{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pickle\n",
"import shap\n",
"\n",
"from sklearn.metrics import roc_auc_score\n",
"\n",
"from lightgbm import LGBMClassifier, plot_importance\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"RANDOM_STATE = 99\n",
"TARGET_VAR = 'flag_bad_usr'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"base_df = pd.read_parquet('data/feats_proba_user_data.parquet')\n",
"new_df = pd.read_parquet('data/pl_new_feats.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raw_df = base_df.merge(new_df, how='left', on=['user_id', 'trx_id'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"raw_df = raw_df.rename(columns={'raw_pd':'raw_pd_v2',\n",
" 'flag_reject_model':'flag_reject_v2'})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(71017, 124)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df.shape"
]
},
{
"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_bad_usr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>len</th>\n",
" <td>71017.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sum</th>\n",
" <td>2958.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.041652</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_bad_usr\n",
"len 71017.000000\n",
"sum 2958.000000\n",
"mean 0.041652"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df.agg({'flag_bad_usr':[len,sum,'mean']})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"raw_df['mo'] = raw_df['transaction_date'].dt.month"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>obs</th>\n",
" <th>bad</th>\n",
" <th>ODR</th>\n",
" </tr>\n",
" <tr>\n",
" <th>mo</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>27729</td>\n",
" <td>1083.0</td>\n",
" <td>0.039057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>37002</td>\n",
" <td>1656.0</td>\n",
" <td>0.044754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>6286</td>\n",
" <td>219.0</td>\n",
" <td>0.034839</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bad ODR\n",
"mo \n",
"6 27729 1083.0 0.039057\n",
"7 37002 1656.0 0.044754\n",
"8 6286 219.0 0.034839"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_odr = raw_df.groupby('mo').agg({'flag_bad_usr':[len,sum,'mean']})\n",
"monthly_odr.columns = ['obs', 'bad', 'ODR']\n",
"monthly_odr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature Transformations\n",
"- Convert payment_type to integer\n",
"- Filter out old engines in calibrated_final\n",
"- Fill NaN with 0 in time_approve_to_pl_hour"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"raw_df['payment_type'] = np.where(raw_df['payment_type'] == '3_months', 0, 1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"x = pd.DataFrame(base_df['engine'].unique()).dropna()\n",
"x = x[~x[0].str.contains('|'.join(['pd3', 'web', '0.0.1']))]\n",
"model_used = x[0].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"model_used"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"raw_df['calibrated_final'] = np.where(raw_df['engine'].isin(model_used), \n",
" raw_df['calibrated_final'], np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"raw_df['time_approve_to_pl_hour'] = raw_df['time_approve_to_pl_hour'].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"raw_df['util_all'] = raw_df['util_pl'].fillna(0) + raw_df['util_non_pl'].fillna(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"features = ['ap_co_plo',\n",
"# 'calibrated_final',\n",
" 'current_dpd',\n",
" 'delin_max_dpd_3mo',\n",
" 'delin_max_dpd_amt_3mo',\n",
" 'flag_bad_pefindo',\n",
" 'flag_good_pefindo',\n",
" 'os_amount',\n",
" 'oth_first_trx_amount',\n",
" 'oth_last_rep_channel',\n",
" 'oth_last_rep_days',\n",
" 'oth_last_rep_dpd',\n",
" 'oth_last_trx_amount',\n",
" 'pf_con_open_count_12mo',\n",
" 'pf_delin_dist_contractcode_30_dpd_3mo',\n",
" 'pf_delin_max_dpd_12mo',\n",
" 'pf_util_creditcard_avg_12mo',\n",
" 'time_approve_to_pl_hour',\n",
" 'util_non_pl',\n",
" 'util_pl',\n",
"# 'util_all',\n",
" \n",
" 'early_settle_co_180',\n",
"# 'early_settle_co_90',\n",
"# 'early_settle_co_ever',\n",
" \n",
"# 'pl_series_jumbo', \n",
"# 'pl_series_mini', \n",
" 'pl_series_jumbomini',\n",
" \n",
" 'pl_trx_co_jumbomini_all_90',\n",
"# 'pl_trx_co_jumbo_5_90',\n",
"# 'pl_trx_co_jumbo_all_90',\n",
"# 'pl_trx_co_jumbomini_5_90',\n",
"# 'pl_trx_co_jumbomini_all_180',\n",
"# 'pl_trx_co_jumbo_5_180',\n",
"# 'pl_trx_co_jumbomini_all_ever',\n",
"# 'pl_trx_co_jumbomini_4_90',\n",
"# 'pl_trx_co_jumbomini_5_180',\n",
"# 'pl_trx_co_jumbo_all_180',\n",
"# 'pl_trx_co_jumbo_5_ever',\n",
"# 'pl_trx_co_jumbomini_5_ever',\n",
" 'pl_trx_co_mini_all_90',\n",
"# 'pl_trx_co_jumbo_4_90',\n",
"# 'pl_trx_co_jumbo_all_ever',\n",
"# 'pl_trx_co_mini_5_90',\n",
"# 'pl_trx_co_mini_all_180',\n",
"# 'pl_trx_co_mini_all_ever',\n",
"# 'pl_trx_co_jumbomini_4_180',\n",
"# 'pl_trx_co_mini_5_180',\n",
"# 'pl_trx_co_mini_4_90',\n",
" 'pl_trx_co_jumbo_4_180',\n",
" \n",
"# 'settle_to_due_max_ever',\n",
" 'settle_to_due_min_ever',\n",
"# 'settle_to_due_avg_ever',\n",
"# 'settle_to_due_med_ever',\n",
" 'settle_to_due_last_pl_ever',\n",
" \n",
" 'td_date',\n",
" 'td_day_of_week',\n",
" 'time_from_prev_pl_settle',\n",
" 'payment_type'\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"features = sorted(features)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(features)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"categorical_feats = ['flag_bad_pefindo',\n",
" 'flag_good_pefindo',\n",
" 'oth_last_rep_channel',\n",
" 'td_day_of_week',\n",
" 'payment_type'\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>user_id</th>\n",
" <th>perc_obs</th>\n",
" </tr>\n",
" <tr>\n",
" <th>flag_bad_usr</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>68059</td>\n",
" <td>0.958348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>2958</td>\n",
" <td>0.041652</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id perc_obs\n",
"flag_bad_usr \n",
"False 68059 0.958348\n",
"True 2958 0.041652"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flag_mean = raw_df.groupby(TARGET_VAR).agg({'user_id':'count'})\n",
"flag_mean['perc_obs'] = flag_mean['user_id']/flag_mean['user_id'].sum()\n",
"flag_mean"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"train_month = [6]\n",
"val_month = [7]\n",
"test_month = [8]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"train = raw_df[raw_df['mo'].isin(train_month)]\n",
"val = raw_df[raw_df['mo'].isin(val_month)]\n",
"test = raw_df[raw_df['mo'].isin(test_month)]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.04165199881718462"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df['flag_bad_usr'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 68059\n",
"True 2958\n",
"Name: flag_bad_usr, dtype: int64"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df['flag_bad_usr'].value_counts(dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"cat_feat_indexes = tuple([raw_df.columns.get_loc(col) for col in categorical_feats])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n",
"Wall time: 7.63 µs\n",
"[1]\tvalid_0's auc: 0.675746\tvalid_0's binary_logloss: 0.164788\tvalid_1's auc: 0.67124\tvalid_1's binary_logloss: 0.183025\n",
"Training until validation scores don't improve for 250 rounds\n",
"[2]\tvalid_0's auc: 0.70743\tvalid_0's binary_logloss: 0.164641\tvalid_1's auc: 0.701395\tvalid_1's binary_logloss: 0.182871\n",
"[3]\tvalid_0's auc: 0.720022\tvalid_0's binary_logloss: 0.164458\tvalid_1's auc: 0.701106\tvalid_1's binary_logloss: 0.182699\n",
"[4]\tvalid_0's auc: 0.723302\tvalid_0's binary_logloss: 0.164297\tvalid_1's auc: 0.702442\tvalid_1's binary_logloss: 0.182536\n",
"[5]\tvalid_0's auc: 0.733381\tvalid_0's binary_logloss: 0.164209\tvalid_1's auc: 0.70638\tvalid_1's binary_logloss: 0.18247\n",
"[6]\tvalid_0's auc: 0.745037\tvalid_0's binary_logloss: 0.164062\tvalid_1's auc: 0.715723\tvalid_1's binary_logloss: 0.18233\n",
"[7]\tvalid_0's auc: 0.751298\tvalid_0's binary_logloss: 0.163954\tvalid_1's auc: 0.718546\tvalid_1's binary_logloss: 0.182243\n",
"[8]\tvalid_0's auc: 0.752799\tvalid_0's binary_logloss: 0.163854\tvalid_1's auc: 0.715165\tvalid_1's binary_logloss: 0.182173\n",
"[9]\tvalid_0's auc: 0.755237\tvalid_0's binary_logloss: 0.163717\tvalid_1's auc: 0.720815\tvalid_1's binary_logloss: 0.182029\n",
"[10]\tvalid_0's auc: 0.752678\tvalid_0's binary_logloss: 0.163611\tvalid_1's auc: 0.716895\tvalid_1's binary_logloss: 0.18194\n",
"[11]\tvalid_0's auc: 0.756382\tvalid_0's binary_logloss: 0.163451\tvalid_1's auc: 0.720205\tvalid_1's binary_logloss: 0.181789\n",
"[12]\tvalid_0's auc: 0.75704\tvalid_0's binary_logloss: 0.16332\tvalid_1's auc: 0.719181\tvalid_1's binary_logloss: 0.181677\n",
"[13]\tvalid_0's auc: 0.760036\tvalid_0's binary_logloss: 0.16317\tvalid_1's auc: 0.723917\tvalid_1's binary_logloss: 0.181519\n",
"[14]\tvalid_0's auc: 0.759141\tvalid_0's binary_logloss: 0.163028\tvalid_1's auc: 0.724221\tvalid_1's binary_logloss: 0.181372\n",
"[15]\tvalid_0's auc: 0.758718\tvalid_0's binary_logloss: 0.162862\tvalid_1's auc: 0.723751\tvalid_1's binary_logloss: 0.181216\n",
"[16]\tvalid_0's auc: 0.758483\tvalid_0's binary_logloss: 0.162763\tvalid_1's auc: 0.723333\tvalid_1's binary_logloss: 0.181123\n",
"[17]\tvalid_0's auc: 0.758946\tvalid_0's binary_logloss: 0.162648\tvalid_1's auc: 0.723779\tvalid_1's binary_logloss: 0.181009\n",
"[18]\tvalid_0's auc: 0.760062\tvalid_0's binary_logloss: 0.162576\tvalid_1's auc: 0.723403\tvalid_1's binary_logloss: 0.180954\n",
"[19]\tvalid_0's auc: 0.759633\tvalid_0's binary_logloss: 0.162417\tvalid_1's auc: 0.723742\tvalid_1's binary_logloss: 0.180803\n",
"[20]\tvalid_0's auc: 0.757943\tvalid_0's binary_logloss: 0.162289\tvalid_1's auc: 0.723154\tvalid_1's binary_logloss: 0.180671\n",
"[21]\tvalid_0's auc: 0.756232\tvalid_0's binary_logloss: 0.162164\tvalid_1's auc: 0.721644\tvalid_1's binary_logloss: 0.180548\n",
"[22]\tvalid_0's auc: 0.755572\tvalid_0's binary_logloss: 0.16205\tvalid_1's auc: 0.720472\tvalid_1's binary_logloss: 0.180451\n",
"[23]\tvalid_0's auc: 0.755098\tvalid_0's binary_logloss: 0.161917\tvalid_1's auc: 0.720734\tvalid_1's binary_logloss: 0.180324\n",
"[24]\tvalid_0's auc: 0.755105\tvalid_0's binary_logloss: 0.161808\tvalid_1's auc: 0.720824\tvalid_1's binary_logloss: 0.180218\n",
"[25]\tvalid_0's auc: 0.755426\tvalid_0's binary_logloss: 0.161686\tvalid_1's auc: 0.721656\tvalid_1's binary_logloss: 0.180099\n",
"[26]\tvalid_0's auc: 0.755081\tvalid_0's binary_logloss: 0.161588\tvalid_1's auc: 0.721964\tvalid_1's binary_logloss: 0.179997\n",
"[27]\tvalid_0's auc: 0.756156\tvalid_0's binary_logloss: 0.161466\tvalid_1's auc: 0.72306\tvalid_1's binary_logloss: 0.179887\n",
"[28]\tvalid_0's auc: 0.756344\tvalid_0's binary_logloss: 0.161369\tvalid_1's auc: 0.723319\tvalid_1's binary_logloss: 0.179791\n",
"[29]\tvalid_0's auc: 0.757039\tvalid_0's binary_logloss: 0.161242\tvalid_1's auc: 0.724423\tvalid_1's binary_logloss: 0.179663\n",
"[30]\tvalid_0's auc: 0.759362\tvalid_0's binary_logloss: 0.161161\tvalid_1's auc: 0.726168\tvalid_1's binary_logloss: 0.179595\n",
"[31]\tvalid_0's auc: 0.759093\tvalid_0's binary_logloss: 0.161072\tvalid_1's auc: 0.726399\tvalid_1's binary_logloss: 0.179505\n",
"[32]\tvalid_0's auc: 0.759252\tvalid_0's binary_logloss: 0.16095\tvalid_1's auc: 0.726625\tvalid_1's binary_logloss: 0.179392\n",
"[33]\tvalid_0's auc: 0.759364\tvalid_0's binary_logloss: 0.160835\tvalid_1's auc: 0.726293\tvalid_1's binary_logloss: 0.179288\n",
"[34]\tvalid_0's auc: 0.760144\tvalid_0's binary_logloss: 0.160719\tvalid_1's auc: 0.726845\tvalid_1's binary_logloss: 0.179182\n",
"[35]\tvalid_0's auc: 0.761643\tvalid_0's binary_logloss: 0.160582\tvalid_1's auc: 0.72793\tvalid_1's binary_logloss: 0.179051\n",
"[36]\tvalid_0's auc: 0.761013\tvalid_0's binary_logloss: 0.160475\tvalid_1's auc: 0.727103\tvalid_1's binary_logloss: 0.178956\n",
"[37]\tvalid_0's auc: 0.762708\tvalid_0's binary_logloss: 0.16038\tvalid_1's auc: 0.728842\tvalid_1's binary_logloss: 0.178871\n",
"[38]\tvalid_0's auc: 0.762674\tvalid_0's binary_logloss: 0.160296\tvalid_1's auc: 0.728479\tvalid_1's binary_logloss: 0.178799\n",
"[39]\tvalid_0's auc: 0.762391\tvalid_0's binary_logloss: 0.160192\tvalid_1's auc: 0.728262\tvalid_1's binary_logloss: 0.178696\n",
"[40]\tvalid_0's auc: 0.762768\tvalid_0's binary_logloss: 0.160102\tvalid_1's auc: 0.728485\tvalid_1's binary_logloss: 0.178613\n",
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"[764]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[765]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[766]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[767]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[768]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[769]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[770]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[771]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[772]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[773]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[774]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"[775]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"Early stopping, best iteration is:\n",
"[525]\tvalid_0's auc: 0.785971\tvalid_0's binary_logloss: 0.146912\tvalid_1's auc: 0.752578\tvalid_1's binary_logloss: 0.166867\n",
"\n",
"defaultdict(<class 'collections.OrderedDict'>, {'valid_0': OrderedDict([('auc', 0.7859709522802609), ('binary_logloss', 0.1469123142059828)]), 'valid_1': OrderedDict([('auc', 0.7525781791788615), ('binary_logloss', 0.1668671296437273)])})\n",
"525\n"
]
},
{
"data": {
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XlxzejseMhilVhShJRKRoRHKq58jgj+Rrm4jIcYtkKu5q7f1y9PKSo04NJDI2pZOQbAsuHQegqxkyadj/InTsh/Z9wXW8FEorIZsFzwTtyTaomBRsH9oFZjBhKqS7INkOqU5IdkC6c+g6ZOTMOA/e8eCI300kg/+lvcE5ee7/wCUFrkQkT+6Q7oa23dC6Hdr2QPve4LptFzRvhc2/DYK8PxaDijqorAsCPJuGWBwsDiUVQcjv3wgTpsGs84P29j1QOQXKJgR9SiohURb8LCmMif2e/3LYRTP497UzZ3Ilr5yhk6/JCEknYevKYNR9aDeUT4RDO6G8JhhZdx+Cqvpgu6QCMqkgjNNd0LId9qyD/Ztg+1OQTQX9s+mj78diQThX1UPjO2DyacFovqIOKsLvkJhyehD4Mf11K/mJZPAfbE8yZUJZocuQQsikoWVbMHWxay10HoDqk6ClCVp3QGlVcGndCdNeEUyNTJgajLZbd0BJOUw/J5gu2bcBYolgKsQsCOHWHfDiI8H0Sn9Bna94GUw+FRouC14symuCkXfVVJh4cjAyr6qHqikKdBl2kQz+VCZLqb5CMTq6WoMRcvPWYI46XhKEsGeDEfTm30DztmDqouMAA541PFYShvUAxy0+8FRKbyc46Sy48FaY2RiMxutOge7WIKi7W4O/ACrroLMZug5Cqit4IYnFg6mUqnqYPA9ieo5KYUQy+JPpLJWVkXxo0dczYs+kYMOD8ORdwf5gKqfA7MUw58JgxFw9LRhBV58MU+YHUzCVdcEoOpsJ5s4raoPreGlwX/GS4LbJtuBFJpMKRuRY8BeCWfBCEy8Lplr6qp4WXFfUQu3sYf9nERlOkUzHZMb1peljRTYDL/8Onv1f2Pp4ENJdzb3Hp70SGt8OU18BtbOCue1YPAhhiwWj9PKJQXAPpCrnU9qxONSEb6CV9vOZjopamHPR8Dw2kSIVyeBPZbKUJnSe/aKTTsJLvwymZzoPBvPlT30nWLWSqICGS4PlbLPOD6ZkZp4fBH48kk9TkYKJ5P8ozfEX2L4XgxF5Vyts+Bkc2BwGfhJS7Uf2rZkN198N86/ufwQuIsMuksGfTGc11TOaDu2GJ74GTaug+eXgTdgePevIp58DJ70SZl8IdQ1QORnKa4NjWrUiMqoiGfypTJaShIJ/WLkHo/YdTwdvnE6cESyV3LUWnv5+MC8/ZT7MXATn3Qy1cyCThNOv6V1vLiJFIZLBn0xrque47d0AO9dAojT41GiyPfiI/4afwcEtR/ePlcAZ18ClH4Xpo/5FayJyHCIZ/KmMU6oRf/6angrebF3/E/j9d48+XlIJ9WfARX8FNTNhyjxo2xt8uKhyskb0ImNMJIM/mclSEteqnsPSyWBtejYNe58PLrVzgjn2l34FT38v7Giw+P0w49wgzKe9Esqq+3/Tte6U0XwEIjKMIhf8mayTyY7DdfxdLdC0OlgHX39G8AGkQztg0lz42W3Q9OTAt62oC84Dc/YSmHLaqJUsIoURueBPZbIA0Q/+jb8I1sEf3Bych2btskHO3BiHRe8JpmXmXQX1p/ee/XHqguAcMSIybkQ2+MuiNsfvDht/HiyXbNsDv/6X3mOJcjjvJjjtKpg4HfasD6Z1KqcELwbTXnH01ExdQ3ARkXEncsGfTEdwxL9mKfzovRxxcrFZi4NTGUw/JxjBW857GicvHPUSRWTsiFzwpzJBOEYm+H/z77Dic4DDqVfC+e+C6WcH6+hNb2CLyLHLK/jN7Grgy0Ac+Ka7/3Of418Ergh3K4Gp7l6bc3wisB74kbvfOhyFD6R3jn8Mh6J7cLnnBnjxYTjjdXDdnVo2KSLDYsjgN7M4cCdwFdAErDKz5e6+rqePu38op/8HgL5zDZ8Ffj0sFQ8hGQb/mFvH37IdNv0i+PTris8F3+7Uc6Ky6+8OzuMuIjIM8hnxLwI2uvtLAGa2FLgOWDdA/yXAp3t2zOw8YBrwINB4QtXmoWeOf8x9cvfBj8P6n/buz7oAFr4FznmLvrBDRIZVPsE/A8j9Jowm4IL+OprZHKABWBHux4DPA28FXjPQHZjZLcAtALNnn9iXWIy55ZwtTfCzj8Pz9wffynTeTcHX8U0/u9CViUhE5RP8/U2WD/DdddwILHM/vKD8fcAD7r7NBnkj0t3vAu4CaGxsHOhn5yU1FqZ6dq6BTSvgD/8TfHerxeDiv4YrPhmcI0dEZATlE/xNwKyc/ZnAjgH63gi8P2f/QuBSM3sfMAEoNbM2d7/teIrNR3exL+fsOAB3Xw2pjt62t9wHp14x8G1ERIZRPsG/CphnZg3AdoJwf1PfTmZ2OjAJWNnT5u5vzjl+M9A4kqEPvcs5i+4buNzh558JTmGc6oC3/ghOuSI41YJW64jIKBoy+N09bWa3Ag8RLOe8292fM7M7gNXuvjzsugRY6u4nNFVzolLFOuL/yfvh6f8Jtq/9fLAmHxT6IjLq8lrH7+4PAA/0abu9z/5nhvgZ3wa+fUzVHYeimuPPpGDzr2HjiiD0p58D73gw+NYpEZECidwnd5PFsqrHHb5/Q7A2H6BiErztJwp9ESm46AV/Mazj//4NsOHBYPuKT8Kpr4H6+cG57UVECixywV/wc/XsXtcb+jPOg0s/oi8TF5GiEsHgL+Acf1cLPHhbcP77jzwffMGJQl9Eikzkgr/3tMyjvJzz4Bb4cvhp24VvhQlTR/f+RUTyVARLX4ZXQd7cdYeHPxVsn/0meP1/jN59i4gco8iN+A9P9YxU8GezsOIO2PciXPxBKJ0Am38TnGDt1Z+AV4/o59NERE5YJIM/ETNisRGa6vndl+HRLwbbz9/f237Kq+Gyj43MfYqIDKPoTfWksyc+zbPqW9C0OtjOZmHlnfBf1wTtP/8MTDsL3rUiWJsPwXLNP/uGTp8sImNCBEf8fmJv7O59Af7vw8H2x7fAsncEZ9IEePmx4Prqf4KZ58HHNuvrD0VkzIlc8Ccz2eNfyrnnefjudb3733k97HoGZi6CN3wN1i6DM18H014RHFfoi8gYFLngT6Wzx//G7iN/B2274NKPQrwEfvVPQfvCN8PkU+HVHx++QkVECiRywZ/MZCk51hF/NgMYbHsCTrsKrvxUMJqPxWHFPwQjfhGRiIhc8Kcyx/Hm7lfPhwObgu0zrumdwrnsb+CSj+hNWxGJlMglWjLtxzbVs+XR3tAHmPqKI48r9EUkYiI34j/mqZ5vXxtcz74QTl4YnFhNRCTCIhf8wZu7ea62ObC5d/s1t8Oci0amKBGRIhK5eYy85/hf/h185Zxgu/GdwYhfRGQciGTw57WO/7dfCK7nXw3X/JvW5IvIuBG5qZ7ufE/ZcHAzLLgO3vjdkS9KRKSIRHPEnxv86e7gtMm5sllo3gqT5o5qbSIixSCCwZ9zrp6W7fCFBXDfLcGpk5MdQXvzFsgkoe6UgtUpIlIoEQz+nDn+TSugYx+svRe+8//gwfCUC9ueDK5PXliYIkVECihywd+RzFBeEn7PbbLtyINrlwXXv/sqVE+HqQtGtzgRkSIQqeDPZp3WrhS1FSVBQ0/wf2ovvPZzkOqAAy/B7rVw/juDE7GJiIwzkQr+Q11p3KGmsjRoSLZDrAQSpTD1zKDtF58NrnXiNREZp/IKfjO72sxeMLONZnbUl8qa2RfN7OnwssHMmsP2c8xspZk9Z2bPmNkNw/0AcrV0pgCCEf+h3dC+D8omBAfrw+B/7j6Yfg7MvXQkSxERKVpDruM3szhwJ3AV0ASsMrPl7r6up4+7fyin/weAnndNO4C3ufuLZnYy8JSZPeTuzcP5IHo0dyYBqKkogc/PDxonzgyvT+7t+Mo/08nXRGTcyucDXIuAje7+EoCZLQWuA9YN0H8J8GkAd9/Q0+juO8xsD1APjEzwdwQj/prKnLn71qbg2gwu+iuwGCx+/0jcvYjImJBP8M8AtuXsNwEX9NfRzOYADcCKfo4tAkqBTf0cuwW4BWD27Nl5lNS/rlQGgIp4tv8Or/3scf9sEZGoyGe+o7+T2Hg/bQA3AsvcPXPEDzCbDvw38HZ3PyqV3f0ud29098b6+vo8SupfOhuUVXXo5d7GRMVx/zwRkSjKJ/ibgFk5+zOBHQP0vRG4J7fBzCYC/wd8yt0fP54i89UT/A33Xtnb+N5HR/IuRUTGnHyCfxUwz8wazKyUINyX9+1kZqcDk4CVOW2lwI+A77r7D4en5IGlM/1M8VRMGum7FREZU4YMfndPA7cCDwHrgXvd/Tkzu8PMXp/TdQmw1P2IM6K9EbgMuDlnuec5w1j/EdKZfmagymtG6u5ERMakvE7L7O4PAA/0abu9z/5n+rnd94DvnUB9xySY6ukT/vHInXlaROSERGoxezqbpZrO3oYlPyhcMSIiRSpawZ9xJtLe25BJFq4YEZEiFa3gz2YptXRvw8zzC1eMiEiRilTwpzJOCWHw/8V3YOL0whYkIlKEIhX8mWxO8MdLC1uMiEiRilTwpzM5Uz0KfhGRfkUq+FNZpzIWBn9CwS8i0p9IBX8m65Rb+OldjfhFRPoVqeBPZbKUxcPzw+lrFUVE+hWp4E9nnHLN8YuIDCpawZ91ymM9I34Fv4hIf6IV/JmsRvwiIkOIVvBnnTKN+EVEBhW54C/XB7hERAYVreDPZCk1reoRERlMtII/672f3E2UFbYYEZEiFangd3edskFEZAiRCv6sQxkpsDjE4oUuR0SkKEUs+J0q74Cy6kKXIiJStCIW/FDl7VA+sdCliIgUrUgFvx8e8dcUuhQRkaIVqeDPulOpEb+IyKAiFfzuhCN+Bb+IyEAiFfxZdyq8QyN+EZFBRCr4PZthUuYAVNUXuhQRkaKVV/Cb2dVm9oKZbTSz2/o5/kUzezq8bDCz5pxjN5nZi+HlpuEsvq8bDn2XUpIw9cyRvBsRkTEtMVQHM4sDdwJXAU3AKjNb7u7revq4+4dy+n8AWBhu1wGfBhoBB54Kb3twWB9F6Lr2HwYbJy8ciR8vIhIJ+Yz4FwEb3f0ld08CS4HrBum/BLgn3P5j4BF3PxCG/SPA1SdS8GD+UHoeLbFamPaKkboLEZExL5/gnwFsy9lvCtuOYmZzgAZgxbHc1sxuMbPVZrZ67969+dQ9oP2JqSd0exGRqMsn+K2fNh+g743AMnfPHMtt3f0ud29098b6+uN/Y9Y8i0fr/WoRkWGXT0o2AbNy9mcCOwboeyO90zzHetsTZjhu/b3WiIhIj3yCfxUwz8wazKyUINyX9+1kZqcDk4CVOc0PAa81s0lmNgl4bdg2IsyzRGyFqojIsBtyVY+7p83sVoLAjgN3u/tzZnYHsNrde14ElgBL3d1zbnvAzD5L8OIBcIe7Hxjeh9DLyGrELyIyhCGDH8DdHwAe6NN2e5/9zwxw27uBu4+zvmNiuOb4RUSGEKmU1Ju7IiJDi1RK6s1dEZGhRSv4PYtbpB6SiMiwi1RKGk7EHpKIyLCLVEpqVY+IyNAiFvwa8YuIDCVSKRlzjfhFRIYSqeDXOn4RkaFFKiWNLGjELyIyqIgFP6DlnCIig4pUSprm+EVEhhSt4NeqHhGRIUUqJWPok7siIkOJVEpqxC8iMrRIpaQ+uSsiMrRIBX8M16oeEZEhRColzV1z/CIiQ4hUSsbIEq7mFxGRAUQq+E1TPSIiQ4pUSsbQVI+IyFAilZLBuXoi9ZBERIZdpFJSq3pERIYWqZQ0skTsIYmIDLtIpWQw4i90FSIixS0ywe/umuoREclDZFLSHdCqHhGRIeWVkmZ2tZm9YGYbzey2Afq80czWmdlzZvb9nPZ/DdvWm9lXzEbmZDrB6dk04hcRGUpiqA5mFgfuBK4CmoBVZrbc3dfl9JkHfAK42N0PmtnUsP0i4GLgVWHXR4HLgV8N54MAyGqqR0QkL/mk5CJgo7u/5O5JYClwXZ8+7wbudPeDAO6+J2x3oBwoBcqAEmD3cBTeVxD8WscvIjKUfFJyBrAtZ78pbMs1H5hvZo+Z2eNmdjWAu68EfgnsDC8Pufv6vndgZreY2WozW713797jeRy465QNIiL5yCcl+5uT9z77CWAe8GpgCfBNM6s1s9OAM4GZBC8WV5rZZUf9MPe73L3R3Rvr6+uPpf7Deqd6tNfg1yYAAAanSURBVJ5TRGQw+QR/EzArZ38msKOfPj9x95S7bwZeIHgheAPwuLu3uXsb8DNg8YmXfbSso6keEZE85JOSq4B5ZtZgZqXAjcDyPn1+DFwBYGZTCKZ+XgK2ApebWcLMSgje2D1qqmc4uDtx01SPiMhQhkxJd08DtwIPEYT2ve7+nJndYWavD7s9BOw3s3UEc/p/4+77gWXAJmAtsAZY4+4/HYHHQTYbzj4p+EVEBjXkck4Ad38AeKBP2+052w58OLzk9skA7znxMvOoMZsJNhT8IiKDikxKZrPZYEPBLyIyqOikpAfBn0jEC1yIiEhxi0zw11UGs1avmjmpwJWIiBS3yAR/z4hfUz0iIoOLTkr2BL9OyC8iMqgIBb+Wc4qI5CM6KampHhGRvEQnJRX8IiJ5iU5KaqpHRCQv0UnJeAIW/CnUnVLoSkREilpep2wYE8pr4I3fKXQVIiJFLzojfhERyYuCX0RknFHwi4iMMwp+EZFxRsEvIjLOKPhFRMYZBb+IyDij4BcRGWfMe051UCTMbC/w8gn8iCnAvmEqZ7iptuNXzPWptuNXzPWNtdrmuHt9PjcuuuA/UWa22t0bC11Hf1Tb8Svm+lTb8Svm+qJcm6Z6RETGGQW/iMg4E8Xgv6vQBQxCtR2/Yq5PtR2/Yq4vsrVFbo5fREQGF8URv4iIDELBLyIyzkQm+M3sajN7wcw2mtltBarhbjPbY2bP5rTVmdkjZvZieD0pbDcz+0pY7zNmdu4I1zbLzH5pZuvN7Dkz+2Cx1Gdm5Wb2pJmtCWv7+7C9wcyeCGv7gZmVhu1l4f7G8Pjckaotp8a4mf3BzO4vwtq2mNlaM3vazFaHbQX/vYb3V2tmy8zs+fC5d2Ex1GZmp4f/Xj2XVjP762KoLafGD4X/H541s3vC/yfD87xz9zF/AeLAJuAUoBRYAywoQB2XAecCz+a0/StwW7h9G/Av4fY1wM8AAxYDT4xwbdOBc8PtamADsKAY6gvvY0K4XQI8Ed7nvcCNYfvXgL8Mt98HfC3cvhH4wSj8bj8MfB+4P9wvptq2AFP6tBX89xre33eAd4XbpUBtsdSWU2Mc2AXMKZbagBnAZqAi5/l283A970b8H3WUfnEXAg/l7H8C+ESBapnLkcH/AjA93J4OvBBufx1Y0l+/UarzJ8BVxVYfUAn8HriA4JOJib6/Y+Ah4MJwOxH2sxGsaSbwC+BK4P7wP39R1BbezxaODv6C/16BiWF4WbHV1qee1wKPFVNtBMG/DagLn0f3A388XM+7qEz19Pwj9WgK24rBNHffCRBeTw3bC1Zz+GfgQoKRdVHUF06lPA3sAR4h+Auu2d3T/dz/4drC4y3A5JGqDfgS8DEgG+5PLqLaABx42MyeMrNbwrZi+L2eAuwF/iucJvummVUVSW25bgTuCbeLojZ33w78O7AV2EnwPHqKYXreRSX4rZ+2Yl+nWpCazWwC8L/AX7t762Bd+2kbsfrcPePu5xCMrhcBZw5y/6NWm5m9Dtjj7k/lNg9y/4X4vV7s7ucCfwK838wuG6TvaNaXIJj6/E93Xwi0E0yfDGTU/+3COfLXAz8cqms/bSNWW/jewnVAA3AyUEXw+x2ohmOqLyrB3wTMytmfCewoUC197Taz6QDh9Z6wfdRrNrMSgtD/H3e/r9jqA3D3ZuBXBPOotWaW6Of+D9cWHq8BDoxQSRcDrzezLcBSgumeLxVJbQC4+47weg/wI4IXzmL4vTYBTe7+RLi/jOCFoBhq6/EnwO/dfXe4Xyy1/RGw2d33unsKuA+4iGF63kUl+FcB88J3vEsJ/nRbXuCaeiwHbgq3byKYW+9pf1u4WmAx0NLzJ+ZIMDMDvgWsd/cvFFN9ZlZvZrXhdgXBk3498Evg+gFq66n5emCFh5Obw83dP+HuM919LsHzaoW7v7kYagMwsyozq+7ZJpivfpYi+L26+y5gm5mdHja9BlhXDLXlWELvNE9PDcVQ21ZgsZlVhv93e/7thud5N9JvnIzWheBd9w0Ec8OfLFAN9xDMx6UIXoHfSTDP9gvgxfC6LuxrwJ1hvWuBxhGu7RKCP/2eAZ4OL9cUQ33Aq4A/hLU9C9wetp8CPAlsJPhTvCxsLw/3N4bHTxml3++r6V3VUxS1hXWsCS/P9Tz3i+H3Gt7fOcDq8Hf7Y2BSEdVWCewHanLaiqK28D7/Hng+/D/x30DZcD3vdMoGEZFxJipTPSIikicFv4jIOKPgFxEZZxT8IiLjjIJfRGScUfCLiIwzCn4RkXHm/wNpcPZj6EK5CgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params_ = {\n",
" # highest impact to control overfitting\n",
" 'num_iterations': 10000,\n",
" 'num_leaves': 100, #should be < 2^(max_depth)\n",
" 'max_depth': 10,\n",
" 'min_data_in_leaf':100,\n",
" 'min_gain_to_split': 10,\n",
" # optional parameters\n",
" 'categorical_feature':cat_feat_indexes,\n",
" 'learning_rate': 0.01,\n",
" 'subsample': 0.5,\n",
" 'colsample_bytree': 0.5,\n",
" 'subsample_freq':1,\n",
" # for regularization\n",
" 'lambda_l2':20,\n",
"}\n",
"\n",
"mod_lgb = LGBMClassifier(n_jobs=-1, random_state=RANDOM_STATE,\n",
" num_iterations=params_['num_iterations'],\n",
" num_leaves=params_['num_leaves'],\n",
" max_depth=params_['max_depth'],\n",
" min_data_in_leaf=params_['min_data_in_leaf'],\n",
" min_gain_to_split=params_['min_gain_to_split'],\n",
" categorical_feature=params_['categorical_feature'],\n",
" learning_rate=params_['learning_rate'],\n",
" subsample=params_['subsample'],\n",
" colsample_bytree=params_['colsample_bytree'],\n",
" subsample_freq=params_['subsample_freq'],\n",
" lambda_l2=params_['lambda_l2']\n",
" )\n",
"\n",
"eval_set = [(train[features], train[TARGET_VAR]),(val[features], val[TARGET_VAR])]\n",
"%time\n",
"mod_lgb.fit(train[features], train[TARGET_VAR], eval_set=eval_set, eval_metric='auc',\n",
" early_stopping_rounds=250, verbose=True)\n",
"\n",
"train_result = mod_lgb.evals_result_['valid_0']['auc']\n",
"val_result = mod_lgb.evals_result_['valid_1']['auc']\n",
"\n",
"plt.plot(train_result)\n",
"plt.plot(val_result)\n",
"\n",
"print()\n",
"print(mod_lgb.best_score_)\n",
"print(mod_lgb.best_iteration_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluate the Model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Gini: 0.5719419045605219\n",
"Val Gini: 0.5053062909358992\n",
"Test Gini: 0.5188236684270697\n"
]
}
],
"source": [
"train['pred'] = mod_lgb.predict_proba(train[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"val['pred'] = mod_lgb.predict_proba(val[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"test['pred'] = mod_lgb.predict_proba(test[features], num_iteration=mod_lgb.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": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5831209033373899\n"
]
}
],
"source": [
"train_val = pd.concat([train, val])\n",
"mod_lgb.fit(train_val[features], train_val[TARGET_VAR])\n",
"\n",
"test['pred_2'] = mod_lgb.predict_proba(test[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"print(roc_auc_score(test[TARGET_VAR], test['pred_2'])*2-1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce2c0cf350>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"test['pred'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.20962648122572697"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['pred'].max()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce58b47ad0>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"test['pred_2'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.25591218564783735"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['pred_2'].max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check Feature Importances"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"feat_imp = pd.DataFrame(features, columns=['feature_name'])\n",
"feat_imp['feature_importance'] = mod_lgb.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>feature_name</th>\n",
" <th>feature_importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>td_date</td>\n",
" <td>602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>delin_max_dpd_3mo</td>\n",
" <td>523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>time_approve_to_pl_hour</td>\n",
" <td>439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>util_pl</td>\n",
" <td>349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>pf_delin_max_dpd_12mo</td>\n",
" <td>236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ap_co_plo</td>\n",
" <td>175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>oth_last_rep_dpd</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>delin_max_dpd_amt_3mo</td>\n",
" <td>165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>oth_last_rep_days</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>oth_last_trx_amount</td>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>os_amount</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>time_from_prev_pl_settle</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>current_dpd</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>settle_to_due_last_pl_ever</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>flag_good_pefindo</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>pf_util_creditcard_avg_12mo</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>oth_first_trx_amount</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>pl_trx_co_mini_all_90</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>settle_to_due_min_ever</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>pf_delin_dist_contractcode_30_dpd_3mo</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>payment_type</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>flag_bad_pefindo</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>pf_con_open_count_12mo</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>pl_trx_co_jumbo_4_180</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>early_settle_co_180</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>td_day_of_week</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>oth_last_rep_channel</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>util_non_pl</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature_name feature_importance\n",
"24 td_date 602\n",
"2 delin_max_dpd_3mo 523\n",
"26 time_approve_to_pl_hour 439\n",
"29 util_pl 349\n",
"16 pf_delin_max_dpd_12mo 236\n",
"20 pl_trx_co_jumbomini_all_90 204\n",
"0 ap_co_plo 175\n",
"11 oth_last_rep_dpd 170\n",
"3 delin_max_dpd_amt_3mo 165\n",
"10 oth_last_rep_days 145\n",
"12 oth_last_trx_amount 142\n",
"7 os_amount 124\n",
"27 time_from_prev_pl_settle 91\n",
"1 current_dpd 90\n",
"22 settle_to_due_last_pl_ever 89\n",
"6 flag_good_pefindo 73\n",
"17 pf_util_creditcard_avg_12mo 52\n",
"8 oth_first_trx_amount 52\n",
"21 pl_trx_co_mini_all_90 26\n",
"23 settle_to_due_min_ever 24\n",
"15 pf_delin_dist_contractcode_30_dpd_3mo 19\n",
"13 payment_type 12\n",
"5 flag_bad_pefindo 8\n",
"14 pf_con_open_count_12mo 2\n",
"19 pl_trx_co_jumbo_4_180 1\n",
"4 early_settle_co_180 1\n",
"25 td_day_of_week 1\n",
"9 oth_last_rep_channel 0\n",
"28 util_non_pl 0\n",
"18 pl_series_jumbomini 0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_imp.sort_values('feature_importance', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce58a4e890>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x1008 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_importance(mod_lgb, figsize=(12,14))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check SHAP Values"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [],
"source": [
"shap_values = shap.TreeExplainer(mod_lgb).shap_values(train[features])"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x972 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"shap.summary_plot(shap_values[1], train[features], max_display = 30)"
]
},
{
"cell_type": "code",
"execution_count": 156,
"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>imp</th>\n",
" <th>feats</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.318381</td>\n",
" <td>time_approve_to_pl_hour</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.315865</td>\n",
" <td>util_pl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.178458</td>\n",
" <td>oth_last_rep_days</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.177225</td>\n",
" <td>delin_max_dpd_3mo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.143365</td>\n",
" <td>delin_max_dpd_amt_3mo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.111691</td>\n",
" <td>pf_delin_max_dpd_12mo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.097750</td>\n",
" <td>util_non_pl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.087660</td>\n",
" <td>ap_co_plo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.082967</td>\n",
" <td>oth_last_rep_dpd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.078717</td>\n",
" <td>td_date</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.073477</td>\n",
" <td>time_from_prev_pl_settle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.068892</td>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.053607</td>\n",
" <td>oth_last_trx_amount</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.036604</td>\n",
" <td>current_dpd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.033149</td>\n",
" <td>flag_good_pefindo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.032629</td>\n",
" <td>os_amount</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.024825</td>\n",
" <td>settle_to_due_last_pl_ever</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.020745</td>\n",
" <td>pf_util_creditcard_avg_12mo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.014011</td>\n",
" <td>pl_trx_co_mini_all_90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.007659</td>\n",
" <td>oth_first_trx_amount</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.004031</td>\n",
" <td>settle_to_due_min_ever</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.003656</td>\n",
" <td>payment_type</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.002845</td>\n",
" <td>pf_delin_dist_contractcode_30_dpd_3mo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.002713</td>\n",
" <td>flag_bad_pefindo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.000611</td>\n",
" <td>early_settle_co_180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.000449</td>\n",
" <td>pf_con_open_count_12mo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.000275</td>\n",
" <td>pl_series_jumbomini</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.000215</td>\n",
" <td>pl_trx_co_jumbo_4_180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.000123</td>\n",
" <td>td_day_of_week</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.000000</td>\n",
" <td>oth_last_rep_channel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.000000</td>\n",
" <td>util_all</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imp feats\n",
"26 0.318381 time_approve_to_pl_hour\n",
"30 0.315865 util_pl\n",
"10 0.178458 oth_last_rep_days\n",
"2 0.177225 delin_max_dpd_3mo\n",
"3 0.143365 delin_max_dpd_amt_3mo\n",
"16 0.111691 pf_delin_max_dpd_12mo\n",
"29 0.097750 util_non_pl\n",
"0 0.087660 ap_co_plo\n",
"11 0.082967 oth_last_rep_dpd\n",
"24 0.078717 td_date\n",
"27 0.073477 time_from_prev_pl_settle\n",
"20 0.068892 pl_trx_co_jumbomini_all_90\n",
"12 0.053607 oth_last_trx_amount\n",
"1 0.036604 current_dpd\n",
"6 0.033149 flag_good_pefindo\n",
"7 0.032629 os_amount\n",
"22 0.024825 settle_to_due_last_pl_ever\n",
"17 0.020745 pf_util_creditcard_avg_12mo\n",
"21 0.014011 pl_trx_co_mini_all_90\n",
"8 0.007659 oth_first_trx_amount\n",
"23 0.004031 settle_to_due_min_ever\n",
"13 0.003656 payment_type\n",
"15 0.002845 pf_delin_dist_contractcode_30_dpd_3mo\n",
"5 0.002713 flag_bad_pefindo\n",
"4 0.000611 early_settle_co_180\n",
"14 0.000449 pf_con_open_count_12mo\n",
"18 0.000275 pl_series_jumbomini\n",
"19 0.000215 pl_trx_co_jumbo_4_180\n",
"25 0.000123 td_day_of_week\n",
"9 0.000000 oth_last_rep_channel\n",
"28 0.000000 util_all"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame({'imp':np.mean(np.abs(shap_values[1]), axis=0), 'feats': features}).sort_values('imp', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe11c123ad0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"test['pred'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.04337575915117538"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['pred'].max()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe146c99e50>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"test['pred_2'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.04896885373705467"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['pred_2'].max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save the model"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# pickle.dump(mod_lgb, open('lgbm_pl_model_v3.p', 'wb'))"
]
}
],
"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": "3.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}