216 lines
No EOL
7.9 KiB
Python
216 lines
No EOL
7.9 KiB
Python
from bisect import bisect
|
|
from math import pi, sqrt, tanh
|
|
from operator import attrgetter, itemgetter
|
|
|
|
from django.db import transaction
|
|
from django.db.models import Count, OuterRef, Subquery
|
|
from django.db.models.functions import Coalesce
|
|
from django.utils import timezone
|
|
|
|
|
|
BETA2 = 328.33 ** 2
|
|
RATING_INIT = 1200 # Newcomer's rating when applying the rating floor/ceiling
|
|
MEAN_INIT = 1400.
|
|
VAR_INIT = 250**2 * (BETA2 / 212**2)
|
|
SD_INIT = sqrt(VAR_INIT)
|
|
VALID_RANGE = MEAN_INIT - 20 * SD_INIT, MEAN_INIT + 20 * SD_INIT
|
|
VAR_PER_CONTEST = 1219.047619 * (BETA2 / 212**2)
|
|
VAR_LIM = (sqrt(VAR_PER_CONTEST**2 + 4 * BETA2 * VAR_PER_CONTEST) - VAR_PER_CONTEST) / 2
|
|
SD_LIM = sqrt(VAR_LIM)
|
|
TANH_C = sqrt(3) / pi
|
|
|
|
|
|
def tie_ranker(iterable, key=attrgetter('points')):
|
|
rank = 0
|
|
delta = 1
|
|
last = None
|
|
buf = []
|
|
for item in iterable:
|
|
new = key(item)
|
|
if new != last:
|
|
for _ in buf:
|
|
yield rank + (delta - 1) / 2.0
|
|
rank += delta
|
|
delta = 0
|
|
buf = []
|
|
delta += 1
|
|
buf.append(item)
|
|
last = key(item)
|
|
for _ in buf:
|
|
yield rank + (delta - 1) / 2.0
|
|
|
|
|
|
def eval_tanhs(tanh_terms, x):
|
|
return sum((wt / sd) * tanh((x - mu) / (2 * sd)) for mu, sd, wt in tanh_terms)
|
|
|
|
|
|
def solve(tanh_terms, y_tg, lin_factor=0, bounds=VALID_RANGE):
|
|
L, R = bounds
|
|
Ly, Ry = None, None
|
|
while R - L > 2:
|
|
x = (L + R) / 2
|
|
y = lin_factor * x + eval_tanhs(tanh_terms, x)
|
|
if y > y_tg:
|
|
R, Ry = x, y
|
|
elif y < y_tg:
|
|
L, Ly = x, y
|
|
else:
|
|
return x
|
|
# Use linear interpolation to be slightly more accurate.
|
|
if Ly is None:
|
|
Ly = lin_factor * L + eval_tanhs(tanh_terms, L)
|
|
if y_tg <= Ly:
|
|
return L
|
|
if Ry is None:
|
|
Ry = lin_factor * R + eval_tanhs(tanh_terms, R)
|
|
if y_tg >= Ry:
|
|
return R
|
|
ratio = (y_tg - Ly) / (Ry - Ly)
|
|
return L * (1 - ratio) + R * ratio
|
|
|
|
|
|
def get_var(times_ranked, cache=[VAR_INIT]):
|
|
while times_ranked >= len(cache):
|
|
next_var = 1. / (1. / (cache[-1] + VAR_PER_CONTEST) + 1. / BETA2)
|
|
cache.append(next_var)
|
|
return cache[times_ranked]
|
|
|
|
|
|
def recalculate_ratings(ranking, old_mean, times_ranked, historical_p):
|
|
n = len(ranking)
|
|
new_p = [0.] * n
|
|
new_mean = [0.] * n
|
|
|
|
# Note: pre-multiply delta by TANH_C to improve efficiency.
|
|
delta = [TANH_C * sqrt(get_var(t) + VAR_PER_CONTEST + BETA2) for t in times_ranked]
|
|
p_tanh_terms = [(m, d, 1) for m, d in zip(old_mean, delta)]
|
|
|
|
# Calculate performance at index i.
|
|
def solve_idx(i, bounds=VALID_RANGE):
|
|
r = ranking[i]
|
|
y_tg = 0
|
|
for d, s in zip(delta, ranking):
|
|
if s > r: # s loses to r
|
|
y_tg += 1. / d
|
|
elif s < r: # s beats r
|
|
y_tg -= 1. / d
|
|
# Otherwise, this is a tie that counts as half a win, as per Elo-MMR.
|
|
new_p[i] = solve(p_tanh_terms, y_tg, bounds=bounds)
|
|
|
|
# Fill all indices between i and j, inclusive. Use the fact that new_p is non-increasing.
|
|
def divconq(i, j):
|
|
if j - i > 1:
|
|
k = (i + j) // 2
|
|
solve_idx(k, bounds=(new_p[j], new_p[i]))
|
|
divconq(i, k)
|
|
divconq(k, j)
|
|
|
|
if n < 2:
|
|
new_p = list(old_mean)
|
|
new_mean = list(old_mean)
|
|
else:
|
|
# Calculate performance.
|
|
solve_idx(0)
|
|
solve_idx(n - 1)
|
|
divconq(0, n - 1)
|
|
|
|
# Calculate mean.
|
|
for i, r in enumerate(ranking):
|
|
tanh_terms = []
|
|
w_prev = 1.
|
|
w_sum = 0.
|
|
for j, h in enumerate([new_p[i]] + historical_p[i]):
|
|
gamma2 = (VAR_PER_CONTEST if j > 0 else 0)
|
|
h_var = get_var(times_ranked[i] + 1 - j)
|
|
k = h_var / (h_var + gamma2)
|
|
w = w_prev * k**2
|
|
# Future optimization: If j is around 20, then w < 1e-3 and it is possible to break early.
|
|
tanh_terms.append((h, sqrt(BETA2) * TANH_C, w))
|
|
w_prev = w
|
|
w_sum += w / BETA2
|
|
w0 = 1. / get_var(times_ranked[i] + 1) - w_sum
|
|
p0 = eval_tanhs(tanh_terms[1:], old_mean[i]) / w0 + old_mean[i]
|
|
new_mean[i] = solve(tanh_terms, w0 * p0, lin_factor=w0)
|
|
|
|
# Display a slightly lower rating to incentivize participation.
|
|
# As times_ranked increases, new_rating converges to new_mean.
|
|
new_rating = [max(1, round(m - (sqrt(get_var(t + 1)) - SD_LIM))) for m, t in zip(new_mean, times_ranked)]
|
|
|
|
return new_rating, new_mean, new_p
|
|
|
|
|
|
def rate_contest(contest):
|
|
from judge.models import Rating, Profile
|
|
|
|
rating_subquery = Rating.objects.filter(user=OuterRef('user'))
|
|
rating_sorted = rating_subquery.order_by('-contest__end_time')
|
|
users = contest.users.order_by('is_disqualified', '-score', 'cumtime', 'tiebreaker') \
|
|
.annotate(submissions=Count('submission'),
|
|
last_rating=Coalesce(Subquery(rating_sorted.values('rating')[:1]), RATING_INIT),
|
|
last_mean=Coalesce(Subquery(rating_sorted.values('mean')[:1]), MEAN_INIT),
|
|
times=Coalesce(Subquery(rating_subquery.order_by().values('user_id')
|
|
.annotate(count=Count('id')).values('count')), 0)) \
|
|
.exclude(user_id__in=contest.rate_exclude.all()) \
|
|
.filter(virtual=0).values('id', 'user_id', 'score', 'cumtime', 'tiebreaker',
|
|
'last_rating', 'last_mean', 'times')
|
|
if not contest.rate_all:
|
|
users = users.filter(submissions__gt=0)
|
|
if contest.rating_floor is not None:
|
|
users = users.exclude(last_rating__lt=contest.rating_floor)
|
|
if contest.rating_ceiling is not None:
|
|
users = users.exclude(last_rating__gt=contest.rating_ceiling)
|
|
|
|
users = list(users)
|
|
participation_ids = list(map(itemgetter('id'), users))
|
|
user_ids = list(map(itemgetter('user_id'), users))
|
|
ranking = list(tie_ranker(users, key=itemgetter('score', 'cumtime', 'tiebreaker')))
|
|
old_mean = list(map(itemgetter('last_mean'), users))
|
|
times_ranked = list(map(itemgetter('times'), users))
|
|
historical_p = [[] for _ in users]
|
|
|
|
user_id_to_idx = {uid: i for i, uid in enumerate(user_ids)}
|
|
for h in Rating.objects.filter(user_id__in=user_ids) \
|
|
.order_by('-contest__end_time') \
|
|
.values('user_id', 'performance'):
|
|
idx = user_id_to_idx[h['user_id']]
|
|
historical_p[idx].append(h['performance'])
|
|
|
|
rating, mean, performance = recalculate_ratings(ranking, old_mean, times_ranked, historical_p)
|
|
|
|
now = timezone.now()
|
|
ratings = [Rating(user_id=i, contest=contest, rating=r, mean=m, performance=perf,
|
|
last_rated=now, participation_id=pid, rank=z)
|
|
for i, pid, r, m, perf, z in zip(user_ids, participation_ids, rating, mean, performance, ranking)]
|
|
with transaction.atomic():
|
|
Rating.objects.bulk_create(ratings)
|
|
|
|
Profile.objects.filter(contest_history__contest=contest, contest_history__virtual=0).update(
|
|
rating=Subquery(Rating.objects.filter(user=OuterRef('id'))
|
|
.order_by('-contest__end_time').values('rating')[:1]))
|
|
|
|
|
|
RATING_LEVELS = ['Newbie', 'Amateur', 'Expert', 'Candidate Master', 'Master', 'Grandmaster', 'Target']
|
|
RATING_VALUES = [1000, 1300, 1600, 1900, 2400, 3000]
|
|
RATING_CLASS = ['rate-newbie', 'rate-amateur', 'rate-expert', 'rate-candidate-master',
|
|
'rate-master', 'rate-grandmaster', 'rate-target']
|
|
|
|
|
|
def rating_level(rating):
|
|
return bisect(RATING_VALUES, rating)
|
|
|
|
|
|
def rating_name(rating):
|
|
return RATING_LEVELS[rating_level(rating)]
|
|
|
|
|
|
def rating_class(rating):
|
|
return RATING_CLASS[rating_level(rating)]
|
|
|
|
|
|
def rating_progress(rating):
|
|
level = bisect(RATING_VALUES, rating)
|
|
if level == len(RATING_VALUES):
|
|
return 1.0
|
|
prev = 0 if not level else RATING_VALUES[level - 1]
|
|
next = RATING_VALUES[level]
|
|
return (rating - prev + 0.0) / (next - prev) |