课程大作业基于python实现的金融风控贷款违约预测源码(可参考借鉴).zip

#!/usr/bin/python3.7 # -*- coding: utf-8 -*- # @Time : 2020/10/20 15:12 # @Author : dly # @File : Main.py # @Desc : import pandas as pd import numpy as np import os import seaborn as sns import matplotlib.pyplot as plt from sklearn import metrics from sklearn.model_selection import KFold, train_test_split, cross_val_score import lightgbm as lgb from bayes_opt import BayesianOptimization # 调整数据类型，减少内存占用 def reduce_mem_usage(df): start_mem = df.memory_usage().sum() / 1024 ** 2 print('Memory usage of df is {:.2f} MB'.format(start_mem)) for col in df.columns: col_type = df[col].dtype if col_type != object: c_min = df[col].min() c_max = df[col].max() if str(col_type)[:3] == 'int': if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: df[col] = df[col].astype(np.int8) elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: df[col] = df[col].astype(np.int16) elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: df[col] = df[col].astype(np.int32) elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: df[col] = df[col].astype(np.int64) else: if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: df[col] = df[col].astype(np.float16) elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max: df[col] = df[col].astype(np.float32) else: df[col] = df[col].astype(np.float64) else: df[col] = df[col].astype('category') end_mem = df.memory_usage().sum() / 1024 ** 2 print('Memory usage of df is {:.2f} MB'.format(end_mem)) print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) return df # 读取数据 data_path = 'C:/Users/Dooooooooo21/Desktop/project/FOUNDATION/' train_all = pd.read_csv(data_path + 'train.csv') test = pd.read_csv(data_path + 'testA.csv') train_all = reduce_mem_usage(train_all) test = reduce_mem_usage(test) y_train = train_all['isDefault'] X_train = train_all.drop(['id', 'issueDate', 'isDefault', 'policyCode'], axis=1) X_test = test.drop(['id', 'issueDate', 'policyCode'], axis=1) def ori_lgb(): # 5折交叉验证 folds = 5 seed = 1001 kf = KFold(n_splits=folds, shuffle=True, random_state=seed) cv_scores = [] for i, (train_index, valid_index) in enumerate(kf.split(X_train, y_train)): print('*' * 20 + str(i + 1) + '*' * 20) X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2) train_matrix = lgb.Dataset(X_train_split, label=y_train_split) valid_matrix = lgb.Dataset(X_val, y_val) params = { 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'learning_rate': 0.01, 'num_leaves': 14, 'max_depth': 19, 'min_data_in_leaf': 37, 'min_child_weight': 1.6, 'bagging_fraction': 0.98, 'feature_fraction': 0.69, 'bagging_freq': 96, 'reg_lambda': 9, 'reg_alpha': 7, 'min_split_gain': 0.4, 'nthread': 8, 'seed': 2020, } model = lgb.train(params, train_set=train_matrix, valid_sets=valid_matrix, num_boost_round=14269, verbose_eval=1000, early_stopping_rounds=200) val_pre_lgb = model.predict(X_val, num_iteration=model.best_iteration) cv_scores.append(metrics.roc_auc_score(y_val, val_pre_lgb)) print(cv_scores) print("lgb_scotrainre_list:{}".format(cv_scores)) print("lgb_score_mean:{}".format(np.mean(cv_scores))) print("lgb_score_std:{}".format(np.std(cv_scores))) def plot_roc(y_val, val_pre_lgb): fpr, tpr, th = metrics.roc_curve(y_val, val_pre_lgb) roc_auc = metrics.auc(fpr, tpr) print('未调参lgb单模型在验证机上的AUC: {}'.format(roc_auc)) # 画roc plt.figure(figsize=(8, 8)) plt.title('val roc') plt.plot(fpr, tpr, 'b', label='Val auc = %0.4f' % roc_auc) plt.xlim(0, 1) plt.ylim(0, 1) plt.legend(loc='best') plt.xlabel('false positive rate') plt.ylabel('true positive rate') # 画对角线 plt.plot([0, 1], [0, 1], 'r--') plt.show() def rf_cv_lgb(num_leaves, max_depth, bagging_fraction, feature_fraction, bagging_freq, min_data_in_leaf, min_child_weight, min_split_gain, reg_lambda, reg_alpha): model_lgb = lgb.LGBMClassifier(boosting_type='gbdt', objective='binary', metrics='auc', learning_rate=0.1, n_estimators=5000, num_leaves=int(num_leaves), max_depth=int(max_depth), bagging_fraction=round(bagging_fraction, 2), feature_fraction=round(feature_fraction, 2), bagging_freq=int(bagging_freq), min_data_in_leaf=int(min_data_in_leaf), min_child_weight=min_child_weight, min_split_gain=min_split_gain, reg_lambda=reg_lambda, reg_alpha=reg_alpha, n_jobs=8) val = cross_val_score(model_lgb, X_train_split, y_train_split, cv=5, scoring='roc_auc').mean() return val # 贝叶斯调参 def bayes(): bayes_lgb = BayesianOptimization(rf_cv_lgb, { 'num_leaves': (10, 200), 'max_depth': (3, 20), 'bagging_fraction': (0.5, 1.0), 'feature_fraction': (0.5, 1.0), 'bagging_freq': (0, 100), 'min_data_in_leaf': (10, 100), 'min_child_weight': (0, 10), 'min_split_gain': (0.0, 1.0), 'reg_alpha': (0.0, 10), 'reg_lambda': (0.0, 10), }) bayes_lgb.maximize(n_iter=10) # 最优参数 print(bayes_lgb.max) # 确定最优的迭代次数 def it(): base_params_lgb = {'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'learning_rate': 0.01, 'num_leaves': 200, 'max_depth': 3, 'min_data_in_leaf': 56, 'min_child_weight': 9.63, 'bagging_fraction': 1.0, 'feature_fraction': 1.0, 'bagging_freq': 100, 'reg_lambda': 10, 'reg_alpha': 1, 'min_split_gain': 1.0, 'nthread': 8, 'seed': 2020, 'verbose': -1, } cv_result_lgb = lgb.cv(train_set=train_matrix, early_stopping_rounds=1000, num_boost_round=20000, nfold=5, stratified=True, shuffle=True, params=base_params_lgb, metrics='auc', seed=0) print('迭代次数{}'.format(len(cv_result_lgb['auc-mean']))) print('最终模型的AUC为{}'.format(max(cv_result_lgb['auc-mean']))) # 最终训练模型 X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2) train_matrix = lgb.Dataset(X_train_split, label=y_train_split) valid_matrix = lgb.Dataset(X_val, y_val) params = { 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'learning_rate': 0.01, 'num_leaves': 14, 'max_depth': 19, 'min_data_in_leaf': 37, 'min_child_weight': 1.6, 'bagging_fraction': 0.98, 'feature_fraction': 0.69, 'bagging_freq': 96, 'reg_lambda': 9, 'reg_alpha': 7, 'min_split_gain': 0.4, 'nthread': 8, 'seed': 2020, } model = lgb.train(params, train_set=train_matrix, num_boost_round=3300, verbose_eval=1000, valid_sets=val