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Running
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CPU Upgrade
File size: 2,094 Bytes
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from .config import *
from .db import *
from .models import *
import pandas as pd
def get_leaderboard(reveal_prelim = False, hide_battle_votes = False):
conn = get_db()
cursor = conn.cursor()
if hide_battle_votes:
sql = '''
SELECT m.name,
SUM(CASE WHEN v.username NOT LIKE '%_battle' AND v.vote = 1 THEN 1 ELSE 0 END) as upvote,
SUM(CASE WHEN v.username NOT LIKE '%_battle' AND v.vote = -1 THEN 1 ELSE 0 END) as downvote
FROM model m
LEFT JOIN vote v ON m.name = v.model
GROUP BY m.name
'''
else:
sql = '''
SELECT name,
SUM(CASE WHEN vote = 1 THEN 1 ELSE 0 END) as upvote,
SUM(CASE WHEN vote = -1 THEN 1 ELSE 0 END) as downvote
FROM model
LEFT JOIN vote ON model.name = vote.model
GROUP BY name
'''
cursor.execute(sql)
data = cursor.fetchall()
df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote'])
df['name'] = df['name'].replace(model_names)
df['votes'] = df['upvote'] + df['downvote']
# Filter out rows with insufficient votes if not revealing preliminary results
if not reveal_prelim:
df = df[df['votes'] > 500]
## ELO SCORE
df['score'] = 1200
for i in range(len(df)):
for j in range(len(df)):
if i != j:
expected_a = 1 / (1 + 10 ** ((df['score'][j] - df['score'][i]) / 400))
expected_b = 1 / (1 + 10 ** ((df['score'][i] - df['score'][j]) / 400))
actual_a = df['upvote'][i] / df['votes'][i] if df['votes'][i] > 0 else 0.5
actual_b = df['upvote'][j] / df['votes'][j] if df['votes'][j] > 0 else 0.5
df.at[i, 'score'] += 32 * (actual_a - expected_a)
df.at[j, 'score'] += 32 * (actual_b - expected_b)
df['score'] = round(df['score'])
## ELO SCORE
df = df.sort_values(by='score', ascending=False)
df['order'] = ['#' + str(i + 1) for i in range(len(df))]
df = df[['order', 'name', 'score', 'votes']]
return df
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