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事件分析标签分组项

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This commit is contained in:
Àî×ÚÕñ 2022-06-10 17:19:41 +08:00
parent 68186cc5fa
commit e6735e8dfa
2 changed files with 78 additions and 29 deletions
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17
api/api_v1/endpoints/query.py
View File

@ -188,7 +188,7 @@ async def event_model(
return schemas.Msg(code=-9, msg='报表配置参数异常')
res = []
is_hide = []
group_label = {}
for idx, item in enumerate(sqls): #列出索引下标
if item.get('is_show') == False:
is_hide.append(idx)
@ -373,6 +373,7 @@ async def event_model(
# q['eventNameDisplay']=item['event_name_display']
res.append(q)
group_label = item['group_label']
# 按总和排序
for item in res:
try:
@ -388,6 +389,20 @@ async def event_model(
sort_key = np.argsort(np.array(item['sum']))[::-1]#将sum中的元素从小到大排列后的结果，提取其对应的索引，然后倒着输出到变量之中
if item.get('groups'):
item['groups'] = np.array(item['groups'])[sort_key].tolist()
groups = []
for gitem in item['groups']:
gb = []
if '(' in gitem:
gitem = gitem.strip('(').strip(')').replace(' ', '').replace("'", '')
true_list = gitem.split(',')
for gstr in true_list:
gb.append(gstr)
# 存在标签分组项
if group_label:
for name, idx in group_label.items():
gb.insert(idx, name)
groups.append(str(gb))
item['groups'] = groups
item['values'] = np.array(item['values'])[sort_key].tolist()
item['sum'] = np.array(item['sum'])[sort_key].tolist()
item['avg'] = np.array(item['avg'])[sort_key].tolist()

90
models/behavior_analysis.py
View File

@ -115,6 +115,7 @@ class BehaviorAnalysis:
self.user_tbl = None
self.event_tbl = None
self.data_in = data_in
self.group_label = {}
self.event_view = dict()
self.events = [dict()]
@ -166,8 +167,8 @@ class BehaviorAnalysis:
self.zone_time = self._get_zone_time()
self.time_particle = self._get_time_particle_size()
self.start_date, self.end_date, self.date_range = self._get_date_range()
self.global_filters = self._get_global_filters()
self.groupby = self._get_group_by()
self.global_filters = self._get_global_filters()
self.unit_num = self._get_unit_num()
self.global_relation = self.event_view.get('relation', 'and')
@ -184,8 +185,29 @@ class BehaviorAnalysis:
return self.event_view.get('unitNum')
def _get_group_by(self):
return [getattr(self.event_tbl.c, item['columnName']) for item in self.event_view.get('groupBy', [])]
res = []
# 存在删选条件
groupBy = self.event_view.get('groupBy', [])
if not groupBy:
return []
for idx, item in enumerate(groupBy):
# 如果是标签
if item['data_type'] == 'user_label':
item.update({
'comparator': "in",
'comparator_name': "是"
})
# 加入分组标签
self.group_label.update({item['columnDesc']: idx})
# 加入events中每个event的filts条件中
if self.events:
for i in self.events:
i['filts'].append(item)
continue
# 不是标签加入分组项中
res.append(getattr(self.event_tbl.c, item['columnName']))
return res
# return [getattr(self.event_tbl.c, item['columnName']) for item in self.event_view.get('groupBy', [])]
def _get_zone_time(self):
return int(self.event_view.get('zone_time', 8))
@ -385,8 +407,8 @@ class BehaviorAnalysis:
sqls = []
event_time_col = getattr(self.event_tbl.c, '#event_time')
for event in self.events:
operator_ = event.get('operator_val','')
#排头显示名
operator_ = event.get('operator_val', '')
# 排头显示名
event_name_display = event.get('eventNameDisplay')
is_show = event.get('is_show', True)
@ -408,7 +430,7 @@ class BehaviorAnalysis:
if event.get('customType') == 'formula':
if event.get('customEvent'):
#组合公式的内容
# 组合公式的内容
formula = event.get('customEvent')
custom = CustomEvent(self.event_tbl, formula, format).parse()
event_name = custom['event_name']
@ -466,22 +488,30 @@ class BehaviorAnalysis:
'values')]
else:
selectd = select_exprs + [
func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])), 2).label(
func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])),
2).label(
'values')]
else:
operator_val=int(operator_)
operator=event['operator'] #运算符号
operator_val = int(operator_)
operator = event['operator'] # 运算符号
if analysis == 'total_count':
selectd = select_exprs + [settings.ARITHMETIC[operator](func.count(),operator_val).label('values')]
selectd = select_exprs + [
settings.ARITHMETIC[operator](func.count(), operator_val).label('values')]
elif analysis == 'touch_user_count':
selectd = select_exprs + [settings.ARITHMETIC[operator](func.count(sa.distinct(e_account_id_col)),operator_val).label('values')]
selectd = select_exprs + [
settings.ARITHMETIC[operator](func.count(sa.distinct(e_account_id_col)),
operator_val).label('values')]
elif analysis == 'touch_user_avg':
selectd = select_exprs + [
settings.ARITHMETIC[operator](func.round((func.count() / func.count(sa.distinct(e_account_id_col))), 2),operator_val).label(
settings.ARITHMETIC[operator](
func.round((func.count() / func.count(sa.distinct(e_account_id_col))), 2),
operator_val).label(
'values')]
else:
selectd = select_exprs + [
settings.ARITHMETIC[operator](func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])), 2),operator_val).label(
settings.ARITHMETIC[operator](
func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])),
2), operator_val).label(
'values')]
if user_filter:
@ -503,10 +533,10 @@ class BehaviorAnalysis:
print(sql)
# 单独付费率的拿出来
if event.get('customEvent') == 'pay.touch_user_count/login.touch_user_count':
stat_date=self.start_date
end_date=self.end_date
game=self.game
sql=f"""
stat_date = self.start_date
end_date = self.end_date
game = self.game
sql = f"""
select aa.date as date,round((a/b)*100,2) as values from
(select toDate(addHours({game}.event."#event_time", 8)) AS date,uniqCombined(if({game}.event."#event_name" = 'pay', {game}.event."#account_id", NULL)) as a from {game}.event
WHERE addHours({game}.event."#event_time", 8) >= '{stat_date}' AND addHours({game}.event."#event_time", 8) <= '{end_date}'
@ -518,11 +548,12 @@ WHERE addHours({game}.event."#event_time", 8) >= '{stat_date}' AND addHours({gam
GROUP BY toDate(addHours({game}.event."#event_time", 8))) as bb on aa.date = bb.date ORDER by date
"""
# 单独把新增付费人数（以设备为维度）拿出来
if event.get('event_attr') == '触发用户数' and ['is_new_device', 'orderid']== [i['columnName'] for i in event.get('filts')]:
stat_date=self.start_date
end_date=self.end_date
game=self.game
sql=f"""SELECT toDate(addHours("#event_time", 8)) as date,
if event.get('event_attr') == '触发用户数' and ['is_new_device', 'orderid'] == [i['columnName'] for i in
event.get('filts')]:
stat_date = self.start_date
end_date = self.end_date
game = self.game
sql = f"""SELECT toDate(addHours("#event_time", 8)) as date,
round(uniqExact("#distinct_id"), 2) AS values FROM
(SELECT toDate(addHours("#event_time", 8)) as date,"#event_time",`#event_name`,`#distinct_id`,`#account_id` from {game}.event WHERE
addHours("#event_time", 8) >= '{stat_date}' AND addHours("#event_time", 8) <= '{end_date}'
@ -534,6 +565,7 @@ addHours("#event_time", 8) >= '{stat_date}' AND addHours("#event_time", 8) <= '{
GROUP BY toDate(addHours("#event_time", 8))"""
sqls.append({'sql': sql,
'group_label':self.group_label,
'groupby': [i.key for i in self.groupby],
'date_range': self.date_range,
'event_name': event_name_display or event_name,
@ -546,7 +578,8 @@ GROUP BY toDate(addHours("#event_time", 8))"""
})
return sqls
#在漏斗分析，事件分析模型里面都有用到这块
# 在漏斗分析，事件分析模型里面都有用到这块
async def funnel_model_sql(self):
"""
SELECT level, count(*) AS values
@ -614,7 +647,7 @@ ORDER BY level
event = self.events[0]
event_name = event['eventName']
analysis = event['analysis']
if analysis in ['list_distinct',"set_distinct","ele_distinct"]:
if analysis in ['list_distinct', "set_distinct", "ele_distinct"]:
analysis = 'max'
e_account_id_col = getattr(self.event_tbl.c, '#account_id').label('uid')
u_account_id_col = getattr(self.user_tbl.c, '#account_id')
@ -680,9 +713,9 @@ ORDER BY level
qry = sa.select(event_date_col, e_account_id_col,
settings.CK_FUNC[analysis](event_attr_col).label('values')) \
.where(and_(*where)) \
.group_by(event_date_col,e_account_id_col)
.group_by(event_date_col, e_account_id_col)
sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
columnName = event.get('label_id','')
columnName = event.get('label_id', '')
if columnName != '':
sql = sql.replace('SELECT', f'SELECT {columnName},', 1)
sql += f',{columnName}'
@ -842,8 +875,9 @@ ORDER BY values desc"""
days = (arrow.get(self.end_date).date() - arrow.get(self.start_date).date()).days
keep = []
cnt = []
retention_n = [*[k for k in range(1, 60)], 70-1, 75-1, 80-1, 85-1, 90-1, 95-1, 100-1, 110-1, 120-1, 150-1, 180-1, 210-1, 240-1, 270-1, 300-1,
360-1]
retention_n = [*[k for k in range(1, 60)], 70 - 1, 75 - 1, 80 - 1, 85 - 1, 90 - 1, 95 - 1, 100 - 1, 110 - 1,
120 - 1, 150 - 1, 180 - 1, 210 - 1, 240 - 1, 270 - 1, 300 - 1,
360 - 1]
"""
cnt0-cnt1 as on1,