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model_monitoring_monthly
Commit 9a3d42b6 authored by 王家华's avatar 王家华
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V1.0

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.idea/misc.xml 0 → 100644
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Monitor_mongoDB/monitoring_VLM_mongodb.py 0 → 100644
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Monitor_mongoDB/psi_from_mongodb.py 0 → 100644
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Monitor_risk_analysis/PSI&VAL_riskanalysis.py
View file @ 9a3d42b6
...@@ -20,8 +20,6 @@ import sklearn.metrics ...@@ -20,8 +20,6 @@ import sklearn.metrics
from django.db import transaction, DatabaseError from django.db import transaction, DatabaseError
sql_bins = ''' sql_bins = '''
SELECT @modelVar,transacted,IF(passdue_day>@passdueday,1,0) as overdue FROM risk_analysis SELECT @modelVar,transacted,IF(passdue_day>@passdueday,1,0) as overdue FROM risk_analysis
WHERE applied_at BETWEEN WHERE applied_at BETWEEN
...@@ -40,18 +38,6 @@ AND !ISNULL(@modelVar) ...@@ -40,18 +38,6 @@ AND !ISNULL(@modelVar)
AND @modelVar > 0 AND @modelVar > 0
''' '''
"""
### sql_bins_360 = '''
SELECT @modelVar,transacted,IF(passdue_day > 15,1,0) as overdue
FROM risk_analysis
WHERE !ISNULL(@modelVar)
AND applied_at >= '2018-08-01' AND applied_at <= '2018-09-01'
AND applied_from IN (@channelID)
AND applied_type IN (@appliedType)
AND !ISNULL(@modelVar)
AND @modelVar > 0
'''
"""
sql_observation = ''' sql_observation = '''
SELECT date_format(applied_at,'%Y-%m') as applied_at,@modelVar SELECT date_format(applied_at,'%Y-%m') as applied_at,@modelVar
...@@ -64,8 +50,7 @@ AND applied_type IN (@appliedType) ...@@ -64,8 +50,7 @@ AND applied_type IN (@appliedType)
AND !ISNULL(@modelVar) AND !ISNULL(@modelVar)
''' '''
######## calculate with T-N mon ###########
######## calculate with natural mon ###########
sql_passdueday = ''' sql_passdueday = '''
(SELECT order_no,'T-1' as applied_at,@modelVar,IF(passdue_day > @passdue_day,1,0) as overdue (SELECT order_no,'T-1' as applied_at,@modelVar,IF(passdue_day > @passdue_day,1,0) as overdue
...@@ -90,8 +75,7 @@ AND applied_type IN (@appliedType) ...@@ -90,8 +75,7 @@ AND applied_type IN (@appliedType)
AND transacted = 1) AND transacted = 1)
''' '''
############ calculate with natural mon #############
############ calculate with T-N mon #############
""" """
sql_passdueday = ''' sql_passdueday = '''
SELECT date_format(loan_start_date,'%Y-%m') as applied_at,@modelVar,IF(passdue_day > @passdueday,1,0) as overdue SELECT date_format(loan_start_date,'%Y-%m') as applied_at,@modelVar,IF(passdue_day > @passdueday,1,0) as overdue
...@@ -106,26 +90,26 @@ AND transacted = 1 ...@@ -106,26 +90,26 @@ AND transacted = 1
''' '''
""" """
passdue_day = 15 passdue_day = 15
#AND applied_from IN (@channelID) #AND applied_from IN (@channelID)
##################################### db config ############################### ##################################### db config ###############################
risk_analysis_config = {'user' : 'jiahua_wang', risk_analysis_config = {'user' : 'fengkong_read_only',
'password' : 'IqHKCIyZ', 'password' : 'mT2HFUgI',
'host' : '172.20.6.9', 'host' : '172.20.6.9',
'port' : 9030, 'port' : 9030,
'database' : 'risk_analysis', 'database' : 'risk_analysis',
'encoding' : 'utf8'} 'encoding' : 'utf8'}
################################################################################# #################################################################################
path = "E:\\Python\\su Project\\plot\\PSI&VAL\\" path = "../plot/PSI_VAL/"
mapping_path = "E:\\Python\\su Project\\query_score.xlsx" mapping_path = "./query_score.xlsx"
mapping = pd.read_excel(mapping_path,sheet_name='score_risk_anlysis') mapping = pd.read_excel(mapping_path,sheet_name='score_risk_anlysis')
modelType = mapping.description.tolist() modelType = mapping.description.tolist()
modelList = mapping.feature.tolist() modelList = mapping.score.tolist()
appliedTypeList = mapping.appliedType.tolist() appliedTypeList = mapping.appliedType.tolist()
channelIDList = mapping.channel.tolist() channelIDList = mapping.channel.tolist()
...@@ -133,21 +117,8 @@ channelIDList = mapping.channel.tolist() ...@@ -133,21 +117,8 @@ channelIDList = mapping.channel.tolist()
del mapping del mapping
#modelList = ['xinyan_xy_fstapply_point','xinyan_xy_reapply_point','xinyan_xy_reloan_point','reloan_v3_point','lxf_v2_point','v6_operator_score_raw','dhb_score','tongdun_score','shuchuang_phone_apply','pingan_markingCriterion','tencent_tencentAntiFraudScore','eleven_bei_score','ljj_old_score','ljj_model_trusty_score']
#modelList = ['xinyan_xy_reloan_point']
#modelType = ['新颜首申分','新颜复申分','新颜复贷分','复贷分','量信分','V6分','电话邦分','同盾分','数创多头','腾讯反欺诈分','十一贝分','量晶晶首贷分','量晶晶复贷分']
#modelType = ['新颜复贷']
#channelIDList = ['217,214,198,1,159481,158748,333,159384,149483,159479,159479,158764,158932,159457,159459,159519','217,214,198,1,159481','158748','333','159384','149483,159479,159479','158764,158932,159457,159459,159519']
#channel = ['全部渠道','内部','汽车之家','融360','平安高净值','平安非高净值','其他外部渠道']
#appliedTypeList = ['1,2,3','1','2','3']
#appliedTypeList = ['1']
#appliedType_type = ['总体','首申','复申','复贷']
appliedType_type = {'1,2,3':'总体','1':'首申','2':'复申','3':'复贷'} appliedType_type = {'1,2,3':'总体','1':'首申','2':'复申','3':'复贷'}
#appliedType_type = ['首申']
passdueday = 15 #more than N days (fstOverdue N+) passdueday = 15 #more than N days (fstOverdue N+)
def connect2DB(db_config): def connect2DB(db_config):
...@@ -420,77 +391,6 @@ for modelVar in modelList: ...@@ -420,77 +391,6 @@ for modelVar in modelList:
except Exception as e: # ZeroDivisionError except Exception as e: # ZeroDivisionError
print('val exception',e) print('val exception',e)
"""
#V5 333
modelVar = 'v5_filter_fraud_point_v5_without_zhima'
channelID = '333'
for appliedType in str(appliedTypeList[modelList.index(modelVar)]).split(';'):
print('appliedType',appliedType)
print('appliedTypeList[model_index]',appliedTypeList[modelList.index(modelVar)])
try:
df_bins = pd.read_sql(sql_bins.replace('@modelVar',modelVar).replace('@appliedType',appliedType).replace('@channelID',channelID).replace('@passdueday',str(passdueday)),conn).dropna(axis=0)
df_observation = pd.read_sql(sql_observation.replace('@modelVar',modelVar).replace('@appliedType',appliedType).replace('@channelID',channelID),conn)
df_observation.loc[:,modelVar] = df_observation.loc[:,modelVar].map(lambda x : np.nan if x < 0 else x)
#df_bins = df_bins.apply(lambda x :np.nan if x < 0 else x)
Nothing,interval = pd.qcut(df_bins.loc[:,modelVar],10,retbins=True,precision=6,duplicates='drop')
interval[0] = 0
del Nothing
BM_count = psi_bins(df_bins,modelVar,interval)
zero_rate,missing_rate,dateList,cols,y,count = dataManipul(df_observation,modelVar,np.array(interval).round(6))
#df_observation_with_bin = pd.cut(df_observation.dropna(axis=0)[modelVar],interval)
# del df_bins
del interval
value_tab = []
rows = []
y_list = []
psi = []
# plot line separated by mon
for mon in dateList:
y_list.append(y.loc[mon].values)
value_tab.append(y.loc[mon].astype('str')+'%')
value_tab.append(count.loc[mon].astype('str')+'(zeroR:'+zero_rate.loc[mon].astype('str')+'%)')
rows.append(str(mon)+' Value');rows.append(str(mon)+' Count')
#(y-10).sum() / np.log10(y/10)
psi.append((((y.loc[mon]-BM_count) * np.log10(y.loc[mon]/BM_count)).sum()/100).round(3))
plotPSI(modelType[modelList.index(modelVar)]+'-'+appliedType_type[appliedType]+'-' + channel[channelID] + ' PSI',y_list,dateList,psi,missing_rate,rows,cols,value_tab,path)
except Exception as e:
print(e)
try:
# Overdue dataframe
df_bins_auc = df_bins[df_bins.transacted == 1]
del df_bins
auc_BM = sklearn.metrics.roc_auc_score(df_bins_auc.overdue, df_bins_auc.loc[:,modelVar])
print('AUC_BM: ',auc_BM)
Nothing,interval = pd.qcut(df_bins_auc.loc[:,modelVar],10,retbins=True,precision=6,duplicates='drop')
interval[0] = 0
del Nothing
df_passdueday = pd.read_sql(sql_passdueday.replace('@modelVar',modelVar).replace('@appliedType',appliedType).replace('@channelID',channelID).replace('@passdueday',str(passdueday)),conn)
count,df_overdue,y,dateList,cols = liftchart(df_passdueday,modelVar,np.array(interval).round(6))
value_tab = []
rows = []
y_list = []
aucri = []
auc = []
for mon in dateList:
y_list.append(y.loc[mon].values)
value_tab.append(y.loc[mon].astype('str')+'%')
value_tab.append(df_overdue.loc[mon].astype('str') + ' (总计 ' + count.loc[mon].astype('str') + ')' )
rows.append(str(mon)+' OverdueRate');rows.append(str(mon)+' Count')
aucri.append(round((sklearn.metrics.roc_auc_score(df_passdueday[df_passdueday.applied_at==mon].overdue, df_passdueday[df_passdueday.applied_at==mon].loc[:,modelVar])/auc_BM),3))
auc.append(round(sklearn.metrics.roc_auc_score(df_passdueday[df_passdueday.applied_at==mon].overdue, df_passdueday[df_passdueday.applied_at==mon].loc[:,modelVar]),3))
auc[-1] = str(auc[-1]) + '\n AUC基准: ' + str(round(auc_BM,3))
plotLiftChart(modelType[modelList.index(modelVar)] + '-' + appliedType_type[appliedType] + '-' + channel[channelID] + ' AUC WITH '+ str(passdueday) + '+',y_list,dateList,aucri,auc,rows,cols,value_tab,path)
except Exception as e:
print(e)
"""
def plot_table_df(dataset, auc, title='untitled', X_label=None, y_label=None, def plot_table_df(dataset, auc, title='untitled', X_label=None, y_label=None,
tab_df=None, plot_tab=True, saved_path=None): tab_df=None, plot_tab=True, saved_path=None):
......
query_score.xlsx
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