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: "🥇", 2: "🥈", 3: "🥉", } 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}
Model B: %{x}
Fraction of A Wins: %{z}" ) 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}
Model B: %{x}
Count: %{z}" ) 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)