一、 建模步骤

(1)从数据中衍生特征

(2)对类别型变量和数值型变量进行补缺

(3)基于卡方分箱法对变量进行分箱

(4)WOE编码后的单变量分析与多变量分析

(5)应用逻辑回归模型

(6)尺度化

(7)模型预测能力

二、代码

import pandas as pdimport datetimeimport collectionsimport numpy as npimport numbersimport randomimport sysimport picklefrom itertools import combinationsfrom sklearn.linear_model import LinearRegressionfrom sklearn.linear_model import LogisticRegressionfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import roc_curvefrom sklearn.metrics import roc_auc_scoreimport statsmodels.api as sm# ##针对图形显示问题# import matplotlib# matplotlib.use('TkAgg')from importlib import reloadfrom matplotlib import pyplot as plt# 如果Jupyter，需要打开下面行代码。#%matplotlib inlinereload(sys)# sys.setdefaultencoding( "utf-8")from scorecard_functions import *from sklearn.linear_model import LogisticRegressionCV# -*- coding: utf-8 -*-######################################## UDF: 自定义函数 ########################################### 对时间窗口，计算累计产比 ###def TimeWindowSelection(df, daysCol, time_windows):    '''    :param df: the dataset containg variabel of days    :param daysCol: the column of days    :param time_windows: the list of time window    :return:    '''    freq_tw = {}    for tw in time_windows:        freq = sum(df[daysCol].apply(lambda x: int(x<=tw)))        freq_tw[tw] = freq    # freq_tw = {dict}: {30: 499175, 60: 524173, 90: 535223, 120: 542683, 150: 548083, 180: 552009, 210: 555009, 240: 557393, 270: 559259, 300: 560823, 330: 562458, 360: 563952}    return freq_twdef DeivdedByZero(nominator, denominator):    '''    当分母为0时，返回0；否则返回正常值    '''    if denominator == 0:        return 0    else:        return nominator*1.0/denominator#对某些统一的字段进行统一def ChangeContent(x):    y = x.upper()    if y == '_MOBILEPHONE':        y = '_PHONE'    return ydef MissingCategorial(df,x):    '''    :param df: the dataresources.    :param x: the column of the dataresources.    :return:    '''    missing_vals = df[x].map(lambda x: int(x!=x))    return sum(missing_vals)*1.0/df.shape[0]def MissingContinuous(df,x):    missing_vals = df[x].map(lambda x: int(np.isnan(x)))    return sum(missing_vals) * 1.0 / df.shape[0]def MakeupRandom(x, sampledList):    if x==x:        return x    else:        randIndex = random.randint(0, len(sampledList)-1)        return sampledList[randIndex]#############################################################Step 0: 数据分析的初始工作, 包括读取数据文件、检查用户Id的一致性等############################################################## F:\chen\download\creditcard\Chimerge\cyc# folderOfData = '/Users/Code/Data Collections/bank default/'folderOfData = 'F:\/chen\/download\/creditcard\/Chimerge\/cyc\/'data1 = pd.read_csv(folderOfData+'PPD_LogInfo_3_1_Training_Set.csv', header = 0)data2 = pd.read_csv(folderOfData+'PPD_Training_Master_GBK_3_1_Training_Set.csv', dtype = {'target': np.int64}, header = 0,encoding = 'gbk')data3 = pd.read_csv(folderOfData+'PPD_Userupdate_Info_3_1_Training_Set.csv', header = 0)# score card:# data=pd.read_csv('F:\/chen\/download\/creditcard\/score card\/data_all_values.csv')############################################################################################## Step 1: 从PPD_LogInfo_3_1_Training_Set &  PPD_Userupdate_Info_3_1_Training_Set数据中衍生特征############################################################################################### compare whether the four city variables matchdata2['city_match'] = data2.apply(lambda x: int(x.UserInfo_2 == x.UserInfo_4 == x.UserInfo_8 == x.UserInfo_20),axis = 1)# score card:# data=pd.read_csv('F:\/chen\/download\/creditcard\/score card\/data_all_values.csv')del data2['UserInfo_2']del data2['UserInfo_4']del data2['UserInfo_8']del data2['UserInfo_20']### 提取申请日期，计算日期差，查看日期差的分布data1['logInfo'] = data1['LogInfo3'].map(lambda x: datetime.datetime.strptime(x,'%Y-%m-%d'))data1['Listinginfo'] = data1['Listinginfo1'].map(lambda x: datetime.datetime.strptime(x,'%Y-%m-%d'))data1['ListingGap'] = data1[['logInfo','Listinginfo']].apply(lambda x: (x[1]-x[0]).days,axis = 1)plt.hist(data1['ListingGap'],bins=200)plt.title('Days between login date and listing date')ListingGap2 = data1['ListingGap'].map(lambda x: min(x,365))plt.hist(ListingGap2,bins=200)# plt.figure()plt.show()timeWindows = TimeWindowSelection(data1, 'ListingGap', range(30,361,30))'''使用180天作为最大的时间窗口计算新特征所有可以使用的时间窗口可以有7 days, 30 days, 60 days, 90 days, 120 days, 150 days and 180 days.在每个时间窗口内，计算总的登录次数，不同的登录方式，以及每种登录方式的平均次数'''time_window = [7, 30, 60, 90, 120, 150, 180]var_list = ['LogInfo1','LogInfo2']data1GroupbyIdx = pd.DataFrame({'Idx':data1['Idx'].drop_duplicates()})for tw in time_window:    data1['TruncatedLogInfo'] = data1['Listinginfo'].map(lambda x: x + datetime.timedelta(-tw))    temp = data1.loc[data1['logInfo'] >= data1['TruncatedLogInfo']]    for var in var_list:        #count the frequences of LogInfo1 and LogInfo2        count_stats = temp.groupby(['Idx'])[var].count().to_dict()        data1GroupbyIdx[str(var)+'_'+str(tw)+'_count'] = data1GroupbyIdx['Idx'].map(lambda x: count_stats.get(x,0))        # count the distinct value of LogInfo1 and LogInfo2        Idx_UserupdateInfo1 = temp[['Idx', var]].drop_duplicates()        uniq_stats = Idx_UserupdateInfo1.groupby(['Idx'])[var].count().to_dict()        data1GroupbyIdx[str(var) + '_' + str(tw) + '_unique'] = data1GroupbyIdx['Idx'].map(lambda x: uniq_stats.get(x,0))        # calculate the average count of each value in LogInfo1 and LogInfo2        # groupbyid,因为之前Idx已经生成，所以groupby,有可能找不到，这样就是0。从而在计算平均值时候会出现分母是01的情况，使用DeicdedbyZero会防止这种情况。        data1GroupbyIdx[str(var) + '_' + str(tw) + '_avg_count'] = data1GroupbyIdx[[str(var)+'_'+str(tw)+'_count',str(var) + '_' + str(tw) + '_unique']].\            apply(lambda x: DeivdedByZero(x[0],x[1]), axis=1)data3['ListingInfo'] = data3['ListingInfo1'].map(lambda x: datetime.datetime.strptime(x,'%Y/%m/%d'))data3['UserupdateInfo'] = data3['UserupdateInfo2'].map(lambda x: datetime.datetime.strptime(x,'%Y/%m/%d'))data3['ListingGap'] = data3[['UserupdateInfo','ListingInfo']].apply(lambda x: (x[1]-x[0]).days,axis = 1)collections.Counter(data3['ListingGap'])hist_ListingGap = np.histogram(data3['ListingGap'])hist_ListingGap = pd.DataFrame({'Freq':hist_ListingGap[0],'gap':hist_ListingGap[1][1:]})hist_ListingGap['CumFreq'] = hist_ListingGap['Freq'].cumsum()hist_ListingGap['CumPercent'] = hist_ListingGap['CumFreq'].map(lambda x: x*1.0/hist_ListingGap.iloc[-1]['CumFreq'])'''对不同表达方法，如： QQ和qQ, Idnumber和idNumber,MOBILEPHONE和PHONE等 进行统一在时间切片内，计算 (1) 更新的频率 (2) 每种更新对象的种类个数 (3) 对重要信息如IDNUMBER,HASBUYCAR, MARRIAGESTATUSID, PHONE的更新。这一点，比如，申请人对自己的重要信息如身份证、是否有车、婚姻状况等等进行更改。表示这个人很有可能存在问题，会衍生出一个变量。 不过这里的程序，有bug。就是它会统计data3里面有多少个 IDNUMBER等等，然后map去对应的ID，但是，如果get不到的话，它会有默认值就是ID自己。所以有时候看到这个值很大，因为就是ID它自己。代码：data3GroupbyIdx['UserupdateInfo_' + str(tw) + str(item)] = data3GroupbyIdx['Idx'].map(lambda x: item_dict.get(x, x))'''data3['UserupdateInfo1'] = data3['UserupdateInfo1'].map(ChangeContent)data3GroupbyIdx = pd.DataFrame({'Idx':data3['Idx'].drop_duplicates()})time_window = [7, 30, 60, 90, 120, 150, 180]for tw in time_window:    data3['TruncatedLogInfo'] = data3['ListingInfo'].map(lambda x: x + datetime.timedelta(-tw))    temp = data3.loc[data3['UserupdateInfo'] >= data3['TruncatedLogInfo']]    #frequency of updating    freq_stats = temp.groupby(['Idx'])['UserupdateInfo1'].count().to_dict()    data3GroupbyIdx['UserupdateInfo_'+str(tw)+'_freq'] = data3GroupbyIdx['Idx'].map(lambda x: freq_stats.get(x,0))    # number of updated types    Idx_UserupdateInfo1 = temp[['Idx','UserupdateInfo1']].drop_duplicates()    uniq_stats = Idx_UserupdateInfo1.groupby(['Idx'])['UserupdateInfo1'].count().to_dict()    data3GroupbyIdx['UserupdateInfo_' + str(tw) + '_unique'] = data3GroupbyIdx['Idx'].map(lambda x: uniq_stats.get(x, x))    #average count of each type    data3GroupbyIdx['UserupdateInfo_' + str(tw) + '_avg_count'] = data3GroupbyIdx[['UserupdateInfo_'+str(tw)+'_freq', 'UserupdateInfo_' + str(tw) + '_unique']]. \        apply(lambda x: x[0] * 1.0 / x[1], axis=1)    #whether the applicant changed items like IDNUMBER,HASBUYCAR, MARRIAGESTATUSID, PHONE    # Idx_UserupdateInfo1 = {DataFrame}    #           Idx      UserupdateInfo1    # 23      10002             [_PHONE]    # 42      10006       [_EDUCATIONID]    # 43      10006         [_HASBUYCAR]    # 44      10006  [_MARRIAGESTATUSID]    # 45      10006             [_PHONE]    # ...       ...                  ...    # 372437   9995                [_QQ]    # 372438   9995  [_RESIDENCEADDRESS]    # 372439   9995    [_RESIDENCEPHONE]    # 372440   9995   [_RESIDENCETYPEID]    # 372441   9995    [_RESIDENCEYEARS]    Idx_UserupdateInfo1['UserupdateInfo1'] = Idx_UserupdateInfo1['UserupdateInfo1'].map(lambda x: [x])    # Idx_UserupdateInfo1_V2 = {Series}Idx    # 8                                                 [_PHONE]    # 16                                                [_PHONE]    # 17                                                [_PHONE]    # 18       [_EDUCATIONID, _HASBUYCAR, _IDNUMBER, _LASTUPD...    # 20       [_EDUCATIONID, _HASBUYCAR, _IDNUMBER, _LASTUPD...    #                                ...    # 91688    [_CITYID, _DISTRICTID, _EDUCATIONID, _HASBUYCA...    # 91689    [_CITYID, _DISTRICTID, _EDUCATIONID, _HASBUYCA...    # 91695    [_DISTRICTID, _LASTUPDATEDATE, _PHONE, _RESIDE...    # 91702    [_CITYID, _DISTRICTID, _EDUCATIONID, _HASBUYCA...    # 91703                                    [_LASTUPDATEDATE]    Idx_UserupdateInfo1_V2 = Idx_UserupdateInfo1.groupby(['Idx'])['UserupdateInfo1'].sum()    for item in ['_IDNUMBER','_HASBUYCAR','_MARRIAGESTATUSID','_PHONE']:        item_dict = Idx_UserupdateInfo1_V2.map(lambda x: int(item in x)).to_dict()        data3GroupbyIdx['UserupdateInfo_' + str(tw) + str(item)] = data3GroupbyIdx['Idx'].map(lambda x: item_dict.get(x, x))# Combine the above features with raw features in PPD_Training_Master_GBK_3_1_Training_SetallData = pd.concat([data2.set_index('Idx'), data3GroupbyIdx.set_index('Idx'), data1GroupbyIdx.set_index('Idx')],axis= 1)allData.to_csv(folderOfData+'allData_0.csv',encoding = 'gbk')######################################## Step 2: 对类别型变量和数值型变量进行补缺#######################################allData = pd.read_csv(folderOfData+'allData_0.csv',header = 0,encoding = 'gbk')allFeatures = list(allData.columns)allFeatures.remove('target')if 'Idx' in allFeatures:    allFeatures.remove('Idx')allFeatures.remove('ListingInfo')#检查是否有常数型变量(如果有的话就拿掉)，并且检查是类别型还是数值型变量"""有的时候，一些虽然是数值型变量，但是取值很少。这个时候可能这些是表示特殊意义的类别型"""numerical_var = []for col in allFeatures:    if len(set(allData[col])) == 1:        print('delete {} from the dataset because it is a constant'.format(col))        del allData[col]        allFeatures.remove(col)    else:        #  排除了缺失值        uniq_valid_vals = [i for i in allData[col] if i == i]        # 通过set()方法，很好地拿到了各个值        uniq_valid_vals = list(set(uniq_valid_vals))        if len(uniq_valid_vals) >= 10 and isinstance(uniq_valid_vals[0], numbers.Real):            numerical_var.append(col)categorical_var = [i for i in allFeatures if i not in numerical_var]#检查变量的最多值的占比情况,以及每个变量中占比最大的值records_count = allData.shape[0]# col_most_values{'学历': 0.86,'收入':0.45, '单位性质'：0.58,'UserInfo_19': 0.909}# col_large_value存放每一个变量col最大值对应的值 = {'UserInfo_19': '广东省', '收入'：23021}col_most_values,col_large_value = {},{}for col in allFeatures:   #  UserInfo_19          target    #   广东省                 30001    #   广西                    2000    #    '''    value_count = {Series}    山西省             945    广东省             2405    广西壮族自治区      1198    新疆维吾尔自治区    204    Name: UserInfo_19, dtype: int64    '''    value_count = allData[col].groupby(allData[col]).count()    col_most_values[col] = max(value_count)/records_count    # 这里要注意, index索引出来的不一定是数字，而是groupby出来的各种值。如果是'学历'这个变量的话，就是：本科、研究生、博士等等    # groupby什么，什么就是index    # 所以这个large_value起名字也很好，就是large_value的值。而不是index.    # print("value_count[value_count== max(value_count)].index = ")    # print(value_count[value_count== max(value_count)].index)    large_value = value_count[value_count== max(value_count)].index[0]    # value_count[value_count == max(value_count)] 这个公式，只会起过滤作用，选择条件是True的数据出来。也即：只有1行数据出来    # 测试了一下： 原来，index的值是：Float64Index([5.0], dtype='float64', name='UserInfo_3')，所以，必须写成index[0],这样index[0] = 5.0    # large_value = value_count[value_count == max(value_count)].index    col_large_value[col] = large_valuecol_most_values_df = pd.DataFrame.from_dict(col_most_values, orient = 'index')col_most_values_df.columns = ['max percent']col_most_values_df = col_most_values_df.sort_values(by = 'max percent', ascending = False)pcnt = list(col_most_values_df[:500]['max percent'])vars = list(col_most_values_df[:500].index)plt.bar(range(len(pcnt)), height = pcnt)plt.title('Largest Percentage of Single Value in Each Variable')plt.show()#               max percent# WeblogInfo_13  0.999700# SocialNetwork_11  0.999600说# WeblogInfo_55  0.999433#计算多数值占比超过90%的字段中，少数值的坏样本率是否会显著高于多数值# print("col_most_values_df[col_most_values_df['max percent']>=0.9].index = ")# print(col_most_values_df[col_most_values_df['max percent']>=0.9].index)large_percent_cols = list(col_most_values_df[col_most_values_df['max percent']>=0.9].index)bad_rate_diff = {}# print("col_large_value = ")# print(col_large_value)# print("large_percent_cols = ")# print(large_percent_cols)for col in large_percent_cols:    large_value = col_large_value[col]    temp = allData[[col,'target']]    temp[col] = temp.apply(lambda x: int(x[col]==large_value),axis=1)    bad_rate = temp.groupby(col).mean()    # print("bad_rate = ")    # print(bad_rate)    if bad_rate.iloc[0]['target'] == 0:        bad_rate_diff[col] = 0        continue    bad_rate_diff[col] = np.log(bad_rate.iloc[0]['target']/bad_rate.iloc[1]['target'])bad_rate_diff_sorted = sorted(bad_rate_diff.items(),key=lambda x: x[1], reverse=True)bad_rate_diff_sorted_values = [x[1] for x in bad_rate_diff_sorted]plt.bar(x = range(len(bad_rate_diff_sorted_values)), height = bad_rate_diff_sorted_values)plt.title("log of bad rate ratio in large varaibles in which 90% is single value")plt.show()#由于所有的少数值的坏样本率并没有显著高于多数值，意味着这些变量可以直接剔除for col in large_percent_cols:    if col in numerical_var:        numerical_var.remove(col)    else:        categorical_var.remove(col)    del allData[col]'''对类别型变量，如果缺失超过80%, 就删除，否则当成特殊的状态'''missing_pcnt_threshould_1 = 0.8for col in categorical_var:    missingRate = MissingCategorial(allData,col)    print('{0} has missing rate as {1}'.format(col,missingRate))    if missingRate > missing_pcnt_threshould_1:        categorical_var.remove(col)        del allData[col]    if 0 < missingRate < missing_pcnt_threshould_1:        uniq_valid_vals = [i for i in allData[col] if i == i]        uniq_valid_vals = list(set(uniq_valid_vals))        if isinstance(uniq_valid_vals[0], numbers.Real):            missing_position = allData.loc[allData[col] != allData[col]][col].index            not_missing_sample = [-1]*len(missing_position)            allData.loc[missing_position, col] = not_missing_sample        else:            # In this way we convert NaN to NAN, which is a string instead of np.nan            allData[col] = allData[col].map(lambda x: str(x).upper())allData_bk = allData.copy()'''检查数值型变量'''missing_pcnt_threshould_2 = 0.8deleted_var = []for col in numerical_var:    missingRate = MissingContinuous(allData, col)    print('{0} has missing rate as {1}'.format(col, missingRate))    if missingRate > missing_pcnt_threshould_2:        deleted_var.append(col)        print('we delete variable {} because of its high missing rate'.format(col))    else:        if missingRate > 0:            not_missing = allData.loc[allData[col] == allData[col]][col]            #makeuped = allData[col].map(lambda x: MakeupRandom(x, list(not_missing)))            # missing_positon 是一个index的list            missing_position = allData.loc[allData[col] != allData[col]][col].index            # 函数random.sample(list, n)作用是随机从list中取出n个元素            not_missing_sample = random.sample(list(not_missing), len(missing_position))            allData.loc[missing_position,col] = not_missing_sample            #del allData[col]            #allData[col] = makeuped            missingRate2 = MissingContinuous(allData, col)            print('missing rate after making up is:{}'.format(str(missingRate2)))if deleted_var != []:    for col in deleted_var:        numerical_var.remove(col)        del allData[col]allData.to_csv(folderOfData+'allData_1.csv', header=True,encoding='gbk', columns = allData.columns, index=False)allData = pd.read_csv(folderOfData+'allData_1.csv', header=0,encoding='gbk')#################################### Step 3: 基于卡方分箱法对变量进行分箱#####################################for each categorical variable, if it has distinct values more than 5, we use the ChiMerge to merge ittrainData = pd.read_csv(folderOfData+'allData_1.csv',header = 0, encoding='gbk',dtype = {'target': np.int64})allFeatures = list(trainData.columns)allFeatures.remove('ListingInfo')allFeatures.remove('target')#allFeatures.remove('Idx')print("开始基于卡方分箱法对变量进行分箱")# 将特征区分为数值型和类别型numerical_var = []for var in allFeatures:    uniq_vals = list(set(trainData[var]))    if np.nan in uniq_vals:        uniq_vals.remove( np.nan)    if len(uniq_vals) >= 10 and isinstance(uniq_vals[0], numbers.Real):        numerical_var.append(var)categorical_var = [i for i in allFeatures if i not in numerical_var]for col in categorical_var:    #for Chinese character, upper() is not valid    if col not in ['UserInfo_7','UserInfo_9','UserInfo_19']:        trainData[col] = trainData[col].map(lambda x: str(x).upper())'''对于类别型变量，按照以下方式处理1，如果变量的取值个数超过5，计算bad rate进行编码2，除此之外，其他任何类别型变量如果有某个取值中，对应的样本全部是坏样本或者是好样本，进行合并。'''deleted_features = []   #将处理过的变量删除，防止对后面建模的干扰encoded_features = {}   #将bad rate编码方式保存下来，在以后的测试和生产环境中需要使用merged_features = {}    #将类别型变量合并方案保留下来var_IV = {}  #save the IV values for binned features       #将IV值和WOE值保留var_WOE = {}for col in categorical_var:    print("现在正在处理 类别型变量。。处理的变量是：")    print('we are processing {}'.format(col))    if len(set(trainData[col]))>5:        print(" now the len this category is > 5 , 需要用bad rate做编码转换成数值型变量，再分箱")        print('{} is encoded with bad rate'.format(col))        col0 = str(col)+'_encoding'        #(1), 计算坏样本率并进行编码        encoding_result = BadRateEncoding(trainData, col, 'target')        # 0        0.082894        # 1        0.058025        # 2        0.082624        # 3        0.058025        # 4        0.058025        #            ...        # 29995    0.077460        # 29996    0.082041        # 29997    0.082624        # 29998    0.058025        # 29999    0.082041        # Name: UserInfo_1, Length: 30000, dtype: float64,        # 'bad_rate': {'-1.0': 0.0, '0.0': 0.0, '1.0': 0.058024568061520024, '2.0': 0.018867924528301886, '3.0': 0.08262393590385578, '4.0': 0.0774604479145264, '5.0': 0.0828936170212766, '6.0': 0.08204081632653061, '7.0': 0.07959479015918958}}        trainData[col0], br_encoding = encoding_result['encoding'],encoding_result['bad_rate']        #(2), 将(1)中的编码后的变量也加入数值型变量列表中，为后面的卡方分箱做准备        numerical_var.append(col0)        #(3), 保存编码结果，保持编码方案用于测试处理        encoded_features[col] = [col0, br_encoding]        # print("encoded_features = ")        # print(encoded_features)        #(4), 删除原始值        deleted_features.append(col)    else:        print("类别型变量处理中。本变量 类别数少于或等于5")        bad_bin = trainData.groupby([col])['target'].sum()        #对于类别数少于5个，但是出现0坏样本的特征需要做处理        if min(bad_bin) == 0:            print("说明出现了 0 坏样本的特征，需要处理")            print('{} has 0 bad sample!'.format(col))            col1 = str(col) + '_mergeByBadRate'            #(1), 找出最优合并方式，使得每一箱同时包含好坏样本            mergeBin = MergeBad0(trainData, col, 'target')            # (2), 依照(1)的结果对值进行合并           trainData[col1] = trainData[col].map(mergeBin)            maxPcnt = MaximumBinPcnt(trainData, col1)            #如果合并后导致有箱占比超过90%，就删除。            if maxPcnt > 0.9:                print("说明合并后导致有箱占比超过90%，需要删除。为什么能够直接删除？不允许计算10%的显著性？")                print('{} is deleted because of large percentage of single bin'.format(col))                deleted_features.append(col)                categorical_var.remove(col)                del trainData[col]                continue            #(3) 如果合并后的新的变量满足要求，就保留下来            print("说明合并后导致有箱占比 没有 超过90%，不需要删除。正常操作即可")            merged_features[col] = [col1, mergeBin]            WOE_IV = CalcWOE(trainData, col1, 'target')            var_WOE[col1] = WOE_IV['WOE']            var_IV[col1] = WOE_IV['IV']            #del trainData[col]            deleted_features.append(col)        else:            print("说明没有  0 坏样本的特征，按照最正常处理方式即可")            WOE_IV = CalcWOE(trainData, col, 'target')            var_WOE[col] = WOE_IV['WOE']            var_IV[col] = WOE_IV['IV']print("处理 类别型变量工作结束！ 开始处理数值型变量")var_cutoff = {}for col in numerical_var:    """    这是最重要的环节    """    print("正在处理数值型变量，变量是：")    print("{} is in processing".format(col))    col1 = str(col) + '_Bin'    #(1),用卡方分箱法进行分箱，并且保存每一个分割的端点。例如端点=[10,20,30]表示将变量分为x<10,1030.    #特别地，缺失值-1不参与分箱    '''    缺失值采用-1表示。如果有缺失值，则不参与分箱    cutOffPoints是分割点    '''    if -1 in set(trainData[col]):        special_attribute = [-1]    else:        special_attribute = []    cutOffPoints = ChiMerge(trainData, col, 'target',special_attribute=special_attribute)    var_cutoff[col] = cutOffPoints    trainData[col1] = trainData[col].map(lambda x: AssignBin(x, cutOffPoints,special_attribute=special_attribute))    #(2), check whether the bad rate is monotone    BRM = BadRateMonotone(trainData, col1, 'target',special_attribute=special_attribute)    if not BRM:        if special_attribute == []:            bin_merged = Monotone_Merge(trainData, 'target', col1)            removed_index = []            for bin in bin_merged:                if len(bin)>1:                    indices = [int(b.replace('Bin ','')) for b in bin]                    removed_index = removed_index+indices[0:-1]                    # removed_index = {list}[0]            # cutOffPoints = {list}: [10750.0, 26020.0, 49531.0, 79315.0]            # removed_point = {list}: [10750.0, 79315.0]            removed_point = [cutOffPoints[k] for k in removed_index]            for p in removed_point:                cutOffPoints.remove(p)            var_cutoff[col] = cutOffPoints            trainData[col1] = trainData[col].map(lambda x: AssignBin(x, cutOffPoints, special_attribute=special_attribute))        else:            cutOffPoints2 = [i for i in cutOffPoints if i not in special_attribute]            temp = trainData.loc[~trainData[col].isin(special_attribute)]            bin_merged = Monotone_Merge(temp, 'target', col1)            removed_index = []            for bin in bin_merged:                if len(bin) > 1:                    indices = [int(b.replace('Bin ', '')) for b in bin]                    # first                    #                    removed_index = removed_index + indices[0:-1]            removed_point = [cutOffPoints2[k] for k in removed_index]            for p in removed_point:                cutOffPoints2.remove(p)            cutOffPoints2 = cutOffPoints2 + special_attribute            var_cutoff[col] = cutOffPoints2            trainData[col1] = trainData[col].map(lambda x: AssignBin(x, cutOffPoints2, special_attribute=special_attribute))    maxPcnt = MaximumBinPcnt(trainData, col1)    if maxPcnt > 0.9:        # del trainData[col1]        deleted_features.append(col)        numerical_var.remove(col)        print('we delete {} because the maximum bin occupies more than 90%'.format(col))        continue    WOE_IV = CalcWOE(trainData, col1, 'target')    var_IV[col] = WOE_IV['IV']    var_WOE[col] = WOE_IV['WOE']    #del trainData[col]print("数值型变量工作结束")print("Step3 基于卡方分箱法对变量进行分箱, 工作完成后的结果")print("var_WOE = ")print(var_WOE)print("var_IV = ")print(var_IV)print("merged_features = ")print(merged_features)trainData.to_csv(folderOfData+'allData_2.csv', header=True,encoding='gbk', columns = trainData.columns, index=False)print("var_cutoff = ")print(var_cutoff)with open(folderOfData+'var_WOE.pkl',"wb") as f:    f.write(pickle.dumps(var_WOE))with open(folderOfData+'var_IV.pkl',"wb") as f:    f.write(pickle.dumps(var_IV))with open(folderOfData+'var_cutoff.pkl',"wb") as f:    f.write(pickle.dumps(var_cutoff))with open(folderOfData+'merged_features.pkl',"wb") as f:    f.write(pickle.dumps(merged_features))######################################### Step 4: WOE编码后的单变量分析与多变量分析#########################################trainData = pd.read_csv(folderOfData+'allData_2.csv', header=0, encoding='gbk')with open(folderOfData+'var_WOE.pkl',"rb") as f:    var_WOE = pickle.load(f)with open(folderOfData+'var_IV.pkl',"rb") as f:    var_IV = pickle.load(f)with open(folderOfData+'var_cutoff.pkl',"rb") as f:    var_cutoff = pickle.load(f)with open(folderOfData+'merged_features.pkl',"rb") as f:    merged_features = pickle.load(f)#将一些看起来像数值变量实际上是类别变量的字段转换成字符num2str = ['SocialNetwork_13','SocialNetwork_12','UserInfo_6','UserInfo_5','UserInfo_10','UserInfo_11','UserInfo_12','UserInfo_13','UserInfo_17']for col in num2str:    trainData[col] = trainData[col].map(lambda x: str(x))for col in var_WOE.keys():    print(col)    col2 = str(col)+"_WOE"    if col in var_cutoff.keys():        cutOffPoints = var_cutoff[col]        special_attribute = []        if - 1 in cutOffPoints:            special_attribute = [-1]        # 需要有给样本赋值bin的过程。因为数值型变量已经安装ChiMerge值进行了分箱。        binValue = trainData[col].map(lambda x: AssignBin(x, cutOffPoints,special_attribute=special_attribute))        trainData[col2] = binValue.map(lambda x: var_WOE[col][x])    else:        trainData[col2] = trainData[col].map(lambda x: var_WOE[col][x])trainData.to_csv(folderOfData+'allData_3.csv', header=True,encoding='gbk', columns = trainData.columns, index=False)### (i) 选择IV高于阈值的变量trainData = pd.read_csv(folderOfData+'allData_3.csv', header=0,encoding='gbk')all_IV = list(var_IV.values())all_IV = sorted(all_IV, reverse=True)plt.bar(x=range(len(all_IV)), height = all_IV)plt.title("IV sorted")plt.show()iv_threshould = 0.02varByIV = [k for k, v in var_IV.items() if v > iv_threshould]### (ii) 检查WOE编码后的变量的两两线性相关性var_IV_selected = {k:var_IV[k] for k in varByIV}var_IV_sorted = sorted(var_IV_selected.items(), key=lambda d:d[1], reverse = True)var_IV_sorted = [i[0] for i in var_IV_sorted]removed_var  = []roh_thresould = 0.6for i in range(len(var_IV_sorted)-1):    if var_IV_sorted[i] not in removed_var:        x1 = var_IV_sorted[i]+"_WOE"        for j in range(i+1,len(var_IV_sorted)):            if var_IV_sorted[j] not in removed_var:                x2 = var_IV_sorted[j] + "_WOE"                roh = np.corrcoef([trainData[x1], trainData[x2]])[0, 1]                if abs(roh) >= roh_thresould:                    print('the correlation coeffient between {0} and {1} is {2}'.format(x1, x2, str(roh)))                    if var_IV[var_IV_sorted[i]] > var_IV[var_IV_sorted[j]]:                        removed_var.append(var_IV_sorted[j])                    else:                        removed_var.append(var_IV_sorted[i])var_IV_sortet_2 = [i for i in var_IV_sorted if i not in removed_var]### (iii)检查是否有变量与其他所有变量的VIF > 10# 由于涉及到Matrix，所以需要把DataFrame转为Matrixfor i in range(len(var_IV_sortet_2)):    x0 = trainData[var_IV_sortet_2[i]+'_WOE']    x0 = np.array(x0)    # 之前，var_IV_sorted_2 = {list} : ['ThirdParty_Info_Period2_6', 'ThirdParty_Info_Period6_6', 'ThirdParty_Info_Period5_6', 'UserInfo_14_encoding', 'ThirdParty_Info_Period4_15', 'ThirdParty_Info_Period1_15', 'ThirdParty_Info_Period3_15', 'ThirdParty_Info_Period6_1', 'ThirdParty_Info_Period5_1', 'ThirdParty_Info_Period5_2', 'UserInfo_16_encoding', 'ThirdParty_Info_Period5_10', 'WeblogInfo_6', 'Idx', 'ThirdParty_Info_Period4_8', 'ThirdParty_Info_Period2_8', 'UserInfo_7_encoding', 'WeblogInfo_20_encoding', 'ThirdParty_Info_Period3_10', 'ThirdParty_Info_Period4_9', 'UserInfo_17', 'ThirdParty_Info_Period1_10', 'ThirdParty_Info_Period1_3', 'ThirdParty_Info_Period2_10', 'ThirdParty_Info_Period4_4', 'WeblogInfo_2_encoding', 'ThirdParty_Info_Period3_3', 'UserInfo_1_encoding', 'LogInfo1_30_avg_count', 'WeblogInfo_5', 'UserInfo_12']    # 之后，X_Col  = {list} : ['ThirdParty_Info_Period6_6_WOE', 'ThirdParty_Info_Period5_6_WOE', 'UserInfo_14_encoding_WOE', 'ThirdParty_Info_Period4_15_WOE', 'ThirdParty_Info_Period1_15_WOE', 'ThirdParty_Info_Period3_15_WOE', 'ThirdParty_Info_Period6_1_WOE', 'ThirdParty_Info_Period5_1_WOE', 'ThirdParty_Info_Period5_2_WOE', 'UserInfo_16_encoding_WOE', 'ThirdParty_Info_Period5_10_WOE', 'WeblogInfo_6_WOE', 'Idx_WOE', 'ThirdParty_Info_Period4_8_WOE', 'ThirdParty_Info_Period2_8_WOE', 'UserInfo_7_encoding_WOE', 'WeblogInfo_20_encoding_WOE', 'ThirdParty_Info_Period3_10_WOE', 'ThirdParty_Info_Period4_9_WOE', 'UserInfo_17_WOE', 'ThirdParty_Info_Period1_10_WOE', 'ThirdParty_Info_Period1_3_WOE', 'ThirdParty_Info_Period2_10_WOE', 'ThirdParty_Info_Period4_4_WOE', 'WeblogInfo_2_encoding_WOE', 'ThirdParty_Info_Period3_3_WOE', 'UserInfo_1_encoding_WOE', 'LogInfo1_30_avg_count_WOE', 'WeblogInfo_5_WOE', 'UserInfo_12_WOE']    X_Col = [k+'_WOE' for k in var_IV_sortet_2 if k != var_IV_sortet_2[i]]    X = trainData[X_Col]    X = np.matrix(X)    regr = LinearRegression()    clr= regr.fit(X, x0)    x_pred = clr.predict(X)    R2 = 1 - ((x_pred - x0) ** 2).sum() / ((x0 - x0.mean()) ** 2).sum()    vif = 1/(1-R2)    if vif > 10:        print("Warning: the vif for {0} is {1}".format(var_IV_sortet_2[i], vif))########################## Step 5: 应用逻辑回归模型########################### 这里 var_IV_sortet_2是变量名称multi_analysis = [i+'_WOE' for i in var_IV_sortet_2]y = trainData['target']X = trainData[multi_analysis].copy()X['intercept'] = [1]*X.shape[0]LR = sm.Logit(y, X).fit()summary = LR.summary2()pvals = LR.pvalues.to_dict()params = LR.params.to_dict()#发现有变量不显著，因此需要单独检验显著性'''把该变量单独拧出来，看是否显著'''varLargeP = {k: v for k,v in pvals.items() if v >= 0.1}varLargeP = sorted(varLargeP.items(), key=lambda d:d[1], reverse = True)varLargeP = [i[0] for i in varLargeP]p_value_list = {}for var in varLargeP:    # trainData[var] = {Series}    X_temp = trainData[var].copy().to_frame()    X_temp['intercept'] = [1] * X_temp.shape[0]    LR = sm.Logit(y, X_temp).fit()    p_value_list[var] = LR.pvalues[var]for k,v in p_value_list.items():    print("{0} has p-value of {1} in univariate regression".format(k,v))#发现有变量的系数为正，因此需要单独检验正确性varPositive = [k for k,v in params.items() if v >= 0]coef_list = {}for var in varPositive:    X_temp = trainData[var].copy().to_frame()    X_temp['intercept'] = [1] * X_temp.shape[0]    LR = sm.Logit(y, X_temp).fit()    coef_list[var] = LR.params[var]for k,v in coef_list.items():    print("{0} has coefficient of {1} in univariate regression".format(k,v))selected_var = [multi_analysis[0]]for var in multi_analysis[1:]:    try_vars = selected_var+[var]    X_temp = trainData[try_vars].copy()    X_temp['intercept'] = [1] * X_temp.shape[0]    LR = sm.Logit(y, X_temp).fit()    #summary = LR.summary2()    pvals, params = LR.pvalues, LR.params    del params['intercept']    if max(pvals)<0.1 and max(params)<0:        selected_var.append(var)# X_temp = {DataFrame}        ThirdParty_Info_Period2_6_WOE  ...  intercept# 0                           0.357555  ...          1# 1                          -0.413268  ...          1# 2                          -0.413268  ...          1# 3                           0.357555  ...          1# 4                           0.357555  ...          1# ...                              ...  ...        ...# 29995                      -0.056315  ...          1# 29996                      -0.056315  ...          1# 29997                       0.357555  ...          1# 29998                       0.357555  ...          1# 29999                       0.357555  ...          1## [30000 rows x 19 columns]# y = {Series} 0        0# 1        0# 2        0# 3        0# 4        0#         ..# 29995    0# 29996    0# 29997    0# 29998    0# 29999    0# Name: target, Length: 30000, dtype: int64# try_vars = {list} : ['ThirdParty_Info_Period2_6_WOE', 'ThirdParty_Info_Period6_6_WOE', 'ThirdParty_Info_Period5_6_WOE', 'UserInfo_14_encoding_WOE', 'ThirdParty_Info_Period1_15_WOE', 'ThirdParty_Info_Period3_15_WOE', 'ThirdParty_Info_Period6_1_WOE', 'UserInfo_16_encoding_WOE', 'WeblogInfo_4_WOE', 'ThirdParty_Info_Period2_8_WOE', 'UserInfo_7_encoding_WOE', 'WeblogInfo_20_encoding_WOE', 'UserInfo_17_WOE', 'ThirdParty_Info_Period1_10_WOE', 'ThirdParty_Info_Period2_10_WOE', 'WeblogInfo_2_encoding_WOE', 'LogInfo1_30_avg_count_WOE', 'UserInfo_12_WOE']# try_vars._len_ = {int} 18LR.summary2()print(LR.summary2())y_pred = LR.predict(X_temp)y_result = pd.DataFrame({'y_pred':y_pred, 'y_real':list(trainData['target'])})roc_auc_score_result = roc_auc_score(trainData['target'], y_pred)print("roc_auc_score_result = ", roc_auc_score_result)################# Step 6: 尺度化#################plt.show()scores = Prob2Score(y_pred, 200, 100)# scores.to_excel("scores.xlsx")plt.title("Score Distribution")plt.xlabel("Score")plt.ylabel("Quantity")plt.hist(scores,bins=100)# plt.title("score-transfer")plt.show()print("完成分数转换！ ")print("Step 6 结束！")######################## Step 7: 生成ROC图#######################print("开始 step 7")import warningswarnings.filterwarnings('ignore')def proba_conversion(x, threshold3 = 0.3):    if (x >= threshold3 ):        return 1    else:        return 0scores = Prob2Score(y_pred, 200, 100)trainData['predication'] = scoresscorecard = pd.DataFrame({'y_pred':y_pred, 'y_real':list(trainData['target']),'score':scores})ks_result = KS(scorecard,'score','y_real')print("ks_result = ", ks_result)# ROC_AUC(df, score, target)roc_auc_score_result2 = ROC_AUC(trainData, 'predication', 'target')# roc_auc_score_result2 = ROC_AUC(trainData, 'y_pred', 'target', )# 也可用sklearn带的函数roc_auc_score_result_directly = roc_auc_score(trainData['target'], y_pred)# print("")print("roc_auc_score = ", roc_auc_score_result_directly)#roc_auc_score_result = roc_auc_score(trainData['target'], y_pred)from sklearn.metrics import confusion_matrix, precision_recall_curveimport itertoolsplt.rcParams['font.sans-serif'] = ['SimHei']def plot_precision_recall():    plt.step(recall, precision, color = 'b', alpha = 0.2, where = 'post')    plt.fill_between(recall, precision, step = 'post', alpha = 0.2, color = 'b')    plt.plot(recall, precision, linewidth=2)    plt.xlim([0.0,1])    plt.ylim([0.0,1.05])    plt.xlabel('召回率')    plt.ylabel('精确率')    plt.title('精确率 - 召回率 曲线')    plt.show()def show_metrics():    tp = cm[1,1]    fn = cm[1,0]    fp = cm[0,1]    tn = cm[0,0]    print('精确率：{:.3f}'.format(tp/(tp+fp)))    print('召回率：{:.3f}'.format(tp/(tp+fn)))    print('F1 值：{:.3f}'.format(  2*(  ( (tp/(tp+fp))*(tp/(tp+fn)) )/( (tp/(tp+fp))+(tp/(tp+fn)) )   )  ))def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):    plt.figure()    plt.imshow(cm, interpolation='nearest', cmap=cmap)    plt.title(title)    plt.colorbar()    tick_marks = np.arange(len(classes))    plt.xticks(tick_marks, classes, rotation=0)    plt.yticks(tick_marks, classes)    thresh = cm.max() / 2.    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):        plt.text(j, i, cm[i, j],                 horizontalalignment='center',                 color='white' if cm[i, j] > thresh else 'black')    plt.tight_layout()    plt.ylabel('True label')    plt.xlabel('Predicted label')    plt.show()print("结束")

三、运行结果

下面图片，是代码在Jupyter Note 上运行后展示的结果

四、具体运行过程

具体运行过程，可以在码云(https://gitee.com/)上关注用户号：abcgz