181 lines
7.5 KiB
Python
181 lines
7.5 KiB
Python
import math
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from bisect import bisect
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from operator import itemgetter
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from django.db import connection, transaction
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from django.db.models import Count
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from django.utils import timezone
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from judge.utils.ranker import tie_ranker
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def rational_approximation(t):
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# Abramowitz and Stegun formula 26.2.23.
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# The absolute value of the error should be less than 4.5 e-4.
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c = [2.515517, 0.802853, 0.010328]
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d = [1.432788, 0.189269, 0.001308]
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numerator = (c[2] * t + c[1]) * t + c[0]
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denominator = ((d[2] * t + d[1]) * t + d[0]) * t + 1.0
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return t - numerator / denominator
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def normal_CDF_inverse(p):
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assert 0.0 < p < 1
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# See article above for explanation of this section.
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if p < 0.5:
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# F^-1(p) = - G^-1(p)
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return -rational_approximation(math.sqrt(-2.0 * math.log(p)))
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else:
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# F^-1(p) = G^-1(1-p)
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return rational_approximation(math.sqrt(-2.0 * math.log(1.0 - p)))
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def WP(RA, RB, VA, VB):
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return (math.erf((RB - RA) / math.sqrt(2 * (VA * VA + VB * VB))) + 1) / 2.0
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def recalculate_ratings(old_rating, old_volatility, actual_rank, times_rated):
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# actual_rank: 1 is first place, N is last place
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# if there are ties, use the average of places (if places 2, 3, 4, 5 tie, use 3.5 for all of them)
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N = len(old_rating)
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new_rating = old_rating[:]
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new_volatility = old_volatility[:]
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if N <= 1:
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return new_rating, new_volatility
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ranking = list(range(N))
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ranking.sort(key=old_rating.__getitem__, reverse=True)
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ave_rating = float(sum(old_rating)) / N
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sum1 = sum(i * i for i in old_volatility) / N
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sum2 = sum((i - ave_rating) ** 2 for i in old_rating) / (N - 1)
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CF = math.sqrt(sum1 + sum2)
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for i in range(N):
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ERank = 0.5
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for j in range(N):
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ERank += WP(old_rating[i], old_rating[j], old_volatility[i], old_volatility[j])
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EPerf = -normal_CDF_inverse((ERank - 0.5) / N)
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APerf = -normal_CDF_inverse((actual_rank[i] - 0.5) / N)
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PerfAs = old_rating[i] + CF * (APerf - EPerf)
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Weight = 1.0 / (1 - (0.42 / (times_rated[i] + 1) + 0.18)) - 1.0
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if old_rating[i] > 2500:
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Weight *= 0.8
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elif old_rating[i] >= 2000:
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Weight *= 0.9
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Cap = 150.0 + 1500.0 / (times_rated[i] + 2)
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new_rating[i] = (old_rating[i] + Weight * PerfAs) / (1.0 + Weight)
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if times_rated[i] == 0:
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new_volatility[i] = 385
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else:
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new_volatility[i] = math.sqrt(((new_rating[i] - old_rating[i]) ** 2) / Weight +
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(old_volatility[i] ** 2) / (Weight + 1))
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if abs(old_rating[i] - new_rating[i]) > Cap:
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if old_rating[i] < new_rating[i]:
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new_rating[i] = old_rating[i] + Cap
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else:
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new_rating[i] = old_rating[i] - Cap
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# try to keep the sum of ratings constant
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adjust = float(sum(old_rating) - sum(new_rating)) / N
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new_rating = list(map(adjust.__add__, new_rating))
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# inflate a little if we have to so people who placed first don't lose rating
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best_rank = min(actual_rank)
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for i in range(N):
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if abs(actual_rank[i] - best_rank) <= 1e-3 and new_rating[i] < old_rating[i] + 1:
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new_rating[i] = old_rating[i] + 1
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return list(map(int, map(round, new_rating))), list(map(int, map(round, new_volatility)))
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def rate_contest(contest):
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from judge.models import Rating, Profile
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cursor = connection.cursor()
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cursor.execute('''
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SELECT judge_rating.user_id, judge_rating.rating, judge_rating.volatility, r.times
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FROM judge_rating INNER JOIN
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judge_contest ON (judge_contest.id = judge_rating.contest_id) INNER JOIN (
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SELECT judge_rating.user_id AS id, MAX(judge_contest.end_time) AS last_time,
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COUNT(judge_rating.user_id) AS times
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FROM judge_contestparticipation INNER JOIN
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judge_rating ON (judge_rating.user_id = judge_contestparticipation.user_id) INNER JOIN
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judge_contest ON (judge_contest.id = judge_rating.contest_id)
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WHERE judge_contestparticipation.contest_id = %s AND judge_contest.end_time < %s AND
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judge_contestparticipation.user_id NOT IN (
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SELECT profile_id FROM judge_contest_rate_exclude WHERE contest_id = %s
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) AND judge_contestparticipation.virtual = 0
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GROUP BY judge_rating.user_id
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ORDER BY judge_contestparticipation.score DESC, judge_contestparticipation.cumtime ASC
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) AS r ON (judge_rating.user_id = r.id AND judge_contest.end_time = r.last_time)
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''', (contest.id, contest.end_time, contest.id))
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data = {user: (rating, volatility, times) for user, rating, volatility, times in cursor.fetchall()}
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cursor.close()
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users = contest.users.order_by('is_disqualified', '-score', 'cumtime').annotate(submissions=Count('submission')) \
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.exclude(user_id__in=contest.rate_exclude.all()).filter(virtual=0, user__is_unlisted=False) \
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.values_list('id', 'user_id', 'score', 'cumtime')
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if not contest.rate_all:
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users = users.filter(submissions__gt=0)
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if contest.rating_floor is not None:
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users = users.exclude(user__rating__lt=contest.rating_floor)
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if contest.rating_ceiling is not None:
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users = users.exclude(user__rating__gt=contest.rating_ceiling)
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users = list(tie_ranker(users, key=itemgetter(2, 3)))
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participation_ids = [user[1][0] for user in users]
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user_ids = [user[1][1] for user in users]
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ranking = list(map(itemgetter(0), users))
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old_data = [data.get(user, (1200, 535, 0)) for user in user_ids]
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old_rating = list(map(itemgetter(0), old_data))
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old_volatility = list(map(itemgetter(1), old_data))
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times_ranked = list(map(itemgetter(2), old_data))
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rating, volatility = recalculate_ratings(old_rating, old_volatility, ranking, times_ranked)
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now = timezone.now()
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ratings = [Rating(user_id=id, contest=contest, rating=r, volatility=v, last_rated=now, participation_id=p, rank=z)
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for id, p, r, v, z in zip(user_ids, participation_ids, rating, volatility, ranking)]
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cursor = connection.cursor()
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cursor.execute('CREATE TEMPORARY TABLE _profile_rating_update(id integer, rating integer)')
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cursor.executemany('INSERT INTO _profile_rating_update VALUES (%s, %s)', list(zip(user_ids, rating)))
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with transaction.atomic():
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Rating.objects.filter(contest=contest).delete()
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Rating.objects.bulk_create(ratings)
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cursor.execute('''
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UPDATE `%s` p INNER JOIN `_profile_rating_update` tmp ON (p.id = tmp.id)
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SET p.rating = tmp.rating
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''' % Profile._meta.db_table)
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cursor.execute('DROP TABLE _profile_rating_update')
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cursor.close()
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return old_rating, old_volatility, ranking, times_ranked, rating, volatility
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RATING_LEVELS = ['Newbie', 'Amateur', 'Expert', 'Candidate Master', 'Master', 'Grandmaster', 'Target']
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RATING_VALUES = [1000, 1200, 1500, 1800, 2200, 3000]
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RATING_CLASS = ['rate-newbie', 'rate-amateur', 'rate-expert', 'rate-candidate-master',
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'rate-master', 'rate-grandmaster', 'rate-target']
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def rating_level(rating):
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return bisect(RATING_VALUES, rating)
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def rating_name(rating):
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return RATING_LEVELS[rating_level(rating)]
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def rating_class(rating):
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return RATING_CLASS[rating_level(rating)]
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def rating_progress(rating):
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level = bisect(RATING_VALUES, rating)
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if level == len(RATING_VALUES):
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return 1.0
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prev = 0 if not level else RATING_VALUES[level - 1]
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next = RATING_VALUES[level]
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return (rating - prev + 0.0) / (next - prev)
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