增加LTV全局筛选功能
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parent
529bf55d37
commit
713ed2d7e2
@ -5,7 +5,6 @@ from motor.motor_asyncio import AsyncIOMotorDatabase
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import crud, schemas
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from api import deps
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from core.config import settings
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from db import get_database
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from db.ckdb import CKDrive, get_ck_db
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from schemas.project import ProjectCreate
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@ -25,6 +24,7 @@ async def create(
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"""创建项目"""
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try:
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res_project = await crud.project.create(db, data_in, current_user=request.user)
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await crud.project_number.createxiangmu(db, data_in)
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except pymongo.errors.DuplicateKeyError:
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return schemas.Msg(code=-1, msg='项目名已存在', data='项目名已存在')
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@ -74,8 +74,28 @@ async def read_project(request: Request,
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resp = await crud.project.get_my_game(db, game_list)
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return schemas.Msg(code=0, msg='ok', data=resp)
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#获取项目名和渠道名project_name
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@router.get("/project_name")
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async def project_name(request: Request,
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db: AsyncIOMotorDatabase = Depends(get_database),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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):
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if request.user.username == 'root':
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res = await crud.project_number.all_xiangmu(db)
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for i in res:
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i['_id'] = str(i['_id'])
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return schemas.Msg(code=0,msg='ok',data=res)
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#添加项目名，渠道名
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@router.post("/add_project_name")
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async def add_project_name(request: Request,
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data_in: schemas.ProjectnumberInsert,
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db: AsyncIOMotorDatabase = Depends(get_database),
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current_user: schemas.UserDB = Depends(deps.get_current_user)):
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#插入数据
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#await crud.project_number.create(db, data_in)
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#修改数据
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await crud.project_number.update(db, data_in)
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return schemas.Msg(code=0, msg='修改成功', data=True)
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@router.get("/detail")
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async def detail(request: Request,
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game: str,
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@ -321,6 +321,112 @@ async def retention_model(request: Request,
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for d in set(res['date_range']) - set(df.index):
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df.loc[d] = 0
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df.sort_index(inplace=True)
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summary_values = {'均值': {}}
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max_retention_n = 1
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#留存人数
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avg = {}
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#流失人数
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avgo = {}
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for date, v in df.T.items():
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#字典中data存在时不替换，否则将data替换成空字典
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tmp = summary_values.setdefault(date, dict())
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tmp['d0'] = int(v.cnt0)
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tmp['p'] = []
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tmp['n'] = []
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tmp['p_outflow'] = []
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tmp['n_outflow'] = []
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for i in retention_n:
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n = (pd.Timestamp.now().date() - date).days
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if i > n:
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continue
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# max_retention_n = i if i > max_retention_n else max_retention_n
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#留存的人数
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avg[i] = avg.setdefault(i, 0) + v[f'cnt{i}']
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#流失的人数
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avgo[i] = avgo.setdefault(i, 0) + v[f'on{i}']
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tmp['p'].append(v[f'p{i}'])
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tmp['n'].append(v[f'cnt{i}'])
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tmp['p_outflow'].append(v[f'op{i}'])
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tmp['n_outflow'].append(v[f'on{i}'])
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tmp = summary_values['均值']
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retention_avg_dict = {}
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for rn in retention_n:
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for rt, rd in df.T.items():
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if rt + datetime.timedelta(days=rn) <= pd.datetime.now().date():
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retention_avg_dict.setdefault(rn, {'cnt0': 0, 'cntn': 0,'o_cnt0':0,'o_cntn':0})
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retention_avg_dict[rn]['cnt0'] += rd['cnt0']
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retention_avg_dict[rn]['cntn'] += rd[f'cnt{rn}']
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retention_avg_dict[rn]['o_cnt0'] += rd['cnt0']
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retention_avg_dict[rn]['o_cntn'] += rd[f'on{rn}']
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tmp['p'] = []
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tmp['n'] = []
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tmp['p_outflow'] = []
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tmp['n_outflow'] = []
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tmp['d0'] = 0
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for rt, rd in retention_avg_dict.items():
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tmp['d0'] = int(df['cnt0'].sum())
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n = round(rd['cntn'] * 100 / rd['cnt0'],2)
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n = 0 if np.isnan(n) else n
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tmp['p'].append(n)
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tmp['n'].append(rd['cntn'])
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n = round(rd['o_cntn'] * 100 / rd['cnt0'],2)
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n = 0 if np.isnan(n) else n
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tmp['p_outflow'].append(n)
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tmp['n_outflow'].append(rd['o_cntn'])
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#次留数
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title = ['日期', '用户数', '次留', *[f'{i + 1}留' for i in retention_n[1:]]]
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# 未到达的日期需要补齐-
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retention_length = len(retention_n)
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for _, items in summary_values.items():
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for key in ['p', 'n', 'p_outflow', 'n_outflow']:
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items[key].extend(['-'] * (retention_length - len(items[key])))
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resp = {
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'summary_values': summary_values,
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# 'values': values,
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'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
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'title': title,
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'filter_item_type': filter_item_type,
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'filter_item': filter_item,
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'start_date': res['start_date'],
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'end_date': res['end_date'],
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'time_particle': res['time_particle']
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}
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return schemas.Msg(code=0, msg='ok', data=resp)
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#计算流失率
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# retention_model
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# retention_model_loss
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#@router.post("/retention_model")
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async def retention_model01(request: Request,
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game: str,
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ckdb: CKDrive = Depends(get_ck_db),
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db: AsyncIOMotorDatabase = Depends(get_database),
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analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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await analysis.init(data_where=current_user.data_where)
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res = await analysis.retention_model_sql2() #初始化开始时间结束时间，sql语句 字典
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sql = res['sql'] #获取到sql语句
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='无数据', data=None)
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date_range = res['date_range'] #时间 列表
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unit_num = res['unit_num'] #int
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retention_n = res['retention_n'] #列表 int
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filter_item_type = res['filter_item_type'] #all
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filter_item = res['filter_item'] #列表 0,1,3,7,14,21,30
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df.set_index('reg_date', inplace=True)
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for d in set(res['date_range']) - set(df.index):
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df.loc[d] = 0
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df.sort_index(inplace=True)
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summary_values = {'均值': {}}
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max_retention_n = 1
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@ -341,7 +447,8 @@ async def retention_model(request: Request,
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# max_retention_n = i if i > max_retention_n else max_retention_n
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avg[i] = avg.setdefault(i, 0) + v[f'cnt{i}']
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avgo[i] = avgo.setdefault(i, 0) + v[f'on{i}']
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tmp['p'].append(v[f'p{i}'])
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tmp['p'].append(round(100-v[f'p{i}'],2))
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#tmp['p'].append(v[f'p{i}'])
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tmp['n'].append(v[f'cnt{i}'])
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tmp['p_outflow'].append(v[f'op{i}'])
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tmp['n_outflow'].append(v[f'on{i}'])
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@ -365,7 +472,8 @@ async def retention_model(request: Request,
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tmp['d0'] = 0
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for rt, rd in retention_avg_dict.items():
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tmp['d0'] = int(df['cnt0'].sum())
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n = round(rd['cntn'] * 100 / rd['cnt0'],2)
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n = round(100-(rd['cntn'] * 100 / rd['cnt0']), 2)
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#n = round(rd['cntn'] * 100 / rd['cnt0'],2)
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n = 0 if np.isnan(n) else n
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tmp['p'].append(n)
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tmp['n'].append(rd['cntn'])
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@ -375,7 +483,7 @@ async def retention_model(request: Request,
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tmp['n_outflow'].append(rd['o_cntn'])
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title = ['日期', '用户数', '次留', *[f'{i + 1}留' for i in retention_n[1:]]]
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title = ['日期', '用户数', '次流失', *[f'{i + 1}流失' for i in retention_n[1:]]]
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# 未到达的日期需要补齐-
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retention_length = len(retention_n)
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@ -398,6 +506,7 @@ async def retention_model(request: Request,
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return schemas.Msg(code=0, msg='ok', data=resp)
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@router.post("/retention_model_export")
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async def retention_model_export(request: Request,
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game: str,
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@ -899,7 +1008,7 @@ async def scatter_model(
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for i in labels:
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v +=1
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if int(i) == 1:
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labels_dict01["1"]=labels_dict["1"]
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labels_dict01["1"]=labels_dict['1']
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else:
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# for number in labels_dict.keys():
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# if number >=i:
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@ -22,7 +22,7 @@ from db.redisdb import get_redis_pool, RedisDrive
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from models.behavior_analysis import BehaviorAnalysis
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from models.user_analysis import UserAnalysis
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from models.x_analysis import XAnalysis
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from utils import DfToStream
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from utils import DfToStream, get_bijiao
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router = APIRouter()
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@ -50,12 +50,35 @@ async def ltv_model_sql(
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) -> schemas.Msg:
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""" ltv模型sql """
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await analysis.init(data_where=current_user.data_where)
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res = analysis.ltv_model_sql()
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sql = res['sql']
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#仅一条筛选条件则是把GM过滤后获取全部数据
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if len(analysis.global_filters)==1 and analysis.global_filters[0]['strftv']=='GM':
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df = await ckdb.query_dataframe(sql)
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#多条筛选条件则合成新的sql
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else:
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new_sql=""""""
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#拆分sql
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split_sql = sql.split('AND 1')
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#获取每一条筛选条件
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for i in analysis.global_filters:
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#剔除GM
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if i['strftv'] != 'GM':
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#获取筛选条件的包含关系
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bijiao=get_bijiao(i["comparator"])
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#获取筛选条件的值
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condition=tuple(i['ftv'])
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#获取事件名
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columnName=i['columnName']
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dd = f""" AND {game}.event.{columnName} {bijiao} {condition}"""
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new_sql+=dd
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split_="""AND 1 """
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news_sql = split_sql[0] + split_+new_sql + split_sql[1] + split_+new_sql+ split_sql[2]+split_+split_sql[3]
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df = await ckdb.query_dataframe(news_sql)
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quota = res['quota'] #字段名
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ltv_n = res['ltv_n']
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df = await ckdb.query_dataframe(sql)
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#df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='查无数据')
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df.fillna(0, inplace=True) #修改原对象，以0填补空缺值
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@ -70,7 +93,7 @@ async def ltv_model_sql(
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# df.sort_values('date', inplace=True) # 根据date进行倒叙排序
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for d in set(res['date_range']) - set(df['date']):
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#在有效日期最后一行补充行数据（值都为0），补充的行数为两个集合的差集长度
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#在有效日期最后一行补充行数据（值都为'-'），补充的行数为两个集合的差集长度
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df.loc[len(df)] = '-'
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#在date此列补充多行数据（值为两个集合差集的子元素）
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df.loc[len(df) - 1, 'date'] = d
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@ -90,9 +113,16 @@ async def ltv_model_sql(
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df2 = df[['注册日期', cat, *[f'sumpay_{i}' for i in ltv_n]]]
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df2.replace('-', 0, inplace=True) #True改变原数据，前面是需要替换的值，后面是替换后的值。 在原数据把下划线替换成0
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#修改下面代码
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# 去除sumpay_1的值为0的列
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new_df2 = (df2.drop(df2[(df2.sumpay_2 == 0)].index))
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#为new_df2排序
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new_df2=new_df2.reset_index(drop=True)
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#求相差天数
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str_time = str(res['date_range'][0])
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split_time = str_time.split('-')
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str_time =new_df2['注册日期'][0]
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str_time01=str(str_time)
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split_time = str_time01.split('-')
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#str_time = str(res['date_range'][0])
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# split_time = str_time.split('-')
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now_time = time.strftime("%Y-%m-%d", time.localtime())
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split_now_time = now_time.split('-')
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today = datetime.datetime(int(split_time[0]), int(split_time[1]), int(split_time[2]))
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@ -102,14 +132,26 @@ async def ltv_model_sql(
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_listData = {}
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for i in ltv_n:
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if i <=newday:
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avgLtv=(df2[[f'sumpay_{i}']][0:newday+1-i].sum()/df2[cat][0:newday+1-i].sum()).round(2)
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#计算均值
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#avgLtv = (new_df2[[f'sumpay_{i}']][0:newday + 1 - i].sum() / new_df2[cat][0:newday + 1 - i].sum()).round(2)
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#12.20号计算LTV均值的时候分母包括当天未充值新增设备数，比剔除掉的计算值偏小
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avgLtv = (df2[[f'sumpay_{i}']][0:newday + 1 - i].sum() / df2[cat][0:newday + 1 - i].sum()).round(2)
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#取出均值
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new_avgLtv=str(avgLtv).split('\n')[0].split(' ')
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new_avgLtv01=new_avgLtv[len(new_avgLtv)-1]
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if new_avgLtv01 == 'NaN':
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_listData[f'sumpay_{i}'] = '-'
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else:
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_listData[f'sumpay_{i}'] = new_avgLtv01
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#原代码
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# avgLtv=(df2[[f'sumpay_{i}']][0:newday+1-i].sum()/df2[cat][0:newday+1-i].sum()).round(2)
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# new_avgLtv=str(avgLtv).split('\n')[0].split(' ')
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# new_avgLtv01=new_avgLtv[len(new_avgLtv)-1]
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# if new_avgLtv01 == 'NaN':
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# _listData[f'sumpay_{i}'] = '-'
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# else:
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# _listData[f'sumpay_{i}'] = new_avgLtv01
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else:
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_listData[f'sumpay_{i}']='-'
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avgLtvlist = pd.Series(_listData)
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@ -117,12 +159,16 @@ async def ltv_model_sql(
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_listname=[]
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#计算总累计LTV最后一个值
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for k, v in _listData.items():
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if v !=0:
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if v != 0 or v!= '-':
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# if v !=0:
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_listname.append(k)
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max_nmu=max(_listname)
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#max_num = (new_df2[[max_nmu]].sum() / new_df2[cat].sum()).round(2)
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max_num=(df2[[max_nmu]].sum()/df2[cat].sum()).round(2)
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max_number=str(max_num[0])
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df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avgLtvlist]
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#原代码
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#df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avgLtvlist]
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# avg_ltv = (df2[[f'sumpay_{i}' for i in ltv_n]].sum() / df2[cat].sum()).round(2)
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#df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avg_ltv]
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@ -141,9 +187,10 @@ async def ltv_model_sql(
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#把列中累计LTV等于0的值改成'-'
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df1.loc[df1['累计LTV']==0, '累计LTV'] = '-'
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#df1.loc[df1['累计LTV']==0, '累计LTV'] = '-'
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#剔除行，列的累计LTV=='-'的剔除出去
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df3 = df1.drop(df1[(df1.累计LTV=='-')].index)
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df3 = df1.drop(df1[(df1.LTV1 == '-')].index)
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#df3 = df1.drop(df1[(df1.累计LTV=='-')].index)
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days = (pd.Timestamp.now().date() - pd.to_datetime(res['start_date']).date()).days
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df1.iloc[len(df1) - 1, days + 4:] = '-'
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@ -14,3 +14,4 @@ from .crud_role import role
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from .crud_check_data import check_data
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from .user_label import user_label
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from .select_map import select_map
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from .crud_project_number import project_number
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33
crud/crud_project_number.py
Normal file
33
crud/crud_project_number.py
Normal file
@ -0,0 +1,33 @@
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from motor.motor_asyncio import AsyncIOMotorDatabase
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import schemas
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from crud.base import CRUDBase
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__all__ = 'project_number',
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from utils import get_uid
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class CRUDProjectNumber(CRUDBase):
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# 获取所有数据
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async def all_xiangmu(self, db: AsyncIOMotorDatabase):
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return await self.find_many(db, {})
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# 修改数据
|
||||
async def update(self, db: AsyncIOMotorDatabase, data_in: schemas.AddProjectnumber):
|
||||
game = data_in.game
|
||||
add_ditch = []
|
||||
for member in data_in.ditch:
|
||||
add_ditch.append(member.dict())
|
||||
await self.update_one(db, {'game': game}, {'$set': {'ditch': add_ditch}})
|
||||
|
||||
# 插入数据
|
||||
async def create(self, db: AsyncIOMotorDatabase, data_in: schemas.ProjectnumberInsert):
|
||||
# await self.update_one(db, {'xiangmu': data_in.xiangmu}, {'$set': data_in.dict()}, upsert=True)
|
||||
await self.update_one(db, {data_in.game, data_in.ditch}, upsert=True)
|
||||
|
||||
# 同步插入项目
|
||||
async def createxiangmu(self, db: AsyncIOMotorDatabase, data_in: schemas.ProjectnumberInsert):
|
||||
await self.insert_one(db, data_in.dict())
|
||||
|
||||
|
||||
project_number = CRUDProjectNumber('project_number')
|
||||
@ -18,3 +18,4 @@ from .role import *
|
||||
from .check_data import *
|
||||
from .userlabel import *
|
||||
from .select_map import *
|
||||
from .project_number import *
|
||||
@ -51,7 +51,7 @@ class ProjectDelMember(BaseModel):
|
||||
class ProjectCreate(ProjectBase):
|
||||
name: str = Field(..., title='项目名')
|
||||
game: str = Field(..., title='游戏代号')
|
||||
|
||||
#qudao:str = Field(...,title='渠道')
|
||||
|
||||
# 查询某个项目看板
|
||||
class ProjectKanban(DBBase):
|
||||
|
||||
17
schemas/project_number.py
Normal file
17
schemas/project_number.py
Normal file
@ -0,0 +1,17 @@
|
||||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
|
||||
|
||||
class ProjectnumberList(BaseModel):
|
||||
main_channel: str
|
||||
ditch: str
|
||||
|
||||
|
||||
class ProjectnumberInsert(BaseModel):
|
||||
game: str
|
||||
ditch: List[ProjectnumberList]
|
||||
name: str
|
||||
|
||||
class AddProjectnumber(BaseModel):
|
||||
game: str
|
||||
ditch: List[ProjectnumberInsert]
|
||||
@ -4,3 +4,11 @@ import time
|
||||
|
||||
def get_uid():
|
||||
return hex(int(time.time() * 10 ** 7) + random.randint(0, 10000))[2:]
|
||||
|
||||
|
||||
# 获取筛选条件的包含关系
|
||||
def get_bijiao(bijiao):
|
||||
if bijiao == '==' or bijiao == 'in' or bijiao == 'like' or bijiao == 'is not null':
|
||||
return "IN"
|
||||
elif bijiao == '!=' or bijiao == 'not like' or bijiao == 'is null':
|
||||
return 'NOT LIKE'
|
||||
|
||||
Loading…
Reference in New Issue
Block a user