如何在Python/pandas中获得证据(WOE)和信息价值(IV)的权重？

import pandas as pd
import numpy as np
from sklearn.utils import resample
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

# 假设数据集为df，其中包含特征列'feature'和目标列'target'
df = pd.read_csv('your_dataset.csv')

# 将目标列转换为数值型
le = LabelEncoder()
df['target'] = le.fit_transform(df['target'])

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(df['feature'], df['target'], test_size=0.2, random_state=42)

def calculate_woe_iv(feature, target):
    df = pd.DataFrame({'feature': feature, 'target': target})
    df['non_target'] = 1 - df['target']
    total_target = df['target'].sum()
    total_non_target = df['non_target'].sum()
    df['target_pct'] = df['target'] / total_target
    df['non_target_pct'] = df['non_target'] / total_non_target
    df['woe'] = np.log(df['target_pct'] / df['non_target_pct'])
    df['iv'] = (df['target_pct'] - df['non_target_pct']) * df['woe']
    iv = df['iv'].sum()
    return df['woe'], iv

woe, iv = calculate_woe_iv(X_train, y_train)