{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from scripts import woe\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": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"raw_df = pd.read_parquet('data/feats_proba_user_data_06012020.parquet').drop(columns=['calibrated_final',\n",
" 'engine'])\n",
"new_df = pd.read_parquet('data/pl_new_feats.parquet')\n",
"# ascore_fix = pd.read_parquet('data/ascore_feat_fix.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"base_df = raw_df.merge(new_df, how='left', on=['user_id', 'trx_id'])\\\n",
"# .merge(ascore_fix, how='left', on=['user_id', 'trx_id'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(105082, 121)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>105082.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sum</th>\n",
" <td>4401.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.041882</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_bad_usr\n",
"len 105082.000000\n",
"sum 4401.000000\n",
"mean 0.041882"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_df.agg({'flag_bad_usr':[len,sum,'mean']})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"base_df['mo'] = base_df['transaction_date'].dt.month"
]
},
{
"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 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>40351</td>\n",
" <td>1662.0</td>\n",
" <td>0.041189</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 40351 1662.0 0.041189"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_odr = base_df.groupby('mo').agg({'flag_bad_usr':[len,sum,'mean']})\n",
"monthly_odr.columns = ['obs', 'bad', 'ODR']\n",
"monthly_odr"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"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>index</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>user_id</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>transaction_date</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>payment_type</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>trx_id</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>performance_window</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>max_dpd</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>first_61_after_trx</td>\n",
" <td>97436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>transaction_id</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>start_date</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>first_dpd_1</td>\n",
" <td>65115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>first_dpd_7</td>\n",
" <td>87328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>first_dpd_15</td>\n",
" <td>92104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>first_dpd_30</td>\n",
" <td>95967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>first_dpd_60</td>\n",
" <td>99780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>first_dpd_90</td>\n",
" <td>102105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>flag_bad_usr</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>flag_bad_trx</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>ap_co_plo</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>current_dpd</td>\n",
" <td>27621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>delin_max_dpd_3mo</td>\n",
" <td>11443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>delin_max_dpd_amt_3mo</td>\n",
" <td>11446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>flag_bad_pefindo</td>\n",
" <td>49247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>flag_good_pefindo</td>\n",
" <td>49247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>os_amount</td>\n",
" <td>27621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>oth_first_trx_amount</td>\n",
" <td>10333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>oth_last_rep_channel</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>oth_last_rep_days</td>\n",
" <td>15996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>oth_last_rep_dpd</td>\n",
" <td>15996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>oth_last_trx_amount</td>\n",
" <td>10333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>pf_con_open_count_12mo</td>\n",
" <td>49247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>pf_delin_dist_contractcode_30_dpd_3mo</td>\n",
" <td>49247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>pf_delin_max_dpd_12mo</td>\n",
" <td>49247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>pf_util_creditcard_avg_12mo</td>\n",
" <td>49247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>time_approve_to_pl_hour</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>util_non_pl</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>util_pl</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>raw_pd</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>flag_reject_model</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index 0\n",
"0 user_id 0\n",
"1 transaction_date 0\n",
"2 payment_type 0\n",
"3 trx_id 0\n",
"4 performance_window 0\n",
"5 max_dpd 1\n",
"6 first_61_after_trx 97436\n",
"7 transaction_id 0\n",
"8 start_date 0\n",
"9 first_dpd_1 65115\n",
"10 first_dpd_7 87328\n",
"11 first_dpd_15 92104\n",
"12 first_dpd_30 95967\n",
"13 first_dpd_60 99780\n",
"14 first_dpd_90 102105\n",
"15 flag_bad_usr 0\n",
"16 flag_bad_trx 0\n",
"17 ap_co_plo 0\n",
"18 current_dpd 27621\n",
"19 delin_max_dpd_3mo 11443\n",
"20 delin_max_dpd_amt_3mo 11446\n",
"21 flag_bad_pefindo 49247\n",
"22 flag_good_pefindo 49247\n",
"23 os_amount 27621\n",
"24 oth_first_trx_amount 10333\n",
"25 oth_last_rep_channel 0\n",
"26 oth_last_rep_days 15996\n",
"27 oth_last_rep_dpd 15996\n",
"28 oth_last_trx_amount 10333\n",
"29 pf_con_open_count_12mo 49247\n",
"30 pf_delin_dist_contractcode_30_dpd_3mo 49247\n",
"31 pf_delin_max_dpd_12mo 49247\n",
"32 pf_util_creditcard_avg_12mo 49247\n",
"33 time_approve_to_pl_hour 0\n",
"34 util_non_pl 0\n",
"35 util_pl 0\n",
"36 raw_pd 0\n",
"37 flag_reject_model 0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nulls = raw_df.isna().sum().reset_index()\n",
"nulls"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"old_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']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Transformation"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"base_df['payment_type'] = np.where(base_df['payment_type'] == '3_months', 1, 0)\n",
"base_df['td_day_of_week'] = base_df['td_day_of_week'].astype(str)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train Val Test Split"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"train_month = [6]\n",
"val_month = [7]\n",
"test_month = [8]\n",
"\n",
"train = base_df[base_df['mo'].isin(train_month)]\n",
"val = base_df[base_df['mo'].isin(val_month)]\n",
"test = base_df[base_df['mo'].isin(test_month)]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def calculate_iv(df, feature, qnt_num, v_type, target_var):\n",
" print(feature)\n",
" if v_type == 'c':\n",
" try:\n",
" _, bins_man = pd.qcut(df[feature], qnt_num, retbins=True, duplicates='drop')\n",
" except:\n",
" return np.nan\n",
" wo_c = woe.WoE(qnt_num=qnt_num, v_type=v_type, bins=bins_man)\n",
" elif v_type == 'd':\n",
" wo_c = woe.WoE(v_type=v_type)\n",
"\n",
" wo_temp = wo_c.fit(df[feature], df[target_var])\n",
" iv = wo_temp.iv\n",
" return iv"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def combine_feature_ivs(df, feature_dict, qnt_num, target_var):\n",
" temp_ls = []\n",
" for key, value in feature_dict.items():\n",
" temp_iv = calculate_iv(df, key, qnt_num, value, target_var)\n",
" temp_ls.append([key, temp_iv])\n",
" \n",
" iv_df = pd.DataFrame(temp_ls)\n",
" iv_df.columns = ['feature', 'iv']\n",
" iv_df = iv_df.sort_values('iv', ascending=False).reset_index(drop=True)\n",
" return iv_df"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def feat_bins_agg_comp(train_df, val_df, feature, bins, target_var, duplicates='drop'):\n",
" train_viz = train_df[[feature, target_var]]\n",
" val_viz = val_df[[feature, target_var]]\n",
" \n",
" if (train_viz[feature].dtype == 'object') or (train_viz[feature].nunique() <= 3):\n",
" train_viz[feature] = train_viz[feature].fillna('missing')\n",
" train_viz = train_viz.groupby(feature).agg({target_var:['count', 'sum', 'mean']})\n",
" train_viz.columns = ['obs', 'bads', 'odr']\n",
" \n",
" val_viz[feature] = val_viz[feature].fillna('missing')\n",
" val_viz = val_viz.groupby(feature).agg({target_var:['count', 'sum', 'mean']})\n",
" val_viz.columns = ['obs', 'bads', 'odr']\n",
" else:\n",
" _, train_bins = pd.qcut(train_viz[feature], bins, retbins=True, duplicates=duplicates)\n",
" train_bins[0] = train_bins[0] - 0.001\n",
" \n",
" train_viz['bin'] = pd.cut(train_viz[feature], bins=train_bins, duplicates=duplicates)\n",
" if train_viz[feature].isna().sum() > 0:\n",
" train_viz['bin'] = train_viz['bin'].cat.add_categories('missing')\n",
" train_viz['bin'] = train_viz['bin'].fillna('missing')\n",
" train_viz = train_viz.groupby('bin').agg({target_var:['count', 'sum', 'mean']})\n",
" train_viz.columns = ['obs', 'bads', 'odr']\n",
" \n",
" val_viz['bin'], val_bins= pd.cut(val_viz[feature], bins=train_bins, retbins=True, duplicates=duplicates)\n",
" if val_viz[feature].isna().sum() > 0:\n",
" val_viz['bin'] = val_viz['bin'].cat.add_categories('missing')\n",
" val_viz['bin'] = val_viz['bin'].fillna('missing')\n",
" val_viz = val_viz.groupby('bin').agg({target_var:['count', 'sum', 'mean']})\n",
" val_viz.columns = ['obs', 'bads', 'odr']\n",
" \n",
" return train_viz, val_viz"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def plot_stability(df, feature):\n",
" zizi = df.groupby('spts_month').agg({feature:['mean', 'median', lambda x: x.isna().mean()]})\n",
" zizi.columns = ['mean', 'median', 'missing_rate']\n",
"\n",
" fig, ax1 = plt.subplots(figsize=(12,8))\n",
"\n",
" ax2 = ax1.twinx()\n",
" ax1.plot(zizi.index, zizi['mean'], marker='o', label='mean')\n",
" ax1.plot(zizi.index, zizi['median'], marker='o', label='median')\n",
" ax2.plot(zizi.index, zizi['missing_rate'], marker='o', c='g', linestyle='dashdot', label='missing_rate')\n",
" \n",
" if df[feature].min() >= 0:\n",
" ax1.set_ylim(bottom=0)\n",
" ax2.set_ylim(bottom=0)\n",
"\n",
" ax1.legend(bbox_to_anchor=(0.12, 1.1))\n",
" ax2.legend(bbox_to_anchor=(1, 1.1))\n",
"\n",
" plt.title(feature)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def eval_feature(train_df, val_df, feature, bins, target_var):\n",
" agg_df_train, agg_df_val = feat_bins_agg_comp(train_df, val_df, feature, bins, target_var)\n",
" print('train set')\n",
" display(agg_df_train)\n",
" print('val set')\n",
" display(agg_df_val)\n",
" \n",
" # vizualization\n",
" X = agg_df_train.index.astype(str)\n",
" Y_1 = agg_df_train['odr']\n",
" Y_2 = agg_df_val['odr']\n",
"\n",
" viz = pd.DataFrame(np.c_[Y_1,Y_2], index=X)\n",
" viz.columns = ['train', 'val']\n",
" viz.plot.bar()\n",
"\n",
"# plt.xticks(rotation='horizontal')\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Select New Features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pl_series"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"pl_series_feats = [col for col in base_df.columns if col.startswith('pl_series_')]\n",
"pl_series_dict = {name:'c' for name in pl_series_feats}"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pl_series_jumbo\n",
"pl_series_mini\n",
"pl_series_jumbomini\n"
]
}
],
"source": [
"pl_series_iv_df = combine_feature_ivs(train, pl_series_dict, 10, TARGET_VAR)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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</th>\n",
" <th>iv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>pl_series_mini</td>\n",
" <td>0.004190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>pl_series_jumbo</td>\n",
" <td>0.003127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.002507</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature iv\n",
"0 pl_series_mini 0.004190\n",
"1 pl_series_jumbo 0.003127\n",
"2 pl_series_jumbomini 0.002507"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl_series_iv_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pl_trx_co"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"pl_trx_feats = [col for col in base_df.columns if col.startswith('pl_trx_co_')]\n",
"pl_trx_dict = {name:'c' for name in pl_trx_feats}"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pl_trx_co_jumbo_4_ever\n",
"pl_trx_co_jumbo_5_ever\n",
"pl_trx_co_mini_5_ever\n",
"pl_trx_co_jumbo_2_ever\n",
"pl_trx_co_jumbo_6_ever\n",
"pl_trx_co_mini_4_ever\n",
"pl_trx_co_jumbo_11_ever\n",
"pl_trx_co_mini_2_ever\n",
"pl_trx_co_mini_6_ever\n",
"pl_trx_co_mini_11_ever\n",
"pl_trx_co_jumbo_7_ever\n",
"pl_trx_co_mini_7_ever\n",
"pl_trx_co_jumbomini_2_ever\n",
"pl_trx_co_jumbomini_4_ever\n",
"pl_trx_co_jumbomini_5_ever\n",
"pl_trx_co_jumbomini_6_ever\n",
"pl_trx_co_jumbomini_7_ever\n",
"pl_trx_co_jumbomini_11_ever\n",
"pl_trx_co_jumbo_all_ever\n",
"pl_trx_co_mini_all_ever\n",
"pl_trx_co_jumbomini_all_ever\n",
"pl_trx_co_jumbo_5_180\n",
"pl_trx_co_mini_5_180\n",
"pl_trx_co_jumbo_4_180\n",
"pl_trx_co_jumbo_2_180\n",
"pl_trx_co_mini_4_180\n",
"pl_trx_co_jumbo_6_180\n",
"pl_trx_co_mini_2_180\n",
"pl_trx_co_mini_6_180\n",
"pl_trx_co_jumbo_7_180\n",
"pl_trx_co_jumbo_11_180\n",
"pl_trx_co_mini_7_180\n",
"pl_trx_co_mini_11_180\n",
"pl_trx_co_jumbomini_2_180\n",
"pl_trx_co_jumbomini_4_180\n",
"pl_trx_co_jumbomini_5_180\n",
"pl_trx_co_jumbomini_6_180\n",
"pl_trx_co_jumbomini_7_180\n",
"pl_trx_co_jumbomini_11_180\n",
"pl_trx_co_jumbo_all_180\n",
"pl_trx_co_mini_all_180\n",
"pl_trx_co_jumbomini_all_180\n",
"pl_trx_co_jumbo_5_90\n",
"pl_trx_co_mini_5_90\n",
"pl_trx_co_jumbo_4_90\n",
"pl_trx_co_jumbo_2_90\n",
"pl_trx_co_mini_4_90\n",
"pl_trx_co_jumbo_6_90\n",
"pl_trx_co_mini_2_90\n",
"pl_trx_co_mini_6_90\n",
"pl_trx_co_jumbomini_2_90\n",
"pl_trx_co_jumbomini_4_90\n",
"pl_trx_co_jumbomini_5_90\n",
"pl_trx_co_jumbomini_6_90\n",
"pl_trx_co_jumbomini_7_90\n",
"pl_trx_co_jumbomini_11_90\n",
"pl_trx_co_jumbo_all_90\n",
"pl_trx_co_mini_all_90\n",
"pl_trx_co_jumbomini_all_90\n"
]
}
],
"source": [
"pl_trx_iv_df = combine_feature_ivs(train, pl_trx_dict, 10, TARGET_VAR)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"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</th>\n",
" <th>iv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>0.073596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>pl_trx_co_jumbo_5_90</td>\n",
" <td>0.063782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>pl_trx_co_jumbo_all_90</td>\n",
" <td>0.062932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>pl_trx_co_jumbomini_5_90</td>\n",
" <td>0.062841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>pl_trx_co_jumbomini_all_180</td>\n",
" <td>0.047566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>pl_trx_co_jumbo_5_180</td>\n",
" <td>0.041087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>pl_trx_co_jumbomini_all_ever</td>\n",
" <td>0.037072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>pl_trx_co_jumbomini_4_90</td>\n",
" <td>0.035368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>pl_trx_co_jumbomini_5_180</td>\n",
" <td>0.035294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>pl_trx_co_jumbo_all_180</td>\n",
" <td>0.033940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>pl_trx_co_jumbo_5_ever</td>\n",
" <td>0.032640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>pl_trx_co_jumbomini_5_ever</td>\n",
" <td>0.031214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>pl_trx_co_mini_all_90</td>\n",
" <td>0.030036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>pl_trx_co_jumbo_4_90</td>\n",
" <td>0.028339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>pl_trx_co_jumbo_all_ever</td>\n",
" <td>0.027919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>pl_trx_co_mini_5_90</td>\n",
" <td>0.021981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>pl_trx_co_mini_all_180</td>\n",
" <td>0.020374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>pl_trx_co_mini_all_ever</td>\n",
" <td>0.018311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>pl_trx_co_jumbomini_4_180</td>\n",
" <td>0.015642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>pl_trx_co_mini_5_180</td>\n",
" <td>0.011672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>pl_trx_co_mini_4_90</td>\n",
" <td>0.010995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>pl_trx_co_jumbo_4_180</td>\n",
" <td>0.010512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>pl_trx_co_mini_5_ever</td>\n",
" <td>0.009827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>pl_trx_co_mini_4_180</td>\n",
" <td>0.005907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>pl_trx_co_jumbomini_2_180</td>\n",
" <td>0.004540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>pl_trx_co_mini_4_ever</td>\n",
" <td>0.004190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>pl_trx_co_jumbomini_2_ever</td>\n",
" <td>0.004188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>pl_trx_co_jumbo_2_180</td>\n",
" <td>0.003801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>pl_trx_co_jumbomini_2_90</td>\n",
" <td>0.003344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>pl_trx_co_jumbo_2_ever</td>\n",
" <td>0.003323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>pl_trx_co_jumbo_4_ever</td>\n",
" <td>0.003127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>pl_trx_co_jumbomini_4_ever</td>\n",
" <td>0.002507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>pl_trx_co_jumbo_2_90</td>\n",
" <td>0.001980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>pl_trx_co_mini_2_90</td>\n",
" <td>0.001752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>pl_trx_co_jumbo_6_90</td>\n",
" <td>0.001589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>pl_trx_co_jumbomini_6_ever</td>\n",
" <td>0.001343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>pl_trx_co_mini_2_180</td>\n",
" <td>0.001307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>pl_trx_co_jumbo_6_ever</td>\n",
" <td>0.001283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>pl_trx_co_mini_2_ever</td>\n",
" <td>0.001259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>pl_trx_co_jumbomini_6_90</td>\n",
" <td>0.001051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>pl_trx_co_jumbomini_6_180</td>\n",
" <td>0.000349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>pl_trx_co_jumbo_6_180</td>\n",
" <td>0.000311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>pl_trx_co_mini_6_ever</td>\n",
" <td>0.000151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>pl_trx_co_mini_6_180</td>\n",
" <td>0.000082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>pl_trx_co_jumbo_11_ever</td>\n",
" <td>0.000064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>pl_trx_co_jumbomini_11_ever</td>\n",
" <td>0.000023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>pl_trx_co_mini_6_90</td>\n",
" <td>0.000012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>pl_trx_co_mini_11_ever</td>\n",
" <td>-0.000011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>pl_trx_co_jumbomini_11_180</td>\n",
" <td>-0.000011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>pl_trx_co_jumbo_11_180</td>\n",
" <td>-0.000046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>pl_trx_co_mini_7_180</td>\n",
" <td>-0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>pl_trx_co_mini_7_ever</td>\n",
" <td>-0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>pl_trx_co_jumbo_7_ever</td>\n",
" <td>-0.000136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>pl_trx_co_jumbo_7_180</td>\n",
" <td>-0.000136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>pl_trx_co_mini_11_180</td>\n",
" <td>-0.000159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>pl_trx_co_jumbomini_7_ever</td>\n",
" <td>-0.000159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>pl_trx_co_jumbomini_7_180</td>\n",
" <td>-0.000159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>pl_trx_co_jumbomini_7_90</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>pl_trx_co_jumbomini_11_90</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature iv\n",
"0 pl_trx_co_jumbomini_all_90 0.073596\n",
"1 pl_trx_co_jumbo_5_90 0.063782\n",
"2 pl_trx_co_jumbo_all_90 0.062932\n",
"3 pl_trx_co_jumbomini_5_90 0.062841\n",
"4 pl_trx_co_jumbomini_all_180 0.047566\n",
"5 pl_trx_co_jumbo_5_180 0.041087\n",
"6 pl_trx_co_jumbomini_all_ever 0.037072\n",
"7 pl_trx_co_jumbomini_4_90 0.035368\n",
"8 pl_trx_co_jumbomini_5_180 0.035294\n",
"9 pl_trx_co_jumbo_all_180 0.033940\n",
"10 pl_trx_co_jumbo_5_ever 0.032640\n",
"11 pl_trx_co_jumbomini_5_ever 0.031214\n",
"12 pl_trx_co_mini_all_90 0.030036\n",
"13 pl_trx_co_jumbo_4_90 0.028339\n",
"14 pl_trx_co_jumbo_all_ever 0.027919\n",
"15 pl_trx_co_mini_5_90 0.021981\n",
"16 pl_trx_co_mini_all_180 0.020374\n",
"17 pl_trx_co_mini_all_ever 0.018311\n",
"18 pl_trx_co_jumbomini_4_180 0.015642\n",
"19 pl_trx_co_mini_5_180 0.011672\n",
"20 pl_trx_co_mini_4_90 0.010995\n",
"21 pl_trx_co_jumbo_4_180 0.010512\n",
"22 pl_trx_co_mini_5_ever 0.009827\n",
"23 pl_trx_co_mini_4_180 0.005907\n",
"24 pl_trx_co_jumbomini_2_180 0.004540\n",
"25 pl_trx_co_mini_4_ever 0.004190\n",
"26 pl_trx_co_jumbomini_2_ever 0.004188\n",
"27 pl_trx_co_jumbo_2_180 0.003801\n",
"28 pl_trx_co_jumbomini_2_90 0.003344\n",
"29 pl_trx_co_jumbo_2_ever 0.003323\n",
"30 pl_trx_co_jumbo_4_ever 0.003127\n",
"31 pl_trx_co_jumbomini_4_ever 0.002507\n",
"32 pl_trx_co_jumbo_2_90 0.001980\n",
"33 pl_trx_co_mini_2_90 0.001752\n",
"34 pl_trx_co_jumbo_6_90 0.001589\n",
"35 pl_trx_co_jumbomini_6_ever 0.001343\n",
"36 pl_trx_co_mini_2_180 0.001307\n",
"37 pl_trx_co_jumbo_6_ever 0.001283\n",
"38 pl_trx_co_mini_2_ever 0.001259\n",
"39 pl_trx_co_jumbomini_6_90 0.001051\n",
"40 pl_trx_co_jumbomini_6_180 0.000349\n",
"41 pl_trx_co_jumbo_6_180 0.000311\n",
"42 pl_trx_co_mini_6_ever 0.000151\n",
"43 pl_trx_co_mini_6_180 0.000082\n",
"44 pl_trx_co_jumbo_11_ever 0.000064\n",
"45 pl_trx_co_jumbomini_11_ever 0.000023\n",
"46 pl_trx_co_mini_6_90 0.000012\n",
"47 pl_trx_co_mini_11_ever -0.000011\n",
"48 pl_trx_co_jumbomini_11_180 -0.000011\n",
"49 pl_trx_co_jumbo_11_180 -0.000046\n",
"50 pl_trx_co_mini_7_180 -0.000094\n",
"51 pl_trx_co_mini_7_ever -0.000094\n",
"52 pl_trx_co_jumbo_7_ever -0.000136\n",
"53 pl_trx_co_jumbo_7_180 -0.000136\n",
"54 pl_trx_co_mini_11_180 -0.000159\n",
"55 pl_trx_co_jumbomini_7_ever -0.000159\n",
"56 pl_trx_co_jumbomini_7_180 -0.000159\n",
"57 pl_trx_co_jumbomini_7_90 NaN\n",
"58 pl_trx_co_jumbomini_11_90 NaN"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl_trx_iv_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## settle to due"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"settle_feats = [col for col in base_df.columns if col.startswith('settle_to_due_')]\n",
"settle_dict = {name:'c' for name in settle_feats}"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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",
"settle_to_due_max_180\n",
"settle_to_due_min_180\n",
"settle_to_due_avg_180\n",
"settle_to_due_med_180\n",
"settle_to_due_last_pl_180\n",
"settle_to_due_max_90\n",
"settle_to_due_min_90\n",
"settle_to_due_avg_90\n",
"settle_to_due_med_90\n",
"settle_to_due_last_pl_90\n"
]
}
],
"source": [
"settle_iv_df = combine_feature_ivs(train, settle_dict, 10, TARGET_VAR)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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</th>\n",
" <th>iv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>settle_to_due_last_pl_ever</td>\n",
" <td>0.102322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>settle_to_due_min_ever</td>\n",
" <td>0.068639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>settle_to_due_med_ever</td>\n",
" <td>0.067720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>settle_to_due_avg_ever</td>\n",
" <td>0.067184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>settle_to_due_max_ever</td>\n",
" <td>0.065497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>settle_to_due_last_pl_180</td>\n",
" <td>0.062922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>settle_to_due_last_pl_90</td>\n",
" <td>0.062542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>settle_to_due_max_180</td>\n",
" <td>0.059961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>settle_to_due_max_90</td>\n",
" <td>0.054297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>settle_to_due_med_90</td>\n",
" <td>0.053747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>settle_to_due_avg_180</td>\n",
" <td>0.051766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>settle_to_due_avg_90</td>\n",
" <td>0.051192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>settle_to_due_min_180</td>\n",
" <td>0.050031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>settle_to_due_med_180</td>\n",
" <td>0.046791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>settle_to_due_min_90</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature iv\n",
"0 settle_to_due_last_pl_ever 0.102322\n",
"1 settle_to_due_min_ever 0.068639\n",
"2 settle_to_due_med_ever 0.067720\n",
"3 settle_to_due_avg_ever 0.067184\n",
"4 settle_to_due_max_ever 0.065497\n",
"5 settle_to_due_last_pl_180 0.062922\n",
"6 settle_to_due_last_pl_90 0.062542\n",
"7 settle_to_due_max_180 0.059961\n",
"8 settle_to_due_max_90 0.054297\n",
"9 settle_to_due_med_90 0.053747\n",
"10 settle_to_due_avg_180 0.051766\n",
"11 settle_to_due_avg_90 0.051192\n",
"12 settle_to_due_min_180 0.050031\n",
"13 settle_to_due_med_180 0.046791\n",
"14 settle_to_due_min_90 NaN"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"settle_iv_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## transaction dates"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"td_feats = [col for col in base_df.columns if col.startswith('td_')]\n",
"td_dict = {'td_date':'c',\n",
" 'td_day_of_week':'d'}"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"td_date\n",
"td_day_of_week\n"
]
}
],
"source": [
"td_iv_df = combine_feature_ivs(train, td_dict, 10, TARGET_VAR)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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</th>\n",
" <th>iv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>td_date</td>\n",
" <td>0.025016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>td_day_of_week</td>\n",
" <td>0.005453</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature iv\n",
"0 td_date 0.025016\n",
"1 td_day_of_week 0.005453"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"td_iv_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## others\n",
"- time_from_prev_pl_settle\n",
"- payment_type"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"other_feats = ['time_from_prev_pl_settle',\n",
" 'payment_type']\n",
"other_dict = {'time_from_prev_pl_settle':'c',\n",
" 'payment_type':'d'}"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time_from_prev_pl_settle\n",
"payment_type\n"
]
}
],
"source": [
"other_iv_df = combine_feature_ivs(train, other_dict, 10, TARGET_VAR)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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</th>\n",
" <th>iv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>time_from_prev_pl_settle</td>\n",
" <td>0.152081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>payment_type</td>\n",
" <td>0.030573</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature iv\n",
"0 time_from_prev_pl_settle 0.152081\n",
"1 payment_type 0.030573"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"other_iv_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Selected Features"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['pl_series_jumbo', 'pl_series_mini', 'pl_series_jumbomini']"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl_series_feats"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['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']"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl_trx_iv_df[pl_trx_iv_df['iv'] > 0.01]['feature'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['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']"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl_trx_iv_df.head(10)['feature'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"features = [\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']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Correlation"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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": 66,
"metadata": {},
"outputs": [],
"source": [
"categorical_feats = set(['td_day_of_week', 'payment_type'])\n",
"corr_feats = list(set(features) - categorical_feats)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa1b5275110>"
]
},
"execution_count": 67,
"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[corr_feats])\n",
"fig, ax = plt.subplots(figsize=(20,20))\n",
"sns.heatmap(corr_viz, annot=True, fmt='.2f', linewidths=.5)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"scrolled": true
},
"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>level_0</th>\n",
" <th>level_1</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>pl_trx_co_mini_all_90</td>\n",
" <td>0.527307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>pl_trx_co_mini_all_180</td>\n",
" <td>0.438438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>pl_trx_co_jumbomini_5_ever</td>\n",
" <td>0.754919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>pl_trx_co_jumbo_all_180</td>\n",
" <td>0.816721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>pl_trx_co_jumbomini_all_180</td>\n",
" <td>0.857210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>590</th>\n",
" <td>settle_to_due_min_ever</td>\n",
" <td>pl_trx_co_jumbo_5_180</td>\n",
" <td>0.076212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>591</th>\n",
" <td>settle_to_due_min_ever</td>\n",
" <td>pl_series_jumbo</td>\n",
" <td>0.251066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>592</th>\n",
" <td>time_from_prev_pl_settle</td>\n",
" <td>pl_trx_co_jumbo_5_180</td>\n",
" <td>0.035727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>593</th>\n",
" <td>time_from_prev_pl_settle</td>\n",
" <td>pl_series_jumbo</td>\n",
" <td>-0.119564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>594</th>\n",
" <td>pl_trx_co_jumbo_5_180</td>\n",
" <td>pl_series_jumbo</td>\n",
" <td>0.196729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>595 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" level_0 level_1 0\n",
"0 pl_trx_co_jumbomini_all_90 pl_trx_co_mini_all_90 0.527307\n",
"1 pl_trx_co_jumbomini_all_90 pl_trx_co_mini_all_180 0.438438\n",
"2 pl_trx_co_jumbomini_all_90 pl_trx_co_jumbomini_5_ever 0.754919\n",
"3 pl_trx_co_jumbomini_all_90 pl_trx_co_jumbo_all_180 0.816721\n",
"4 pl_trx_co_jumbomini_all_90 pl_trx_co_jumbomini_all_180 0.857210\n",
".. ... ... ...\n",
"590 settle_to_due_min_ever pl_trx_co_jumbo_5_180 0.076212\n",
"591 settle_to_due_min_ever pl_series_jumbo 0.251066\n",
"592 time_from_prev_pl_settle pl_trx_co_jumbo_5_180 0.035727\n",
"593 time_from_prev_pl_settle pl_series_jumbo -0.119564\n",
"594 pl_trx_co_jumbo_5_180 pl_series_jumbo 0.196729\n",
"\n",
"[595 rows x 3 columns]"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eval_corr = corr_viz.stack().reset_index()\n",
"eval_corr"
]
},
{
"cell_type": "code",
"execution_count": 93,
"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>level_0</th>\n",
" <th>level_1</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>316</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>time_from_prev_pl_settle</td>\n",
" <td>-0.162879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>settle_to_due_min_ever</td>\n",
" <td>-0.088119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>td_date</td>\n",
" <td>0.089201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_5_90</td>\n",
" <td>0.089879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>305</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbomini_5_90</td>\n",
" <td>0.104989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>245</th>\n",
" <td>pl_trx_co_mini_5_90</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.107039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>settle_to_due_med_ever</td>\n",
" <td>0.123366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>317</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_5_180</td>\n",
" <td>0.133409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>309</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_mini_5_180</td>\n",
" <td>0.134257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>settle_to_due_avg_ever</td>\n",
" <td>0.139578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>299</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_all_90</td>\n",
" <td>0.150375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbomini_5_180</td>\n",
" <td>0.151124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>early_settle_co_90</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.158521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>313</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_5_ever</td>\n",
" <td>0.167958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>pl_trx_co_jumbomini_5_ever</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.185439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>301</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>settle_to_due_last_pl_ever</td>\n",
" <td>0.193015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>pl_trx_co_jumbo_all_180</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.245129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>pl_trx_co_jumbomini_all_90</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.245950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>settle_to_due_max_ever</td>\n",
" <td>0.314259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_all_ever</td>\n",
" <td>0.314310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>304</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>early_settle_co_180</td>\n",
" <td>0.327345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>pl_trx_co_jumbomini_all_180</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.377142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_4_90</td>\n",
" <td>0.380526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>pl_trx_co_mini_all_90</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.388383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>303</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbomini_all_ever</td>\n",
" <td>0.441288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>310</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>early_settle_co_ever</td>\n",
" <td>0.457474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>219</th>\n",
" <td>pl_trx_co_mini_4_90</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.493123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>pl_trx_co_mini_all_180</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.507465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_mini_all_ever</td>\n",
" <td>0.537357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>296</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbo_4_180</td>\n",
" <td>0.582379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>312</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbomini_4_90</td>\n",
" <td>0.632787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>pl_series_mini</td>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>0.645545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>318</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_series_jumbo</td>\n",
" <td>0.678197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>pl_series_jumbomini</td>\n",
" <td>pl_trx_co_jumbomini_4_180</td>\n",
" <td>0.890908</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" level_0 level_1 0\n",
"316 pl_series_jumbomini time_from_prev_pl_settle -0.162879\n",
"315 pl_series_jumbomini settle_to_due_min_ever -0.088119\n",
"307 pl_series_jumbomini td_date 0.089201\n",
"311 pl_series_jumbomini pl_trx_co_jumbo_5_90 0.089879\n",
"305 pl_series_jumbomini pl_trx_co_jumbomini_5_90 0.104989\n",
"245 pl_trx_co_mini_5_90 pl_series_jumbomini 0.107039\n",
"295 pl_series_jumbomini settle_to_due_med_ever 0.123366\n",
"317 pl_series_jumbomini pl_trx_co_jumbo_5_180 0.133409\n",
"309 pl_series_jumbomini pl_trx_co_mini_5_180 0.134257\n",
"302 pl_series_jumbomini settle_to_due_avg_ever 0.139578\n",
"299 pl_series_jumbomini pl_trx_co_jumbo_all_90 0.150375\n",
"308 pl_series_jumbomini pl_trx_co_jumbomini_5_180 0.151124\n",
"270 early_settle_co_90 pl_series_jumbomini 0.158521\n",
"313 pl_series_jumbomini pl_trx_co_jumbo_5_ever 0.167958\n",
"105 pl_trx_co_jumbomini_5_ever pl_series_jumbomini 0.185439\n",
"301 pl_series_jumbomini settle_to_due_last_pl_ever 0.193015\n",
"135 pl_trx_co_jumbo_all_180 pl_series_jumbomini 0.245129\n",
"9 pl_trx_co_jumbomini_all_90 pl_series_jumbomini 0.245950\n",
"298 pl_series_jumbomini settle_to_due_max_ever 0.314259\n",
"300 pl_series_jumbomini pl_trx_co_jumbo_all_ever 0.314310\n",
"304 pl_series_jumbomini early_settle_co_180 0.327345\n",
"164 pl_trx_co_jumbomini_all_180 pl_series_jumbomini 0.377142\n",
"314 pl_series_jumbomini pl_trx_co_jumbo_4_90 0.380526\n",
"42 pl_trx_co_mini_all_90 pl_series_jumbomini 0.388383\n",
"303 pl_series_jumbomini pl_trx_co_jumbomini_all_ever 0.441288\n",
"310 pl_series_jumbomini early_settle_co_ever 0.457474\n",
"219 pl_trx_co_mini_4_90 pl_series_jumbomini 0.493123\n",
"74 pl_trx_co_mini_all_180 pl_series_jumbomini 0.507465\n",
"306 pl_series_jumbomini pl_trx_co_mini_all_ever 0.537357\n",
"296 pl_series_jumbomini pl_trx_co_jumbo_4_180 0.582379\n",
"312 pl_series_jumbomini pl_trx_co_jumbomini_4_90 0.632787\n",
"192 pl_series_mini pl_series_jumbomini 0.645545\n",
"318 pl_series_jumbomini pl_series_jumbo 0.678197\n",
"297 pl_series_jumbomini pl_trx_co_jumbomini_4_180 0.890908"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"this_feat = 'pl_series_jumbomini'\n",
"eval_corr[(eval_corr['level_0'] == this_feat) | (eval_corr['level_1'] == this_feat)].sort_values([0])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"scrolled": true
},
"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</th>\n",
" <th>iv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>pl_trx_co_jumbomini_4_90</td>\n",
" <td>0.035368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>pl_trx_co_mini_5_90</td>\n",
" <td>0.021981</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature iv\n",
"7 pl_trx_co_jumbomini_4_90 0.035368\n",
"15 pl_trx_co_mini_5_90 0.021981"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compare_feats = ['pl_trx_co_mini_5_90', 'pl_trx_co_jumbomini_4_90']\n",
"pl_trx_iv_df[pl_trx_iv_df['feature'].isin(compare_feats)]"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"selected_feats = [\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": "markdown",
"metadata": {},
"source": [
"# Univariate Analysis"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['user_id',\n",
" 'transaction_date',\n",
" 'payment_type',\n",
" 'trx_id',\n",
" 'performance_window',\n",
" 'max_dpd',\n",
" 'first_61_after_trx',\n",
" 'transaction_id',\n",
" 'start_date',\n",
" 'first_dpd_1',\n",
" 'first_dpd_7',\n",
" 'first_dpd_15',\n",
" 'first_dpd_30',\n",
" 'first_dpd_60',\n",
" 'first_dpd_90',\n",
" 'flag_bad_usr',\n",
" 'flag_bad_trx',\n",
" 'ap_co_plo',\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",
" 'raw_pd',\n",
" 'flag_reject_model',\n",
" 'pl_series_jumbo',\n",
" 'pl_series_mini',\n",
" 'pl_series_jumbomini',\n",
" 'pl_trx_co_jumbo_4_ever',\n",
" 'pl_trx_co_jumbo_5_ever',\n",
" 'pl_trx_co_mini_5_ever',\n",
" 'pl_trx_co_jumbo_2_ever',\n",
" 'pl_trx_co_jumbo_6_ever',\n",
" 'pl_trx_co_mini_4_ever',\n",
" 'pl_trx_co_jumbo_11_ever',\n",
" 'pl_trx_co_mini_2_ever',\n",
" 'pl_trx_co_mini_6_ever',\n",
" 'pl_trx_co_mini_11_ever',\n",
" 'pl_trx_co_jumbo_7_ever',\n",
" 'pl_trx_co_mini_7_ever',\n",
" 'pl_trx_co_jumbomini_2_ever',\n",
" 'pl_trx_co_jumbomini_4_ever',\n",
" 'pl_trx_co_jumbomini_5_ever',\n",
" 'pl_trx_co_jumbomini_6_ever',\n",
" 'pl_trx_co_jumbomini_7_ever',\n",
" 'pl_trx_co_jumbomini_11_ever',\n",
" 'pl_trx_co_jumbo_all_ever',\n",
" 'pl_trx_co_mini_all_ever',\n",
" 'pl_trx_co_jumbomini_all_ever',\n",
" 'pl_trx_co_jumbo_5_180',\n",
" 'pl_trx_co_mini_5_180',\n",
" 'pl_trx_co_jumbo_4_180',\n",
" 'pl_trx_co_jumbo_2_180',\n",
" 'pl_trx_co_mini_4_180',\n",
" 'pl_trx_co_jumbo_6_180',\n",
" 'pl_trx_co_mini_2_180',\n",
" 'pl_trx_co_mini_6_180',\n",
" 'pl_trx_co_jumbo_7_180',\n",
" 'pl_trx_co_jumbo_11_180',\n",
" 'pl_trx_co_mini_7_180',\n",
" 'pl_trx_co_mini_11_180',\n",
" 'pl_trx_co_jumbomini_2_180',\n",
" 'pl_trx_co_jumbomini_4_180',\n",
" 'pl_trx_co_jumbomini_5_180',\n",
" 'pl_trx_co_jumbomini_6_180',\n",
" 'pl_trx_co_jumbomini_7_180',\n",
" 'pl_trx_co_jumbomini_11_180',\n",
" 'pl_trx_co_jumbo_all_180',\n",
" 'pl_trx_co_mini_all_180',\n",
" 'pl_trx_co_jumbomini_all_180',\n",
" 'pl_trx_co_jumbo_5_90',\n",
" 'pl_trx_co_mini_5_90',\n",
" 'pl_trx_co_jumbo_4_90',\n",
" 'pl_trx_co_jumbo_2_90',\n",
" 'pl_trx_co_mini_4_90',\n",
" 'pl_trx_co_jumbo_6_90',\n",
" 'pl_trx_co_mini_2_90',\n",
" 'pl_trx_co_mini_6_90',\n",
" 'pl_trx_co_jumbomini_2_90',\n",
" 'pl_trx_co_jumbomini_4_90',\n",
" 'pl_trx_co_jumbomini_5_90',\n",
" 'pl_trx_co_jumbomini_6_90',\n",
" 'pl_trx_co_jumbomini_7_90',\n",
" 'pl_trx_co_jumbomini_11_90',\n",
" 'pl_trx_co_jumbo_all_90',\n",
" 'pl_trx_co_mini_all_90',\n",
" 'pl_trx_co_jumbomini_all_90',\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",
" 'early_settle_co_ever',\n",
" 'settle_to_due_max_180',\n",
" 'settle_to_due_min_180',\n",
" 'settle_to_due_avg_180',\n",
" 'settle_to_due_med_180',\n",
" 'settle_to_due_last_pl_180',\n",
" 'early_settle_co_180',\n",
" 'settle_to_due_max_90',\n",
" 'settle_to_due_min_90',\n",
" 'settle_to_due_avg_90',\n",
" 'settle_to_due_med_90',\n",
" 'settle_to_due_last_pl_90',\n",
" 'early_settle_co_90',\n",
" 'time_from_prev_pl_settle',\n",
" 'td_date',\n",
" 'td_day_of_week',\n",
" 'mo']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.columns.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" NaN 49247\n",
" 0.0 31291\n",
"-999.0 6492\n",
" 1.0 915\n",
" 3.0 726\n",
" ... \n",
" 2321.0 1\n",
" 4641.0 1\n",
" 1081.0 1\n",
" 336.0 1\n",
" 1508.0 1\n",
"Name: pf_delin_max_dpd_12mo, Length: 1647, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_df['pf_delin_max_dpd_12mo'].value_counts(dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ap_co_plo\n",
"train set\n"
]
},
{
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" <th></th>\n",
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" <th>odr</th>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 3274 147.0 0.044899\n",
"(1.0, 16.0] 2596 190.0 0.073190"
]
},
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"text": [
"val set\n"
]
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 4501 250.0 0.055543\n",
"(1.0, 16.0] 3534 290.0 0.082060"
]
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\n",
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"<Figure size 432x288 with 1 Axes>"
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"calibrated_final\n",
"train set\n"
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" obs bads odr\n",
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WuLTaXgI8mJlZ7R+onsrpA84EnmjP6JKkOlreuqnuuV8FPABMA+7KzE0RcT0wmJlrgTuBlRExxNiV/EB17qaIuBfYDOwFrszMN6foZ5EOBm8x6rATYxfekqRS+c5YSSqcoZekwhl6SSqcoZekwtV5w5T0/1pE/Ce/+Ua/XcAg8K3MHD34U0n1eUUvtfYcsBv4l+rPL4GXgN+uXkuHNB+vlFqIiPWZ+XvN9kXEpsw8p1OzSXV4RS+1Njsi5u17UW3Pql6+0ZmRpPq8Ry+1dg3wSEQ8y9gH9fUBV0TEMbzz8dzSIctbN1INETETOJux0D/tL2B1ODH0Ug0R8TtAL+P+Lzgz/61jA0mT4K0bqYWIWAmcDmwE9n0oXwKGXocFr+ilFiJiCzA//Y9FhymfupFa+xlwSqeHkPaXt26k1mYBmyPiCeDX+3Zm5mc7N5JUn6GXWlvR6QGkA+E9ekkqnFf00gQi4pHMvDAiXuPdH2oWQGbm+zo0mjQpXtFLUuF86kZqISJOr94ZS0R8MiK+EBEndHouqS5DL7V2H/BmRJwB3MnYZ938e2dHkuoz9FJrb2XmXuBzwD9m5tXAnA7PJNVm6KXW9kTEUuBS4L+qfTM6OI80KYZeau0y4OPAjZn584joA77T4Zmk2nzqRpqEiDgRODUzf9rpWaS6vKKXWoiIhyPifRHxfuAnwLcj4h86PZdUl6GXWjs+M38J/Anw7cy8APiDDs8k1WbopdamR8Qc4M9455ex0mHD0EutXQ88AAxl5pMR8VvAMx2eSarNX8ZKUuH8UDNpAhHxd5n5tYj4J979oWYAZOYXOjCWNGmGXprYluqfgzQJvXS48NaN1EJEfAT4e6CXdy6OMjPP69hQ0iQYeqmFiNgK/C3wFPDWvv2Z+ULHhpImwVs3Umsjmbm200NI+8sreqmFiLgIWAr8D+/+y8H/o2NDSZPgFb3U2mXA2Yx9YuW+WzcJGHodFgy91NqHMvODnR5C2l++M1Zq7bGImN/pIaT95T16qYWI2AKcDvycsXv0gY9X6jBi6KUWIuK0Zvt9vFKHC0MvSYXzHr0kFc7QS1LhDL3UICJ6I+JnTfb/q0/f6HDkc/RSTZn5l52eQdofXtFLzU2PiLsj4qcRsSYijq7+kvB+gIjYHRE3RsRPIuKxiDi50wNLEzH0UnNnAXdUz8r/Erii4fgxwGOZ+SFgPfBXB3k+qTZDLzW3LTN/UG1/B7iw4fgbvPMXhW9g7LPqpUOSoZeaa3yDSePrPfnOm1DexN936RBm6KXm5kXEx6vtpcAjnRxGOhCGXmpuC3BpRPwUeD/wzx2eR9pvfgSCJBXOK3pJKpyhl6TCGXpJKpyhl6TCGXpJKpyhl6TCGXpJKpyhl6TC/R+EgZ3b6805jgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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"output_type": "display_data"
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"output_type": "stream",
"text": [
"current_dpd\n",
"train set\n"
]
},
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" <td>2410</td>\n",
" <td>88.0</td>\n",
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" <th>(-30.0, -28.0]</th>\n",
" <td>1797</td>\n",
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" <th>(-24.0, -20.0]</th>\n",
" <td>2057</td>\n",
" <td>64.0</td>\n",
" <td>0.031113</td>\n",
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" <tr>\n",
" <th>(-20.0, -16.0]</th>\n",
" <td>1847</td>\n",
" <td>53.0</td>\n",
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" <tr>\n",
" <th>(-16.0, -12.0]</th>\n",
" <td>1940</td>\n",
" <td>65.0</td>\n",
" <td>0.033505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-12.0, -7.0]</th>\n",
" <td>2418</td>\n",
" <td>95.0</td>\n",
" <td>0.039289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-7.0, -4.0]</th>\n",
" <td>1772</td>\n",
" <td>70.0</td>\n",
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" <tr>\n",
" <th>(-4.0, -1.0]</th>\n",
" <td>2182</td>\n",
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" <th>(-1.0, 60.0]</th>\n",
" <td>1831</td>\n",
" <td>124.0</td>\n",
" <td>0.067723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>7126</td>\n",
" <td>269.0</td>\n",
" <td>0.037749</td>\n",
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"</table>\n",
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-356.0, -30.0] 2410 88.0 0.036515\n",
"(-30.0, -28.0] 1797 82.0 0.045632\n",
"(-28.0, -24.0] 2349 70.0 0.029800\n",
"(-24.0, -20.0] 2057 64.0 0.031113\n",
"(-20.0, -16.0] 1847 53.0 0.028695\n",
"(-16.0, -12.0] 1940 65.0 0.033505\n",
"(-12.0, -7.0] 2418 95.0 0.039289\n",
"(-7.0, -4.0] 1772 70.0 0.039503\n",
"(-4.0, -1.0] 2182 103.0 0.047204\n",
"(-1.0, 60.0] 1831 124.0 0.067723\n",
"missing 7126 269.0 0.037749"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
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" <tr>\n",
" <th>bin</th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-356.0, -30.0]</th>\n",
" <td>3087</td>\n",
" <td>108.0</td>\n",
" <td>0.034985</td>\n",
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" <tr>\n",
" <th>(-30.0, -28.0]</th>\n",
" <td>2549</td>\n",
" <td>112.0</td>\n",
" <td>0.043939</td>\n",
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" <tr>\n",
" <th>(-28.0, -24.0]</th>\n",
" <td>3425</td>\n",
" <td>149.0</td>\n",
" <td>0.043504</td>\n",
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" <tr>\n",
" <th>(-24.0, -20.0]</th>\n",
" <td>2648</td>\n",
" <td>97.0</td>\n",
" <td>0.036631</td>\n",
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" <tr>\n",
" <th>(-20.0, -16.0]</th>\n",
" <td>2383</td>\n",
" <td>72.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>(-16.0, -12.0]</th>\n",
" <td>2370</td>\n",
" <td>91.0</td>\n",
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" <tr>\n",
" <th>(-12.0, -7.0]</th>\n",
" <td>3132</td>\n",
" <td>118.0</td>\n",
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" <tr>\n",
" <th>(-7.0, -4.0]</th>\n",
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" <tr>\n",
" <th>(-4.0, -1.0]</th>\n",
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" <tr>\n",
" <th>(-1.0, 60.0]</th>\n",
" <td>2317</td>\n",
" <td>220.0</td>\n",
" <td>0.094950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>9948</td>\n",
" <td>410.0</td>\n",
" <td>0.041214</td>\n",
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-356.0, -30.0] 3087 108.0 0.034985\n",
"(-30.0, -28.0] 2549 112.0 0.043939\n",
"(-28.0, -24.0] 3425 149.0 0.043504\n",
"(-24.0, -20.0] 2648 97.0 0.036631\n",
"(-20.0, -16.0] 2383 72.0 0.030214\n",
"(-16.0, -12.0] 2370 91.0 0.038397\n",
"(-12.0, -7.0] 3132 118.0 0.037676\n",
"(-7.0, -4.0] 2287 102.0 0.044600\n",
"(-4.0, -1.0] 2856 177.0 0.061975\n",
"(-1.0, 60.0] 2317 220.0 0.094950\n",
"missing 9948 410.0 0.041214"
]
},
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},
{
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\n",
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"<Figure size 432x288 with 1 Axes>"
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"delin_max_dpd_3mo\n",
"train set\n"
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(-358.0, -16.0] 2484 131.0 0.052738\n",
"(-16.0, -5.0] 2722 63.0 0.023145\n",
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"(0.0, 1.0] 2140 94.0 0.043925\n",
"(1.0, 2.0] 1102 59.0 0.053539\n",
"(2.0, 6.0] 2523 110.0 0.043599\n",
"(6.0, 60.0] 2372 216.0 0.091062\n",
"missing 2969 148.0 0.049848"
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" }\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>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-358.0, -16.0]</th>\n",
" <td>3426</td>\n",
" <td>176.0</td>\n",
" <td>0.051372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-16.0, -5.0]</th>\n",
" <td>3643</td>\n",
" <td>102.0</td>\n",
" <td>0.027999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-5.0, -2.0]</th>\n",
" <td>4175</td>\n",
" <td>94.0</td>\n",
" <td>0.022515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-2.0, -1.0]</th>\n",
" <td>3474</td>\n",
" <td>80.0</td>\n",
" <td>0.023028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-1.0, 0.0]</th>\n",
" <td>7399</td>\n",
" <td>254.0</td>\n",
" <td>0.034329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.0, 1.0]</th>\n",
" <td>2819</td>\n",
" <td>126.0</td>\n",
" <td>0.044697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>1495</td>\n",
" <td>59.0</td>\n",
" <td>0.039465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 6.0]</th>\n",
" <td>3224</td>\n",
" <td>232.0</td>\n",
" <td>0.071960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(6.0, 60.0]</th>\n",
" <td>3087</td>\n",
" <td>328.0</td>\n",
" <td>0.106252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>4260</td>\n",
" <td>205.0</td>\n",
" <td>0.048122</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-358.0, -16.0] 3426 176.0 0.051372\n",
"(-16.0, -5.0] 3643 102.0 0.027999\n",
"(-5.0, -2.0] 4175 94.0 0.022515\n",
"(-2.0, -1.0] 3474 80.0 0.023028\n",
"(-1.0, 0.0] 7399 254.0 0.034329\n",
"(0.0, 1.0] 2819 126.0 0.044697\n",
"(1.0, 2.0] 1495 59.0 0.039465\n",
"(2.0, 6.0] 3224 232.0 0.071960\n",
"(6.0, 60.0] 3087 328.0 0.106252\n",
"missing 4260 205.0 0.048122"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
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"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"delin_max_dpd_amt_3mo\n",
"train set\n"
]
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" <th></th>\n",
" <th>obs</th>\n",
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" <th>odr</th>\n",
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" <tr>\n",
" <th>bin</th>\n",
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" <td>14</td>\n",
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" <tr>\n",
" <th>(886.0, 299519.0]</th>\n",
" <td>2472</td>\n",
" <td>101.0</td>\n",
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" <tr>\n",
" <th>(299519.0, 698891.0]</th>\n",
" <td>2482</td>\n",
" <td>121.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>(698891.0, 7366229.0]</th>\n",
" <td>2474</td>\n",
" <td>218.0</td>\n",
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" <tr>\n",
" <th>missing</th>\n",
" <td>20287</td>\n",
" <td>643.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 886.0] 14 0.0 0.000000\n",
"(886.0, 299519.0] 2472 101.0 0.040858\n",
"(299519.0, 698891.0] 2482 121.0 0.048751\n",
"(698891.0, 7366229.0] 2474 218.0 0.088116\n",
"missing 20287 643.0 0.031695"
]
},
"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
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"text": [
"val set\n"
]
},
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" <td>2932</td>\n",
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" <td>2850</td>\n",
" <td>191.0</td>\n",
" <td>0.067018</td>\n",
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" <tr>\n",
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" <td>3029</td>\n",
" <td>318.0</td>\n",
" <td>0.104985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>28174</td>\n",
" <td>1012.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 886.0] 17 0.0 0.000000\n",
"(886.0, 299519.0] 2932 135.0 0.046044\n",
"(299519.0, 698891.0] 2850 191.0 0.067018\n",
"(698891.0, 7366229.0] 3029 318.0 0.104985\n",
"missing 28174 1012.0 0.035920"
]
},
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},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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"text": [
"flag_bad_pefindo\n",
"train set\n"
]
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" obs bads odr\n",
"flag_bad_pefindo \n",
"0.0 12696 503.0 0.039619\n",
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" <th>missing</th>\n",
" <td>17317</td>\n",
" <td>663.0</td>\n",
" <td>0.038286</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"flag_bad_pefindo \n",
"0.0 18009 836.0 0.046421\n",
"1.0 1676 157.0 0.093675\n",
"missing 17317 663.0 0.038286"
]
},
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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"flag_good_pefindo\n",
"train set\n"
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"text/plain": [
" obs bads odr\n",
"flag_good_pefindo \n",
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"1.0 6894 258.0 0.037424\n",
"missing 13618 478.0 0.035101"
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"text/plain": [
" obs bads odr\n",
"flag_good_pefindo \n",
"0.0 9829 597.0 0.060739\n",
"1.0 9856 396.0 0.040179\n",
"missing 17317 663.0 0.038286"
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"os_amount\n",
"train set\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 224810.0]</th>\n",
" <td>2061</td>\n",
" <td>61.0</td>\n",
" <td>0.029597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(224810.0, 493030.0]</th>\n",
" <td>2060</td>\n",
" <td>75.0</td>\n",
" <td>0.036408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(493030.0, 758725.0]</th>\n",
" <td>2060</td>\n",
" <td>91.0</td>\n",
" <td>0.044175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(758725.0, 1125206.0]</th>\n",
" <td>2063</td>\n",
" <td>95.0</td>\n",
" <td>0.046049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1125206.0, 1645836.0]</th>\n",
" <td>2058</td>\n",
" <td>68.0</td>\n",
" <td>0.033042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1645836.0, 2253266.0]</th>\n",
" <td>2060</td>\n",
" <td>82.0</td>\n",
" <td>0.039806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2253266.0, 3027367.0]</th>\n",
" <td>2061</td>\n",
" <td>78.0</td>\n",
" <td>0.037846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3027367.0, 4134348.0]</th>\n",
" <td>2060</td>\n",
" <td>98.0</td>\n",
" <td>0.047573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4134348.0, 6151729.0]</th>\n",
" <td>2060</td>\n",
" <td>86.0</td>\n",
" <td>0.041748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(6151729.0, 35305575.0]</th>\n",
" <td>2061</td>\n",
" <td>80.0</td>\n",
" <td>0.038816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>7125</td>\n",
" <td>269.0</td>\n",
" <td>0.037754</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 224810.0] 2061 61.0 0.029597\n",
"(224810.0, 493030.0] 2060 75.0 0.036408\n",
"(493030.0, 758725.0] 2060 91.0 0.044175\n",
"(758725.0, 1125206.0] 2063 95.0 0.046049\n",
"(1125206.0, 1645836.0] 2058 68.0 0.033042\n",
"(1645836.0, 2253266.0] 2060 82.0 0.039806\n",
"(2253266.0, 3027367.0] 2061 78.0 0.037846\n",
"(3027367.0, 4134348.0] 2060 98.0 0.047573\n",
"(4134348.0, 6151729.0] 2060 86.0 0.041748\n",
"(6151729.0, 35305575.0] 2061 80.0 0.038816\n",
"missing 7125 269.0 0.037754"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 224810.0]</th>\n",
" <td>2727</td>\n",
" <td>88.0</td>\n",
" <td>0.032270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(224810.0, 493030.0]</th>\n",
" <td>2930</td>\n",
" <td>99.0</td>\n",
" <td>0.033788</td>\n",
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" <tr>\n",
" <th>(493030.0, 758725.0]</th>\n",
" <td>2776</td>\n",
" <td>141.0</td>\n",
" <td>0.050793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(758725.0, 1125206.0]</th>\n",
" <td>2886</td>\n",
" <td>157.0</td>\n",
" <td>0.054401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1125206.0, 1645836.0]</th>\n",
" <td>2746</td>\n",
" <td>127.0</td>\n",
" <td>0.046249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1645836.0, 2253266.0]</th>\n",
" <td>2667</td>\n",
" <td>134.0</td>\n",
" <td>0.050244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2253266.0, 3027367.0]</th>\n",
" <td>2634</td>\n",
" <td>108.0</td>\n",
" <td>0.041002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3027367.0, 4134348.0]</th>\n",
" <td>2678</td>\n",
" <td>139.0</td>\n",
" <td>0.051904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4134348.0, 6151729.0]</th>\n",
" <td>2575</td>\n",
" <td>139.0</td>\n",
" <td>0.053981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(6151729.0, 35305575.0]</th>\n",
" <td>2436</td>\n",
" <td>115.0</td>\n",
" <td>0.047209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>9947</td>\n",
" <td>409.0</td>\n",
" <td>0.041118</td>\n",
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"</table>\n",
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 224810.0] 2727 88.0 0.032270\n",
"(224810.0, 493030.0] 2930 99.0 0.033788\n",
"(493030.0, 758725.0] 2776 141.0 0.050793\n",
"(758725.0, 1125206.0] 2886 157.0 0.054401\n",
"(1125206.0, 1645836.0] 2746 127.0 0.046249\n",
"(1645836.0, 2253266.0] 2667 134.0 0.050244\n",
"(2253266.0, 3027367.0] 2634 108.0 0.041002\n",
"(3027367.0, 4134348.0] 2678 139.0 0.051904\n",
"(4134348.0, 6151729.0] 2575 139.0 0.053981\n",
"(6151729.0, 35305575.0] 2436 115.0 0.047209\n",
"missing 9947 409.0 0.041118"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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},
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"name": "stdout",
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"text": [
"oth_first_trx_amount\n",
"train set\n"
]
},
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" <tr>\n",
" <th>(194238.0, 342800.0]</th>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
" <th>(1500000.0, 2500000.0]</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>2685</td>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(600.0, 50000.0] 2654 121.0 0.045592\n",
"(50000.0, 97061.0] 2355 93.0 0.039490\n",
"(97061.0, 116935.0] 2504 112.0 0.044728\n",
"(116935.0, 194238.0] 2504 72.0 0.028754\n",
"(194238.0, 342800.0] 2505 79.0 0.031537\n",
"(342800.0, 750506.0] 2505 123.0 0.049102\n",
"(750506.0, 1500000.0] 2542 109.0 0.042880\n",
"(1500000.0, 2500000.0] 2516 78.0 0.031002\n",
"(2500000.0, 4128984.0] 2454 85.0 0.034637\n",
"(4128984.0, 28599000.0] 2505 98.0 0.039122\n",
"missing 2685 113.0 0.042086"
]
},
"metadata": {},
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"text": [
"val set\n"
]
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" </tr>\n",
" <tr>\n",
" <th>(50000.0, 97061.0]</th>\n",
" <td>3122</td>\n",
" <td>132.0</td>\n",
" <td>0.042281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(97061.0, 116935.0]</th>\n",
" <td>3363</td>\n",
" <td>159.0</td>\n",
" <td>0.047279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(116935.0, 194238.0]</th>\n",
" <td>3292</td>\n",
" <td>131.0</td>\n",
" <td>0.039793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(194238.0, 342800.0]</th>\n",
" <td>3283</td>\n",
" <td>131.0</td>\n",
" <td>0.039903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(342800.0, 750506.0]</th>\n",
" <td>3506</td>\n",
" <td>189.0</td>\n",
" <td>0.053908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(750506.0, 1500000.0]</th>\n",
" <td>3692</td>\n",
" <td>207.0</td>\n",
" <td>0.056067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1500000.0, 2500000.0]</th>\n",
" <td>2902</td>\n",
" <td>104.0</td>\n",
" <td>0.035837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2500000.0, 4128984.0]</th>\n",
" <td>3185</td>\n",
" <td>120.0</td>\n",
" <td>0.037677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4128984.0, 28599000.0]</th>\n",
" <td>3156</td>\n",
" <td>127.0</td>\n",
" <td>0.040241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>3834</td>\n",
" <td>175.0</td>\n",
" <td>0.045644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(600.0, 50000.0] 3667 181.0 0.049359\n",
"(50000.0, 97061.0] 3122 132.0 0.042281\n",
"(97061.0, 116935.0] 3363 159.0 0.047279\n",
"(116935.0, 194238.0] 3292 131.0 0.039793\n",
"(194238.0, 342800.0] 3283 131.0 0.039903\n",
"(342800.0, 750506.0] 3506 189.0 0.053908\n",
"(750506.0, 1500000.0] 3692 207.0 0.056067\n",
"(1500000.0, 2500000.0] 2902 104.0 0.035837\n",
"(2500000.0, 4128984.0] 3185 120.0 0.037677\n",
"(4128984.0, 28599000.0] 3156 127.0 0.040241\n",
"missing 3834 175.0 0.045644"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
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"name": "stdout",
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"text": [
"oth_last_rep_channel\n",
"train set\n"
]
},
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" <tr>\n",
" <th>bin</th>\n",
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" <th>(1.0, 3.0]</th>\n",
" <td>21975</td>\n",
" <td>841.0</td>\n",
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" <tr>\n",
" <th>(3.0, 4.0]</th>\n",
" <td>5273</td>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(1.0, 3.0] 21975 841.0 0.038271\n",
"(3.0, 4.0] 5273 214.0 0.040584\n",
"(4.0, 5.0] 136 6.0 0.044118"
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"text": [
"val set\n"
]
},
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(1.0, 3.0] 29633 1338.0 0.045152\n",
"(3.0, 4.0] 6804 287.0 0.042181\n",
"(4.0, 5.0] 159 7.0 0.044025"
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"oth_last_rep_days\n",
"train set\n"
]
},
{
"data": {
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" <tr>\n",
" <th>bin</th>\n",
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" <td>2841</td>\n",
" <td>125.0</td>\n",
" <td>0.043999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>1512</td>\n",
" <td>32.0</td>\n",
" <td>0.021164</td>\n",
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" <th>(2.0, 5.0]</th>\n",
" <td>2523</td>\n",
" <td>58.0</td>\n",
" <td>0.022989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(5.0, 10.0]</th>\n",
" <td>2370</td>\n",
" <td>45.0</td>\n",
" <td>0.018987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(10.0, 18.0]</th>\n",
" <td>2227</td>\n",
" <td>26.0</td>\n",
" <td>0.011675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(18.0, 475.0]</th>\n",
" <td>2128</td>\n",
" <td>50.0</td>\n",
" <td>0.023496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>14128</td>\n",
" <td>747.0</td>\n",
" <td>0.052874</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 2841 125.0 0.043999\n",
"(1.0, 2.0] 1512 32.0 0.021164\n",
"(2.0, 5.0] 2523 58.0 0.022989\n",
"(5.0, 10.0] 2370 45.0 0.018987\n",
"(10.0, 18.0] 2227 26.0 0.011675\n",
"(18.0, 475.0] 2128 50.0 0.023496\n",
"missing 14128 747.0 0.052874"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
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" <td>3497</td>\n",
" <td>167.0</td>\n",
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" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>1920</td>\n",
" <td>59.0</td>\n",
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" <th>(2.0, 5.0]</th>\n",
" <td>4037</td>\n",
" <td>107.0</td>\n",
" <td>0.026505</td>\n",
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" <tr>\n",
" <th>(5.0, 10.0]</th>\n",
" <td>3222</td>\n",
" <td>69.0</td>\n",
" <td>0.021415</td>\n",
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" <tr>\n",
" <th>(10.0, 18.0]</th>\n",
" <td>2503</td>\n",
" <td>55.0</td>\n",
" <td>0.021974</td>\n",
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" <tr>\n",
" <th>(18.0, 475.0]</th>\n",
" <td>2469</td>\n",
" <td>56.0</td>\n",
" <td>0.022681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>19354</td>\n",
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" <td>0.059058</td>\n",
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"</table>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 3497 167.0 0.047755\n",
"(1.0, 2.0] 1920 59.0 0.030729\n",
"(2.0, 5.0] 4037 107.0 0.026505\n",
"(5.0, 10.0] 3222 69.0 0.021415\n",
"(10.0, 18.0] 2503 55.0 0.021974\n",
"(18.0, 475.0] 2469 56.0 0.022681\n",
"missing 19354 1143.0 0.059058"
]
},
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"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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"text": [
"oth_last_rep_dpd\n",
"train set\n"
]
},
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" <td>2394</td>\n",
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" <td>2481</td>\n",
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" <th>(-14.0, -9.0]</th>\n",
" <td>2129</td>\n",
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" <td>2504</td>\n",
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" <tr>\n",
" <th>(-5.0, -2.0]</th>\n",
" <td>3286</td>\n",
" <td>98.0</td>\n",
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" <th>(-2.0, -1.0]</th>\n",
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" <tr>\n",
" <th>missing</th>\n",
" <td>4249</td>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(-359.0, -38.0] 2394 97.0 0.040518\n",
"(-38.0, -27.0] 2689 110.0 0.040907\n",
"(-27.0, -21.0] 2159 78.0 0.036128\n",
"(-21.0, -14.0] 2481 82.0 0.033051\n",
"(-14.0, -9.0] 2129 61.0 0.028652\n",
"(-9.0, -5.0] 2504 67.0 0.026757\n",
"(-5.0, -2.0] 3286 98.0 0.029823\n",
"(-2.0, -1.0] 1484 42.0 0.028302\n",
"(-1.0, 0.0] 2272 97.0 0.042694\n",
"(0.0, 60.0] 2082 126.0 0.060519\n",
"missing 4249 225.0 0.052954"
]
},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
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" <td>3316</td>\n",
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" <th>(-9.0, -5.0]</th>\n",
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" <td>223.0</td>\n",
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" <tr>\n",
" <th>missing</th>\n",
" <td>6016</td>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(-359.0, -38.0] 3162 158.0 0.049968\n",
"(-38.0, -27.0] 3456 150.0 0.043403\n",
"(-27.0, -21.0] 2821 108.0 0.038284\n",
"(-21.0, -14.0] 3316 116.0 0.034982\n",
"(-14.0, -9.0] 2917 101.0 0.034625\n",
"(-9.0, -5.0] 3249 95.0 0.029240\n",
"(-5.0, -2.0] 4182 160.0 0.038259\n",
"(-2.0, -1.0] 2122 90.0 0.042413\n",
"(-1.0, 0.0] 2926 139.0 0.047505\n",
"(0.0, 60.0] 2835 223.0 0.078660\n",
"missing 6016 316.0 0.052527"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"oth_last_trx_amount\n",
"train set\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(11.0, 25000.0]</th>\n",
" <td>4570</td>\n",
" <td>232.0</td>\n",
" <td>0.050766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(25000.0, 31321.0]</th>\n",
" <td>438</td>\n",
" <td>11.0</td>\n",
" <td>0.025114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(31321.0, 53000.0]</th>\n",
" <td>2938</td>\n",
" <td>137.0</td>\n",
" <td>0.046630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(53000.0, 86837.0]</th>\n",
" <td>2071</td>\n",
" <td>68.0</td>\n",
" <td>0.032834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(86837.0, 100000.0]</th>\n",
" <td>2519</td>\n",
" <td>92.0</td>\n",
" <td>0.036522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(100000.0, 153750.0]</th>\n",
" <td>2502</td>\n",
" <td>69.0</td>\n",
" <td>0.027578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(153750.0, 361683.0]</th>\n",
" <td>2492</td>\n",
" <td>70.0</td>\n",
" <td>0.028090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(361683.0, 1162010.0]</th>\n",
" <td>2505</td>\n",
" <td>92.0</td>\n",
" <td>0.036727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1162010.0, 3000000.0]</th>\n",
" <td>2618</td>\n",
" <td>101.0</td>\n",
" <td>0.038579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3000000.0, 28967085.0]</th>\n",
" <td>2391</td>\n",
" <td>97.0</td>\n",
" <td>0.040569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>2685</td>\n",
" <td>114.0</td>\n",
" <td>0.042458</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(11.0, 25000.0] 4570 232.0 0.050766\n",
"(25000.0, 31321.0] 438 11.0 0.025114\n",
"(31321.0, 53000.0] 2938 137.0 0.046630\n",
"(53000.0, 86837.0] 2071 68.0 0.032834\n",
"(86837.0, 100000.0] 2519 92.0 0.036522\n",
"(100000.0, 153750.0] 2502 69.0 0.027578\n",
"(153750.0, 361683.0] 2492 70.0 0.028090\n",
"(361683.0, 1162010.0] 2505 92.0 0.036727\n",
"(1162010.0, 3000000.0] 2618 101.0 0.038579\n",
"(3000000.0, 28967085.0] 2391 97.0 0.040569\n",
"missing 2685 114.0 0.042458"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(11.0, 25000.0]</th>\n",
" <td>5694</td>\n",
" <td>315.0</td>\n",
" <td>0.055321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(25000.0, 31321.0]</th>\n",
" <td>635</td>\n",
" <td>37.0</td>\n",
" <td>0.058268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(31321.0, 53000.0]</th>\n",
" <td>3921</td>\n",
" <td>180.0</td>\n",
" <td>0.045907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(53000.0, 86837.0]</th>\n",
" <td>2957</td>\n",
" <td>123.0</td>\n",
" <td>0.041596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(86837.0, 100000.0]</th>\n",
" <td>3345</td>\n",
" <td>152.0</td>\n",
" <td>0.045441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(100000.0, 153750.0]</th>\n",
" <td>3490</td>\n",
" <td>118.0</td>\n",
" <td>0.033811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(153750.0, 361683.0]</th>\n",
" <td>3289</td>\n",
" <td>99.0</td>\n",
" <td>0.030100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(361683.0, 1162010.0]</th>\n",
" <td>3407</td>\n",
" <td>146.0</td>\n",
" <td>0.042853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1162010.0, 3000000.0]</th>\n",
" <td>3390</td>\n",
" <td>159.0</td>\n",
" <td>0.046903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3000000.0, 28967085.0]</th>\n",
" <td>3039</td>\n",
" <td>152.0</td>\n",
" <td>0.050016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>3835</td>\n",
" <td>175.0</td>\n",
" <td>0.045632</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(11.0, 25000.0] 5694 315.0 0.055321\n",
"(25000.0, 31321.0] 635 37.0 0.058268\n",
"(31321.0, 53000.0] 3921 180.0 0.045907\n",
"(53000.0, 86837.0] 2957 123.0 0.041596\n",
"(86837.0, 100000.0] 3345 152.0 0.045441\n",
"(100000.0, 153750.0] 3490 118.0 0.033811\n",
"(153750.0, 361683.0] 3289 99.0 0.030100\n",
"(361683.0, 1162010.0] 3407 146.0 0.042853\n",
"(1162010.0, 3000000.0] 3390 159.0 0.046903\n",
"(3000000.0, 28967085.0] 3039 152.0 0.050016\n",
"missing 3835 175.0 0.045632"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pf_con_open_count_12mo\n",
"train set\n"
]
},
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" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
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" <td>3344</td>\n",
" <td>136.0</td>\n",
" <td>0.040670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>2045</td>\n",
" <td>93.0</td>\n",
" <td>0.045477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 3.0]</th>\n",
" <td>1103</td>\n",
" <td>63.0</td>\n",
" <td>0.057117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3.0, 5.0]</th>\n",
" <td>1138</td>\n",
" <td>52.0</td>\n",
" <td>0.045694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(5.0, 27.0]</th>\n",
" <td>1000</td>\n",
" <td>65.0</td>\n",
" <td>0.065000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>19099</td>\n",
" <td>674.0</td>\n",
" <td>0.035290</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 3344 136.0 0.040670\n",
"(1.0, 2.0] 2045 93.0 0.045477\n",
"(2.0, 3.0] 1103 63.0 0.057117\n",
"(3.0, 5.0] 1138 52.0 0.045694\n",
"(5.0, 27.0] 1000 65.0 0.065000\n",
"missing 19099 674.0 0.035290"
]
},
"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
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" <td>4526</td>\n",
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" <th>(2.0, 3.0]</th>\n",
" <td>1555</td>\n",
" <td>95.0</td>\n",
" <td>0.061093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3.0, 5.0]</th>\n",
" <td>1673</td>\n",
" <td>105.0</td>\n",
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" <td>1754</td>\n",
" <td>87.0</td>\n",
" <td>0.049601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>24642</td>\n",
" <td>987.0</td>\n",
" <td>0.040054</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 4526 236.0 0.052143\n",
"(1.0, 2.0] 2852 146.0 0.051192\n",
"(2.0, 3.0] 1555 95.0 0.061093\n",
"(3.0, 5.0] 1673 105.0 0.062762\n",
"(5.0, 27.0] 1754 87.0 0.049601\n",
"missing 24642 987.0 0.040054"
]
},
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},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pf_delin_dist_contractcode_30_dpd_3mo\n",
"train set\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
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" <td>734</td>\n",
" <td>66.0</td>\n",
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" <tr>\n",
" <th>missing</th>\n",
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 33.0] 734 66.0 0.089918\n",
"missing 26995 1017.0 0.037674"
]
},
"metadata": {},
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"text": [
"val set\n"
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" obs bads odr\n",
"bin \n",
"(0.0, 33.0] 1178 130.0 0.110357\n",
"missing 35824 1526.0 0.042597"
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"<Figure size 432x288 with 1 Axes>"
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"pf_delin_max_dpd_12mo\n",
"train set\n"
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(-999.0, 0.0] 7595 243.0 0.031995\n",
"(0.0, 3.0] 511 23.0 0.045010\n",
"(3.0, 23.0] 1393 91.0 0.065327\n",
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" 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>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-999.0, 0.0]</th>\n",
" <td>11081</td>\n",
" <td>427.0</td>\n",
" <td>0.038534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.0, 3.0]</th>\n",
" <td>650</td>\n",
" <td>21.0</td>\n",
" <td>0.032308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3.0, 23.0]</th>\n",
" <td>2053</td>\n",
" <td>135.0</td>\n",
" <td>0.065757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(23.0, 97.0]</th>\n",
" <td>1965</td>\n",
" <td>167.0</td>\n",
" <td>0.084987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(97.0, 90717.0]</th>\n",
" <td>1714</td>\n",
" <td>171.0</td>\n",
" <td>0.099767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>19539</td>\n",
" <td>735.0</td>\n",
" <td>0.037617</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-999.0, 0.0] 11081 427.0 0.038534\n",
"(0.0, 3.0] 650 21.0 0.032308\n",
"(3.0, 23.0] 2053 135.0 0.065757\n",
"(23.0, 97.0] 1965 167.0 0.084987\n",
"(97.0, 90717.0] 1714 171.0 0.099767\n",
"missing 19539 735.0 0.037617"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
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"pf_util_creditcard_avg_12mo\n",
"train set\n"
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" obs bads odr\n",
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"(-999.0, 0.0] 492 15.0 0.030488\n",
"(0.0, 1.0] 4646 205.0 0.044124\n",
"(1.0, 2.0] 801 34.0 0.042447\n",
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"(0.0, 1.0] 6499 285.0 0.043853\n",
"(1.0, 2.0] 991 60.0 0.060545\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time_approve_to_pl_hour\n",
"train set\n"
]
},
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
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" <td>1292</td>\n",
" <td>85.0</td>\n",
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" <tr>\n",
" <th>(14.0, 167.0]</th>\n",
" <td>2743</td>\n",
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" <th>(167.0, 649.0]</th>\n",
" <td>2761</td>\n",
" <td>113.0</td>\n",
" <td>0.040927</td>\n",
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" <tr>\n",
" <th>(649.0, 1554.0]</th>\n",
" <td>2771</td>\n",
" <td>114.0</td>\n",
" <td>0.041140</td>\n",
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" <th>(1554.0, 2958.0]</th>\n",
" <td>2774</td>\n",
" <td>116.0</td>\n",
" <td>0.041817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2958.0, 4346.0]</th>\n",
" <td>2772</td>\n",
" <td>95.0</td>\n",
" <td>0.034271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4346.0, 5606.0]</th>\n",
" <td>2773</td>\n",
" <td>74.0</td>\n",
" <td>0.026686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(5606.0, 8022.0]</th>\n",
" <td>2772</td>\n",
" <td>78.0</td>\n",
" <td>0.028139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(8022.0, 12280.0]</th>\n",
" <td>2774</td>\n",
" <td>105.0</td>\n",
" <td>0.037851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(12280.0, 24907.0]</th>\n",
" <td>2772</td>\n",
" <td>58.0</td>\n",
" <td>0.020924</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 14.0] 1292 85.0 0.065789\n",
"(14.0, 167.0] 2743 122.0 0.044477\n",
"(167.0, 649.0] 2761 113.0 0.040927\n",
"(649.0, 1554.0] 2771 114.0 0.041140\n",
"(1554.0, 2958.0] 2774 116.0 0.041817\n",
"(2958.0, 4346.0] 2772 95.0 0.034271\n",
"(4346.0, 5606.0] 2773 74.0 0.026686\n",
"(5606.0, 8022.0] 2772 78.0 0.028139\n",
"(8022.0, 12280.0] 2774 105.0 0.037851\n",
"(12280.0, 24907.0] 2772 58.0 0.020924"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 14.0]</th>\n",
" <td>1949</td>\n",
" <td>125.0</td>\n",
" <td>0.064135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(14.0, 167.0]</th>\n",
" <td>4082</td>\n",
" <td>186.0</td>\n",
" <td>0.045566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(167.0, 649.0]</th>\n",
" <td>3628</td>\n",
" <td>135.0</td>\n",
" <td>0.037211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(649.0, 1554.0]</th>\n",
" <td>3722</td>\n",
" <td>244.0</td>\n",
" <td>0.065556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1554.0, 2958.0]</th>\n",
" <td>3976</td>\n",
" <td>198.0</td>\n",
" <td>0.049799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2958.0, 4346.0]</th>\n",
" <td>3139</td>\n",
" <td>133.0</td>\n",
" <td>0.042370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4346.0, 5606.0]</th>\n",
" <td>3254</td>\n",
" <td>108.0</td>\n",
" <td>0.033190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(5606.0, 8022.0]</th>\n",
" <td>3785</td>\n",
" <td>125.0</td>\n",
" <td>0.033025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(8022.0, 12280.0]</th>\n",
" <td>3687</td>\n",
" <td>110.0</td>\n",
" <td>0.029835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(12280.0, 24907.0]</th>\n",
" <td>3579</td>\n",
" <td>102.0</td>\n",
" <td>0.028500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 14.0] 1949 125.0 0.064135\n",
"(14.0, 167.0] 4082 186.0 0.045566\n",
"(167.0, 649.0] 3628 135.0 0.037211\n",
"(649.0, 1554.0] 3722 244.0 0.065556\n",
"(1554.0, 2958.0] 3976 198.0 0.049799\n",
"(2958.0, 4346.0] 3139 133.0 0.042370\n",
"(4346.0, 5606.0] 3254 108.0 0.033190\n",
"(5606.0, 8022.0] 3785 125.0 0.033025\n",
"(8022.0, 12280.0] 3687 110.0 0.029835\n",
"(12280.0, 24907.0] 3579 102.0 0.028500"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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ICaNGjXpu34GZma1VO4l+NrCLpPGSNgGmADOazpkBHJ63JwPXRERI2gr4KfCFiPi/TgVtZmbtGzTR5z73o0gjZu4ELomI+ZJOkPSOfNrZwEhJC4HPAP1DMI8Cdga+LGle/tqu49+FmZkNqJ0+eiJiJjCzad9xDdvLgUNaPO9rwNfWMUYzM1sHnhlrZlZzTvRmZjXnRG9mVnNO9GZmNedEb2ZWc070ZmY150RvZlZzTvRmZjXnRG9mVnNO9GZmNedEb2ZWc070ZmY150RvZlZzTvRmZjXnRG9mVnNO9GZmNedEb2ZWc070ZmY150RvZlZzTvRmZjXnRG9mVnNO9GZmNddWopc0UdLdkhZKOqbF8WGSLs7Hb5Q0Lu8fKelaSU9K+nZnQzczs3YMmuglDQFOBw4CdgUOk7Rr02lHAI9FxM7AKcDJef9y4MvA5zoWsZmZPSfttOj3AxZGxH0R8TQwDZjUdM4k4Ny8PR04UJIi4o8R8QtSwjczsy5oJ9GPBhY1PO7L+1qeExErgWXAyE4EaGZm66adRK8W++J5nDPwBaSpkuZImrNkyZJ2n2ZmZm1oJ9H3ATs2PB4DLB7oHEkbAyOAR9sNIiLOjIgJETFh1KhR7T7NzMza0E6inw3sImm8pE2AKcCMpnNmAIfn7cnANRHRdovezMyqs/FgJ0TESklHAVcBQ4BzImK+pBOAORExAzgbOF/SQlJLfkr/8yXdD7wA2ETSwcBbImJB578VMzNrZdBEDxARM4GZTfuOa9heDhwywHPHrUN8Zma2jjwz1sys5pzozcxqzonezKzmnOjNzGrOid7MrOac6M3Mas6J3sys5pzozcxqzonezKzmnOjNzGrOid7MrOac6M3Mas6J3sys5pzozcxqzonezKzmnOjNzGrOid7MrOac6M3Mas6J3sys5pzozcxqzonezKzmnOjNzGrOid7MrObaSvSSJkq6W9JCSce0OD5M0sX5+I2SxjUc+0Lef7ekt3YudDMza8egiV7SEOB04CBgV+AwSbs2nXYE8FhE7AycApycn7srMAXYDZgInJH/PzMzK6SdFv1+wMKIuC8ingamAZOazpkEnJu3pwMHSlLePy0i/hwRvwEW5v/PzMwKaSfRjwYWNTzuy/tanhMRK4FlwMg2n2tmZhXauI1z1GJftHlOO89F0lRgan74pKS724hrQIJtgd8PeMJXWoXVeb0QRy/E0CtxrBcx9EocvRBDr8TRCzG0F8eLBzrQTqLvA3ZseDwGWDzAOX2SNgZGAI+2+Vwi4kzgzDZiaYukORExoVP/3/ocRy/E0CtxOIbeiqMXYuiVOKqOoZ2um9nALpLGS9qEdHN1RtM5M4DD8/Zk4JqIiLx/Sh6VMx7YBbipM6GbmVk7Bm3RR8RKSUcBVwFDgHMiYr6kE4A5ETEDOBs4X9JCUkt+Sn7ufEmXAAuAlcDHI+KZir4XMzNroZ2uGyJiJjCzad9xDdvLgUMGeO6JwInrEOPz0bFuoHXUC3H0QgzQG3E4htV6IY5eiAF6I45KY1DqYTEzs7pyCQQzs5pzojczq7m2+uh7maRt2jjtLxHxeIUx/GGwU4DfRcRLq4ohx/GuNk5bnu+5VBXDaW2c9oeIOLaqGHIcXX9NJDWPTmvl0Yj4UFUx9Eockm5r47QlEXFgVTHkOHohX3ymjdP+GBHf69g11/c+eknLSWPz1zabYEhEjK0whlsi4pXrek4H4lgKXM7afxavi4idKozhAeC4QU47JiJeUVUMOY6uvyaS7gU+urZTgNMjYreqYuiVOCTNB942SAwzImKPqmLIcfRCvvgd8J1BYnhfJxsh632LHriznTd0xTG8u0PnrKsrIuIjaztB0gUVx3BKRJy7thMkbV1xDNAbr8mXIuL6tZ0g6SsVx9ArcfxDRDwwSAxHVhwD9Ea+OD8iThgkhs07ecE6tOiH5+Gd63ROh2LZnlTLJ4DFEfFw1de0tfNr0nty90lExGNduHbP5IuS1vtED5ArZe5HwxsauCkKfXOS9gK+Syr98GDePQZ4HDgyIm4uEUeO5eWkqqGNP4sZEXFnoetvTCpb/U7gRQ0xXA6cHRErCsXR9ddE0gjgC8DBwKi8+xHSz+KkKvuBey0OSWOBbwAHkl4DAS8AriF15d1fdQwNsXQ1X+QY3kp6PRpjuDwirqzkeut7opf0FuAM4F7WfEPvTHpD/6xADPNIH01vbNr/auB7EbFn1THk6x0NHEYqJd2Xd48hzVSeFhEnFYjhItIb+dymGA4HtomIQ6uOIcfR9ddE0lWkRHZuRDyU972Q9LN4U0S8ueoYeiUOSb8CTgWm98+Oz2tTHAL8U0S8uuoY8jV7IV+cCrwUOI813yMfBO6NiE91/Jo1SPR3Agc1twhybZ2ZVd/0y9e6NyJ2GeDYwrwgS+Uk3QPs1txqzjWK5g8UY4djuDsiXjZQfFWPPGq4Vtdfk0F+FgMeq2Mcg7weAx6rII5eyBct3wf5k8Y9Vfws6jCOfmNW/1Vs9CAwtFAMV0j6qaRDJb0mfx0q6adAJR/FBvAXUndJsx3ysRIek3SIpFW/W5I2knQoULJPthdekwck/XO+TwCkewb5k9eitTyvjnHMlXSGpFdJelH+epWkM4Cqb3426oV8sVxSqwWY9gUquTdQhxb9F4D3kLor+n9pdyR1V1wSEf9aKI6DWN03LtIv04wqx6y3iGEi8G3Sx9L+n8VY0sfSo6rq/2uKYRxpKck3sjqxbwVcS+qL/U3VMTTE0tXXJI8uOibHsF3e/TCpquvJEfHohhJH/lR5BGu+HouA/ybdu/lz1THkOLqeLyTtTRpeuSWr/+jsCPyB1H00t+PXXN8TPYCkV9D6Db2gq4F1QW5J999o6v9ZzO5G1VBJI0m/Y2tfUMGsoF7JF/k+yaoY+u+fVHKtOiT6XiZpaqSFVTZ4kl5Y5S/zc4ij66+JpL1Ljsbq5Tgk/V1E/KSbMdRdHfroByTp+G7HwNpnvxUjqRfeSGd3O4CsF16Tf+x2AFkvxLFvtwOA3sgXkir5o1vrFr2kt0fEf3c7jl4gaYeI+F234zDrVXXOF7Vu0Zd60SR9UtKOg5/ZPd1M8oWmtjdfcxtJx0n6qJIvSfqJpG+qTAkGJG2Sh8z1P36DpM/mm8TF9FAcL5D0rDpLkiqtb9OuuiZ5qGmLvuR47Xy9ZcAfgV8DFwE/jIglpa7fEMcWwD+TariMAZ7OMX03Ir5fKIbmynwizcr8OkBE/FuhOGYCt5NmX74ib18CvBnYMyImFYjhVuCAiHhM0udJs4VnAq8H5kbEMVXH0CtxSHoPacLUI6RhjB+KiNn52M0RsXfVMeRrdX3mtqRHgR+RckX/+trVXnN9T/SSniC9WLC673Uz4E+kehovKBDDLcA+wJuAQ4F3AHNJL+SPIuKJqmPIcVwOXAZcTRpCtjlpGNmxwIMR8cUCMTxBSiLzWf16/BPpTU5ElCjihaR5EbFXbsn2RcTo5mMFYrgjInbP23OAv4mIp3KyubnqSo29FIfSTOWDIuJ3eQz5ecAXI+JHKlDZtSGOrs/clnQ38B+kWezjgOnARRExq7KLRsR6/ZV/YOcB2zfs+03hGG5uejyUlOwvItXYLhXHrU2PZ+d/NwLuKhTD2PyLezKwWd53Xxd+L24Dts7xLAPG5f0jgQWFYvglsHvevhLYOm8PB+4o+LPoehzA7U2PdyA1hj7Z/P6pOI6713LsnkIx3NywPZb0Kfxm4D7g61Vcc73vo4+ITwD/DlyU+8o3YnULv5Q1RnFExIqImBERh5FeyFL+KGl/SDeWgEdzPH9pjrEqEfHbiJhMSi4/lzS5xHVb+FfgLmA28BHgLEk/J/0BOLVQDB8DLpR0HqnLYo6kc4BfkLuyNqA4nmjsn490z+gA0nj2SuvxN+mFmdur3ov5/fKNSF1XBwGVTBxb77tu+uUX7ihSkaSdIqJVKYCqrv3SiLin1PXWEscewFmkgkl3AB+JiHskjQIOi4h2Vn/qZDybAV8BXhURryt57Xz9IaTf8ZW5m2IvUhdWsRvTOYa3kF6T/un3V0WhypW9EoekPYE/RcS9TfuHAu+JiAsLxTGONWduizRzu7+KZuUztyX9W0S0s8pU565Zl0TfT9IOwCujYOmBFjHsDOxJWuRgg5ud24vyjeqXkrqRiibZpji6OkFJXawF32s2pJnb633XTbOI+F1/kpdUqgzstZK2zdsfIN2MPAi4WNInSsSwltiuKXy9iQ3bW0k6S9Jtkn6ghqJaBeI4o2F7f2AB8P+A2yWtbUm7Tsawd/MXMEPSK/N2EZLGSpom6RHgRmC2pEfyvnGFYtgxX+9/JX0xt+T7j/24RAz5WptI+qCkAyNiKfAWSd+W9PHGmCqOYWNJ/yDpyvzeuFXSFZI+VlUMtWvRN5L026hw7ceG6zSOapgNTIyIpbnrYlaUG13RvACzSK3YuwFKxNE4VE7SWcBDwH8C7wJeHxEHVx1DiziuBT4bETdLegmpeNWEAjH8BZjFmv2ur877IiLeWHUMOY6u14LP90cuJX3vR5BGqb09v09Kjrq5kNR1tRlp9M0WpKGOB5Ly4eEFYig+8me9XzNWA69wL9IIixJWSBodEQ8CT5LG1EN6gw8pFAPA/aQKeF8DniL9DP4XeHvBGBpNiNXDGE+RVPmbaAAv6O8uiYj7cpIr4T3AJ4BvNnzK/E1EvKHQ9fttGxEXN+7ICX+apK8WimFURHw3b39C0vuBGyS9g7KDJ/4qIvbI92weBF4UEc8oraV8a6EY9o5nrwHQB8xSWlOi49b7RA/8DfB+UoJt1L9cWAmfBn4m6VLS+PFrJF2ZY/uvQjEQEe+Q9E7gTOBbETFD0ooYZFHmDttOadKUgBdIUqz+2Fiyq/Dl+ROOgHGSto40YWgjCtUdj4jp+ffgq5I+DHyW8iPCINeCJ7UgG0vzHk65WvBD1bAWa0RcIOkh4CrSfI9SNlIqmbw5qVU/gjQ6bRjl6tE/JukQ4NI8Iq5/MMkhVDTypw6Jfhbpbv6zVrlXmphQuYi4TtJrgPeSakzPJbXmPxERd5WIoSGWyyT9jJRcPgpsUvL6pG6aLfP2ucC2wBKlkqzzCsbRvFJQ/6esbYDjSgUREU8Cn1Zaw/ZcVv9sSvogqbvkKzSV5qVcobmzgFcBq96nEXF1TnjfKBQDpO/3LtIn7S8BP5R0H6lLbVqhGKaQRv6cIal/5M8I0poNU6q4YK376Dd0eUjbXzd8ZLYukiRgy4j4Q7dj2ZBJehFARCyWtBVpRvtvI+KmLsRSZORP7Ubd9BpJxeqeq6l4Fan1urkKF69qiml/SZ9RWpS55HW3kHSCpPmSlklaImmWpA8VjGEzpSX8Pi9pOKllfYGkbygN9ywVxzuVhlUiaZSkcyXdLuliSWMKxVB8pMlAImJxRCzO249HxPTSST7/fk4m/U4cJmmiGiZxdZoTfQcoVUps9TUSKDKUL5tNmvyBUvGqE4FNgc9IKrWk4k0N239PWtpwS+BfJBUp4pVdSJpS/lZSl8VpwAeAN0gqNRv0+8D2wHjgp6S6698ifVT/TqEYAE6M1csFfpvUhXYQcAXl7iGdT5qwdjzpPfG3pNdlT+CCQjEgaY/8B3+RpDPVUMm08Xe34hjeQ+qmmUia5Lkf6XdznqS/quSiVdRV2NC+gGdISeU3DV/9j58uGMcdDdtzgE3z9sbAbYViuKVhezZptAWkm1+3l4ghX68X6v7My/+KNMxUDY+LvB75enc3bM9tFWPJGFocK1JjJl/rF6QEuxXwOdLgiZ3ysVsKxXAbq+tAbUuaoQywB/DLKq7pFn1n3EcqAzu+4eslETGetAhzKX+QtHve/j2paBWkRF/qtd5I0tYNfY9LACLij8DKQjFAD9T96RfpXTwz/9v/uOTNsetyN9ameftgAElvIBV8K6EXaswAbBERV0bqsvkWqUV9paRXU+41EWn4M6RBAtsBRMRtpLLaHVeHUTctSTqXVKr49Ii4o+LLnUqqlPjbFsdKjijoL151K6uLV11PaimU6q4YQRp1JCCU14nNfdIlE+zHSIXMXkaqRf8RSH3UwOmFYpgjaYuIeDIiPtK/U6m4V5HS1dlRpBEm/aPQPi3pj8B/k6caYyUAABQQSURBVLoMSmg10qS/xkwlI00GIEkjImIZQERcK+ndpMlc2xSKYSbpj8v1pC60H+bAtqGi90htR91I2pdUOXK/iDi62/GUoh4potUirs1IpaQrLxq1PmiaX1DyuiOAjSNN/++KUiNNBrj2e0n1jmY17R8LfDki/r5QHG8DdiV1Mf4879sIGBoRHa9gWdtEb72nv3Vb8HovJ40bv7HxupImRsSVpeJoium8iPhgN67dEEPxAm95ktIUUvXQ/8kJ9zXAncCZUWBlp14m6R0RMdAs/3X//9f3RJ9bKF8ADgZG5d2PkJYGO6nbLVlbTYVqD+VrfRL4OCmR7AV8KiIuz8eKLF2nZ5fnEPAGUncFEfGOqmPIcZwREUfm7f2BH5CWmNwZ+IcoUOlVPVBjJscxBPgoqbbMlRHxfw3Hjo2IrxWI4V3Nu0jdiUcCRMSPOn3NOvTRX0J64xwQEQ8BKM3C/BCp76tIBUtL9Ow1Y1cdIr25S/l7YJ+IeFKpQuN0SeMi4t8pd69gDKlq5lmkG30CJpCqaJbUWLTsq8DB0VDgjdRnXLVeqDED8D3SH5ubgNMkXR+ra8O/i1QnqmqXkFb6alxXenNSTaog/QHsqDqMuhkXESf3J3mAiHgoIk6i7OpOzyJpB0nDuhlDF3yddGN6y6avLSj7+zakv7smIu4nrWZ0kKR/o1yin0C6Mf0lYFlEXAc8FRHXR4uSHYWsUeCNckX3+mvMbMnqGjNQtsYMpHt2742IU0klGbaQ9KP8Pi31e/HXpPktN5EWB/ow8PuI+HDjTftOqkOL/gFJ/wycGxEPAyjVPf8Qqws4dcv5wE6SLo2Iz3UriDxBaBlwVoGbcDcDP46IuS3i+GjF1270kKS9ImIepJozkv4OOAeoZlJKkzyU8xRJP8z/Pkx33nNdL/BGb9SYgYbaTxGxEpgq6ThSr0CRT5wRMVtprYxPkAogHk3FQzvr0Ee/NXAMae3J7fLuh0kFm06O1TMCu0KSgF0jYn4XYzgY2AnYs+obgXk449JWIyokbd//x7hqSlP7VzZ+0ms49trGvtlSJP0t8NqI+GLh6764adfiiFihtFjO66roEx4gjq7XmMldRRc034zPjZDvRETRcgySRgOnkEp6v6Sy66zvib5X5IS+H2mURwCLgZu6MYTOzKxRHfroB6RCy7UpFey6l2fX8bhXBYt5qYcKR5mtb1Ru6dEh+X36VUmvbTp2bCXXrHODU9J/lpgAIelO4KB8069x/3jS1Pfm2uhVxVF8iTKzuig1/Fdpic3+kT8fAFaN/Klq6G+tE30pku4FXpFv7jTu3wRYEBE7F4rj7nj2EmX9x+6JiJeWiMOsV7WY27DqEPDGiKh8tStJt0VevzkPNz2DVNzsMNIa0x1fP7cOo276J01NZM3+8ZLT/s8BZkuaxppLtU2h3Ao+0IUlytol6UhgaY6tZHGz5jiuBlaQaiD9ZEONIcdRcjTWQDGU/ln0wtKjxUf+rPd99JI+SBrSdwDp49DmpNmHc/OxykXEv5KWERRpjOxr8vb78rFSpgCTgYcl3aO00PBDpIkgJQtHtSJgfyqYDPIcfRA4FmgeibKhxQCp62AladRHt5T+WaxaerTp6zpWF32r2hxJExt3RMQJpLUBxlVxwfW+60ZpXdhXNbfe87DLGzfU7opuFo7qNUpVASMiuvapphdi6BX+WZS33rfoyeVwW+wvXne8FUnHd+O6EbHGWPZcFqIISW+V9B1JMyRdnrcnDv7MjsYwVtI0SUuAG0lda4/kfeM2lBhyHF0fjdUrP4sNVR366E8Ebpb0M1b3j48l1bj5ateiWu1ZM0S75GzSsM9KSTqVVBnxPNYc+fNJSQdFxKeqjiG7mLROwPsi4pkc2xDS/YpprFn/pc4xQJqh/Thp+G/zaKwLgBKjsXrlZ7FBWu+7bmBVN81bSTdjxeoa7P5oWNhAo3vyhLJ7ImKXQnHcO9C11nasbjHka3V9NFav/Cw2VOt9i16SckIfsF5GPqfoX7RuDGeUtFUPlGVeLmm/FtPa9wWWF4xjrqQzSHMKGkdCHQ7csgHFAL0xGqtXfhY9S9IOwKPhhUeeTdJ1pGXALo+I3zbs34Q0yuNw4NqI+H6FMTzB6vsE/fcFNiMtZRgRUck6kC3iWAlcB1xEelMXT/p5NvJ3SFUK+7sJdgT+ABzZqthZRXFsAhxBqoHU/0lvEWn5vLOreDP1Ygw5jnGkZfzeyOrEvhVwLXBMFFj1q1d+FgNR2aVHB4rhalJNqo4XQaxDoh9OWg/0fcB4Ul/kpqQbzT8jvXDzKo7hP0hlVz/fUEHzN5EWBy9G0u2kRVgOI80r+AUp6V8eEU+t7bkVxPJCGrrSWhUXs/I8Gqs19cjSo7mLs+NFENf7RN8ojyDYllTzu2hrVtI+wDeBHwPfBhZWWY1ugBhWTZ+WtClpIYMpwOtJ9yzeWzKeXiXp77o5UalXYshxvLDbf4R75WdRUk7oxYog1mF45SoRsSIifteNLovcJfGm/PB6YHjpGGgYThoRT0XEJRHxLuAlwFVdiGcNkm7udgzZvt0OgN6IAcrO3B5IsZ+FpBGSTpJ0l6Sl+evOvG+rQjEUL4JYqxZ9r8g3VV4ZBdbibLru5yLiWyWvabY+kXQVqdTAufHspUcPjIjKK1iqC0UQnegrJunNEfHzbsfRDXnY68qIeKLbsQBIOi8qXnil6XqbkLrOFkfE1ZLeSyqPcSdwZkSsKBVLU1w7A3sCd0bEgkLXHAs8EhHLc7fFh4C9SWvq/mep+keDDDUd8FiHYyheBNGJvmIqVPo0X+soYFpE/D6/mc8B9iDV8PhoRNxeIIYXASeRRldsQVoImhzLiaWSm55dpVCkGkjXAETEOwrEcCFpCPNmpEECW5Bq/RxIeu8dXnUMOY5rgUPy78UHgC8DN5DWTD0zIv6jQAx3kG50/knSyaTRJT8mjQQiKlortUUcPwOupvXSo2+OiDet5emdiuELwHtIQ8KbiyBeUkV9LCf6DmiRVFYdolDp0xzH/IjYLW//lFSV8DJJB5CS7GvX+h90JoZrgBMi4jpJ7yJVCzyWNBpou4iYWnUMOY6bSa3Fs0g3u0QagTQFIAoszq1cjlapFO2DwIsi4pncor01cqnaAnHcERG75+3ZwMSIWCppM1JZ3MrjkLQgInbN23OBfRvG9N8aEXtWHUO+Vk8sPSrpFaw51LQPmFHVJ6z1fsJUj+iF0qew5uu5XURcBpCT7paFYhgZqRIgEfEjSV+KiD8Cx0q6q1AMABOAT5EWov58RMyT9FSJBN9go/xxfHNSq34E8CgwjHKLcgOskDQ6Ih4k/Y7+Me//M2mx7hIWSXpjRFwD3E9qwT6Qh3sWkydXHp2/uiYi7iR14RXhRN8Zq0qfNh9Qqq5ZynRJ3wdOAC6T9E+s7ir47dqe2EFLJL2f1EXybtKbun84WbFRXrm1eIqkH+Z/H6b87/vZwF2kZPol4IeS7iPVdRlwJncFPg38TNKlwHzgGklXkhoo/1Uoho8C5ykV+VsGzJN0C7A18JlCMayVpL0joqsjwyQdHxHHd/z/dddNvUj6EPCPpD7QYaQ+wB+TPpYuK3D9scC3gF2BeaTW9O9yy+2AiLi06hgGiOvvgNdExBcLX/dFABGxOA/fexPw2xYlIqqOYwRpzYSXkv7g9ZEm0pX8lNXfZdEYw+z+LpxuU6GlRweJ4e0R8d8d/3+d6NedNHgtnXbOsWpJ2qZUH2wvx5Dj2C4iHunCdbenYZJQ/w1Rq1atJkx10bWSPpFbs6tI2kTSG5XqaBQZYTEQFVrhfoBr39OFax7bsL1rjmGOpPslvaqLMcwtGUO+9jbNX8BNkrbO2yVi2EvSLFItpm+QZpFfL2mWUn2kotSiDr+kbQtdu/j6AG7Rd4Ba19sZTuqbLVJvZzClhnlqdYG3xkVfulHgrbEcxE+Bb0fEFZL2A06NiNdsCDHka/8FeKBp9xhS10mUKNUhaR7wDxFxY9P+VwPfKzjq5g2k+vzDSFUzp/ZPXGp8vSqO4SJSjjiXZ68PsE1EdHx9AN+M7YCIWE5ayf0MdbfeztqGeZYa3fB9eqDAW5MXRcQVABFxk1IdoA0phn8m3Rv4fP9cii68Jps3J3mAiJglqcjw4+wbwFsjYr6kycDPJX0gImZBsRXp9m4xMasPmFXVp18n+g7LE4J+16XLd32YZ0R8QqnA20WS+gu8deNj40vyHz4BYyRtFhF/ysdKDW3shRiIiG9JmkYafbQI+BfKvyZX5E8157HmJKEPAlcWjGOTyJUhI2K6UjmCH0k6hnI/k+LrAzjR10tPDPOMiLmS3gQcRfcKvE1qerwRrLoZ+J0NKAYAIqIPOETS24Gfk7rTSl7/k5IO4tmThE6PsjWhVqihYmdu2R8I/IQ0Uq2EKaT1Ac6Q9BjpZ7EVaUjylCou6D56q5S6VODNBpa7jXaKLi2w0U25AbIkIm5t2j8COCoiTiwcT5H1ATzqpkbypKR1PmcdYxibb073X2sicJCkf1QqBVCEpD0atodKOlbSDElfV5r6XyKGjSR9WNJP8siKuZKmKZWkKEbJeyQdkl+T1wBTJR2ZuwxKxHBU/6gWSTtJukHSY5JulPRXJWIAiIirm5N83r+sVJJXGo33QUkHRsRS4C2Svi3p4x51Y4NSbyyr2CvFqxpHvPw/0s3o/wIOJpVpqLyKpaT/Io12uRqYTFpO8X9J0+8vjwLFxHIcZ5DqumySYxhGWsLvbcDDEfGpAjF0vQ5TvvYLSHWXxgBXRMQPGo6dERFHFoiheLE7J/oa6YVhnuqd4lW3RMQr8/a8HMeK3KItUlBMuahZw+NZEfFqScOAeVFB3fEB4rg9Iv4qtxYfAnaIiKfzJ6xbIqLyFrUaSgBLmh0R+zYcu63E65GvdSlp0Y9ZpPfKCuC9EfHngsMrixe7883YGumRYZ49UbwKGCHpnaTuyWF5NBQREZJKtW5WSNopIn6tNCno6RzDnwvGALAyX3dFTrL9cayU9EyhGHqhDhOkexPvzts/lvQlUu2fystWNyhe7M6Jvqa6OMyzV4pXXQ/0v3lnSdo+Ih5WWk2o1MLYnyfNml5OegNPAZA0ijTKo5SHJG0REU9GxMT+nfln8XSJACLiS0p1mC5idR2mqaRuvfeViCEbJmmj/k+ZEXGipD5Sff4tCsVQvNidu26sEurh4lUl5Y/jI6seVfF8KE1U2rwbNW+6RdI3gJ9FxNVN+ycC/xERuxSKo2ixOyd66yip9wu8qeDyjvnm36iI+HXT/j0i4rYSMbQi6etRuJJn0/XHA68kLZ1XtILmhsiJ3jqqF0b+DEbl6v68BzgVeITUdfOhiJidjxW58ZevdVrzLuADpFmqRMQnC8Tw44g4OG9PIv1crgNeC3y95O+DpLeSRl+tqqJJ+n0tMkM3D/09M1//CuDoSAuiIOmmiOj4LHb30VunTSSNZrgot9qaR/6cUvXIH+iZuj9fBPaJVI9/P+B8SV+MiB9Rrq4KwLtISfVnDdedAswtGMOLG7aPJi2x+Zs8tv5/SDWSKifpVFKX4nmsWVDsk5IOKjHUlDRg4njSyJ+PAr+Q9I78qc/j6G390sWRPyhNLR+o7s/FEbF9gRhubxy6qDRL+CekqoUfKtii3xL4Kmks/ecj4kFJ90WBqpUNMTTOa1ij1do4FLZAHPdExEtb7BdwT4k+eknzImKvhsdvILXwPwCcUcXvhVv0VpkujvyB3qj780T/8EqA3LI/gDTSZLdCMRARTwD/pFRs7oI8Yan0rPg9Jf2B9Id2mHK9mdylV2rdWoDlkvZrcdNzX2B5oRgkaUTkFd8i4lpJ7yZ1eVayPoBb9GYVkbQn6Y/NvU37hwLviYgLuxCTgCOBv46I95e+fot4tgJeERG/KnS9vUkF5bZkddfNjqQZw0dGROXdWZLeC9wXqTRy4/6xwJejguUMneitlnph9E8vxNArcfRCDE3XeiENVTQjV7OsKxc1s7rqheUdeyGGXomjF2Lov+brgBG59T4ceL+kt5W4dr7+EKWlBL8q6bVNx44d6HnrdE236K2O1Bt1f7oew1ri2JTU0NvQfhankhbh2Ri4ilSC4Qrg9aS6P58vEMNZpNIHN5FuwF4fEZ/JxyoZdutEb7XXzdE/vRRDr8TR5dFY84HdSX/oHgRGR6q0OpSU6HcvEMOqIm5Khc3OIP08DgNmVTECyaNurPa6PPqnZ2LolTi6HEPkwnb95Tj6W7p/oVxX9iYNwawkrQ1wHGmFqUrq7TjRm9mG5KeS/pfUbXQWcImkWaSumxsKxTBH0sTGmbgRcYKkxVS0xKS7bsxsgyLpr0kt+1mSdgLeSSqVPL2uhfec6M1sg9FrwzxbXLuSgnseXmlmG5KeGeY5gLOr+E/dojezDUaPDDVdW8G9N0bE5h2/phO9mW2IujXMsxsF9zzqxsw2SF0c5lm84J5b9GZmNeebsWZmBeUKout8znPhRG9mVlbxkT/uujEzK6gbBd6c6M3MuqTUyB8nejOzmnMfvZlZzTnRm5nVnBO9WRNJ4yTd0WL/WZJ27UZMZuvCM2PN2hQRH+12DGbPh1v0Zq1tLOlcSbdJmi5pM0nXSZoAIOlJSSdKulXSLEkdr09i1ilO9GatvQw4M6/t+QfgyKbjm5PW99yTtDLR3xeOz6xtTvRmrS2KiP/L2xcA+zcdfxr4Sd6eC4wrFJfZc+ZEb9Za8wST5scrGlYhegbf77Ie5kRv1trYvLYowGHAL7oZjNm6cKI3a+1O4HBJtwHbAN/pcjxmz5tLIJiZ1Zxb9GZmNedEb2ZWc070ZmY150RvZlZzTvRmZjXnRG9mVnNO9GZmNedEb2ZWc/8fB9p7z37IzowAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
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"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"util_non_pl\n",
"train set\n"
]
},
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" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 14450 502.0 0.03474\n",
"(1.0, 7.0] 25 0.0 0.00000"
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 18437 696.0 0.03775\n",
"(1.0, 7.0] 24 0.0 0.00000"
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gC8CzIy3ezMzeu5pnAUXEEUlLgE1AA7AyIrZLuh3ojYgeYAWwWlIfpU/+3UfXl7QbOA2YIOkq4LPAS8Cm7I9/A/AA8LejumdmZnZMNQMAICI2Ahsr2m4tmx4Crh5m3fZhNntBvhLNzKwefCWwmVmiHABmZolyAJiZJcoBYGaWKAeAmVmiHABmZolyAJiZJcoBYGaWKAeAmVmiHABmZolyAJiZJcoBYGaWKAeAmVmiHABmZolyAJiZJcoBYGaWKAeAmVmiHABmZolyAJiZJSpXAEiaL2mnpD5JS6ssnyhpbbZ8s6T2rL1F0kOSDkn6m4p1LpD0TLbOPZI0GjtkZmb51AwASQ3AMuAKYDZwraTZFd0WAfsjYibwLeDurH0IuAX4L1U2/W1gMTAre81/LztgZmbvTZ4jgLlAX0Tsioi3gDVAV0WfLmBVNr0emCdJEfFGRPycUhC8Q9JU4LSIeCwiAvgecNXx7IiZmY1MngCYBuwpm+/P2qr2iYgjwAGgpcY2+2tsEwBJiyX1SuodHBzMUa6ZmeWRJwCqjc3He+jznvpHxPKI6IyIztbW1mNs0szMRiJPAPQD08vm24CB4fpIagQmA6/V2GZbjW2amVkd5QmALcAsSR2SJgDdQE9Fnx5gYTa9AHgwG9uvKiJ+DRyUdFF29s/1wH0jrt7MzN6zxlodIuKIpCXAJqABWBkR2yXdDvRGRA+wAlgtqY/SJ//uo+tL2g2cBkyQdBXw2YjYAXwV+J/AScD92cvMzMZIzQAAiIiNwMaKtlvLpoeAq4dZt32Y9l7gvLyFmpnZ6PKVwGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiXIAmJklKlcASJovaaekPklLqyyfKGlttnyzpPayZTdn7Tslfa6sfbekZyRtk9Q7GjtjZmb51XwmsKQGYBlwOdAPbJHUkz3Y/ahFwP6ImCmpG7gbuEbSbEoPiD8X+CDwgKQPR8Tb2Xqfjoi9o7g/ZmaWU54jgLlAX0Tsioi3gDVAV0WfLmBVNr0emCdJWfuaiHgzIv4Z6Mu2Z2ZmBcsTANOAPWXz/Vlb1T4RcQQ4ALTUWDeAn0raKmnxyEs3M7PjUXMICFCVtsjZ51jrXhwRA5LOBH4m6fmIePSPfnkpHBYDzJgxI0e5ZmaWR54jgH5getl8GzAwXB9JjcBk4LVjrRsRR3++CmxgmKGhiFgeEZ0R0dna2pqjXDMzyyNPAGwBZknqkDSB0pe6PRV9eoCF2fQC4MGIiKy9OztLqAOYBTwhaZKkUwEkTQI+Czx7/LtjZmZ51RwCiogjkpYAm4AGYGVEbJd0O9AbET3ACmC1pD5Kn/y7s3W3S1oH7ACOADdGxNuSzgI2lL4nphH4QUT8pA77Z2Zmw8jzHQARsRHYWNF2a9n0EHD1MOveCdxZ0bYL+PhIizUzs9HjK4HNzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUbkCQNJ8STsl9UlaWmX5RElrs+WbJbWXLbs5a98p6XN5t2lmZvVVMwAkNQDLgCuA2cC1kmZXdFsE7I+ImcC3gLuzdWcD3cC5wHzgXkkNObdpZmZ1lOcIYC7QFxG7IuItYA3QVdGnC1iVTa8H5klS1r4mIt6MiH8G+rLt5dmmmZnVUWOOPtOAPWXz/cCFw/WJiCOSDgAtWfvjFetOy6ZrbRMASYuBxdnsIUk7c9RsNQimAHuLrqOmv1bRFVgxToz354nj7GqNeQKg2v/AyNlnuPZqRx6V2yw1RiwHlh+rQBs5Sb0R0Vl0HWbV+P05NvIMAfUD08vm24CB4fpIagQmA68dY9082zQzszrKEwBbgFmSOiRNoPSlbk9Fnx5gYTa9AHgwIiJr787OEuoAZgFP5NymmZnVUc0hoGxMfwmwCWgAVkbEdkm3A70R0QOsAFZL6qP0yb87W3e7pHXADuAIcGNEvA1QbZujv3t2DB5Ws/czvz/HgEof1M3MLDW+EtjMLFEOADOzRDkAzMwSlec6ADvBSfpVjm6DETGv7sWYVZD0L7W6AL+OiA+PRT0pcQCkoQH4/DGWC5+Ga8V5MSI+cawOkp4aq2JS4rOAEiDpkoj4+fH2MasHSX8SEbuOt4+NnAMgMZI+AERE7C+6FrNyks6idK+wAAYi4pWCSxr3HAAJkDQD+O/APOB1SkM+pwEPAksjYndx1VnqJM0BvkPpFjIvZ81tlN6rN0TEk0XVNt45ABIg6THgfwDry67EbgCuBv5zRFxUZH2WNknbgK9ExOaK9ouA70bEx4upbPxzACRA0gsRMWuky8zGQo33Z1/2oCmrA58FlIatku6l9NCeo89hmE7pBn4+u8KKdr+kHwPf493vz+uBnxRWVQJ8BJCA7I6riyg9dW0ape8A9gA/AlZExJsFlmeGpCt49/uzH+iJiI2FFjbOOQDMzBLlW0EkTtIXiq7BbDjZI2GtThwA9smiCzA7Bj8Uuo48BGRmligfASRO0uVF12Bpk/SfJE2v3dNGm48AEifp/0XEjKLrsHRJOgC8AbwI/APwjxExWGxVafB1AAmQNNydPgW0jGUtZlXsAi4ALgOuAf5a0lZKYfBPEXGwyOLGMx8BJEDSfuAvgUOVi4C1EXHW2FdlViLpyYj407L5JuAK4FrgsohoLay4cc5HAGl4HPhtRDxSuUDSzgLqMSv3rjN9IuIwpedT9Eg6qZiS0uAjADMrlKQPR8T/LbqOFDkAzMwS5dNAzcwS5QAwM0uUA8DMLFEOgIRJWiXp25LOK7oWs0qSHpB0v29YWD/+Ejhhkj4JzADmRsRNRddjVk7SB4GpwEURsazoesYjB4CZvW9I+gAQEbG/6FpS4CGgBEiaLOkuSc9L2pe9nsvaTi+6PkubpBmS1kgaBDYDWyS9mrW1F1vd+OYASMM6YD9waUS0REQL8GngdeAfC63MDNYCG4B/HRGzsofATwX+N7Cm0MrGOQ8BJUDSzog4Z6TLzMaCpBciYtZIl9nx8xFAGl6S9F8lvXPTN0lnSbqJ0sPhzYq0VdK9ki6U9MHsdaGke4Gnii5uPPMRQAIknQEsBbqAM7PmVyjdcOvuiHitqNrMJE0AFlF6f06jdHO4PcCPgBUR8WaB5Y1rDgAzs0R5CChxkv60di+zYvgisPpyANhXiy7A7Bg+WXQB45mHgMzMEuUngiVC0mRgPqUv2QIYADZFxOuFFmZ2DJIuj4ifFV3HeOUhoARIuh54ErgUOBmYROlCsK3ZMrP3qxVFFzCeeQgoAdlzfy+s/LSfnR66OSI+XExlZiCpZ7hFwGciYtJY1pMSDwGlQZSGfSr9nooHcpsV4FPAXwKHKtoFzB37ctLhAEjDncCTkn7KH678nQFcDvy3wqoyK3kc+G1EPFK5IDt6tTrxEFAisuGez/GHKy37KX0J7NvumiXKAZAASYoa/9B5+pjVg9+fxfFZQGl4SNJ/lDSjvFHSBEmfkbQKWFhQbWZ+fxbERwAJkNQM/DvgOqCD0nMATqL0AeCnwLKI2FZchZayYd6fzUADfn/WlQMgMZKagCnA73wRmL3f+P05thwAZmaJ8ncAZmaJcgCYmSXKAWCWk6R2Sc9Waf87SbOLqMnsePhKYLPjFBH/vugazN4LHwGYjUyjpFWSfiVpvaSTJT0sqRNA0iFJd0p6WtLjks4qumCz4TgAzEbmHGB5RJwP/AtwQ8XyScDjEfFx4FHgy2Ncn1luDgCzkdkTEb/Ipv8XcEnF8reA/5NNbwXax6gusxFzAJiNTOWFM5Xzh8vuWfM2/p7N3sccAGYjM0PSv8mmrwV+XmQxZsfDAWA2Ms8BCyX9CvgA8O2C6zF7z3wrCDOzRPkIwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS9T/B9lXDa6T/1gAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"util_pl\n",
"train set\n"
]
},
{
"data": {
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 27729 1083.0 0.039057"
]
},
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"text": [
"val set\n"
]
},
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"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.0, 1.0] 37002 1656.0 0.044754"
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},
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"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": [
"for feat in old_features:\n",
" print(feat)\n",
" eval_feature(train, val, feat, 10, TARGET_VAR)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NaN 13236\n",
"0.0 6719\n",
"1.0 6073\n",
"2.0 1330\n",
"3.0 273\n",
"4.0 60\n",
"5.0 27\n",
"7.0 5\n",
"6.0 5\n",
"8.0 1\n",
"Name: early_settle_co_180, dtype: int64"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['early_settle_co_180'].value_counts(dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"early_settle_co_180\n",
"train set\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-0.001, 1.0]</th>\n",
" <td>12792</td>\n",
" <td>492.0</td>\n",
" <td>0.038462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>1330</td>\n",
" <td>75.0</td>\n",
" <td>0.056391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 8.0]</th>\n",
" <td>371</td>\n",
" <td>24.0</td>\n",
" <td>0.064690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>13236</td>\n",
" <td>492.0</td>\n",
" <td>0.037171</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 12792 492.0 0.038462\n",
"(1.0, 2.0] 1330 75.0 0.056391\n",
"(2.0, 8.0] 371 24.0 0.064690\n",
"missing 13236 492.0 0.037171"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" }\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>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-0.001, 1.0]</th>\n",
" <td>17646</td>\n",
" <td>835.0</td>\n",
" <td>0.047320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>1746</td>\n",
" <td>116.0</td>\n",
" <td>0.066438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 8.0]</th>\n",
" <td>464</td>\n",
" <td>31.0</td>\n",
" <td>0.066810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>17146</td>\n",
" <td>674.0</td>\n",
" <td>0.039309</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 17646 835.0 0.047320\n",
"(1.0, 2.0] 1746 116.0 0.066438\n",
"(2.0, 8.0] 464 31.0 0.066810\n",
"missing 17146 674.0 0.039309"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pl_series_jumbomini\n",
"train set\n"
]
},
{
"data": {
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" <th>bin</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>(0.999, 2.0]</th>\n",
" <td>20048</td>\n",
" <td>761.0</td>\n",
" <td>0.037959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 3.0]</th>\n",
" <td>4984</td>\n",
" <td>204.0</td>\n",
" <td>0.040931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(3.0, 15.0]</th>\n",
" <td>2697</td>\n",
" <td>118.0</td>\n",
" <td>0.043752</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.999, 2.0] 20048 761.0 0.037959\n",
"(2.0, 3.0] 4984 204.0 0.040931\n",
"(3.0, 15.0] 2697 118.0 0.043752"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.999, 2.0] 25054 1093.0 0.043626\n",
"(2.0, 3.0] 7338 334.0 0.045516\n",
"(3.0, 15.0] 4605 229.0 0.049729"
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
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"pl_trx_co_jumbomini_all_90\n",
"train set\n"
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-0.001, 1.0]</th>\n",
" <td>20237</td>\n",
" <td>673.0</td>\n",
" <td>0.033256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>2525</td>\n",
" <td>113.0</td>\n",
" <td>0.044752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 4.0]</th>\n",
" <td>2777</td>\n",
" <td>140.0</td>\n",
" <td>0.050414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4.0, 175.0]</th>\n",
" <td>2190</td>\n",
" <td>157.0</td>\n",
" <td>0.071689</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 20237 673.0 0.033256\n",
"(1.0, 2.0] 2525 113.0 0.044752\n",
"(2.0, 4.0] 2777 140.0 0.050414\n",
"(4.0, 175.0] 2190 157.0 0.071689"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
"text/html": [
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" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>(1.0, 2.0]</th>\n",
" <td>3278</td>\n",
" <td>153.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>(2.0, 4.0]</th>\n",
" <td>3723</td>\n",
" <td>177.0</td>\n",
" <td>0.047542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4.0, 175.0]</th>\n",
" <td>3785</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 26215 981.0 0.037421\n",
"(1.0, 2.0] 3278 153.0 0.046675\n",
"(2.0, 4.0] 3723 177.0 0.047542\n",
"(4.0, 175.0] 3785 345.0 0.091149"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
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"name": "stdout",
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"text": [
"pl_trx_co_mini_all_90\n",
"train set\n"
]
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"bin \n",
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 33192 1403.0 0.042269\n",
"(1.0, 24.0] 3806 253.0 0.066474"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pl_trx_co_jumbo_4_180\n",
"train set\n"
]
},
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"data": {
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" <thead>\n",
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" <th></th>\n",
" <th>obs</th>\n",
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" <th>odr</th>\n",
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" <tr>\n",
" <th>bin</th>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 25726 976.0 0.037938\n",
"(1.0, 8.0] 2003 107.0 0.053420"
]
},
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"val set\n"
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"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 1.0] 34384 1492.0 0.043392\n",
"(1.0, 8.0] 2618 164.0 0.062643"
]
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{
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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"name": "stdout",
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"text": [
"settle_to_due_min_ever\n",
"train set\n"
]
},
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" <th></th>\n",
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" <th>bads</th>\n",
" <th>odr</th>\n",
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" <tr>\n",
" <th>bin</th>\n",
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" <td>143.0</td>\n",
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" <tr>\n",
" <th>(0.0, 2.0]</th>\n",
" <td>1483</td>\n",
" <td>48.0</td>\n",
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" <th>(2.0, 5.0]</th>\n",
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" <th>(5.0, 10.0]</th>\n",
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" <td>1771</td>\n",
" <td>54.0</td>\n",
" <td>0.030491</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(25.0, 33.0]</th>\n",
" <td>1680</td>\n",
" <td>63.0</td>\n",
" <td>0.037500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(33.0, 55.0]</th>\n",
" <td>1748</td>\n",
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" <tr>\n",
" <th>(84.0, 180.0]</th>\n",
" <td>1707</td>\n",
" <td>108.0</td>\n",
" <td>0.063269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>10265</td>\n",
" <td>390.0</td>\n",
" <td>0.037993</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-55.001, 0.0] 2575 143.0 0.055534\n",
"(0.0, 2.0] 1483 48.0 0.032367\n",
"(2.0, 5.0] 1449 54.0 0.037267\n",
"(5.0, 10.0] 1586 35.0 0.022068\n",
"(10.0, 17.0] 1734 45.0 0.025952\n",
"(17.0, 25.0] 1771 54.0 0.030491\n",
"(25.0, 33.0] 1680 63.0 0.037500\n",
"(33.0, 55.0] 1748 69.0 0.039474\n",
"(55.0, 84.0] 1731 74.0 0.042750\n",
"(84.0, 180.0] 1707 108.0 0.063269\n",
"missing 10265 390.0 0.037993"
]
},
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"name": "stdout",
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"text": [
"val set\n"
]
},
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" 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>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-55.001, 0.0]</th>\n",
" <td>3818</td>\n",
" <td>236.0</td>\n",
" <td>0.061812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.0, 2.0]</th>\n",
" <td>2275</td>\n",
" <td>97.0</td>\n",
" <td>0.042637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 5.0]</th>\n",
" <td>2044</td>\n",
" <td>59.0</td>\n",
" <td>0.028865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(5.0, 10.0]</th>\n",
" <td>2159</td>\n",
" <td>61.0</td>\n",
" <td>0.028254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(10.0, 17.0]</th>\n",
" <td>2452</td>\n",
" <td>74.0</td>\n",
" <td>0.030179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(17.0, 25.0]</th>\n",
" <td>2337</td>\n",
" <td>79.0</td>\n",
" <td>0.033804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(25.0, 33.0]</th>\n",
" <td>2134</td>\n",
" <td>120.0</td>\n",
" <td>0.056232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(33.0, 55.0]</th>\n",
" <td>2034</td>\n",
" <td>86.0</td>\n",
" <td>0.042281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(55.0, 84.0]</th>\n",
" <td>2282</td>\n",
" <td>119.0</td>\n",
" <td>0.052147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(84.0, 180.0]</th>\n",
" <td>2117</td>\n",
" <td>152.0</td>\n",
" <td>0.071800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>13350</td>\n",
" <td>573.0</td>\n",
" <td>0.042921</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-55.001, 0.0] 3818 236.0 0.061812\n",
"(0.0, 2.0] 2275 97.0 0.042637\n",
"(2.0, 5.0] 2044 59.0 0.028865\n",
"(5.0, 10.0] 2159 61.0 0.028254\n",
"(10.0, 17.0] 2452 74.0 0.030179\n",
"(17.0, 25.0] 2337 79.0 0.033804\n",
"(25.0, 33.0] 2134 120.0 0.056232\n",
"(33.0, 55.0] 2034 86.0 0.042281\n",
"(55.0, 84.0] 2282 119.0 0.052147\n",
"(84.0, 180.0] 2117 152.0 0.071800\n",
"missing 13350 573.0 0.042921"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"settle_to_due_last_pl_ever\n",
"train set\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>(-55.001, 0.0]</th>\n",
" <td>1847</td>\n",
" <td>112.0</td>\n",
" <td>0.060639</td>\n",
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" <tr>\n",
" <th>(0.0, 4.0]</th>\n",
" <td>1938</td>\n",
" <td>59.0</td>\n",
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" <tr>\n",
" <th>(4.0, 9.0]</th>\n",
" <td>1491</td>\n",
" <td>28.0</td>\n",
" <td>0.018779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(9.0, 17.0]</th>\n",
" <td>1830</td>\n",
" <td>44.0</td>\n",
" <td>0.024044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(17.0, 25.0]</th>\n",
" <td>1628</td>\n",
" <td>44.0</td>\n",
" <td>0.027027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(25.0, 34.0]</th>\n",
" <td>1758</td>\n",
" <td>61.0</td>\n",
" <td>0.034699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(34.0, 57.0]</th>\n",
" <td>1774</td>\n",
" <td>68.0</td>\n",
" <td>0.038331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(57.0, 81.0]</th>\n",
" <td>1745</td>\n",
" <td>68.0</td>\n",
" <td>0.038968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(81.0, 116.0]</th>\n",
" <td>1721</td>\n",
" <td>93.0</td>\n",
" <td>0.054038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(116.0, 180.0]</th>\n",
" <td>1732</td>\n",
" <td>116.0</td>\n",
" <td>0.066975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>10265</td>\n",
" <td>390.0</td>\n",
" <td>0.037993</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-55.001, 0.0] 1847 112.0 0.060639\n",
"(0.0, 4.0] 1938 59.0 0.030444\n",
"(4.0, 9.0] 1491 28.0 0.018779\n",
"(9.0, 17.0] 1830 44.0 0.024044\n",
"(17.0, 25.0] 1628 44.0 0.027027\n",
"(25.0, 34.0] 1758 61.0 0.034699\n",
"(34.0, 57.0] 1774 68.0 0.038331\n",
"(57.0, 81.0] 1745 68.0 0.038968\n",
"(81.0, 116.0] 1721 93.0 0.054038\n",
"(116.0, 180.0] 1732 116.0 0.066975\n",
"missing 10265 390.0 0.037993"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
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"<style scoped>\n",
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"\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>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-55.001, 0.0]</th>\n",
" <td>2458</td>\n",
" <td>158.0</td>\n",
" <td>0.064280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(0.0, 4.0]</th>\n",
" <td>2840</td>\n",
" <td>108.0</td>\n",
" <td>0.038028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(4.0, 9.0]</th>\n",
" <td>2056</td>\n",
" <td>50.0</td>\n",
" <td>0.024319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(9.0, 17.0]</th>\n",
" <td>2472</td>\n",
" <td>69.0</td>\n",
" <td>0.027913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(17.0, 25.0]</th>\n",
" <td>2213</td>\n",
" <td>83.0</td>\n",
" <td>0.037506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(25.0, 34.0]</th>\n",
" <td>2249</td>\n",
" <td>104.0</td>\n",
" <td>0.046243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(34.0, 57.0]</th>\n",
" <td>2313</td>\n",
" <td>90.0</td>\n",
" <td>0.038911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(57.0, 81.0]</th>\n",
" <td>2448</td>\n",
" <td>110.0</td>\n",
" <td>0.044935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(81.0, 116.0]</th>\n",
" <td>2355</td>\n",
" <td>132.0</td>\n",
" <td>0.056051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(116.0, 180.0]</th>\n",
" <td>2248</td>\n",
" <td>179.0</td>\n",
" <td>0.079626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>13350</td>\n",
" <td>573.0</td>\n",
" <td>0.042921</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-55.001, 0.0] 2458 158.0 0.064280\n",
"(0.0, 4.0] 2840 108.0 0.038028\n",
"(4.0, 9.0] 2056 50.0 0.024319\n",
"(9.0, 17.0] 2472 69.0 0.027913\n",
"(17.0, 25.0] 2213 83.0 0.037506\n",
"(25.0, 34.0] 2249 104.0 0.046243\n",
"(34.0, 57.0] 2313 90.0 0.038911\n",
"(57.0, 81.0] 2448 110.0 0.044935\n",
"(81.0, 116.0] 2355 132.0 0.056051\n",
"(116.0, 180.0] 2248 179.0 0.079626\n",
"missing 13350 573.0 0.042921"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"td_date\n",
"train set\n"
]
},
{
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" <th>(10.0, 13.0]</th>\n",
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" <th>(16.0, 20.0]</th>\n",
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" <tr>\n",
" <th>(20.0, 24.0]</th>\n",
" <td>2490</td>\n",
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],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.999, 3.0] 3619 125.0 0.034540\n",
"(3.0, 7.0] 2437 83.0 0.034058\n",
"(7.0, 10.0] 2566 88.0 0.034295\n",
"(10.0, 13.0] 3077 104.0 0.033799\n",
"(13.0, 16.0] 2354 77.0 0.032710\n",
"(16.0, 20.0] 3305 155.0 0.046899\n",
"(20.0, 24.0] 2490 103.0 0.041365\n",
"(24.0, 26.0] 2407 115.0 0.047777\n",
"(26.0, 28.0] 2961 141.0 0.047619\n",
"(28.0, 30.0] 2513 92.0 0.036610"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
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" </tr>\n",
" <tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(0.999, 3.0] 5131 183.0 0.035666\n",
"(3.0, 7.0] 4140 178.0 0.042995\n",
"(7.0, 10.0] 3872 146.0 0.037707\n",
"(10.0, 13.0] 3096 132.0 0.042636\n",
"(13.0, 16.0] 2858 130.0 0.045486\n",
"(16.0, 20.0] 3559 167.0 0.046923\n",
"(20.0, 24.0] 3140 170.0 0.054140\n",
"(24.0, 26.0] 3167 196.0 0.061888\n",
"(26.0, 28.0] 2471 103.0 0.041684\n",
"(28.0, 30.0] 3462 151.0 0.043616"
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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"text": [
"td_day_of_week\n",
"train set\n"
]
},
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" <td>3161</td>\n",
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"text/plain": [
" obs bads odr\n",
"td_day_of_week \n",
"0 3831 140.0 0.036544\n",
"1 4111 184.0 0.044758\n",
"2 3401 132.0 0.038812\n",
"3 3161 122.0 0.038595\n",
"4 4379 177.0 0.040420\n",
"5 4724 169.0 0.035775\n",
"6 4122 159.0 0.038574"
]
},
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"text": [
"val set\n"
]
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" <td>270.0</td>\n",
" <td>0.041551</td>\n",
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" <td>4714</td>\n",
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" <td>0.040126</td>\n",
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"text/plain": [
" obs bads odr\n",
"td_day_of_week \n",
"0 7290 324.0 0.044444\n",
"1 6254 260.0 0.041573\n",
"2 6498 270.0 0.041551\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time_from_prev_pl_settle\n",
"train set\n"
]
},
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"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>obs</th>\n",
" <th>bads</th>\n",
" <th>odr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-0.001, 2.0]</th>\n",
" <td>10707</td>\n",
" <td>550.0</td>\n",
" <td>0.051368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 8.0]</th>\n",
" <td>1691</td>\n",
" <td>33.0</td>\n",
" <td>0.019515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(8.0, 18.0]</th>\n",
" <td>1629</td>\n",
" <td>22.0</td>\n",
" <td>0.013505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(18.0, 40.7]</th>\n",
" <td>1690</td>\n",
" <td>38.0</td>\n",
" <td>0.022485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(40.7, 439.0]</th>\n",
" <td>1747</td>\n",
" <td>50.0</td>\n",
" <td>0.028620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>10265</td>\n",
" <td>390.0</td>\n",
" <td>0.037993</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 2.0] 10707 550.0 0.051368\n",
"(2.0, 8.0] 1691 33.0 0.019515\n",
"(8.0, 18.0] 1629 22.0 0.013505\n",
"(18.0, 40.7] 1690 38.0 0.022485\n",
"(40.7, 439.0] 1747 50.0 0.028620\n",
"missing 10265 390.0 0.037993"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"val set\n"
]
},
{
"data": {
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" <tr>\n",
" <th>bin</th>\n",
" <th></th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-0.001, 2.0]</th>\n",
" <td>14879</td>\n",
" <td>875.0</td>\n",
" <td>0.058808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(2.0, 8.0]</th>\n",
" <td>2782</td>\n",
" <td>69.0</td>\n",
" <td>0.024802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(8.0, 18.0]</th>\n",
" <td>1611</td>\n",
" <td>37.0</td>\n",
" <td>0.022967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(18.0, 40.7]</th>\n",
" <td>1692</td>\n",
" <td>47.0</td>\n",
" <td>0.027778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(40.7, 439.0]</th>\n",
" <td>2689</td>\n",
" <td>55.0</td>\n",
" <td>0.020454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>13349</td>\n",
" <td>573.0</td>\n",
" <td>0.042925</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bads odr\n",
"bin \n",
"(-0.001, 2.0] 14879 875.0 0.058808\n",
"(2.0, 8.0] 2782 69.0 0.024802\n",
"(8.0, 18.0] 1611 37.0 0.022967\n",
"(18.0, 40.7] 1692 47.0 0.027778\n",
"(40.7, 439.0] 2689 55.0 0.020454\n",
"missing 13349 573.0 0.042925"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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"output_type": "display_data"
},
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"payment_type\n",
"train set\n"
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},
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"text/plain": [
" obs bads odr\n",
"payment_type \n",
"0 23477 977.0 0.041615\n",
"1 4252 106.0 0.024929"
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" obs bads odr\n",
"payment_type \n",
"0 30907 1464.0 0.047368\n",
"1 6095 192.0 0.031501"
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},
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"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": [
"for feat in selected_feats:\n",
" print(feat)\n",
" eval_feature(train, val, feat, 10, TARGET_VAR)"
]
},
{
"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": "3.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}