Python获取pandas数据帧中所有特征组合的条件概率

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import CategoricalNB

# 假设数据集文件名为data.csv，特征列为feature1、feature2、feature3，目标列为target
data = pd.read_csv('data.csv')

# 将特征列编码为整数，以便进行条件概率计算
encoder = LabelEncoder()
for feature in ['feature1', 'feature2', 'feature3']:
    data[feature] = encoder.fit_transform(data[feature])

# 创建一个空的数据帧来存储条件概率
probabilities = pd.DataFrame(columns=['feature1', 'feature2', 'feature3', 'target', 'probability'])

# 遍历所有特征组合，并计算条件概率
for feature1 in data['feature1'].unique():
    for feature2 in data['feature2'].unique():
        for feature3 in data['feature3'].unique():
            # 选择当前特征组合的数据子集
            subset = data[(data['feature1'] == feature1) & (data['feature2'] == feature2) & (data['feature3'] == feature3)]
            
            # 计算条件概率
            probability = len(subset[subset['target'] == 1]) / len(subset)
            
            # 将结果添加到数据帧中
            probabilities = probabilities.append({'feature1': feature1, 'feature2': feature2, 'feature3': feature3, 'target': 1, 'probability': probability}, ignore_index=True)

print(probabilities)