import math
from operator import attrgetter, itemgetter
from django.db import migrations, models
from django.db.models import Count, OuterRef, Subquery
from django.db.models.functions import Coalesce
from django.utils import timezone
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 rational_approximation(t):
# Abramowitz and Stegun formula 26.2.23.
# The absolute value of the error should be less than 4.5 e-4.
c = [2.515517, 0.802853, 0.010328]
d = [1.432788, 0.189269, 0.001308]
numerator = (c[2] * t + c[1]) * t + c[0]
denominator = ((d[2] * t + d[1]) * t + d[0]) * t + 1.0
return t - numerator / denominator
def normal_CDF_inverse(p):
assert 0.0 < p < 1
# See article above for explanation of this section.
if p < 0.5:
# F^-1(p) = - G^-1(p)
return -rational_approximation(math.sqrt(-2.0 * math.log(p)))
else:
# F^-1(p) = G^-1(1-p)
return rational_approximation(math.sqrt(-2.0 * math.log(1.0 - p)))
def WP(RA, RB, VA, VB):
return (math.erf((RB - RA) / math.sqrt(2 * (VA * VA + VB * VB))) + 1) / 2.0
def recalculate_ratings(
old_rating, old_volatility, actual_rank, times_rated, is_disqualified
):
# actual_rank: 1 is first place, N is last place
# if there are ties, use the average of places (if places 2, 3, 4, 5 tie, use 3.5 for all of them)
N = len(old_rating)
new_rating = old_rating[:]
new_volatility = old_volatility[:]
if N <= 1:
return new_rating, new_volatility
ranking = list(range(N))
ranking.sort(key=old_rating.__getitem__, reverse=True)
ave_rating = float(sum(old_rating)) / N
sum1 = sum(i * i for i in old_volatility) / N
sum2 = sum((i - ave_rating) ** 2 for i in old_rating) / (N - 1)
CF = math.sqrt(sum1 + sum2)
for i in range(N):
ERank = 0.5
for j in range(N):
ERank += WP(
old_rating[i], old_rating[j], old_volatility[i], old_volatility[j]
)
EPerf = -normal_CDF_inverse((ERank - 0.5) / N)
APerf = -normal_CDF_inverse((actual_rank[i] - 0.5) / N)
PerfAs = old_rating[i] + CF * (APerf - EPerf)
Weight = 1.0 / (1 - (0.42 / (times_rated[i] + 1) + 0.18)) - 1.0
if old_rating[i] > 2500:
Weight *= 0.8
elif old_rating[i] >= 2000:
Weight *= 0.9
Cap = 150.0 + 1500.0 / (times_rated[i] + 2)
new_rating[i] = (old_rating[i] + Weight * PerfAs) / (1.0 + Weight)
if abs(old_rating[i] - new_rating[i]) > Cap:
if old_rating[i] < new_rating[i]:
new_rating[i] = old_rating[i] + Cap
else:
new_rating[i] = old_rating[i] - Cap
if times_rated[i] == 0:
new_volatility[i] = 385
else:
new_volatility[i] = math.sqrt(
((new_rating[i] - old_rating[i]) ** 2) / Weight
+ (old_volatility[i] ** 2) / (Weight + 1)
)
if is_disqualified[i]:
# DQed users can manipulate TopCoder ratings to get higher volatility in order to increase their rating
# later on, prohibit this by ensuring their volatility never increases in this situation
new_volatility[i] = min(new_volatility[i], old_volatility[i])
# try to keep the sum of ratings constant
adjust = float(sum(old_rating) - sum(new_rating)) / N
new_rating = list(map(adjust.__add__, new_rating))
# inflate a little if we have to so people who placed first don't lose rating
best_rank = min(actual_rank)
for i in range(N):
if (
abs(actual_rank[i] - best_rank) <= 1e-3
and new_rating[i] < old_rating[i] + 1
):
new_rating[i] = old_rating[i] + 1
return list(map(int, map(round, new_rating))), list(
map(int, map(round, new_volatility))
)
def tc_rate_contest(contest, 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]), 1200),
volatility=Coalesce(Subquery(rating_sorted.values("volatility")[:1]), 535),
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",
"is_disqualified",
"last_rating",
"volatility",
"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))
is_disqualified = list(map(itemgetter("is_disqualified"), users))
ranking = list(tie_ranker(users, key=itemgetter("score", "cumtime", "tiebreaker")))
old_rating = list(map(itemgetter("last_rating"), users))
old_volatility = list(map(itemgetter("volatility"), users))
times_ranked = list(map(itemgetter("times"), users))
rating, volatility = recalculate_ratings(
old_rating, old_volatility, ranking, times_ranked, is_disqualified
)
now = timezone.now()
ratings = [
Rating(
user_id=i,
contest=contest,
rating=r,
volatility=v,
last_rated=now,
participation_id=p,
rank=z,
)
for i, p, r, v, z in zip(
user_ids, participation_ids, rating, volatility, ranking
)
]
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]
)
)
# inspired by rate_all_view
def rate_tc(apps, schema_editor):
Contest = apps.get_model("judge", "Contest")
Rating = apps.get_model("judge", "Rating")
Profile = apps.get_model("judge", "Profile")
with schema_editor.connection.cursor() as cursor:
cursor.execute("TRUNCATE TABLE `%s`" % Rating._meta.db_table)
Profile.objects.update(rating=None)
for contest in Contest.objects.filter(
is_rated=True, end_time__lte=timezone.now()
).order_by("end_time"):
tc_rate_contest(contest, Rating, Profile)
# inspired by rate_all_view
def rate_elo_mmr(apps, schema_editor):
Rating = apps.get_model("judge", "Rating")
Profile = apps.get_model("judge", "Profile")
with schema_editor.connection.cursor() as cursor:
cursor.execute("TRUNCATE TABLE `%s`" % Rating._meta.db_table)
Profile.objects.update(rating=None)
# Don't populate Rating
class Migration(migrations.Migration):
dependencies = [
("judge", "0117_auto_20211209_0612"),
]
operations = [
migrations.RunPython(migrations.RunPython.noop, rate_tc, atomic=True),
migrations.AddField(
model_name="rating",
name="mean",
field=models.FloatField(verbose_name="raw rating"),
),
migrations.AddField(
model_name="rating",
name="performance",
field=models.FloatField(verbose_name="contest performance"),
),
migrations.RemoveField(
model_name="rating",
name="volatility",
field=models.IntegerField(verbose_name="volatility"),
),
migrations.RunPython(rate_elo_mmr, migrations.RunPython.noop, atomic=True),
]