python卡⽅分箱算法_python基于卡⽅值分箱算法的实现⽰例原理很简单，初始分20箱或更多，先确保每箱中都含有0，1标签，对不包含0，1标签的箱向前合并，计算各箱卡⽅值，对卡⽅值最⼩的箱向后合并，代码如下
import pandas as pd
import numpy as np
import scipy
from scipy import stats
def chi_bin(DF,var,target,binnum=5,maxcut=20):
'''
DF:data
var:variable
target:target / label
binnum: the number of bins output
maxcut: initial bins number
'''
data=DF[[var,target]]
#equifrequent cut the var into maxcut bins
data["cut"],breaks=pd.qcut(data[var],q=maxcut,duplicates="drop",retbins=True)
#count 1,0 in each bin
count_1=data.loc[data[target]==1].groupby("cut")[target].count()
count_0=data.loc[data[target]==0].groupby("cut")[target].count()
#get bins value: min,max,count 0,count 1
bins_value=[*zip(breaks[:maxcut-1],breaks[1:],count_0,count_1)]
#define woe
def woe_value(bins_value):
df_woe=pd.DataFrame(bins_value)
lumns=["min","max","count_0","count_1"]
df_woe["total"]=unt_1+unt_0
df_woe["bad_rate"]=unt_1/al
df_woe["woe"]=np.log((unt_0/unt_0.sum())/(unt_1/unt_1.sum()))
return df_woe
#define iv
def iv_value(df_woe):
rate=(unt_0/unt_0.sum())-(unt_1/unt_1.sum())
iv=np.sum(rate * df_woe.woe)
return iv
#make sure every bin contain 1 and 0
##first bin merge backwards
for i in range(len(bins_value)):
if 0 in bins_value[0][2:]:
bins_value[0:2]=[(
bins_value[0][0],
bins_value[1][1],
bins_value[0][2]+bins_value[1][2],
bins_value[0][3]+bins_value[1][3])]
continue
##bins merge forwards
if 0 in bins_value[i][2:]:
bins_value[i-1:i+1]=[(
bins_value[i-1][0],
bins_value[i][1],
bins_value[i-1][2]+bins_value[i][2],
bins_value[i-1][3]+bins_value[i][3])]
break
else:
break
#calculate chi-square merge the minimum chisquare while len(bins_value)>binnum:
chi_squares=[]
for i in range(len(bins_value)-1):
a=bins_value[i][2:]
b=bins_value[i+1][2:]
chi_square=scipy.stats.chi2_contingency([a,b])[0] chi_squares.append(chi_square)
#merge the minimum chisquare backwards
i = chi_squares.index(min(chi_squares))
bins_value[i:i+2]=[(
bins_value[i][0],
bins_value[i+1][1],
bins_value[i][2]+bins_value[i+1][2],
bins_value[i][3]+bins_value[i+1][3])]
df_woe=woe_value(bins_value)
#print bin number and iv
print("箱数：{},iv:{:.6f}".format(len(bins_value),iv_value(df_woe)))
#return bins and woe information
return woe_value(bins_value)
以下是效果：
初始分成10箱，⽬标为3箱
chi_bin(data,"age","SeriousDlqin2yrs",binnum=3,maxcut=10)
箱数：8,iv:0.184862
快速排序python实现箱数：7,iv:0.184128
箱数：6,iv:0.179518
箱数：5,iv:0.176980
箱数：4,iv:0.172406
箱数：3,iv:0.160015
min max count_0 count_1 total bad_rate woe
0 0.0 52.0 70293 7077 77370 0.091470 -0.266233
1 52.0 61.0 29318 1774 3109
2 0.057056 0.242909
2 61.0 72.0 26332 865 27197 0.031805 0.853755
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时间： 2020-07-17