数据挖掘实践（金融风控-贷款违约预测）：Baseline

数据挖掘实践（金融风控-贷款违约预测）：Baseline

1.相关内容

数据挖掘实践（金融风控-贷款违约预测）（一）：赛题理解

数据挖掘实践（金融风控-贷款违约预测）（二）：数据分析

数据挖掘实践（金融风控-贷款违约预测）（三）：特征工程

数据挖掘实践（金融风控-贷款违约预测）（四）：建模与调参

数据挖掘实践（金融风控-贷款违约预测）（五）：模型融合

2.Baseline代码

import pandas as pd
import os
import gc
import lightgbm as lgb
import xgboost as xgb
from catboost import CatBoostRegressor
from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, log_loss
import matplotlib.pyplot as plt
import time
import warnings
warnings.filterwarnings('ignore')

train = pd.read_csv('train.csv')
testA = pd.read_csv('testA.csv')
print(train.head())

data = pd.concat([train, testA], axis=0, ignore_index=True)

# 数据预处理
print(sorted(data['grade'].unique()))
print(sorted(data['subGrade'].unique()))

print(data['employmentLength'].value_counts(dropna=False).sort_index())

# 首先对employmentLength进行转换到数值
data['employmentLength'].replace(to_replace='10+ years', value='10 years', inplace=True)
data['employmentLength'].replace('< 1 year', '0 years', inplace=True)


def employmentLength_to_int(s):
    if pd.isnull(s):
        return s
    else:
        return np.int8(s.split()[0])

data['employmentLength'] = data['employmentLength'].apply(employmentLength_to_int)
print(data['employmentLength'].value_counts(dropna=False).sort_index())

# 对earliesCreditLine进行预处理
print(data['earliesCreditLine'].sample(5))

data['earliesCreditLine'] = data['earliesCreditLine'].apply(lambda s: int(s[-4:]))
print(data['earliesCreditLine'].describe())

print(data.head())

# 类别特征处理
# 部分类别特征
cate_features = ['grade', 'subGrade', 'employmentTitle', 'homeOwnership', 'verificationStatus', 'purpose', 'postCode', 'regionCode', \
                 'applicationType', 'initialListStatus', 'title', 'policyCode']
for f in cate_features:
    print(f, '类型数：', data[f].nunique())
# 类型数在2之上，又不是高维稀疏的
data = pd.get_dummies(data, columns=['grade', 'subGrade', 'homeOwnership', 'verificationStatus', 'purpose', 'regionCode'], drop_first=True)
# 高维类别特征需要进行转换
for f in ['employmentTitle', 'postCode', 'title']:
    data[f+'_cnts'] = data.groupby([f])['id'].transform('count')
    data[f+'_rank'] = data.groupby([f])['id'].rank(ascending=False).astype(int)
    del data[f]

# 训练数据/测试数据准备
features = [f for f in data.columns if f not in ['id','issueDate','isDefault']]

train = data[data.isDefault.notnull()].reset_index(drop=True)
test = data[data.isDefault.isnull()].reset_index(drop=True)

x_train = train[features]
x_test = test[features]

y_train = train['isDefault']

# 模型训练
def cv_model(clf, train_x, train_y, test_x, clf_name):
    folds = 5
    seed = 2020
    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)

    train = np.zeros(train_x.shape[0])
    test = np.zeros(test_x.shape[0])

    cv_scores = []

    for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):
        print('************************************ {} ************************************'.format(str(i + 1)))
        trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], \
                                     train_y[valid_index]

        if clf_name == "lgb":
            train_matrix = clf.Dataset(trn_x, label=trn_y)
            valid_matrix = clf.Dataset(val_x, label=val_y)

            params = {
    
    
                'boosting_type': 'gbdt',
                'objective': 'binary',
                'metric': 'auc',
                'min_child_weight': 5,
                'num_leaves': 2 ** 5,
                'lambda_l2': 10,
                'feature_fraction': 0.8,
                'bagging_fraction': 0.8,
                'bagging_freq': 4,
                'learning_rate': 0.1,
                'seed': 2020,
                'nthread': 28,
                'n_jobs': 24,
                'silent': True,
                'verbose': -1,
            }

            model = clf.train(params, train_matrix, 50000, valid_sets=[train_matrix, valid_matrix], verbose_eval=200,
                              early_stopping_rounds=200)
            val_pred = model.predict(val_x, num_iteration=model.best_iteration)
            test_pred = model.predict(test_x, num_iteration=model.best_iteration)

            # print(list(sorted(zip(features, model.feature_importance("gain")), key=lambda x: x[1], reverse=True))[:20])

        if clf_name == "xgb":
            train_matrix = clf.DMatrix(trn_x, label=trn_y)
            valid_matrix = clf.DMatrix(val_x, label=val_y)
            test_matrix = clf.DMatrix(test_x)

            params = {
    
    'booster': 'gbtree',
                      'objective': 'binary:logistic',
                      'eval_metric': 'auc',
                      'gamma': 1,
                      'min_child_weight': 1.5,
                      'max_depth': 5,
                      'lambda': 10,
                      'subsample': 0.7,
                      'colsample_bytree': 0.7,
                      'colsample_bylevel': 0.7,
                      'eta': 0.04,
                      'tree_method': 'exact',
                      'seed': 2020,
                      'nthread': 36,
                      "silent": True,
                      }

            watchlist = [(train_matrix, 'train'), (valid_matrix, 'eval')]

            model = clf.train(params, train_matrix, num_boost_round=50000, evals=watchlist, verbose_eval=200,
                              early_stopping_rounds=200)
            val_pred = model.predict(valid_matrix, ntree_limit=model.best_ntree_limit)
            test_pred = model.predict(test_matrix, ntree_limit=model.best_ntree_limit)

        if clf_name == "cat":
            params = {
    
    'learning_rate': 0.05, 'depth': 5, 'l2_leaf_reg': 10, 'bootstrap_type': 'Bernoulli',
                      'od_type': 'Iter', 'od_wait': 50, 'random_seed': 11, 'allow_writing_files': False}

            model = clf(iterations=20000, **params)
            model.fit(trn_x, trn_y, eval_set=(val_x, val_y),
                      cat_features=[], use_best_model=True, verbose=500)

            val_pred = model.predict(val_x)
            test_pred = model.predict(test_x)

        train[valid_index] = val_pred
        test = test_pred / kf.n_splits
        cv_scores.append(roc_auc_score(val_y, val_pred))

        print(cv_scores)

    print("%s_scotrainre_list:" % clf_name, cv_scores)
    print("%s_score_mean:" % clf_name, np.mean(cv_scores))
    print("%s_score_std:" % clf_name, np.std(cv_scores))
    return train, test

def lgb_model(x_train, y_train, x_test):
    lgb_train, lgb_test = cv_model(lgb, x_train, y_train, x_test, "lgb")
    return lgb_train, lgb_test

def xgb_model(x_train, y_train, x_test):
    xgb_train, xgb_test = cv_model(xgb, x_train, y_train, x_test, "xgb")
    return xgb_train, xgb_test

def cat_model(x_train, y_train, x_test):
    cat_train, cat_test = cv_model(CatBoostRegressor, x_train, y_train, x_test, "cat")
    return cat_train, cat_test

lgb_train, lgb_test = lgb_model(x_train, y_train, x_test)

xgb_train, xgb_test = xgb_model(x_train, y_train, x_test)

cat_train, cat_test = cat_model(x_train, y_train, x_test)

rh_test = lgb_test*0.5 + xgb_test*0.5
testA['isDefault'] = rh_test
testA[['id', 'isDefault']].to_csv('test_sub.csv', index=False)

结果：

参考资料

1. 数据挖掘实践（金融风控）
2. 零基础入门数据挖掘 - 贷款违约预测