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Running
on
Zero
File size: 12,162 Bytes
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import argparse
from collections import defaultdict
import datetime
import json
import math
import pickle
from pytz import timezone
import numpy as np
import pandas as pd
import plotly.express as px
from tqdm import tqdm
from .model_registry import get_model_info
from .basic_stats import get_log_files
from .clean_battle_data import clean_battle_data
pd.options.display.float_format = "{:.2f}".format
def compute_elo(battles, K=4, SCALE=400, BASE=10, INIT_RATING=1000):
rating = defaultdict(lambda: INIT_RATING)
for rd, model_a, model_b, winner in battles[
["model_a", "model_b", "winner"]
].itertuples():
ra = rating[model_a]
rb = rating[model_b]
ea = 1 / (1 + BASE ** ((rb - ra) / SCALE))
eb = 1 / (1 + BASE ** ((ra - rb) / SCALE))
if winner == "model_a":
sa = 1
elif winner == "model_b":
sa = 0
elif winner == "tie" or winner == "tie (bothbad)":
sa = 0.5
else:
raise Exception(f"unexpected vote {winner}")
rating[model_a] += K * (sa - ea)
rating[model_b] += K * (1 - sa - eb)
return dict(rating)
def get_bootstrap_result(battles, func_compute_elo, num_round=1000):
rows = []
for i in tqdm(range(num_round), desc="bootstrap"):
tmp_battles = battles.sample(frac=1.0, replace=True)
rows.append(func_compute_elo(tmp_battles))
df = pd.DataFrame(rows)
return df[df.median().sort_values(ascending=False).index]
def compute_elo_mle_with_tie(df, SCALE=400, BASE=10, INIT_RATING=1000):
from sklearn.linear_model import LogisticRegression
models = pd.concat([df["model_a"], df["model_b"]]).unique()
models = pd.Series(np.arange(len(models)), index=models)
# duplicate battles
df = pd.concat([df, df], ignore_index=True)
p = len(models.index)
n = df.shape[0]
X = np.zeros([n, p])
X[np.arange(n), models[df["model_a"]]] = +math.log(BASE)
X[np.arange(n), models[df["model_b"]]] = -math.log(BASE)
# one A win => two A win
Y = np.zeros(n)
Y[df["winner"] == "model_a"] = 1.0
# one tie => one A win + one B win
# find tie + tie (both bad) index
tie_idx = (df["winner"] == "tie") | (df["winner"] == "tie (bothbad)")
tie_idx[len(tie_idx) // 2 :] = False
Y[tie_idx] = 1.0
lr = LogisticRegression(fit_intercept=False)
lr.fit(X, Y)
elo_scores = SCALE * lr.coef_[0] + INIT_RATING
# calibrate llama-13b to 800 if applicable
if "llama-13b" in models.index:
elo_scores += 800 - elo_scores[models["llama-13b"]]
return pd.Series(elo_scores, index=models.index).sort_values(ascending=False)
def get_median_elo_from_bootstrap(bootstrap_df):
median = dict(bootstrap_df.quantile(0.5))
median = {k: int(v + 0.5) for k, v in median.items()}
return median
def compute_pairwise_win_fraction(battles, model_order, limit_show_number=None):
# Times each model wins as Model A
a_win_ptbl = pd.pivot_table(
battles[battles["winner"] == "model_a"],
index="model_a",
columns="model_b",
aggfunc="size",
fill_value=0,
)
# Table counting times each model wins as Model B
b_win_ptbl = pd.pivot_table(
battles[battles["winner"] == "model_b"],
index="model_a",
columns="model_b",
aggfunc="size",
fill_value=0,
)
# Table counting number of A-B pairs
num_battles_ptbl = pd.pivot_table(
battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0
)
# Computing the proportion of wins for each model as A and as B
# against all other models
row_beats_col_freq = (a_win_ptbl + b_win_ptbl.T) / (
num_battles_ptbl + num_battles_ptbl.T
)
if model_order is None:
prop_wins = row_beats_col_freq.mean(axis=1).sort_values(ascending=False)
model_order = list(prop_wins.keys())
if limit_show_number is not None:
model_order = model_order[:limit_show_number]
# Arrange ordering according to proprition of wins
row_beats_col = row_beats_col_freq.loc[model_order, model_order]
return row_beats_col
def visualize_leaderboard_table(rating):
models = list(rating.keys())
models.sort(key=lambda k: -rating[k])
emoji_dict = {
1: "π₯",
2: "π₯",
3: "π₯",
}
md = ""
md += "| Rank | Model | Elo Rating | Description |\n"
md += "| --- | --- | --- | --- |\n"
for i, model in enumerate(models):
rank = i + 1
minfo = get_model_info(model)
emoji = emoji_dict.get(rank, "")
md += f"| {rank} | {emoji} [{model}]({minfo.link}) | {rating[model]:.0f} | {minfo.description} |\n"
return md
def visualize_pairwise_win_fraction(battles, model_order):
row_beats_col = compute_pairwise_win_fraction(battles, model_order)
fig = px.imshow(
row_beats_col,
color_continuous_scale="RdBu",
text_auto=".2f",
height=700,
width=700,
)
fig.update_layout(
xaxis_title="Model B",
yaxis_title="Model A",
xaxis_side="top",
title_y=0.07,
title_x=0.5,
)
fig.update_traces(
hovertemplate="Model A: %{y}<br>Model B: %{x}<br>Fraction of A Wins: %{z}<extra></extra>"
)
return fig
def visualize_battle_count(battles, model_order):
ptbl = pd.pivot_table(
battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0
)
battle_counts = ptbl + ptbl.T
fig = px.imshow(
battle_counts.loc[model_order, model_order],
text_auto=True,
height=700,
width=700,
)
fig.update_layout(
xaxis_title="Model B",
yaxis_title="Model A",
xaxis_side="top",
title_y=0.07,
title_x=0.5,
)
fig.update_traces(
hovertemplate="Model A: %{y}<br>Model B: %{x}<br>Count: %{z}<extra></extra>"
)
return fig
def visualize_average_win_rate(battles, limit_show_number):
row_beats_col_freq = compute_pairwise_win_fraction(
battles, None, limit_show_number=limit_show_number
)
fig = px.bar(
row_beats_col_freq.mean(axis=1).sort_values(ascending=False),
text_auto=".2f",
height=500,
width=700,
)
fig.update_layout(
yaxis_title="Average Win Rate", xaxis_title="Model", showlegend=False
)
return fig
def visualize_bootstrap_elo_rating(df, df_final, limit_show_number):
bars = (
pd.DataFrame(
dict(
lower=df.quantile(0.025),
rating=df_final,
upper=df.quantile(0.975),
)
)
.reset_index(names="model")
.sort_values("rating", ascending=False)
)
bars = bars[:limit_show_number]
bars["error_y"] = bars["upper"] - bars["rating"]
bars["error_y_minus"] = bars["rating"] - bars["lower"]
bars["rating_rounded"] = np.round(bars["rating"], 2)
fig = px.scatter(
bars,
x="model",
y="rating",
error_y="error_y",
error_y_minus="error_y_minus",
text="rating_rounded",
height=500,
width=700,
)
fig.update_layout(xaxis_title="Model", yaxis_title="Rating")
return fig
def report_elo_analysis_results(battles_json, rating_system="bt", num_bootstrap=100, anony_only=True):
battles = pd.DataFrame(battles_json)
battles = battles.sort_values(ascending=True, by=["tstamp"])
# Only use anonymous votes
if anony_only:
battles = battles[battles["anony"]].reset_index(drop=True)
battles_no_ties = battles[~battles["winner"].str.contains("tie")]
# Online update
elo_rating_online = compute_elo(battles)
if rating_system == "bt":
bootstrap_df = get_bootstrap_result(
battles, compute_elo_mle_with_tie, num_round=num_bootstrap
)
elo_rating_final = compute_elo_mle_with_tie(battles)
elif rating_system == "elo":
bootstrap_df = get_bootstrap_result(
battles, compute_elo, num_round=num_bootstrap
)
elo_rating_median = get_median_elo_from_bootstrap(bootstrap_df)
elo_rating_final = elo_rating_median
model_order = list(elo_rating_final.keys())
model_order.sort(key=lambda k: -elo_rating_final[k])
limit_show_number = 25 # limit show number to make plots smaller
model_order = model_order[:limit_show_number]
# leaderboard_table_df: elo rating, variance, 95% interval, number of battles
leaderboard_table_df = pd.DataFrame(
{
"rating": elo_rating_final,
"variance": bootstrap_df.var(),
"rating_q975": bootstrap_df.quantile(0.975),
"rating_q025": bootstrap_df.quantile(0.025),
"num_battles": battles["model_a"].value_counts()
+ battles["model_b"].value_counts(),
}
)
# Plots
leaderboard_table = visualize_leaderboard_table(elo_rating_final)
win_fraction_heatmap = visualize_pairwise_win_fraction(battles_no_ties, model_order)
battle_count_heatmap = visualize_battle_count(battles_no_ties, model_order)
average_win_rate_bar = visualize_average_win_rate(
battles_no_ties, limit_show_number
)
bootstrap_elo_rating = visualize_bootstrap_elo_rating(
bootstrap_df, elo_rating_final, limit_show_number
)
last_updated_tstamp = battles["tstamp"].max()
last_updated_datetime = datetime.datetime.fromtimestamp(
last_updated_tstamp, tz=timezone("US/Pacific")
).strftime("%Y-%m-%d %H:%M:%S %Z")
return {
"rating_system": rating_system,
"elo_rating_online": elo_rating_online,
"elo_rating_final": elo_rating_final,
"leaderboard_table": leaderboard_table,
"win_fraction_heatmap": win_fraction_heatmap,
"battle_count_heatmap": battle_count_heatmap,
"average_win_rate_bar": average_win_rate_bar,
"bootstrap_elo_rating": bootstrap_elo_rating,
"last_updated_datetime": last_updated_datetime,
"last_updated_tstamp": last_updated_tstamp,
"bootstrap_df": bootstrap_df,
"leaderboard_table_df": leaderboard_table_df,
}
def pretty_print_elo_rating(rating):
model_order = list(rating.keys())
model_order.sort(key=lambda k: -rating[k])
for i, model in enumerate(model_order):
print(f"{i+1:2d}, {model:25s}, {rating[model]:.0f}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--clean-battle-file", type=str)
parser.add_argument("--max-num-files", type=int)
parser.add_argument("--num-bootstrap", type=int, default=100)
parser.add_argument(
"--rating-system", type=str, choices=["bt", "elo"], default="bt"
)
parser.add_argument("--exclude-tie", action="store_true", default=False)
args = parser.parse_args()
np.random.seed(42)
if args.clean_battle_file:
# Read data from a cleaned battle files
battles = pd.read_json(args.clean_battle_file)
else:
# Read data from all log files
log_files = get_log_files(args.max_num_files)
battles = clean_battle_data(log_files)
anony_results = report_elo_analysis_results(
battles, rating_system=args.rating_system, num_bootstrap=args.num_bootstrap, anony_only=True
)
full_results = report_elo_analysis_results(
battles, rating_system=args.rating_system, num_bootstrap=args.num_bootstrap, anony_only=False
)
print("# Online Elo")
pretty_print_elo_rating(anony_results["elo_rating_online"])
print("# Median")
pretty_print_elo_rating(anony_results["elo_rating_final"])
print(f"last update : {anony_results['last_updated_datetime']}")
last_updated_tstamp = anony_results["last_updated_tstamp"]
cutoff_date = datetime.datetime.fromtimestamp(
last_updated_tstamp, tz=timezone("US/Pacific")
).strftime("%Y%m%d")
results = {
"anony": anony_results,
"full": full_results,
}
with open(f"elo_results_{cutoff_date}.pkl", "wb") as fout:
pickle.dump(results, fout)
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