原理很簡單，初始分20箱或更多，先確保每箱中都含有0，1標簽，對不包含0，1標簽的箱向前合并，計算各箱卡方值，對卡方值最小的箱向后合并，代碼如下
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import pandas as pdimport numpy as npimport scipyfrom scipy import statsdef 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) 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())) return df_woe #define iv def iv_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) 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("箱數：{},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，目標為3箱
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chi_bin(data,"age","SeriousDlqin2yrs",binnum=3,maxcut=10) |
箱數：8,iv:0.184862
箱數：7,iv:0.184128
箱數：6,iv:0.179518
箱數：5,iv:0.176980
箱數：4,iv:0.172406
箱數：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
到此這篇關于python 基于卡方值分箱算法的實現示例的文章就介紹到這了,更多相關python 卡方值分箱算法內容請搜索服務器之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持服務器之家！
原文鏈接：https://blog.csdn.net/wyzwyzwyzo/article/details/107354019
