{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import boto3\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import roc_auc_score\n",
"\n",
"from lightgbm import LGBMClassifier, plot_importance\n",
"\n",
"from datetime import datetime\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"RANDOM_STATE = 99\n",
"TARGET_VAR = 'flag_trx'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(95874, 18)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base = pd.read_parquet('s3://finaccel-ml-model-data-production/zhilal/prop_1st_trx_at_activation/prop_1st_trx_activ_20201028-20201120_v20201216.parquet')\n",
"base.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define flag_trx"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"46 0.327972\n",
"45 0.283956\n",
"47 0.214240\n",
"44 0.173832\n",
"Name: week, dtype: float64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base['week'].value_counts(normalize=True, dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"base[TARGET_VAR] = base['first_trx_dt_filtered'].notna()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True 0.640455\n",
"False 0.359545\n",
"Name: flag_trx, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base[TARGET_VAR].value_counts(normalize=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"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>flag_trx</th>\n",
" </tr>\n",
" <tr>\n",
" <th>first_app_type_1</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BASIC</th>\n",
" <td>0.678601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PREMIUM</th>\n",
" <td>0.710470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STARTER</th>\n",
" <td>0.352063</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_trx\n",
"first_app_type_1 \n",
"BASIC 0.678601\n",
"PREMIUM 0.710470\n",
"STARTER 0.352063"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base.groupby(['first_app_type_1']).agg({TARGET_VAR:'mean'})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">flag_trx</th>\n",
" </tr>\n",
" <tr>\n",
" <th>first_app_type_1</th>\n",
" <th>BASIC</th>\n",
" <th>PREMIUM</th>\n",
" <th>STARTER</th>\n",
" </tr>\n",
" <tr>\n",
" <th>week</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.677950</td>\n",
" <td>0.734268</td>\n",
" <td>0.374529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.684743</td>\n",
" <td>0.731696</td>\n",
" <td>0.373450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>0.675926</td>\n",
" <td>0.696512</td>\n",
" <td>0.324002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.675306</td>\n",
" <td>0.684144</td>\n",
" <td>0.335714</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_trx \n",
"first_app_type_1 BASIC PREMIUM STARTER\n",
"week \n",
"44 0.677950 0.734268 0.374529\n",
"45 0.684743 0.731696 0.373450\n",
"46 0.675926 0.696512 0.324002\n",
"47 0.675306 0.684144 0.335714"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show = base.groupby(['week', 'first_app_type_1']).agg({TARGET_VAR:'mean'}).unstack()\n",
"show\n",
"# show.columns = ['basic', 'premium', 'starter']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show = base.groupby(['week', 'first_app_type_1'], as_index=False).agg({TARGET_VAR:'mean'})\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,8))\n",
"sns.lineplot(data=show, x='week', y=TARGET_VAR, hue='first_app_type_1', ax=ax)\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
"plt.ylim([0,1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">flag_trx</th>\n",
" </tr>\n",
" <tr>\n",
" <th>first_app_type_1</th>\n",
" <th>BASIC</th>\n",
" <th>PREMIUM</th>\n",
" <th>STARTER</th>\n",
" </tr>\n",
" <tr>\n",
" <th>week</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>11374</td>\n",
" <td>2638</td>\n",
" <td>2654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>19159</td>\n",
" <td>4193</td>\n",
" <td>3872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>22933</td>\n",
" <td>4903</td>\n",
" <td>3608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>14854</td>\n",
" <td>3166</td>\n",
" <td>2520</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_trx \n",
"first_app_type_1 BASIC PREMIUM STARTER\n",
"week \n",
"44 11374 2638 2654\n",
"45 19159 4193 3872\n",
"46 22933 4903 3608\n",
"47 14854 3166 2520"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base.groupby(['week', 'first_app_type_1']).agg({TARGET_VAR:'count'}).unstack()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BASIC 0.712602\n",
"PREMIUM 0.155412\n",
"STARTER 0.131986\n",
"Name: first_app_type_1, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base['first_app_type_1'].value_counts(normalize=True, dropna=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Features"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"s3 = boto3.client('s3')\n",
"\n",
"def get_matching_s3_keys(bucket, prefix='', suffix=''):\n",
" \"\"\"\n",
" Generate the keys in an S3 bucket.\n",
"\n",
" :param bucket: Name of the S3 bucket.\n",
" :param prefix: Only fetch keys that start with this prefix (optional).\n",
" :param suffix: Only fetch keys that end with this suffix (optional).\n",
" \"\"\"\n",
" kwargs = {'Bucket': bucket, 'Prefix': prefix}\n",
" while True:\n",
" resp = s3.list_objects_v2(**kwargs)\n",
" for obj in resp['Contents']:\n",
" key = obj['Key']\n",
" if key.endswith(suffix):\n",
" yield key\n",
"\n",
" try:\n",
" kwargs['ContinuationToken'] = resp['NextContinuationToken']\n",
" except KeyError:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"amp_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/prop_1st_trx_at_activation/amp_features/', \n",
" suffix = '.parquet'):\n",
" amp_ls.append(key)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"ap_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/prop_1st_trx_at_activation/ap_features_fixed/', \n",
" suffix = '.parquet'):\n",
" ap_ls.append(key)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"ec_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/prop_1st_trx_at_activation/ec_features/', \n",
" suffix = '.parquet'):\n",
" ec_ls.append(key)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"izi_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/prop_1st_trx_at_activation/izi_features/', \n",
" suffix = '.parquet'):\n",
" izi_ls.append(key)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def combine_batch_files(file_ls):\n",
" result_df = pd.DataFrame()\n",
" for file in file_ls:\n",
" temp_df = pd.read_parquet('s3://finaccel-ml-model-data-production/'+file)\n",
" result_df = pd.concat([result_df, temp_df])\n",
" return result_df.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"amp_feats = combine_batch_files(amp_ls)\n",
"ap_feats = combine_batch_files(ap_ls)\n",
"ec_feats = combine_batch_files(ec_ls)\n",
"izi_feats = combine_batch_files(izi_ls)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"ap_feats = ap_feats.drop(columns=['week'])\n",
"ec_feats = ec_feats.drop(columns=['week'])\n",
"izi_feats = izi_feats.drop(columns=['week'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amp_feats shape (17892, 106)\n",
"ap_feats shape (87747, 72)\n",
"ec_feats shape (28966, 55)\n",
"izi_feats shape (95730, 99)\n"
]
}
],
"source": [
"print('amp_feats shape', amp_feats.shape)\n",
"print('ap_feats shape', ap_feats.shape)\n",
"print('ec_feats shape', ec_feats.shape)\n",
"print('izi_feats shape', izi_feats.shape)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"acq_feats = pd.read_parquet('s3://finaccel-ml-model-data-production/zhilal/prop_1st_trx_at_activation/ACQ_features.parquet')\n",
"de_feats = pd.read_parquet('s3://finaccel-ml-model-data-production/zhilal/prop_1st_trx_at_activation/de_features/DE_features.parquet')\n",
"ph_feats = pd.read_parquet('s3://finaccel-ml-model-data-production/zhilal/prop_1st_trx_at_activation/ph_features/PH_features.parquet')\n",
"ab_feats = pd.read_parquet('AB_features.parquet')\n",
"pf_feats = pd.read_parquet('PF_features.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"acq_feats shape (95874, 3)\n",
"de_feats shape (95874, 19)\n",
"ph_feats shape (95874, 3)\n",
"ab_feats shape (95874, 18)\n",
"pf_feats shape (95874, 11)\n"
]
}
],
"source": [
"print('acq_feats shape', acq_feats.shape)\n",
"print('de_feats shape', de_feats.shape)\n",
"print('ph_feats shape', ph_feats.shape)\n",
"print('ab_feats shape', ab_feats.shape)\n",
"print('pf_feats shape', pf_feats.shape)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(95874, 378)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feats_df = base[['user_id']].merge(amp_feats, how='left', on='user_id')\\\n",
" .merge(ap_feats, how='left', on='user_id')\\\n",
" .merge(ec_feats, how='left', on='user_id')\\\n",
" .merge(izi_feats, how='left', on='user_id')\\\n",
" .merge(acq_feats, how='left', on='user_id')\\\n",
" .merge(de_feats, how='left', on='user_id')\\\n",
" .merge(ph_feats, how='left', on='user_id')\\\n",
" .merge(ab_feats, how='left', on='user_id')\\\n",
" .merge(pf_feats, how='left', on='user_id')\n",
"feats_df.shape\n",
"\n",
"# del amp_feats, ap_feats, ec_feats, izi_feats, acq_feats, de_feats, ph_feats, ab_feats, pf_feats"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"user_id 0\n",
"obs_date 77982\n",
"amp_ses_dist_co_1 90095\n",
"amp_ses_dist_co_3 85463\n",
"amp_ses_dist_co_7 82581\n",
" ... \n",
"pf_delin_max_dpd_12mo 33865\n",
"pf_delin_avg_dpd_6mo 33865\n",
"pf_util_creditcard_avg_6mo 33865\n",
"pf_delin_creditcard_avg_dpd_amt_3mo 33865\n",
"pf_max_tenure_ever 33940\n",
"Length: 378, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feats_df.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Combine Base and Feature Datasets"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(95874, 396)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main = base.merge(feats_df, how='left', on='user_id')\n",
"# main = main[~(main['user_id'].isin(['19690648']))].reset_index(drop=True)\n",
"main.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Transformation"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def convert_feat_str_to_int(df, feature):\n",
" unique_vals = sorted(df[feature].unique().tolist())\n",
" mapping = dict(zip(unique_vals, [i for i in range(0, len(unique_vals))]))\n",
" df[feature] = df[feature].map(mapping)\n",
" return df, mapping"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# create dummies for de_source_of_registration\n",
"# de_sor = pd.get_dummies(main[['user_id', 'de_source_of_registration']], prefix='de_sor')\n",
"# main = main.merge(de_sor, how='left', on='user_id')\n",
"\n",
"# convert sources of registration into integers\n",
"main, de_sor_map = convert_feat_str_to_int(main, 'de_source_of_registration')\n",
"main, de_reg_map = convert_feat_str_to_int(main, 'de_region')\n",
"main, de_pp_map = convert_feat_str_to_int(main, 'de_phone_provider')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'BUKACICILAN_ANDROID': 0,\n",
" 'IOS_NATIVE_APP': 1,\n",
" 'KLOP_ANDROID': 2,\n",
" 'LINKAJA_ANDROID': 3,\n",
" 'NATIVE_APP': 4,\n",
" 'TOKOPEDIA_ANDROID': 5,\n",
" 'TOKOPEDIA_IOS': 6}"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"de_sor_map"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'BANTEN': 0, 'DI YOGYAKARTA': 1, 'DKI JAKARTA': 2, 'JAVA': 3, 'OTHERS': 4}"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"de_reg_map"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'axis': 0,\n",
" 'indosat': 1,\n",
" 'missing': 2,\n",
" 'smartfren': 3,\n",
" 'telkomsel': 4,\n",
" 'tri': 5,\n",
" 'xl': 6}"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"de_pp_map"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['PREMIUM', 'BASIC', 'STARTER'], dtype=object)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main['first_app_type_1'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"app_cat_dict = {\n",
" 'STARTER':1,\n",
" 'BASIC':2,\n",
" 'PREMIUM':3\n",
"}\n",
"\n",
"main['app_type_cat'] = main['first_app_type_1'].map(app_cat_dict)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# fill nan referral with 0\n",
"main['referral'] = main['referral'].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"main['first_app_type'] = main['first_app_type'].replace({150:10})"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"main['prm_ascore'] = np.where(main['first_app_type_1'] == 'PREMIUM', main['first_a_score'], np.nan)\n",
"main['bsc_ascore'] = np.where(main['first_app_type_1'] == 'BASIC', main['first_a_score'], np.nan)\n",
"main['str_ascore'] = np.where(main['first_app_type_1'] == 'STARTER', main['first_a_score'], np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"main['prm_credit_limit'] = np.where(main['first_app_type_1'] == 'PREMIUM', main['credit_limit'], np.nan)\n",
"main['bsc_credit_limit'] = np.where(main['first_app_type_1'] == 'BASIC', main['credit_limit'], np.nan)\n",
"main['str_credit_limit'] = np.where(main['first_app_type_1'] == 'STARTER', main['credit_limit'], np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"main['submit_to_approval_time'] = main['submit_to_approval_time'].fillna(main['submit_to_approval_time'].median())\n",
"main['approval_to_activation_time'] = main['approval_to_activation_time'].fillna(main['approval_to_activation_time'].median())"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"main['de_education'] = np.where(main['de_education'] > 5, 3, main['de_education'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train - Val - Test Split"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(95874, 403)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main.shape"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"44 16666\n",
"45 27224\n",
"46 31444\n",
"47 20540\n",
"Name: week, dtype: int64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main['week'].value_counts().sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"temp, test = train_test_split(main, test_size=0.33, random_state=99)\n",
"train, val = train_test_split(temp, test_size=0.5, random_state=99)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# train_period = [27, 28, 29, 30, 31, 32, 33]\n",
"# val_period = [34, 35, 36, 37, 38, 39]\n",
"# test_period = [40, 41, 42]\n",
"\n",
"# period_col = 'week'\n",
"# train = main[main[period_col].isin(train_period)]\n",
"# val = main[main[period_col].isin(val_period)]\n",
"# test = main[main[period_col].isin(test_period)]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train size: 32117\n",
"Val size: 32118\n",
"Test size: 31639\n"
]
}
],
"source": [
"print('Train size:', train.shape[0])\n",
"print('Val size:', val.shape[0])\n",
"print('Test size:', test.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"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>train</th>\n",
" <th>val</th>\n",
" <th>test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>32117.000000</td>\n",
" <td>32118.000000</td>\n",
" <td>31639.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sum</th>\n",
" <td>20597.000000</td>\n",
" <td>20554.000000</td>\n",
" <td>20252.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.641311</td>\n",
" <td>0.639953</td>\n",
" <td>0.640096</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" train val test\n",
"count 32117.000000 32118.000000 31639.000000\n",
"sum 20597.000000 20554.000000 20252.000000\n",
"mean 0.641311 0.639953 0.640096"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_target = train.agg({TARGET_VAR:['count', 'sum', 'mean']})\n",
"val_target = val.agg({TARGET_VAR:['count', 'sum', 'mean']})\n",
"test_target = test.agg({TARGET_VAR:['count', 'sum', 'mean']})\n",
"\n",
"train_val_test_target = train_target.merge(val_target, how='left', left_index=True, right_index=True)\\\n",
" .merge(test_target, how='left', left_index=True, right_index=True)\n",
"train_val_test_target.columns = ['train', 'val', 'test']\n",
"train_val_test_target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Features"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"features = [\n",
"# 'amp_last_event_to_obs_len',\n",
"# 'amp_ses_dist_co_ever', \n",
" 'ins_co_payday_apps_ia',\n",
" 'ins_co_finance_apps_ia_curated',\n",
" 'ins_co_shopping_apps_ia_curated',\n",
" 'ap_all_co_nonsys_v2',\n",
" 'ap_last_install',\n",
"# 'ec_sum_suc_trx_amt_180',\n",
" 'iz_inquiries_dist_name_max_360',\n",
"# 'iz_inquiries_dist_name_sum_velocity_30_180',\n",
"# 'iz_inquiries_dist_name_diff_360',\n",
" 'de_age',\n",
" 'de_submit_phoneprice',\n",
" 'de_phone_agerelease',\n",
"# 'ab_co_submit_img',\n",
" 'approval_to_activation_time',\n",
" 'bsc_ascore',\n",
" 'bsc_credit_limit',\n",
" 'join_to_submit_time',\n",
" 'prm_ascore',\n",
" 'prm_credit_limit',\n",
" 'str_ascore',\n",
" 'str_credit_limit',\n",
" 'submit_to_approval_time',\n",
" 'telco_experian_score',\n",
" 'telco_tsel_score',\n",
" 'de_source_of_registration',\n",
" 'de_education',\n",
"# 'de_phone_provider',\n",
" 'de_region',\n",
" 'referral', \n",
"# 'af_flag_organic',\n",
" 'first_app_type',\n",
" 'pf_delin_max_dpd_12mo',\n",
"# 'pf_delin_dist_code_30_dpd_3mo',\n",
" 'pf_util_creditcard_avg_6mo',\n",
"# 'pf_con_dist_count_12mo',\n",
"]\n",
"\n",
"features = sorted(features)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"27"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(features)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"['ap_all_co_nonsys_v2',\n",
" 'ap_last_install',\n",
" 'approval_to_activation_time',\n",
" 'bsc_ascore',\n",
" 'bsc_credit_limit',\n",
" 'de_age',\n",
" 'de_education',\n",
" 'de_phone_agerelease',\n",
" 'de_region',\n",
" 'de_source_of_registration',\n",
" 'de_submit_phoneprice',\n",
" 'first_app_type',\n",
" 'ins_co_finance_apps_ia_curated',\n",
" 'ins_co_payday_apps_ia',\n",
" 'ins_co_shopping_apps_ia_curated',\n",
" 'iz_inquiries_dist_name_max_360',\n",
" 'join_to_submit_time',\n",
" 'pf_delin_max_dpd_12mo',\n",
" 'pf_util_creditcard_avg_6mo',\n",
" 'prm_ascore',\n",
" 'prm_credit_limit',\n",
" 'referral',\n",
" 'str_ascore',\n",
" 'str_credit_limit',\n",
" 'submit_to_approval_time',\n",
" 'telco_experian_score',\n",
" 'telco_tsel_score']"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Correlation"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"def corr_upper_triangle(df):\n",
" # Create correlation matrix\n",
" corr_matrix = df.corr()\n",
" \n",
" # Select upper triangle of correlation matrix\n",
" upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
" \n",
" return upper"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x1440 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"corr_viz = corr_upper_triangle(train[features])\n",
"fig, ax = plt.subplots(figsize=(20,20))\n",
"sns.heatmap(corr_viz, annot=True, fmt='.2f', linewidths=.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train Model"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"categorical_feats = [\n",
" 'first_app_type', \n",
" 'de_source_of_registration', \n",
" 'de_education', \n",
" 'de_region',\n",
"# 'de_phone_provider', \n",
" 'referral',\n",
"# 'af_flag_organic',\n",
"]\n",
"cat_feat_indexes = tuple([train.columns.get_loc(col) for col in categorical_feats])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1]\tvalid_0's auc: 0.698329\tvalid_0's binary_logloss: 0.641809\tvalid_1's auc: 0.67944\tvalid_1's binary_logloss: 0.643602\n",
"[2]\tvalid_0's auc: 0.702587\tvalid_0's binary_logloss: 0.633052\tvalid_1's auc: 0.683413\tvalid_1's binary_logloss: 0.635638\n",
"[3]\tvalid_0's auc: 0.706334\tvalid_0's binary_logloss: 0.625723\tvalid_1's auc: 0.685977\tvalid_1's binary_logloss: 0.629165\n",
"[4]\tvalid_0's auc: 0.708402\tvalid_0's binary_logloss: 0.619597\tvalid_1's auc: 0.687314\tvalid_1's binary_logloss: 0.623841\n",
"[5]\tvalid_0's auc: 0.709906\tvalid_0's binary_logloss: 0.614515\tvalid_1's auc: 0.687902\tvalid_1's binary_logloss: 0.619583\n",
"[6]\tvalid_0's auc: 0.709906\tvalid_0's binary_logloss: 0.614515\tvalid_1's auc: 0.687902\tvalid_1's binary_logloss: 0.619583\n",
"[7]\tvalid_0's auc: 0.711591\tvalid_0's binary_logloss: 0.61011\tvalid_1's auc: 0.688391\tvalid_1's binary_logloss: 0.616087\n",
"[8]\tvalid_0's auc: 0.712113\tvalid_0's binary_logloss: 0.610597\tvalid_1's auc: 0.688432\tvalid_1's binary_logloss: 0.61662\n",
"[9]\tvalid_0's auc: 0.713215\tvalid_0's binary_logloss: 0.606723\tvalid_1's auc: 0.688938\tvalid_1's binary_logloss: 0.613511\n",
"[10]\tvalid_0's auc: 0.715487\tvalid_0's binary_logloss: 0.603171\tvalid_1's auc: 0.689328\tvalid_1's binary_logloss: 0.611068\n",
"[11]\tvalid_0's auc: 0.716378\tvalid_0's binary_logloss: 0.603984\tvalid_1's auc: 0.689303\tvalid_1's binary_logloss: 0.611879\n",
"[12]\tvalid_0's auc: 0.717557\tvalid_0's binary_logloss: 0.600772\tvalid_1's auc: 0.689902\tvalid_1's binary_logloss: 0.609518\n",
"[13]\tvalid_0's auc: 0.718903\tvalid_0's binary_logloss: 0.598069\tvalid_1's auc: 0.690396\tvalid_1's binary_logloss: 0.607656\n",
"[14]\tvalid_0's auc: 0.718894\tvalid_0's binary_logloss: 0.59922\tvalid_1's auc: 0.690428\tvalid_1's binary_logloss: 0.60849\n",
"[15]\tvalid_0's auc: 0.719653\tvalid_0's binary_logloss: 0.598949\tvalid_1's auc: 0.690608\tvalid_1's binary_logloss: 0.608443\n",
"[16]\tvalid_0's auc: 0.720689\tvalid_0's binary_logloss: 0.596335\tvalid_1's auc: 0.690921\tvalid_1's binary_logloss: 0.606727\n",
"[17]\tvalid_0's auc: 0.720611\tvalid_0's binary_logloss: 0.597464\tvalid_1's auc: 0.690971\tvalid_1's binary_logloss: 0.607516\n",
"[18]\tvalid_0's auc: 0.721257\tvalid_0's binary_logloss: 0.597272\tvalid_1's auc: 0.691136\tvalid_1's binary_logloss: 0.607444\n",
"[19]\tvalid_0's auc: 0.722225\tvalid_0's binary_logloss: 0.594778\tvalid_1's auc: 0.691209\tvalid_1's binary_logloss: 0.605875\n",
"[20]\tvalid_0's auc: 0.723263\tvalid_0's binary_logloss: 0.592567\tvalid_1's auc: 0.690944\tvalid_1's binary_logloss: 0.604711\n",
"[21]\tvalid_0's auc: 0.723651\tvalid_0's binary_logloss: 0.593152\tvalid_1's auc: 0.69092\tvalid_1's binary_logloss: 0.605183\n",
"[22]\tvalid_0's auc: 0.724775\tvalid_0's binary_logloss: 0.591062\tvalid_1's auc: 0.691183\tvalid_1's binary_logloss: 0.603965\n",
"[23]\tvalid_0's auc: 0.726033\tvalid_0's binary_logloss: 0.589168\tvalid_1's auc: 0.691487\tvalid_1's binary_logloss: 0.602932\n",
"[24]\tvalid_0's auc: 0.72653\tvalid_0's binary_logloss: 0.589335\tvalid_1's auc: 0.691507\tvalid_1's binary_logloss: 0.603121\n",
"[25]\tvalid_0's auc: 0.727699\tvalid_0's binary_logloss: 0.587589\tvalid_1's auc: 0.691541\tvalid_1's binary_logloss: 0.602326\n",
"[26]\tvalid_0's auc: 0.728747\tvalid_0's binary_logloss: 0.585902\tvalid_1's auc: 0.691696\tvalid_1's binary_logloss: 0.60158\n",
"[27]\tvalid_0's auc: 0.729903\tvalid_0's binary_logloss: 0.584304\tvalid_1's auc: 0.69176\tvalid_1's binary_logloss: 0.600953\n",
"[28]\tvalid_0's auc: 0.730997\tvalid_0's binary_logloss: 0.582864\tvalid_1's auc: 0.691903\tvalid_1's binary_logloss: 0.600418\n",
"[29]\tvalid_0's auc: 0.731453\tvalid_0's binary_logloss: 0.582972\tvalid_1's auc: 0.691978\tvalid_1's binary_logloss: 0.600535\n",
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"[45]\tvalid_0's auc: 0.739251\tvalid_0's binary_logloss: 0.576988\tvalid_1's auc: 0.693676\tvalid_1's binary_logloss: 0.599337\n",
"[46]\tvalid_0's auc: 0.739954\tvalid_0's binary_logloss: 0.575742\tvalid_1's auc: 0.693567\tvalid_1's binary_logloss: 0.599037\n",
"[47]\tvalid_0's auc: 0.741201\tvalid_0's binary_logloss: 0.574505\tvalid_1's auc: 0.693668\tvalid_1's binary_logloss: 0.598705\n",
"[48]\tvalid_0's auc: 0.741033\tvalid_0's binary_logloss: 0.57526\tvalid_1's auc: 0.693675\tvalid_1's binary_logloss: 0.598934\n",
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"[54]\tvalid_0's auc: 0.741713\tvalid_0's binary_logloss: 0.576192\tvalid_1's auc: 0.693653\tvalid_1's binary_logloss: 0.599409\n",
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"[56]\tvalid_0's auc: 0.742248\tvalid_0's binary_logloss: 0.575652\tvalid_1's auc: 0.693865\tvalid_1's binary_logloss: 0.599171\n",
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"[72]\tvalid_0's auc: 0.751458\tvalid_0's binary_logloss: 0.567477\tvalid_1's auc: 0.694386\tvalid_1's binary_logloss: 0.597726\n"
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"[73]\tvalid_0's auc: 0.752552\tvalid_0's binary_logloss: 0.566472\tvalid_1's auc: 0.69453\tvalid_1's binary_logloss: 0.597514\n",
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"[91]\tvalid_0's auc: 0.760876\tvalid_0's binary_logloss: 0.560252\tvalid_1's auc: 0.69477\tvalid_1's binary_logloss: 0.597257\n",
"[92]\tvalid_0's auc: 0.760621\tvalid_0's binary_logloss: 0.560862\tvalid_1's auc: 0.6948\tvalid_1's binary_logloss: 0.597328\n",
"[93]\tvalid_0's auc: 0.761548\tvalid_0's binary_logloss: 0.559903\tvalid_1's auc: 0.694953\tvalid_1's binary_logloss: 0.597128\n",
"[94]\tvalid_0's auc: 0.762453\tvalid_0's binary_logloss: 0.558932\tvalid_1's auc: 0.694973\tvalid_1's binary_logloss: 0.597028\n",
"[95]\tvalid_0's auc: 0.763484\tvalid_0's binary_logloss: 0.558026\tvalid_1's auc: 0.694973\tvalid_1's binary_logloss: 0.596984\n",
"[96]\tvalid_0's auc: 0.763293\tvalid_0's binary_logloss: 0.558595\tvalid_1's auc: 0.69502\tvalid_1's binary_logloss: 0.597035\n",
"[97]\tvalid_0's auc: 0.763173\tvalid_0's binary_logloss: 0.559039\tvalid_1's auc: 0.695027\tvalid_1's binary_logloss: 0.597096\n",
"[98]\tvalid_0's auc: 0.762992\tvalid_0's binary_logloss: 0.559442\tvalid_1's auc: 0.69508\tvalid_1's binary_logloss: 0.59712\n",
"[99]\tvalid_0's auc: 0.763686\tvalid_0's binary_logloss: 0.558565\tvalid_1's auc: 0.694945\tvalid_1's binary_logloss: 0.597073\n",
"[100]\tvalid_0's auc: 0.76466\tvalid_0's binary_logloss: 0.557671\tvalid_1's auc: 0.694647\tvalid_1's binary_logloss: 0.597112\n",
"\n",
"TRAINING TIME 0:00:02.713603\n",
"\n",
"defaultdict(<class 'dict'>, {'valid_0': {'auc': 0.7646600957933464, 'binary_logloss': 0.5576708153043006}, 'valid_1': {'auc': 0.6946469596063143, 'binary_logloss': 0.5971123176213756}})\n",
"None\n"
]
},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lgbm = LGBMClassifier(boosting_type='dart', random_state=RANDOM_STATE)\n",
"\n",
"fit_params = {\n",
"# 'early_stopping_rounds':250,\n",
" 'eval_metric':'auc',\n",
" 'eval_set':[(train[features], train[TARGET_VAR]),(val[features], val[TARGET_VAR])],\n",
" 'categorical_feature':cat_feat_indexes}\n",
"\n",
"st_tm = datetime.now()\n",
"lgbm.fit(train[features], train[TARGET_VAR], **fit_params)\n",
"\n",
"print()\n",
"print('TRAINING TIME', datetime.now() - st_tm)\n",
"\n",
"train_result = lgbm.evals_result_['valid_0']['auc']\n",
"val_result = lgbm.evals_result_['valid_1']['auc']\n",
"\n",
"plt.plot(train_result)\n",
"plt.plot(val_result)\n",
"\n",
"print()\n",
"print(lgbm.best_score_)\n",
"print(lgbm.best_iteration_)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Gini: 0.5293201915866927\n",
"Val Gini: 0.38929391921262857\n",
"Test Gini: 0.3920703682645823\n"
]
}
],
"source": [
"train['pred'] = lgbm.predict_proba(train[features], num_iteration=lgbm.best_iteration_)[:,1]\n",
"val['pred'] = lgbm.predict_proba(val[features], num_iteration=lgbm.best_iteration_)[:,1]\n",
"test['pred'] = lgbm.predict_proba(test[features], num_iteration=lgbm.best_iteration_)[:,1]\n",
"\n",
"print('Train Gini:', roc_auc_score(train[TARGET_VAR], train['pred'])*2-1)\n",
"print('Val Gini:',roc_auc_score(val[TARGET_VAR], val['pred'])*2-1)\n",
"print('Test Gini:',roc_auc_score(test[TARGET_VAR], test['pred'])*2-1)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"# split weeks\n",
"# Train Gini: 0.5352690377981153\n",
"# Val Gini: 0.3931025838510549\n",
"# Test Gini: 0.39579129004229685"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test PREMIUM Gini: 0.2814416167388132\n",
"Test BASIC Gini: 0.3201665658310815\n",
"Test STARTER Gini: 0.18125335083183458\n"
]
}
],
"source": [
"test_prm = test[test['first_app_type_1'] == 'PREMIUM'].reset_index(drop=True)\n",
"test_prm['pred'] = lgbm.predict_proba(test_prm[features], num_iteration=lgbm.best_iteration_)[:,1]\n",
"\n",
"test_bsc = test[test['first_app_type_1'] == 'BASIC'].reset_index(drop=True)\n",
"test_bsc['pred'] = lgbm.predict_proba(test_bsc[features], num_iteration=lgbm.best_iteration_)[:,1]\n",
"\n",
"test_str = test[test['first_app_type_1'] == 'STARTER'].reset_index(drop=True)\n",
"test_str['pred'] = lgbm.predict_proba(test_str[features], num_iteration=lgbm.best_iteration_)[:,1]\n",
"\n",
"print('Test PREMIUM Gini:',roc_auc_score(test_prm[TARGET_VAR], test_prm['pred'])*2-1)\n",
"print('Test BASIC Gini:',roc_auc_score(test_bsc[TARGET_VAR], test_bsc['pred'])*2-1)\n",
"print('Test STARTER Gini:',roc_auc_score(test_str[TARGET_VAR], test_str['pred'])*2-1)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"# Test PREMIUM Gini: 0.2815643581833447\n",
"# Test BASIC Gini: 0.3212115688971755\n",
"# Test STARTER Gini: 0.26352105926651204"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PREMIUM\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">flag_trx</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>sum</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bins</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.265, 0.563]</th>\n",
" <td>489</td>\n",
" <td>229</td>\n",
" <td>0.468303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.563, 0.613]</th>\n",
" <td>489</td>\n",
" <td>317</td>\n",
" <td>0.648262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.613, 0.644]</th>\n",
" <td>489</td>\n",
" <td>320</td>\n",
" <td>0.654397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.644, 0.67]</th>\n",
" <td>489</td>\n",
" <td>343</td>\n",
" <td>0.701431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.67, 0.697]</th>\n",
" <td>489</td>\n",
" <td>349</td>\n",
" <td>0.713701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.697, 0.722]</th>\n",
" <td>488</td>\n",
" <td>364</td>\n",
" <td>0.745902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.722, 0.746]</th>\n",
" <td>489</td>\n",
" <td>365</td>\n",
" <td>0.746421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.746, 0.774]</th>\n",
" <td>489</td>\n",
" <td>378</td>\n",
" <td>0.773006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.774, 0.808]</th>\n",
" <td>489</td>\n",
" <td>392</td>\n",
" <td>0.801636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.808, 0.904]</th>\n",
" <td>489</td>\n",
" <td>438</td>\n",
" <td>0.895706</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_trx \n",
" count sum mean\n",
"bins \n",
"(0.265, 0.563] 489 229 0.468303\n",
"(0.563, 0.613] 489 317 0.648262\n",
"(0.613, 0.644] 489 320 0.654397\n",
"(0.644, 0.67] 489 343 0.701431\n",
"(0.67, 0.697] 489 349 0.713701\n",
"(0.697, 0.722] 488 364 0.745902\n",
"(0.722, 0.746] 489 365 0.746421\n",
"(0.746, 0.774] 489 378 0.773006\n",
"(0.774, 0.808] 489 392 0.801636\n",
"(0.808, 0.904] 489 438 0.895706"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('PREMIUM')\n",
"test_prm['bins'] = pd.qcut(test_prm['pred'], 10)\n",
"test_prm.groupby('bins').agg({TARGET_VAR:['count', 'sum', \n",
" 'mean']})"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BASIC\n"
]
},
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">flag_trx</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>sum</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bins</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.285, 0.518]</th>\n",
" <td>2264</td>\n",
" <td>1062</td>\n",
" <td>0.469081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.518, 0.563]</th>\n",
" <td>2263</td>\n",
" <td>1238</td>\n",
" <td>0.547061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.563, 0.599]</th>\n",
" <td>2263</td>\n",
" <td>1315</td>\n",
" <td>0.581087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.599, 0.633]</th>\n",
" <td>2263</td>\n",
" <td>1434</td>\n",
" <td>0.633672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.633, 0.668]</th>\n",
" <td>2264</td>\n",
" <td>1476</td>\n",
" <td>0.651943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.668, 0.703]</th>\n",
" <td>2263</td>\n",
" <td>1534</td>\n",
" <td>0.677861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.703, 0.744]</th>\n",
" <td>2263</td>\n",
" <td>1622</td>\n",
" <td>0.716748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.744, 0.79]</th>\n",
" <td>2263</td>\n",
" <td>1762</td>\n",
" <td>0.778612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.79, 0.836]</th>\n",
" <td>2263</td>\n",
" <td>1868</td>\n",
" <td>0.825453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.836, 0.924]</th>\n",
" <td>2264</td>\n",
" <td>1998</td>\n",
" <td>0.882509</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_trx \n",
" count sum mean\n",
"bins \n",
"(0.285, 0.518] 2264 1062 0.469081\n",
"(0.518, 0.563] 2263 1238 0.547061\n",
"(0.563, 0.599] 2263 1315 0.581087\n",
"(0.599, 0.633] 2263 1434 0.633672\n",
"(0.633, 0.668] 2264 1476 0.651943\n",
"(0.668, 0.703] 2263 1534 0.677861\n",
"(0.703, 0.744] 2263 1622 0.716748\n",
"(0.744, 0.79] 2263 1762 0.778612\n",
"(0.79, 0.836] 2263 1868 0.825453\n",
"(0.836, 0.924] 2264 1998 0.882509"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('BASIC')\n",
"test_bsc['bins'] = pd.qcut(test_bsc['pred'], 10)\n",
"test_bsc.groupby('bins').agg({TARGET_VAR:['count', 'sum', \n",
" 'mean']})"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"STARTER\n"
]
},
{
"data": {
"text/html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" text-align: left;\n",
" }\n",
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" .dataframe thead tr:last-of-type th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">flag_trx</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>sum</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bins</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.218, 0.283]</th>\n",
" <td>412</td>\n",
" <td>88</td>\n",
" <td>0.213592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.283, 0.305]</th>\n",
" <td>412</td>\n",
" <td>125</td>\n",
" <td>0.303398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.305, 0.322]</th>\n",
" <td>411</td>\n",
" <td>108</td>\n",
" <td>0.262774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.322, 0.338]</th>\n",
" <td>412</td>\n",
" <td>135</td>\n",
" <td>0.327670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.338, 0.354]</th>\n",
" <td>412</td>\n",
" <td>139</td>\n",
" <td>0.337379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.354, 0.371]</th>\n",
" <td>411</td>\n",
" <td>150</td>\n",
" <td>0.364964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.371, 0.391]</th>\n",
" <td>412</td>\n",
" <td>170</td>\n",
" <td>0.412621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.391, 0.414]</th>\n",
" <td>411</td>\n",
" <td>166</td>\n",
" <td>0.403893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.414, 0.447]</th>\n",
" <td>412</td>\n",
" <td>175</td>\n",
" <td>0.424757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.447, 0.607]</th>\n",
" <td>412</td>\n",
" <td>192</td>\n",
" <td>0.466019</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_trx \n",
" count sum mean\n",
"bins \n",
"(0.218, 0.283] 412 88 0.213592\n",
"(0.283, 0.305] 412 125 0.303398\n",
"(0.305, 0.322] 411 108 0.262774\n",
"(0.322, 0.338] 412 135 0.327670\n",
"(0.338, 0.354] 412 139 0.337379\n",
"(0.354, 0.371] 411 150 0.364964\n",
"(0.371, 0.391] 412 170 0.412621\n",
"(0.391, 0.414] 411 166 0.403893\n",
"(0.414, 0.447] 412 175 0.424757\n",
"(0.447, 0.607] 412 192 0.466019"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('STARTER')\n",
"test_str['bins'] = pd.qcut(test_str['pred'], 10)\n",
"test_str.groupby('bins').agg({TARGET_VAR:['count', 'sum', \n",
" 'mean']})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature Importances"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": false
},
"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>feature_name</th>\n",
" <th>feature_importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>de_age</td>\n",
" <td>549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>approval_to_activation_time</td>\n",
" <td>271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>bsc_ascore</td>\n",
" <td>227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>pf_delin_max_dpd_12mo</td>\n",
" <td>220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ins_co_payday_apps_ia</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>de_submit_phoneprice</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>str_ascore</td>\n",
" <td>116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ins_co_shopping_apps_ia_curated</td>\n",
" <td>103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>join_to_submit_time</td>\n",
" <td>101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ap_all_co_nonsys_v2</td>\n",
" <td>101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>telco_tsel_score</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>de_phone_agerelease</td>\n",
" <td>98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>iz_inquiries_dist_name_max_360</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>submit_to_approval_time</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>pf_util_creditcard_avg_6mo</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>telco_experian_score</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>bsc_credit_limit</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>first_app_type</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>prm_ascore</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ins_co_finance_apps_ia_curated</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ap_last_install</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>referral</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>de_education</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>de_region</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>prm_credit_limit</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>de_source_of_registration</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>str_credit_limit</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature_name feature_importance\n",
"5 de_age 549\n",
"2 approval_to_activation_time 271\n",
"3 bsc_ascore 227\n",
"17 pf_delin_max_dpd_12mo 220\n",
"13 ins_co_payday_apps_ia 146\n",
"10 de_submit_phoneprice 124\n",
"22 str_ascore 116\n",
"14 ins_co_shopping_apps_ia_curated 103\n",
"16 join_to_submit_time 101\n",
"0 ap_all_co_nonsys_v2 101\n",
"26 telco_tsel_score 100\n",
"7 de_phone_agerelease 98\n",
"15 iz_inquiries_dist_name_max_360 91\n",
"24 submit_to_approval_time 84\n",
"18 pf_util_creditcard_avg_6mo 84\n",
"25 telco_experian_score 80\n",
"4 bsc_credit_limit 76\n",
"11 first_app_type 73\n",
"19 prm_ascore 72\n",
"12 ins_co_finance_apps_ia_curated 63\n",
"1 ap_last_install 59\n",
"21 referral 50\n",
"6 de_education 43\n",
"8 de_region 33\n",
"20 prm_credit_limit 23\n",
"9 de_source_of_registration 13\n",
"23 str_credit_limit 0"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_imp = pd.DataFrame(features, columns=['feature_name'])\n",
"feat_imp['feature_importance'] = lgbm.feature_importances_\n",
"feat_imp.sort_values('feature_importance', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# base.columns.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"# main[['user_id','activation_dt',TARGET_VAR,'first_app_type_1']+features].head()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"# main[['user_id','activation_dt',TARGET_VAR,'first_app_type_1']+features].to_csv('prop_1st_trx_activ_20201028-20201120_features.csv')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"# train['flag_split'] = 'train'\n",
"# val['flag_split'] = 'val'\n",
"# test['flag_split'] = 'test'\n",
"\n",
"# split = pd.concat([train[['user_id', 'flag_split']], \n",
"# val[['user_id', 'flag_split']], \n",
"# test[['user_id', 'flag_split']]]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"# split.shape"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"# split['flag_split'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"# split.to_csv('prop_1st_trx_activ_train_val_test_split.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}