class BaseFeatureMonitor(object):
def __init__(self, config_file=None, product=None):
self.config = self.set_config(config_file)
self.mysql_risk = MySql(**self.config['mysql_risk'])
self.mysql_risk_table = None
self.mongo_derivable = Mongo(**self.config['mongo_derivable'])
self.mongo_derivable_table = None
self.except_handler = DingdingExceptionHandler(self.config['robots'])
self.product = product
self.ssh_config = self.config['model_file_remote_ssh']
def set_config(self, config_file):
with open(config_file, 'r') as f:
file = f.read()
config = yaml.load(file)
return config
def get_model_path_from_mysql(self, table=None):
pass
def get_top_features(self, monitor_flag):
model_path_list = self.get_model_path_from_mysql()
final_features = []
# 连接远程服务器
ssh_client = paramiko.Transport(self.ssh_config['hostname'], self.ssh_config['port'])
ssh_client.connect(username=self.ssh_config['username'], password=self.ssh_config['password'])
sftp = paramiko.SFTPClient.from_transport(ssh_client)
for model_dict in [model for model in model_path_list if model['monitor_flag'] == monitor_flag]:
remote_model_path = model_dict['model_path']
# 判断本地模型文件所在目录是否存在，没有就创建
if not os.path.isdir(os.path.split(remote_model_path)[0]):
os.makedirs(os.path.split(remote_model_path)[0])
# 将远程文件下载到本地
sftp.get(remote_model_path, remote_model_path)
with open(remote_model_path, 'rb') as f:
model_info = pickle.load(f)
top_columns = []
try:
model = model_info['model']
enum = model.get_params()['enum']
mm = model.get_params()['clf']
top_columns = []
for i, v in enumerate(
sorted(zip(map(lambda x: round(x, 4), mm.feature_importances_), enum.clean_col_names),
reverse=True)):
if i <= 30:
top_columns.append(v[1])
except Exception as e:
logging.error(e)
final_features.extend(top_columns)
sftp.close()
no_final_features = ['ALPHA_Behavior_submit_date', 'ALPHA_Behavior_submit_hour',
'ALPHA_Behavior_submit_weekday', 'X_DNA_Behavior_submit_date',
'X_DNA_Behavior_submit_hour', 'X_DNA_Behavior_submit_weekday'] # 这些不监控
final_features = list(set(final_features) - set(no_final_features))
logging.info('{}-top_features: {}'.format(self.product, final_features))
return final_features
def get_appid_from_mysql(self, start_time, diff_day, diff_hour):
"""获取所需要的11天的所有appid信息"""
end_time = (datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') + timedelta(days=diff_day)).strftime(
"%Y-%m-%d %H:%M:%S")
start_hour = 0
end_hour = start_hour + diff_hour
sql = '''select upper(app_id) as app_id,flow_type,work_flag,date(create_time) as date
from {}
where create_time >= '{}'
and create_time < '{}'
and hour(create_time) >= {}
and hour(create_time) <= {}
'''.format(self.mysql_risk_table, start_time, end_time, start_hour, end_hour)
res = self.mysql_risk.query(sql)
return pd.DataFrame(res)
def get_features(self, df_appid, top_feature):
appids = list(set(df_appid['app_id'].tolist()))
qry = {'_id': {'$in': appids}}
qry1 = {feature: 1 for feature in top_feature}
res = self.mongo_derivable.get_collection(self.mongo_derivable_table).find(qry, qry1, batch_size=500)
res_list = list(res)
return pd.DataFrame(res_list)
def psi(self, df_feature_1, df_feature_2, feature, bin_num=10):
df_feature_1['label'] = 0
df_feature_2['label'] = 1
df_feature = pd.concat([df_feature_1, df_feature_2])
df_feature = df_feature.replace('null', np.nan)
df_feature = df_feature.replace('NaN', np.nan)
df_feature = df_feature.apply(pd.to_numeric, errors='ignore')
enum = EnumMapper(maximum_enum_num=100)
enum.fit(df_feature)
df_feature = enum.transform(df_feature)
if feature in df_feature.columns.tolist():
df_psi = df_feature[[feature, 'label']].copy()
if df_psi[feature].dtype not in ['int', 'float'] and df_psi[feature].unique().shape[0] > 20:
# print("The unique number of feature is {}".format(df_psi[feature].unique().shape[0]))
return None, 999
else:
if df_psi[feature].unique().shape[0] > 2:
df_psi['bins'] = pd.qcut(df_psi[feature], 10, precision=2, duplicates='drop')
nan_df = df_psi[df_psi[feature].map(lambda x: pd.isnull(x))].reset_index(drop=True)
if not nan_df.empty:
df_psi['bins'] = df_psi['bins'].cat.add_categories('-999')
df_psi['bins'] = df_psi['bins'].fillna('-999')
else:
df_psi['bins'] = df_psi[feature].map(lambda x: -999 if pd.isnull(x) else x)
group_df = df_psi.groupby(['bins', 'label']).size().unstack('label')
group_df = group_df.fillna(0)
group_df['b_rate'] = group_df[0] / group_df[0].sum()
group_df['a_rate'] = group_df[1] / group_df[1].sum()
e = 0.000000000001
group_df['psi_part'] = group_df.apply(
lambda group_df: (group_df['a_rate'] - group_df['b_rate']) * math.log(
(group_df['a_rate'] + e) / (group_df['b_rate'] + e)), axis=1)
return group_df, group_df.psi_part.sum()
else:
return None, 99
def psi_classified(self, start_time, diff_day, diff_hour, timedetail):
"""psi分类监控"""
ls_top_loss_rate = [] # 发送到钉钉的丢失率监控列表
ls_top_psi = [] # 发送到钉钉的psi监控列表
total_appids_df = self.get_appid_from_mysql(start_time, diff_day, diff_hour) # 获取所需要的11天的所有appid信息
total_appids_df.date = total_appids_df.date.map(lambda x: str(x)) # 将里面date字段的类型转换为str
# 开卡初审
top_features = self.get_top_features(monitor_flag='cp')
cp_ls_top_loss_rate, cp_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='c',
work_flag='precheck')
if cp_ls_top_loss_rate:
ls_top_loss_rate.append('#######开卡初审#######')
ls_top_loss_rate.extend(cp_ls_top_loss_rate)
if cp_ls_top_psi:
ls_top_psi.append('#######开卡初审#######')
ls_top_psi.extend(cp_ls_top_psi)
# 开卡复审
top_features = self.get_top_features(monitor_flag='cf')
cf_ls_top_loss_rate, cf_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='c',
work_flag='finalcheck')
if cf_ls_top_loss_rate:
ls_top_loss_rate.append('#######开卡复审#######')
ls_top_loss_rate.extend(cf_ls_top_loss_rate)
if cf_ls_top_psi:
ls_top_psi.append('#######开卡复审#######')
ls_top_psi.extend(cf_ls_top_psi)
# 首贷提现初审
top_features = self.get_top_features(monitor_flag='fp')
fp_ls_top_loss_rate, fp_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='f',
work_flag='precheck')
if fp_ls_top_loss_rate:
ls_top_loss_rate.append('#######首贷提现初审#######')
ls_top_loss_rate.extend(fp_ls_top_loss_rate)
if fp_ls_top_psi:
ls_top_psi.append('#######首贷提现初审#######')
ls_top_psi.extend(fp_ls_top_psi)
# 首贷提现复审
top_features = self.get_top_features(monitor_flag='ff')
ff_ls_top_loss_rate, ff_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='f',
work_flag='finalcheck')
if ff_ls_top_loss_rate:
ls_top_loss_rate.append('#######首贷提现复审#######')
ls_top_loss_rate.extend(ff_ls_top_loss_rate)
if ff_ls_top_psi:
ls_top_psi.append('#######首贷提现复审#######')
ls_top_psi.extend(ff_ls_top_psi)
# 复贷初审
top_features = self.get_top_features(monitor_flag='wp')
wp_ls_top_loss_rate, wp_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='w',
work_flag='precheck')
if wp_ls_top_loss_rate:
ls_top_loss_rate.append('#######复贷初审#######')
ls_top_loss_rate.extend(wp_ls_top_loss_rate)
if wp_ls_top_psi:
ls_top_psi.append('#######复贷初审#######')
ls_top_psi.extend(wp_ls_top_psi)
# 复贷复审
top_features = self.get_top_features(monitor_flag='wf')
wf_ls_top_loss_rate, wf_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='w',
work_flag='finalcheck')
if wf_ls_top_loss_rate:
ls_top_loss_rate.append('#######复贷复审#######')
ls_top_loss_rate.extend(wf_ls_top_loss_rate)
if wf_ls_top_psi:
ls_top_psi.append('#######复贷复审#######')
ls_top_psi.extend(wf_ls_top_psi)
# 结清调额
top_features = self.get_top_features(monitor_flag='q')
q_ls_top_loss_rate, q_ls_top_psi = self.psi_distr(start_time, total_appids_df, top_features, flow_type='q',
work_flag='finalcheck')
if q_ls_top_loss_rate:
ls_top_loss_rate.append('#######结清调额#######')
ls_top_loss_rate.extend(q_ls_top_loss_rate)
if q_ls_top_psi:
ls_top_psi.append('#######结清调额#######')
ls_top_psi.extend(q_ls_top_psi)
if ls_top_loss_rate:
ls_top_loss_rate.insert(0, '*******{}-丢失率报警*******'.format(self.product))
ls_top_loss_rate.insert(1, '时间：{}'.format(datetime.now().strftime('%Y-%m-%d ') + timedetail))
self.except_handler.handle(msg=ls_top_loss_rate)
if ls_top_psi:
ls_top_psi.insert(0, '*******{}-psi报警*******'.format(self.product))
ls_top_psi.insert(1, '时间：{}'.format(datetime.now().strftime('%Y-%m-%d ') + timedetail))
self.except_handler.handle(msg=ls_top_psi)
def psi_distr(self, start_time, total_appids_df, top_features, flow_type, work_flag):
the_psi_date = (datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') + timedelta(days=10)).strftime(
'%Y-%m-%d') # 所监控的日期
logging.info('所监控的日期为：{}'.format(the_psi_date))
# 所监控日期前十天对应类型的appids
df_appid1 = total_appids_df.query(
"flow_type=='{}' and work_flag=='{}' and date!='{}'".format(flow_type, work_flag,
the_psi_date)).reset_index(drop=True)
df_appid1 = df_appid1.sample(min(10000, df_appid1.shape[0]))
logging.info('flow_type:{} work_flag:{} 前十天全部app_id个数：{}'.format(flow_type, work_flag, len(df_appid1)))
# 所监控日期对应类型的appids
df_appid2 = total_appids_df.query(
"flow_type=='{}' and work_flag=='{}' and date=='{}'".format(flow_type, work_flag,
the_psi_date)).reset_index(drop=True)
df_appid2 = df_appid2.sample(min(1000, df_appid2.shape[0]))
logging.info('flow_type:{} work_flag:{} 所监控的app_id个数：{}'.format(flow_type, work_flag, len(df_appid2)))
dict_report = {}
ls_top_psi = []
ls_top_loss_rate = []
df_feature_all_1 = self.get_features(df_appid1, top_features)
df_feature_all_2 = self.get_features(df_appid2, top_features)
for feature in top_features:
df_feature_1 = pd.DataFrame(df_feature_all_1, columns=[feature])
df_feature_2 = pd.DataFrame(df_feature_all_2, columns=[feature])
# 这里添加计算空值逻辑
feature_precent = df_feature_2.iloc[:, 0].isna().tolist().count(True) / df_feature_2.shape[0]
if feature_precent > 0.7:
ls_top_loss_rate.append("{}--loss_rate:{}".format(feature, round(feature_precent, 3)))
dict_report[feature] = self.psi(df_feature_1, df_feature_2, feature, bin_num=10)[1]
if dict_report[feature] > 0.25:
ls_top_psi.append("{}--psi:{}".format(feature, round(dict_report[feature], 3)))
return ls_top_loss_rate, ls_top_psi
# 前一天
def job1(self):
"""比较昨天top特征的分布与昨天的前10天top特征的分布"""
logging.info('{} start handle feature_monitor job1!'.format(self.product))
start_time = (datetime.now() - timedelta(days=11)).strftime(
'%Y-%m-%d') + ' 00:00:00' # 获取所监控及其对比的前10天所有appid的开始时间
diff_day = 11 # 获取从开始时间往后11天的数据
diff_hour = 24 # 获取每天从0时到24时的数据
self.psi_classified(start_time, diff_day, diff_hour, timedetail='上午')
logging.info('{} end handle feature_monitor job1!'.format(self.product))
# 当天
def job2(self):
"""比较当天(0-15时)的top特征的分布与前10天(0-16时)top特征的分布"""
logging.info('{} start handle feature_monitor job2!'.format(self.product))
start_time = (datetime.now() - timedelta(days=10)).strftime(
'%Y-%m-%d') + ' 00:00:00' # 获取所监控及其对比的前10天所有appid的开始时间
diff_day = 11 # 获取从开始时间往后11天的数据
diff_hour = 15 # 获取每天从0时到15时的数据
self.psi_classified(start_time, diff_day, diff_hour, timedetail='下午')
logging.info('{} end handle feature_monitor job2!'.format(self.product))
class BaseFeatureMonitor(object):
def __init__(self, config_file=None, product=None):
self.config = self.set_config(config_file)
self.mysql_risk = MySql(**self.config['mysql_risk'])
self.mysql_risk_table = None
self.mongo_derivable = Mongo(**self.config['mongo_derivable'])
self.mongo_derivable_table = None
self.except_handler = DingdingExceptionHandler(
self.config['robots_psi'])
self.product = product
self.ssh_config = self.config['model_file_remote_ssh']
def set_config(self, config_file):
with open(config_file, 'r') as f:
file = f.read()
config = yaml.load(file)
return config
def get_model_path_from_mysql(self, table=None):
pass
def get_top_features(self):
""""
"""
model_path_list = self.get_model_path_from_mysql()
model_path_df = pd.DataFrame(model_path_list)
group_df = model_path_df.groupby('monitor_flag').apply(
lambda x: x.model_path.unique()).rename(
'model_path_list').reset_index()
group_df['top_features'] = group_df['model_path_list'].map(
lambda x: self.top30_features(x))
return group_df[['monitor_flag', 'top_features']]
def top30_features(self, model_path):
final_features = []
# 连接远程服务器
ssh_client = paramiko.Transport(self.ssh_config['hostname'],
self.ssh_config['port'])
ssh_client.connect(username=self.ssh_config['username'],
password=self.ssh_config['password'])
sftp = paramiko.SFTPClient.from_transport(ssh_client)
for remote_model_path in model_path:
# remote_model_path = model_dict['model_path']
# 判断本地模型文件所在目录是否存在，没有就创建
if not os.path.isdir(os.path.split(remote_model_path)[0]):
os.makedirs(os.path.split(remote_model_path)[0])
# 将远程文件下载到本地
sftp.get(remote_model_path, remote_model_path)
with open(remote_model_path, 'rb') as f:
model_info = pickle.load(f)
top_columns = []
try:
model = model_info['model']
enum = model.get_params()['enum']
mm = model.get_params()['clf']
top_columns = []
for i, v in enumerate(
sorted(zip(
map(lambda x: round(x, 4),
mm.feature_importances_),
enum.clean_col_names),
reverse=True)):
if i <= 30:
top_columns.append(v[1])
except Exception as e:
logging.error(e)
final_features.extend(top_columns)
sftp.close()
no_final_features = [
'ALPHA_Behavior_submit_date', 'ALPHA_Behavior_submit_hour',
'ALPHA_Behavior_submit_weekday', 'X_DNA_Behavior_submit_date',
'X_DNA_Behavior_submit_hour', 'X_DNA_Behavior_submit_weekday'
] # 这些不监控
final_features = list(set(final_features) - set(no_final_features))
# logging.info('{}-top_features: {}'.format(self.product, final_features))
return final_features
def get_appid_from_mysql(self, start_time, diff_day, diff_hour):
"""获取所需要的11天的所有appid信息"""
end_time = (datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') +
timedelta(days=diff_day)).strftime("%Y-%m-%d %H:%M:%S")
start_hour = 0
end_hour = start_hour + diff_hour
sql = '''select upper(app_id) as app_id,flow_type,work_flag,date(create_time) as date
from {}
where create_time >= '{}'
and create_time < '{}'
and hour(create_time) >= {}
and hour(create_time) <= {}
'''.format(self.mysql_risk_table, start_time, end_time,
start_hour, end_hour)
res = self.mysql_risk.query(sql)
return pd.DataFrame(res)
def get_features(self, df_appid, top_feature):
appids = list(set(df_appid['app_id'].tolist()))
qry = {'_id': {'$in': appids}}
qry1 = {feature: 1 for feature in top_feature}
res = self.mongo_derivable.get_collection(
self.mongo_derivable_table).find(qry, qry1, batch_size=500)
res_list = list(res)
return pd.DataFrame(res_list)
@staticmethod
def cal_psi(x, y):
e = 0.00001
if x == 0 or y == 0:
psi = (x - y) * math.log((x + e) / (y + e))
else:
psi = (x - y) * math.log(x / y)
return round(psi, 3)
def psi(self, df_feature_1, df_feature_2, feature, bin_num=10):
df_feature_1['label'] = 0
df_feature_2['label'] = 1
df_feature = pd.concat([df_feature_1, df_feature_2])
df_feature = df_feature.replace('null', np.nan)
df_feature = df_feature.replace('NaN', np.nan)
df_feature = df_feature.apply(pd.to_numeric, errors='ignore')
enum = EnumMapper(maximum_enum_num=100)
enum.fit(df_feature)
df_feature = enum.transform(df_feature)
if feature in df_feature.columns.tolist():
df_psi = df_feature[[feature, 'label']].copy()
if df_psi[feature].dtype not in [
'int', 'float'
] and df_psi[feature].unique().shape[0] > 20:
# print("The unique number of feature is {}".format(df_psi[feature].unique().shape[0]))
return None, 999
else:
if df_psi[feature].unique().shape[0] > 2:
df_psi['bins'] = pd.qcut(df_psi[feature],
10,
precision=2,
duplicates='drop')
nan_df = df_psi[df_psi[feature].map(
lambda x: pd.isnull(x))].reset_index(drop=True)
if not nan_df.empty:
df_psi['bins'] = df_psi['bins'].cat.add_categories(
'(-999.1, -999]')
df_psi['bins'] = df_psi['bins'].fillna(
'(-999.1, -999]')
else:
df_psi['bins'] = df_psi[feature].map(
lambda x: -999 if pd.isnull(x) else x)
group_df = df_psi.groupby(['bins',
'label']).size().unstack('label')
group_df = group_df.fillna(0)
group_df['b_rate'] = group_df[0] / group_df[0].sum()
group_df['a_rate'] = group_df[1] / group_df[1].sum()
group_df = group_df.map(lambda x: round(x, 4))
group_df['psi_part'] = list(
map(lambda x, y: self.cal_psi(x, y), group_df.b_rate,
group_df.a_rate))
group_df = group_df.apply(lambda x: round(x, 3))
group_df = group_df.reset_index()
return group_df, group_df.psi_part.sum()
else:
return None, 99
@staticmethod
def define_mf(x, y):
if x == 'c' and y == 'precheck':
return 'cp'
elif x == 'c' and y == 'finalcheck':
return 'cf'
elif x == 'f' and y == 'precheck':
return 'fp'
elif x == 'f' and y == 'finalcheck':
return 'ff'
elif x == 'w' and y == 'precheck':
return 'wp'
elif x == 'w' and y == 'finalcheck':
return 'wf'
elif x == 'q' and y == 'finalcheck':
return 'q'
def psi_classified(self, start_time, diff_day, diff_hour, timedetail):
"""psi分类监控"""
total_appids_df = self.get_appid_from_mysql(
start_time, diff_day, diff_hour) # 获取所需要的11天的所有appid信息
total_appids_df.date = total_appids_df.date.map(
lambda x: str(x)) # 将里面date字段的类型转换为str
total_appids_df['monitor_flag'] = list(
map(lambda x, y: self.define_mf(x, y), total_appids_df.flow_type,
total_appids_df.work_flag))
cp_ls_top_psi = [] # 发送到钉钉的psi监控列表
features_df = self.get_top_features()
for monitor_flag in total_appids_df.monitor_flag.unique().tolist():
top_features = features_df.query(
"monitor_flag=='{}'".format(monitor_flag)).top_features.values
if top_features:
top_psi = self.psi_distr(start_time, total_appids_df,
top_features[0], monitor_flag)
if monitor_flag == 'cp' and top_psi:
cp_ls_top_psi.append('=======开卡初审=======')
elif monitor_flag == 'cf' and top_psi:
cp_ls_top_psi.append('=======开卡复审=======')
elif monitor_flag == 'fp' and top_psi:
cp_ls_top_psi.append('=======首贷提现初审=======')
elif monitor_flag == 'ff' and top_psi:
cp_ls_top_psi.append('=======首贷提现复审=======')
elif monitor_flag == 'wp' and top_psi:
cp_ls_top_psi.append('=======复贷初审=======')
elif monitor_flag == 'wf' and top_psi:
cp_ls_top_psi.append('=======复贷复审=======')
elif monitor_flag == 'q' and top_psi:
cp_ls_top_psi.append('=======结清调额=======')
else:
pass
cp_ls_top_psi.extend(top_psi)
logging.info('warming psi list: {}'.format(cp_ls_top_psi))
if cp_ls_top_psi:
cp_ls_top_psi.insert(0,
'*******{}-psi报警*******'.format(self.product))
cp_ls_top_psi.insert(
1, '时间：{}'.format(datetime.now().strftime('%Y-%m-%d ') +
timedetail))
self.except_handler.handle(msg=cp_ls_top_psi)
def psi_distr(self, start_time, total_appids_df, top_features,
monitor_flag):
the_psi_date = (datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') +
timedelta(days=10)).strftime('%Y-%m-%d') # 所监控的日期
logging.info('所监控的日期为：{}'.format(the_psi_date))
# 所监控日期前十天对应类型的appids
df_appid1 = total_appids_df.query(
"monitor_flag=='{}' and date!='{}'".format(
monitor_flag, the_psi_date)).reset_index(drop=True)
df_appid1 = df_appid1.sample(min(10000, df_appid1.shape[0]))
logging.info('monitor_flag:{} 前十天全部app_id个数：{}'.format(
monitor_flag, len(df_appid1)))
# 所监控日期对应类型的appids
df_appid2 = total_appids_df.query(
"monitor_flag=='{}' and date=='{}'".format(
monitor_flag, the_psi_date)).reset_index(drop=True)
df_appid2 = df_appid2.sample(min(1000, df_appid2.shape[0]))
logging.info('monitor_flag:{} 所监控的app_id个数：{}'.format(
monitor_flag, len(df_appid2)))
ls_top_psi = []
df_feature_all_1 = self.get_features(df_appid1, top_features)
df_feature_all_2 = self.get_features(df_appid2, top_features)
psi_dict = {}
for feature in top_features:
df_feature_1 = pd.DataFrame(df_feature_all_1, columns=[feature])
df_feature_2 = pd.DataFrame(df_feature_all_2, columns=[feature])
df, psi = self.psi(df_feature_1, df_feature_2, feature, bin_num=10)
psi_dict.update({feature: psi})
if psi > 0.25:
ls_top_psi.append("{}--psi:{}".format(feature, round(psi, 3)))
df['bins'] = df['bins'].map(lambda x: str(x))
max_index = df.query("psi_part=={}".format(
df.psi_part.max()))['bins'].values[0]
str_text = ''
if str(max_index) == '(-999.1, -999]':
str_text += '原因：缺失值变化导致, '
else:
str_text += '原因：区间{}变化所致, '.format(max_index)
if df.query("bins=='{}'".format(max_index)).a_rate.values[0] > \
df.query("bins=='{}'".format(max_index)).b_rate.values[0]:
str_text += '当前比例大于过去比例。'
else:
str_text += '当前比例小于过去比例。'
ls_top_psi.append(str_text)
ls_top_psi.append('==' * 18)
ls_top_psi.append(str(df))
ls_top_psi.append('==' * 18)
logging.info('{} calculate psi done :{}'.format(
monitor_flag, psi_dict))
return ls_top_psi
# 前一天
def job1(self):
"""比较昨天top特征的分布与昨天的前10天top特征的分布"""
logging.info('{} start handle psi_monitor job1!'.format(self.product))
start_time = (datetime.now() - timedelta(days=11)).strftime(
'%Y-%m-%d') + ' 00:00:00' # 获取所监控及其对比的前10天所有appid的开始时间
diff_day = 11 # 获取从开始时间往后11天的数据
diff_hour = 24 # 获取每天从0时到24时的数据
self.psi_classified(start_time,
diff_day,
diff_hour,
timedetail='前一天0-24时分布变化')
logging.info('{} end handle psi_monitor job1!'.format(self.product))
# 当天
def job2(self):
"""比较当天(0-16时)的top特征的分布与前10天(0-16时)top特征的分布"""
logging.info('{} start handle feature_monitor job2!'.format(
self.product))
start_time = (datetime.now() - timedelta(days=10)).strftime(
'%Y-%m-%d') + ' 00:00:00' # 获取所监控及其对比的前10天所有appid的开始时间
diff_day = 11 # 获取从开始时间往后11天的数据
diff_hour = 15 # 获取每天从0时到15时的数据
self.psi_classified(start_time,
diff_day,
diff_hour,
timedetail='当天0-16时分布变化')
logging.info('{} end handle psi_monitor job2!'.format(self.product))
# 当天
def job3(self):
"""比较当天(0-18时)的top特征的分布与前10天(0-18时)top特征的分布"""
logging.info('{} start handle feature_monitor job2!'.format(
self.product))
start_time = (datetime.now() -
timedelta(days=10)).strftime('%Y-%m-%d') + ' 00:00:00'
diff_day = 11 # 获取从开始时间往后11天的数据
diff_hour = 17 # 获取每天从0时到18时的数据
self.psi_classified(start_time,
diff_day,
diff_hour,
timedetail='当天0-18时分布变化')
logging.info('{} end handle psi_monitor job3!'.format(self.product))