事件分析标签分组项
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@ -188,7 +188,7 @@ async def event_model(
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return schemas.Msg(code=-9, msg='报表配置参数异常')
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res = []
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is_hide = []
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group_label = {}
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for idx, item in enumerate(sqls): #列出索引下标
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if item.get('is_show') == False:
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is_hide.append(idx)
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@ -373,6 +373,7 @@ async def event_model(
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# q['eventNameDisplay']=item['event_name_display']
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res.append(q)
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group_label = item['group_label']
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# 按总和排序
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for item in res:
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try:
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@ -388,6 +389,20 @@ async def event_model(
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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
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if item.get('groups'):
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item['groups'] = np.array(item['groups'])[sort_key].tolist()
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groups = []
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for gitem in item['groups']:
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gb = []
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if '(' in gitem:
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gitem = gitem.strip('(').strip(')').replace(' ', '').replace("'", '')
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true_list = gitem.split(',')
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for gstr in true_list:
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gb.append(gstr)
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# 存在标签分组项
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if group_label:
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for name, idx in group_label.items():
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gb.insert(idx, name)
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groups.append(str(gb))
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item['groups'] = groups
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item['values'] = np.array(item['values'])[sort_key].tolist()
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item['sum'] = np.array(item['sum'])[sort_key].tolist()
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item['avg'] = np.array(item['avg'])[sort_key].tolist()
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@ -115,6 +115,7 @@ class BehaviorAnalysis:
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self.user_tbl = None
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self.event_tbl = None
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self.data_in = data_in
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self.group_label = {}
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self.event_view = dict()
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self.events = [dict()]
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@ -166,8 +167,8 @@ class BehaviorAnalysis:
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self.zone_time = self._get_zone_time()
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self.time_particle = self._get_time_particle_size()
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self.start_date, self.end_date, self.date_range = self._get_date_range()
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self.global_filters = self._get_global_filters()
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self.groupby = self._get_group_by()
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self.global_filters = self._get_global_filters()
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self.unit_num = self._get_unit_num()
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self.global_relation = self.event_view.get('relation', 'and')
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@ -184,8 +185,29 @@ class BehaviorAnalysis:
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return self.event_view.get('unitNum')
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def _get_group_by(self):
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return [getattr(self.event_tbl.c, item['columnName']) for item in self.event_view.get('groupBy', [])]
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res = []
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# 存在删选条件
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groupBy = self.event_view.get('groupBy', [])
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if not groupBy:
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return []
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for idx, item in enumerate(groupBy):
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# 如果是标签
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if item['data_type'] == 'user_label':
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item.update({
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'comparator': "in",
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'comparator_name': "是"
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})
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# 加入分组标签
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self.group_label.update({item['columnDesc']: idx})
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# 加入events中每个event的filts条件中
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if self.events:
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for i in self.events:
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i['filts'].append(item)
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continue
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# 不是标签加入分组项中
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res.append(getattr(self.event_tbl.c, item['columnName']))
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return res
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# return [getattr(self.event_tbl.c, item['columnName']) for item in self.event_view.get('groupBy', [])]
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def _get_zone_time(self):
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return int(self.event_view.get('zone_time', 8))
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@ -466,22 +488,30 @@ class BehaviorAnalysis:
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'values')]
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else:
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selectd = select_exprs + [
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func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])), 2).label(
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func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])),
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2).label(
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'values')]
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else:
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operator_val = int(operator_)
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operator = event['operator'] # 运算符号
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if analysis == 'total_count':
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selectd = select_exprs + [settings.ARITHMETIC[operator](func.count(),operator_val).label('values')]
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selectd = select_exprs + [
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settings.ARITHMETIC[operator](func.count(), operator_val).label('values')]
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elif analysis == 'touch_user_count':
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selectd = select_exprs + [settings.ARITHMETIC[operator](func.count(sa.distinct(e_account_id_col)),operator_val).label('values')]
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selectd = select_exprs + [
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settings.ARITHMETIC[operator](func.count(sa.distinct(e_account_id_col)),
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operator_val).label('values')]
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elif analysis == 'touch_user_avg':
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selectd = select_exprs + [
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settings.ARITHMETIC[operator](func.round((func.count() / func.count(sa.distinct(e_account_id_col))), 2),operator_val).label(
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settings.ARITHMETIC[operator](
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func.round((func.count() / func.count(sa.distinct(e_account_id_col))), 2),
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operator_val).label(
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'values')]
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else:
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selectd = select_exprs + [
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settings.ARITHMETIC[operator](func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])), 2),operator_val).label(
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settings.ARITHMETIC[operator](
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func.round(getattr(func, analysis)(getattr(self.event_tbl.c, event['event_attr_id'])),
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2), operator_val).label(
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'values')]
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if user_filter:
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@ -518,7 +548,8 @@ WHERE addHours({game}.event."#event_time", 8) >= '{stat_date}' AND addHours({gam
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GROUP BY toDate(addHours({game}.event."#event_time", 8))) as bb on aa.date = bb.date ORDER by date
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"""
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# 单独把新增付费人数（以设备为维度）拿出来
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if event.get('event_attr') == '触发用户数' and ['is_new_device', 'orderid']== [i['columnName'] for i in event.get('filts')]:
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if event.get('event_attr') == '触发用户数' and ['is_new_device', 'orderid'] == [i['columnName'] for i in
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event.get('filts')]:
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stat_date = self.start_date
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end_date = self.end_date
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game = self.game
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@ -534,6 +565,7 @@ addHours("#event_time", 8) >= '{stat_date}' AND addHours("#event_time", 8) <= '{
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GROUP BY toDate(addHours("#event_time", 8))"""
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sqls.append({'sql': sql,
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'group_label':self.group_label,
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'groupby': [i.key for i in self.groupby],
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'date_range': self.date_range,
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'event_name': event_name_display or event_name,
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@ -546,6 +578,7 @@ GROUP BY toDate(addHours("#event_time", 8))"""
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})
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return sqls
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# 在漏斗分析，事件分析模型里面都有用到这块
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async def funnel_model_sql(self):
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"""
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@ -842,7 +875,8 @@ ORDER BY values desc"""
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days = (arrow.get(self.end_date).date() - arrow.get(self.start_date).date()).days
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keep = []
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cnt = []
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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,
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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,
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120 - 1, 150 - 1, 180 - 1, 210 - 1, 240 - 1, 270 - 1, 300 - 1,
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360 - 1]
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"""
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