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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)
@dataclass
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
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