【風控建模】XGBoost算法構建信用評分卡模型

正火速編輯!
在這裏插入圖片描述
需要數據集,請留言聯繫筆者!!!
話不多說,直接上代碼。

主程序

import pandas as pd
from xgboost.sklearn import XGBClassifier
from sklearn.model_selection import GridSearchCV
import datetime
import collections
import numpy as np
import numbers
import random
import sys
_path = r'C:\Users\A3\Desktop\LR_scorecard'
sys.path.append(_path)
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from importlib import reload
from matplotlib import pyplot as plt
reload(sys)
#sys.setdefaultencoding( "utf-8")
# -*- coding: utf-8 -*-

### 對時間窗口,計算累計產比 ###
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
    return freq_tw


def DeivdedByZero(nominator, denominator):
    '''
    當分母爲0時,返回0;否則返回正常值
    '''
    if denominator == 0:
        return 0
    else:
        return nominator*1.0/denominator


#對某些統一的字段進行統一
def ChangeContent(x):
    y = x.upper()
    if y == '_MOBILEPHONE':
        y = '_PHONE'
    return y

def MissingCategorial(df,x):
    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]


def Outlier_Dectection(df,x):
    '''
    :param df:
    :param x:
    :return:
    '''
    p25, p75 = np.percentile(df[x], 25),np.percentile(df[x], 75)
    d = p75 - p25
    upper, lower =  p75 + 1.5*d, p25-1.5*d
    truncation = df[x].map(lambda x: max(min(upper, x), lower))
    return truncation

############################################################
#Step 0: 數據分析的初始工作, 包括讀取數據文件、檢查用戶Id的一致性等#
############################################################

folderOfData = 'C:/Users/A3/Desktop/XGBoost_scorecard/'
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', header = 0,encoding = 'gbk')
data3 = pd.read_csv(folderOfData+'PPD_Userupdate_Info_3_1_Training_Set.csv', header = 0)

#將數據集分爲訓練集與測試集
all_ids = data2['Idx']
train_ids, test_ids = train_test_split(all_ids, test_size=0.3)
train_ids = pd.DataFrame(train_ids)
test_ids = pd.DataFrame(test_ids)


data1_train = pd.merge(left=train_ids,right = data1, on='Idx', how='inner')
data2_train = pd.merge(left=train_ids,right = data2, on='Idx', how='inner')
data3_train = pd.merge(left=train_ids,right = data3, on='Idx', how='inner')

data1_test = pd.merge(left=test_ids,right = data1, on='Idx', how='inner')
data2_test = pd.merge(left=test_ids,right = data2, on='Idx', how='inner')
data3_test = pd.merge(left=test_ids,right = data3, on='Idx', how='inner')



#############################################################################################
# Step 1: 從PPD_LogInfo_3_1_Training_Set &  PPD_Userupdate_Info_3_1_Training_Set數據中衍生特徵#
#############################################################################################
# compare whether the four city variables match
data2_train['city_match'] = data2_train.apply(lambda x: int(x.UserInfo_2 == x.UserInfo_4 == x.UserInfo_8 == x.UserInfo_20),axis = 1)
del data2_train['UserInfo_2']
del data2_train['UserInfo_4']
del data2_train['UserInfo_8']
del data2_train['UserInfo_20']

### 提取申請日期,計算日期差,查看日期差的分佈
data1_train['logInfo'] = data1_train['LogInfo3'].map(lambda x: datetime.datetime.strptime(x,'%Y-%m-%d'))
data1_train['Listinginfo'] = data1_train['Listinginfo1'].map(lambda x: datetime.datetime.strptime(x,'%Y-%m-%d'))
data1_train['ListingGap'] = data1_train[['logInfo','Listinginfo']].apply(lambda x: (x[1]-x[0]).days,axis = 1)

### 提取申請日期,計算日期差,查看日期差的分佈
'''
使用180天作爲最大的時間窗口計算新特徵
所有可以使用的時間窗口可以有7 days, 30 days, 60 days, 90 days, 120 days, 150 days and 180 days.
在每個時間窗口內,計算總的登錄次數,不同的登錄方式,以及每種登錄方式的平均次數
'''
time_window = [7, 30, 60, 90, 120, 150, 180]
var_list = ['LogInfo1','LogInfo2']
data1GroupbyIdx = pd.DataFrame({'Idx':data1_train['Idx'].drop_duplicates()})

for tw in time_window:
    data1_train['TruncatedLogInfo'] = data1_train['Listinginfo'].map(lambda x: x + datetime.timedelta(-tw))
    temp = data1_train.loc[data1_train['logInfo'] >= data1_train['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
        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_train['ListingInfo'] = data3_train['ListingInfo1'].map(lambda x: datetime.datetime.strptime(x,'%Y/%m/%d'))
data3_train['UserupdateInfo'] = data3_train['UserupdateInfo2'].map(lambda x: datetime.datetime.strptime(x,'%Y/%m/%d'))
data3_train['ListingGap'] = data3_train[['UserupdateInfo','ListingInfo']].apply(lambda x: (x[1]-x[0]).days,axis = 1)
collections.Counter(data3_train['ListingGap'])
hist_ListingGap = np.histogram(data3_train['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'])

'''
對 QQ和qQ, Idnumber和idNumber,MOBILEPHONE和PHONE 進行統一
在時間切片內,計算
 (1) 更新的頻率
 (2) 每種更新對象的種類個數
 (3) 對重要信息如IDNUMBER,HASBUYCAR, MARRIAGESTATUSID, PHONE的更新
'''
data3_train['UserupdateInfo1'] = data3_train['UserupdateInfo1'].map(ChangeContent)
data3GroupbyIdx = pd.DataFrame({'Idx':data3_train['Idx'].drop_duplicates()})

time_window = [7, 30, 60, 90, 120, 150, 180]
for tw in time_window:
    data3_train['TruncatedLogInfo'] = data3_train['ListingInfo'].map(lambda x: x + datetime.timedelta(-tw))
    temp = data3_train.loc[data3_train['UserupdateInfo'] >= data3_train['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['UserupdateInfo1'] = Idx_UserupdateInfo1['UserupdateInfo1'].map(lambda x: [x])
    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_Set
allData = pd.concat([data2_train.set_index('Idx'), data3GroupbyIdx.set_index('Idx'), data1GroupbyIdx.set_index('Idx')],axis= 1)
allData.to_csv(folderOfData+'allData_0.csv',encoding = 'gbk')




########################################
# Step 2: 對類別型變量和數值型變量進行預處理#
########################################
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')

#檢查是否有常數型變量,並且檢查是類別型還是數值型變量
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:
        uniq_valid_vals = [i for i in allData[col] if i == i]
        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]


#檢查變量的最多值的佔比情況,以及每個變量中佔比最大的值
records_count = allData.shape[0]
col_most_values,col_large_value = {},{}
for col in allFeatures:
    value_count = allData[col].groupby(allData[col]).count()
    col_most_values[col] = max(value_count)/records_count
    large_value = value_count[value_count== max(value_count)].index[0]
    col_large_value[col] = large_value
col_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')

#計算多數值佔比超過90%的字段中,少數值的壞樣本率是否會顯著高於多數值
large_percent_cols = list(col_most_values_df[col_most_values_df['max percent']>=0.9].index)
bad_rate_diff = {}
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()
    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)

#由於所有的少數值的壞樣本率並沒有顯著高於多數值,意味着這些變量可以直接剔除
for col in large_percent_cols:
    if col in numerical_var:
        numerical_var.remove(col)
    else:
        categorical_var.remove(col)
    del allData[col]

'''
對類別型變量,如果缺失超過80%, 就刪除,否則保留。
'''
missing_pcnt_threshould_1 = 0.8
for 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]
allData_bk = allData.copy()

'''
用one-hot對類別型變量進行編碼
'''
dummy_map = {}
dummy_columns = []
for raw_col in categorical_var:
    dummies = pd.get_dummies(allData.loc[:, raw_col], prefix=raw_col)
    col_onehot = pd.concat([allData[raw_col], dummies], axis=1)
    col_onehot = col_onehot.drop_duplicates()
    allData = pd.concat([allData, dummies], axis=1)
    del allData[raw_col]
    dummy_map[raw_col] = col_onehot
    dummy_columns = dummy_columns + list(dummies)




with open(folderOfData+'dummy_map.pkl',"wb") as f:
    f.write(pickle.dumps(dummy_map))

with open(folderOfData+'dummy_columns.pkl',"wb") as f:
    f.write(pickle.dumps(dummy_columns))


'''
檢查數值型變量
'''
missing_pcnt_threshould_2 = 0.8
deleted_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_position = allData.loc[allData[col] != allData[col]][col].index
            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]

'''
對極端值變量做處理。
'''
max_min_standardized = {}
for col in numerical_var:
    truncation = Outlier_Dectection(allData, col)
    upper, lower = max(truncation), min(truncation)
    d = upper - lower
    if d == 0:
        print("{} is almost a constant".format(col))
        numerical_var.remove(col)
        continue
    allData[col] = truncation.map(lambda x: (upper - x)/d)
    max_min_standardized[col] = [lower, upper]



with open(folderOfData+'max_min_standardized.pkl',"wb") as f:
    f.write(pickle.dumps(max_min_standardized))


allData.to_csv(folderOfData+'allData_1_XGBoost.csv', header=True,encoding='gbk', columns = allData.columns, index=False)

allData = pd.read_csv(folderOfData+'allData_1_XGBoost.csv', header=0,encoding='gbk')


####################################
# Step 3: 構建XGBoost模型用於違約預測 #
####################################
allData = pd.read_csv(folderOfData+'allData_1_XGBoost.csv', header=0,encoding='gbk')
all_features = list(allData.columns)
all_features.remove('target')
#all_features.remove('ListingInfo')
X_train, y_train = allData[all_features], allData['target']

param_test1 = {'max_depth':range(3,10,2), 'min_child_weight':range(1,6,2)}
gsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1, n_estimators=100, gamma=0, subsample=0.8, colsample_bytree=0.8,
                                                  objective= 'binary:logistic',nthread=4,scale_pos_weight=1,seed=27),
                        param_grid = param_test1,scoring='roc_auc',n_jobs=4,iid=False,cv=5)
gsearch1.fit(X_train,y_train)
best_max_depth, best_min_child_weight = gsearch1.best_params_['max_depth'],gsearch1.best_params_['min_child_weight']   #9,3

param_test2 = {'gamma':[i/10.0 for i in range(0,5)]}
gsearch2 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1, n_estimators=100, subsample=0.8, colsample_bytree=0.8, max_depth= best_max_depth,
                                                  min_child_weight=best_min_child_weight, objective= 'binary:logistic',nthread=4,scale_pos_weight=1,seed=27),
                        param_grid = param_test2,scoring='roc_auc',n_jobs=4,iid=False,cv=5)
gsearch2.fit(X_train,y_train)
best_gamma = gsearch2.best_params_['gamma']  #0


param_test3 = {'subsample':[i/10.0 for i in range(6,10)],'colsample_bytree':[i/10.0 for i in range(6,10)]}
gsearch3 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1, n_estimators=100, max_depth= best_max_depth, gamma=best_gamma,
                                                  min_child_weight=best_min_child_weight, objective= 'binary:logistic',nthread=4,scale_pos_weight=1,seed=27),
                        param_grid = param_test3,scoring='roc_auc',n_jobs=4,iid=False,cv=5)
gsearch3.fit(X_train,y_train)
best_colsample_bytree, best_subsample = gsearch3.best_params_['colsample_bytree'], gsearch3.best_params_['subsample']  #0.8, 0.6


param_test4 = {'reg_alpha':[0.01,0.1,1,10,50,100,200,500]}
gsearch4 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1, n_estimators=100, max_depth= best_max_depth, gamma=best_gamma,
                                                  colsample_bytree = best_colsample_bytree, subsample = best_subsample,
                                                  min_child_weight=best_min_child_weight, objective= 'binary:logistic',nthread=4,scale_pos_weight=1,seed=27),
                        param_grid = param_test4,scoring='roc_auc',n_jobs=4,iid=False,cv=5)
gsearch4.fit(X_train,y_train)
best_reg_alpha = gsearch4.best_params_['reg_alpha']   #50


param_test5 = {'n_estimators':range(100,401,10)}
gsearch5 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1,  max_depth= best_max_depth, gamma=best_gamma,
                                                  colsample_bytree = best_colsample_bytree, subsample = best_subsample,reg_alpha=best_reg_alpha,
                                                  min_child_weight=best_min_child_weight, objective= 'binary:logistic',nthread=4,scale_pos_weight=1,seed=27),
                        param_grid = param_test5,scoring='roc_auc',n_jobs=4,iid=False,cv=5)
gsearch5.fit(X_train,y_train)
best_n_estimators = gsearch5.best_params_     #390


#用獲取得到的最優參數再次訓練模型
best_xgb = XGBClassifier(learning_rate =0.1, n_estimators=100, max_depth= best_max_depth, gamma=best_gamma,
                         colsample_bytree = best_colsample_bytree, subsample = best_subsample, reg_alpha=best_reg_alpha,
                         min_child_weight=best_min_child_weight, objective= 'binary:logistic',nthread=4,scale_pos_weight=1,seed=27)
best_xgb.fit(X_train,y_train)
y_pred = best_xgb.predict_proba(X_train)[:,1]
roc_auc_score(y_train, y_pred)   #0.8
feature_importance = best_xgb.feature_importances_


#利用特徵重要性篩去一部分無用的變量
X_train_temp = X_train.copy()
features_in_model = all_features
while(min(feature_importance)<0.00001):
    features_in_model = [features_in_model[i] for i in range(len(feature_importance)) if feature_importance[i] > 0.00001]
    X_train_temp= X_train_temp[features_in_model]
    best_xgb.fit(X_train_temp, y_train)
    feature_importance = best_xgb.feature_importances_

y_pred = best_xgb.predict_proba(X_train_temp)[:,1]
roc_auc_score(y_train, y_pred)  # 0.8
print('There are {} features in the raw data'.format(X_train.shape[1]))  #400
print('There are {} features in the reduced data'.format(X_train_temp.shape[1]))  #158

功能模塊

import numpy as np
import pandas as pd

def SplitData(df, col, numOfSplit, special_attribute=[]):
    '''
    :param df: 按照col排序後的數據集
    :param col: 待分箱的變量
    :param numOfSplit: 切分的組別數
    :param special_attribute: 在切分數據集的時候,某些特殊值需要排除在外
    :return: 在原數據集上增加一列,把原始細粒度的col重新劃分成粗粒度的值,便於分箱中的合併處理
    '''
    df2 = df.copy()
    if special_attribute != []:
        df2 = df.loc[~df[col].isin(special_attribute)]
    N = df2.shape[0]
    n = int(N/numOfSplit)
    splitPointIndex = [i*n for i in range(1,numOfSplit)]
    rawValues = sorted(list(df2[col]))
    splitPoint = [rawValues[i] for i in splitPointIndex]
    splitPoint = sorted(list(set(splitPoint)))
    return splitPoint

def MaximumBinPcnt(df,col):
    '''
    :return: 數據集df中,變量col的分佈佔比
    '''
    N = df.shape[0]
    total = df.groupby([col])[col].count()
    pcnt = total*1.0/N
    return max(pcnt)



def Chi2(df, total_col, bad_col):
    '''
    :param df: 包含全部樣本總計與壞樣本總計的數據框
    :param total_col: 全部樣本的個數
    :param bad_col: 壞樣本的個數
    :return: 卡方值
    '''
    df2 = df.copy()
    # 求出df中,總體的壞樣本率和好樣本率
    badRate = sum(df2[bad_col])*1.0/sum(df2[total_col])
    # 當全部樣本只有好或者壞樣本時,卡方值爲0
    if badRate in [0,1]:
        return 0
    df2['good'] = df2.apply(lambda x: x[total_col] - x[bad_col], axis = 1)
    goodRate = sum(df2['good']) * 1.0 / sum(df2[total_col])
    # 期望壞(好)樣本個數=全部樣本個數*平均壞(好)樣本佔比
    df2['badExpected'] = df[total_col].apply(lambda x: x*badRate)
    df2['goodExpected'] = df[total_col].apply(lambda x: x * goodRate)
    badCombined = zip(df2['badExpected'], df2[bad_col])
    goodCombined = zip(df2['goodExpected'], df2['good'])
    badChi = [(i[0]-i[1])**2/i[0] for i in badCombined]
    goodChi = [(i[0] - i[1]) ** 2 / i[0] for i in goodCombined]
    chi2 = sum(badChi) + sum(goodChi)
    return chi2



def BinBadRate(df, col, target, grantRateIndicator=0):
    '''
    :param df: 需要計算好壞比率的數據集
    :param col: 需要計算好壞比率的特徵
    :param target: 好壞標籤
    :param grantRateIndicator: 1返回總體的壞樣本率,0不返回
    :return: 每箱的壞樣本率,以及總體的壞樣本率(當grantRateIndicator==1時)
    '''
    total = df.groupby([col])[target].count()
    total = pd.DataFrame({'total': total})
    bad = df.groupby([col])[target].sum()
    bad = pd.DataFrame({'bad': bad})
    regroup = total.merge(bad, left_index=True, right_index=True, how='left')
    regroup.reset_index(level=0, inplace=True)
    regroup['bad_rate'] = regroup.apply(lambda x: x.bad * 1.0 / x.total, axis=1)
    dicts = dict(zip(regroup[col],regroup['bad_rate']))
    if grantRateIndicator==0:
        return (dicts, regroup)
    N = sum(regroup['total'])
    B = sum(regroup['bad'])
    overallRate = B * 1.0 / N
    return (dicts, regroup, overallRate)



def AssignGroup(x, bin):
    '''
    :return: 數值x在區間映射下的結果。例如,x=2,bin=[0,3,5], 由於0<x<3,x映射成3
    '''
    N = len(bin)
    if x<=min(bin):
        return min(bin)
    elif x>max(bin):
        return 10e10
    else:
        for i in range(N-1):
            if bin[i] < x <= bin[i+1]:
                return bin[i+1]


def ChiMerge(df, col, target, max_interval=5,special_attribute=[],minBinPcnt=0):
    '''
    :param df: 包含目標變量與分箱屬性的數據框
    :param col: 需要分箱的屬性
    :param target: 目標變量,取值0或1
    :param max_interval: 最大分箱數。如果原始屬性的取值個數低於該參數,不執行這段函數
    :param special_attribute: 不參與分箱的屬性取值
    :param minBinPcnt:最小箱的佔比,默認爲0
    :return: 分箱結果
    '''
    colLevels = sorted(list(set(df[col])))
    N_distinct = len(colLevels)
    if N_distinct <= max_interval:  #如果原始屬性的取值個數低於max_interval,不執行這段函數
        print("The number of original levels for {} is less than or equal to max intervals".format(col))
        return colLevels[:-1]
    else:
        if len(special_attribute)>=1:
            df1 = df.loc[df[col].isin(special_attribute)]
            df2 = df.loc[~df[col].isin(special_attribute)]
        else:
            df2 = df.copy()
        N_distinct = len(list(set(df2[col])))

        # 步驟一: 通過col對數據集進行分組,求出每組的總樣本數與壞樣本數
        if N_distinct > 100:
            split_x = SplitData(df2, col, 100)
            df2['temp'] = df2[col].map(lambda x: AssignGroup(x, split_x))
        else:
            df2['temp'] = df2[col]
        # 總體bad rate將被用來計算expected bad count
        (binBadRate, regroup, overallRate) = BinBadRate(df2, 'temp', target, grantRateIndicator=1)

        # 首先,每個單獨的屬性值將被分爲單獨的一組
        # 對屬性值進行排序,然後兩兩組別進行合併
        colLevels = sorted(list(set(df2['temp'])))
        groupIntervals = [[i] for i in colLevels]

        # 步驟二:建立循環,不斷合併最優的相鄰兩個組別,直到:
        # 1,最終分裂出來的分箱數<=預設的最大分箱數
        # 2,每箱的佔比不低於預設值(可選)
        # 3,每箱同時包含好壞樣本
        # 如果有特殊屬性,那麼最終分裂出來的分箱數=預設的最大分箱數-特殊屬性的個數
        split_intervals = max_interval - len(special_attribute)
        while (len(groupIntervals) > split_intervals):  # 終止條件: 當前分箱數=預設的分箱數
            # 每次循環時, 計算合併相鄰組別後的卡方值。具有最小卡方值的合併方案,是最優方案
            chisqList = []
            for k in range(len(groupIntervals)-1):
                temp_group = groupIntervals[k] + groupIntervals[k+1]
                df2b = regroup.loc[regroup['temp'].isin(temp_group)]
                chisq = Chi2(df2b, 'total', 'bad')
                chisqList.append(chisq)
            best_comnbined = chisqList.index(min(chisqList))
            groupIntervals[best_comnbined] = groupIntervals[best_comnbined] + groupIntervals[best_comnbined+1]
            # 當將最優的相鄰的兩個變量合併在一起後,需要從原來的列表中將其移除。例如,將[3,4,5] 與[6,7]合併成[3,4,5,6,7]後,需要將[3,4,5] 與[6,7]移除,保留[3,4,5,6,7]
            groupIntervals.remove(groupIntervals[best_comnbined+1])
        groupIntervals = [sorted(i) for i in groupIntervals]
        cutOffPoints = [max(i) for i in groupIntervals[:-1]]

        # 檢查是否有箱沒有好或者壞樣本。如果有,需要跟相鄰的箱進行合併,直到每箱同時包含好壞樣本
        groupedvalues = df2['temp'].apply(lambda x: AssignBin(x, cutOffPoints))
        df2['temp_Bin'] = groupedvalues
        (binBadRate,regroup) = BinBadRate(df2, 'temp_Bin', target)
        [minBadRate, maxBadRate] = [min(binBadRate.values()),max(binBadRate.values())]
        while minBadRate ==0 or maxBadRate == 1:
            # 找出全部爲好/壞樣本的箱
            indexForBad01 = regroup[regroup['bad_rate'].isin([0,1])].temp_Bin.tolist()
            bin=indexForBad01[0]
            # 如果是最後一箱,則需要和上一個箱進行合併,也就意味着分裂點cutOffPoints中的最後一個需要移除
            if bin == max(regroup.temp_Bin):
                cutOffPoints = cutOffPoints[:-1]
            # 如果是第一箱,則需要和下一個箱進行合併,也就意味着分裂點cutOffPoints中的第一個需要移除
            elif bin == min(regroup.temp_Bin):
                cutOffPoints = cutOffPoints[1:]
            # 如果是中間的某一箱,則需要和前後中的一個箱進行合併,依據是較小的卡方值
            else:
                # 和前一箱進行合併,並且計算卡方值
                currentIndex = list(regroup.temp_Bin).index(bin)
                prevIndex = list(regroup.temp_Bin)[currentIndex - 1]
                df3 = df2.loc[df2['temp_Bin'].isin([prevIndex, bin])]
                (binBadRate, df2b) = BinBadRate(df3, 'temp_Bin', target)
                chisq1 = Chi2(df2b, 'total', 'bad')
                # 和後一箱進行合併,並且計算卡方值
                laterIndex = list(regroup.temp_Bin)[currentIndex + 1]
                df3b = df2.loc[df2['temp_Bin'].isin([laterIndex, bin])]
                (binBadRate, df2b) = BinBadRate(df3b, 'temp_Bin', target)
                chisq2 = Chi2(df2b, 'total', 'bad')
                if chisq1 < chisq2:
                    cutOffPoints.remove(cutOffPoints[currentIndex - 1])
                else:
                    cutOffPoints.remove(cutOffPoints[currentIndex])
            # 完成合並之後,需要再次計算新的分箱準則下,每箱是否同時包含好壞樣本
            groupedvalues = df2['temp'].apply(lambda x: AssignBin(x, cutOffPoints))
            df2['temp_Bin'] = groupedvalues
            (binBadRate, regroup) = BinBadRate(df2, 'temp_Bin', target)
            [minBadRate, maxBadRate] = [min(binBadRate.values()), max(binBadRate.values())]
        # 需要檢查分箱後的最小佔比
        if minBinPcnt > 0:
            groupedvalues = df2['temp'].apply(lambda x: AssignBin(x, cutOffPoints))
            df2['temp_Bin'] = groupedvalues
            valueCounts = groupedvalues.value_counts().to_frame()
            N = sum(valueCounts['temp'])
            valueCounts['pcnt'] = valueCounts['temp'].apply(lambda x: x * 1.0 / N)
            valueCounts = valueCounts.sort_index()
            minPcnt = min(valueCounts['pcnt'])
            while minPcnt < minBinPcnt and len(cutOffPoints) > 2:
                # 找出佔比最小的箱
                indexForMinPcnt = valueCounts[valueCounts['pcnt'] == minPcnt].index.tolist()[0]
                # 如果佔比最小的箱是最後一箱,則需要和上一個箱進行合併,也就意味着分裂點cutOffPoints中的最後一個需要移除
                if indexForMinPcnt == max(valueCounts.index):
                    cutOffPoints = cutOffPoints[:-1]
                # 如果佔比最小的箱是第一箱,則需要和下一個箱進行合併,也就意味着分裂點cutOffPoints中的第一個需要移除
                elif indexForMinPcnt == min(valueCounts.index):
                    cutOffPoints = cutOffPoints[1:]
                # 如果佔比最小的箱是中間的某一箱,則需要和前後中的一個箱進行合併,依據是較小的卡方值
                else:
                    # 和前一箱進行合併,並且計算卡方值
                    currentIndex = list(valueCounts.index).index(indexForMinPcnt)
                    prevIndex = list(valueCounts.index)[currentIndex - 1]
                    df3 = df2.loc[df2['temp_Bin'].isin([prevIndex, indexForMinPcnt])]
                    (binBadRate, df2b) = BinBadRate(df3, 'temp_Bin', target)
                    chisq1 = Chi2(df2b, 'total', 'bad')
                    # 和後一箱進行合併,並且計算卡方值
                    laterIndex = list(valueCounts.index)[currentIndex + 1]
                    df3b = df2.loc[df2['temp_Bin'].isin([laterIndex, indexForMinPcnt])]
                    (binBadRate, df2b) = BinBadRate(df3b, 'temp_Bin', target)
                    chisq2 = Chi2(df2b, 'total', 'bad')
                    if chisq1 < chisq2:
                        cutOffPoints.remove(cutOffPoints[currentIndex - 1])
                    else:
                        cutOffPoints.remove(cutOffPoints[currentIndex])
                groupedvalues = df2['temp'].apply(lambda x: AssignBin(x, cutOffPoints))
                df2['temp_Bin'] = groupedvalues
                valueCounts = groupedvalues.value_counts().to_frame()
                valueCounts['pcnt'] = valueCounts['temp'].apply(lambda x: x * 1.0 / N)
                valueCounts = valueCounts.sort_index()
                minPcnt = min(valueCounts['pcnt'])
        cutOffPoints = special_attribute + cutOffPoints
        return cutOffPoints



def BadRateEncoding(df, col, target):
    '''
    :return: 在數據集df中,用壞樣本率給col進行編碼。target表示壞樣本標籤
    '''
    regroup = BinBadRate(df, col, target, grantRateIndicator=0)[1]
    br_dict = regroup[[col,'bad_rate']].set_index([col]).to_dict(orient='index')
    for k, v in br_dict.items():
        br_dict[k] = v['bad_rate']
    badRateEnconding = df[col].map(lambda x: br_dict[x])
    return {'encoding':badRateEnconding, 'bad_rate':br_dict}


def AssignBin(x, cutOffPoints,special_attribute=[]):
    '''
    :param x: 某個變量的某個取值
    :param cutOffPoints: 上述變量的分箱結果,用切分點表示
    :param special_attribute:  不參與分箱的特殊取值
    :return: 分箱後的對應的第幾個箱,從0開始
    例如, cutOffPoints = [10,20,30], 對於 x = 7, 返回 Bin 0;對於x=23,返回Bin 2; 對於x = 35, return Bin 3。
    對於特殊值,返回的序列數前加"-"
    '''
    cutOffPoints2 = [i for i in cutOffPoints if i not in special_attribute]
    numBin = len(cutOffPoints2)
    if x in special_attribute:
        i = special_attribute.index(x)+1
        return 'Bin {}'.format(0-i)
    if x<=cutOffPoints2[0]:
        return 'Bin 0'
    elif x > cutOffPoints2[-1]:
        return 'Bin {}'.format(numBin)
    else:
        for i in range(0,numBin):
            if cutOffPoints2[i] < x <=  cutOffPoints2[i+1]:
                return 'Bin {}'.format(i+1)



def CalcWOE(df, col, target):
    '''
    :param df: 包含需要計算WOE的變量和目標變量
    :param col: 需要計算WOE、IV的變量,必須是分箱後的變量,或者不需要分箱的類別型變量
    :param target: 目標變量,0、1表示好、壞
    :return: 返回WOE和IV
    '''
    total = df.groupby([col])[target].count()
    total = pd.DataFrame({'total': total})
    bad = df.groupby([col])[target].sum()
    bad = pd.DataFrame({'bad': bad})
    regroup = total.merge(bad, left_index=True, right_index=True, how='left')
    regroup.reset_index(level=0, inplace=True)
    N = sum(regroup['total'])
    B = sum(regroup['bad'])
    regroup['good'] = regroup['total'] - regroup['bad']
    G = N - B
    regroup['bad_pcnt'] = regroup['bad'].map(lambda x: x*1.0/B)
    regroup['good_pcnt'] = regroup['good'].map(lambda x: x * 1.0 / G)
    regroup['WOE'] = regroup.apply(lambda x: np.log(x.good_pcnt*1.0/x.bad_pcnt),axis = 1)
    WOE_dict = regroup[[col,'WOE']].set_index(col).to_dict(orient='index')
    for k, v in WOE_dict.items():
        WOE_dict[k] = v['WOE']
    IV = regroup.apply(lambda x: (x.good_pcnt-x.bad_pcnt)*np.log(x.good_pcnt*1.0/x.bad_pcnt),axis = 1)
    IV = sum(IV)
    return {"WOE": WOE_dict, 'IV':IV}


def FeatureMonotone(x):
    '''
    :return: 返回序列x中有幾個元素不滿足單調性,以及這些元素的位置。
    例如,x=[1,3,2,5], 元素3比前後兩個元素都大,不滿足單調性;元素2比前後兩個元素都小,也不滿足單調性。
    故返回的不滿足單調性的元素個數爲2,位置爲1和2.
    '''
    monotone = [x[i]<x[i+1] and x[i] < x[i-1] or x[i]>x[i+1] and x[i] > x[i-1] for i in range(1,len(x)-1)]
    index_of_nonmonotone = [i+1 for i in range(len(monotone)) if monotone[i]]
    return {'count_of_nonmonotone':monotone.count(True), 'index_of_nonmonotone':index_of_nonmonotone}

## 判斷某變量的壞樣本率是否單調
def BadRateMonotone(df, sortByVar, target,special_attribute = []):
    '''
    :param df: 包含檢驗壞樣本率的變量,和目標變量
    :param sortByVar: 需要檢驗壞樣本率的變量
    :param target: 目標變量,0、1表示好、壞
    :param special_attribute: 不參與檢驗的特殊值
    :return: 壞樣本率單調與否
    '''
    df2 = df.loc[~df[sortByVar].isin(special_attribute)]
    if len(set(df2[sortByVar])) <= 2:
        return True
    regroup = BinBadRate(df2, sortByVar, target)[1]
    combined = zip(regroup['total'],regroup['bad'])
    badRate = [x[1]*1.0/x[0] for x in combined]
    badRateNotMonotone = FeatureMonotone(badRate)['count_of_nonmonotone']
    if badRateNotMonotone > 0:
        return False
    else:
        return True

def MergeBad0(df,col,target, direction='bad'):
    '''
     :param df: 包含檢驗0%或者100%壞樣本率
     :param col: 分箱後的變量或者類別型變量。檢驗其中是否有一組或者多組沒有壞樣本或者沒有好樣本。如果是,則需要進行合併
     :param target: 目標變量,0、1表示好、壞
     :return: 合併方案,使得每個組裏同時包含好壞樣本
     '''
    regroup = BinBadRate(df, col, target)[1]
    if direction == 'bad':
        # 如果是合併0壞樣本率的組,則跟最小的非0壞樣本率的組進行合併
        regroup = regroup.sort_values(by  = 'bad_rate')
    else:
        # 如果是合併0好樣本率的組,則跟最小的非0好樣本率的組進行合併
        regroup = regroup.sort_values(by='bad_rate',ascending=False)
    regroup.index = range(regroup.shape[0])
    col_regroup = [[i] for i in regroup[col]]
    del_index = []
    for i in range(regroup.shape[0]-1):
        col_regroup[i+1] = col_regroup[i] + col_regroup[i+1]
        del_index.append(i)
        if direction == 'bad':
            if regroup['bad_rate'][i+1] > 0:
                break
        else:
            if regroup['bad_rate'][i+1] < 1:
                break
    col_regroup2 = [col_regroup[i] for i in range(len(col_regroup)) if i not in del_index]
    newGroup = {}
    for i in range(len(col_regroup2)):
        for g2 in col_regroup2[i]:
            newGroup[g2] = 'Bin '+str(i)
    return newGroup


def Monotone_Merge(df, target, col):
    '''
    :return:將數據集df中,不滿足壞樣本率單調性的變量col進行合併,使得合併後的新的變量中,壞樣本率單調,輸出合併方案。
    例如,col=[Bin 0, Bin 1, Bin 2, Bin 3, Bin 4]是不滿足壞樣本率單調性的。合併後的col是:
    [Bin 0&Bin 1, Bin 2, Bin 3, Bin 4].
    合併只能在相鄰的箱中進行。
    迭代地尋找最優合併方案。每一步迭代時,都嘗試將所有非單調的箱進行合併,每一次嘗試的合併都是跟前後箱進行合併再做比較
    '''
    def MergeMatrix(m, i,j,k):
        '''
        :param m: 需要合併行的矩陣
        :param i,j: 合併第i和j行
        :param k: 刪除第k行
        :return: 合併後的矩陣
        '''
        m[i, :] = m[i, :] + m[j, :]
        m = np.delete(m, k, axis=0)
        return m

    def Merge_adjacent_Rows(i, bad_by_bin_current, bins_list_current, not_monotone_count_current):
        '''
        :param i: 需要將第i行與前、後的行分別進行合併,比較哪種合併方案最佳。判斷準則是,合併後非單調性程度減輕,且更加均勻
        :param bad_by_bin_current:合併前的分箱矩陣,包括每一箱的樣本個數、壞樣本個數和壞樣本率
        :param bins_list_current: 合併前的分箱方案
        :param not_monotone_count_current:合併前的非單調性元素個數
        :return:分箱後的分箱矩陣、分箱方案、非單調性元素個數和衡量均勻性的指標balance
        '''
        i_prev = i - 1
        i_next = i + 1
        bins_list = bins_list_current.copy()
        bad_by_bin = bad_by_bin_current.copy()
        not_monotone_count = not_monotone_count_current
        #合併方案a:將第i箱與前一箱進行合併
        bad_by_bin2a = MergeMatrix(bad_by_bin.copy(), i_prev, i, i)
        bad_by_bin2a[i_prev, -1] = bad_by_bin2a[i_prev, -2] / bad_by_bin2a[i_prev, -3]
        not_monotone_count2a = FeatureMonotone(bad_by_bin2a[:, -1])['count_of_nonmonotone']
        # 合併方案b:將第i行與後一行進行合併
        bad_by_bin2b = MergeMatrix(bad_by_bin.copy(), i, i_next, i_next)
        bad_by_bin2b[i, -1] = bad_by_bin2b[i, -2] / bad_by_bin2b[i, -3]
        not_monotone_count2b = FeatureMonotone(bad_by_bin2b[:, -1])['count_of_nonmonotone']
        balance = ((bad_by_bin[:, 1] / N).T * (bad_by_bin[:, 1] / N))[0, 0]
        balance_a = ((bad_by_bin2a[:, 1] / N).T * (bad_by_bin2a[:, 1] / N))[0, 0]
        balance_b = ((bad_by_bin2b[:, 1] / N).T * (bad_by_bin2b[:, 1] / N))[0, 0]
        #滿足下述2種情況時返回方案a:(1)方案a能減輕非單調性而方案b不能;(2)方案a和b都能減輕非單調性,但是方案a的樣本均勻性優於方案b
        if not_monotone_count2a < not_monotone_count_current and not_monotone_count2b >= not_monotone_count_current or \
                                        not_monotone_count2a < not_monotone_count_current and not_monotone_count2b < not_monotone_count_current and balance_a < balance_b:
            bins_list[i_prev] = bins_list[i_prev] + bins_list[i]
            bins_list.remove(bins_list[i])
            bad_by_bin = bad_by_bin2a
            not_monotone_count = not_monotone_count2a
            balance = balance_a
        # 同樣地,滿足下述2種情況時返回方案b:(1)方案b能減輕非單調性而方案a不能;(2)方案a和b都能減輕非單調性,但是方案b的樣本均勻性優於方案a
        elif not_monotone_count2a >= not_monotone_count_current and not_monotone_count2b < not_monotone_count_current or \
                                        not_monotone_count2a < not_monotone_count_current and not_monotone_count2b < not_monotone_count_current and balance_a > balance_b:
            bins_list[i] = bins_list[i] + bins_list[i_next]
            bins_list.remove(bins_list[i_next])
            bad_by_bin = bad_by_bin2b
            not_monotone_count = not_monotone_count2b
            balance = balance_b
        #如果方案a和b都不能減輕非單調性,返回均勻性更優的合併方案
        else:
            if balance_a< balance_b:
                bins_list[i] = bins_list[i] + bins_list[i_next]
                bins_list.remove(bins_list[i_next])
                bad_by_bin = bad_by_bin2b
                not_monotone_count = not_monotone_count2b
                balance = balance_b
            else:
                bins_list[i] = bins_list[i] + bins_list[i_next]
                bins_list.remove(bins_list[i_next])
                bad_by_bin = bad_by_bin2b
                not_monotone_count = not_monotone_count2b
                balance = balance_b
        return {'bins_list': bins_list, 'bad_by_bin': bad_by_bin, 'not_monotone_count': not_monotone_count,
                'balance': balance}


    N = df.shape[0]
    [badrate_bin, bad_by_bin] = BinBadRate(df, col, target)
    bins = list(bad_by_bin[col])
    bins_list = [[i] for i in bins]
    badRate = sorted(badrate_bin.items(), key=lambda x: x[0])
    badRate = [i[1] for i in badRate]
    not_monotone_count, not_monotone_position = FeatureMonotone(badRate)['count_of_nonmonotone'], FeatureMonotone(badRate)['index_of_nonmonotone']
    #迭代地尋找最優合併方案,終止條件是:當前的壞樣本率已經單調,或者當前只有2箱
    while (not_monotone_count > 0 and len(bins_list)>2):
        #當非單調的箱的個數超過1個時,每一次迭代中都嘗試每一個箱的最優合併方案
        all_possible_merging = []
        for i in not_monotone_position:
            merge_adjacent_rows = Merge_adjacent_Rows(i, np.mat(bad_by_bin), bins_list, not_monotone_count)
            all_possible_merging.append(merge_adjacent_rows)
        balance_list = [i['balance'] for i in all_possible_merging]
        not_monotone_count_new = [i['not_monotone_count'] for i in all_possible_merging]
        #如果所有的合併方案都不能減輕當前的非單調性,就選擇更加均勻的合併方案
        if min(not_monotone_count_new) >= not_monotone_count:
            best_merging_position = balance_list.index(min(balance_list))
        #如果有多個合併方案都能減輕當前的非單調性,也選擇更加均勻的合併方案
        else:
            better_merging_index = [i for i in range(len(not_monotone_count_new)) if not_monotone_count_new[i] < not_monotone_count]
            better_balance = [balance_list[i] for i in better_merging_index]
            best_balance_index = better_balance.index(min(better_balance))
            best_merging_position = better_merging_index[best_balance_index]
        bins_list = all_possible_merging[best_merging_position]['bins_list']
        bad_by_bin = all_possible_merging[best_merging_position]['bad_by_bin']
        not_monotone_count = all_possible_merging[best_merging_position]['not_monotone_count']
        not_monotone_position = FeatureMonotone(bad_by_bin[:, 3])['index_of_nonmonotone']
    return bins_list





def Prob2Score(prob, basePoint, PDO):
    #將概率轉化成分數且爲正整數
    y = np.log(prob/(1-prob))
    return (basePoint+PDO/np.log(2)*(-y)).map(lambda x: int(x))



### 計算KS值
def KS(df, score, target):
    '''
    :param df: 包含目標變量與預測值的數據集
    :param score: 得分或者概率
    :param target: 目標變量
    :return: KS值
    '''
    total = df.groupby([score])[target].count()
    bad = df.groupby([score])[target].sum()
    all = pd.DataFrame({'total':total, 'bad':bad})
    all['good'] = all['total'] - all['bad']
    all[score] = all.index
    all = all.sort_values(by=score,ascending=False)
    all.index = range(len(all))
    all['badCumRate'] = all['bad'].cumsum() / all['bad'].sum()
    all['goodCumRate'] = all['good'].cumsum() / all['good'].sum()
    KS = all.apply(lambda x: x.badCumRate - x.goodCumRate, axis=1)
    return max(KS)


發表評論
所有評論
還沒有人評論,想成為第一個評論的人麼? 請在上方評論欄輸入並且點擊發布.
相關文章