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
importpandasaspd
importnumpyasnp
importscipy
fromscipyimportstats
defchi_bin(DF,var,target,binnum=5,maxcut=20):
'''
DF:data
var:variable
target:target/label
binnum:thenumberofbinsoutput
maxcut:initialbinsnumber
'''
data=DF[[var,target]]
#equifrequentcutthevarintomaxcutbins
data["cut"],breaks=pd.qcut(data[var],q=maxcut,duplicates="drop",retbins=True)
#count1,0ineachbin
count_1=data.loc[data[target]==1].groupby("cut")[target].count()
count_0=data.loc[data[target]==0].groupby("cut")[target].count()
#getbinsvalue:min,max,count0,count1
bins_value=[*zip(breaks[:maxcut-1],breaks[1:],count_0,count_1)]
#definewoe
defwoe_value(bins_value):
df_woe=pd.DataFrame(bins_value)
df_woe.columns=["min","max","count_0","count_1"]
df_woe["total"]=df_woe.count_1+df_woe.count_0
df_woe["bad_rate"]=df_woe.count_1/df_woe.total
df_woe["woe"]=np.log((df_woe.count_0/df_woe.count_0.sum())/(df_woe.count_1/df_woe.count_1.sum()))
returndf_woe
#defineiv
defiv_value(df_woe):
rate=(df_woe.count_0/df_woe.count_0.sum())-(df_woe.count_1/df_woe.count_1.sum())
iv=np.sum(rate*df_woe.woe)
returniv
#makesureeverybincontain1and0
##firstbinmergebackwards
foriinrange(len(bins_value)):
if0inbins_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
##binsmergeforwards
if0inbins_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
#calculatechi-squaremergetheminimumchisquare
whilelen(bins_value)>binnum:
chi_squares=[]
foriinrange(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)
#mergetheminimumchisquarebackwards
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)
#printbinnumberandiv
print("箱数：{},iv:{:.6f}".format(len(bins_value),iv_value(df_woe)))
#returnbinsandwoeinformation
returnwoe_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
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 31092 0.057056 0.242909
2 61.0 72.0 26332 865 27197 0.031805 0.853755
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