credit-part2-data-preparation-5-features_engineer.ipynb

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    "# Feature Engineering\n",
    "\n",
    "Feature engineering is the act of taking raw data and extracting features from it that are suitable for tasks like machine learning. Most machine learning algorithms work with tabular data. When we talk about features, we are referring to the information stored in the columns of these tables. \n",
    "\n",
    "Most machine learning algorithms require their input data to be represented as a vector or a matrix, and many assume that the data is distributed normally. In the real world, more often than not you will receive data that is not in this format. You will also need to work with many different types of data, some data types you will often encounter are: continuous variables, categorical data, ordinal data, boolean values, and dates and times. Dealing with these is manageable, but requires a well thought out approach. Feature engineering is often overlooked in machine learning discussions, but any real-world practitioner will confirm that data manipulation and feature engineering is the most important aspect of the project.\n",
    "\n",
    "## Load Lib "
   ]
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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",
    "\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\n",
    "from sklearn.metrics import make_scorer,mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import roc_curve,confusion_matrix\n",
    "from datetime import datetime, date\n",
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.linear_model import LinearRegression, RidgeCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#import tensorflow as tf \n",
    "#from tensorflow.keras import layers\n",
    "#from tensorflow.keras.callbacks import EarlyStopping\n",
    "#from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "#import smogn\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "# For training random forest model\n",
    "import lightgbm as lgb\n",
    "from scipy import sparse\n",
    "from sklearn.neighbors import KNeighborsRegressor \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "# Model selection\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression,f_classif,chi2\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.feature_selection import mutual_info_classif,VarianceThreshold\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from lightgbm import LGBMClassifier\n",
    "import lightgbm as lgbm\n",
    "#from catboost import CatBoostRegressor, CatBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import svm\n",
    "from xgboost import XGBClassifier,XGBRegressor\n",
    "from sklearn import set_config\n",
    "from itertools import combinations\n",
    "# Cluster :\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "#from yellowbrick.cluster import KElbowVisualizer\n",
    "#import smong \n",
    "import category_encoders as ce\n",
    "import warnings\n",
    "#import optuna \n",
    "from joblib import Parallel, delayed\n",
    "import joblib \n",
    "from sklearn import set_config\n",
    "from typing import List, Optional, Union\n",
    "set_config(display='diagram')\n",
    "warnings.filterwarnings('ignore')"
   ]
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   "source": [
    "\n",
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "a6e739ce",
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 250 ms, sys: 76.6 ms, total: 327 ms\n",
      "Wall time: 467 ms\n"
     ]
    },
    {
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       "      <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",
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       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
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       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
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       "      <td>0.957151</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.658180</td>\n",
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       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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      "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": "0719fe97",
   "metadata": {
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     "start_time": "2021-12-27T11:25:46.114302",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Convert Dtypes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "031f29d9",
   "metadata": {
    "execution": {
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     "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": "79ded568",
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    "papermill": {
     "duration": 0.040314,
     "end_time": "2021-12-27T11:25:46.413886",
     "exception": false,
     "start_time": "2021-12-27T11:25:46.373572",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reduce Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fe10d3c",
   "metadata": {
    "execution": {
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     "exception": false,
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     "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": "4cc209a1",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "# X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "377ff4aa",
   "metadata": {
    "execution": {
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    },
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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": 6,
   "id": "b01834f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T11:25:46.825205Z",
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     "shell.execute_reply.started": "2021-12-24T09:12:35.710621Z"
    },
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     "exception": false,
     "start_time": "2021-12-27T11:25:46.779202",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = train['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae29f7bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T11:25:46.921002Z",
     "iopub.status.busy": "2021-12-27T11:25:46.920213Z",
     "iopub.status.idle": "2021-12-27T11:25:46.987210Z",
     "shell.execute_reply": "2021-12-27T11:25:46.986094Z",
     "shell.execute_reply.started": "2021-12-24T09:12:35.730643Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DebtRatio                               30457\n",
       "NumberOfTime30-59DaysPastDueNotWorse    23982\n",
       "SeriousDlqin2yrs                        10026\n",
       "NumberOfTimes90DaysLate                  8338\n",
       "NumberOfTime60-89DaysPastDueNotWorse     7604\n",
       "MonthlyIncome                            1557\n",
       "NumberOfDependents                        991\n",
       "NumberOfOpenCreditLinesAndLoans           442\n",
       "RevolvingUtilizationOfUnsecuredLines      339\n",
       "NumberRealEstateLoansOrLines              209\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_outlier_counts(df, outlier_threshold=1.5):\n",
    "    Q1 = df.quantile(0.25)\n",
    "    Q3 = df.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    outlier_counts = ((df < (Q1 - outlier_threshold * IQR)) | (df > (Q3 + outlier_threshold * IQR))).sum()\n",
    "    return outlier_counts[outlier_counts>0].sort_values(ascending=False)\n",
    "\n",
    "get_outlier_counts(train, outlier_threshold=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a16c79",
   "metadata": {
    "papermill": {
     "duration": 0.04102,
     "end_time": "2021-12-27T11:25:47.070598",
     "exception": false,
     "start_time": "2021-12-27T11:25:47.029578",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Relationship between missing data and target\n",
    "Let's evaluate the target  observations where the information is missing. We will do this for each variable that shows missing data.\n",
    "if there a relation we should mention this as new feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9fcd0ea6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T11:25:47.157232Z",
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    },
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     "start_time": "2021-12-27T11:25:47.112104",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MonthlyIncome         0.198207\n",
       "NumberOfDependents    0.026160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list of the variables that contain missing values\n",
    "vars_with_na = [var for var in train.columns if train[var].isnull().sum() > 0]\n",
    "\n",
    "# determine percentage of missing values (expressed as decimals)\n",
    "# and display the result ordered by % of missin data\n",
    "\n",
    "train[vars_with_na].isnull().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8985ced3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T11:25:47.266150Z",
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     "shell.execute_reply.started": "2021-12-24T09:12:35.839288Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def analyse_na_value(df, var):\n",
    "\n",
    "    # copy of the dataframe, so that we do not override the original data\n",
    "    # see the link for more details about pandas.copy()\n",
    "    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.copy.html\n",
    "    df = df.copy()\n",
    "\n",
    "    # let's make an interim variable that indicates 1 if the\n",
    "    # observation was missing or 0 otherwise\n",
    "    df[var] = np.where(df[var].isnull(), 1, 0)\n",
    "\n",
    "    # let's compare the median SalePrice in the observations where data is missing\n",
    "    # vs the observations where data is available\n",
    "\n",
    "    # determine the median price in the groups 1 and 0,\n",
    "    # and the standard deviation of the sale price,\n",
    "    # and we capture the results in a temporary dataset\n",
    "    tmp = df.groupby(var)['SeriousDlqin2yrs'].agg(['mean', 'std','count'])\n",
    "\n",
    "    # plot into a bar graph\n",
    "    tmp.plot(kind=\"barh\", y=\"mean\", legend=False,\n",
    "             xerr=\"std\", title=\"SeriousDlqin2yrs\", color='green')\n",
    "\n",
    "    plt.show()"
   ]
  },
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   "execution_count": 10,
   "id": "99b5d8c7",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fe11f58a250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "train[vars_with_na].isnull().mean().sort_values(\n",
    "    ascending=False).plot.bar(figsize=(10, 4))\n",
    "plt.ylabel('Percentage of missing data')\n",
    "plt.axhline(y=0.90, color='r', linestyle='-')\n",
    "plt.axhline(y=0.80, color='g', linestyle='-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a42d77e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T11:25:47.707316Z",
     "iopub.status.busy": "2021-12-27T11:25:47.706669Z",
     "iopub.status.idle": "2021-12-27T11:25:48.098699Z",
     "shell.execute_reply": "2021-12-27T11:25:48.099213Z",
     "shell.execute_reply.started": "2021-12-24T09:12:36.135097Z"
    },
    "papermill": {
     "duration": 0.438395,
     "end_time": "2021-12-27T11:25:48.099385",
     "exception": false,
     "start_time": "2021-12-27T11:25:47.660990",
     "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's run the function on each variable with missing data\n",
    "\n",
    "for var in vars_with_na:\n",
    "    analyse_na_value(train, var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45de9a9f",
   "metadata": {
    "papermill": {
     "duration": 0.045778,
     "end_time": "2021-12-27T11:25:48.189607",
     "exception": false,
     "start_time": "2021-12-27T11:25:48.143829",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In some variables, the average SeriousDlqin2yrs  in our data  where the information is missing, differs from the average SeriousDlqin2yrsin data where information exists. This suggests that data being missing could be a good predictor of credit fault .\n",
    "For this we should keep add indicator when we have missing value ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05881c9e",
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     "start_time": "2021-12-27T11:25:48.234089",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Imput before Descritaziation : \n",
    "\n",
    "## IterativeImputer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b58cf537",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 542 µs, sys: 0 ns, total: 542 µs\n",
      "Wall time: 551 µs\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "param =  {   \"verbosity\": 0,\n",
    "            #\"objective\": \"binary:logistic\",\n",
    "            #\"eval_metric\": \"auc\",\n",
    "            'random_state': 42,\n",
    "            # regression\n",
    "            'objective':'reg:squarederror', \n",
    "             'eval_metric': 'mae',\n",
    "            #early_stopping_rounds=100 ,\n",
    "            #'gpu_id':0, \n",
    "            #'predictor':\"gpu_predictor\",\n",
    "            # use exact for small dataset.\n",
    "            #\"tree_method\": \"exact\",\n",
    "            # big data :\n",
    "            # 'tree_method': 'gpu_hist',\n",
    "            # defines booster, gblinear for linear functions.\n",
    "             'booster': 'gbtree', \n",
    "            'lambda': 8.544792472633987e-07,\n",
    "            'alpha': 0.31141671752487043,\n",
    "            'subsample': 0.8779467596981366, \n",
    "            'colsample_bytree': 0.9759532762677546,\n",
    "            'learning_rate': 0.008686087328805853, \n",
    "            'n_estimators': 6988,\n",
    "            'max_depth': 9,\n",
    "            'min_child_weight': 2, \n",
    "            'eta': 3.7603213457541647e-06,\n",
    "            'gamma': 2.1478058456847449e-07,\n",
    "            'grow_policy': 'lossguide'}\n",
    "                \n",
    "\n",
    "#model_xgb = XGBRegressor(\n",
    "       #objective=\"mae\",\n",
    " #   **xgb_params2)\n",
    "\n",
    "numeric_transformer1 = Pipeline(\n",
    "                            steps=[\n",
    "                            ('imputer', SimpleImputer(strategy='median'\n",
    "                                                      ,add_indicator=True)),\n",
    "                            ('scaler', PowerTransformer()),#(Numerical Input, Numerical Output)\n",
    "                            # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                            #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                           # ('reducedim',  SelectPercentile(f_classif,percentile=90))\n",
    "                            ]\n",
    "                            )\n",
    "numeric_transformer2 = Pipeline(\n",
    "                            steps=[\n",
    "                            #('imputer', SimpleImputer(strategy='median'\n",
    "                             #                         ,add_indicator=True)),\n",
    "                            ('scaler', PowerTransformer()),#(Numerical Input, Numerical Output)\n",
    "                            # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                            #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                           # ('reducedim',  SelectPercentile(f_classif,percentile=90))\n",
    "                            ]\n",
    "                            )\n",
    "\n",
    "pipe_xgbr1 = Pipeline(\n",
    "                    steps=[\n",
    "                        ('preprocessor', numeric_transformer1),\n",
    "                        ('classifier', XGBRegressor(\n",
    "                      #objective=\"mae\",\n",
    "                       **param))\n",
    "                    ]\n",
    "                )\n",
    "pipe_xgbr2 = Pipeline(\n",
    "                    steps=[\n",
    "                        ('preprocessor', numeric_transformer2),\n",
    "                        ('classifier', XGBRegressor(\n",
    "                      #objective=\"mae\",\n",
    "                       **param))\n",
    "                    ]\n",
    "                )\n",
    "\n",
    "#imputer=IterativeImputer(estimator=XGBRegressor(\n",
    "#                      #objective=\"mae\",\n",
    " #                     **param),max_iter=2,\n",
    "  #                       add_indicator=True,\n",
    "   #                      random_state=42)\n",
    "#train=train.drop(['Unnamed: 0','SeriousDlqin2yrs'], axis=1)\n",
    "#test=test.drop(['Unnamed: 0','SeriousDlqin2yrs'], axis=1)\n",
    "#imputer.fit(train)\n",
    "#train = imputer.transform(train)\n",
    "#test = imputer.transform(test)\n",
    "#features=['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
    "#      'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
    " #      'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
    " #     'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
    " #     'NumberOfDependents']\n",
    "#train=pd.DataFrame(train,columns=features)\n",
    "#test=pd.DataFrame(test,columns=features)\n",
    "#train= reduce_mem_usage(train)\n",
    "#test= reduce_mem_usage(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddadcac",
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     "start_time": "2021-12-27T11:25:48.428620",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Rgression Imputer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9998482f",
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    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 14min 12s, sys: 6.37 s, total: 1h 14min 18s\n",
      "Wall time: 19min 1s\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": 14,
   "id": "67733d5e",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float16\n",
      " 1   age                                   150000 non-null  int8   \n",
      " 2   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int8   \n",
      " 3   DebtRatio                             150000 non-null  float32\n",
      " 4   MonthlyIncome                         150000 non-null  float32\n",
      " 5   NumberOfOpenCreditLinesAndLoans       150000 non-null  int8   \n",
      " 6   NumberOfTimes90DaysLate               150000 non-null  int8   \n",
      " 7   NumberRealEstateLoansOrLines          150000 non-null  int8   \n",
      " 8   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int8   \n",
      " 9   NumberOfDependents                    146076 non-null  float16\n",
      "dtypes: float16(2), float32(2), int8(6)\n",
      "memory usage: 2.6 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82edd49a",
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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 10 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   RevolvingUtilizationOfUnsecuredLines  101503 non-null  float16\n",
      " 1   age                                   101503 non-null  int8   \n",
      " 2   NumberOfTime30-59DaysPastDueNotWorse  101503 non-null  int8   \n",
      " 3   DebtRatio                             101503 non-null  float32\n",
      " 4   MonthlyIncome                         101503 non-null  float32\n",
      " 5   NumberOfOpenCreditLinesAndLoans       101503 non-null  int8   \n",
      " 6   NumberOfTimes90DaysLate               101503 non-null  int8   \n",
      " 7   NumberRealEstateLoansOrLines          101503 non-null  int8   \n",
      " 8   NumberOfTime60-89DaysPastDueNotWorse  101503 non-null  int8   \n",
      " 9   NumberOfDependents                    98877 non-null   float16\n",
      "dtypes: float16(2), float32(2), int8(6)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 29min 57s, sys: 5.67 s, total: 1h 30min 3s\n",
      "Wall time: 23min 1s\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": 17,
   "id": "2cd55ce2",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float16\n",
      " 1   age                                   150000 non-null  int8   \n",
      " 2   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int8   \n",
      " 3   DebtRatio                             150000 non-null  float32\n",
      " 4   MonthlyIncome                         150000 non-null  float32\n",
      " 5   NumberOfOpenCreditLinesAndLoans       150000 non-null  int8   \n",
      " 6   NumberOfTimes90DaysLate               150000 non-null  int8   \n",
      " 7   NumberRealEstateLoansOrLines          150000 non-null  int8   \n",
      " 8   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int8   \n",
      " 9   NumberOfDependents                    150000 non-null  float16\n",
      "dtypes: float16(2), float32(2), int8(6)\n",
      "memory usage: 2.6 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1aa88164",
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    "execution": {
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     "status": "completed"
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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 10 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   RevolvingUtilizationOfUnsecuredLines  101503 non-null  float16\n",
      " 1   age                                   101503 non-null  int8   \n",
      " 2   NumberOfTime30-59DaysPastDueNotWorse  101503 non-null  int8   \n",
      " 3   DebtRatio                             101503 non-null  float32\n",
      " 4   MonthlyIncome                         101503 non-null  float32\n",
      " 5   NumberOfOpenCreditLinesAndLoans       101503 non-null  int8   \n",
      " 6   NumberOfTimes90DaysLate               101503 non-null  int8   \n",
      " 7   NumberRealEstateLoansOrLines          101503 non-null  int8   \n",
      " 8   NumberOfTime60-89DaysPastDueNotWorse  101503 non-null  int8   \n",
      " 9   NumberOfDependents                    101503 non-null  float16\n",
      "dtypes: float16(2), float32(2), int8(6)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccaa101",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# Features Engineer1\n",
    "## Add Bin \n",
    "It's always good to bin data when we have lot of outlier and skweness "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf48b07b",
   "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": 20,
   "id": "d8621512",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:52.958797Z",
     "iopub.status.busy": "2021-12-27T12:07:52.958062Z",
     "iopub.status.idle": "2021-12-27T12:07:52.962830Z",
     "shell.execute_reply": "2021-12-27T12:07:52.963294Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.382214Z"
    },
    "papermill": {
     "duration": 0.056981,
     "end_time": "2021-12-27T12:07:52.963465",
     "exception": false,
     "start_time": "2021-12-27T12:07:52.906484",
     "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": "f6e2dab3",
   "metadata": {
    "papermill": {
     "duration": 0.046665,
     "end_time": "2021-12-27T12:07:53.058051",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.011386",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Final X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "26632328",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:53.156771Z",
     "iopub.status.busy": "2021-12-27T12:07:53.155712Z",
     "iopub.status.idle": "2021-12-27T12:07:53.159690Z",
     "shell.execute_reply": "2021-12-27T12:07:53.160162Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.39677Z"
    },
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     "duration": 0.055224,
     "end_time": "2021-12-27T12:07:53.160338",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.105114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Pour le train test\n",
    "target= \"SeriousDlqin2yrs\"\n",
    "X = train_final# axis=1\n",
    "X_test_final =test_final# axis=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd1490a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:53.264041Z",
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     "shell.execute_reply": "2021-12-27T12:07:53.287488Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.406265Z"
    },
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     "duration": 0.07934,
     "end_time": "2021-12-27T12:07:53.287723",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.208383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101503 entries, 0 to 101502\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                    Non-Null Count   Dtype   \n",
      "---  ------                                    --------------   -----   \n",
      " 0   RevolvingUtilizationOfUnsecuredLines      101503 non-null  float16 \n",
      " 1   DebtRatio                                 101503 non-null  float32 \n",
      " 2   MonthlyIncome                             101503 non-null  float32 \n",
      " 3   cat_RevolvingUtilizationOfUnsecuredLines  101503 non-null  category\n",
      " 4   cat_DebtRatio                             101503 non-null  category\n",
      " 5   cat_MonthlyIncome                         101503 non-null  category\n",
      " 6   NumberOfDependents                        101503 non-null  float16 \n",
      " 7   NumberOfTime60-89DaysPastDueNotWorse      101503 non-null  int8    \n",
      " 8   NumberRealEstateLoansOrLines              101503 non-null  int8    \n",
      " 9   NumberOfTimes90DaysLate                   101503 non-null  int8    \n",
      " 10  NumberOfOpenCreditLinesAndLoans           101503 non-null  int8    \n",
      " 11  NumberOfTime30-59DaysPastDueNotWorse      101503 non-null  int8    \n",
      " 12  age                                       101503 non-null  int8    \n",
      " 13  cat_NumberOfDependents                    101503 non-null  category\n",
      " 14  cat_NumberOfTime60-89DaysPastDueNotWorse  101503 non-null  category\n",
      " 15  cat_NumberRealEstateLoansOrLines          101503 non-null  category\n",
      " 16  cat_NumberOfTimes90DaysLate               101503 non-null  category\n",
      " 17  cat_NumberOfOpenCreditLinesAndLoans       101503 non-null  category\n",
      " 18  cat_NumberOfTime30-59DaysPastDueNotWorse  101503 non-null  category\n",
      " 19  cat_age                                   101503 non-null  category\n",
      "dtypes: category(10), float16(2), float32(2), int8(6)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "X_test_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec7bfe4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:53.389398Z",
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     "shell.execute_reply.started": "2021-12-24T10:04:16.435334Z"
    },
    "papermill": {
     "duration": 0.074972,
     "end_time": "2021-12-27T12:07:53.413765",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.338793",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                    Non-Null Count   Dtype   \n",
      "---  ------                                    --------------   -----   \n",
      " 0   RevolvingUtilizationOfUnsecuredLines      150000 non-null  float16 \n",
      " 1   DebtRatio                                 150000 non-null  float32 \n",
      " 2   MonthlyIncome                             150000 non-null  float32 \n",
      " 3   cat_RevolvingUtilizationOfUnsecuredLines  150000 non-null  category\n",
      " 4   cat_DebtRatio                             150000 non-null  category\n",
      " 5   cat_MonthlyIncome                         150000 non-null  category\n",
      " 6   NumberOfDependents                        150000 non-null  float16 \n",
      " 7   NumberOfTime60-89DaysPastDueNotWorse      150000 non-null  int8    \n",
      " 8   NumberRealEstateLoansOrLines              150000 non-null  int8    \n",
      " 9   NumberOfTimes90DaysLate                   150000 non-null  int8    \n",
      " 10  NumberOfOpenCreditLinesAndLoans           150000 non-null  int8    \n",
      " 11  NumberOfTime30-59DaysPastDueNotWorse      150000 non-null  int8    \n",
      " 12  age                                       150000 non-null  int8    \n",
      " 13  cat_NumberOfDependents                    150000 non-null  category\n",
      " 14  cat_NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  category\n",
      " 15  cat_NumberRealEstateLoansOrLines          150000 non-null  category\n",
      " 16  cat_NumberOfTimes90DaysLate               150000 non-null  category\n",
      " 17  cat_NumberOfOpenCreditLinesAndLoans       150000 non-null  category\n",
      " 18  cat_NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  category\n",
      " 19  cat_age                                   150000 non-null  category\n",
      "dtypes: category(10), float16(2), float32(2), int8(6)\n",
      "memory usage: 4.0 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e408c95a",
   "metadata": {
    "papermill": {
     "duration": 0.046977,
     "end_time": "2021-12-27T12:07:53.509540",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.462563",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Features Engineer2\n",
    "## Create some combination  between features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f0a0b31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:53.609508Z",
     "iopub.status.busy": "2021-12-27T12:07:53.608700Z",
     "iopub.status.idle": "2021-12-27T12:07:53.611531Z",
     "shell.execute_reply": "2021-12-27T12:07:53.610875Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.462317Z"
    },
    "papermill": {
     "duration": 0.054959,
     "end_time": "2021-12-27T12:07:53.611689",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.556730",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del train\n",
    "del test \n",
    "del train_final\n",
    "del test_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec9df18",
   "metadata": {
    "papermill": {
     "duration": 0.048194,
     "end_time": "2021-12-27T12:07:53.707627",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.659433",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Extract final cat  and num Features \n",
    "## Cat Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "305ceff8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:53.815377Z",
     "iopub.status.busy": "2021-12-27T12:07:53.814276Z",
     "iopub.status.idle": "2021-12-27T12:07:53.819644Z",
     "shell.execute_reply": "2021-12-27T12:07:53.820088Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.469303Z"
    },
    "papermill": {
     "duration": 0.063659,
     "end_time": "2021-12-27T12:07:53.820277",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.756618",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cat_RevolvingUtilizationOfUnsecuredLines', 'cat_DebtRatio',\n",
       "       'cat_MonthlyIncome', 'cat_NumberOfDependents',\n",
       "       'cat_NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'cat_NumberRealEstateLoansOrLines', 'cat_NumberOfTimes90DaysLate',\n",
       "       'cat_NumberOfOpenCreditLinesAndLoans',\n",
       "       'cat_NumberOfTime30-59DaysPastDueNotWorse', 'cat_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "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": "86ac6ccb",
   "metadata": {
    "papermill": {
     "duration": 0.047393,
     "end_time": "2021-12-27T12:07:53.919058",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.871665",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Num Features**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed036db8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:54.019396Z",
     "iopub.status.busy": "2021-12-27T12:07:54.018380Z",
     "iopub.status.idle": "2021-12-27T12:07:54.031446Z",
     "shell.execute_reply": "2021-12-27T12:07:54.030911Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.491535Z"
    },
    "papermill": {
     "duration": 0.064648,
     "end_time": "2021-12-27T12:07:54.031633",
     "exception": false,
     "start_time": "2021-12-27T12:07:53.966985",
     "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": 26,
     "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": "code",
   "execution_count": 27,
   "id": "734c2288",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:54.144886Z",
     "iopub.status.busy": "2021-12-27T12:07:54.144206Z",
     "iopub.status.idle": "2021-12-27T12:07:54.146797Z",
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     "shell.execute_reply.started": "2021-12-24T10:04:16.507269Z"
    },
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     "end_time": "2021-12-27T12:07:54.147724",
     "exception": false,
     "start_time": "2021-12-27T12:07:54.081075",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FeaturesEngineer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        X=X.copy()\n",
    "        # Calculate some metrics across rows\n",
    "        X[\"num_missing\"]  = X.isnull().sum(axis=1)\n",
    "        X[\"std_row\"] = X.std(axis=1)\n",
    "        X[\"sem_row\"] = X.sem(axis=1)\n",
    "        X[\"abs_sum_row\"] = X.abs().sum(axis=1)\n",
    "        X[\"mean_row\"] = X.mean(axis=1)\n",
    "        X[\"max_row\"]= X.max(axis=1)\n",
    "        X[\"min_row\"]= X.min(axis=1)\n",
    "        X['Weighted_Sum_PastDue'] = 2 * X['NumberOfTime30-59DaysPastDueNotWorse'] + 3 * X['NumberOfTime60-89DaysPastDueNotWorse'] + 6 * X['NumberOfTimes90DaysLate']\n",
    "        X['90days_out_of_TotalPastDue'] = X['NumberOfTimes90DaysLate'] / (X['Weighted_Sum_PastDue'])\n",
    "        X.loc[X['Weighted_Sum_PastDue']==0, '90days_out_of_TotalPastDue'] = 0\n",
    "        X['RemainingLines'] = X['NumberOfOpenCreditLinesAndLoans'] - X['NumberRealEstateLoansOrLines']\n",
    "        X['Loans_vs_Other_Lines'] = X['RemainingLines'] / (1 + X['NumberRealEstateLoansOrLines'])\n",
    "        X['Debt_per_Real_Estate_Loan'] = X['DebtRatio'] / X['NumberRealEstateLoansOrLines']\n",
    "        X.loc[X['NumberRealEstateLoansOrLines']==0, 'Debt_per_Real_Estate_Loan'] = 0\n",
    "        X['Disposable_Income_per_person'] = (X['MonthlyIncome'] - X['DebtRatio']) / (X['NumberOfDependents'] + 1)\n",
    "        X.loc[X['NumberOfDependents']==0, 'Disposable_Income_per_person'] = 0\n",
    "        X['RemainingLines_per_person'] = X['RemainingLines'] / (X['NumberOfDependents'] + 1)\n",
    "        X['NumberRE_X_DebtRatio_X_age'] = X['NumberRealEstateLoansOrLines'] * X['DebtRatio'] / X['age']\n",
    "        X['RevolvingUtilization_per_age'] = X['RevolvingUtilizationOfUnsecuredLines'] / X['age']\n",
    "        X[X==np.inf]=np.nan\n",
    "        X=reduce_mem_usage(X)\n",
    "        return X  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a53a1eb",
   "metadata": {
    "papermill": {
     "duration": 0.049184,
     "end_time": "2021-12-27T12:07:54.244992",
     "exception": false,
     "start_time": "2021-12-27T12:07:54.195808",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Features Engineer3\n",
    "# Poly features + Sparse Features + Kmeans \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "26e7170a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:54.351964Z",
     "iopub.status.busy": "2021-12-27T12:07:54.349876Z",
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     "shell.execute_reply": "2021-12-27T12:07:54.354563Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.527586Z"
    },
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     "duration": 0.061499,
     "end_time": "2021-12-27T12:07:54.355234",
     "exception": false,
     "start_time": "2021-12-27T12:07:54.293735",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MiniKmeansTransformerEncoder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, num_clusters = 11, encoder=ce.woe.WOEEncoder()):\n",
    "        self.num_clusters = num_clusters\n",
    "        self.encoder= encoder\n",
    "        if self.num_clusters > 0:\n",
    "            self.kmeans = MiniBatchKMeans(n_clusters=self.num_clusters, random_state=0)\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        if self.num_clusters > 0:\n",
    "            self.kmeans.fit(X)\n",
    "            preds=self.kmeans.predict(X)\n",
    "            preds=pd.DataFrame(preds, columns=['kmeans']).astype('category')\n",
    "            self.encoder.fit(preds,y)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        pred_classes = self.kmeans.predict(X)\n",
    "        pred_classes=pd.DataFrame(pred_classes, columns=['kmeans']).astype('category')\n",
    "        pred_encoded = self.encoder.transform(pred_classes)\n",
    "        return np.hstack((X, pred_encoded))\n",
    "        #return pred_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75e463ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:54.460708Z",
     "iopub.status.busy": "2021-12-27T12:07:54.459995Z",
     "iopub.status.idle": "2021-12-27T12:07:54.467169Z",
     "shell.execute_reply": "2021-12-27T12:07:54.466637Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.54215Z"
    },
    "papermill": {
     "duration": 0.06395,
     "end_time": "2021-12-27T12:07:54.467321",
     "exception": false,
     "start_time": "2021-12-27T12:07:54.403371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SparseInteractions(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, degree=2, feature_name_separator=\"_\"):\n",
    "        self.degree = degree\n",
    "        self.feature_name_separator = feature_name_separator\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "        \n",
    "    def transform(self, X):\n",
    "        if not sparse.isspmatrix_csc(X):\n",
    "            X = sparse.csc_matrix(X)\n",
    "            \n",
    "        if hasattr(X, \"columns\"):\n",
    "            self.orig_col_names = X.columns\n",
    "        else:\n",
    "            self.orig_col_names = np.array([str(i) for i in range(X.shape[1])])\n",
    "            \n",
    "        spi = self._create_sparse_interactions(X)\n",
    "        return spi\n",
    "    \n",
    "    \n",
    "    def get_feature_names(self):\n",
    "        return self.feature_names\n",
    "    \n",
    "    def _create_sparse_interactions(self, X):\n",
    "        out_mat = []\n",
    "        self.feature_names = self.orig_col_names.tolist()\n",
    "        \n",
    "        for sub_degree in range(2, self.degree + 1):\n",
    "            for col_ixs in combinations(range(X.shape[1]), sub_degree):\n",
    "                # add name for new column\n",
    "                name = self.feature_name_separator.join(self.orig_col_names[list(col_ixs)])\n",
    "                self.feature_names.append(name)\n",
    "                \n",
    "                # get column multiplications value\n",
    "                out = X[:, col_ixs[0]]    \n",
    "                for j in col_ixs[1:]:\n",
    "                    out = out.multiply(X[:, j])\n",
    "\n",
    "                out_mat.append(out)\n",
    "\n",
    "        return sparse.hstack([X] + out_mat)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f8e3c8",
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     "start_time": "2021-12-27T12:07:54.515561",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Complete Pipe \n",
    "\n",
    "1. Features Engineer\n",
    "\n",
    "1. Kmeans\n",
    "\n",
    "1. Sparse\n",
    "\n",
    "1. Poly\n",
    "\n",
    "1. Scaler/Transformer\n",
    "\n",
    "1. imput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d5be31b",
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    "execution": {
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cat pipeline\n",
    "categorical_transformer = Pipeline(\n",
    "                    steps=[\n",
    "                        ('imputer', SimpleImputer(strategy='most_frequent',\n",
    "                                                  fill_value='missing',\n",
    "                                                  add_indicator=True)),\n",
    "                        ('encoder',  ce.woe.WOEEncoder()),#(Numerical Input, Categorical Output)\n",
    "                        #('sparse_features', SparseInteractions(degree=2)),\n",
    "                        #('reducedim',  SelectPercentile( mutual_info_classif, percentile=90))\n",
    "\n",
    "                    ]\n",
    "                    ) \n",
    "#Define vnum pipeline\n",
    "numeric_transformer = Pipeline(\n",
    "                            steps=[\n",
    "                            ('imputer', SimpleImputer(strategy='median'\n",
    "                                                      ,add_indicator=True)),\n",
    "                            #('general_features',FeaturesEngineer()),\n",
    "                            ('scaler', PowerTransformer()),#(Numerical Input, Numerical Output)\n",
    "                            # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                            #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                            ('polynominal_features', PolynomialFeatures(degree=2)),\n",
    "                            ('kmeans',MiniKmeansTransformerEncoder()),\n",
    "                            #('reducedim',  SelectPercentile(f_classif,percentile=90))\n",
    "                            ]\n",
    "                            )\n",
    "# Features union cat + num \n",
    "# WOE+PowerTransformer\n",
    "preprocessor_woe_powertransformer = ColumnTransformer(\n",
    "            transformers=[\n",
    "                ('numerical', numeric_transformer, num_columns),\n",
    "               # ('categorical', categorical_transformer, cat_columns)\n",
    "            ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761fa677",
   "metadata": {
    "papermill": {
     "duration": 0.047999,
     "end_time": "2021-12-27T12:07:54.768688",
     "exception": false,
     "start_time": "2021-12-27T12:07:54.720689",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Pipe V15 num+ poly +kmeans "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "38658c7e",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2021-12-27T12:07:54.947287Z",
     "shell.execute_reply": "2021-12-27T12:07:54.946776Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.583058Z"
    },
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     "exception": false,
     "start_time": "2021-12-27T12:07:54.817775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"991bffc4-f650-4cea-b4b7-08da8e99c86f\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"991bffc4-f650-4cea-b4b7-08da8e99c86f\">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",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder())]),\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=\"17a684fe-3698-42ab-832c-d49c4eaec8e3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"17a684fe-3698-42ab-832c-d49c4eaec8e3\">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=\"782762ea-801d-42ae-9407-b0c28950cdeb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"782762ea-801d-42ae-9407-b0c28950cdeb\">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=\"56dbca80-eec3-413d-bd71-9de2b312ff97\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"56dbca80-eec3-413d-bd71-9de2b312ff97\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"01dfe863-216c-4f70-a527-bb0b18471ab4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"01dfe863-216c-4f70-a527-bb0b18471ab4\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</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=\"a992bbc8-2c5e-4aed-920f-25545ca009c8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a992bbc8-2c5e-4aed-920f-25545ca009c8\">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=\"95f86f42-222e-42c0-b0f6-1473dc56deaa\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"95f86f42-222e-42c0-b0f6-1473dc56deaa\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('numerical',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=True,\n",
       "                                                                strategy='median')),\n",
       "                                                 ('scaler', PowerTransformer()),\n",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder())]),\n",
       "                                 Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor_woe_powertransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "02a76fb4",
   "metadata": {
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     "iopub.status.idle": "2021-12-27T12:07:55.459088Z",
     "shell.execute_reply": "2021-12-27T12:07:55.458429Z",
     "shell.execute_reply.started": "2021-12-24T10:04:16.676988Z"
    },
    "papermill": {
     "duration": 0.462883,
     "end_time": "2021-12-27T12:07:55.459247",
     "exception": false,
     "start_time": "2021-12-27T12:07:54.996364",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4416f51c-8f2e-4186-b724-115d2cf34f6d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4416f51c-8f2e-4186-b724-115d2cf34f6d\">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",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(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=\"cabcdc8a-d7bc-400b-8f93-05954a70e51e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"cabcdc8a-d7bc-400b-8f93-05954a70e51e\">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",
       "                                                 ('polynominal_features',\n",
       "                                                  PolynomialFeatures()),\n",
       "                                                 ('kmeans',\n",
       "                                                  MiniKmeansTransformerEncoder())]),\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=\"d103fdcb-4cb1-4475-a977-476c9a24df56\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d103fdcb-4cb1-4475-a977-476c9a24df56\">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=\"7b218000-57a1-4efa-9799-eeb21cf385fc\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7b218000-57a1-4efa-9799-eeb21cf385fc\">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=\"a23e1608-d1c9-48f0-9084-e19c80cf48dd\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a23e1608-d1c9-48f0-9084-e19c80cf48dd\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2d8ee8d4-adeb-4098-9ea9-c62ed905a49e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"2d8ee8d4-adeb-4098-9ea9-c62ed905a49e\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</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=\"fc6f989f-b2ea-4e14-a820-98f4f118a621\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fc6f989f-b2ea-4e14-a820-98f4f118a621\">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=\"8d53a20f-2114-4a8b-aeee-9f79c5c70c6b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8d53a20f-2114-4a8b-aeee-9f79c5c70c6b\">WOEEncoder</label><div class=\"sk-toggleable__content\"><pre>WOEEncoder()</pre></div></div></div></div></div></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=\"0662ede8-6c8e-41a4-a8bf-4fcd0ab1b8d6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"0662ede8-6c8e-41a4-a8bf-4fcd0ab1b8d6\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(learning_rate=0.0018069834369607075, max_depth=8, metric='auc',\n",
       "               min_samples_leaf=47, n_estimators=5000, random_state=42,\n",
       "               subsample=0.8573598985000007, 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",
       "                                                                  ('polynominal_features',\n",
       "                                                                   PolynomialFeatures()),\n",
       "                                                                  ('kmeans',\n",
       "                                                                   MiniKmeansTransformerEncoder())]),\n",
       "                                                  Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_param={'learning_rate': 0.0018069834369607075,\n",
    "                             'max_depth': 8,\n",
    "                             #'max_features': 4,\n",
    "                             'min_samples_leaf': 47,\n",
    "                             #'min_samples_split': 389,\n",
    "                             'subsample': 0.8573598985000007,\n",
    "                             #'n_iter_no_change': 300,\n",
    "                             'n_estimators': 5000,\n",
    "                            # 'verbose': 0,\n",
    "                             'random_state': 42,\n",
    "                             'metric': 'auc',\n",
    "                            #\"device_type\" : \"gpu\",\n",
    "                            'boosting_type': 'gbdt',\n",
    "                           # 'tree_method': \"gpu_hist\"\n",
    "                           }\n",
    "\n",
    "pipe_lgbm_woe_powertransformer = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor_woe_powertransformer),\n",
    "        #('reducedim',  SelectPercentile( mutual_info_classif, percentile=90)),\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',\n",
    "         lgbm.LGBMClassifier( \n",
    "            #n_jobs=-1,\n",
    "                                           verbose=-1,\n",
    "                                           **lgbm_param))\n",
    "    ]\n",
    ")\n",
    "pipe_lgbm_woe_powertransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3340618b",
   "metadata": {
    "papermill": {
     "duration": 0.049827,
     "end_time": "2021-12-27T12:07:55.558932",
     "exception": false,
     "start_time": "2021-12-27T12:07:55.509105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Test Pipes with CV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57892d5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:55.667432Z",
     "iopub.status.busy": "2021-12-27T12:07:55.662816Z",
     "iopub.status.idle": "2021-12-27T12:07:55.670365Z",
     "shell.execute_reply": "2021-12-27T12:07:55.669776Z",
     "shell.execute_reply.started": "2021-12-24T10:04:17.08847Z"
    },
    "papermill": {
     "duration": 0.061263,
     "end_time": "2021-12-27T12:07:55.670504",
     "exception": false,
     "start_time": "2021-12-27T12:07:55.609241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_roc(y,y_pred):\n",
    "    from sklearn.metrics import auc\n",
    "    fpr, tpr, thresholds = roc_curve(y_true=y, y_score=y_pred)\n",
    "    auc = auc(fpr, tpr)\n",
    "    plt.figure(1)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.plot(fpr, tpr, label='Model(area = {:.3f})'.format(auc))\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.title('ROC curve')\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c41270b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:07:55.778403Z",
     "iopub.status.busy": "2021-12-27T12:07:55.777690Z",
     "iopub.status.idle": "2021-12-27T12:29:43.718230Z",
     "shell.execute_reply": "2021-12-27T12:29:43.719365Z",
     "shell.execute_reply.started": "2021-12-24T10:04:17.099244Z"
    },
    "papermill": {
     "duration": 1307.99956,
     "end_time": "2021-12-27T12:29:43.719603",
     "exception": false,
     "start_time": "2021-12-27T12:07:55.720043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 0 | AUC: 0.8657539562494199\n",
      "Fold 0 | AP: 0.4047756473151977\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 1 | AUC: 0.8611598938983577\n",
      "Fold 1 | AP: 0.379292219452668\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 2 | AUC: 0.8668629390815906\n",
      "Fold 2 | AP: 0.40098136396712847\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 3 | AUC: 0.8643201637496598\n",
      "Fold 3 | AP: 0.39404684599344414\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 4 | AUC: 0.8654409351783188\n",
      "Fold 4 | AP: 0.3979264775774182\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 5 | AUC: 0.8737750098992159\n",
      "Fold 5 | AP: 0.4402509617428701\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 6 | AUC: 0.8586249418579615\n",
      "Fold 6 | AP: 0.3626198678839718\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 7 | AUC: 0.8586598056922303\n",
      "Fold 7 | AP: 0.40537275313966387\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 8 | AUC: 0.8750652716570005\n",
      "Fold 8 | AP: 0.44248855688486455\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_samples_leaf=47, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=47\n",
      "Fold 9 | AUC: 0.8640952842136842\n",
      "Fold 9 | AP: 0.37326552455008405\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Overall mean auc : 0.865375820147744\n",
      "\n",
      "Overall mean mean average : 0.4001020218507312\n",
      "CPU times: user 1h 23min 29s, sys: 23 s, total: 1h 23min 52s\n",
      "Wall time: 21min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# evaluate each strategy on the dataset\n",
    "results = list()\n",
    "# Setting a 10-fold stratified cross-validation (note: shuffle=True)\n",
    "SEED = 42\n",
    "FOLDS = 10\n",
    "skf = StratifiedKFold(n_splits=FOLDS, shuffle=True, random_state=SEED)\n",
    "# CV interations\n",
    "# Create arrays for the features and the response variable\n",
    "roc_auc = list()\n",
    "average_precision = list()\n",
    "#X1=X.iloc[0:2000,:].copy()\n",
    "#y1=y[0:2000].copy()\n",
    "oof = np.empty((X.shape[0],))\n",
    "#oof_bin = np.empty((X.shape[0],))\n",
    "predictions=[]\n",
    "mean_auc = 0\n",
    "mean_ap=0\n",
    "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",
    "    pipe_lgbm_woe_powertransformer.fit(X_train,y_train)\n",
    "    preds = pipe_lgbm_woe_powertransformer.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",
    "    print(f\"Fold {fold} | AUC: {auc_score}\")\n",
    "    ap=average_precision_score(y_true=y_test, y_score=preds)\n",
    "    average_precision.append(ap)\n",
    "    print(f\"Fold {fold} | AP: {ap}\")\n",
    "    plot_roc(y_test,preds)\n",
    "    mean_auc += auc_score / FOLDS\n",
    "    mean_ap+=ap/FOLDS\n",
    "    predictions.append(pipe_lgbm_woe_powertransformer.predict_proba(X_test_final)[:,1]) \n",
    "    #Mean of the predictions\n",
    "print(f\"\\nOverall mean auc : {mean_auc}\")\n",
    "print(f\"\\nOverall mean mean average : {mean_ap}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0eab60b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:29:43.864362Z",
     "iopub.status.busy": "2021-12-27T12:29:43.862830Z",
     "iopub.status.idle": "2021-12-27T12:29:43.865045Z",
     "shell.execute_reply": "2021-12-27T12:29:43.865529Z",
     "shell.execute_reply.started": "2021-12-24T10:27:42.450955Z"
    },
    "papermill": {
     "duration": 0.075435,
     "end_time": "2021-12-27T12:29:43.865703",
     "exception": false,
     "start_time": "2021-12-27T12:29:43.790268",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#pipe_lgbm_woe_powertransformer.fit(X,y)\n",
    "#predictions =pipe_lgbm_woe_powertransformer.predict_proba(X_test_final)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bc77d6bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:29:44.087242Z",
     "iopub.status.busy": "2021-12-27T12:29:44.086559Z",
     "iopub.status.idle": "2021-12-27T12:29:44.099339Z",
     "shell.execute_reply": "2021-12-27T12:29:44.099867Z",
     "shell.execute_reply.started": "2021-12-24T10:27:42.456908Z"
    },
    "papermill": {
     "duration": 0.16591,
     "end_time": "2021-12-27T12:29:44.100065",
     "exception": false,
     "start_time": "2021-12-27T12:29:43.934155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictions = np.mean(np.column_stack(predictions), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2861228",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-27T12:29:44.242280Z",
     "iopub.status.busy": "2021-12-27T12:29:44.241584Z",
     "iopub.status.idle": "2021-12-27T12:29:44.694315Z",
     "shell.execute_reply": "2021-12-27T12:29:44.693736Z",
     "shell.execute_reply.started": "2021-12-24T10:27:42.485821Z"
    },
    "papermill": {
     "duration": 0.524545,
     "end_time": "2021-12-27T12:29:44.694454",
     "exception": false,
     "start_time": "2021-12-27T12:29:44.169909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.060599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.041063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.015531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.068244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.108679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101498</th>\n",
       "      <td>101499</td>\n",
       "      <td>0.032904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101499</th>\n",
       "      <td>101500</td>\n",
       "      <td>0.394093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101500</th>\n",
       "      <td>101501</td>\n",
       "      <td>0.009195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101501</th>\n",
       "      <td>101502</td>\n",
       "      <td>0.067507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101502</th>\n",
       "      <td>101503</td>\n",
       "      <td>0.031662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101503 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  Probability\n",
       "0            1     0.060599\n",
       "1            2     0.041063\n",
       "2            3     0.015531\n",
       "3            4     0.068244\n",
       "4            5     0.108679\n",
       "...        ...          ...\n",
       "101498  101499     0.032904\n",
       "101499  101500     0.394093\n",
       "101500  101501     0.009195\n",
       "101501  101502     0.067507\n",
       "101502  101503     0.031662\n",
       "\n",
       "[101503 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the predictions to a CSV file\n",
    "sub = pd.read_csv('../input/GiveMeSomeCredit/sampleEntry.csv')\n",
    "sub['Probability']=predictions\n",
    "sub.to_csv('pipe_lgbm_woe_powertransformer_estimator_XGB_CV_onlynum_fe_V3.csv', index=False)\n",
    "sub"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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