Clémentine
merge refactor
460d762
raw
history blame
6.31 kB
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 src.assets.text_content import PLOT_1_TITLE, PLOT_2_TITLE, PLOT_3_TITLE, PLOT_4_TITLE
from src.utils_display import make_clickable_model, EloEvalColumn
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[EloEvalColumn.model.name] = make_clickable_model(base_model)
data_dict[EloEvalColumn.gpt4.name] = self.gpt_4_all
data_dict[EloEvalColumn.human_all.name] = self.human_all
data_dict[EloEvalColumn.human_instruct.name] = self.human_instruct
data_dict[EloEvalColumn.human_code_instruct.name] = 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