接着上一篇，用lightgbm建立baseline，发现模型效果得到了很大优化（模型分提升为0.73)

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from sklearn.model_selection import cross_val_score,KFold

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import lightgbm as lgb

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from lightgbm import LGBMClassifier

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from lightgbm import early_stopping

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from sklearn.metrics import roc_auc_score

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bad_chars= $':','""','\\\\',''$

for feature in application_train.columns:

if any(bad_char in feature for bad_char in bad_chars):

print(f"Feature '{feature}'包含非法字符。")

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#去掉特殊字符import pandas as pd

import re

def clean_column_names(df):

"""

清理DataFrame列名，去除特殊字符，使其符合JSON格式要求。

参数:

df (pd.DataFrame): 输入的DataFrame。

pd.DataFrame: 列名已清理的DataFrame。

"""

定义一个函数，用于替换单个列名中的特殊字符

def replace_chars(col_name):

替换掉所有非字母数字和下划线的字符

return re.sub(r'\W+', '_', col_name)

应用替换函数到所有列名

df.columns = $replace_chars(col) for col in df.columns$

return df

In $217$ :

application_train=clean_column_names(application_train)

application_test=clean_column_names(application_test)

In $218$ :

bad_chars= $':','""','\\\\',''$

for feature in application_train.columns:

if any(bad_char in feature for bad_char in bad_chars):

print(f"Feature '{feature}'包含非法字符。")

In $238$ :

def fit(train=application_train, valid=application_test):

"""

模型训练函数，

参数：train训练集

valid测试集

返回值：

valid_auc:验证集上AUC指标

feature_importances:特征重要性

test_results:测试集结果

"""

test = valid.copy()

x_train = train.drop( $'SK_ID_CURR', 'TARGET'$ , axis=1)

y_train = train $'TARGET'$

五折交叉验证

folds = KFold(n_splits=5, shuffle=True, random_state=1412)

定义变量保存预测结果

oof_preds = np.zeros(y_train.shape $0$ )

test_preds = np.zeros(test.shape $0$ )

提取特征名

feature_names = list(x_train.columns)

空数组用于存储特征重要性值

feature_importance_values = np.zeros(len(feature_names))

实例化模型

lgb = LGBMClassifier(n_estimators=10000, early_stopping_round=200, random_state=24)

for fold_idx, (train_idx, valid_idx) in enumerate(folds.split(x_train)):

X = x_train.iloc $train_idx$

y = y_train.iloc $train_idx$

valid_X = x_train.iloc $valid_idx$

valid_y = y_train.iloc $valid_idx$

定义早停回调函数

callbacks = $early_stopping(stopping_rounds=200)$

拟合模型

lgb.fit(X, y, eval_set= $(X, y), (valid_X, valid_y)$ , callbacks=callbacks)

记录特征重要性

feature_importance_values += lgb.feature_importances_ / folds.n_splits

在验证集上进行预测

proba = lgb.predict_proba(valid_X, num_iteration=lgb.best_iteration_)

oof_preds $valid_idx$ = proba $:, 1$ # 选择正类概率

test_preds += lgb.predict_proba(test $feature_names$ , num_iteration=lgb.best_iteration_) $:, 1$

valid_auc = roc_auc_score(y_train, oof_preds)

feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})

test $'TARGET'$ = test_preds

return valid_auc, feature_importances, test $\['SK_ID_CURR', 'TARGET'$ ]

In $246$ :

valid_auc,feature_importance,submission=fit(application_train $:50000$ ,application_test)

#发现报错了Do not support special JSON characters in feature name，原因是有些列名里有特殊的字符,这是get_dummies时产生的

In $247$ :

valid_auc #0.7421786519800682

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#看下特征重要性

feature_importance.sort_values(by='importance',ascending=False)

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submission.to_csv('baseline_model_lightgbm.csv',index=False)#提交后成绩0.73

In $251$ :

application_train.to_csv('original_application_train.csv')#保存下结果

application_test.to_csv('original_application_test.csv')

KAGGLE竞赛实战2-捷信金融违约预测竞赛-part2-用lightgbm建立baseline

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