Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| from collections import defaultdict | |
| from dataclasses import dataclass | |
| from typing import Dict, List | |
| import numpy as np | |
| import pandas as pd | |
| from datasets import load_dataset | |
| from content import PLOT_1_TITLE, PLOT_2_TITLE, PLOT_3_TITLE, PLOT_4_TITLE | |
| from utils import make_clickable_model | |
| from visualizations import (get_bootstrap_result, switch_model_a_b, | |
| visualize_battle_count, visualize_bootstrap_scores, | |
| visualize_pairwise_win_fraction, | |
| visualize_rating_count) | |
| class EloEvalResult: | |
| model: str | |
| gpt_4_all: int | |
| human_all: int | |
| human_instruct: int | |
| human_code_instruct: int | |
| tie_allowed: bool | |
| def to_dict(self): | |
| base_model = f"{self.model}" | |
| data_dict = {} | |
| data_dict["Model"] = make_clickable_model(base_model) | |
| data_dict["GPT-4 (all)"] = self.gpt_4_all | |
| data_dict["Human (all)"] = self.human_all | |
| data_dict["Human (instruct)"] = self.human_instruct | |
| data_dict["Human (code-instruct)"] = self.human_code_instruct | |
| return data_dict | |
| def create_eval_df(df, tie_allowed): | |
| responses = [] | |
| for _, row in df.iterrows(): | |
| if row["status"] == "canceled": | |
| continue | |
| rating = row["response"]["annotations"]["Preference"] | |
| if rating == "NaN": | |
| continue | |
| scores = row["response"]["responses"] | |
| if any(s["Preference"] == "" for s in scores): | |
| continue | |
| response = { | |
| "id": row["task_id"], | |
| "prompt": row["params"]["templateVariables"]["prompt"], | |
| "model_a": row["params"]["templateVariables"]["modela"], | |
| "model_b": row["params"]["templateVariables"]["modelb"], | |
| "response_a": row["params"]["templateVariables"]["response1"], | |
| "response_b": row["params"]["templateVariables"]["response2"], | |
| "rating": int(rating), | |
| "ratings": [np.array([s["Preference"] for s in scores], dtype=np.int32)], | |
| } | |
| if tie_allowed: | |
| response["win"] = "model_a" if response["rating"] < 4 else "model_b" if response["rating"] > 5 else "tie" | |
| else: | |
| response["win"] = "model_a" if response["rating"] < 5 else "model_b" | |
| responses.append(response) | |
| return pd.DataFrame(responses) | |
| def create_eval_df_for_gpt(df, tie_allowed): | |
| responses = [] | |
| for _, row in df.iterrows(): | |
| response = { | |
| "id": row["review_id"], | |
| "prompt": row["question"], | |
| "model_a": row["model1"], | |
| "model_b": row["model2"], | |
| "response_a": row["answer1"], | |
| "response_b": row["answer2"], | |
| "rating": row["score"][0], | |
| } | |
| if tie_allowed: | |
| response["win"] = "model_a" if response["rating"] < 4 else "model_b" if response["rating"] > 5 else "tie" | |
| else: | |
| response["win"] = "model_a" if response["rating"] < 5 else "model_b" | |
| responses.append(response) | |
| return pd.DataFrame(responses) | |
| # Compute the Elo rating for each model | |
| def compute_elo(df, k=32, scale=400, base=10, initial_rating=1000): | |
| rating = defaultdict(lambda: initial_rating) | |
| for _, model_a, model_b, win in df[["model_a", "model_b", "win"]].itertuples(): | |
| ra = rating[model_a] | |
| rb = rating[model_b] | |
| ea = 1 / (1 + base ** ((rb - ra) / scale)) | |
| eb = 1 / (1 + base ** ((ra - rb) / scale)) | |
| if win == "model_a": | |
| sa = 1 | |
| elif win == "model_b": | |
| sa = 0 | |
| elif win == "tie" or win == "tie (bothbad)": | |
| sa = 0.5 | |
| else: | |
| raise Exception(f"unexpected vote {win}") | |
| rating[model_a] += k * (sa - ea) | |
| rating[model_b] += k * (1 - sa - eb) | |
| return rating | |
| def convert_rating_from_float_to_int(df): | |
| return {model: int(rating) for model, rating in compute_elo(df).items()} | |
| def get_elo_results(df_instruct, df_code_instruct, tie_allowed): | |
| df_all = pd.concat([df_instruct, df_code_instruct]) | |
| df_gpt_4 = load_dataset( | |
| "gpt_4_evals/data/", split="train", revision="e007baaf6e505731c08a0bc1a833a1f8f8cb8846" | |
| ).to_pandas() | |
| dfs = [df_instruct, df_code_instruct, df_all] | |
| elo_ratings = [convert_rating_from_float_to_int(create_eval_df(df, tie_allowed=tie_allowed)) for df in dfs] | |
| gpt_4_elo_ratings = convert_rating_from_float_to_int(create_eval_df_for_gpt(df_gpt_4, tie_allowed=tie_allowed)) | |
| elo_ratings.append(gpt_4_elo_ratings) | |
| results = [ | |
| EloEvalResult( | |
| model=model_name, | |
| gpt_4_all=elo_ratings[3][model_name], | |
| human_all=elo_ratings[2][model_name], | |
| human_instruct=elo_ratings[0][model_name], | |
| human_code_instruct=elo_ratings[1][model_name], | |
| tie_allowed=tie_allowed, | |
| ) | |
| for model_name in elo_ratings[0].keys() | |
| ] | |
| return results | |
| def get_elo_results_dicts(df_instruct, df_code_instruct, tie_allowed) -> List[Dict]: | |
| eval_results = get_elo_results(df_instruct, df_code_instruct, tie_allowed) | |
| return [r.to_dict() for r in eval_results] | |
| def get_elo_plots(df_instruct, df_code_instruct, tie_allowed): | |
| df_instruct = create_eval_df(df_instruct, tie_allowed=tie_allowed) | |
| df_code_instruct = create_eval_df(df_code_instruct, tie_allowed=tie_allowed) | |
| df_all = pd.concat([df_instruct, df_code_instruct]) | |
| game = df_all[["model_a", "model_b", "win"]] | |
| game_switch = switch_model_a_b(game) | |
| plot_1 = visualize_pairwise_win_fraction(game_switch, PLOT_1_TITLE) | |
| plot_2 = visualize_battle_count(game_switch, PLOT_2_TITLE) | |
| BOOTSTRAP_ROUNDS = 1000 | |
| if "bootstrap_elo_lu" not in globals(): | |
| bootstrap_elo_lu = get_bootstrap_result(game_switch, compute_elo, BOOTSTRAP_ROUNDS) | |
| plot_3 = visualize_bootstrap_scores(bootstrap_elo_lu, PLOT_3_TITLE) | |
| plot_4 = visualize_rating_count(game, PLOT_4_TITLE) | |
| return plot_1, plot_2, plot_3, plot_4 | |