credit-part3-modeling-4-imbalanced-undersample.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "id": "696cffc3",
   "metadata": {
    "papermill": {
     "duration": 0.056674,
     "end_time": "2022-01-03T14:47:47.455680",
     "exception": false,
     "start_time": "2022-01-03T14:47:47.399006",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this notebook, we will use the imbalanced-learn pipeline, to set up various under-sampling solutions, in a way that we train the model on re-sampled data, but we evaluate performance on non resampled data.\n",
    "\n",
    "![image.png](attachment:5c8cb5d2-3b60-4e40-b35d-568b7f4877da.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc9b6a13",
   "metadata": {
    "_cell_guid": "ddedf45c-cdd2-4013-973f-6ccf6c78347a",
    "_uuid": "20c9b28e-4c93-447d-8210-cad89f404354",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-01-03T14:47:47.566820Z",
     "iopub.status.busy": "2022-01-03T14:47:47.565973Z",
     "iopub.status.idle": "2022-01-03T14:47:51.221904Z",
     "shell.execute_reply": "2022-01-03T14:47:51.220920Z",
     "shell.execute_reply.started": "2022-01-03T13:06:45.183250Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 3.710295,
     "end_time": "2022-01-03T14:47:51.222061",
     "exception": false,
     "start_time": "2022-01-03T14:47:47.511766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
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    }
   ],
   "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 yellowbrick.classifier import (\n",
    "    ClassificationReport,\n",
    "    DiscriminationThreshold,\n",
    ")\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,ClassifierMixin\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,cross_validate\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# Imbalanced data \n",
    "from imblearn.datasets import fetch_datasets\n",
    "# to correctly set up the cross-validation\n",
    "from imblearn.pipeline import make_pipeline\n",
    "from imblearn.under_sampling import (\n",
    "    RandomUnderSampler,\n",
    "    CondensedNearestNeighbour,\n",
    "    TomekLinks,\n",
    "    OneSidedSelection,\n",
    "    EditedNearestNeighbours,\n",
    "    RepeatedEditedNearestNeighbours,\n",
    "    AllKNN,\n",
    "    NeighbourhoodCleaningRule,\n",
    "    NearMiss,\n",
    "    InstanceHardnessThreshold\n",
    ")\n",
    "\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": "2f4d5b93",
   "metadata": {
    "_cell_guid": "38c6befb-892d-4ef3-9c63-13be5959b671",
    "_uuid": "2953f9f3-02f8-4fa9-a7fc-cb93565c2183",
    "papermill": {
     "duration": 0.034803,
     "end_time": "2022-01-03T14:47:51.290762",
     "exception": false,
     "start_time": "2022-01-03T14:47:51.255959",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0c4d36d",
   "metadata": {
    "_cell_guid": "98a5b4a6-90e9-449d-96cc-0d85c9ff1c77",
    "_uuid": "e086592f-9f6a-4592-80b5-c1e7ad227e02",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-01-03T14:47:51.361935Z",
     "iopub.status.busy": "2022-01-03T14:47:51.361431Z",
     "iopub.status.idle": "2022-01-03T14:47:51.778362Z",
     "shell.execute_reply": "2022-01-03T14:47:51.777876Z",
     "shell.execute_reply.started": "2022-01-03T13:06:48.966556Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.454453,
     "end_time": "2022-01-03T14:47:51.778538",
     "exception": false,
     "start_time": "2022-01-03T14:47:51.324085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 208 ms, sys: 52.3 ms, total: 261 ms\n",
      "Wall time: 397 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": "25d6bbdd",
   "metadata": {
    "papermill": {
     "duration": 0.033821,
     "end_time": "2022-01-03T14:47:51.847085",
     "exception": false,
     "start_time": "2022-01-03T14:47:51.813264",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1-Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b2442f",
   "metadata": {
    "papermill": {
     "duration": 0.033517,
     "end_time": "2022-01-03T14:47:51.915911",
     "exception": false,
     "start_time": "2022-01-03T14:47:51.882394",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Convert Dtypes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f06a453",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:47:52.008852Z",
     "iopub.status.busy": "2022-01-03T14:47:52.007349Z",
     "iopub.status.idle": "2022-01-03T14:47:52.062531Z",
     "shell.execute_reply": "2022-01-03T14:47:52.061606Z",
     "shell.execute_reply.started": "2022-01-03T13:06:49.419756Z"
    },
    "papermill": {
     "duration": 0.112897,
     "end_time": "2022-01-03T14:47:52.062676",
     "exception": false,
     "start_time": "2022-01-03T14:47:51.949779",
     "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": "21fd0f0d",
   "metadata": {
    "papermill": {
     "duration": 0.033688,
     "end_time": "2022-01-03T14:47:52.131443",
     "exception": false,
     "start_time": "2022-01-03T14:47:52.097755",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Imbalanced target\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b6c2b47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:47:52.206493Z",
     "iopub.status.busy": "2022-01-03T14:47:52.205755Z",
     "iopub.status.idle": "2022-01-03T14:47:52.208597Z",
     "shell.execute_reply": "2022-01-03T14:47:52.208999Z",
     "shell.execute_reply.started": "2022-01-03T13:06:49.528382Z"
    },
    "papermill": {
     "duration": 0.043779,
     "end_time": "2022-01-03T14:47:52.209117",
     "exception": false,
     "start_time": "2022-01-03T14:47:52.165338",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.93316\n",
       "1    0.06684\n",
       "Name: SeriousDlqin2yrs, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.SeriousDlqin2yrs.value_counts() / len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5358711b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:47:52.282030Z",
     "iopub.status.busy": "2022-01-03T14:47:52.281196Z",
     "iopub.status.idle": "2022-01-03T14:47:52.475514Z",
     "shell.execute_reply": "2022-01-03T14:47:52.474794Z"
    },
    "papermill": {
     "duration": 0.232188,
     "end_time": "2022-01-03T14:47:52.475645",
     "exception": false,
     "start_time": "2022-01-03T14:47:52.243457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the target variable\n",
    "g = sns.countplot(train['SeriousDlqin2yrs'])\n",
    "g.set_xticklabels(['Pay','Not Pay'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e75dd6e",
   "metadata": {
    "papermill": {
     "duration": 0.036188,
     "end_time": "2022-01-03T14:47:52.549128",
     "exception": false,
     "start_time": "2022-01-03T14:47:52.512940",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Reduce Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eecf8daa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:47:52.635759Z",
     "iopub.status.busy": "2022-01-03T14:47:52.634334Z",
     "iopub.status.idle": "2022-01-03T14:47:52.675171Z",
     "shell.execute_reply": "2022-01-03T14:47:52.675740Z",
     "shell.execute_reply.started": "2022-01-03T13:06:49.549523Z"
    },
    "papermill": {
     "duration": 0.091961,
     "end_time": "2022-01-03T14:47:52.675931",
     "exception": false,
     "start_time": "2022-01-03T14:47:52.583970",
     "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": "2b05b58c",
   "metadata": {
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     "start_time": "2022-01-03T14:47:52.712107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8836ce96",
   "metadata": {
    "execution": {
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     "exception": false,
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     "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": 8,
   "id": "4d51ee47",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "y = train['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dcd062",
   "metadata": {
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     "start_time": "2022-01-03T14:47:52.939181",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Imput before Descritaziation : \n",
    "\n",
    "### IterativeImputer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b9d12ea",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 347 µs, sys: 49 µs, total: 396 µs\n",
      "Wall time: 371 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",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2b62caa",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-01-03T13:06:49.685049Z"
    },
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     "duration": 133.428577,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 9s, sys: 2.46 s, total: 2min 12s\n",
      "Wall time: 2min 13s\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": 11,
   "id": "16525509",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 34s, sys: 962 ms, total: 2min 35s\n",
      "Wall time: 2min 35s\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": "code",
   "execution_count": 12,
   "id": "ae2dd0f4",
   "metadata": {
    "execution": {
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   "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": 13,
   "id": "0a84223b",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-01-03T14:52:43.249336Z",
     "shell.execute_reply.started": "2022-01-03T13:11:40.133209Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2022-01-03T14:52:43.204755",
     "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": "029e887d",
   "metadata": {
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     "start_time": "2022-01-03T14:52:43.285475",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Final X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bdbbda5",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "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": 15,
   "id": "8ac5d080",
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   "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()"
   ]
  },
  {
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   "execution_count": 16,
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    {
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     "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": 17,
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   "outputs": [],
   "source": [
    "del train\n",
    "del test \n",
    "del train_final\n",
    "del test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97527566",
   "metadata": {
    "execution": {
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   "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": 18,
     "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": "90c4105d",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Num Features\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "df443812",
   "metadata": {
    "execution": {
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     "exception": false,
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     "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": 19,
     "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": "541f9149",
   "metadata": {
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     "end_time": "2022-01-03T14:52:44.002258",
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     "start_time": "2022-01-03T14:52:43.965391",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# FE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c0b003e",
   "metadata": {
    "execution": {
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     "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": "markdown",
   "id": "9ac4dab8",
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     "start_time": "2022-01-03T14:52:44.122730",
     "status": "completed"
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    "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\n",
    "\n",
    "1. Add bin data \n",
    "\n",
    "## Pipe : \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0b83b286",
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    "execution": {
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   "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",
    "            ])\n",
    "#Define vnum pipeline\n",
    "numeric_transformer_final = 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=95))\n",
    "                            ]\n",
    "                            )\n",
    "# Features union cat + num \n",
    "# WOE+PowerTransformer\n",
    "preprocessor_woe_powertransformer_final = ColumnTransformer(\n",
    "            transformers=[\n",
    "                ('numerical', numeric_transformer_final, num_columns),\n",
    "               # ('categorical', categorical_transformer, cat_columns)\n",
    "            ])"
   ]
  },
  {
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     "status": "completed"
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    "tags": []
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   "source": [
    "\n"
   ]
  },
  {
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    {
     "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=\"6345fcea-97d0-4362-96ff-406c0ee65f8a\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6345fcea-97d0-4362-96ff-406c0ee65f8a\">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",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('reducedim', SelectPercentile(percentile=95)),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(device_type='gpu',\n",
       "                                learning_rate=0.0018069834369607075,\n",
       "                                max_depth=8, metric='auc', min_samples_leaf=47,\n",
       "                                n_estimators=5000, random_state=42,\n",
       "                                subsample=0.8573598985000007, verbose=-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=\"7e98f05e-cb80-4d76-85f6-9e8175b2d127\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7e98f05e-cb80-4d76-85f6-9e8175b2d127\">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=\"706f33e2-0808-4e5e-b270-19e5978ce9f6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"706f33e2-0808-4e5e-b270-19e5978ce9f6\">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=\"a70b2690-11fc-418e-8b1a-2fc8828e4cbf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a70b2690-11fc-418e-8b1a-2fc8828e4cbf\">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=\"2c08955f-1fea-4ba3-83ad-265f1dc2a8df\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"2c08955f-1fea-4ba3-83ad-265f1dc2a8df\">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=\"24101d18-b746-48f9-9a1c-1dc0c3a90f47\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"24101d18-b746-48f9-9a1c-1dc0c3a90f47\">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=\"18a98bae-d71d-40cd-a97c-96dadf03979a\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"18a98bae-d71d-40cd-a97c-96dadf03979a\">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=\"5d85ba7f-2aba-4e35-aabe-d80cbc1c46a3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5d85ba7f-2aba-4e35-aabe-d80cbc1c46a3\">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=\"73b553e2-7dc1-41ef-9030-53fb2e8f168c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"73b553e2-7dc1-41ef-9030-53fb2e8f168c\">SelectPercentile</label><div class=\"sk-toggleable__content\"><pre>SelectPercentile(percentile=95)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"aac39051-de9f-4cda-8df1-79afcec24930\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"aac39051-de9f-4cda-8df1-79afcec24930\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(device_type='gpu', learning_rate=0.0018069834369607075,\n",
       "               max_depth=8, metric='auc', min_samples_leaf=47,\n",
       "               n_estimators=5000, random_state=42, subsample=0.8573598985000007,\n",
       "               verbose=-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",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('reducedim', SelectPercentile(percentile=95)),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(device_type='gpu',\n",
       "                                learning_rate=0.0018069834369607075,\n",
       "                                max_depth=8, metric='auc', min_samples_leaf=47,\n",
       "                                n_estimators=5000, random_state=42,\n",
       "                                subsample=0.8573598985000007, verbose=-1))])"
      ]
     },
     "execution_count": 22,
     "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': -1,\n",
    "                                 'random_state': 42,\n",
    "                                 'metric': 'auc',\n",
    "                                \"device_type\" : \"gpu\",\n",
    "                                'boosting_type': 'gbdt',\n",
    "                                #'tree_method': \"gpu_hist\"\n",
    "                               }\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_lgbm2)\n",
    "    ]\n",
    ")\n",
    "pipe1_model_lgbm_woe_powertransformer2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2a84de",
   "metadata": {
    "papermill": {
     "duration": 0.03785,
     "end_time": "2022-01-03T14:52:44.812235",
     "exception": false,
     "start_time": "2022-01-03T14:52:44.774385",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#  Train_Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11582359",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:52:44.902000Z",
     "iopub.status.busy": "2022-01-03T14:52:44.901172Z",
     "iopub.status.idle": "2022-01-03T14:52:45.004803Z",
     "shell.execute_reply": "2022-01-03T14:52:45.005265Z",
     "shell.execute_reply.started": "2022-01-03T13:11:40.661010Z"
    },
    "papermill": {
     "duration": 0.150833,
     "end_time": "2022-01-03T14:52:45.005425",
     "exception": false,
     "start_time": "2022-01-03T14:52:44.854592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "###############################################################################\n",
    "#                                  Get data                                 #\n",
    "###############################################################################\n",
    "X_train,X_test, y_train,  y_test = train_test_split(X, y,stratify=y, \n",
    "                                                       test_size=0.25,\n",
    "                                                        random_state=42,\n",
    "                                                        shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa881d8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:52:45.088828Z",
     "iopub.status.busy": "2022-01-03T14:52:45.085071Z",
     "iopub.status.idle": "2022-01-03T14:52:48.671889Z",
     "shell.execute_reply": "2022-01-03T14:52:48.671267Z",
     "shell.execute_reply.started": "2022-01-03T13:11:40.768632Z"
    },
    "papermill": {
     "duration": 3.628102,
     "end_time": "2022-01-03T14:52:48.672017",
     "exception": false,
     "start_time": "2022-01-03T14:52:45.043915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "preprocessor_woe_powertransformer_final.fit(X_train,y_train)\n",
    "X_train_pre = preprocessor_woe_powertransformer_final.transform(X_train)\n",
    "X_test_pre = preprocessor_woe_powertransformer_final.transform(X_test)\n",
    "#del x_final\n",
    "#del x_test_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f9f8f0",
   "metadata": {
    "papermill": {
     "duration": 0.037275,
     "end_time": "2022-01-03T14:52:48.747694",
     "exception": false,
     "start_time": "2022-01-03T14:52:48.710419",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1-Simple train : \n",
    "\n",
    "## Full classification report\n",
    "\n",
    "\n",
    "        Precision, Recall, F-measure, Support\n",
    "\n",
    "        Precision = tp / (tp + fp)\n",
    "\n",
    "        Recall = tp / (tp + fn)\n",
    "\n",
    "        F1 = 2 (precision recall) / (precision + recall)\n",
    "\n",
    "        Support = Number of cases on each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f855a45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:52:48.827553Z",
     "iopub.status.busy": "2022-01-03T14:52:48.826819Z",
     "iopub.status.idle": "2022-01-03T14:55:13.075490Z",
     "shell.execute_reply": "2022-01-03T14:55:13.075039Z",
     "shell.execute_reply.started": "2022-01-03T13:11:44.644941Z"
    },
    "papermill": {
     "duration": 144.290435,
     "end_time": "2022-01-03T14:55:13.075608",
     "exception": false,
     "start_time": "2022-01-03T14:52:48.785173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'LGBMClassifier Classification Report'}>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://www.scikit-yb.org/en/latest/api/classifier/classification_report.html\n",
    "visualizer = ClassificationReport(model_lgbm2)\n",
    "visualizer.fit(X_train_pre,y_train)        # Fit the visualizer and the model\n",
    "visualizer.score(X_train_pre,y_train)        # Evaluate the model on the test data\n",
    "visualizer.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8232f927",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:55:13.158229Z",
     "iopub.status.busy": "2022-01-03T14:55:13.157447Z",
     "iopub.status.idle": "2022-01-03T14:57:14.160832Z",
     "shell.execute_reply": "2022-01-03T14:57:14.160386Z",
     "shell.execute_reply.started": "2022-01-03T13:14:07.407741Z"
    },
    "papermill": {
     "duration": 121.045531,
     "end_time": "2022-01-03T14:57:14.160949",
     "exception": false,
     "start_time": "2022-01-03T14:55:13.115418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Threshold Plot for LGBMClassifier'}, xlabel='discrimination threshold', ylabel='score'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = DiscriminationThreshold(model_lgbm2,\n",
    "                                     n_trials=1,\n",
    "                                     cv=0.5,\n",
    "                                     argmax='fscore',\n",
    "                                     random_state=0,\n",
    "                                     is_fitted='auto',\n",
    "                                     exclude = \"queue_rate\")\n",
    "\n",
    "visualizer.fit(X_train_pre,y_train)        # Fit the visualizer and the model\n",
    "visualizer.score(X_train_pre,y_train)        # Evaluate the model on the test data\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3023c6",
   "metadata": {
    "papermill": {
     "duration": 0.040676,
     "end_time": "2022-01-03T14:57:14.242758",
     "exception": false,
     "start_time": "2022-01-03T14:57:14.202082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Undersample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3348ba1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:57:14.333406Z",
     "iopub.status.busy": "2022-01-03T14:57:14.332780Z",
     "iopub.status.idle": "2022-01-03T14:57:14.335035Z",
     "shell.execute_reply": "2022-01-03T14:57:14.335433Z",
     "shell.execute_reply.started": "2022-01-03T14:13:37.277211Z"
    },
    "papermill": {
     "duration": 0.051835,
     "end_time": "2022-01-03T14:57:14.335572",
     "exception": false,
     "start_time": "2022-01-03T14:57:14.283737",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "undersampler_dict = {\n",
    "    'random': RandomUnderSampler(\n",
    "        sampling_strategy='auto',\n",
    "        random_state=42,\n",
    "        replacement=False),\n",
    "\n",
    "#    'cnn': CondensedNearestNeighbour(\n",
    " #       sampling_strategy='auto',\n",
    "  #      random_state=0,\n",
    "   #     n_neighbors=1,\n",
    "    #    n_jobs=4),\n",
    "\n",
    "    'tomek': TomekLinks(\n",
    "        sampling_strategy='auto',\n",
    "        n_jobs=-1),\n",
    "\n",
    "    'oss': OneSidedSelection(\n",
    "        sampling_strategy='auto',\n",
    "        random_state=42,\n",
    "        n_neighbors=1,\n",
    "        n_jobs=-1),\n",
    "\n",
    "    'enn': EditedNearestNeighbours(\n",
    "        sampling_strategy='auto',\n",
    "        n_neighbors=3,\n",
    "        kind_sel='all',\n",
    "        n_jobs=4),\n",
    "\n",
    "    'renn': RepeatedEditedNearestNeighbours(\n",
    "        sampling_strategy='auto',\n",
    "        n_neighbors=3,\n",
    "        kind_sel='all',\n",
    "        n_jobs=4,\n",
    "        max_iter=100),\n",
    "\n",
    "    'allknn': AllKNN(\n",
    "        sampling_strategy='auto',\n",
    "        n_neighbors=5,\n",
    "        kind_sel='all',\n",
    "        n_jobs=4),\n",
    "\n",
    "    'ncr': NeighbourhoodCleaningRule(\n",
    "        sampling_strategy='auto',\n",
    "        n_neighbors=3,\n",
    "        kind_sel='all',\n",
    "        n_jobs=4,\n",
    "        threshold_cleaning=0.5),\n",
    "\n",
    "   # 'nm1': NearMiss(\n",
    "    #    sampling_strategy='auto',\n",
    "     #   version=1,\n",
    "     #   n_neighbors=3,\n",
    "     #   n_jobs=4),\n",
    "\n",
    "    #'nm2': NearMiss(\n",
    "     #   sampling_strategy='auto',\n",
    "     #   version=2,\n",
    "     #   n_neighbors=3,\n",
    "     #   n_jobs=4),\n",
    "\n",
    "    # we set up the instance hardness threshold\n",
    "    # with the same classifier that we intend to use in our data\n",
    "    'iht': InstanceHardnessThreshold(\n",
    "        estimator=model_lgbm2,\n",
    "        sampling_strategy='auto',\n",
    "        random_state=42,\n",
    "        n_jobs=4,\n",
    "        cv=3)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "38309812",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T14:57:14.421024Z",
     "iopub.status.busy": "2022-01-03T14:57:14.420218Z",
     "iopub.status.idle": "2022-01-03T14:57:14.427516Z",
     "shell.execute_reply": "2022-01-03T14:57:14.427056Z",
     "shell.execute_reply.started": "2022-01-03T14:13:37.333046Z"
    },
    "papermill": {
     "duration": 0.05123,
     "end_time": "2022-01-03T14:57:14.427626",
     "exception": false,
     "start_time": "2022-01-03T14:57:14.376396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# function to train random forests and evaluate the performance\n",
    "\n",
    "# ***with cross-validation***\n",
    "\n",
    "def run_model(X_train, y_train, undersampler=None):\n",
    "    \n",
    "    # set up the classifier\n",
    "    rf = model_lgbm2\n",
    "    \n",
    "    # set up a scaler \n",
    "    # (as many undersampling techniques use KNN\n",
    "    # we put the variables in the same scale)\n",
    "    scaler = MinMaxScaler()\n",
    "    \n",
    "    # without undersampling:\n",
    "    if not undersampler:\n",
    "\n",
    "        model = model_lgbm2\n",
    "    \n",
    "    # set up a pipeline with undersampling:\n",
    "    else:\n",
    "        \n",
    "        # important to scale before the under-sampler\n",
    "        # as the many of methods require the variables in \n",
    "        # a similar scale\n",
    "        model = make_pipeline(\n",
    "            scaler,\n",
    "            undersampler,\n",
    "            model_lgbm2,\n",
    "        )\n",
    "        \n",
    "        \n",
    "    # When we make a pipeline and then run the training of the model\n",
    "    # with cross-validation, the procedure works as follows:\n",
    "    \n",
    "    # 1) take 2 of the 3 fold as train set\n",
    "    # 2) resample the 2 fold (aka, the train set)\n",
    "    # 3) train the model on the resampled data from point 2\n",
    "    # 4) evaluate performance on the 3rd fold, that was not resampled\n",
    "    \n",
    "    # this way, we make sure that we are not evaluating the performance\n",
    "    # of our classifier on the under-sampled data\n",
    "    \n",
    "    cv_results = cross_validate(\n",
    "        model, # the lgbm model  or the pipeline\n",
    "        X_train, # the data that will be used in the cross-validation\n",
    "        y_train, # the target\n",
    "        scoring=\"roc_auc\", # the metric that we want to evaluate\n",
    "        cv=4, # the cross-validation fold\n",
    "    )\n",
    "\n",
    "    print(\n",
    "        'model_lgbm2 roc_auc: {0} +/- {1}'.format(\n",
    "        cv_results['test_score'].mean(), cv_results['test_score'].std()\n",
    "        )\n",
    "    )\n",
    "    return cv_results['test_score'].mean(), cv_results['test_score'].std()"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8625688276997037 +/- 0.0035080236891726173\n",
      "\n",
      "random\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.858181429188133 +/- 0.004612587761779285\n",
      "\n",
      "tomek\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8627041498279308 +/- 0.003584230592089494\n",
      "\n",
      "oss\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8627229021333344 +/- 0.003784828443950451\n",
      "\n",
      "enn\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8630909788287942 +/- 0.0038026055124852757\n",
      "\n",
      "renn\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8621382365638033 +/- 0.003615991141402151\n",
      "\n",
      "allknn\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8621110226669315 +/- 0.0040351051926343535\n",
      "\n",
      "ncr\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.8621691036387884 +/- 0.003973914786246707\n",
      "\n",
      "iht\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "model_lgbm2 roc_auc: 0.7991996119828295 +/- 0.010744213905743065\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# now we train several models, with the different under-samplers and\n",
    "# with cross-validation\n",
    "# to save the results\n",
    "pr_mean_dict = {}\n",
    "pr_std_dict = {} \n",
    "# initiate a dictionary per dataset\n",
    "#pr_mean_dict[dataset] = {}\n",
    "#pr_std_dict[dataset] = {}\n",
    "\n",
    "# load dataset\n",
    "#data = fetch_datasets()[dataset]\n",
    "\n",
    "# separate dataset into train and test\n",
    "#X_train, X_test, y_train, y_test = train_test_split(\n",
    " #   data.data,  \n",
    "  #  data.target, \n",
    "   # test_size=0.3,\n",
    "   # random_state=0,\n",
    "#)\n",
    "\n",
    "\n",
    "# train model on data without re-sampling\n",
    "# with cross-validation\n",
    "aps_mean, aps_std = run_model(X_train_pre, y_train)\n",
    " # store results\n",
    "pr_mean_dict['full_data'] = aps_mean\n",
    "pr_std_dict['full_data'] = aps_std\n",
    "    \n",
    "print()\n",
    "for undersampler in undersampler_dict.keys():\n",
    "\n",
    "    print(undersampler)\n",
    "\n",
    "    # resample, train and evaluate performance\n",
    "    # with cross-validation\n",
    "    aps_mean, aps_std = run_model(X_train_pre, y_train, undersampler_dict[undersampler])\n",
    "     # store results\n",
    "    pr_mean_dict[undersampler] = aps_mean\n",
    "    pr_std_dict[undersampler] = aps_std\n",
    "    print()\n",
    "print()"
   ]
  },
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_mean_s = pd.Series(pr_mean_dict)\n",
    "pr_std_s = pd.Series(pr_std_dict)\n",
    "pr_mean_s.plot.bar(yerr=[pr_std_s, pr_std_s])\n",
    "plt.title(\"train with different undersampling method\")\n",
    "plt.ylabel(\"roc_auc\")\n",
    "plt.axhline(pr_mean_dict['full_data'], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94297b36",
   "metadata": {
    "papermill": {
     "duration": 0.053777,
     "end_time": "2022-01-03T16:52:34.516603",
     "exception": false,
     "start_time": "2022-01-03T16:52:34.462826",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Different under-sampling techniques work best for different datasets.\n",
    "ENN, RENN and AllKNN tend to produce similar results, so we may as well just choose one of the 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "02b8a497",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T16:52:34.628870Z",
     "iopub.status.busy": "2022-01-03T16:52:34.628243Z",
     "iopub.status.idle": "2022-01-03T16:52:34.631503Z",
     "shell.execute_reply": "2022-01-03T16:52:34.632085Z"
    },
    "papermill": {
     "duration": 0.062687,
     "end_time": "2022-01-03T16:52:34.632252",
     "exception": false,
     "start_time": "2022-01-03T16:52:34.569565",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
      "Wall time: 7.63 µs\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "#pipe_woe_powertransformer.fit(X,y)\n",
    "#x_pre = pipe_woe_powertransformer_red.transform(X)\n",
    "#x_test_final_pre = pipe_woe_powertransformer_red.transform(X_test_final)\n",
    "#del x_final\n",
    "#del x_test_final\n",
    "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": "markdown",
   "id": "d6f5086d",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-12-31T12:33:50.384931Z",
     "iopub.status.idle": "2021-12-31T12:33:50.385528Z",
     "shell.execute_reply": "2021-12-31T12:33:50.385321Z",
     "shell.execute_reply.started": "2021-12-31T12:33:50.385297Z"
    },
    "papermill": {
     "duration": 0.053795,
     "end_time": "2022-01-03T16:52:34.740042",
     "exception": false,
     "start_time": "2022-01-03T16:52:34.686247",
     "status": "completed"
    },
    "tags": []
   },
   "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": "5b814743",
   "metadata": {
    "papermill": {
     "duration": 0.053134,
     "end_time": "2022-01-03T16:52:34.846777",
     "exception": false,
     "start_time": "2022-01-03T16:52:34.793643",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Prepare data + undersample in all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb67e745",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T16:52:34.958697Z",
     "iopub.status.busy": "2022-01-03T16:52:34.957987Z",
     "iopub.status.idle": "2022-01-03T16:52:34.960818Z",
     "shell.execute_reply": "2022-01-03T16:52:34.961409Z"
    },
    "papermill": {
     "duration": 0.061661,
     "end_time": "2022-01-03T16:52:34.961572",
     "exception": false,
     "start_time": "2022-01-03T16:52:34.899911",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 6.2 µs\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "#pipe_woe_powertransformer.fit(X,y)\n",
    "#x_pre = pipe_woe_powertransformer_red.transform(X)\n",
    "#x_test_final_pre = pipe_woe_powertransformer_red.transform(X_test_final)\n",
    "#model = make_pipeline(\n",
    " #           scaler,\n",
    "  #          undersampler,\n",
    "   #         model_lgbm2,\n",
    "    #    )\n",
    "#del x_final\n",
    "#del x_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b078ca83",
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     "exception": false,
     "start_time": "2022-01-03T16:52:35.016393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e8490f9d",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-01-03T16:52:35.188184Z",
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    },
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     "end_time": "2022-01-03T16:52:35.190897",
     "exception": false,
     "start_time": "2022-01-03T16:52:35.130867",
     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "#predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a123df15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-03T16:52:35.304885Z",
     "iopub.status.busy": "2022-01-03T16:52:35.304017Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "#predictions = np.mean(np.column_stack(predictions), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63064a2f",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-12-31T12:33:50.392054Z",
     "iopub.status.idle": "2021-12-31T12:33:50.392674Z",
     "shell.execute_reply": "2021-12-31T12:33:50.392439Z",
     "shell.execute_reply.started": "2021-12-31T12:33:50.392415Z"
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     "end_time": "2022-01-03T16:52:35.422339",
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     "start_time": "2022-01-03T16:52:35.366415",
     "status": "completed"
    },
    "tags": []
   },
   "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('pipe1_model_lgbm_woe_powertransformer2_simple.csv', index=False)\n",
    "    sub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41cc89b",
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     "start_time": "2022-01-03T16:52:35.477054",
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   "source": [
    "# Conclusion: \n",
    "\n",
    "**Advantage and disadvantages of Under-sampling**\n",
    "\n",
    "**Advantages;**\n",
    "\n",
    "It can help improve run time and storage problems by reducing the number of training data samples when the training data set is huge.\n",
    "\n",
    "**Disadvantages;**\n",
    "\n",
    "It can discard potentially useful information which could be important for building rule classifiers.\n",
    "The sample chosen by random under-sampling may be a biased sample. And it will not be an accurate representation of the population. Thereby, resulting in inaccurate results with the actual test data set.\n",
    "\n",
    "credit: \n",
    "\n",
    "**Imbalanced:**\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2020/07/10-techniques-to-deal-with-class-imbalance-in-machine-learning/\n",
    "\n",
    "https://github.com/DeepSparkChaker/machine-learning-imbalanced-data/tree/master/Section-04-Undersampling\n",
    "\n",
    "https://github.com/DeepSparkChaker/machine-learning-imbalanced-data/blob/master/Section-04-Undersampling/04-12-Under-sampling-method-comparison-Precision-cross-val.ipynb\n",
    "\n",
    "**stacking:**\n",
    "\n",
    "https://towardsdatascience.com/stacking-classifiers-for-higher-predictive-performance-566f963e4840\n",
    "\n",
    "https://www.dataquest.io/blog/introduction-to-ensembles/\n",
    "\n",
    "https://scikit-learn.org/stable/modules/ensemble.html\n",
    "\n",
    "https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/\n",
    "\n",
    "\n",
    "https://mlfromscratch.com/model-stacking-explained/#/\n",
    "\n",
    "http://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/\n",
    "\n",
    "http://rasbt.github.io/mlxtend/user_guide/classifier/StackingCVClassifier/\n",
    "\n",
    "\n",
    "https://www.kaggle.com/bannourchaker/7ensembling-stacking-averaging-scratchvssklearn/edit/run/73916270\n",
    "\n",
    "https://www.kaggle.com/bannourchaker/averagingmodels\n",
    "\n",
    "https://mlfromscratch.com/model-stacking-explained/#/\n",
    "\n",
    "https://neptune.ai/blog/ensemble-learning-guide\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2021/08/ensemble-stacking-for-machine-learning-and-deep-learning/\n",
    "\n",
    "https://www.kaggle.com/prashant111/adaboost-classifier-tutorial\n",
    "\n",
    "https://www.kaggle.com/bannourchaker/stacking/edit/run/75840835\n",
    "\n",
    "https://www.kaggle.com/bannourchaker/8-ensembling-blending-scratchvsoop\n",
    "\n",
    "**Bagging:**\n",
    "\n",
    "https://vitalflux.com/bagging-classifier-python-code-example/\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/\n",
    "\n",
    "**complete guide Stacking :**\n",
    "\n",
    "https://neptune.ai/blog/ensemble-learning-guide\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2021/08/ensemble-stacking-for-machine-learning-and-deep-learning/\n",
    "\n",
    "https://www.kaggle.com/prashant111/adaboost-classifier-tutorial"
   ]
  }
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