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'''
This file is part of Open-MoE-LLM-Leaderboard and is modified based on work
under the Apache 2.0 License from the arena-hard project.
(https://github.com/lm-sys/arena-hard)
Original Copyright (c) 2024 Tianle Li*, Wei-Lin Chiang*, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica
See the NOTICE file distributed with this work for additional
information regarding copyright ownership.
'''
import pandas as pd
from tqdm import tqdm
import numpy as np
from sklearn.linear_model import LogisticRegression
import math
from collections import defaultdict
from tqdm import tqdm
from src.backend.tasks.arena_hard.arena_utils import (
chat_completion_openai,
load_questions,
load_model_answers,
get_endpoint,
make_config,
)
def get_score(judgment, pattern, pairwise=True):
matches = pattern.findall(judgment)
matches = [m for m in matches if m != ""]
if len(set(matches)) == 0:
return None, True
elif len(set(matches)) == 1:
if pairwise:
return matches[0].strip("\n"), False
return int(matches[0])
else:
return None, False
# get answer from model
def get_answer(model, conv, temperature, max_tokens, endpoint_dict=None):
api_dict = get_endpoint(endpoint_dict["endpoints"])
# if endpoint_dict["api_type"] == "anthropic":
# output = chat_completion_anthropic(model, conv, temperature, max_tokens)
# elif endpoint_dict["api_type"] == "azure":
# output = chat_completion_openai_azure(model, conv, temperature, max_tokens, api_dict)
output = chat_completion_openai(model, conv, temperature, max_tokens, api_dict)
return output
def judgment(**args):
question = args["question"]
answer = args["answer"]
reference = args["reference"]
baseline = args["baseline_answer"]
configs = args["configs"]
# output_file = args["output_file"]
model = configs["judge_model"]
num_games = 2 if configs["pairwise"] else 1
# output = {
# "question_id":question["question_id"],
# "judge": model,
# "model": "custom_model",
# "games":[]
# }
output = [question["question_id"]]
for game in range(num_games):
conv = [{"role": "system", "content": configs["system_prompt"]}]
for template in configs["prompt_template"]:
prompt_args = {}
prompt_args[f"question_{1}"] = question["content"]
base = 1
if baseline:
if game % 2 == 1: # swap position
temp = baseline
baseline = answer
answer = temp
if game == 0:
for i, turn in enumerate(baseline["choices"][0]["turns"]):
prompt_args[f"answer_{i+1}"] = turn["content"]
base += 1
if game == 1:
prompt_args[f"answer_{1}"] = baseline
base += 1
if answer:
prompt_args[f"answer_{base}"] = answer
if reference:
for j, ref_answer in enumerate(reference):
for i, turn in enumerate(ref_answer["choices"][0]["turns"]):
prompt_args[f"ref_answer_{i+j+1}"] = turn["content"]
user_prompt = template.format(**prompt_args)
conv.append({"role": "user", "content": user_prompt})
judgment = ""
for _ in range(2):
new_judgment = get_answer(
model,
conv,
configs["temperature"],
configs["max_tokens"],
args["endpoint_dict"],
)
judgment += ("\n" + new_judgment)
score, try_again = get_score(judgment, args["regex_pattern"])
conv.append({"role": "assistant", "content": new_judgment})
if not try_again:
break
conv.append({"role": "user", "content": "continue your judgment and finish by outputting a final verdict label"})
print("Finish judgment!!!")
# result = {
# "user_prompt": conv[1]["content"],
# "judgment": judgment,
# "score":score
# }
output.append(score)
return output
def get_battles_from_scores(score_list, first_game_only=False, WEIGHT=3):
arena_hard_battles = pd.DataFrame()
print("Turning score list into battles...")
for scores in tqdm(score_list):
question_id, score1, score2 = scores
# Process game 1
output = {"question_id": question_id,
"model_a": "gpt-4-0314",
"model_b": f"custom_model"} # Unique identifier for model
weight = 1
if score1 == "A=B":
output["winner"] = "tie"
elif score1 == "A>B":
output["winner"] = "model_a"
elif score1 == "A>>B":
output["winner"] = "model_a"
weight = WEIGHT
elif score1 == "B>A":
output["winner"] = "model_b"
elif score1 == "B>>A":
output["winner"] = "model_b"
weight = WEIGHT
else:
weight = 0
if weight:
arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])
if not first_game_only:
# Process game 2
output = {"question_id": question_id,
"model_a": "gpt-4-0314",
"model_b": f"custom_model"} # Unique identifier for model
weight = 1
if score2 == "A=B":
output["winner"] = "tie"
elif score2 == "A>B":
output["winner"] = "model_b"
elif score2 == "A>>B":
output["winner"] = "model_b"
weight = WEIGHT
elif score2 == "B>A":
output["winner"] = "model_a"
elif score2 == "B>>A":
output["winner"] = "model_a"
weight = WEIGHT
else:
weight = 0
if weight:
arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])
arena_hard_battles.to_json("./arena_hard_battles.jsonl", lines=True, orient="records")
return arena_hard_battles
def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000):
models = pd.concat([df["model_a"], df["model_b"]]).unique()
models = pd.Series(np.arange(len(models)), index=models)
LOW_RATING = 100
# 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
if len(np.unique(Y)) == 1:
# If there's only one class in the data, assign default ratings
elo_scores = np.full(p, LOW_RATING)
elo_scores[models["gpt-4-0314"]] = INIT_RATING
else:
lr = LogisticRegression(fit_intercept=False, penalty=None, tol=1e-8)
lr.fit(X,Y)
elo_scores = SCALE * lr.coef_[0] + INIT_RATING
# set anchor as gpt-4-0314 = 1000
if "gpt-4-0314" in models.index:
elo_scores += 1000 - elo_scores[models["gpt-4-0314"]]
return pd.Series(elo_scores, index = models.index).sort_values(ascending=False)
def predict_win_rate(elo_ratings, SCALE=400, BASE=10, INIT_RATING=1000):
names = sorted(list(elo_ratings.keys()))
wins = defaultdict(lambda: defaultdict(lambda: 0))
for a in names:
for b in names:
ea = 1 / (1 + BASE ** ((elo_ratings[b] - elo_ratings[a]) / SCALE))
wins[a][b] = ea
wins[b][a] = 1 - ea
data = {
a: [wins[a][b] if a != b else np.NAN for b in names]
for a in names
}
df = pd.DataFrame(data, index=names)
df.index.name = "model_a"
df.columns.name = "model_b"
return df.T
def get_win_rate_column(df, column, baseline="gpt-4-0314"):
to_dict = df[["model", column]].set_index("model").to_dict()[column]
win_rate_table = predict_win_rate(to_dict)
return win_rate_table[baseline].fillna(0.5).apply(lambda x: round(x * 100, 2)) |