188 lines
No EOL
7.4 KiB
Python
188 lines
No EOL
7.4 KiB
Python
import math
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from bisect import bisect
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from operator import attrgetter, itemgetter
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from django.db import transaction
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from django.db.models import Count, OuterRef, Subquery
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from django.db.models.functions import Coalesce
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from django.utils import timezone
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def tie_ranker(iterable, key=attrgetter('points')):
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rank = 0
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delta = 1
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last = None
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buf = []
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for item in iterable:
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new = key(item)
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if new != last:
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for _ in buf:
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yield rank + (delta - 1) / 2.0
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rank += delta
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delta = 0
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buf = []
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delta += 1
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buf.append(item)
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last = key(item)
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for _ in buf:
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yield rank + (delta - 1) / 2.0
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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, is_disqualified):
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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 is_disqualified[i]:
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# DQed users can manipulate TopCoder ratings to get higher volatility in order to increase their rating
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# later on, prohibit this by ensuring their volatility never increases in this situation
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new_volatility[i] = min(new_volatility[i], old_volatility[i])
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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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rating_subquery = Rating.objects.filter(user=OuterRef('user'))
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rating_sorted = rating_subquery.order_by('-contest__end_time')
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users = contest.users.order_by('is_disqualified', '-score', 'cumtime', 'tiebreaker') \
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.annotate(submissions=Count('submission'),
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last_rating=Coalesce(Subquery(rating_sorted.values('rating')[:1]), 1200),
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volatility=Coalesce(Subquery(rating_sorted.values('volatility')[:1]), 535),
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times=Coalesce(Subquery(rating_subquery.order_by().values('user_id')
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.annotate(count=Count('id')).values('count')), 0)) \
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.exclude(user_id__in=contest.rate_exclude.all()) \
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.filter(virtual=0).values('id', 'user_id', 'score', 'cumtime', 'tiebreaker', 'is_disqualified',
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'last_rating', 'volatility', 'times')
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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(last_rating__lt=contest.rating_floor)
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if contest.rating_ceiling is not None:
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users = users.exclude(last_rating__gt=contest.rating_ceiling)
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users = list(users)
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participation_ids = list(map(itemgetter('id'), users))
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user_ids = list(map(itemgetter('user_id'), users))
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is_disqualified = list(map(itemgetter('is_disqualified'), users))
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ranking = list(tie_ranker(users, key=itemgetter('score', 'cumtime', 'tiebreaker')))
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old_rating = list(map(itemgetter('last_rating'), users))
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old_volatility = list(map(itemgetter('volatility'), users))
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times_ranked = list(map(itemgetter('times'), users))
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rating, volatility = recalculate_ratings(old_rating, old_volatility, ranking, times_ranked, is_disqualified)
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now = timezone.now()
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ratings = [Rating(user_id=i, contest=contest, rating=r, volatility=v, last_rated=now, participation_id=p, rank=z)
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for i, p, r, v, z in zip(user_ids, participation_ids, rating, volatility, ranking)]
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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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Profile.objects.filter(contest_history__contest=contest, contest_history__virtual=0) \
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.update(rating=Subquery(Rating.objects.filter(user=OuterRef('id')) \
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.order_by('-contest__end_time').values('rating')[:1]))
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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) |