1. 代码

基于jupyter notebook

#导包

import numpy as np

import math

import pandas as pd

from sklearn.utils.multiclass import type_of_target

from scipy import stats

#求woe值和iv值

def woe(X, y, event):

res_woe = [] #列表存放woe字典

res_iv = [] #列表存放iv

X1 = feature_discretion(X) #对连续型特征进行处理

for i in range(0, X1.shape[-1]): #遍历所有特征

x = X1[:, i] #单个特征

woe_dict, iv1 = woe_single_x(x, y, event) #计算单个特征的woe值

res_woe.append(woe_dict)

res_iv.append(iv1)

return np.array(res_woe), np.array(res_iv) #返回数组

#求单个特征的woe值

def woe_single_x(x, y, event):

event_total, non_event_total = count_binary(y, event) #计算好人坏人总数

x_labels = np.unique(x) #特征中的分段

woe_dict = {} #存放每个分段的名称 以及 其对应的woe值

iv = 0

for x1 in x_labels: #遍历每个分段

y1 = y[np.where(x == x1)[0]]

event_count, non_event_count = count_binary(y1, event=event)

rate_event = 1.0 * event_count / event_total

rate_non_event = 1.0 * non_event_count / non_event_total

#woe无穷大时处理

if rate_event == 0:

print()#print("{'",x1,"'}"+":全是好人") #只输出不做处理

elif rate_non_event == 0:

print()#print("{'",x1,"'}"+":全是坏人")

else:

woe1 = math.log(rate_event / rate_non_event)

woe_dict[x1] = woe1

iv += (rate_event - rate_non_event) * woe1

return woe_dict, iv

#计算个数

def count_binary(a, event):

event_count = (a == event).sum()

non_event_count = a.shape[-1] - event_count

return event_count, non_event_count

#判断特征数据是否为离散型

def feature_discretion(X):

temp = []

for i in range(0, X.shape[-1]):

x = X[:, i]

x_type = type_of_target(x)

if pd.Series(list(x)).dtype != 'O':

x1 = discrete(x)

temp.append(x1)

else:

temp.append(x)

return np.array(temp).T

#对连续型特征进行离散化

def discrete(x):

res = np.array([0] * x.shape[-1], dtype=int)

for i in range(5):

point1 = stats.scoreatpercentile(x, i * 20)

point2 = stats.scoreatpercentile(x, (i + 1) * 20)

x1 = x[np.where((x >= point1) & (x <= point2))]

mask = np.in1d(x, x1)

res[mask] = (i + 1)

return res

2. 数据

读取数据

df = pd.read_csv("telephone_test.csv")

目标变量

y=df['status'].values

所有特征

X=df.drop(['status','sid', 'uid'],axis=1).values

3. 结果

将特征的IV值和特征构建成字典并按IV值对特征进行排序

dic = dict(zip(a,b)) #a为IV值列表，b为特征列表

dic_sort= sorted(dic.items(),key = lambda x:x[1],reverse = True)

部分截图