Update Feb24
This commit is contained in:
martin
parent
61e68b069b
commit
7620e297de
82
celery/script.py
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82
celery/script.py
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# %%
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import os
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import time
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from celery import Celery
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from celery.result import AsyncResult
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from celery.contrib.abortable import AbortableAsyncResult
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# Conference.objects.filter(scenario=scenario,name__in=["UEL","UECL"]).update(reopt=True)
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CELERY_RESULT_BACKEND="redis://localhost:6379"
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CELERY_BROKER_URL="redis://localhost:6379"
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celery = Celery('leagues',
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backend=CELERY_RESULT_BACKEND,
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broker=CELERY_BROKER_URL
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)
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celery.conf.update(
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worker_redirect_stdouts=False,
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timezone='Europe/Berlin',
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worker_hijack_root_logger=False,
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)
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celery.autodiscover_tasks()
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# Inspect all nodes.
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i = celery.control.inspect()
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# Show the items that have an ETA or are scheduled for later processing
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i.scheduled()
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# Show tasks that are currently active.
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i.active()
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# Show tasks that have been claimed by workers
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i.reserved()
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# # # SEND TASK
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# # task = celery.send_task('test_task', args=[], kwargs={})
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# task = AbortableAsyncResult('b5b1bfff-318c-4052-bbec-f758a1e5925e')
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# task.revoke(terminate=True)
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# while not task.ready():
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# print(task.result)
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# print(task.info)
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# print(task.state)
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# print(task.traceback)
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# print(task.task_id)
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# print(task.status)
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# time.sleep(1)
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# # REVOKE TASK
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# celery.control.revoke('task.id', terminate=True)
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# # # GET TASK RESULT
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# job = AbortableAsyncResult('b5b1bfff-318c-4052-bbec-f758a1e5925e')
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# job.update_state(state='SUCCESS', result='result')
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# %%
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#
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print(job.status,job.info)
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# %%
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# l = [{2:"d"},{1:"b"}]
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# def test(a):
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# a = sorted(a, key=lambda x: list(x.keys())[0])
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# a[0][2] = "Z"
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# a.append({3:"c"})
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# print(a)
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# return a
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# print(l)
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# g = test(l)
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# print(l)
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# print(g)
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# %%
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41
celery/task.py
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41
celery/task.py
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from .tasks.optimize import optimize
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from .functions import *
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import json
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from celery.contrib.abortable import AbortableTask
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from leagues.celery import celery, TASK_TIME_LIMIT
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from leagues.settings import RUN_ENV, SOLVER
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from scheduler.models import *
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@celery.task(name='test_task', bind=True, time_limit=TASK_TIME_LIMIT, base=AbortableTask)
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def test_task(self):
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status_dict = {
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c: {} for c in range(10)
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}
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i = 0
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c = 0
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terminate = False
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self.update_state(state='STARTED', meta={'info':c})
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while(c < 10):
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i += 1
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status_dict[c][i] = "".join(random.choices(["A","B","C","D","E","F","G","H","I","J"], k=100))
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status_dict['info'] = "RUNNING"
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self.update_state(state='TESTING', meta=json.dumps(status_dict))
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if i == 100000:
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c += 1
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i = 0
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print("TESTING",c)
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if terminate:
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return "TEST ABORTED"
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if self.is_aborted():
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terminate = True
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return "TEST SUCCESSFULL"
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@ -332,7 +332,7 @@ class Draw_Simulator:
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self.pots = ['D','C','B','A']
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if use_db:
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scenario_id = 10081
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scenario_id = 9529
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scenario = Scenario.objects.get(id=scenario_id)
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self.basepots = {
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@ -1039,8 +1039,9 @@ class Draw_Simulator:
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visited_countries[t1['id']].add(t2['country'])
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blockings, breaks = self.create_schedule(sol_dict, n)
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# blockings, breaks = self.create_schedule(sol_dict, n)
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blockings = defaultdict(lambda:0)
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breaks = defaultdict(lambda:0)
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@ -1158,7 +1159,7 @@ class Draw_Simulator:
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# print("\t",game[0],game[1],sol_dict[game[0]][game[1]])
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# for game in self.awayGames[g, p]:
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# print("\t",game[0],game[1],sol_dict[game[0]][game[1]])
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base_scenario_id = 10081
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base_scenario_id = 9529
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base_scenario = Scenario.objects.get(id=base_scenario_id)
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scenario = copy_scenario(base_scenario,f" - {self.opponent_func.__name__} - {nSim}")
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@ -1283,7 +1284,7 @@ funcs = [
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]
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scenario_id = 10081
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scenario_id = 9529
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scenario = Scenario.objects.get(id=scenario_id)
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Scenario.objects.filter(base_scenario=scenario).delete()
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@ -1291,7 +1292,7 @@ Scenario.objects.filter(base_scenario=scenario).delete()
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stats = {}
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for func in funcs:
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simulator = Draw_Simulator(algorithm='XP', opponent_func = func, html_output=True,use_db=True)
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nSim = 1000
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nSim = 5
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stats[func.__name__] = simulator.simulate(nSim)
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0
research/__init__.py
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0
research/__init__.py
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3
research/admin.py
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research/admin.py
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from django.contrib import admin
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# Register your models here.
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5
research/apps.py
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research/apps.py
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from django.apps import AppConfig
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class ResearchConfig(AppConfig):
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name = 'research'
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0
research/migrations/__init__.py
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0
research/migrations/__init__.py
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289
research/ml/learners.py
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research/ml/learners.py
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from django.db.models import F
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import pandas as pd
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import pickle
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.ensemble import GradientBoostingRegressor
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from scheduler.models import *
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from common.functions import distanceInKmByGPS
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class AttendanceLearner:
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def __init__(self,season_id):
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self.season = Season.objects.filter(id=season_id).first()
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self.rf_regressor = None
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self.predictions = None
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self.ids = []
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def save(self):
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with open('research/ml/models/{}.mod'.format(self.season.id), 'wb') as f:
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pickle.dump(self.rf_regressor, f)
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def load(self):
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with open('research/ml/models/{}.mod'.format(self.season.id), 'rb') as f:
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self.rf_regressor = pickle.load(f)
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def __get_pivot_tables(self):
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games = Game.objects.filter(season=self.season).exclude(historic_season=None)
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# create dataset
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df = pd.DataFrame.from_records(games.values())
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# remove outliers
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out_fields = ['attendance']
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for field in out_fields:
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q_low = df[field].quantile(0.01)
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q_hi = df[field].quantile(0.99)
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df = df[(df[field] < q_hi) & (df[field] > q_low)]
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# pivots
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pivot_table = {}
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pivot_table['homeTeam_mean_attendance'] = df.pivot_table('attendance','homeTeam_id',aggfunc='mean')
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pivot_table['homeTeam_max_attendance'] = df.pivot_table('attendance','homeTeam_id',aggfunc='max')
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return pivot_table
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def __prepare_training_data(self):
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games = Game.objects.filter(season=self.season).exclude(historic_season=None).annotate(
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home=F('homeTeam__shortname'),
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away=F('awayTeam__shortname'),
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home_lat=F('homeTeam__latitude'),
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home_lon=F('homeTeam__longitude'),
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home_attr=F('homeTeam__attractivity'),
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away_lat=F('awayTeam__latitude'),
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away_lon=F('awayTeam__longitude'),
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away_attr=F('awayTeam__attractivity'),
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home_country=F('homeTeam__country'),
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away_country=F('awayTeam__country'),
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)
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# create dataset
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df = pd.DataFrame.from_records(games.values())
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# data cleaning
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df['time'] = df['time'].replace('','0')
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df = df[df['attendance'] != 0]
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# remove outliers
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out_fields = ['attendance']
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for field in out_fields:
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q_low = df[field].quantile(0.01)
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q_hi = df[field].quantile(0.99)
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df = df[(df[field] < q_hi) & (df[field] > q_low)]
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# pivots
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self.pivot_homeTeam_mean = df.pivot_table('attendance','homeTeam_id',aggfunc='mean')
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self.pivot_homeTeam_max = df.pivot_table('attendance','homeTeam_id',aggfunc='max')
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# add more features
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df['weekday'] = df.apply(lambda r: r['date'].weekday(), axis=1)
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df['day'] = df.apply(lambda r: r['date'].day, axis=1)
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df['month'] = df.apply(lambda r: r['date'].month, axis=1)
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df['year'] = df.apply(lambda r: r['date'].year, axis=1)
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df['distance'] = df.apply(lambda r: distanceInKmByGPS(r['home_lon'],r['home_lat'],r['away_lon'],r['away_lat']), axis=1)
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df['weekend'] = df.apply(lambda r: int(r['weekday'] in [6,7]), axis=1)
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df['winter_season'] = df.apply(lambda r: int(r['month'] in [1,2,3,10,11,12]), axis=1)
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df['home_base'] = df.apply(lambda r: self.pivot_homeTeam_mean.loc[r['homeTeam_id'],'attendance'], axis=1)
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df['stadium_size'] = df.apply(lambda r: self.pivot_homeTeam_max.loc[r['homeTeam_id'],'attendance'], axis=1)
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df['early'] = df.apply(lambda r: r['time'].replace(':','') < "1800", axis=1)
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df['before2010'] = df.apply(lambda r: r['historic_season'].split('-')[0] < "2010", axis=1)
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# one hot encoding
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ohe_fields = ['home_country']
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for field in ohe_fields:
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ohe = OneHotEncoder()
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transformed = ohe.fit_transform(df[[field]])
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df[ohe.categories_[0]] = transformed.toarray()
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# sort label to last index
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cols = list(df.columns)
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cols.append(cols.pop(cols.index('attendance')))
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df = df[cols]
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remove_columns = ['season_id', 'resultEntered', 'reversible', 'reschedule', 'homeGoals', 'awayGoals',
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'homeGoals2', 'awayGoals2', 'homeGoals3', 'awayGoals3', 'home', 'away', 'date', 'time',
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'id', 'homeTeam_id', 'awayTeam_id', 'historic_season',
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'home_country','home_lat','home_lon','away_lat','away_lon','away_country','year']
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feature_cols = list(set(df.columns[:-1]) - set(remove_columns))
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label = 'attendance'
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self.X = df[feature_cols]
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self.y = df[label]
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def __prepare_prediction_data(self):
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games = Game.objects.filter(season=self.season,historic_season=None).annotate(
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home=F('homeTeam__shortname'),
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away=F('awayTeam__shortname'),
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home_lat=F('homeTeam__latitude'),
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home_lon=F('homeTeam__longitude'),
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home_attr=F('homeTeam__attractivity'),
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away_lat=F('awayTeam__latitude'),
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away_lon=F('awayTeam__longitude'),
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away_attr=F('awayTeam__attractivity'),
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home_country=F('homeTeam__country'),
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away_country=F('awayTeam__country'),
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)
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# create dataset
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df = pd.DataFrame.from_records(games.values())
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self.ids = list(df['id'].values)
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# pivots
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pivot_tables = self.__get_pivot_tables()
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# add more features
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df['weekday'] = df.apply(lambda r: r['date'].weekday(), axis=1)
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df['day'] = df.apply(lambda r: r['date'].day, axis=1)
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df['month'] = df.apply(lambda r: r['date'].month, axis=1)
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df['year'] = df.apply(lambda r: r['date'].year, axis=1)
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df['distance'] = df.apply(lambda r: distanceInKmByGPS(r['home_lon'],r['home_lat'],r['away_lon'],r['away_lat']), axis=1)
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df['weekend'] = df.apply(lambda r: int(r['weekday'] in [6,7]), axis=1)
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df['winter_season'] = df.apply(lambda r: int(r['month'] in [1,2,3,10,11,12]), axis=1)
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df['home_base'] = df.apply(lambda r: pivot_tables['homeTeam_mean_attendance'].loc[r['homeTeam_id'],'attendance'], axis=1)
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df['stadium_size'] = df.apply(lambda r: pivot_tables['homeTeam_max_attendance'].loc[r['homeTeam_id'],'attendance'], axis=1)
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df['early'] = df.apply(lambda r: r['time'].replace(':','') < "1800", axis=1)
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df['before2010'] = df.apply(lambda r: 0, axis=1)
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# one hot encoding
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ohe_fields = ['home_country']
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for field in ohe_fields:
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ohe = OneHotEncoder()
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transformed = ohe.fit_transform(df[[field]])
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df[ohe.categories_[0]] = transformed.toarray()
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# sort label to last index
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cols = list(df.columns)
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cols.append(cols.pop(cols.index('attendance')))
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df = df[cols]
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remove_columns = ['season_id', 'resultEntered', 'reversible', 'reschedule', 'homeGoals', 'awayGoals',
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'homeGoals2', 'awayGoals2', 'homeGoals3', 'awayGoals3', 'home', 'away', 'date', 'time',
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'id', 'homeTeam_id', 'awayTeam_id', 'historic_season',
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'home_country','home_lat','home_lon','away_lat','away_lon','away_country','year']
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feature_cols = list(set(df.columns[:-1]) - set(remove_columns))
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label = 'attendance'
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print (feature_cols)
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self.X = df[feature_cols]
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self.y = df[label]
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def train(self):
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self.__prepare_training_data()
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X_train, X_test, y_train, y_test = train_test_split(
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self.X, self.y, test_size=0.3, random_state=1)
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# self.rf_regressor = RandomForestRegressor(n_estimators = 200 , random_state = 42)
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self.rf_regressor = GradientBoostingRegressor(n_estimators = 200 , random_state = 42)
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self.rf_regressor.fit(X_train,y_train)
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self.score = self.rf_regressor.score(X_test,y_test)
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def predict(self):
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self.__prepare_prediction_data()
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print(len(self.ids))
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print(len(self.rf_regressor.predict(self.X)))
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self.predictions = dict(zip(self.ids,self.rf_regressor.predict(self.X)))
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print(self.rf_regressor.predict(self.X))
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print(self.predictions)
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def predict_games(self,games):
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teamObjects = Team.objects.filter(season=self.season ).values()
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dayObjects = Day.objects.filter(season=self.season ).values()
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teams=[]
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t_name={}
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t_country={}
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t_lon = {}
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t_lat = {}
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t_attractivity ={}
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getTeamById={}
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for t in teamObjects:
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# print (t['name'], t['id'])
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teams.append(t['id'])
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t_name[t['id']]= t['name']
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t_country[t['id']]= t['country']
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t_attractivity[t['id']]=t['attractivity']
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t_lon[t['id']]=t['longitude']
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t_lat[t['id']]=t['latitude']
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getTeamById[t['id']]=t['name']
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distance ={}
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distanceById ={}
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attractivity ={}
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travelDict= self.season.travelDict()
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for t1 in teamObjects:
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for t2 in teamObjects:
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# print (t1['name'],t2['name'],distance[t1['name'],t2['name']] ," -> ", travelDict[t1['id']][t2['id']]['distance'] )
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distance[t1['id'],t2['id']] = travelDict[t1['id']][t2['id']]['distance']
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attractivity[t1['id'],t2['id']] = int(100*t1['attractivity']*t2['attractivity']);
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wds= {0:'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri', 5:'Sat', 6:'Sun'}
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dayObjects = Day.objects.filter(season=self.season).values()
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days = [ d['id'] for d in dayObjects ]
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d_round = { d['id'] : d['round'] for d in dayObjects }
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getDateTimeDay={}
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getNiceDay={}
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getWeekDay={}
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getMonth={}
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getRound={}
|
||||
days = [ d['id'] for d in dayObjects if d['round']>0 ]
|
||||
for d in dayObjects:
|
||||
dt = parse(d['day'])
|
||||
getDateTimeDay[d['id']] = dt
|
||||
getNiceDay[d['id']] = str(dt.strftime('%a, %b %d, %Y'))
|
||||
getWeekDay[d['id']] = str(wds[dt.weekday()])
|
||||
getMonth[d['id']] = dt.month
|
||||
|
||||
def getFeatureVector(t1,t2,d):
|
||||
gm={'homeTeam_id': t1, 'awayTeam_id': t2, 'month': d.month,
|
||||
'home_attractivity': t_attractivity[t1], 'away_attractivity': t_attractivity[t2],
|
||||
'weekday': d.weekday, 'home_lat': t_lat[t1] , 'home_lon': t_lon[t1] ,
|
||||
'away_lat': t_lat[t2], 'away_lon': t_lon[t2], 'weekday': d.weekday in [1,2,3,4],
|
||||
# 'home_country': t_country[t1], 'away_country': t_country[t2] ,
|
||||
'distance' : distance[t1,t2]}
|
||||
return list(gm.values())
|
||||
|
||||
games_train = Game.objects.filter(season=self.season).exclude(historic_season=None)
|
||||
self.X= []
|
||||
self.y =[]
|
||||
for game in games_train:
|
||||
self.X.append(getFeatureVector(game.homeTeam.id, game.awayTeam.id,game.date) )
|
||||
self.y.append(game.attendance)
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(
|
||||
self.X, self.y, test_size=0.3, random_state=1)
|
||||
|
||||
# self.rf_regressor = RandomForestRegressor(n_estimators = 200 , random_state = 42)
|
||||
self.rf_regressor = GradientBoostingRegressor(n_estimators = 200 , random_state = 42)
|
||||
self.rf_regressor.fit(X_train,y_train)
|
||||
self.score = self.rf_regressor.score(X_test,y_test)
|
||||
print (self.score)
|
||||
|
||||
self.X= []
|
||||
self.y =[]
|
||||
for (t1,t2,d) in games:
|
||||
self.X.append(getFeatureVector(t1,t2,getDateTimeDay[d]) )
|
||||
|
||||
result = dict(zip(games,self.rf_regressor.predict(self.X)))
|
||||
|
||||
for g in games:
|
||||
result[g] = int (result[g])
|
||||
|
||||
return result
|
||||
|
||||
|
||||
|
||||
4
research/ml/models/.gitignore
vendored
Normal file
4
research/ml/models/.gitignore
vendored
Normal file
@ -0,0 +1,4 @@
|
||||
# Ignore everything in this directory
|
||||
*
|
||||
# Except this file
|
||||
!.gitignore
|
||||
3
research/models.py
Normal file
3
research/models.py
Normal file
@ -0,0 +1,3 @@
|
||||
from django.db import models
|
||||
|
||||
# Create your models here.
|
||||
84
research/simulator/simulator.py
Normal file
84
research/simulator/simulator.py
Normal file
@ -0,0 +1,84 @@
|
||||
from scheduler.models import *
|
||||
from scheduler.helpers import *
|
||||
from scheduler.solver.optimizer import optimize_2phases, optimize_simulate, optimize_sequentially
|
||||
|
||||
from celery.result import AsyncResult
|
||||
|
||||
from leagues.settings import RUN_ENV
|
||||
from leagues.celery import celery
|
||||
|
||||
|
||||
|
||||
class UefaSimulator:
|
||||
|
||||
def __init__(self,scenario_id):
|
||||
self.scenario = Scenario.objects.get(id=scenario_id)
|
||||
self.param = {
|
||||
"NAME":"undefined",
|
||||
"SIMC":0,
|
||||
"OPPONENTS":"",
|
||||
"RUNMODE":"",
|
||||
"LOCALSEARCHTIME":0,
|
||||
"RESET":False,
|
||||
"CREATE_ONLY":False,
|
||||
"SIM_ONLY":False,
|
||||
"UCL24PATTERNS":False,
|
||||
"KEEP_SCENARIOS":False,
|
||||
}
|
||||
self.param.update(self.scenario.parse_sim_script())
|
||||
|
||||
|
||||
def simulate(self):
|
||||
|
||||
if self.param['RESET'] == "True":
|
||||
self.scenario.simulation_scenarios.all().delete()
|
||||
setup = {
|
||||
'STATS': {
|
||||
"STARTED":datetime.datetime.now().strftime("%d.%m.%Y, %H:%M:%S"),
|
||||
"FINISHED":0,
|
||||
}
|
||||
}
|
||||
else:
|
||||
setup = json.loads(self.scenario.sim_report)
|
||||
setup['STATS'] = {
|
||||
"STARTED":datetime.datetime.now().strftime("%d.%m.%Y, %H:%M:%S"),
|
||||
"FINISHED":0,
|
||||
}
|
||||
setup['STATS'].update(self.param)
|
||||
|
||||
self.scenario.sim_report = json.dumps(setup)
|
||||
self.scenario.save()
|
||||
|
||||
|
||||
# RUN SCENARIOS
|
||||
if RUN_ENV == 'celery':
|
||||
print("RUNNING IN CELERY")
|
||||
task = celery.send_task('optimize_simulate',args=[self.scenario.id, self.param, "Simulator", True, self.param['RUNMODE'], int(self.param['LOCALSEARCHTIME'])])
|
||||
self.scenario.task_id = task.id
|
||||
self.scenario.stopComputation = False
|
||||
self.scenario.save()
|
||||
else:
|
||||
optimize_simulate(self.scenario.id, self.param, "Simulator", True, runMode=self.param['RUNMODE'], localsearch_time=int(self.param['LOCALSEARCHTIME']))
|
||||
|
||||
|
||||
def improve(self):
|
||||
|
||||
# IMPROVE ALL SCENARIOS SCENARIOS
|
||||
|
||||
scenarios = []
|
||||
if self.scenario.sim_report:
|
||||
kpis = json.loads(self.scenario.sim_report)
|
||||
for kpi in kpis:
|
||||
if kpi.isnumeric() and Scenario.objects.filter(id=kpi).exists():
|
||||
scenarios.append(kpi)
|
||||
|
||||
|
||||
|
||||
|
||||
if RUN_ENV == 'celery':
|
||||
task = celery.send_task('optimize_sequentially',args=[scenarios, "Simulator", True, self.param['RUNMODE'], int(self.param['LOCALSEARCHTIME'])])
|
||||
self.scenario.task_id = task.id
|
||||
self.scenario.stopComputation = False
|
||||
self.scenario.save()
|
||||
else:
|
||||
optimize_sequentially(scenarios, user_name="Simulator", user_is_staff=True, runMode=self.param['RUNMODE'], localsearch_time=int(self.param['LOCALSEARCHTIME']))
|
||||
3
research/tests.py
Normal file
3
research/tests.py
Normal file
@ -0,0 +1,3 @@
|
||||
from django.test import TestCase
|
||||
|
||||
# Create your tests here.
|
||||
18
research/urls.py
Normal file
18
research/urls.py
Normal file
@ -0,0 +1,18 @@
|
||||
from django.urls import re_path
|
||||
from research.views import *
|
||||
from common.decorators import admin_only
|
||||
|
||||
app_name = 'research'
|
||||
|
||||
# Learners
|
||||
urlpatterns = [
|
||||
re_path(r'^master_attendance/$', admin_only(master_attendance), name='master_attendance'),
|
||||
re_path(r'^histgames/$', admin_only(histgames), name='histgames'),
|
||||
]
|
||||
|
||||
# Simulator
|
||||
urlpatterns += [
|
||||
re_path(r'^simulator/$', admin_only(simulator), name='simulator'),
|
||||
re_path(r'^simulation_report/$', admin_only(simulation_report), name='simulation_report'),
|
||||
|
||||
]
|
||||
356
research/views.py
Normal file
356
research/views.py
Normal file
@ -0,0 +1,356 @@
|
||||
from django.http import JsonResponse, HttpResponse
|
||||
from django.shortcuts import render
|
||||
import json
|
||||
import csv
|
||||
|
||||
from scheduler.models import *
|
||||
from scheduler.views_func import ComputationProgressView
|
||||
from .ml.learners import AttendanceLearner
|
||||
from .simulator.simulator import UefaSimulator
|
||||
|
||||
|
||||
def histgames(request):
|
||||
games = Game.objects.filter(season=request.session['season'])
|
||||
context = {
|
||||
'games': games
|
||||
}
|
||||
return render(request, 'historic_games.html', context)
|
||||
|
||||
|
||||
def master_attendance(request):
|
||||
if request.method == 'GET':
|
||||
season = Season.objects.filter(id=request.session['season'])
|
||||
context = {
|
||||
'season': season
|
||||
}
|
||||
return render(request, 'attendance.html', context)
|
||||
|
||||
elif request.method == "POST":
|
||||
if request.POST['type'] == 'train':
|
||||
print("TRAINING")
|
||||
learner = AttendanceLearner(request.session['season'])
|
||||
learner.train()
|
||||
learner.save()
|
||||
return JsonResponse({'score': learner.score})
|
||||
elif request.POST['type'] == 'predict':
|
||||
print("PREDICT")
|
||||
scenario = Scenario.objects.get(id=request.session['scenario'])
|
||||
|
||||
def createGamesFromSolution(scenario_id):
|
||||
scenario = Scenario.objects.get(id=scenario_id)
|
||||
season = scenario.season
|
||||
|
||||
games = []
|
||||
Game.objects.filter(
|
||||
season=season, historic_season=None).delete()
|
||||
for game in scenario.solutionlist():
|
||||
day = Day.objects.get(id=game[0])
|
||||
home = Team.objects.get(id=game[1])
|
||||
away = Team.objects.get(id=game[2])
|
||||
round = game[3]
|
||||
time = game[4]
|
||||
games.append(Game(
|
||||
season=season,
|
||||
homeTeam=home,
|
||||
awayTeam=away,
|
||||
round=round,
|
||||
date=parse(day.day),
|
||||
time=time,
|
||||
))
|
||||
Game.objects.bulk_create(games)
|
||||
|
||||
createGamesFromSolution(scenario.id)
|
||||
|
||||
learner = AttendanceLearner(request.session['season'])
|
||||
learner.load()
|
||||
learner.predict()
|
||||
|
||||
games = Game.objects.filter(
|
||||
season=scenario.season, historic_season=None)
|
||||
for g in games:
|
||||
g.attendance = learner.predictions[g.id]
|
||||
g.save()
|
||||
|
||||
return JsonResponse({})
|
||||
|
||||
|
||||
def simulator(request):
|
||||
|
||||
if request.method == "GET":
|
||||
scenario = Scenario.objects.get(id=request.session['scenario'])
|
||||
|
||||
response = json.loads(ComputationProgressView(request).content)
|
||||
progress = response['task_status'] if 'task_status' in response else ""
|
||||
|
||||
scenarios = []
|
||||
scenarios_exist = []
|
||||
all_kpis = []
|
||||
get_kpi = defaultdict(lambda:{})
|
||||
get_kpi_text = defaultdict(lambda:{})
|
||||
if scenario.sim_report:
|
||||
kpis = json.loads(scenario.sim_report)
|
||||
scenarios = list(kpis.keys())[1:]
|
||||
for s in scenarios:
|
||||
for kpi in [kpit.split("__") for kpit in kpis[s].split("___")]:
|
||||
if not kpi[0] in all_kpis:
|
||||
all_kpis.append(kpi[0])
|
||||
|
||||
|
||||
get_kpi_val = { (s,kpi) : 0 for s in scenarios for kpi in all_kpis}
|
||||
|
||||
for s in scenarios:
|
||||
scenarioObj = Scenario.objects.filter(id=s).first()
|
||||
kpis_dict = {}
|
||||
if scenarioObj:
|
||||
scenarios_exist.append(str(scenarioObj.id))
|
||||
# If exists update with most recent data
|
||||
if scenarioObj.sol_kpis != "":
|
||||
for kpi in scenarioObj.kpis():
|
||||
kpis_dict[kpi[0]] = kpi[1:]
|
||||
kpis_dict["Datetime"] = [scenarioObj.lastComputation.strftime('%Y:%m:%d-%H:%M:%S'),""]
|
||||
|
||||
|
||||
for kpi in [kpit.split("__") for kpit in kpis[s].split("___")]:
|
||||
des1=kpi[0]
|
||||
val1= kpis_dict[des1][0] if des1 in kpis_dict else kpi[1]
|
||||
get_kpi[s][des1]= val1
|
||||
val1=(val1.split(" "))[0]
|
||||
if val1.isnumeric():
|
||||
get_kpi_val[(s, des1)]= int(float(val1))
|
||||
if len(kpi)>2:
|
||||
if des1 in kpis_dict and len(kpis_dict[des1])>1:
|
||||
get_kpi_text[s][des1]= kpis_dict[des1][1]
|
||||
else:
|
||||
get_kpi_text[s][des1]= kpi[2]
|
||||
# else:
|
||||
# get_kpi_text[s][des1] = val1
|
||||
#
|
||||
|
||||
all_kpis=[kpi for kpi in all_kpis if len([sc for sc in scenarios if get_kpi[sc][kpi] != "0 of 0" ])>0]
|
||||
|
||||
|
||||
|
||||
context = {
|
||||
'scenario': scenario,
|
||||
'scenarios':scenarios,
|
||||
'scenarios_exist':scenarios_exist,
|
||||
'all_kpis':all_kpis,
|
||||
'get_kpi': get_kpi,
|
||||
'get_kpi_text': get_kpi_text,
|
||||
'progress':progress
|
||||
}
|
||||
return render(request, "simulator.html", context)
|
||||
|
||||
elif request.method == "POST":
|
||||
|
||||
if request.POST['method'] == "submit_params":
|
||||
s = Scenario.objects.get(id=request.session['scenario'])
|
||||
s.sim_script = request.POST['payload'].replace("\n", ";").replace("\"","").replace("\'","")
|
||||
s.save()
|
||||
return HttpResponse("OK")
|
||||
|
||||
elif request.POST['method'] == "start_simulation":
|
||||
s = Scenario.objects.get(id=request.session['scenario'])
|
||||
sim = UefaSimulator(s.id)
|
||||
sim.simulate()
|
||||
return HttpResponse("OK")
|
||||
|
||||
elif request.POST['method'] == "start_improvement":
|
||||
s = Scenario.objects.get(id=request.session['scenario'])
|
||||
sim = UefaSimulator(s.id)
|
||||
sim.improve()
|
||||
return HttpResponse("OK")
|
||||
|
||||
|
||||
def findall(p, s):
|
||||
i = s.find(p)
|
||||
while i != -1:
|
||||
yield i
|
||||
i = s.find(p, i+1)
|
||||
|
||||
|
||||
def simulation_report(request):
|
||||
|
||||
baseScenario = Scenario.objects.get(id=request.session['scenario'])
|
||||
|
||||
response = HttpResponse(content_type='text/csv')
|
||||
response['Content-Disposition'] = 'attachment; filename="{}_{}_violations.csv"'.format(baseScenario.season.nicename,baseScenario.name)
|
||||
|
||||
writer = csv.writer(response,delimiter=",")
|
||||
|
||||
# writer.writerow(['', 'Team Name', 'Date', 'Type', 'Time', 'Comment', 'Tag'])
|
||||
# for b in Blocking.objects.filter(scenario=scenario):
|
||||
# writer.writerow(["BLOCKING",b.team.name,parse(b.day.day).date(),b.type,b.time,b.comment,b.tag])
|
||||
|
||||
prioVal ={'A': 25 , 'B': 5 , 'C': 1, 'Hard' : 1000}
|
||||
|
||||
quality_dict = {}
|
||||
for ref_scenario in baseScenario.simulation_scenarios.all().order_by('name'):
|
||||
for scenario in ref_scenario.simulation_scenarios.all().order_by('name').reverse():
|
||||
|
||||
total_vio = 0
|
||||
objectiveFunctionWeights = ObjectiveFunctionWeight.objects.filter(scenario=scenario).values()
|
||||
|
||||
gew={}
|
||||
for ow in objectiveFunctionWeights:
|
||||
gew[ow['name']]= ow['use'] * ow['prio']
|
||||
|
||||
|
||||
if scenario.sol_kpis != "":
|
||||
kpis_dict = {}
|
||||
for kpi in scenario.kpis():
|
||||
kpis_dict[kpi[0]] = kpi[1:]
|
||||
kpis_dict["Datetime"] = [scenario.lastComputation.strftime('%Y:%m:%d-%H:%M:%S'),""]
|
||||
|
||||
|
||||
uclpatterns = SpecialWish.objects.filter(scenario=scenario,name='UCL24Patterns',active=True).exists()
|
||||
quality_scen = {"Overall Quality":kpis_dict["Overall Quality"],"Datetime":kpis_dict["Datetime"],"UCLpatterns":[uclpatterns]}
|
||||
first_row = ["" for i in range(5)]
|
||||
|
||||
# PRIO BASED
|
||||
# for prio in ['Hard','A','B','C']:
|
||||
# pairing_vios = Pairing.objects.filter(scenario=scenario,prio=prio,active=True).exclude(violation="")
|
||||
# pairing_viols_count = 0
|
||||
# for v in pairing_vios:
|
||||
# pairing_viols_count += len(v.violation.split("__"))
|
||||
# quality_scen["Pairing"][prio] = [pairing_viols_count,5*prioVal[prio] * pairing_viols_count * gew["Pairings"]]
|
||||
# total_vio += 5*prioVal[prio] * pairing_viols_count * gew["Pairings"]
|
||||
|
||||
# ha_vios = HAWish.objects.filter(scenario=scenario,prio=prio,active=True).exclude(violation="")
|
||||
# ha_vios_count = 0
|
||||
# for v in ha_vios:
|
||||
# many = {}
|
||||
# few = {}
|
||||
# for i in range(10):
|
||||
# many[i] = len(list(findall(f"{i} too many",v.violation)))
|
||||
# few[i] = len(list(findall(f"{i} too few",v.violation)))
|
||||
# ha_vios_count += i*(many[i] + few[i])
|
||||
# quality_scen["HAWish"][prio] = [ha_vios_count,prioVal[prio] * ha_vios_count * gew["Home-/Away"]]
|
||||
# total_vio += prioVal[prio] * ha_vios_count * gew["Home-/Away"]
|
||||
|
||||
# enc_vios = EncWish.objects.filter(scenario=scenario,prio=prio,active=True).exclude(violation="").exclude(violation="Not evaluated yet!")
|
||||
# enc_vios_count = 0
|
||||
# for v in enc_vios:
|
||||
# many = {}
|
||||
# few = {}
|
||||
# for i in range(10):
|
||||
# many[i] = len(list(findall(f"{i} too many",v.violation)))
|
||||
# few[i] = len(list(findall(f"{i} too few",v.violation)))
|
||||
# enc_vios_count += i*(many[i] + few[i])
|
||||
|
||||
# # for a in s.split(":"):
|
||||
# # if "too" in a:
|
||||
# # print("\t",a)
|
||||
# # ha_vios_count += int(a.split("too")[0].strip())
|
||||
# # quality_scen["HAWish"][prio] = [ha_vios_count,prioVal[prio] * ha_vios_count * gew["Home-/Away"]]
|
||||
|
||||
|
||||
# quality_scen["EncWish"][prio] = [enc_vios_count,5*prioVal[prio] * enc_vios_count * gew["Encounters"]]
|
||||
# total_vio += 5*prioVal[prio] * enc_vios_count * gew["Encounters"]
|
||||
|
||||
# quality_scen["PAIR+HA+ENC"] = total_vio
|
||||
|
||||
dynamic_rows = []
|
||||
|
||||
|
||||
""" PAIRINGS """
|
||||
pairings = Pairing.objects.filter(scenario=scenario,active=True)
|
||||
pairing_tags = pairings.values_list('comment',flat=True).distinct()
|
||||
|
||||
for tag in pairing_tags:
|
||||
first_row.append("PAIRINGS")
|
||||
row = f"{tag}"
|
||||
dynamic_rows.append(row)
|
||||
pairing_vios = pairings.filter(comment=tag).exclude(violation="")
|
||||
pairing_viols_count = 0
|
||||
pairing_viols_obj = 0
|
||||
for v in pairing_vios:
|
||||
pairing_viols_count += len(v.violation.split("__"))
|
||||
pairing_viols_obj += 5*prioVal[v.prio] * len(v.violation.split("__"))
|
||||
quality_scen[row] = [pairing_viols_count,pairing_viols_obj * gew["Pairings"]]
|
||||
total_vio += pairing_viols_obj * gew["Pairings"]
|
||||
|
||||
|
||||
""" HOME AWAY """
|
||||
tags = Tag.objects.filter(season=scenario.season,scheduler_hawishes__isnull=False).distinct()
|
||||
|
||||
for tag in tags:
|
||||
first_row.append("HOME AWAY")
|
||||
row = f"{tag.name}"
|
||||
dynamic_rows.append(row)
|
||||
ha_vios = tag.scheduler_hawishes.filter(scenario=scenario,active=True).exclude(violation="")
|
||||
ha_vios_count = 0
|
||||
ha_vios_obj = 0
|
||||
for v in ha_vios:
|
||||
many = {}
|
||||
few = {}
|
||||
for i in range(10):
|
||||
many[i] = len(list(findall(f"{i} too many",v.violation)))
|
||||
few[i] = len(list(findall(f"{i} too few",v.violation)))
|
||||
ha_vios_count += i*(many[i] + few[i])
|
||||
ha_vios_obj += i*(many[i] + few[i]) * prioVal[v.prio]
|
||||
quality_scen[row] = [ha_vios_count, ha_vios_obj * gew["Home-/Away"]]
|
||||
total_vio += ha_vios_obj * gew["Home-/Away"]
|
||||
|
||||
|
||||
""" ENCOUNTERS """
|
||||
tags = Tag.objects.filter(season=scenario.season,scheduler_encwishes__isnull=False).distinct()
|
||||
|
||||
for tag in tags:
|
||||
first_row.append("ENCOUNTERS")
|
||||
row = f"{tag.name}"
|
||||
dynamic_rows.append(row)
|
||||
# enc_vios = EncWish.objects.filter(scenario=scenario,prio=prio,active=True).exclude(violation="").exclude(violation="Not evaluated yet!")
|
||||
enc_vios = tag.scheduler_encwishes.filter(scenario=scenario,active=True).exclude(violation="").exclude(violation="Not evaluated yet!")
|
||||
enc_vios_count = 0
|
||||
enc_vios_obj = 0
|
||||
for v in enc_vios:
|
||||
many = {}
|
||||
few = {}
|
||||
for i in range(10):
|
||||
many[i] = len(list(findall(f"{i} too many",v.violation)))
|
||||
few[i] = len(list(findall(f"{i} too few",v.violation)))
|
||||
enc_vios_count += i*(many[i] + few[i])
|
||||
enc_vios_obj += i*(many[i] + few[i]) * prioVal[v.prio] * 5
|
||||
# for a in s.split(":"):
|
||||
# if "too" in a:
|
||||
# print("\t",a)
|
||||
# ha_vios_count += int(a.split("too")[0].strip())
|
||||
# quality_scen["HAWish"][prio] = [ha_vios_count,prioVal[prio] * ha_vios_count * gew["Home-/Away"]]
|
||||
quality_scen[row] = [enc_vios_count,enc_vios_obj * gew["Encounters"]]
|
||||
total_vio += enc_vios_obj * gew["Encounters"]
|
||||
|
||||
quality_scen["PAIR+HA+ENC"] = total_vio
|
||||
|
||||
|
||||
|
||||
quality_dict[scenario.name] = quality_scen
|
||||
|
||||
|
||||
writer.writerow(first_row[:5]+[""]+first_row[5:]+[""]+first_row[5:])
|
||||
rows = ["AScenario","Datetime","UCLPatterns","Overall Quality","PAIR+HA+ENC"]
|
||||
rows += [""]
|
||||
rows += [f"#({r})" for r in dynamic_rows]
|
||||
rows += [""]
|
||||
rows += [f"obj({r})" for r in dynamic_rows]
|
||||
writer.writerow(rows)
|
||||
|
||||
for scenario in quality_dict.keys():
|
||||
ll = []
|
||||
ll.append(f"{scenario}")
|
||||
ll.append(f"{quality_dict[scenario]['Datetime'][0]}")
|
||||
ll.append(f"{quality_dict[scenario]['UCLpatterns'][0]}")
|
||||
ll.append(f"{quality_dict[scenario]['Overall Quality'][0]}")
|
||||
ll.append(f"{quality_dict[scenario]['PAIR+HA+ENC']}")
|
||||
ll.append("")
|
||||
for row in dynamic_rows:
|
||||
ll.append(f"{quality_dict[scenario][row][0]}")
|
||||
ll.append("")
|
||||
for row in dynamic_rows:
|
||||
ll.append(f"{quality_dict[scenario][row][1]}")
|
||||
writer.writerow(ll)
|
||||
|
||||
|
||||
|
||||
|
||||
return response
|
||||
85
scripts/check_data.py
Normal file
85
scripts/check_data.py
Normal file
@ -0,0 +1,85 @@
|
||||
# %%
|
||||
PROJECT_PATH = '/home/md/Work/ligalytics/leagues_stable/'
|
||||
import os, sys
|
||||
sys.path.insert(0, PROJECT_PATH)
|
||||
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "leagues.settings")
|
||||
os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"
|
||||
|
||||
from leagues import settings
|
||||
# settings.DATABASES['default']['NAME'] = PROJECT_PATH+'/db.sqlite3'
|
||||
settings.DATABASES['default']['ENGINE'] = 'django.db.backends.postgresql_psycopg2'
|
||||
settings.DATABASES['default']['HOST'] = '0.0.0.0'
|
||||
settings.DATABASES['default']['PORT'] = '5432'
|
||||
settings.DATABASES['default']['USER'] = 'postgres'
|
||||
settings.DATABASES['default']['PASSWORD'] = 'secret123'
|
||||
settings.DATABASES['default']['NAME'] = 'mypgsqldb'
|
||||
settings.DATABASES['default']['ATOMIC_REQUESTS'] = False
|
||||
settings.DATABASES['default']['AUTOCOMMIT'] = True
|
||||
settings.DATABASES['default']['CONN_MAX_AGE'] = 0
|
||||
settings.DATABASES['default']['CONN_HEALTH_CHECKS'] = False
|
||||
settings.DATABASES['default']['OPTIONS'] = {}
|
||||
|
||||
os.environ["XPRESSDIR"] = "/opt/xpressmp"
|
||||
os.environ["XPRESS"] = "/opt/xpressmp/bin"
|
||||
os.environ["LD_LIBRARY_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["DYLD_LIBRARY_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["SHLIB_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["LIBPATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["PYTHONPATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprs.jar"
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprb.jar" + os.pathsep + os.environ["CLASSPATH"]
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprm.jar" + os.pathsep + os.environ["CLASSPATH"]
|
||||
os.environ["PATH"] = os.environ["XPRESSDIR"] + "/bin" + os.pathsep + os.environ["PATH"]
|
||||
|
||||
|
||||
import django
|
||||
django.setup()
|
||||
|
||||
from scheduler.models import *
|
||||
import pulp
|
||||
from pulp import lpSum, value, XPRESS, GUROBI, PULP_CBC_CMD
|
||||
from django.db.models import Q
|
||||
from django.template.loader import render_to_string
|
||||
|
||||
from qualifiers.models import *
|
||||
from common.models import GlobalTeam, GlobalCountry
|
||||
from scheduler.models import *
|
||||
|
||||
from qualifiers.draws import groupTeams, optimize_inversions4
|
||||
from scheduler.solver.tasks.optimize import optimize
|
||||
from scheduler.solver.tasks.optimize_submodels import ueluecl24_basicmodell_v2
|
||||
|
||||
from common.security import ESCAPE_WORDS
|
||||
|
||||
import random
|
||||
import time
|
||||
import json
|
||||
import csv
|
||||
import networkx as nx
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import timedelta
|
||||
|
||||
from django.contrib.sessions.models import Session
|
||||
|
||||
|
||||
|
||||
|
||||
for word in ESCAPE_WORDS:
|
||||
print("WORD",word)
|
||||
for hawish in HAWish.objects.all():
|
||||
if hawish.reason.find(word) >= 0:
|
||||
print(f"\t{hawish.scenario.season} found |{word}| in |{hawish.reason}|")
|
||||
for encwish in EncWish.objects.all():
|
||||
if encwish.reason.find(word) >= 0:
|
||||
print(f"\t{encwish.scenario.season} found |{word}| in |{encwish.reason}|")
|
||||
for pairing in Pairing.objects.all():
|
||||
if pairing.comment.find(word) >= 0:
|
||||
print(f"\t{pairing.scenario.season} found |{word}| in |{pairing.comment}|")
|
||||
for team in Team.objects.all():
|
||||
if team.comments:
|
||||
if team.comments.find(word) >= 0:
|
||||
print(f"\t{team.season} found |{word}| in |{team.comments}|")
|
||||
for blocking in Blocking.objects.all():
|
||||
if blocking.comment.find(word) >= 0:
|
||||
print(f"\t{blocking.scenario.season} found |{word}| in |{blocking.comment}|")
|
||||
|
||||
78
solver/debug.py
Executable file
78
solver/debug.py
Executable file
@ -0,0 +1,78 @@
|
||||
# %%
|
||||
PROJECT_PATH = '/home/md/Work/ligalytics/leagues_stable/'
|
||||
import os, sys
|
||||
sys.path.insert(0, PROJECT_PATH)
|
||||
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "leagues.settings")
|
||||
os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"
|
||||
|
||||
from leagues import settings
|
||||
# settings.DATABASES['default']['NAME'] = PROJECT_PATH+'/db.sqlite3'
|
||||
settings.DATABASES['default']['ENGINE'] = 'django.db.backends.postgresql_psycopg2'
|
||||
settings.DATABASES['default']['HOST'] = '0.0.0.0'
|
||||
settings.DATABASES['default']['PORT'] = '5432'
|
||||
settings.DATABASES['default']['USER'] = 'postgres'
|
||||
settings.DATABASES['default']['PASSWORD'] = 'secret123'
|
||||
settings.DATABASES['default']['NAME'] = 'mypgsqldb'
|
||||
settings.DATABASES['default']['ATOMIC_REQUESTS'] = False
|
||||
settings.DATABASES['default']['AUTOCOMMIT'] = True
|
||||
settings.DATABASES['default']['CONN_MAX_AGE'] = 0
|
||||
settings.DATABASES['default']['CONN_HEALTH_CHECKS'] = False
|
||||
settings.DATABASES['default']['OPTIONS'] = {}
|
||||
|
||||
os.environ["XPRESSDIR"] = "/opt/xpressmp"
|
||||
os.environ["XPRESS"] = "/opt/xpressmp/bin"
|
||||
os.environ["LD_LIBRARY_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["DYLD_LIBRARY_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["SHLIB_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["LIBPATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["PYTHONPATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprs.jar"
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprb.jar" + os.pathsep + os.environ["CLASSPATH"]
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprm.jar" + os.pathsep + os.environ["CLASSPATH"]
|
||||
os.environ["PATH"] = os.environ["XPRESSDIR"] + "/bin" + os.pathsep + os.environ["PATH"]
|
||||
|
||||
|
||||
import django
|
||||
django.setup()
|
||||
|
||||
from scheduler.models import *
|
||||
import pulp
|
||||
from pulp import lpSum, value, XPRESS, GUROBI, PULP_CBC_CMD
|
||||
from django.db.models import Q
|
||||
from django.template.loader import render_to_string
|
||||
|
||||
from qualifiers.models import *
|
||||
from common.models import GlobalTeam, GlobalCountry
|
||||
from scheduler.models import Season, Scenario, Team, DayObj, CountryClash, Country
|
||||
|
||||
from qualifiers.draws import groupTeams, optimize_inversions4
|
||||
from scheduler.solver.tasks.optimize import optimize
|
||||
from scheduler.solver.tasks.optimize_submodels import ueluecl24_basicmodell_v2
|
||||
|
||||
import random
|
||||
import time
|
||||
import json
|
||||
import csv
|
||||
import networkx as nx
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import timedelta
|
||||
|
||||
from django.contrib.sessions.models import Session
|
||||
|
||||
|
||||
scenario = Scenario.objects.get(id=9343)
|
||||
# scenario = Scenario.objects.get(id=9368)
|
||||
# scenario = Scenario.objects.get(id=1)
|
||||
season = scenario.season
|
||||
|
||||
s2 = scenario.id
|
||||
user_name = 'md'
|
||||
user_is_staff = True
|
||||
runMode = 'Improve'
|
||||
localsearch_time = 0
|
||||
RUN_ENV = 'local'
|
||||
SOLVER = 'xpress'
|
||||
|
||||
|
||||
sol = optimize(task=None, s2=s2, user_name=user_name, user_is_staff=user_is_staff,
|
||||
runMode=runMode, localsearch_time=localsearch_time, RUN_ENV=RUN_ENV, solver=SOLVER)
|
||||
114
uefa/cycle24/research/script.py
Executable file
114
uefa/cycle24/research/script.py
Executable file
@ -0,0 +1,114 @@
|
||||
# %%
|
||||
PROJECT_PATH = '/home/md/Work/ligalytics/leagues_stable/'
|
||||
import os, sys
|
||||
sys.path.insert(0, PROJECT_PATH)
|
||||
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "leagues.settings")
|
||||
os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"
|
||||
|
||||
from leagues import settings
|
||||
# settings.DATABASES['default']['NAME'] = PROJECT_PATH+'/db.sqlite3'
|
||||
settings.DATABASES['default']['ENGINE'] = 'django.db.backends.postgresql_psycopg2'
|
||||
settings.DATABASES['default']['HOST'] = '0.0.0.0'
|
||||
settings.DATABASES['default']['PORT'] = '5432'
|
||||
settings.DATABASES['default']['USER'] = 'postgres'
|
||||
settings.DATABASES['default']['PASSWORD'] = 'secret123'
|
||||
settings.DATABASES['default']['NAME'] = 'mypgsqldb'
|
||||
settings.DATABASES['default']['ATOMIC_REQUESTS'] = False
|
||||
settings.DATABASES['default']['AUTOCOMMIT'] = True
|
||||
settings.DATABASES['default']['CONN_MAX_AGE'] = 0
|
||||
settings.DATABASES['default']['CONN_HEALTH_CHECKS'] = False
|
||||
settings.DATABASES['default']['OPTIONS'] = {}
|
||||
|
||||
os.environ["XPRESSDIR"] = "/opt/xpressmp"
|
||||
os.environ["XPRESS"] = "/opt/xpressmp/bin"
|
||||
os.environ["LD_LIBRARY_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["DYLD_LIBRARY_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["SHLIB_PATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["LIBPATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["PYTHONPATH"] = os.environ["XPRESSDIR"] + "/lib"
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprs.jar"
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprb.jar" + os.pathsep + os.environ["CLASSPATH"]
|
||||
os.environ["CLASSPATH"] = os.environ["XPRESSDIR"] + "/lib/xprm.jar" + os.pathsep + os.environ["CLASSPATH"]
|
||||
os.environ["PATH"] = os.environ["XPRESSDIR"] + "/bin" + os.pathsep + os.environ["PATH"]
|
||||
|
||||
|
||||
import django
|
||||
django.setup()
|
||||
|
||||
from scheduler.models import *
|
||||
import pulp
|
||||
from pulp import lpSum, value, XPRESS, GUROBI, PULP_CBC_CMD
|
||||
from django.db.models import Q
|
||||
from django.template.loader import render_to_string
|
||||
|
||||
from scheduler.models import *
|
||||
|
||||
|
||||
import random
|
||||
import time
|
||||
import json
|
||||
import csv
|
||||
import networkx as nx
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import timedelta
|
||||
|
||||
from django.contrib.sessions.models import Session
|
||||
from pprint import pprint
|
||||
|
||||
ucl_basescenario = Scenario.objects.get(id=9543)
|
||||
# scenario = Scenario.objects.get(id=9368)
|
||||
# scenario = Scenario.objects.get(id=1)
|
||||
season = ucl_basescenario.season
|
||||
|
||||
|
||||
days = Day.objects.filter(season=season)
|
||||
teams = Team.objects.filter(season=season)
|
||||
|
||||
getTeamById = {team.id:team for team in teams}
|
||||
getDayById = {day.id:day for day in days}
|
||||
|
||||
user_name = 'md'
|
||||
user_is_staff = True
|
||||
runMode = 'New'
|
||||
localsearch_time = 0
|
||||
RUN_ENV = 'local'
|
||||
SOLVER = 'xpress'
|
||||
|
||||
|
||||
solverstats = json.loads(ucl_basescenario.solverstats_json)
|
||||
|
||||
solverstats.keys()
|
||||
|
||||
# home_in_round = {
|
||||
# (r,t):0 for t in teams for r in range(1,season.nRounds+1)
|
||||
# }
|
||||
|
||||
|
||||
# away_in_round = {
|
||||
# (r,t):0 for t in teams for r in range(1,season.nRounds+1)
|
||||
# }
|
||||
|
||||
# scenarios = season.scenarios.exclude(id=ucl_basescenario.id)
|
||||
|
||||
# for scenario in scenarios:
|
||||
|
||||
# solverstats = json.loads(scenario.solverstats_json)
|
||||
|
||||
# if solverstats.get('info-Improve'):
|
||||
# print(solverstats['info-Improve'])
|
||||
# break
|
||||
|
||||
|
||||
# # solution = scenario.sol_solution()
|
||||
|
||||
# for game in solverstats['premodell']['partial_solution']:
|
||||
# home_in_round[(getDayById[game['days'][0]].round,getTeamById[game['home'][0]])] += 1
|
||||
# away_in_round[(getDayById[game['days'][0]].round,getTeamById[game['away'][0]])] += 1
|
||||
# # sol = optimize(task=None, s2=ucl_basescenario.id, user_name=user_name, user_is_staff=user_is_staff,
|
||||
# # runMode=runMode, localsearch_time=localsearch_time, RUN_ENV=RUN_ENV, solver=SOLVER)
|
||||
# home_in_round = sorted(home_in_round.items(), key=lambda x: x[0][0])
|
||||
# pprint(home_in_round)
|
||||
# away_in_round = sorted(away_in_round.items(), key=lambda x: x[0][0])
|
||||
# pprint(away_in_round)
|
||||
|
||||
# %%
|
||||
285
uefa/feb24_checkandschedule/uefa_tester_24.py
Normal file
285
uefa/feb24_checkandschedule/uefa_tester_24.py
Normal file
@ -0,0 +1,285 @@
|
||||
import csv
|
||||
import operator
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
import timeit
|
||||
import datetime
|
||||
# import networkx as nx
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
from dateutil.parser import parse
|
||||
from math import sqrt, pow, sin, cos, atan2, pi
|
||||
|
||||
try:
|
||||
import simplejson as json
|
||||
except:
|
||||
import json
|
||||
|
||||
from gurobipy import *
|
||||
|
||||
# import config
|
||||
|
||||
getTeamsOfPot = { p: [ p+str(i) for i in range(1,10) ] for p in ["A","B","C","D"] }
|
||||
|
||||
allTeams = getTeamsOfPot['A']+getTeamsOfPot['B']+getTeamsOfPot['C']+getTeamsOfPot['D']
|
||||
activeTeams= allTeams
|
||||
pots = getTeamsOfPot.keys()
|
||||
# print (pots)
|
||||
|
||||
gamesAgainst = { (p1,p2) : 2 for p1 in pots for p2 in pots}
|
||||
|
||||
# for p in pots:
|
||||
# gamesAgainst[(p,p)]=2
|
||||
|
||||
# for (p1,p2) in [('A','D'), ('D','A'), ('B','C'), ('C','B') ]:
|
||||
# gamesAgainst[(p1,p2)]=2
|
||||
|
||||
nMatchDays=8
|
||||
|
||||
|
||||
# for p1 in pots:
|
||||
# for p2 in pots:
|
||||
# print (p1,p2,gamesAgainst[(p1,p2)])
|
||||
# print()
|
||||
|
||||
# print (allTeams)
|
||||
|
||||
cntr=0
|
||||
|
||||
mbus = Model("Draw")
|
||||
|
||||
possAssigns= [ (t1,t2) for t1 in allTeams for t2 in allTeams if t1!=t2]
|
||||
# print (possAssigns)
|
||||
|
||||
|
||||
plays = mbus.addVars(possAssigns, vtype=GRB.BINARY)
|
||||
|
||||
for (t1,t2) in possAssigns:
|
||||
if t1<t2:
|
||||
mbus.addConstr( plays[(t1,t2)] ==plays[(t2,t1)] )
|
||||
|
||||
for p in pots:
|
||||
for t in getTeamsOfPot[p]:
|
||||
for p2 in pots:
|
||||
mbus.addConstr( quicksum(plays[(t,t2)] for t2 in getTeamsOfPot[p2] if t!=t2 ) == gamesAgainst[(p,p2)])
|
||||
|
||||
|
||||
# for (t1,t2) in [("A1","A2"), ("A2","A3"), ("A3","A1"),
|
||||
# ("B1","B2"), ("B2","B3"), ("B3","B1"),
|
||||
# ("C1","C2"), ("C2","C3"), ("C3","C1"),
|
||||
# # ("D1","D2"), ("D2","D3"), ("D3","D1"),
|
||||
# ] :
|
||||
# mbus.addConstr( plays[(t1,t2)] == 1)
|
||||
|
||||
|
||||
# for (p1,p2) in [ ("A","B"), ("B","C"), ("C","D"), ("D","A") ]:
|
||||
# for (n1,n2) in [ (1,1),(1,2),(3,2),(3,3),(2,3) ,(2,1)]:
|
||||
# mbus.addConstr( plays[( p1+str(n1) , p2+str(n2))] == 1)
|
||||
|
||||
|
||||
# potpairs = [ (p1,p2) for p1 in pots for p2 in pots if p1!=p2]
|
||||
# clusters = [[1,2,3], [4,5,6], [7,8,9]]
|
||||
|
||||
# for cl in clusters:
|
||||
# for (p1,p2) in potpairs:
|
||||
# if len(cl)==2:
|
||||
# mbus.addConstr( quicksum(plays[( p1+str(n1),p2+str(n2))] for n1 in cl for n2 in cl ) == 4)
|
||||
# print ( [( p1+str(n1),p2+str(n2)) for n1 in cl for n2 in cl ])
|
||||
|
||||
# for (p1,p2) in [ ("A","B"), ("B","C"), ("C","D"), ("D","A") ]:
|
||||
# for (n1,n2) in [ (1,1),(1,2),(3,2),(3,3),(2,3) ,(2,1)]:
|
||||
# mbus.addConstr( plays[( p1+str(n1) , p2+str(n2))] == 1)
|
||||
|
||||
|
||||
badExample=False
|
||||
|
||||
if badExample:
|
||||
for p in pots:
|
||||
for i in [2,3]:
|
||||
mbus.addConstr( plays[( p+str(i-1),p+str(i))]==1)
|
||||
# print ( p+str(i-1),p+str(i))
|
||||
mbus.addConstr( plays[( p+"3",p+"1")]==1)
|
||||
|
||||
for p1 in pots:
|
||||
for i in [1,2,3]:
|
||||
for p2 in pots:
|
||||
if p1!=p2:
|
||||
mbus.addConstr( quicksum(plays[( p1+str(i),p2+str(j))] for j in [4,5,6] ) ==2)
|
||||
|
||||
mbus.Params.OutputFlag = 0
|
||||
|
||||
debug=False
|
||||
|
||||
|
||||
now = datetime.now()
|
||||
|
||||
nSims=10
|
||||
|
||||
if len(sys.argv)>1:
|
||||
nSims=int(sys.argv[1])
|
||||
|
||||
|
||||
solutions =[]
|
||||
solution_strings =[]
|
||||
for i in range(nSims):
|
||||
if i%10==0:
|
||||
print(i)
|
||||
cntr+=1
|
||||
mbus.setObjective(quicksum([ int(100*random.random()) * plays[pa] for pa in possAssigns ]))
|
||||
# mbus.setObjective(quicksum([ max(0,int(pa[0][1]) - int(pa[1][1] )) * plays[pa] for pa in possAssigns ]))
|
||||
|
||||
# print ("DRAWING !!!!")
|
||||
|
||||
mbus.optimize()
|
||||
sol= [ pa for pa in possAssigns if plays[pa].x>0.9]
|
||||
solutions.append(sol.copy())
|
||||
|
||||
if debug:
|
||||
print ("Solution")
|
||||
# exit(0)
|
||||
print ("INPOT")
|
||||
for p in pots:
|
||||
print ("")
|
||||
print ("POT",p)
|
||||
for (t1,t2) in [(t1,t2) for (t1,t2) in sol if t1<t2 and t1[0]==p and t2[0]==p]:
|
||||
print ( t1 , "-" , t2)
|
||||
|
||||
print ("OUTPOT")
|
||||
for p in pots:
|
||||
print ("")
|
||||
print ("POT",p)
|
||||
for (t1,t2) in [(t1,t2) for (t1,t2) in sol if t1[0]==p and t2[0]!=p]:
|
||||
print ( t1 , "-" , t2)
|
||||
|
||||
solstring =""
|
||||
for pa in possAssigns:
|
||||
solstring+= str(int(plays[pa].x +0.1) )
|
||||
if nSims < 1000:
|
||||
solution_strings.append(solstring)
|
||||
|
||||
mds =range(1,nMatchDays+1)
|
||||
|
||||
pas2= [ (t1,t2,md) for md in mds for (t1,t2) in sol]
|
||||
# print ("pas2", pas2)
|
||||
|
||||
cal = Model("Calendar")
|
||||
|
||||
x = cal.addVars(pas2, vtype=GRB.BINARY)
|
||||
|
||||
computeCalendarsHA = True
|
||||
computeCalendarsHA = False
|
||||
if computeCalendarsHA:
|
||||
|
||||
pos_patterns = [
|
||||
[1,0,1,1,0,0,1,0 ],
|
||||
[1,0,1,0,1,0,1,0 ],
|
||||
[1,0,1,0,1,0,0,1 ],
|
||||
[1,0,1,0,0,1,1,0 ],
|
||||
[1,0,1,0,0,1,0,1 ],
|
||||
[1,0,0,1,1,0,1,0 ],
|
||||
[1,0,0,1,1,0,0,1 ],
|
||||
[1,0,0,1,0,1,1,0 ],
|
||||
[1,0,0,1,0,1,0,1 ],
|
||||
]
|
||||
|
||||
# add reverse patterns (all those starting with 0,1 )
|
||||
pos_patterns+=[ [ 1-p[i] for i in range(8)] for p in pos_patterns ]
|
||||
# print ("pos_patterns",pos_patterns)
|
||||
|
||||
positions = range (len(pos_patterns))
|
||||
|
||||
pos = cal.addVars( [(t,p) for t in allTeams for p in positions], vtype=GRB.CONTINUOUS)
|
||||
|
||||
for t in allTeams:
|
||||
cal.addConstr( quicksum( [ pos[t,p] for p in positions ]) ==1)
|
||||
for md in mds:
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] for (t1,t2) in sol if t ==t1 ) == quicksum( [ pos[t,p] for p in positions if pos_patterns[p][md-1]==1 ]) )
|
||||
|
||||
for (t1,t2) in sol :
|
||||
if t1<t2:
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] + x[(t2,t1,md)] for md in mds) <=1 )
|
||||
|
||||
for t in allTeams:
|
||||
# every team 4 home games
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] for (t1,t2) in sol if t ==t1 for md in mds ) == 4)
|
||||
# every team one game a day
|
||||
for md in mds:
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] for (t1,t2) in sol if t in [t1,t2] ) == 1)
|
||||
|
||||
for p in pots:
|
||||
for t in getTeamsOfPot[p]:
|
||||
for p2 in pots:
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] for (t1,t2) in sol if t ==t1 and t2 in getTeamsOfPot[p2] for md in mds ) >= int(0.5*gamesAgainst[(p,p2)]) )
|
||||
else:
|
||||
for (t1,t2) in sol :
|
||||
if t1<t2:
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] for md in mds) ==1 )
|
||||
for md in mds:
|
||||
cal.addConstr( x[(t1,t2,md)] == x[(t2,t1,md)] )
|
||||
|
||||
for t in activeTeams:
|
||||
for md in mds:
|
||||
cal.addConstr( quicksum(x[(t1,t2,md)] for (t1,t2) in sol if t==t1 ) == 1)
|
||||
|
||||
cal.setObjective(quicksum( -x[pa] for pa in x.keys() ))
|
||||
|
||||
cal.Params.OutputFlag = 0
|
||||
# print ("SCHEDULING !!!!")
|
||||
cal.optimize()
|
||||
|
||||
# cal_sol= [pa for pa in x.keys() if x[pa].x>0.9 ]
|
||||
# print( cal_sol)
|
||||
# for pa in cal_sol :
|
||||
# print (pa)
|
||||
# # print (cal.status)
|
||||
# print(cal.objVal)
|
||||
|
||||
if cal.status!=2 or cal.objVal>-288:
|
||||
sol= [ (t1,t2) for (t1,t2) in sol if t1<t2]
|
||||
# sol= [ (t1,t2) for (t1,t2) in sol if t1[0]=="A" and t2[0]=="D" ]
|
||||
print ("No calendar for ", sol)
|
||||
print ("Tested " , str(i+1) , " calendars" )
|
||||
exit(0)
|
||||
|
||||
pas2_sol= [ pas for pas in pas2 if x[pas].x>0.9]
|
||||
# print (pas2_sol)
|
||||
|
||||
|
||||
showSolution= False
|
||||
showSolution= True
|
||||
if showSolution and debug:
|
||||
for md in mds:
|
||||
print (md,":")
|
||||
for (t1,t2,md2) in pas2_sol:
|
||||
# if md2==md and t1<t2 and "C" in [t1[0],t2[0]] :
|
||||
if md2==md :
|
||||
print (" " ,t1, " -" ,t2)
|
||||
|
||||
for t in activeTeams:
|
||||
for md in mds:
|
||||
if not computeCalendarsHA and sum(x[(t1,t2,md)].x for (t1,t2) in sol if t==t1 ) < 1:
|
||||
print ("ALERT ",t,md)
|
||||
|
||||
# print (solutions)
|
||||
# oldGm = [ gg for gg in solutions[1] if gg not in solutions[0]]
|
||||
if len(solutions)>1 and showSolution:
|
||||
newGm = [ gg for gg in solutions[-1] if gg not in solutions[-2]]
|
||||
|
||||
# print (oldGm)
|
||||
# print (newGm)
|
||||
# print (solution_strings)
|
||||
|
||||
print (len(set(solution_strings)) )
|
||||
|
||||
f = open("draw_feasibility/log_"+datetime.now().strftime("%Y%m%d_%H%M%S")+"_"+str(nSims)+".txt", "a")
|
||||
for s in solutions:
|
||||
for s2 in s:
|
||||
f.write(s2[0]+s2[1])
|
||||
f.write("\n")
|
||||
f.close()
|
||||
|
||||
print ("done" , datetime.now()-now )
|
||||
|
||||
@ -43,11 +43,12 @@ from django.template.loader import render_to_string
|
||||
|
||||
from qualifiers.models import *
|
||||
from common.models import GlobalTeam, GlobalCountry
|
||||
from scheduler.models import Season, Scenario, Team, DayObj, CountryClash, Country
|
||||
from scheduler.models import Season, Scenario, Team, DayObj, CountryClash, Country, Day
|
||||
|
||||
from qualifiers.draws import groupTeams, optimize_inversions4
|
||||
from scheduler.solver.tasks.optimize import optimize
|
||||
from scheduler.solver.tasks.optimize_submodels import ueluecl24_basicmodell_v2
|
||||
from scheduler.solver.optimizer import optimize_2phases
|
||||
|
||||
import random
|
||||
import time
|
||||
@ -60,114 +61,49 @@ from datetime import timedelta
|
||||
from django.contrib.sessions.models import Session
|
||||
|
||||
|
||||
scenario = Scenario.objects.get(id=9051)
|
||||
# scenario = Scenario.objects.get(id=9368)
|
||||
# scenario = Scenario.objects.get(id=1)
|
||||
season = scenario.season
|
||||
# scenario = Scenario.objects.get(id=9534)
|
||||
# scenario = Scenario.objects.get(id=9379)
|
||||
|
||||
# scenario = Scenario.objects.get(id=9361)
|
||||
scenario = Scenario.objects.get(id=9579)
|
||||
season = scenario.season
|
||||
s2 = scenario.id
|
||||
user_name = 'md'
|
||||
user_is_staff = True
|
||||
runMode = 'New'
|
||||
localsearch_time = 0
|
||||
runMode = 'Improve'
|
||||
localsearch_time = 30
|
||||
RUN_ENV = 'local'
|
||||
SOLVER = 'xpress'
|
||||
|
||||
|
||||
# Conference.objects.filter(scenario=scenario,name__in=["UEL","UECL"]).update(reopt=True)
|
||||
nRounds = season.nRounds
|
||||
rounds=range(1,nRounds+1)
|
||||
dayObjects = Day.objects.filter(season=season).values()
|
||||
getDays = { r : [] for r in rounds}
|
||||
for d in dayObjects:
|
||||
if d['round']>0 :
|
||||
getDays[d['round']].append(d['id'])
|
||||
|
||||
|
||||
|
||||
sol = optimize(task=None, s2=s2, user_name=user_name, user_is_staff=user_is_staff,
|
||||
runMode=runMode, localsearch_time=localsearch_time, RUN_ENV=RUN_ENV, solver=SOLVER)
|
||||
|
||||
# sol = optimize_2phases(s2, user_name, user_is_staff, runMode=runMode, localsearch_time=localsearch_time, RUN_ENV=RUN_ENV, SOLVER=SOLVER)
|
||||
# partial_solution = []
|
||||
# res_objective, res_games = ueluecl24_basicmodell_v2(SOLVER, s2, partial_solution)
|
||||
|
||||
# print(res_games)
|
||||
|
||||
# res_games = [(42255, 42321, 1), (42251, 42252, 1), (42244, 42317, 1), (42291, 42288, 1), (42290, 42310, 1), (42289, 42327, 1), (42277, 42282, 1), (42326, 42312, 1), (42325, 42314, 1), (42324, 42280, 1), (42319, 42320, 1), (42316, 42322, 1), (42315, 42273, 1), (42311, 42296, 1), (42313, 42305, 1), (42318, 42299, 1), (42323, 42329, 1), (42328, 42304, 1), (42330, 42336, 2), (42333, 42343, 2), (42250, 42293, 2), (42273, 42290, 2), (42271, 42300, 2), (42253, 42266, 2), (42252, 42311, 2), (42305, 42326, 2), (42304, 42319, 2), (42299, 42289, 2), (42297, 42332, 2), (42296, 42323, 2), (42288, 42277, 2), (42282, 42313, 2), (42280, 42315, 2), (42279, 42348, 2), (42278, 42286, 2), (42346, 42345, 2), (42342, 42254, 2), (42340, 42261, 2), (42339, 42247, 2), (42338, 42331, 2), (42335, 42344, 2), (42329, 42324, 2), (42327, 42328, 2), (42322, 42318, 2), (42321, 42251, 2), (42320, 42316, 2), (42317, 42291, 2), (42314, 42255, 2), (42312, 42244, 2), (42310, 42325, 2), (42307, 42337, 2), (42347, 42349, 2), (42350, 42334, 2), (42267, 42341, 2), (42293, 42307, 3), (42273, 42304, 3), (42266, 42342, 3), (42261, 42267, 3), (42254, 42338, 3), (42247, 42350, 3), (42305, 42312, 3), (42300, 42297, 3), (42299, 42244, 3), (42296, 42315, 3), (42291, 42280, 3), (42290, 42325, 3), (42286, 42333, 3), (42282, 42318, 3), (42277, 42313, 3), (42349, 42339, 3), (42345, 42340, 3), (42344, 42278, 3), (42341, 42346, 3), (42336, 42335, 3), (42348, 42330, 3), (42334, 42253, 3), (42331, 42250, 3), (42329, 42252, 3), (42327, 42317, 3), (42324, 42326, 3), (42322, 42251, 3), (42321, 42314, 3), (42320, 42288, 3), (42316, 42255, 3), (42311, 42319, 3), (42310, 42289, 3), (42328, 42323, 3), (42332, 42271, 3), (42337, 42279, 3), (42343, 42347, 3), (42255, 42311, 4), (42250, 42350, 4), (42271, 42293, 4), (42266, 42333, 4), (42254, 42348, 4), (42253, 42339, 4), (42252, 42305, 4), (42251, 42310, 4), (42244, 42282, 4), (42304, 42290, 4), (42300, 42349, 4), (42289, 42321, 4), (42286, 42340, 4), (42280, 42329, 4), (42277, 42322, 4), (42346, 42336, 4), (42341, 42337, 4), (42342, 42344, 4), (42338, 42297, 4), (42335, 42261, 4), (42334, 42345, 4), (42331, 42330, 4), (42326, 42316, 4), (42325, 42291, 4), (42319, 42273, 4), (42317, 42328, 4), (42315, 42299, 4), (42314, 42327, 4), (42312, 42324, 4), (42313, 42320, 4), (42318, 42296, 4), (42323, 42288, 4), (42332, 42247, 4), (42343, 42279, 4), (42347, 42307, 4), (42267, 42278, 4), (42330, 42254, 5), (42333, 42334, 5), (42293, 42347, 5), (42273, 42312, 5), (42261, 42332, 5), (42247, 42300, 5), (42244, 42315, 5), (42304, 42316, 5), (42299, 42322, 5), (42297, 42250, 5), (42289, 42305, 5), (42288, 42325, 5), (42282, 42327, 5), (42280, 42251, 5), (42279, 42267, 5), (42278, 42335, 5), (42349, 42271, 5), (42345, 42338, 5), (42344, 42253, 5), (42340, 42341, 5), (42339, 42343, 5), (42336, 42342, 5), (42348, 42266, 5), (42326, 42290, 5), (42324, 42255, 5), (42321, 42252, 5), (42320, 42329, 5), (42319, 42310, 5), (42317, 42311, 5), (42307, 42331, 5), (42313, 42314, 5), (42318, 42291, 5), (42323, 42277, 5), (42328, 42296, 5), (42337, 42286, 5), (42350, 42346, 5), (42330, 42342, 6), (42255, 42289, 6), (42250, 42300, 6), (42261, 42346, 6), (42252, 42319, 6), (42251, 42323, 6), (42305, 42244, 6), (42297, 42271, 6), (42296, 42280, 6), (42291, 42324, 6), (42290, 42282, 6), (42288, 42304, 6), (42286, 42293, 6), (42279, 42334, 6), (42278, 42347, 6), (42349, 42332, 6), (42341, 42254, 6), (42340, 42348, 6), (42339, 42338, 6), (42336, 42333, 6), (42335, 42253, 6), (42331, 42266, 6), (42329, 42273, 6), (42327, 42321, 6), (42325, 42320, 6), (42322, 42313, 6), (42316, 42318, 6), (42315, 42328, 6), (42314, 42317, 6), (42312, 42277, 6), (42311, 42326, 6), (42310, 42299, 6), (42307, 42344, 6), (42343, 42247, 6), (42350, 42337, 6), (42267, 42345, 6), (42333, 42339, 7), (42293, 42336, 7), (42271, 42343, 7), (42266, 42297, 7), (42254, 42261, 7), (42253, 42330, 7), (42247, 42279, 7), (42300, 42307, 7), (42346, 42286, 7), (42345, 42331, 7), (42344, 42350, 7), (42342, 42250, 7), (42338, 42349, 7), (42348, 42267, 7), (42334, 42278, 7), (42332, 42341, 7), (42337, 42340, 7), (42347, 42335, 7), (42255, 42325, 7), (42273, 42318, 7), (42305, 42304, 7), (42296, 42288, 7), (42280, 42320, 7), (42277, 42310, 7), (42326, 42319, 7), (42324, 42244, 7), (42322, 42327, 7), (42321, 42290, 7), (42317, 42282, 7), (42315, 42291, 7), (42314, 42252, 7), (42312, 42316, 7), (42311, 42251, 7), (42313, 42329, 7), (42323, 42289, 7), (42328, 42299, 7), (42252, 42328, 8), (42251, 42273, 8), (42244, 42323, 8), (42304, 42313, 8), (42299, 42255, 8), (42291, 42314, 8), (42290, 42311, 8), (42289, 42277, 8), (42288, 42321, 8), (42282, 42280, 8), (42329, 42305, 8), (42327, 42315, 8), (42325, 42322, 8), (42320, 42312, 8), (42319, 42324, 8), (42316, 42317, 8), (42310, 42296, 8), (42318, 42326, 8)]
|
||||
|
||||
|
||||
# ueluecl24_basicmodell_v2(SOLVER,scenario,[],[],maxTime=3600)
|
||||
# partial_solution = []
|
||||
# for (t1,t2,r) in res_games:
|
||||
# partial_solution.append({"home": [t1], "away": [t2], "days": getDays[r], "time": None , "changeable": False })
|
||||
|
||||
|
||||
# %%
|
||||
|
||||
# hawishes = HAWish.objects.filter(scenario=scenario)
|
||||
|
||||
# # elemHaWishes ={e['id'] : [] for e in hawishes}
|
||||
# # elemHaWishTeams={}
|
||||
# # elemHaWishDays ={}
|
||||
# # elemHaWishFirstDay ={}
|
||||
# # elemHaWishNum ={}
|
||||
|
||||
# cntr =1
|
||||
# for e in hawishes:
|
||||
# elemTeams = [[t] for t in e.get_teams()] if e.forEachTeam else [e.get_teams()]
|
||||
# elemRounds = [[d] for d in e.get_rounds()] if e.forEachDay else [e.get_rounds()]
|
||||
|
||||
# if e.homeAway == "HOME":
|
||||
# if e.minGames > 0:
|
||||
# for teamset in elemTeams:
|
||||
# for roundset in elemRounds:
|
||||
# model += lpSum([x[r,t.id,t2.id] for t in teamset for r in roundset for t2 in teams if (r,t.id,t2.id) in x.keys()]) >= e.minGames - seedViolation[seed.id]
|
||||
# res_objective, res_games = ueluecl24_basicmodell_v2(SOLVER, s2, partial_solution)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# # elemHaWishes[e['id']]=[]
|
||||
# # allElemDays=[]
|
||||
# # thisDaySet=[]
|
||||
# # lastDaySet = []
|
||||
# # # print("elemDays",elemDays)
|
||||
# # for d in elemDays :
|
||||
# # # print (e)
|
||||
# # if (e['forEachDay'] or e['forOneDay']) and e['timeframe']!=1:
|
||||
# # thisDaySet=[]
|
||||
# # # day1= parse(getDayById[d[0]]['day'])
|
||||
# # day1= getDateTimeDay[d[0]]
|
||||
# # if e['timeframe']>1:
|
||||
# # day2=day1 + datetime.timedelta(days=e['timeframe']-1)
|
||||
# # for d3 in days:
|
||||
# # # dt = parse(getDayById[d3]['day'])
|
||||
# # dt= getDateTimeDay[d3]
|
||||
# # if day1<=dt and dt<=day2 :
|
||||
# # thisDaySet.append(d3)
|
||||
# # else:
|
||||
# # r1 = getDayById[d[0]]['round']
|
||||
# # for d3 in days:
|
||||
# # dt= getDateTimeDay[d3]
|
||||
# # # dt = parse(getDayById[d3]['day'])
|
||||
# # if day1<=dt and r1 <= getRoundByDay[d3] and getRoundByDay[d3]< r1 + (-e['timeframe']) :
|
||||
# # thisDaySet.append(d3)
|
||||
# # # print (" ROUND HA WISH ", e['reason'], thisDaySet, e['timeframe'])
|
||||
# # else:
|
||||
# # thisDaySet=d
|
||||
|
||||
# # # only create wish id new day set is superset
|
||||
# # if len([d for d in thisDaySet if d not in lastDaySet])>0:
|
||||
# # for t in elemTeams:
|
||||
# # cntr+=1
|
||||
# # elemHaWishes[e['id']].append(cntr)
|
||||
# # elemHaWishTeams[cntr]=t
|
||||
# # elemHaWishDays[cntr]=thisDaySet.copy()
|
||||
# # elemHaWishFirstDay[cntr]=d[0]
|
||||
# # elemHaWishNum[cntr]=1
|
||||
# # if e['maxGames'] ==0:
|
||||
# # elemHaWishNum[cntr]=len(elemHaWishDays[cntr])*len(elemHaWishTeams[cntr])
|
||||
# # if e['minGames'] > 0:
|
||||
# # elemHaWishNum[cntr]=e['minGames']
|
||||
# # lastDaySet = thisDaySet.copy()
|
||||
# # allElemDays+= thisDaySet
|
||||
|
||||
# # hawRounds[e['id']]=[]
|
||||
# # for d3 in set(allElemDays):
|
||||
# # hawRounds[e['id']]+=getRoundsByDay[d3]
|
||||
# # hawRounds[e['id']]=sorted(list(set(hawRounds[e['id']])))
|
||||
# # hawRoundsString[e['id']]= ""
|
||||
# # for r in hawRounds[e['id']]:
|
||||
# # hawRoundsString[e['id']]+= str(r)+"_"
|
||||
# # if hawRoundsString[e['id']]!="":
|
||||
# # hawRoundsString[e['id']]=hawRoundsString[e['id']][:-1]
|
||||
# # e['nWishes'] = sum ( elemHaWishNum[ee] for ee in elemHaWishes[e['id']] )
|
||||
|
||||
# # if e['forOneDay']:
|
||||
# # e['nWishes'] = e['forOneDayNum'] * len(elemTeams)
|
||||
|
||||
# # nElemHaWishes= sum(w['nWishes'] for w in hawishes)
|
||||
|
||||
# # %%
|
||||
|
||||
@ -108,14 +108,14 @@ def getVal(v):
|
||||
|
||||
|
||||
# basescenario = Scenario.objects.get(id=9306)
|
||||
basescenario = Scenario.objects.get(id=9279)
|
||||
# scenario = Scenario.objects.get(id=9308)
|
||||
basescenario = Scenario.objects.get(id=9414)
|
||||
|
||||
s2 = basescenario.id
|
||||
user_name = 'md'
|
||||
user_is_staff = True
|
||||
runMode = 'New'
|
||||
localsearch_time = 60
|
||||
localsearch_time = 0
|
||||
RUN_ENV = 'local'
|
||||
SOLVER = 'xpress'
|
||||
|
||||
|
||||
22
uefa/uefa_api/api.py
Normal file
22
uefa/uefa_api/api.py
Normal file
@ -0,0 +1,22 @@
|
||||
# %%
|
||||
import requests
|
||||
|
||||
|
||||
prod_url = 'https://uefadigitalapi.developer.azure-api.net'
|
||||
prod_primary_key = '7dfa861240aa40f8a834990c24f1a66d'
|
||||
prod_secondary_key = '4451dcc1ad4f41b2aa6af96cc5a1256a'
|
||||
pre_url = 'https://uefadigitalapipre.developer.azure-api.net'
|
||||
pre_primary_key = '1decf93425944f8b9e6dc7226a3b8477'
|
||||
pre_secondary_key = '14771f5c67b74836a59f777cb543cc0f'
|
||||
|
||||
|
||||
|
||||
r=requests.get("https://api.digital.uefa.com/comp/v2/competitions/1/seasons", headers={"Cache-Control":"no-cache","Ocp-Apim-Subscription-Key":"7dfa861240aa40f8a834990c24f1a66d"})
|
||||
# %%
|
||||
r.json()
|
||||
# %%
|
||||
|
||||
r=requests.get("https://api.pre.digital.uefa.com/comp/v2/competitions/1/seasons", headers={"Cache-Control":"no-cache","Ocp-Apim-Subscription-Key":"14771f5c67b74836a59f777cb543cc0f"})
|
||||
# %%
|
||||
r.json()
|
||||
# %%
|
||||
Loading…
x
Reference in New Issue
Block a user