本文围绕Kaggle的Home Credit Default Risk赛题展开，利用客户申请表等7张表数据构建模型预测客户还款能力。通过数据清洗、特征工程，融合多表信息生成衍生特征，经LightGBM模型训练，最终线上评分为0.78277，为信用记录不足人群的贷款评估提供参考。
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由于信用记录不足或不存在，许多人往往被划分为低信用借贷人而难以获得贷款。 为了确保这些人群获得贷款，Home Credit公司利用替代数据（包括电信和交易信息）预测客户的还款能力。
Home Credit一共提供了7张表，一共218个字段，其中训练集样本约31万(逾期8%)，测试集样本约5万。
application_train/test 客户申请表
包含了
bureau/bureau_balance 由其他金融机构提供给征信中心的客户信用记录历史(月数据)
包含了客户在征信中心的
以时间序列(按行)的形式进行记录.
POS_CASH_balance 客户在Home Credit数据库中POS(point of sales)和现金贷款历史(月数据)
包含了客户
credit_card_balance 客户在Home Credit数据库中信用卡的snapshot历史(月数据)
包含了客户
等情况.
previous_application 客户先前的申请记录
包含了客户所有历史申请记录(申请信息, 申请结果等).
installments_payments 客户先前信用卡的还款记录
包含了客户的还款情况
[1] https://zhuanlan.zhihu.com/p/43541825
[2] https://www.kaggle.com/xucheng/cv-7993-private-score-7996/
[3] https://zhuanlan.zhihu.com/p/40790434
[4] https://www.kaggle.com/tahmidnafi/cse499
[5] https://blog.csdn.net/zhangchen2449/article/details/83338978
| Field | Description |
|---|---|
| SK_ID_CURR | 此次申请的ID |
| TARGET | 申请人本次申请的还款风险：1-风险较高；0-风险较低 |
| NAME_CONTRACT_TYPE | 贷款类型：cash(现金)还是revolving(周转金，一次申请，多次循环提取) |
| CODE_GENDER | 申请人性别 |
| FLAG_OWN_CAR | 申请人是否有车 |
| FLAG_OWN_REALTY | 申请人是否有房 |
| CNT_CHILDREN | 申请人子女个数 |
| AMT_INCOME_TOTAL | 申请人收入状况 |
| AMT_CREDIT | 此次申请的贷款金额 |
| AMT_ANNUITY | 贷款年金 |
| AMT_GOODS_PRICE | 如果是消费贷款，改字段表示商品的实际价格 |
| NAME_TYPE_SUITE | 申请人此次申请的陪同人员 |
| NAME_INCOME_TYPE | 申请人收入类型 |
| NAME_EDUCATION_TYPE | 申请人受教育程度 |
| NAME_FAMILY_STATUS | 申请人婚姻状况 |
| NAME_HOUSING_TYPE | 申请人居住状况（租房，已购房，和父母一起住等） |
| REGION_POPULATION_RELATIVE | 申请人居住地人口密度，已标准化 |
| DAYS_BIRTH | 申请人出生日（距离申请当日的天数，负值） |
| DAYS_EMPLOYED | 申请人当前工作的工作年限（距离申请当日的天数，负值） |
| DAYS_REGISTRATION | 申请人最近一次修改注册信息的时间（距离申请当日的天数，负值） |
| DAYS_ID_PUBLISH | 申请人最近一次修改申请贷款的身份证明文件的时间（距离申请当日的天数，负值） |
| FLAG_MOBIL | 申请人是否提供个人电话（1-yes，0-no） |
| FLAG_EMP_PHONE | 申请人是否提供家庭电话（1-yes，0-no） |
| FLAG_WORK_PHONE | 申请人是否提供工作电话（1-yes，0-no） |
| FLAG_CONT_MOBILE | 申请人个人电话是否能拨通（1-yes，0-no） |
| FLAG_EMAIL | 申请人是否提供电子邮箱（1-yes，0-no） |
| OCCUPATION_TYPE | 申请人职务 |
| REGION_RATING_CLIENT | ben公司对申请人居住区域的评分等级（1,2,3） |
| REGION_RATING_CLIENT_W_CITY | 在考虑所在城市的情况下，ben公司对申请人居住区域的评分等级（1,2,3） |
| WEEKDAY_APPR_PROCESS_START | 申请人发起申请日是星期几 |
| HOUR_APPR_PROCESS_START | 申请人发起申请的hour |
| REG_REGION_NOT_LIVE_REGION | 申请人提供的的永久地址和联系地址是否匹配（1-不匹配，2-匹配，区域级别的） |
| REG_REGION_NOT_WORK_REGION | 申请人提供的的永久地址和工作地址是否匹配（1-不匹配，2-匹配，区域级别的） |
| LIVE_REGION_NOT_WORK_REGION | 申请人提供的的联系地址和工作地址是否匹配（1-不匹配，2-匹配，区域级别的） |
| REG_CITY_NOT_LIVE_CITY | 申请人提供的的永久地址和联系地址是否匹配（1-不匹配，2-匹配，城市级别的） |
| REG_CITY_NOT_WORK_CITY | 申请人提供的的永久地址和工作地址是否匹配（1-不匹配，2-匹配，城市级别的） |
| LIVE_CITY_NOT_WORK_CITY | 申请人提供的的联系地址和工作地址是否匹配（1-不匹配，2-匹配，城市级别的） |
| ORGANIZATION_TYPE | 申请人工作所属组织类型 |
| EXT_SOURCE_1 | 外部数据源1的标准化评分 |
| EXT_SOURCE_2 | 外部数据源2的标准化评分 |
| EXT_SOURCE_3 | 外部数据源3的标准化评分 |
| APARTMENTS_AVG EMERGENCYSTATE_MODE | 申请人居住环境各项指标的标准化评分 |
| OBS_30_CNT_SOCIAL_CIRC LE DEF_60_CNT_SOCIAL_CIRCLE | 这部分字段含义没看懂 |
| DAYS_LAST_PHONE_CHANGE | 申请人最近一次修改手机号码的时间（距离申请当日的天数，负值） |
| FLAG_DOCUMENT_2 FLAG_DOCUMENT_21 | 申请人是否额外提供了文件2,3,4. . .21 |
| AMT_REQ_CREDIT_BUREAU_HOUR | 申请人发起申请前1个小时以内，被查询征信的次数 |
| AMT_REQ_CREDIT_BUREAU_DAY | 申请人发起申请前一天以内，被查询征信的次数 |
| AMT_REQ_CREDIT_BUREAU_WEEK | 申请人发起申请前一周以内，被查询征信的次数 |
| AMT_REQ_CREDIT_BUREAU_MONTH | 申请人发起申请前一个月以内，被查询征信的次数 |
| AMT_REQ_CREDIT_BUREAU_QRT | 申请人发起申请前一个季度以内，被查询征信的次数 |
| AMT_REQ_CREDIT_BUREAU_YEAR | 申请人发起申请前一年以内，被查询征信的次数 |
#!unzip -q -o data/data105246/home_credit_default_risk.zip -d /home/aistudio/data
unzip: cannot find or open data/data104475/IEEE_CIS_Fraud_Detection.zip, data/data104475/IEEE_CIS_Fraud_Detection.zip.zip or data/data104475/IEEE_CIS_Fraud_Detection.zip.ZIP.In [22]
# 安装依赖包!pip install xgboost !pip install lightgbm
Looking in indexes: https://mirror.baidu.com/pypi/simple/ Requirement already satisfied: xgboost in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (1.3.3) Requirement already satisfied: scipy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from xgboost) (1.6.3) Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from xgboost) (1.20.3) Looking in indexes: https://mirror.baidu.com/pypi/simple/ Requirement already satisfied: lightgbm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (3.1.1) Requirement already satisfied: scikit-learn!=0.22.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from lightgbm) (0.24.2) Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from lightgbm) (1.20.3) Requirement already satisfied: scipy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from lightgbm) (1.6.3) Requirement already satisfied: wheel in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from lightgbm) (0.36.2) Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0) Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn!=0.22.0->lightgbm) (0.14.1)In [23]
import osimport gcimport numpy as npimport pandas as pdfrom scipy.stats import kurtosisfrom sklearn.metrics import roc_auc_scorefrom sklearn.preprocessing import MinMaxScalerfrom sklearn.impute import SimpleImputerfrom sklearn.linear_model import LogisticRegressionimport matplotlib.pyplot as pltimport seaborn as snsimport warningsfrom sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFoldimport xgboost as xgbfrom xgboost import XGBClassifier warnings.simplefilter(action='ignore', category=FutureWarning)from lightgbm import LGBMClassifierIn [24]
DATA_DIRECTORY = "./data"df_train = pd.read_csv(os.path.join(DATA_DIRECTORY, 'application_train.csv')) df_test = pd.read_csv(os.path.join(DATA_DIRECTORY, 'application_test.csv')) df = df_train.append(df_test)del df_train, df_test; gc.collect()
39In [25]
df = df[df['AMT_INCOME_TOTAL'] < 20000000] df = df[df['CODE_GENDER'] != 'XNA'] df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace=True) df['DAYS_LAST_PHONE_CHANGE'].replace(0, np.nan, inplace=True)In [26]
def get_age_group(days_birth):
age_years = -days_birth / 365
if age_years < 27: return 1
elif age_years < 40: return 2
elif age_years < 50: return 3
elif age_years < 65: return 4
elif age_years < 99: return 5
else: return 0
In [27]
docs = [f for f in df.columns if 'FLAG_DOC' in f] df['DOCUMENT_COUNT'] = df[docs].sum(axis=1) df['NEW_DOC_KURT'] = df[docs].kurtosis(axis=1) df['AGE_RANGE'] = df['DAYS_BIRTH'].apply(lambda x: get_age_group(x))In [28]
df['EXT_SOURCES_PROD'] = df['EXT_SOURCE_1'] * df['EXT_SOURCE_2'] * df['EXT_SOURCE_3']
df['EXT_SOURCES_WEIGHTED'] = df.EXT_SOURCE_1 * 2 + df.EXT_SOURCE_2 * 1 + df.EXT_SOURCE_3 * 3np.warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')for function_name in ['min', 'max', 'mean', 'nanmedian', 'var']:
feature_name = 'EXT_SOURCES_{}'.format(function_name.upper())
df[feature_name] = eval('np.{}'.format(function_name))(
df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']], axis=1)
In [29]
df['CREDIT_TO_ANNUITY_RATIO'] = df['AMT_CREDIT'] / df['AMT_ANNUITY'] df['CREDIT_TO_GOODS_RATIO'] = df['AMT_CREDIT'] / df['AMT_GOODS_PRICE'] df['ANNUITY_TO_INCOME_RATIO'] = df['AMT_ANNUITY'] / df['AMT_INCOME_TOTAL'] df['CREDIT_TO_INCOME_RATIO'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL'] df['INCOME_TO_EMPLOYED_RATIO'] = df['AMT_INCOME_TOTAL'] / df['DAYS_EMPLOYED'] df['INCOME_TO_BIRTH_RATIO'] = df['AMT_INCOME_TOTAL'] / df['DAYS_BIRTH'] df['EMPLOYED_TO_BIRTH_RATIO'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH'] df['ID_TO_BIRTH_RATIO'] = df['DAYS_ID_PUBLISH'] / df['DAYS_BIRTH'] df['CAR_TO_BIRTH_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_BIRTH'] df['CAR_TO_EMPLOYED_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_EMPLOYED'] df['PHONE_TO_BIRTH_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_BIRTH']In [30]
def do_mean(df, group_cols, counted, agg_name):
gp = df[group_cols + [counted]].groupby(group_cols)[counted].mean().reset_index().rename(
columns={counted: agg_name})
df = df.merge(gp, on=group_cols, how='left') del gp
gc.collect() return df
In [31]
def do_median(df, group_cols, counted, agg_name):
gp = df[group_cols + [counted]].groupby(group_cols)[counted].median().reset_index().rename(
columns={counted: agg_name})
df = df.merge(gp, on=group_cols, how='left') del gp
gc.collect() return df
In [32]
def do_std(df, group_cols, couIn [33]nted, agg_name): gp = df[group_cols + [counted]].groupby(group_cols)[counted].std().reset_index().rename( columns={counted: agg_name}) df = df.merge(gp, on=group_cols, how='left') del gp gc.collect() return df
nted, agg_name):
gp = df[group_cols + [counted]].groupby(group_cols)[counted].std().reset_index().rename(
columns={counted: agg_name})
df = df.merge(gp, on=group_cols, how='left') del gp
gc.collect() return dfdef do_sum(df, group_cols, counted, agg_name):
gp = df[group_cols + [counted]].groupby(group_cols)[counted].sum().reset_index().rename(
columns={counted: agg_name})
df = df.merge(gp, on=group_cols, how='left') del gp
gc.collect() return df
In [34]
group = ['ORGANIZATION_TYPE', 'NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'AGE_RANGE', 'CODE_GENDER'] df = do_median(df, group, 'EXT_SOURCES_MEAN', 'GROUP_EXT_SOURCES_MEDIAN') df = do_std(df, group, 'EXT_SOURCES_MEAN', 'GROUP_EXT_SOURCES_STD') df = do_mean(df, group, 'AMT_INCOME_TOTAL', 'GROUP_INCOME_MEAN') df = do_std(df, group, 'AMT_INCOME_TOTAL', 'GROUP_INCOME_STD') df = do_mean(df, group, 'CREDIT_TO_ANNUITY_RATIO', 'GROUP_CREDIT_TO_ANNUITY_MEAN') df = do_std(df, group, 'CREDIT_TO_ANNUITY_RATIO', 'GROUP_CREDIT_TO_ANNUITY_STD') df = do_mean(df, group, 'AMT_CREDIT', 'GROUP_CREDIT_MEAN') df = do_mean(df, group, 'AMT_ANNUITY', 'GROUP_ANNUITY_MEAN') df = do_std(df, group, 'AMT_ANNUITY', 'GROUP_ANNUITY_STD')In [35]
def label_encoder(df, categorical_columns=None):
if not categorical_columns:
categorical_columns = [col for col in df.columns if df[col].dtype == 'object'] for col in categorical_columns:
df[col], uniques = pd.factorize(df[col]) return df, categorical_columns
In [36]
def drop_application_columns(df):
drop_list = [ 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'HOUR_APPR_PROCESS_START', 'FLAG_EMP_PHONE', 'FLAG_MOBIL', 'FLAG_CONT_MOBILE', 'FLAG_EMAIL', 'FLAG_PHONE', 'FLAG_OWN_REALTY', 'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_WORK_CITY', 'OBS_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_YEAR',
'COMMONAREA_MODE', 'NONLIVINGAREA_MODE', 'ELEVATORS_MODE', 'NONLIVINGAREA_AVG', 'FLOORSMIN_MEDI', 'LANDAREA_MODE', 'NONLIVINGAREA_MEDI', 'LIVINGAPARTMENTS_MODE', 'FLOORSMIN_AVG', 'LANDAREA_AVG', 'FLOORSMIN_MODE', 'LANDAREA_MEDI', 'COMMONAREA_MEDI', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'BASEMENTAREA_AVG', 'BASEMENTAREA_MODE', 'NONLIVINGAPARTMENTS_MEDI', 'BASEMENTAREA_MEDI',
'LIVINGAPARTMENTS_AVG', 'ELEVATORS_AVG', 'YEARS_BUILD_MEDI', 'ENTRANCES_MODE', 'NONLIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE', 'LIVINGAPARTMENTS_MEDI', 'YEARS_BUILD_MODE', 'YEARS_BEGINEXPLUATATION_AVG', 'ELEVATORS_MEDI', 'LIVINGAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MODE', 'NONLIVINGAPARTMENTS_AVG', 'HOUSETYPE_MODE', 'FONDKAPREMONT_MODE', 'EMERGENCYSTATE_MODE'
] for doc_num in [2,4,5,6,7,9,10,11,12,13,14,15,16,17,19,20,21]:
drop_list.append('FLAG_DOCUMENT_{}'.format(doc_num))
df.drop(drop_list, axis=1, inplace=True) return df
In [37]
df, le_encoded_cols = label_encoder(df, None) df = drop_application_columns(df)In [38]
df = pd.get_dummies(df)In [39]
bureau = pd.read_csv(os.path.join(DATA_DIRECTORY, 'bureau.csv'))In [40]
bureau['CREDIT_DURATION'] = -bureau['DAYS_CREDIT'] + bureau['DAYS_CREDIT_ENDDATE'] bureau['ENDDATE_DIF'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT'] bureau['DEBT_PERCENTAGE'] = bureau['AMT_CREDIT_SUM'] / bureau['AMT_CREDIT_SUM_DEBT'] bureau['DEBT_CREDIT_DIFF'] = bureau['AMT_CREDIT_SUM'] - bureau['AMT_CREDIT_SUM_DEBT'] bureau['CREDIT_TO_ANNUITY_RATIO'] = bureau['AMT_CREDIT_SUM'] / bureau['AMT_ANNUITY']In [41]
def one_hot_encoder(df, categorical_columns=None, nan_as_category=True):
original_columns = list(df.columns) if not categorical_columns:
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)
categorical_columns = [c for c in df.columns if c not in original_columns] return df, categorical_columns
In [42]
def group(df_to_agg, prefix, aggregations, aggregate_by= 'SK_ID_CURR'):
agg_df = df_to_agg.groupby(aggregate_by).agg(aggregations)
agg_df.columns = pd.Index(['{}{}_{}'.format(prefix, e[0], e[1].upper()) for e in agg_df.columns.tolist()]) return agg_df.reset_index()
In [43]
def group_and_merge(df_to_agg, df_to_merge, prefix, aggregations, aggregate_by= 'SK_ID_CURR'):
agg_df = group(df_to_agg, prefix, aggregations, aggregate_by= aggregate_by) return df_to_merge.merge(agg_df, how='left', on= aggregate_by)
In [44]
def get_bureau_balance(path, num_rows= None):
bb = pd.read_csv(os.path.join(path, 'bureau_balance.csv'))
bb, categorical_cols = one_hot_encoder(bb, nan_as_category= False) # Calculate rate for each category with decay
bb_processed = bb.groupby('SK_ID_BUREAU')[categorical_cols].mean().reset_index() # Min, Max, Count and mean duration of payments (months)
agg = {'MONTHS_BALANCE': ['min', 'max', 'mean', 'size']}
bb_processed = group_and_merge(bb, bb_processed, '', agg, 'SK_ID_BUREAU') del bb; gc.collect() return bb_processed
In [45]
bureau, categorical_cols = one_hot_encoder(bureau, nan_as_category= False)
bureau = bureau.merge(get_bureau_balance(DATA_DIRECTORY), how='left', on='SK_ID_BUREAU')
bureau['STATUS_12345'] = 0for i in range(1,6):
bureau['STATUS_12345'] += bureau['STATUS_{}'.format(i)]
In [46]
features = ['AMT_CREDIT_MAX_OVERDUE', 'AMT_CREDIT_SUM_OVERDUE', 'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'DEBT_PERCENTAGE', 'DEBT_CREDIT_DIFF', 'STATUS_0', 'STATUS_12345']
agg_length = bureau.groupby('MONTHS_BALANCE_SIZE')[features].mean().reset_index()
agg_length.rename({feat: 'LL_' + feat for feat in features}, axis=1, inplace=True)
bureau = bureau.merge(agg_length, how='left', on='MONTHS_BALANCE_SIZE')del agg_length; gc.collect()
39In [47]
BUREAU_AGG = { 'SK_ID_BUREAU': ['nunique'], 'DAYS_CREDIT': ['min', 'max', 'mean'], 'DAYS_CREDIT_ENDDATE': ['min', 'max'], 'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'], 'AMT_CREDIT_SUM': ['max', 'mean', 'sum'], 'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'], 'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'], 'AMT_ANNUITY': ['mean'], 'DEBT_CREDIT_DIFF': ['mean', 'sum'], 'MONTHS_BALANCE_MEAN': ['mean', 'var'], 'MONTHS_BALANCE_SIZE': ['mean', 'sum'], 'STATUS_0': ['mean'], 'STATUS_1': ['mean'], 'STATUS_12345': ['mean'], 'STATUS_C': ['mean'], 'STATUS_X': ['mean'], 'CREDIT_ACTIVE_Active': ['mean'], 'CREDIT_ACTIVE_Closed': ['mean'], 'CREDIT_ACTIVE_Sold': ['mean'], 'CREDIT_TYPE_Consumer credit': ['mean'], 'CREDIT_TYPE_Credit card': ['mean'], 'CREDIT_TYPE_Car loan': ['mean'], 'CREDIT_TYPE_Mortgage': ['mean'], 'CREDIT_TYPE_Microloan': ['mean'], 'LL_AMT_CREDIT_SUM_OVERDUE': ['mean'], 'LL_DEBT_CREDIT_DIFF': ['mean'], 'LL_STATUS_12345': ['mean'],
}
BUREAU_ACTIVE_AGG = { 'DAYS_CREDIT': ['max', 'mean'], 'DAYS_CREDIT_ENDDATE': ['min', 'max'], 'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'], 'AMT_CREDIT_SUM': ['max', 'sum'], 'AMT_CREDIT_SUM_DEBT': ['mean', 'sum'], 'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean'], 'DAYS_CREDIT_UPDATE': ['min', 'mean'], 'DEBT_PERCENTAGE': ['mean'], 'DEBT_CREDIT_DIFF': ['mean'], 'CREDIT_TO_ANNUITY_RATIO': ['mean'], 'MONTHS_BALANCE_MEAN': ['mean', 'var'], 'MONTHS_BALANCE_SIZE': ['mean', 'sum'],
}
BUREAU_CLOSED_AGG = { 'DAYS_CREDIT': ['max', 'var'], 'DAYS_CREDIT_ENDDATE': ['max'], 'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'], 'AMT_CREDIT_SUM_OVERDUE': ['mean'], 'AMT_CREDIT_SUM': ['max', 'mean', 'sum'], 'AMT_CREDIT_SUM_DEBT': ['max', 'sum'], 'DAYS_CREDIT_UPDATE': ['max'], 'ENDDATE_DIF': ['mean'], 'STATUS_12345': ['mean'],
}
BUREAU_LOAN_TYPE_AGG = { 'DAYS_CREDIT': ['mean', 'max'], 'AMT_CREDIT_MAX_OVERDUE': ['mean', 'max'], 'AMT_CREDIT_SUM': ['mean', 'max'], 'AMT_CREDIT_SUM_DEBT': ['mean', 'max'], 'DEBT_PERCENTAGE': ['mean'], 'DEBT_CREDIT_DIFF': ['mean'], 'DAYS_CREDIT_ENDDATE': ['max'],
}
BUREAU_TIME_AGG = { 'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'], 'AMT_CREDIT_SUM_OVERDUE': ['mean'], 'AMT_CREDIT_SUM': ['max', 'sum'], 'AMT_CREDIT_SUM_DEBT': ['mean', 'sum'], 'DEBT_PERCENTAGE': ['mean'], 'DEBT_CREDIT_DIFF': ['mean'], 'STATUS_0': ['mean'], 'STATUS_12345': ['mean'],
}
In [48]
agg_bureau = group(bureau, 'BUREAU_', BUREAU_AGG)
active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]
agg_bureau = group_and_merge(active,agg_bureau,'BUREAU_ACTIVE_',BUREAU_ACTIVE_AGG)
closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]
agg_bureau = group_and_merge(closed,agg_bureau,'BUREAU_CLOSED_',BUREAU_CLOSED_AGG)del active, closed; gc.collect()for credit_type in ['Consumer credit', 'Credit card', 'Mortgage', 'Car loan', 'Microloan']:
type_df = bureau[bureau['CREDIT_TYPE_' + credit_type] == 1]
prefix = 'BUREAU_' + credit_type.split(' ')[0].upper() + '_'
agg_bureau = group_and_merge(type_df, agg_bureau, prefix, BUREAU_LOAN_TYPE_AGG) del type_df; gc.collect()for time_frame in [6, 12]:
prefix = "BUREAU_LAST{}M_".format(time_frame)
time_frame_df = bureau[bureau['DAYS_CREDIT'] >= -30*time_frame]
agg_bureau = group_and_merge(time_frame_df, agg_bureau, prefix, BUREAU_TIME_AGG) del time_frame_df; gc.collect()
In [49]
sort_bureau = bureau.sort_values(by=['DAYS_CREDIT'])
gr = sort_bureau.groupby('SK_ID_CURR')['AMT_CREDIT_MAX_OVERDUE'].last().reset_index()
gr.rename({'AMT_CREDIT_MAX_OVERDUE': 'BUREAU_LAST_LOAN_MAX_OVERDUE'}, inplace=True)
agg_bureau = agg_bureau.merge(gr, on='SK_ID_CURR', how='left')
agg_bureau['BUREAU_DEBT_OVER_CREDIT'] = \
agg_bureau['BUREAU_AMT_CREDIT_SUM_DEBT_SUM']/agg_bureau['BUREAU_AMT_CREDIT_SUM_SUM']
agg_bureau['BUREAU_ACTIVE_DEBT_OVER_CREDIT'] = \
agg_bureau['BUREAU_ACTIVE_AMT_CREDIT_SUM_DEBT_SUM']/agg_bureau['BUREAU_ACTIVE_AMT_CREDIT_SUM_SUM']
In [50]
df = pd.merge(df, agg_bureau, on='SK_ID_CURR', how='left')del agg_bureau, bureau gc.collect()
39In [51]
prev = pd.read_csv(os.path.join(DATA_DIRECTORY, 'previous_application.csv')) pay = pd.read_csv(os.path.join(DATA_DIRECTORY, 'installments_payments.csv'))In [52]
PREVIOUS_AGG = { 'SK_ID_PREV': ['nunique'], 'AMT_ANNUITY': ['min', 'max', 'mean'], 'AMT_DOWN_PAYMENT': ['max', 'mean'], 'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'], 'RATE_DOWN_PAYMENT': ['max', 'mean'], 'DAYS_DECISION': ['min', 'max', 'mean'], 'CNT_PAYMENT': ['max', 'mean'], 'DAYS_TERMINATION': ['max'], # Engineered features
'CREDIT_TO_ANNUITY_RATIO': ['mean', 'max'], 'APPLICATION_CREDIT_DIFF': ['min', 'max', 'mean'], 'APPLICATION_CREDIT_RATIO': ['min', 'max', 'mean', 'var'], 'DOWN_PAYMENT_TO_CREDIT': ['mean'],
}
PREVIOUS_ACTIVE_AGG = { 'SK_ID_PREV': ['nunique'], 'SIMPLE_INTERESTS': ['mean'], 'AMT_ANNUITY': ['max', 'sum'], 'AMT_APPLICATION': ['max', 'mean'], 'AMT_CREDIT': ['sum'], 'AMT_DOWN_PAYMENT': ['max', 'mean'], 'DAYS_DECISION': ['min', 'mean'], 'CNT_PAYMENT': ['mean', 'sum'], 'DAYS_LAST_DUE_1ST_VERSION': ['min', 'max', 'mean'], # Engineered features
'AMT_PAYMENT': ['sum'], 'INSTALMENT_PAYMENT_DIFF': ['mean', 'max'], 'REMAINING_DEBT': ['max', 'mean', 'sum'], 'REPAYMENT_RATIO': ['mean'],
}
PREVIOUS_LATE_PAYMENTS_AGG = { 'DAYS_DECISION': ['min', 'max', 'mean'], 'DAYS_LAST_DUE_1ST_VERSION': ['min', 'max', 'mean'], # Engineered features
'APPLICATION_CREDIT_DIFF': ['min'], 'NAME_CONTRACT_TYPE_Consumer loans': ['mean'], 'NAME_CONTRACT_TYPE_Cash loans': ['mean'], 'NAME_CONTRACT_TYPE_Revolving loans': ['mean'],
}
PREVIOUS_LOAN_TYPE_AGG = { 'AMT_CREDIT': ['sum'], 'AMT_ANNUITY': ['mean', 'max'], 'SIMPLE_INTERESTS': ['min', 'mean', 'max', 'var'], 'APPLICATION_CREDIT_DIFF': ['min', 'var'], 'APPLICATION_CREDIT_RATIO': ['min', 'max', 'mean'], 'DAYS_DECISION': ['max'], 'DAYS_LAST_DUE_1ST_VERSION': ['max', 'mean'], 'CNT_PAYMENT': ['mean'],
}
PREVIOUS_TIME_AGG = { 'AMT_CREDIT': ['sum'], 'AMT_ANNUITY': ['mean', 'max'], 'SIMPLE_INTERESTS': ['mean', 'max'], 'DAYS_DECISION': ['min', 'mean'], 'DAYS_LAST_DUE_1ST_VERSION': ['min', 'max', 'mean'], # Engineered features
'APPLICATION_CREDIT_DIFF': ['min'], 'APPLICATION_CREDIT_RATIO': ['min', 'max', 'mean'], 'NAME_CONTRACT_TYPE_Consumer loans': ['mean'], 'NAME_CONTRACT_TYPE_Cash loans': ['mean'], 'NAME_CONTRACT_TYPE_Revolving loans': ['mean'],
}
PREVIOUS_APPROVED_AGG = { 'SK_ID_PREV': ['nunique'], 'AMT_ANNUITY': ['min', 'max', 'mean'], 'AMT_CREDIT': ['min', 'max', 'mean'], 'AMT_DOWN_PAYMENT': ['max'], 'AMT_GOODS_PRICE': ['max'], 'HOUR_APPR_PROCESS_START': ['min', 'max'], 'DAYS_DECISION': ['min', 'mean'], 'CNT_PAYMENT': ['max', 'mean'], 'DAYS_TERMINATION': ['mean'], # Engineered features
'CREDIT_TO_ANNUITY_RATIO': ['mean', 'max'], 'APPLICATION_CREDIT_DIFF': ['max'], 'APPLICATION_CREDIT_RATIO': ['min', 'max', 'mean'], # The following features are only for approved applications
'DAYS_FIRST_DRAWING': ['max', 'mean'], 'DAYS_FIRST_DUE': ['min', 'mean'], 'DAYS_LAST_DUE_1ST_VERSION': ['min', 'max', 'mean'], 'DAYS_LAST_DUE': ['max', 'mean'], 'DAYS_LAST_DUE_DIFF': ['min', 'max', 'mean'], 'SIMPLE_INTERESTS': ['min', 'max', 'mean'],
}
PREVIOUS_REFUSED_AGG = { 'AMT_APPLICATION': ['max', 'mean'], 'AMT_CREDIT': ['min', 'max'], 'DAYS_DECISION': ['min', 'max', 'mean'], 'CNT_PAYMENT': ['max', 'mean'], # Engineered features
'APPLICATION_CREDIT_DIFF': ['min', 'max', 'mean', 'var'], 'APPLICATION_CREDIT_RATIO': ['min', 'mean'], 'NAME_CONTRACT_TYPE_Consumer loans': ['mean'], 'NAME_CONTRACT_TYPE_Cash loans': ['mean'], 'NAME_CONTRACT_TYPE_Revolving loans': ['mean'],
}
In [53]
ohe_columns = [ 'NAME_CONTRACT_STATUS', 'NAME_CONTRACT_TYPE', 'CHANNEL_TYPE', 'NAME_TYPE_SUITE', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION', 'NAME_PRODUCT_TYPE', 'NAME_CLIENT_TYPE'] prev, categorical_cols = one_hot_encoder(prev, ohe_columns, nan_as_category= False)In [54]
prev['APPLICATION_CREDIT_DIFF'] = prev['AMT_APPLICATION'] - prev['AMT_CREDIT'] prev['APPLICATION_CREDIT_RATIO'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT'] prev['CREDIT_TO_ANNUITY_RATIO'] = prev['AMT_CREDIT']/prev['AMT_ANNUITY'] prev['DOWN_PAYMENT_TO_CREDIT'] = prev['AMT_DOWN_PAYMENT'] / prev['AMT_CREDIT'] total_payment = prev['AMT_ANNUITY'] * prev['CNT_PAYMENT'] prev['SIMPLE_INTERESTS'] = (total_payment/prev['AMT_CREDIT'] - 1)/prev['CNT_PAYMENT']In [55]
approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
active_df = approved[approved['DAYS_LAST_DUE'] == 365243]
active_pay = pay[pay['SK_ID_PREV'].isin(active_df['SK_ID_PREV'])]
active_pay_agg = active_pay.groupby('SK_ID_PREV')[['AMT_INSTALMENT', 'AMT_PAYMENT']].sum()
active_pay_agg.reset_index(inplace= True)
active_pay_agg['INSTALMENT_PAYMENT_DIFF'] = active_pay_agg['AMT_INSTALMENT'] - active_pay_agg['AMT_PAYMENT']
active_df = active_df.merge(active_pay_agg, on= 'SK_ID_PREV', how= 'left')
active_df['REMAINING_DEBT'] = active_df['AMT_CREDIT'] - active_df['AMT_PAYMENT']
active_df['REPAYMENT_RATIO'] = active_df['AMT_PAYMENT'] / active_df['AMT_CREDIT']
active_agg_df = group(active_df, 'PREV_ACTIVE_', PREVIOUS_ACTIVE_AGG)
active_agg_df['TOTAL_REPAYMENT_RATIO'] = active_agg_df['PREV_ACTIVE_AMT_PAYMENT_SUM']/\
active_agg_df['PREV_ACTIVE_AMT_CREDIT_SUM']del active_pay, active_pay_agg, active_df; gc.collect()
0In [56]
prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True) prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True) prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True) prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True) prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)In [57]
prev['DAYS_LAST_DUE_DIFF'] = prev['DAYS_LAST_DUE_1ST_VERSION'] - prev['DAYS_LAST_DUE'] approved['DAYS_LAST_DUE_DIFF'] = approved['DAYS_LAST_DUE_1ST_VERSION'] - approved['DAYS_LAST_DUE']In [58]
categorical_agg = {key: ['mean'] for key in categorical_cols}
In [59]
agg_prev = group(prev, 'PREV_', {**PREVIOUS_AGG, **categorical_agg})
agg_prev = agg_prev.merge(active_agg_df, how='left', on='SK_ID_CURR')del active_agg_df; gc.collect()
0In [60]
agg_prev = group_and_merge(approved, agg_prev, 'APPROVED_', PREVIOUS_APPROVED_AGG) refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1] agg_prev = group_and_merge(refused, agg_prev, 'REFUSED_', PREVIOUS_REFUSED_AGG)del approved, refused; gc.collect()
0In [61]
for loan_type in ['Consumer loans', 'Cash loans']:
type_df = prev[prev['NAME_CONTRACT_TYPE_{}'.format(loan_type)] == 1]
prefix = 'PREV_' + loan_type.split(" ")[0] + '_'
agg_prev = group_and_merge(type_df, agg_prev, prefix, PREVIOUS_LOAN_TYPE_AGG) del type_df; gc.collect()
In [62]
pay['LATE_PAYMENT'] = pay['DAYS_ENTRY_PAYMENT'] - pay['DAYS_INSTALMENT'] pay['LATE_PAYMENT'] = pay['LATE_PAYMENT'].apply(lambda x: 1 if x > 0 else 0) dpd_id = pay[pay['LATE_PAYMENT'] > 0]['SK_ID_PREV'].unique()In [63]
agg_dpd = group_and_merge(prev[prev['SK_ID_PREV'].isin(dpd_id)], agg_prev, 'PREV_LATE_', PREVIOUS_LATE_PAYMENTS_AGG)del agg_dpd, dpd_id; gc.collect()
0In [64]
for time_frame in [12, 24]:
time_frame_df = prev[prev['DAYS_DECISION'] >= -30*time_frame]
prefix = 'PREV_LAST{}M_'.format(time_frame)
agg_prev = group_and_merge(time_frame_df, agg_prev, prefix, PREVIOUS_TIME_AGG) del time_frame_df; gc.collect()del prev; gc.collect()
0In [65]
df = pd.merge(df, agg_prev, on='SK_ID_CURR', how='left')In [66]
train = df[df['TARGET'].notnull()] test = df[df['TARGET'].isnull()]del df gc.collect()
98In [67]
labels = train['TARGET'] test_lebels=test['TARGET'] train = train.drop(columns=['TARGET']) test = test.drop(columns=['TARGET'])In [68]
feature = list(train.columns) train.replace([np.inf, -np.inf], np.nan, inplace=True) test.replace([np.inf, -np.inf], np.nan, inplace=True) test_df = test.copy() train_df = train.copy() train_df['TARGET'] = labels test_df['TARGET'] = test_lebelsIn [69]
imputer = SimpleImputer(strategy = 'median') imputer.fit(train) imputer.fit(test) train = imputer.transform(train) test = imputer.transform(test)In [70]
scaler = MinMaxScaler(feature_range = (0, 1)) scaler.fit(train) scaler.fit(test) train = scaler.transform(train) test = scaler.transform(test)In [71]
from lightgbm import LGBMClassifier lgbmc = LGBMClassifier() lgbmc.fit(train, labels)
LGBMClassifier()In [72]
lgbm_pred = lgbmc.predict_proba(test)[:, 1]In [74]
submit = test_df[['SK_ID_CURR']] submit['TARGET'] = lgbm_predIn [75]
submit.to_csv('lgbm.csv', index = False)
| 数据集 | Home Credit Default Risk |
|---|---|
| 线上评分 | 0.78277 |
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