特征工程之分箱--Best-KS分箱

变量的KS值

KS(Kolmogorov-Smirnov)用于模型风险区分能力进行评估，指标衡量的是好坏样本累计部分之间的差距 。KS值越大，表示该变量越能将正，负客户的区分程度越大。通常来说，KS>0.2即表示特征有较好的准确率。强调一下，这
里的KS值是变量的KS值，而不是模型的KS值。（后面的模型评估里会重点讲解模型的KS值）。
KS的计算方式：

1. 计算每个评分区间的好坏账户数。
2. 计算各每个评分区间的累计好账户数占总好账户数比率（good%)和累计坏账户数占总坏账户数比率（bad%）。
3. 计算每个评分区间累计坏账户比与累计好账户占比差的绝对值（累计good%-累计bad%），然后对这些绝对值取最大值记得到KS值。

Best-KS分箱

Best-KS分箱的算法执行过程是一个逐步拆分的过程：

1. 将特征值值进行从小到大的排序。
2. 计算出KS最大的那个值，即为切点，记为D。然后把数据切分成两部分。
3. 重复步骤2，进行递归，D左右的数据进一步切割。直到KS的箱体数达到我们的预设阈值即可。

Best-KS分箱的特点：

• 连续型变量：分箱后的KS值<=分箱前的KS值
• 分箱过程中，决定分箱后的KS值是某一个切点，而不是多个切点的共同作用。这个切点的位置是原始KS值最大的位置。

1.简单版

# -*- coding: utf-8 -*-
"""
创建KS分箱实验
"""
import pandas as pd


def best_ks_box(data, var_name, box_num):
    data = data[[var_name, '是否违约']]
    """
    KS值函数
    """

    def ks_bin(data_, limit):
        g = data_.iloc[:, 1].value_counts()[0]
        b = data_.iloc[:, 1].value_counts()[1]
        data_cro = pd.crosstab(data_.iloc[:, 0], data_.iloc[:, 1])
        data_cro[0] = data_cro[0] / g
        data_cro[1] = data_cro[1] / b
        data_cro_cum = data_cro.cumsum()
        ks_list = abs(data_cro_cum[1] - data_cro_cum[0])
        ks_list_index = ks_list.nlargest(len(ks_list)).index.tolist()
        for i in ks_list_index:
            data_1 = data_[data_.iloc[:, 0] <= i]
            data_2 = data_[data_.iloc[:, 0] > i]
            if len(data_1) >= limit and len(data_2) >= limit:
                break
        return i

    # 测试： ks_bin(data,data.shape[0]/7)

    """
    区间选取函数
    """

    def ks_zone(data_, list_):
        list_zone = list()
        list_.sort()
        n = 0
        for i in list_:
            m = sum(data_.iloc[:, 0] <= i) - n
            n = sum(data_.iloc[:, 0] <= i)
            list_zone.append(m)
        list_zone.append(50000 - sum(list_zone))
        max_index = list_zone.index(max(list_zone))
        if max_index == 0:
            rst = [data_.iloc[:, 0].unique().min(), list_[0]]
        elif max_index == len(list_):
            rst = [list_[-1], data_.iloc[:, 0].unique().max()]
        else:
            rst = [list_[max_index - 1], list_[max_index]]
        return rst

    #    测试： ks_zone(data_,[23])    #左开右闭

    data_ = data.copy()
    limit_ = data.shape[0] / 20  # 总体的5%
    """"
    循环体
    """
    zone = list()
    for i in range(box_num - 1):
        ks_ = ks_bin(data_, limit_)
        zone.append(ks_)
        new_zone = ks_zone(data, zone)
        data_ = data[(data.iloc[:, 0] > new_zone[0]) & (data.iloc[:, 0] <= new_zone[1])]

    """
    构造分箱明细表
    """
    zone.append(data.iloc[:, 0].unique().max())
    zone.append(data.iloc[:, 0].unique().min())
    zone.sort()
    df_ = pd.DataFrame(columns=[0, 1])
    for i in range(len(zone) - 1):
        if i == 0:
            data_ = data[(data.iloc[:, 0] >= zone[i]) & (data.iloc[:, 0] <= zone[i + 1])]
        else:
            data_ = data[(data.iloc[:, 0] > zone[i]) & (data.iloc[:, 0] <= zone[i + 1])]
        data_cro = pd.crosstab(data_.iloc[:, 0], data_.iloc[:, 1])
        df_.loc['{0}-{1}'.format(data_cro.index.min(), data_cro.index.max())] = data_cro.apply(sum)
    return df_


data = pd.read_excel('测试1.xlsx')
var_name = '年龄'
print(best_ks_box(data, var_name, 5))

2.复杂版 

# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
#import missingno as msno
plt.style.use('fivethirtyeight')
import warnings
import datetime
warnings.filterwarnings('ignore')
#%matplotlib inline
#from tqdm import tqdm

import re
import math
import time
import itertools
import random

from logging import Logger
from logging.handlers import TimedRotatingFileHandler
import os

#######################################################KS分箱的主体逻辑##############################################
def init_logger(logger_name,logging_path):
    if not os.path.exists(logging_path):
        os.makedirs(logging_path)
    if logger_name not in Logger.manager.loggerDict:
        logger  = logging.getLogger(logger_name)
        logger.setLevel(logging.DEBUG)
        handler = TimedRotatingFileHandler(filename=logging_path+"/%sAll.log"%logger_name,when='D',backupCount = 7)
        datefmt = '%Y-%m-%d %H:%M:%S'
        format_str = '[%(asctime)s]: %(name)s %(filename)s[line:%(lineno)s] %(levelname)s  %(message)s'
        formatter = logging.Formatter(format_str,datefmt)
        handler.setFormatter(formatter)
        handler.setLevel(logging.INFO)
        logger.addHandler(handler)
        console= logging.StreamHandler()
        console.setLevel(logging.INFO)
        console.setFormatter(formatter)
        logger.addHandler(console)
        handler = TimedRotatingFileHandler(filename=logging_path+"/%sError.log"%logger_name,when='D',backupCount=7)
        datefmt = '%Y-%m-%d %H:%M:%S'
        format_str = '[%(asctime)s]: %(name)s %(filename)s[line:%(lineno)s] %(levelname)s  %(message)s'
        formatter = logging.Formatter(format_str,datefmt)
        handler.setFormatter(formatter)
        handler.setLevel(logging.ERROR)
        logger.addHandler(handler)
    logger = logging.getLogger(logger_name)
    return logger

def get_max_ks(date_df, start, end, rate, factor_name, bad_name, good_name, total_name,total_all):
    '''
    计算最大的ks值
    :param date_df: 数据源
    :param start: 第一条数据的index
    :param end: 最后一条数据的index
    :param rate:
    :param factor_name:
    :param bad_name:
    :param good_name:
    :param total_name:
    :param total_all:
    :return:最大ks值切点的index
    '''
    ks = ''
    #获取黑名单数据
    bad = date_df.loc[start:end,bad_name]
    #获取白名单数据
    good = date_df.loc[start:end,good_name]

   #np.cumsum累加。计算黑白的数量占比，累计差
    bad_good_cum = list(abs(np.cumsum(bad/sum(bad)) - np.cumsum(good/sum(good))))  
    if bad_good_cum:
        #找到最大的ks
        max_ks = max(bad_good_cum)
        #找到最大ks的切点index。
        index_max = bad_good_cum.index(max_ks)
        t = start + index_max
        len1 = sum(date_df.loc[start:t,total_name])
        len2 = sum(date_df.loc[t+1:end,total_name])
        #这个就是rate起的效果，一旦按照最大ks切点切割数据，要保证两边的数据量都不能小于一个阈值
        if len1 >= rate*total_all:
            if len2 >= rate*total_all:
                ks = t
    #如果分割之后，任意一部分数据的数量小于rate这个阈值，那么ks就返回为空了。
    return ks

def cut_fun(x,date_df,types,rate,factor_name,bad_name,good_name,total_name,total_all):
    '''

    :param x: List，就是保存了date_df的第一条index和最后一条index的List。
    :param date_df: 数据源
    :param types: 不知道是什么意思
    :param rate: rate的含义也是一直不清楚
    :param factor_name: 待分箱的特征字段
    :param bad_name:
    :param good_name:
    :param total_name:
    :param total_all:
    :return: 数据的start index,切点index,end index。
    '''
    if types == 'upper':
        #起始从date_df的第一条开始
        start = x[0]
    else:
        start = x[0]+1
    #结束时date_df的最后一条
    end = x[1]
    t = ''
    #很明显start != end,所以就执行这个函数体
    if start != end:
        #计算得到最大ks切点index的值，并且把值存入t。
        t = get_max_ks(date_df,start,end,rate,factor_name,bad_name,good_name,total_name,total_all)
    if t:
        #把t存入x。
        x.append(t)
        #这个时候x存着[start，切点，end]
        x.sort()
    if t == 0:
        x.append(t)
        x.sort()

    return x

def cut_while_fun(t_list,date_df,rate,factor_name,bad_name,good_name,total_name,total_all):
    '''

    :param t_list: start_index,分箱切点 ,end_index
    :param date_df:
    :param rate:
    :param factor_name:
    :param bad_name:
    :param good_name:
    :param total_name:
    :param total_all:
    :return:
    '''
    if len(t_list) != 2:
        #切点左边数据
        t_up = [t_list[0],t_list[1]]
        #切点右边数据
        t_down = [t_list[1],t_list[2]]

        #递归对左边数据进行切割
        if t_list[1]-t_list[0] > 1 and sum(date_df.loc[t_up[0]:t_up[1],total_name]) >= rate * sum(date_df[total_name]):

            t_up = cut_fun(t_up,date_df,'upper',rate,factor_name,good_name,bad_name,total_name,total_all)
        else:
            t_up = []

        #递归对右边数据进行切割
        if t_list[2]-t_list[1] > 1 and sum(date_df.loc[t_down[0]+1:t_down[1],total_name]) >= rate * sum(date_df[total_name]):
            t_down = cut_fun(t_down,date_df,'down',rate,factor_name,good_name,bad_name,total_name,total_all)
        else:
            t_down = []
    else:
        t_up = []
        t_down = []
    return t_up,t_down

def ks_auto(date_df,piece,rate,factor_name,bad_name,good_name,total_name,total_all):
    '''
    :param date_df: 数据源
    :param piece: 分箱数目
    :param rate: 最小数量占比，就是把数据通过切点分成两半部分之后，要保证两部分的数量都必须不能小于这个占比rate。
    :param factor_name: 待分箱的特征名称
    :param bad_name: 黑名单特征名称
    :param good_name: 白名单特征名称
    :param total_name: 总和的特诊名称
    :param total_all: 总共数据量
    :return: 返回整个分箱的间隔点，用List保存。这里是以date_df的index为分割点的。
    '''
    t1 = 0
    #数据源的大小，条数
    t2 = len(date_df)-1
    num = len(date_df)
    #还不知道这样做的目的是什么。
    if num > pow(2,piece-1):
        num = pow(2,piece-1)

    #新定义一个list,这个list是什么含义
    t_list = [t1,t2]
    tt =[]
    i = 1
    #如果数据源的条数大于1，就表示有分箱的资格
    if len(date_df) > 1:
        #这个是为了获取date_df数据的[start_index，切点_index, end_index]
        #将数据根据ks最大处进行二分
        t_list = cut_fun(t_list,date_df,'upper',rate,factor_name,bad_name,good_name,total_name,total_all)
        tt.append(t_list)
        for t_new in tt:
            #>2说明，分箱是成功的。
            if len(t_new) > 2:
                #
                up_down = cut_while_fun(t_new,date_df,rate,factor_name,bad_name,good_name,total_name,total_all)
                t_up = up_down[0]
                if len(t_up) > 2:
                    #
                    t_list = list(set(t_list+t_up))
                    tt.append(t_up)
                t_down = up_down[1]
                if len(t_down) > 2:
                    t_list = list(set(t_list+t_down))
                    tt.append(t_down)
                i += 1
                #注意循环的停止条件
                #1. i表示通过箱数限制break
                #2. len(t_list)还不是很清楚
                if len(t_list)-1 > num:
                    break
                if i >= piece:
                    break
    if len(date_df) > 0:
        #这里有个疑问，我感觉有问题
        #这里为啥要获取第一条数据，total的数量
        length1 = date_df.loc[0,total_name]
        if length1 >= rate*total_all:
            if 0 not in t_list:
                t_list.append(0)
        else:
            t_list.remove(0)
    t_list.sort()
    return t_list

def get_combine(t_list, date_df, piece):
    '''
    :param t_list: 这个值分箱间隔点
    :param date_df: 数据源
    :param piece: 分箱的箱数，表示第几箱。
    :return: 枚举所有的分箱可能组合
    '''
    t1 = 0
    t2 = len(date_df)-1
    list0 = t_list[1:len(t_list)-1]
    combine = []
    if len(t_list)-2 < piece:
        c = len(t_list)-2
    else:
        c = piece-1
    #获取list0的所有子序列。子序列长度是c
    list1 = list(itertools.combinations(list0, c))
    if list1:
        #向list1收尾添加数据，头部添加t1-1,尾部添加t2
        combine = [sorted(x + (t1-1,t2)) for x in list1]
    return combine

def cal_iv(date_df,items,bad_name,good_name,total_name):
    '''

    :param date_df:
    :param items:
    :param bad_name:
    :param good_name:
    :param total_name:
    :return: 返回计算的IV值
    '''
    iv0 = 0
    bad0 = np.array([sum(date_df.ix[x[0]:x[1],bad_name]) for x in items])
    good0 = np.array([sum(date_df.ix[x[0]:x[1],good_name]) for x in items])
    bad_rate0 = np.array([sum(date_df.ix[x[0]:x[1],bad_name])*1.0/sum(date_df.ix[x[0]:x[1],total_name]) for x in items])
    if 0 in bad0:
        return iv0
    if 0 in good0:
        return iv0
    good_per0 = good0*1.0/sum(date_df[good_name])
    bad_per0 = bad0*1.0/sum(date_df[bad_name])
    woe0 = [math.log(x,math.e) for x in good_per0/bad_per0]
    if sorted(woe0, reverse=False) == list(woe0) and sorted(bad_rate0, reverse=True) == list(bad_rate0):
        iv0 = sum(woe0*(good_per0-bad_per0))
    elif sorted(woe0, reverse=True) == list(woe0) and sorted(bad_rate0, reverse=False) == list(bad_rate0):
        iv0 = sum(woe0*(good_per0-bad_per0))
    return iv0

def choose_best_combine(date_df,combine,bad_name,good_name,total_name):
    '''
    :param date_df: 数据源
    :param combine: 所有的分箱可能
    :param bad_name:
    :param good_name:
    :param total_name:
    :return: 通过最大IV值，来得到最优的分箱方法
    '''
    z = [0]*len(combine)
    for i in range(len(combine)):
        item = combine[i]
        z[i] = (list(zip([x+1 for x in item[0:len(item)-1]],item[1:])))
    #计算最大的IV值
    iv_list = [cal_iv(date_df,x,bad_name,good_name,total_name) for x in z]
    iv_max = max(iv_list)
    if iv_max == 0:
        return ''
    index_max = iv_list.index(iv_max)
    combine_max = z[index_max]
    #返回最好的分箱组合

    #[(0, 180), (181, 268), (269, 348), (349, 450), (451, 605)] 类似于这种数据

    return combine_max

def verify_woe(x):
    if re.match('^d*.?d+$', str(x)):
        return x
    else:
        return 0

def best_df(date_df, items, na_df, rate, factor_name, total_name, bad_name, good_name,total_all,good_all,bad_all):
    '''

    :param date_df:
    :param items: 分箱间隔，数组[(0, 180), (181, 268), (269, 348), (349, 450), (451, 605)]
    :param na_df:
    :param rate:
    :param factor_name:
    :param total_name:
    :param bad_name:
    :param good_name:
    :param total_all:
    :param good_all:
    :param bad_all:
    :return:分箱之后的指标保存为dataframe，并返回。
    '''
    df0 = pd.DataFrame()

    if items:
        piece0 = ['['+str(date_df.ix[x[0],factor_name])+','+str(date_df.ix[x[1],factor_name])+']' for x in items]
        bad0 = [sum(date_df.ix[x[0]:x[1],bad_name]) for x in items]
        good0 = [sum(date_df.ix[x[0]:x[1],good_name]) for x in items]

        if len(na_df) > 0:
            piece0 = np.array(list(piece0) + ['['+str(x)+','+str(x)+']' for x in list(na_df[factor_name])])
            bad0 = np.array(list(bad0) + list(na_df[bad_name]))
            good0 = np.array(list(good0) + list(na_df[good_name]))
        else:
            piece0 = np.array(list(piece0))
            bad0 = np.array(list(bad0))
            good0 = np.array(list(good0))

        #bad0,good0都是list数据结构
        total0 = bad0 + good0
        #计算每一个箱子的总数量占比
        total_per0 = total0*1.0/total_all
        #当前箱子的黑名单比例
        bad_rate0 = bad0*1.0/total0
        #当前箱子的白名单比例
        good_rate0 = 1 - bad_rate0
        #当前箱子的白名单在整体白名单数据的比例
        good_per0 = good0*1.0/good_all
        #当前箱子黑名单在在整体黑名单数据的比例
        bad_per0 = bad0*1.0/bad_all
        #先将这些数据保存为数框
        df0 = pd.DataFrame(list(zip(piece0,total0,bad0,good0,total_per0,bad_rate0,good_rate0,good_per0,bad_per0)),columns=['Bin','Total_Num','Bad_Num','Good_Num','Total_Pcnt','Bad_Rate','Good_Rate','Good_Pcnt','Bad_Pcnt'])
        #通过bad_rate进行排序
        df0 = df0.sort_values(by='Bad_Rate',ascending=False)
        df0.index = list(range(len(df0)))
        bad_per0 = np.array(list(df0['Bad_Pcnt']))
        good_per0 = np.array(list(df0['Good_Pcnt']))
        bad_rate0 = np.array(list(df0['Bad_Rate']))
        good_rate0 = np.array(list(df0['Good_Rate']))
        bad_cum = np.cumsum(bad_per0)
        good_cum = np.cumsum(good_per0)
        #
        woe0 = [math.log(x, math.e) for x in good_per0/bad_per0]
        #这里要注意当woe是无穷大的情况
        #这种情况是因为在某些箱体中，黑名单数量或者白名单数量为0造成的
        if 'inf' in str(woe0):
            woe0 = [verify_woe(x) for x in woe0]
        iv0 = woe0*(good_per0-bad_per0)
        gini = 1-pow(good_rate0,2)-pow(bad_rate0,2)
        df0['Bad_Cum'] = bad_cum
        df0['Good_Cum'] = good_cum
        df0["Woe"] = woe0
        df0["IV"] = iv0
        df0['Gini'] = gini
        #就是累计到KS最大的那个点
        df0['KS'] = abs(df0['Good_Cum'] - df0['Bad_Cum'])
    #返回数框
    return df0

def all_information(date_df, na_df, piece, rate, factor_name, total_name, bad_name, good_name,total_all,good_all,bad_all):
    '''

    :param date_df: 这是经过处理之后的数据源，主要是针对factor_name统计flag_name的good,bad数量的数据
    :param na_df:   这是个空的df。
    :param piece:  分片大小，就是箱数
    :param rate: 值是0.05,这个值目前的含义不明
    :param factor_name:  分箱特征
    :param total_name:  总和的特征名称
    :param bad_name:   黑名单的特征名称
    :param good_name:  白名单的特征名称
    :param total_all:  总和数量
    :param good_all: 白名单数量
    :param bad_all:  黑名单数量
    :return:分箱之后的所有结果
    '''
    #新创建的一个List
    p_sort = list(range(piece+1))
    #倒着排序，就是从大到小排序
    p_sort.sort(reverse=True)

    t_list = ks_auto(date_df,piece,rate,factor_name,bad_name,good_name,total_name,total_all)

    #就是说明不需要分箱
    if len(t_list) < 3:
        df1 = pd.DataFrame()
        print('Warning: this data cannot get bins or the bins does not satisfy monotonicity')
        return df1
    df1 = pd.DataFrame()
    for c in p_sort[:piece-1]:
        #枚举所有的分箱可能组合。
        combine = get_combine(t_list,date_df,c)

        #选出最好的分箱
        best_combine = choose_best_combine(date_df,combine,bad_name,good_name,total_name)
        #按照最佳的分箱数组，分箱
        df1 = best_df(date_df,best_combine,na_df,rate,factor_name,total_name,bad_name,good_name,total_all,good_all,bad_all)
        if len(df1) != 0:
            gini = sum(df1['Gini']*df1['Total_Num']/sum(df1['Total_Num']))
            print('piece_count:',str(len(df1)))
            print('IV_All_Max:',str(sum(df1['IV'])))
            print('Best_KS:',str(max(df1['KS'])))
            print('Gini_index:',str(gini))
            print(df1)
            #把分箱之后的各个指标存为df，并且返回。
            return df1
    if len(df1) == 0:
        logger.warning('Warning: this data cannot get bins or the bins does not satisfy monotonicity')
        return df1

def fun_group_by(date_df,factor_name,bad_name,good_name):
    df_bad = date_df.groupby(factor_name)[bad_name].agg([(bad_name,'sum')])
    df_good = date_df.groupby(factor_name)[good_name].agg([(good_name,'sum')])
    df_bad = df_bad.reset_index()
    df_good = df_good.reset_index()
    good_dict = dict(list(zip(list(df_good[factor_name]),list(df_good[good_name]))))
    df_bad[good_name] = df_bad[factor_name].map(good_dict)
    df_bad[factor_name]= df_bad[factor_name].apply(lambda x : verify_factor(x))
    df_bad = df_bad.sort_values(by=[factor_name],ascending=True)
    df_bad[factor_name] = df_bad[factor_name].astype(str)
    return df_bad

def verify_factor(x):
    '''

    :param x:
    :return:
    '''
    if re.match('^-?d*.?d+$',x):
        x = float(x)
    return x

def path_df(path,sep,factor_name):
    data = pd.read_csv(path,sep=sep)
    data[factor_name] = data[factor_name].astype(str).map(lambda x: x.upper())
    data[factor_name] = data[factor_name].apply(lambda x: re.sub(' ','MISSING',x))
    return data

def verify_df_multiple(date_df,factor_name,total_name,bad_name,good_name):
    date_df = date_df.fillna(0)
    if (bad_name in date_df.columns) & (good_name in date_df.columns) & (total_name not in date_df.columns):
        date_df[good_name] = date_df[good_name].astype(float)
        date_df[bad_name] = date_df[bad_name].astype(float)
        date_df[total_name] = date_df[bad_name] + date_df[good_name]
        date_df = date_df.drop(date_df[date_df[total_name] == 0].index)
    if total_name in date_df.columns:
        date_df = date_df.drop(date_df[date_df[total_name] == 0].index)
        if bad_name in date_df.columns and good_name in date_df.columns:
            date_df['check'] = date_df[good_name] + date_df[bad_name] - date_df[total_name]
            date_df_check = date_df[date_df['check'] != 0]
            if len(date_df_check) > 0:
                date_df = pd.DataFrame()
                print('Error: total amounts is not equal to the sum of bad & good amounts')
                print(date_df_check)
        elif bad_name in date_df.columns:
            date_df['check'] = date_df[total_name] - date_df[bad_name]
            date_df_check = date_df[date_df['check'] < 0]
            if len(date_df_check) > 0:
                date_df = pd.DataFrame()
                print('Error: total amounts is smaller than bad amounts')
                print(date_df_check)
            else:
                date_df[good_name] = date_df[total_name] - date_df[bad_name]
        elif good_name in date_df.columns:
            date_df['check'] = date_df[total_name] - date_df[good_name]
            date_df_check = date_df[date_df['check'] < 0]
            if len(date_df_check) > 0:
                date_df = pd.DataFrame()
                print('Error: total amounts is smaller than good amounts')
                print(date_df_check)
            else:
                date_df[bad_name] = date_df[total_name] - date_df[good_name]
        else:
            print('Error: lack of bad or good data')
            date_df = pd.DataFrame()
    elif bad_name not in date_df.columns :
        print('Error: lack of bad data')
        date_df = pd.DataFrame()
    elif good_name not in date_df.columns:
        print('Error: lack of good data')
        date_df = pd.DataFrame()
    if len(date_df) != 0:
        date_df[good_name] = date_df[good_name].astype(int)
        date_df[bad_name] = date_df[bad_name].astype(int)
        date_df[factor_name] = date_df[factor_name].apply(lambda x: verify_factor(x))
        date_df = date_df.sort_values(by=[factor_name],ascending=True)
        date_df[factor_name] = date_df[factor_name].astype(str)
        del date_df['check']
    return date_df

def verify_df_two(date_df,flag_name,factor_name):
    '''
    验证数据集
    :param date_df:
    :param flag_name:
    :param factor_name:
    :return:
    '''
    #先删除flag_name为空的数据
    date_df = date_df.drop(date_df[date_df[flag_name].isnull()].index)
    #获取flag_name值大于1的数据。如果是二分类，flag_name只会是0和1，不应该出现大于1的情况。
    check = date_df[date_df[flag_name] > 1]
    if len(check) != 0 :
        print('Error: there exits the number bigger than one in the data')
        date_df = pd.DataFrame()
        return date_df
    elif len(date_df) != 0 :
        #这是正常，说明是二分类问题，并且转化flag_name的值为int类型。
        date_df[flag_name] = date_df[flag_name].astype(int)
        return date_df
    else:
        print('Error: the data is wrong')
        date_df = pd.DataFrame()
        return date_df

def universal_df(data,flag_name,factor_name,total_name,bad_name,good_name):
    '''
    转换数据，统计每一个值的黑白个数
    :param data:
    :param flag_name:
    :param factor_name:
    :param total_name:
    :param bad_name:
    :param good_name:
    :return:
    '''
    if flag_name != '':
        # 只读取factor_name和flag_name这两个特征的值
        data = data[[factor_name,flag_name]]
        # 确保数据的flag_name是二元化，并且不会有空值。
        data = verify_df_two(data,flag_name,factor_name)
        if len(data) != 0:
            # 根据 flag_name,factor_name聚合，统计flag_name的数量
            data = data[flag_name].groupby([data[factor_name],data[flag_name]]).count()
            #把series转化成新的 dataframe
            data = data.unstack()
            data = data.reset_index()
            #定义新的data列名
            data.columns = [factor_name,'good','bad']

            # 将factor_name数据的值类型进行校验，看是不是数值型，然后转化成float.
            data[factor_name] = data[factor_name].apply(lambda x: verify_factor(x))
            #把data按照factor_name进行升序排序。
            data = data.sort_values(by=[factor_name],ascending=True)
            #空缺值用0填补
            data = data.fillna(0)
            #对data新增total字段
            data['total'] = data['good']+data['bad']
            #将data的factor_name字段改成str类型
            data[factor_name] = data[factor_name].astype(str)
    else:
        data =[x.upper() for x in data[factor_name].astype(str)]
        verify_df_multiple(data,factor_name,total_name,bad_name,good_name)
        if len(data[factor_name]) != len(set(data[factor_name])):
            data = fun_group_by(data,factor_name,bad_name,good_name)
    print('universal_df')
    return data

def Best_KS_Bin(path='',data=pd.DataFrame(),sep=',',flag_name='',factor_name='name',total_name='total',bad_name='bad',good_name='good',piece=5,rate=0.05,not_in_list=[]):
    time0 = time.time()
    if len(data) != 0:
        # 如果factor_name是字符串类型，那就全部转化成大写。
        data[factor_name] = [x.upper() for x in data[factor_name].astype(str)]
    elif path != '':
        #如果path不为空，那么就从path里加载数据
        data = path_df(path,sep,factor_name)
        data[factor_name] = [x.upper() for x in data[factor_name].astype(str)]
    else:
        data = pd.DataFrame()
        print('Error: there is no data')
        time1 = time.time()
        print('spend time(s):', round(time1-time0,0))
        return data

    #这里就是返回数据里factor_name列数据的每个值的统计
    data = universal_df(data,flag_name,factor_name,total_name,bad_name,good_name)

    # 总的样本数
    total_all = sum(data['total'])
    # 白名单个数
    good_all = sum(data['good'])
    # 黑名单个数
    bad_all = sum(data['bad'])
    if len(data) != 0:
        not_list = [x.upper() for x in not_in_list]
        if not_in_list:
            not_name = not_list
            if 'NA' in not_list or 'NAN' in not_list or '' in not_list:
                not_name = not_list + ['NAN']
            elif ' ' in not_list:
                not_name = not_list + ['MISSING']
            na_df = data[data[factor_name].isin(not_name)]
            date_df = data.drop(data[data[factor_name].isin(not_name)].index)
            if (0 in na_df[good_name]) or (0 in na_df[bad_name]):
                not_value = list(set(list(na_df[na_df[good_name] == 0][factor_name]) + list(na_df[na_df[bad_name] == 0][factor_name])))
                print("Warning: the count of good or bad for the value in 'not_in_list' is 0. The value ("+str(not_value)+") will not get the separate bin. ")
                na_df_new = na_df[na_df[factor_name].isin(not_value)]
                na_df = na_df.drop(na_df[na_df[factor_name].isin(not_value)].index)
                na_df.index = list(range(len(na_df)))
                na_df_new[factor_name] = na_df_new[factor_name].map(lambda x: verify_factor(x))
                date_df[factor_name] = date_df[factor_name].map(lambda x: verify_factor(x))
                date_df = na_df_new.append(date_df)
                date_df = date_df.sort_values(by=factor_name,ascending=True)
                type_len = list(set([type(x) for x in list(date_df[factor_name])]))
                if len(type_len) > 1:
                    other_df = date_df[date_df[factor_name].apply(lambda x: type(x) == str)]
                    date_df = date_df[date_df[factor_name].apply(lambda x: type(x) == float)]
                    date_df = other_df.append(date_df)
        else:
            #在not_in_list不为空的时候，执行如下逻辑
            na_df = pd.DataFrame()
            date_df = data
        #重新定义data_df的index
        date_df.index = list(range(len(date_df)))
        if len(date_df) > 0:
            # 计算分箱
            bin_df = all_information(date_df,na_df,piece,rate,factor_name,total_name,bad_name,good_name,total_all,good_all,bad_all)
        else:
            time1 = time.time()
            print('spend time(s):', round(time1-time0,0))
            return data
        time1 = time.time()
        #统计分箱消耗时长
        print('spend time(s):', round(time1-time0,0))
        return bin_df
    else:
        time1 = time.time()
        print('spend time(s):', round(time1-time0,0))
        return data
    
###############################################对KS分箱之后进行IV排名#########################################
def sort_band_by_iv():
    tmp_df=pd.DataFrame()
    indexvalue=1
    for filename in os.listdir('/home/liuweitang/yellow_model/eda/band_result'):
        if 'csv' in filename:
            print(filename)
            try:
                band_result=pd.read_csv('/home/liuweitang/yellow_model/eda/band_result/%s'%filename)
                ks=band_result['KS'].max()
                iv_sum=band_result['IV'].sum()
                df=pd.DataFrame({
                    'band':[filename],
                     'ks':[ks],
                     'iv_sum':[iv_sum]
                })
                tmp_df=tmp_df.append(df)
            except Exception as err:
                pass
        
    tmp_df.reset_index(drop=True, inplace=True)
    tmp_df.info()
    tmp_df.sort_values(by=['iv_sum'], ascending=False, inplace=True)
    print(tmp_df)
    tmp_df.to_csv('/home/liuweitang/yellow_model/eda/IVSort/IV.csv',index=False)

####################################################数据合并#####################################################
#数据合并
#就是开房次数和异性同住次数特征表进行合并,并且将数据合并之后的数据保存到本地。
def merge_data(lgzsPath,yxtzPath):
    lgzs_data=pd.read_csv(lgzsPath)
    yxtz_data=pd.read_csv(yxtzPath)
    result_data=pd.merge(yxtz_data,lgzs_data,how='inner',left_on='gmsfhm_rzsj',right_on='gmsfhm_rzsj')
    result_data.rename(columns={'label_x':'label'}, inplace=True)
    now_time=time.strftime('%Y%m%d',time.localtime(time.time()))
    result_data.to_csv('/home/liuweitang/yellow_model/data/input/new/yxtz_lgzs_merge_%s.csv'%now_time,index=False)


###################################################KS分箱的主类#################################################
class KS_Bin():
    def __init__(self,path,flag,notBandColList):
        '''
        :param path: 数据源路径
        :param flag: 目标值1-0值
        :param colList: 需要分箱的数据列
        '''
        
        line = os.popen("head -1 %s"%path)
        line=line.readlines()[0]
        if "$" in line:
            self.df=pd.read_csv(path,sep='$',engine='c')
        else:
            self.df=pd.read_csv(path, sep=',', engine='c')
        if 'bad' in self.df['label'].drop_duplicates().values:
            self.df[flag]=self.df[flag].map(lambda x: 1 if x=='bad' else 0)
            
        self.flag=flag
        self.path=path
        not_band_list=[]
        for col in self.df.columns.tolist():
            if col not in notBandColList:
                not_band_list.append(col)
        self.colList=not_band_list
        print(self.colList)
    def to_band(self):
        for col in tqdm(self.colList):
            ks_data = Best_KS_Bin(data=self.df, flag_name=self.flag, factor_name=col)
            #将分箱数据导出来
          
            self.binData_csv(ks_data, '/home/liuweitang/yellow_model/eda/band_result/%s_binResult.csv'%col)
            # 用WOE值代替分类值
            for row in ks_data.index:
                bin= ks_data.loc[row].Bin
                woe= ks_data.loc[row].Woe
                binStart = float(bin.split(',')[0][1:])
                binEnd=float(bin.split(',')[1][:-1])
                self.df[col]=self.df[col].map(lambda x: float(x))
                #用WOE值代替原来的值
                self.df.loc[(self.df[col] >= binStart) & (self.df[col] <= binEnd),'%s_band'%col] = woe
        print('save data')
        self.save_band_data()
       
    def binData_csv(self,df,csvPath):
         df.to_csv(csvPath,index=False)
    
    def save_band_data(self):
        '''
          这里就是把分箱之后的字段提取出，作为新的数据进行保存
        '''
        band_list=[]
        #这两个字段现在写死了，看后期怎么玩，其实可以拿出来，当做参数，这样子就可以通用化。
        #目前只是我们的业务，所以自己写了。
        band_list.append('gmsfhm_rzsj')   
        band_list.append('label')
        for col in self.df.columns.tolist():
            if 'band' in col:
                band_list.append(col)

        band_data=self.df[band_list]
        filename=self.path.split('/')[-1]
        filename=filename.split('.')[0]+'_band'
        band_data.to_csv('/home/liuweitang/yellow_model/data/input/new/%s.csv'%filename,index=False)
        

if __name__=="__main__":
    # print('start band lgzs')
    # #这里是分箱lgzs的数据
    # lgzs_not_band_col=[
    #         'gmsfhm_rzsj',
    #         'label'
    #     ]
    # 
    # lgzs_data_path='/home/liuweitang/yellow_model/feature/raw/train_openning_feature_20180508.txt'
    # lgzs_ks_bin=KS_Bin(lgzs_data_path,flag='label', notBandColList=lgzs_not_band_col)
    # lgzs_ks_bin.to_band()
    # 
    # print('band lgzs finished')
    # 
    # print('band yxtz start')
    # #这里对yxtz的数据分箱。
    # yxtz_col_list=[
    #     'gmsfhm_rzsj',
    #     'label'
    #  ]
    # yxtz_data_path='/home/liuweitang/yellow_model/data/mk/tmp_good_people_in_yxtz_lwt2.txt'
    # yxtz_ks_bin=KS_Bin(yxtz_data_path,flag='label', notBandColList=yxtz_col_list)
    # yxtz_ks_bin.to_band()
    # print('band yxtz finished')
    # 
    # print('start iv rank')
    # #对所有分箱之后的特征IV值排名保存
    # sort_band_by_iv()
    # 
    # print('start merge band_data')
    # #合并数据
    # lgzs_band='/home/liuweitang/yellow_model/data/input/new/'+lgzs_data_path.split(".")[0]+'_band.csv'
    # yxtz_band='/home/liuweitang/yellow_model/data/input/new/'+yxtz_data_path.split(".")[0]+'_band.csv'
    # merge_data(lgzs_band,yxtz_band)
    


    data=pd.read_csv('application_test.csv')
    data['FLAG_OWN_CAR']=data['FLAG_OWN_CAR'].map(lambda x:1 if x=='Y' else 0)

    Best_KS_Bin(data=data,factor_name='AMT_INCOME_TOTAL',flag_name='FLAG_OWN_CAR')

    print(data[['FLAG_OWN_CAR','AMT_INCOME_TOTAL']].head())