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python 基于卡方值分箱算法的實(shí)現(xiàn)示例

更新時(shí)間：2020年07月17日 09:41:04 作者：wyzwyzwyzo

這篇文章主要介紹了python 基于卡方值分箱算法的實(shí)現(xiàn)示例，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

原理很簡(jiǎn)單，初始分20箱或更多，先確保每箱中都含有0，1標(biāo)簽，對(duì)不包含0，1標(biāo)簽的箱向前合并，計(jì)算各箱卡方值，對(duì)卡方值最小的箱向后合并，代碼如下

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)
    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(shù)：{},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(biāo)為3箱

chi_bin(data,"age","SeriousDlqin2yrs",binnum=3,maxcut=10)

箱數(shù)：8,iv:0.184862
箱數(shù)：7,iv:0.184128
箱數(shù)：6,iv:0.179518
箱數(shù)：5,iv:0.176980
箱數(shù)：4,iv:0.172406
箱數(shù)：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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