1072 lines
42 KiB
Python
1072 lines
42 KiB
Python
import datetime
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from collections import defaultdict
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import mimetypes
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from urllib.parse import quote
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import pandas as pd
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import numpy as np
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from fastapi import APIRouter, Depends, Request
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from fastapi.encoders import jsonable_encoder
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from motor.motor_asyncio import AsyncIOMotorDatabase
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from fastapi.responses import StreamingResponse
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import crud, schemas
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from common import *
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from api import deps
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from db import get_database
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from db.ckdb import get_ck_db, CKDrive
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from db.redisdb import get_redis_pool, RedisDrive
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from models.behavior_analysis import BehaviorAnalysis, CombinationEvent
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from models.user_analysis import UserAnalysis
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from utils import DfToStream
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router = APIRouter()
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@router.post("/sql")
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async def query_sql(
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request: Request,
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game: str,
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data_in: schemas.Sql,
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ckdb: CKDrive = Depends(get_ck_db),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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"""原 sql 查询 """
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sql = data_in.sql
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sql = sql.replace('$game', game)
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data = await ckdb.execute(sql)
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return schemas.Msg(code=0, msg='ok', data=data)
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@router.post("/sql_export")
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async def query_sql(
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request: Request,
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game: str,
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data_in: schemas.Sql,
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ckdb: CKDrive = Depends(get_ck_db),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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):
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"""sql 导出 """
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file_name = quote(f'result.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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sql = data_in.sql
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sql = sql.replace('$game', game)
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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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df_to_stream = DfToStream((df, 'result'))
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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@router.post("/event_model_sql")
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async def event_model_sql(
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request: Request,
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game: str,
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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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""" 事件分析模型 sql"""
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await analysis.init(data_where=current_user.data_where)
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data = await analysis.event_model_sql()
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return schemas.Msg(code=0, msg='ok', data=data)
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@router.post("/event_model_export")
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async def event_model_export(request: Request,
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game: str,
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ckdb: CKDrive = Depends(get_ck_db),
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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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):
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""" 事件分析模型 数据导出"""
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await analysis.init(data_where=current_user.data_where)
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sqls = await analysis.event_model_sql()
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file_name = quote(f'{sqls[0]["report_name"]}.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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excels = []
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for item in sqls:
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if item.get('combination_event'):
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continue
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sql = item['sql']
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event_name = item['event_name']
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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continue
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if 'date' in df:
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df.sort_values('date', inplace=True)
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try:
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df['date'] = df['date'].dt.tz_localize(None)
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except:
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pass
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excels.append((df, event_name))
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df_to_stream = DfToStream(*excels)
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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#
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# @router.get("/event_model_export")
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# async def event_model_export(request: Request,
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# game: str,
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# report_id: str,
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# ckdb: CKDrive = Depends(get_ck_db),
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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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# ):
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# """ 事件分析模型 数据导出"""
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# analysis = BehaviorAnalysis(game, schemas.CkQuery(report_id=report_id), get_redis_pool())
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# await analysis.init(data_where=current_user.data_where)
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# sqls = analysis.event_model_sql()
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# res = []
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# file_name = f'{sqls[0]["report_name"]}.xlsx'
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# mime = mimetypes.guess_type(file_name)[0]
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# for item in sqls[:1]:
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# sql = item['sql']
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# event_name = item['event_name']
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# df = await ckdb.query_dataframe(sql)
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# file = df_to_stream(df, event_name)
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# return StreamingResponse(file, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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#
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@router.post("/event_model")
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async def event_model(
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request: Request,
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game: str,
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data_in: schemas.CkQuery,
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ckdb: CKDrive = Depends(get_ck_db),
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rdb: RedisDrive = Depends(get_redis_pool),
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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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""" 事件分析"""
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await analysis.init(data_where=current_user.data_where)
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sqls = await analysis.event_model_sql()
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res = []
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is_hide = []
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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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#event_name:事件名，日充总额
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#format：float浮点型
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q = {
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'groups': [],
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'values': [],
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'sum': [],
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'avg': [],
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'event_name': item['event_name'],
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'format': item['format'],
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'last_value': 0,
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'start_date': item['start_date'],
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'end_date': item['end_date'],
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'time_particle': item['time_particle']
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}
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# 处理组合问题，如combination_event不存在则跳过
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if item.get('combination_event'):
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combination_event = CombinationEvent(res, item.get('combination_event'), item['format'])
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values, sum_, avg = combination_event.parse()
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q['values'].append(values)
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q['sum'].append(sum_)
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q['avg'].append(avg)
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q['date_range'] = item['date_range']
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for last_value in values[::-1]:
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if last_value > 0:
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q['last_value'] = float(last_value)
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break
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res.append(q)
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continue
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#sql语句
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sql = item['sql']
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groupby = item['groupby']
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date_range = item['date_range'] #获取的要查询的每一天的时间
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q['date_range'] = date_range #把要查询的时间加入q字典中
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df = await ckdb.query_dataframe(sql) #以sql语句查出数据，df是二维列表
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df.fillna(0, inplace=True)#以0填补空数据
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#获取第一矩阵的长度
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if df.shape[0] == 0:
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df = pd.DataFrame({'date': date_range, 'values': 0 * len(date_range)})
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# continue
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# return schemas.Msg(code=0, msg='ok', data=[q])
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if item['time_particle'] == 'total':
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# for group, df_group in df.groupby(groupby):
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# df_group.reset_index(drop=True, inplace=True)
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q['groups'].append(groupby)
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q['values'].append(df['values'].to_list())
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q['sum'].append(round(float(df['values'].sum()), 2))
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q['avg'].append(round(float(df['values'].mean()), 2))
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for last_value in df['values'].values[::-1]:
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if last_value > 0:
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q['last_value'] = float(last_value)
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break
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if groupby and (set(groupby) & set(df) == set(groupby)):
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q['date_range'] = [f'{i}' for i in df.set_index(groupby).index]
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else:
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q['date_range'] = ['合计']
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res.append(q)
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continue
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if groupby and (set(groupby) & set(df)) == set(groupby):
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# 有分组
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for group, df_group in df.groupby(groupby):
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#在原数据上将索引重新转换为列，新索引的列删除
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df_group.reset_index(drop=True, inplace=True)
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q['groups'].append(str(group))
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concat_data = []
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for i in set(date_range) - set(df_group['date']):
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if len(groupby) > 1:
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concat_data.append((i, *group, 0))
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else:
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concat_data.append((i, group, 0))
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df_group = pd.concat([df_group, pd.DataFrame(concat_data, columns=df_group.columns)])
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df_group.sort_values('date', inplace=True)
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q['values'].append(df_group['values'].to_list())
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q['sum'].append(round(float(df_group['values'].sum()), 2))
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q['avg'].append(round(float(df_group['values'].mean()), 2))
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for last_value in df['values'].values[::-1]:
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if last_value > 0:
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q['last_value'] = float(last_value)
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break
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else:
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# 无分组
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concat_data = []
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for i in set(date_range) - set(df['date']):
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concat_data.append((i, 0))
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#纵向拼接两个表
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df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
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#在原数据上按data排序
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df.sort_values('date', inplace=True)
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if len(df) >= 2:
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q['chain_ratio'] = division((df.iloc[-1, 1] - df.iloc[-2, 1]) * 100, df.iloc[-2, 1], 2)
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if len(df) >= 8:
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q['wow'] = division((df.iloc[-1, 1] - df.iloc[-8, 1]) * 100, df.iloc[-8, 1], 2) or 0
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q['values'].append(df['values'].to_list())
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for last_value in df['values'].values[::-1]:
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if last_value > 0:
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q['last_value'] = float(last_value)
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break
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#求所有值的和
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q['sum'].append(round(float(df['values'].sum()), 2))
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#求平均值
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q['avg'].append(round(float(df['values'].mean()), 2))
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# q['eventNameDisplay']=item['event_name_display']
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res.append(q)
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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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if item['time_particle'] in ('P1D', 'P1W'): #按格式修改年月日
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item['date_range'] = [d.strftime('%Y-%m-%d') for d in item['date_range']]
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elif item['time_particle'] in ('P1M',):
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item['date_range'] = [d.strftime('%Y-%m') for d in item['date_range']]
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else:
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item['date_range'] = [d.strftime('%Y-%m-%d %H:%M:%S') for d in item['date_range']]
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except:
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pass
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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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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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res = [item for idx, item in enumerate(res) if idx not in is_hide]
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return schemas.Msg(code=0, msg='ok', data=res)
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@router.post("/retention_model_sql")
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async def retention_model_sql(
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request: Request,
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game: str,
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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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"""留存查询 sql"""
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await analysis.init(data_where=current_user.data_where)
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data = await analysis.retention_model_sql2()
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return schemas.Msg(code=0, msg='ok', data=[data])
|
||
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||
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@router.post("/retention_model")
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async def retention_model(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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||
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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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avg = {}
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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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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['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']
|
||
retention_avg_dict = {}
|
||
|
||
for rn in retention_n:
|
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for rt, rd in df.T.items():
|
||
if rt + datetime.timedelta(days=rn) <= pd.datetime.now().date():
|
||
retention_avg_dict.setdefault(rn, {'cnt0': 0, 'cntn': 0,'o_cnt0':0,'o_cntn':0})
|
||
retention_avg_dict[rn]['cnt0'] += rd['cnt0']
|
||
retention_avg_dict[rn]['cntn'] += rd[f'cnt{rn}']
|
||
|
||
retention_avg_dict[rn]['o_cnt0'] += rd['cnt0']
|
||
retention_avg_dict[rn]['o_cntn'] += rd[f'on{rn}']
|
||
|
||
tmp['p'] = []
|
||
tmp['n'] = []
|
||
tmp['p_outflow'] = []
|
||
tmp['n_outflow'] = []
|
||
tmp['d0'] = 0
|
||
for rt, rd in retention_avg_dict.items():
|
||
tmp['d0'] = int(df['cnt0'].sum())
|
||
n = round(rd['cntn'] * 100 / rd['cnt0'],2)
|
||
n = 0 if np.isnan(n) else n
|
||
tmp['p'].append(n)
|
||
tmp['n'].append(rd['cntn'])
|
||
n = round(rd['o_cntn'] * 100 / rd['cnt0'],2)
|
||
n = 0 if np.isnan(n) else n
|
||
tmp['p_outflow'].append(n)
|
||
tmp['n_outflow'].append(rd['o_cntn'])
|
||
|
||
|
||
title = ['日期', '用户数', '次留', *[f'{i + 1}留' for i in retention_n[1:]]]
|
||
|
||
# 未到达的日期需要补齐-
|
||
retention_length = len(retention_n)
|
||
for _, items in summary_values.items():
|
||
for key in ['p', 'n', 'p_outflow', 'n_outflow']:
|
||
items[key].extend(['-'] * (retention_length - len(items[key])))
|
||
|
||
resp = {
|
||
'summary_values': summary_values,
|
||
# 'values': values,
|
||
'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
|
||
'title': title,
|
||
'filter_item_type': filter_item_type,
|
||
'filter_item': filter_item,
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
|
||
}
|
||
return schemas.Msg(code=0, msg='ok', data=resp)
|
||
|
||
|
||
@router.post("/retention_model_export")
|
||
async def retention_model_export(request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
):
|
||
""" 留存分析模型 数据导出"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
data = await analysis.retention_model_sql2()
|
||
file_name = quote(f'留存分析.xlsx')
|
||
mime = mimetypes.guess_type(file_name)[0]
|
||
|
||
sql = data['sql']
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
df_to_stream = DfToStream((df, '留存分析'))
|
||
with df_to_stream as d:
|
||
export = d.to_stream()
|
||
return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
|
||
|
||
|
||
@router.post("/retention_model_del", deprecated=True)
|
||
async def retention_model_del(
|
||
request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
db: AsyncIOMotorDatabase = Depends(get_database),
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""留存数据模型"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
res = await analysis.retention_model_sql()
|
||
sql = res['sql']
|
||
date_range = res['date_range']
|
||
event_a, event_b = res['event_name']
|
||
unit_num = res['unit_num']
|
||
title = await crud.event_mana.get_show_name(db, game, event_a)
|
||
title = f'{title}用户数'
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
concat_data = []
|
||
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
|
||
df['date'] = df['date'].apply(lambda x: x.date())
|
||
# 计算整体
|
||
summary_df = df.groupby('date')[['val_a', 'val_b', 'amount_a']].sum()
|
||
summary_values = {}
|
||
for i, d1 in enumerate(date_range):
|
||
a = set(summary_df.loc[d1]['val_a']) if d1 in summary_df.index else set()
|
||
if not a:
|
||
continue
|
||
key = d1.strftime('%Y-%m-%d')
|
||
for j, d2 in enumerate(date_range[i:]):
|
||
if j > unit_num:
|
||
break
|
||
b = set(summary_df.loc[d2]['val_b']) if d2 in summary_df.index else set()
|
||
tmp = summary_values.setdefault(key, {})
|
||
tmp.setdefault('d0', len(a))
|
||
tmp.setdefault('p', []).append(division(len(a & b) * 100, len(a)))
|
||
tmp.setdefault('n', []).append(len(a & b))
|
||
tmp.setdefault('p_outflow', []).append(round(100 - division(len(a & b) * 100, len(a)), 2))
|
||
tmp.setdefault('n_outflow', []).append(len(a) - len(a & b))
|
||
groups = set([tuple(i) for i in df[res['groupby']].values])
|
||
df.set_index(res['groupby'], inplace=True)
|
||
df.sort_index(inplace=True)
|
||
values = {}
|
||
days = [i for i in range((date_range[-1] - date_range[0]).days + 1)][:unit_num + 1]
|
||
for i, d1 in enumerate(date_range):
|
||
for g in groups:
|
||
if len(g) == 1:
|
||
continue
|
||
a = set(df.loc[g]['val_a']) if g in df.index else set()
|
||
if not a:
|
||
continue
|
||
key = d1.strftime("%Y-%m-%d")
|
||
tmp_g = values.setdefault(key, {})
|
||
for j, d2 in enumerate(date_range[i:]):
|
||
if j > unit_num:
|
||
break
|
||
b = set(df.loc[g]['val_b']) if g in df.index else set()
|
||
tmp = tmp_g.setdefault(','.join(g[1:]), {})
|
||
tmp.setdefault('d0', len(a))
|
||
tmp.setdefault('p', []).append(division(len(a & b) * 100, len(a)))
|
||
tmp.setdefault('n', []).append(len(a & b))
|
||
data = {
|
||
'summary_values': summary_values,
|
||
'values': values,
|
||
'days': days,
|
||
'date_range': [d.strftime('%Y-%m-%d') for d in date_range][:unit_num + 1],
|
||
'title': title,
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
return schemas.Msg(code=0, msg='ok', data=data)
|
||
|
||
|
||
@router.post("/funnel_model_sql")
|
||
async def funnel_model_sql(
|
||
request: Request,
|
||
game: str,
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""漏斗数据模型 sql"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
data = await analysis.funnel_model_sql()
|
||
return schemas.Msg(code=0, msg='ok', data=[data])
|
||
|
||
|
||
@router.post("/funnel_model")
|
||
async def funnel_model(
|
||
request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
db: AsyncIOMotorDatabase = Depends(get_database),
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""漏斗数据模型"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
res = await analysis.funnel_model_sql()
|
||
sql = res['sql']
|
||
#查询的时间
|
||
date_range = res['date_range']
|
||
cond_level = res['cond_level']
|
||
groupby = res['groupby']
|
||
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
# 补齐level数据
|
||
concat_data = []
|
||
for key, tmp_df in df.groupby(['date'] + groupby):
|
||
not_exists_level = {i for i in range(1, len(cond_level) + 1)} - set(tmp_df['level'])
|
||
for item in not_exists_level:
|
||
key = key if isinstance(key, tuple) else (key,)
|
||
concat_data.append((*key, item, 0))
|
||
#合并数据
|
||
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
|
||
|
||
# df.set_index('date',inplace=True)
|
||
data_list = []
|
||
date_data = {}
|
||
if df.shape == (0, 0):
|
||
return schemas.Msg(code=0, msg='ok', data={'list': data_list, 'level': cond_level})
|
||
|
||
tmp = {'title': '总体'}
|
||
#以level分组后的和
|
||
tmp_df = df[['level', 'values']].groupby('level').sum()
|
||
#在原数据上对索引进行排序
|
||
tmp_df.sort_index(inplace=True)
|
||
for i in tmp_df.index:
|
||
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
|
||
|
||
tmp['n'] = tmp_df['values'].to_list()
|
||
tmp['p1'] = [100]
|
||
# tmp['p2'] = []
|
||
for i, v in tmp_df.loc[2:, 'values'].items():
|
||
tmp['p1'].append(round(v * 100 / tmp_df.loc[1, 'values'], 2))
|
||
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
|
||
data_list.append(tmp)
|
||
|
||
# 补齐日期
|
||
all_idx = {(dt, lv) for dt in date_range for lv in range(1, len(cond_level) + 1)}
|
||
concat_data = []
|
||
for i in all_idx - set(df.set_index(['date', 'level']).index):
|
||
concat_data.append((*i, 0))
|
||
summary_df = pd.concat(
|
||
[df[['date', 'level', 'values']], pd.DataFrame(concat_data, columns=['date', 'level', 'values'])])
|
||
for key, tmp_df in summary_df.groupby('date'):
|
||
tmp_df = tmp_df.groupby('level').sum()
|
||
tmp_df.sort_index(inplace=True)
|
||
for i in tmp_df.index:
|
||
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
|
||
|
||
tmp = dict()
|
||
|
||
tmp['n'] = tmp_df['values'].to_list()
|
||
tmp['p1'] = [100]
|
||
# tmp['p2'] = []
|
||
for i, v in tmp_df.loc[2:, 'values'].items():
|
||
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
|
||
var = 0 if np.isnan(var) else var
|
||
tmp['p1'].append(var)
|
||
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
|
||
|
||
_ = date_data.setdefault(key.strftime('%Y-%m-%d'), {})
|
||
_['总体'] = tmp
|
||
|
||
if groupby:
|
||
# 补齐数据
|
||
concat_data = []
|
||
idx = set(df.set_index(['date'] + groupby).index)
|
||
all_idx = {(*j, i) for i in range(1, len(cond_level) + 1) for j in idx}
|
||
for i in all_idx - set(df.set_index(list(('date', *groupby, 'level'))).index):
|
||
concat_data.append((*i, 0))
|
||
|
||
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
|
||
# df.sort_values(list((*groupby, 'level')), inplace=True, ascending=False)
|
||
|
||
for key, tmp_df in df.groupby(groupby):
|
||
tmp = {'title': key}
|
||
tmp_df = tmp_df.groupby('level').sum()
|
||
tmp_df.sort_index(inplace=True)
|
||
for i in tmp_df.index:
|
||
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
|
||
|
||
tmp['n'] = tmp_df['values'].to_list()
|
||
tmp['p1'] = [100]
|
||
# tmp['p2'] = []
|
||
for i, v in tmp_df.loc[2:, 'values'].items():
|
||
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
|
||
var = 0 if np.isnan(var) else var
|
||
tmp['p1'].append(var)
|
||
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
|
||
data_list.append(tmp)
|
||
|
||
for key, tmp_df in df.groupby(['date'] + groupby):
|
||
|
||
tmp_df = tmp_df.groupby('level').sum()
|
||
tmp_df.sort_index(inplace=True)
|
||
for i in tmp_df.index:
|
||
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
|
||
|
||
tmp = dict()
|
||
|
||
tmp['n'] = tmp_df['values'].to_list()
|
||
tmp['p1'] = [100]
|
||
# tmp['p2'] = []
|
||
for i, v in tmp_df.loc[2:, 'values'].items():
|
||
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
|
||
var = 0 if np.isnan(var) else var
|
||
tmp['p1'].append(var)
|
||
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
|
||
|
||
_ = date_data.setdefault(key[0].strftime('%Y-%m-%d'), {})
|
||
_[key[1]] = tmp
|
||
title = (groupby or ['总体']) + cond_level
|
||
resp = {'list': data_list,
|
||
'date_data': date_data,
|
||
'title': title,
|
||
'level': cond_level,
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
return schemas.Msg(code=0, msg='ok', data=resp)
|
||
|
||
|
||
@router.post("/scatter_model_sql")
|
||
async def scatter_model_sql(
|
||
request: Request,
|
||
game: str,
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""分布分析 sql"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
data = await analysis.scatter_model_sql()
|
||
return schemas.Msg(code=0, msg='ok', data=[data])
|
||
|
||
|
||
@router.post("/scatter_model_export")
|
||
async def retention_model_export(request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
):
|
||
""" 分布分析 数据导出"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
res = await analysis.scatter_model_sql()
|
||
file_name = quote(f'分布分析.xlsx')
|
||
mime = mimetypes.guess_type(file_name)[0]
|
||
sql = res['sql']
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
interval_type = res['interval_type']
|
||
analysis = res['analysis']
|
||
groupby = res['groupby']
|
||
quota_interval_arr = res['quota_interval_arr']
|
||
# 兼容合计的
|
||
if res['time_particle'] == 'total':
|
||
df['date'] = '合计'
|
||
if analysis != 'number_of_days' and interval_type != 'discrete':
|
||
max_v = int(df['values'].max())
|
||
min_v = int(df['values'].min())
|
||
interval = (max_v - min_v) // 10 or 1
|
||
resp = {'list': dict(),
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
|
||
if not quota_interval_arr:
|
||
resp['label'] = [f'[{i},{i + interval})' for i in range(min_v, max_v, interval)]
|
||
bins = [i for i in range(min_v, max_v + interval, interval)]
|
||
else:
|
||
quota_interval_arr = [-float('inf')] + quota_interval_arr + [float('inf')]
|
||
resp['label'] = []
|
||
bins = [quota_interval_arr[0]]
|
||
for i, v in enumerate(quota_interval_arr[1:]):
|
||
resp['label'].append(f'[{quota_interval_arr[i]},{v})')
|
||
bins.append(v)
|
||
|
||
# 这是整体的
|
||
for key, tmp_df in df.groupby('date'):
|
||
bins_s = pd.cut(tmp_df['values'], bins=bins,
|
||
right=False).value_counts()
|
||
bins_s.sort_index(inplace=True)
|
||
total = int(bins_s.sum())
|
||
if res['time_particle'] == 'total':
|
||
resp['list']['合计'] = dict()
|
||
resp['list']['合计']['总体'] = {'n': bins_s.to_list(), 'total': total,
|
||
'p': round(bins_s * 100 / total, 2).to_list(),
|
||
'title': '总体'}
|
||
else:
|
||
resp['list'][key.strftime('%Y-%m-%d')] = dict()
|
||
resp['list'][key.strftime('%Y-%m-%d')]['总体'] = {'n': bins_s.to_list(), 'total': total,
|
||
'p': round(bins_s * 100 / total, 2).to_list(),
|
||
'title': '总体'}
|
||
|
||
# 分组的
|
||
if groupby:
|
||
export_df = pd.DataFrame(columns=resp['label'])
|
||
|
||
for key, tmp_df in df.groupby(['date', *groupby]):
|
||
bins_s = pd.cut(tmp_df['values'], bins=bins,
|
||
right=False).value_counts()
|
||
bins_s.sort_index(inplace=True)
|
||
total = int(bins_s.sum())
|
||
title = '.'.join(key[1:])
|
||
date = key[0]
|
||
resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
|
||
'p': round(bins_s * 100 / total, 2).to_list(),
|
||
'title': title
|
||
}
|
||
|
||
export_df.loc[(date.strftime('%Y-%m-%d'), title)] = bins_s.to_list()
|
||
|
||
df_to_stream = DfToStream((export_df, '分布分析'), (df, '分布分析原始数据'), index=True)
|
||
with df_to_stream as d:
|
||
export = d.to_stream()
|
||
return StreamingResponse(export, media_type=mime,
|
||
headers={'Content-Disposition': f'filename="{file_name}"'})
|
||
|
||
|
||
# elif analysis == 'number_of_days':
|
||
else:
|
||
resp = {'list': {}, 'label': [],
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
labels = [str(i) for i in sorted(df['values'].unique())]
|
||
resp['label'] = labels
|
||
for key, tmp_df in df.groupby(['date']):
|
||
total = len(tmp_df)
|
||
if res['time_particle'] == 'total':
|
||
dt = '合计'
|
||
else:
|
||
dt = key.strftime('%Y-%m-%d')
|
||
labels_dict = {}
|
||
for key2, tmp_df2 in tmp_df.groupby('values'):
|
||
label = str(key2)
|
||
n = len(tmp_df2)
|
||
labels_dict[label] = n
|
||
|
||
resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
|
||
'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
|
||
|
||
export_df = pd.DataFrame(columns=resp['label'])
|
||
for d, v in resp['list'].items():
|
||
export_df.loc[d] = v['总体']['n']
|
||
|
||
df_to_stream = DfToStream((export_df, '分布分析'), (df, '分布分析原始数据'), index=True)
|
||
with df_to_stream as d:
|
||
export = d.to_stream()
|
||
return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
|
||
|
||
|
||
@router.post("/scatter_model")
|
||
async def scatter_model(
|
||
request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
db: AsyncIOMotorDatabase = Depends(get_database),
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""分布分析 模型"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
res = await analysis.scatter_model_sql()
|
||
sql = res['sql']
|
||
|
||
#查询买量渠道owner为kuaiyou3的日注册玩家等级分布
|
||
# sql_list=sql.split("GROUP BY")
|
||
# sql01 = """and xiangsu.event.owner_name='kuaiyou3'GROUP BY"""""
|
||
# new_sql=sql_list[0]+sql01+sql_list[1]
|
||
|
||
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
df.fillna(0, inplace=True)
|
||
#转换数据类型为int
|
||
df['values'] = df['values'].astype(int)
|
||
interval_type = res['interval_type']
|
||
analysis = res['analysis']
|
||
groupby = res['groupby']
|
||
quota_interval_arr = res['quota_interval_arr']
|
||
# 兼容合计的
|
||
if res['time_particle'] == 'total':
|
||
df['date'] = '合计'
|
||
|
||
if analysis != 'number_of_days' and interval_type != 'discrete':
|
||
max_v = int(df['values'].max())
|
||
min_v = int(df['values'].min())
|
||
interval = (max_v - min_v) // 10 or 1
|
||
resp = {'list': dict(),
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
|
||
if not quota_interval_arr:
|
||
resp['label'] = [f'[{i},{i + interval})' for i in range(min_v, max_v, interval)]
|
||
bins = [i for i in range(min_v, max_v + interval, interval)]
|
||
else:
|
||
quota_interval_arr = [-float('inf')] + quota_interval_arr + [float('inf')]
|
||
resp['label'] = []
|
||
bins = [quota_interval_arr[0]]
|
||
for i, v in enumerate(quota_interval_arr[1:]):
|
||
resp['label'].append(f'[{quota_interval_arr[i]},{v})')
|
||
bins.append(v)
|
||
|
||
# 这是整体的
|
||
for key, tmp_df in df.groupby('date'):
|
||
bins_s = pd.cut(tmp_df['values'], bins=bins,
|
||
right=False).value_counts()
|
||
bins_s.sort_index(inplace=True)
|
||
total = int(bins_s.sum())
|
||
if res['time_particle'] == 'total':
|
||
resp['list']['合计'] = dict()
|
||
resp['list']['合计']['总体'] = {'n': bins_s.to_list(), 'total': total,
|
||
'p': round(bins_s * 100 / total, 2).to_list(),
|
||
'title': '总体'}
|
||
else:
|
||
resp['list'][key.strftime('%Y-%m-%d')] = dict()
|
||
resp['list'][key.strftime('%Y-%m-%d')]['总体'] = {'n': bins_s.to_list(), 'total': total,
|
||
'p': round(bins_s * 100 / total, 2).to_list(),
|
||
'title': '总体'}
|
||
# 分组的
|
||
if groupby:
|
||
for key, tmp_df in df.groupby(['date', *groupby]):
|
||
bins_s = pd.cut(tmp_df['values'], bins=bins,
|
||
right=False).value_counts()
|
||
bins_s.sort_index(inplace=True)
|
||
total = int(bins_s.sum())
|
||
title = '.'.join(key[1:])
|
||
date = key[0]
|
||
resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
|
||
'p': round((bins_s * 100 / total).fillna(0),
|
||
2).to_list(),
|
||
'title': title
|
||
}
|
||
return schemas.Msg(code=0, msg='ok', data=resp)
|
||
|
||
# elif analysis == 'number_of_days':
|
||
else:
|
||
resp = {'list': {}, 'label': [],
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
labels = [str(i) for i in sorted(df['values'].unique())]
|
||
resp['label'] = labels
|
||
for key, tmp_df in df.groupby(['date']):
|
||
total = len(tmp_df)
|
||
if res['time_particle'] == 'total':
|
||
dt = '合计'
|
||
else:
|
||
dt = key.strftime('%Y-%m-%d')
|
||
|
||
labels_dict = {}
|
||
for key2, tmp_df2 in tmp_df.groupby('values'):
|
||
label = str(key2)
|
||
n = len(tmp_df2)
|
||
labels_dict[label] = n
|
||
|
||
#如需要2之后所有之和，则执行下面代码，返回值为字典的labels_dict01
|
||
labels_dict01={}
|
||
v=-1
|
||
for i in labels:
|
||
v +=1
|
||
if int(i) == 1:
|
||
labels_dict01["1"]=labels_dict["1"]
|
||
else:
|
||
# for number in labels_dict.keys():
|
||
# if number >=i:
|
||
values=list(labels_dict.values())
|
||
n=sum(values[v:])
|
||
labels_dict01[i]=n
|
||
#传入百分比数据
|
||
list_p=[]
|
||
for i in labels:
|
||
number_int=round(labels_dict01.get(i, 0) * 100 / total, 2)
|
||
number_str=str(number_int)+'%'
|
||
list_p.append(number_str)
|
||
|
||
resp['list'][dt] = {'总体': {'n': [labels_dict01.get(i, 0) for i in labels], 'total': total,
|
||
'p': list_p}}
|
||
# resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
|
||
# 'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
|
||
return schemas.Msg(code=0, msg='ok', data=resp)
|
||
|
||
# bins_s = pd.cut(tmp_df['values'], bins=bins,
|
||
# right=False).value_counts()
|
||
# bins_s.sort_index(inplace=True)
|
||
# total = int(bins_s.sum())
|
||
# resp['list'][key.strftime('%Y-%m-%d')] = dict()
|
||
# resp['list'][key.strftime('%Y-%m-%d')]['总体'] = {'n': bins_s.to_list(), 'total': total,
|
||
# 'p': round(bins_s * 100 / total, 2).to_list(),
|
||
# 'title': '总体'}
|
||
|
||
|
||
@router.post("/trace_model_sql")
|
||
async def trace_model_sql(
|
||
request: Request,
|
||
game: str,
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""路径分析 sql"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
data = await analysis.trace_model_sql()
|
||
return schemas.Msg(code=0, msg='ok', data=[data])
|
||
|
||
|
||
@router.post("/trace_model")
|
||
async def trace_model_sql(
|
||
request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""路径分析"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
res = await analysis.trace_model_sql()
|
||
sql = res['sql']
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
chain_dict = defaultdict(dict)
|
||
nodes = {'流失'}
|
||
for event_names, count in zip(df['event_chain'], df['values']):
|
||
chain_len = len(event_names)
|
||
for i, event_name in enumerate(event_names):
|
||
if i >= 10:
|
||
continue
|
||
next_event = event_names[i + 1] if i < chain_len - 1 else '流失'
|
||
key = (f'{event_name}{i}', f'{next_event}{i + 1}')
|
||
nodes.update(key)
|
||
chain_dict[i][key] = chain_dict[i].setdefault(key, 0) + count
|
||
|
||
links = []
|
||
for _, items in chain_dict.items():
|
||
for keys, val in items.items():
|
||
links.append({
|
||
"source": keys[0],
|
||
"target": keys[1],
|
||
"value": val
|
||
})
|
||
# nodes = set()
|
||
# for item in links:
|
||
# nodes.update((
|
||
# item['source'],
|
||
# item['target'])
|
||
# )
|
||
data = {
|
||
'nodes': [{'name': item} for item in nodes],
|
||
'links': links,
|
||
'start_date': res['start_date'],
|
||
'end_date': res['end_date'],
|
||
'time_particle': res['time_particle']
|
||
}
|
||
return schemas.Msg(code=0, msg='ok', data=data)
|
||
|
||
|
||
@router.post("/user_property_model_sql")
|
||
async def user_property_sql(
|
||
request: Request,
|
||
game: str,
|
||
analysis: UserAnalysis = Depends(UserAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""用户属性sql"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
data = analysis.property_model()
|
||
return schemas.Msg(code=0, msg='ok', data=[data])
|
||
|
||
|
||
@router.post("/user_property_model_export")
|
||
async def user_property_model_export(
|
||
request: Request,
|
||
game: str,
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
analysis: UserAnalysis = Depends(UserAnalysis),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
):
|
||
"""用户属性 导出"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
data = analysis.property_model()
|
||
file_name = quote(f'用户属性.xlsx')
|
||
mime = mimetypes.guess_type(file_name)[0]
|
||
|
||
sql = data['sql']
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
df_to_stream = DfToStream((df, '用户属性'))
|
||
with df_to_stream as d:
|
||
export = d.to_stream()
|
||
return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
|
||
|
||
|
||
@router.post("/user_property_model")
|
||
async def user_property_model(
|
||
request: Request,
|
||
game: str,
|
||
analysis: UserAnalysis = Depends(UserAnalysis),
|
||
ckdb: CKDrive = Depends(get_ck_db),
|
||
current_user: schemas.UserDB = Depends(deps.get_current_user)
|
||
) -> schemas.Msg:
|
||
"""用户属性分析"""
|
||
await analysis.init(data_where=current_user.data_where)
|
||
res = analysis.property_model()
|
||
sql = res['sql']
|
||
quota = res['quota']
|
||
groupby = res['groupby']
|
||
df = await ckdb.query_dataframe(sql)
|
||
if df.empty:
|
||
return schemas.Msg(code=-9, msg='无数据', data=None)
|
||
|
||
# 没有分组
|
||
data = {}
|
||
|
||
if not groupby:
|
||
data['总体'] = int(df['values'][0])
|
||
title = ['总体', quota]
|
||
|
||
else:
|
||
sum_s = df.groupby(groupby)['values'].sum()
|
||
for key, val in sum_s.items():
|
||
if isinstance(key, tuple):
|
||
key = ','.join([str(i) for i in key])
|
||
else:
|
||
key = str(key)
|
||
data[key] = val
|
||
title = ['.'.join(groupby), quota]
|
||
|
||
return schemas.Msg(code=0, msg='ok', data={
|
||
'value': data,
|
||
'title': title
|
||
})
|