卡方分箱 python

卡方分箱是一种离散化方法，目的是将连续变量离散化为若干个分段，使得每个分段内变量的分布差异较小，不同分段之间的分布差异较大。在python中可以使用以下步骤实现：

1. 导入需要的库

import pandas as pd
import numpy as np
import scipy.stats as ss

2. 读入数据

data = pd.read_csv('data.csv')

3. 确定分箱个数

可以通过经验法则、等频分箱或等距分箱等方法确定分箱个数。

# 等频分箱
data['rank'] = pd.qcut(data['feature'], 10, labels=False)

4. 计算卡方值

卡方值可以衡量变量在不同分箱之间的差异程度。

def chi2(df, total_col, bad_col, overall_rate):
    df2 = df.copy()
    df2['expected'] = df[total_col].apply(lambda x: x * overall_rate)
    combined = zip(df2['expected'], df2[bad_col])
    chi = [(i[0]-i[1])**2/i[0] for i in combined]
    return sum(chi)

total = data.groupby('rank').count()['feature']
bad = data.groupby('rank').sum()['target']
overall_rate = np.sum(bad) / np.sum(total)
chi2_value = chi2(pd.concat([total, bad], axis=1), 'feature', 'target', overall_rate)

5. 合并分箱

根据卡方值，将相邻的分箱合并，直到满足预设的最小卡方值。

def merge(data, col, target, max_interval, min_chi2):
    total = data.groupby(col).count()[target]
    bad = data.groupby(col).sum()[target]
    overall_rate = np.sum(bad) / np.sum(total)
    group_intervals = [[i] for i in total.index]
    while len(group_intervals) > max_interval:
        chi2_values = []
        for i in range(len(group_intervals)-1):
            new_interval = group_intervals[i] + group_intervals[i+1]
            df1 = pd.concat([total.loc[new_interval], bad.loc[new_interval]], axis=1)
            df2 = pd.concat([total.loc[group_intervals[i]], bad.loc[group_intervals[i]]], axis=1)
            df3 = pd.concat([total.loc[group_intervals[i+1]], bad.loc[group_intervals[i+1]]], axis=1)
            chi2_values.append(chi2(df1, 'feature', 'target', overall_rate) - chi2(df2, 'feature', 'target', overall_rate) - chi2(df3, 'feature', 'target', overall_rate))
        if min(chi2_values) < min_chi2:
            merge_index = chi2_values.index(min(chi2_values))
            group_intervals[merge_index] = group_intervals[merge_index] + group_intervals.pop(merge_index+1)
        else:
            break
    return group_intervals

intervals = merge(data, 'rank', 'target', 10, 3.841)

6. 将分箱结果应用到数据中

def apply_intervals(x, intervals):
    for i in range(len(intervals)):
        if x in intervals[i]:
            return i

data['rank_new'] = data['rank'].apply(lambda x: apply_intervals(x, intervals))

以上就是使用python实现卡方分箱的步骤。需要注意的是，卡方分箱并不是一种万能的离散化方法，应根据具体情况选择合适的方法。