5 col3\" >0.381870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46492_level0_row6\" class=\"row_heading level0 row6\" >2</th>\n",
       "      <td id=\"T_46492_row6_col0\" class=\"data row6 col0\" >m3_xgbr</td>\n",
       "      <td id=\"T_46492_row6_col1\" class=\"data row6 col1\" >0.855243</td>\n",
       "      <td id=\"T_46492_row6_col2\" class=\"data row6 col2\" >0.294449</td>\n",
       "      <td id=\"T_46492_row6_col3\" class=\"data row6 col3\" >0.372796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff415ac7790>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = sns.light_palette(\"blue\", as_cmap=True)\n",
    "\n",
    "df_results.sort_values(by='auc',ascending=False).style.background_gradient(cmap=cm).highlight_max(axis=0, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "10d62be3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:38.851774Z",
     "iopub.status.busy": "2021-12-27T15:31:38.851007Z",
     "iopub.status.idle": "2021-12-27T15:31:38.875568Z",
     "shell.execute_reply": "2021-12-27T15:31:38.876088Z",
     "shell.execute_reply.started": "2021-12-27T14:34:36.546441Z"
    },
    "papermill": {
     "duration": 0.107957,
     "end_time": "2021-12-27T15:31:38.876285",
     "exception": false,
     "start_time": "2021-12-27T15:31:38.768328",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_04a50_row0_col0 {\n",
       "  background-color: red;\n",
       "}\n",
       "#T_04a50_row0_col1 {\n",
       "  background-color: #2020fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row0_col2, #T_04a50_row3_col1, #T_04a50_row4_col3 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "  background-color: red;\n",
       "}\n",
       "#T_04a50_row0_col3 {\n",
       "  background-color: #1616fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row1_col1, #T_04a50_row1_col3, #T_04a50_row6_col2 {\n",
       "  background-color: #f0f0f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_04a50_row1_col2 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row2_col1 {\n",
       "  background-color: #1d1dfe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row2_col2 {\n",
       "  background-color: #0d0dfe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row2_col3 {\n",
       "  background-color: #1919fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row3_col2 {\n",
       "  background-color: #2424fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row3_col3 {\n",
       "  background-color: #1414fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row4_col1 {\n",
       "  background-color: #1212fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row4_col2 {\n",
       "  background-color: #6868fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row5_col1 {\n",
       "  background-color: #3535fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row5_col2 {\n",
       "  background-color: #7b7bf9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row5_col3 {\n",
       "  background-color: #3a3afc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row6_col1 {\n",
       "  background-color: #5d5dfa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_04a50_row6_col3 {\n",
       "  background-color: #a9a9f6;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_04a50_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >model</th>\n",
       "      <th class=\"col_heading level0 col1\" >auc</th>\n",
       "      <th class=\"col_heading level0 col2\" >f1_score</th>\n",
       "      <th class=\"col_heading level0 col3\" >average_precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row0\" class=\"row_heading level0 row0\" >6</th>\n",
       "      <td id=\"T_04a50_row0_col0\" class=\"data row0 col0\" >m7_lgbm2</td>\n",
       "      <td id=\"T_04a50_row0_col1\" class=\"data row0 col1\" >0.864804</td>\n",
       "      <td id=\"T_04a50_row0_col2\" class=\"data row0 col2\" >0.294483</td>\n",
       "      <td id=\"T_04a50_row0_col3\" class=\"data row0 col3\" >0.400505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_04a50_row1_col0\" class=\"data row1 col0\" >m3_xgbr</td>\n",
       "      <td id=\"T_04a50_row1_col1\" class=\"data row1 col1\" >0.855243</td>\n",
       "      <td id=\"T_04a50_row1_col2\" class=\"data row1 col2\" >0.294449</td>\n",
       "      <td id=\"T_04a50_row1_col3\" class=\"data row1 col3\" >0.372796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
       "      <td id=\"T_04a50_row2_col0\" class=\"data row2 col0\" >m6_lgbm1</td>\n",
       "      <td id=\"T_04a50_row2_col1\" class=\"data row2 col1\" >0.864935</td>\n",
       "      <td id=\"T_04a50_row2_col2\" class=\"data row2 col2\" >0.292513</td>\n",
       "      <td id=\"T_04a50_row2_col3\" class=\"data row2 col3\" >0.400006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row3\" class=\"row_heading level0 row3\" >1</th>\n",
       "      <td id=\"T_04a50_row3_col0\" class=\"data row3 col0\" >m2_catboost</td>\n",
       "      <td id=\"T_04a50_row3_col1\" class=\"data row3 col1\" >0.866293</td>\n",
       "      <td id=\"T_04a50_row3_col2\" class=\"data row3 col2\" >0.289173</td>\n",
       "      <td id=\"T_04a50_row3_col3\" class=\"data row3 col3\" >0.400756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row4\" class=\"row_heading level0 row4\" >0</th>\n",
       "      <td id=\"T_04a50_row4_col0\" class=\"data row4 col0\" >m1_hgbc</td>\n",
       "      <td id=\"T_04a50_row4_col1\" class=\"data row4 col1\" >0.865434</td>\n",
       "      <td id=\"T_04a50_row4_col2\" class=\"data row4 col2\" >0.279313</td>\n",
       "      <td id=\"T_04a50_row4_col3\" class=\"data row4 col3\" >0.403261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row5\" class=\"row_heading level0 row5\" >4</th>\n",
       "      <td id=\"T_04a50_row5_col0\" class=\"data row5 col0\" >m5_RF</td>\n",
       "      <td id=\"T_04a50_row5_col1\" class=\"data row5 col1\" >0.863872</td>\n",
       "      <td id=\"T_04a50_row5_col2\" class=\"data row5 col2\" >0.276701</td>\n",
       "      <td id=\"T_04a50_row5_col3\" class=\"data row5 col3\" >0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04a50_level0_row6\" class=\"row_heading level0 row6\" >3</th>\n",
       "      <td id=\"T_04a50_row6_col0\" class=\"data row6 col0\" >m4_LR</td>\n",
       "      <td id=\"T_04a50_row6_col1\" class=\"data row6 col1\" >0.862013</td>\n",
       "      <td id=\"T_04a50_row6_col2\" class=\"data row6 col2\" >0.259481</td>\n",
       "      <td id=\"T_04a50_row6_col3\" class=\"data row6 col3\" >0.381870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff4144bfa10>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.sort_values(by='f1_score',ascending=False).style.background_gradient(cmap=cm).highlight_max(axis=0, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b040a42b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:39.037579Z",
     "iopub.status.busy": "2021-12-27T15:31:39.036889Z",
     "iopub.status.idle": "2021-12-27T15:31:39.054430Z",
     "shell.execute_reply": "2021-12-27T15:31:39.054955Z",
     "shell.execute_reply.started": "2021-12-27T14:31:10.538648Z"
    },
    "papermill": {
     "duration": 0.099635,
     "end_time": "2021-12-27T15:31:39.055134",
     "exception": false,
     "start_time": "2021-12-27T15:31:38.955499",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_95174_row0_col1 {\n",
       "  background-color: #1212fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row0_col2 {\n",
       "  background-color: #6868fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row0_col3, #T_95174_row1_col1, #T_95174_row2_col2, #T_95174_row6_col2 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row1_col2 {\n",
       "  background-color: #2424fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row1_col3 {\n",
       "  background-color: #1414fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row2_col1 {\n",
       "  background-color: #2020fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row2_col3 {\n",
       "  background-color: #1616fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row3_col1 {\n",
       "  background-color: #1d1dfe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row3_col2 {\n",
       "  background-color: #0d0dfe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row3_col3 {\n",
       "  background-color: #1919fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row4_col1 {\n",
       "  background-color: #3535fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row4_col2 {\n",
       "  background-color: #7b7bf9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row4_col3 {\n",
       "  background-color: #3a3afc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row5_col1 {\n",
       "  background-color: #5d5dfa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_95174_row5_col2, #T_95174_row6_col1, #T_95174_row6_col3 {\n",
       "  background-color: #f0f0f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_95174_row5_col3 {\n",
       "  background-color: #a9a9f6;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_95174_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >model</th>\n",
       "      <th class=\"col_heading level0 col1\" >auc</th>\n",
       "      <th class=\"col_heading level0 col2\" >f1_score</th>\n",
       "      <th class=\"col_heading level0 col3\" >average_precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_95174_row0_col0\" class=\"data row0 col0\" >m1_hgbc</td>\n",
       "      <td id=\"T_95174_row0_col1\" class=\"data row0 col1\" >0.865434</td>\n",
       "      <td id=\"T_95174_row0_col2\" class=\"data row0 col2\" >0.279313</td>\n",
       "      <td id=\"T_95174_row0_col3\" class=\"data row0 col3\" >0.403261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_95174_row1_col0\" class=\"data row1 col0\" >m2_catboost</td>\n",
       "      <td id=\"T_95174_row1_col1\" class=\"data row1 col1\" >0.866293</td>\n",
       "      <td id=\"T_95174_row1_col2\" class=\"data row1 col2\" >0.289173</td>\n",
       "      <td id=\"T_95174_row1_col3\" class=\"data row1 col3\" >0.400756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
       "      <td id=\"T_95174_row2_col0\" class=\"data row2 col0\" >m7_lgbm2</td>\n",
       "      <td id=\"T_95174_row2_col1\" class=\"data row2 col1\" >0.864804</td>\n",
       "      <td id=\"T_95174_row2_col2\" class=\"data row2 col2\" >0.294483</td>\n",
       "      <td id=\"T_95174_row2_col3\" class=\"data row2 col3\" >0.400505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row3\" class=\"row_heading level0 row3\" >5</th>\n",
       "      <td id=\"T_95174_row3_col0\" class=\"data row3 col0\" >m6_lgbm1</td>\n",
       "      <td id=\"T_95174_row3_col1\" class=\"data row3 col1\" >0.864935</td>\n",
       "      <td id=\"T_95174_row3_col2\" class=\"data row3 col2\" >0.292513</td>\n",
       "      <td id=\"T_95174_row3_col3\" class=\"data row3 col3\" >0.400006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_95174_row4_col0\" class=\"data row4 col0\" >m5_RF</td>\n",
       "      <td id=\"T_95174_row4_col1\" class=\"data row4 col1\" >0.863872</td>\n",
       "      <td id=\"T_95174_row4_col2\" class=\"data row4 col2\" >0.276701</td>\n",
       "      <td id=\"T_95174_row4_col3\" class=\"data row4 col3\" >0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row5\" class=\"row_heading level0 row5\" >3</th>\n",
       "      <td id=\"T_95174_row5_col0\" class=\"data row5 col0\" >m4_LR</td>\n",
       "      <td id=\"T_95174_row5_col1\" class=\"data row5 col1\" >0.862013</td>\n",
       "      <td id=\"T_95174_row5_col2\" class=\"data row5 col2\" >0.259481</td>\n",
       "      <td id=\"T_95174_row5_col3\" class=\"data row5 col3\" >0.381870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95174_level0_row6\" class=\"row_heading level0 row6\" >2</th>\n",
       "      <td id=\"T_95174_row6_col0\" class=\"data row6 col0\" >m3_xgbr</td>\n",
       "      <td id=\"T_95174_row6_col1\" class=\"data row6 col1\" >0.855243</td>\n",
       "      <td id=\"T_95174_row6_col2\" class=\"data row6 col2\" >0.294449</td>\n",
       "      <td id=\"T_95174_row6_col3\" class=\"data row6 col3\" >0.372796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff414465e50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.sort_values(by='average_precision_score',ascending=False).style.background_gradient(cmap=cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "50711c79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:39.217787Z",
     "iopub.status.busy": "2021-12-27T15:31:39.217136Z",
     "iopub.status.idle": "2021-12-27T15:31:39.220809Z",
     "shell.execute_reply": "2021-12-27T15:31:39.221320Z"
    },
    "papermill": {
     "duration": 0.086429,
     "end_time": "2021-12-27T15:31:39.221498",
     "exception": false,
     "start_time": "2021-12-27T15:31:39.135069",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#pipe_lgbm_woe_powertransformer.fit(X,y)\n",
    "#predictions =pipe_lgbm_woe_powertransformer.predict_proba(X_test_final)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8b3dde0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:39.383608Z",
     "iopub.status.busy": "2021-12-27T15:31:39.382941Z",
     "iopub.status.idle": "2021-12-27T15:31:39.399042Z",
     "shell.execute_reply": "2021-12-27T15:31:39.399615Z"
    },
    "papermill": {
     "duration": 0.100281,
     "end_time": "2021-12-27T15:31:39.399828",
     "exception": false,
     "start_time": "2021-12-27T15:31:39.299547",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictions = np.mean(np.column_stack(predictions), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "12d75d84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T15:31:39.563982Z",
     "iopub.status.busy": "2021-12-27T15:31:39.563350Z",
     "iopub.status.idle": "2021-12-27T15:31:40.054983Z",
     "shell.execute_reply": "2021-12-27T15:31:40.055539Z"
    },
    "papermill": {
     "duration": 0.575245,
     "end_time": "2021-12-27T15:31:40.055723",
     "exception": false,
     "start_time": "2021-12-27T15:31:39.480478",
     "status": "completed"
    },
    "tags": []
   },
   "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>Id</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.049593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.034282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.014106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.057494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.117421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101498</th>\n",
       "      <td>101499</td>\n",
       "      <td>0.031084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101499</th>\n",
       "      <td>101500</td>\n",
       "      <td>0.381699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101500</th>\n",
       "      <td>101501</td>\n",
       "      <td>0.006950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101501</th>\n",
       "      <td>101502</td>\n",
       "      <td>0.065035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101502</th>\n",
       "      <td>101503</td>\n",
       "      <td>0.030633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101503 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  Probability\n",
       "0            1     0.049593\n",
       "1            2     0.034282\n",
       "2            3     0.014106\n",
       "3            4     0.057494\n",
       "4            5     0.117421\n",
       "...        ...          ...\n",
       "101498  101499     0.031084\n",
       "101499  101500     0.381699\n",
       "101500  101501     0.006950\n",
       "101501  101502     0.065035\n",
       "101502  101503     0.030633\n",
       "\n",
       "[101503 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the predictions to a CSV file\n",
    "sub = pd.read_csv('../input/GiveMeSomeCredit/sampleEntry.csv')\n",
    "sub['Probability']=predictions\n",
    "sub.to_csv('pipe_lgbm_woe_powertransformer_estimator_XGB_CV_onlynum_fe_V3.csv', index=False)\n",
    "sub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1fa2bb2",
   "metadata": {
    "papermill": {
     "duration": 0.078933,
     "end_time": "2021-12-27T15:31:40.215811",
     "exception": false,
     "start_time": "2021-12-27T15:31:40.136878",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Conclusion: \n",
    "\n",
    "1. A model that meets the requirements and constraints of project stakeholders.\n",
    "1. A model that is sufficiently skillful given the time and resources available.\n",
    "1. A model that is skillful as compared to naive models.\n",
    "1. A model that is skillful relative to other tested models.\n",
    "1. A model that is skillful relative to the state-of-the-art."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 3333.703774,
   "end_time": "2021-12-27T15:31:41.816603",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-12-27T14:36:08.112829",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}