1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
|
import json
import os
from src.utils import normalize_answer
from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm
import logging
logger = logging.getLogger(__name__)
class Evaluator:
def __init__(self, llm_model, predictions_path):
self.llm_model = llm_model
self.predictions_path = predictions_path
self.prediction_results = self.load_predictions()
def load_predictions(self):
prediction_results = json.load(open(self.predictions_path))
return prediction_results
def calculate_llm_accuracy(self,pre_answer,gold_ans):
system_prompt = """You are an expert evaluator.
"""
user_prompt = f"""Please evaluate if the generated answer is correct by comparing it with the gold answer.
Generated answer: {pre_answer}
Gold answer: {gold_ans}
The generated answer should be considered correct if it:
1. Contains the key information from the gold answer
2. Is factually accurate and consistent with the gold answer
3. Does not contain any contradicting information
Respond with ONLY 'correct' or 'incorrect'.
Response:
"""
response = self.llm_model.infer([{"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}])
if response.strip().lower() == "correct":
return 1.0
else:
return 0.0
def calculate_contain(self,pre_answers,gold_ans):
if pre_answers is None or pre_answers == "" or (isinstance(pre_answers, str) and pre_answers.strip() == ""):
return 0
if gold_ans is None or gold_ans == "" or (isinstance(gold_ans, str) and gold_ans.strip() == ""):
return 0
s1 = normalize_answer(pre_answers)
s2 = normalize_answer(gold_ans)
if s2 in s1:
return 1
else:
return 0
def evaluate_sig_sample(self,idx,prediction):
pre_answer = prediction["pred_answer"]
gold_ans = prediction["gold_answer"]
# llm_acc = 0.0
llm_acc = self.calculate_llm_accuracy(pre_answer, gold_ans)
contain_acc = self.calculate_contain(pre_answer, gold_ans)
return idx, llm_acc, contain_acc
def evaluate(self,max_workers):
llm_scores = [0.0] * len(self.prediction_results)
contain_scores = [0.0] * len(self.prediction_results)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = {
executor.submit(self.evaluate_sig_sample, idx, pred): idx
for idx, pred in enumerate(self.prediction_results)
}
completed = 0
total_llm_score = 0.0
total_contain_score = 0.0
pbar = tqdm(total=len(futures), desc="Evaluating samples", unit="sample")
for future in as_completed(futures):
idx, llm_acc, contain_acc = future.result()
llm_scores[idx] = llm_acc
contain_scores[idx] = contain_acc
self.prediction_results[idx]["llm_accuracy"] = llm_acc
self.prediction_results[idx]["contain_accuracy"] = contain_acc
total_llm_score += llm_acc
total_contain_score += contain_acc
completed += 1
current_llm_acc = total_llm_score / completed
current_contain_acc = total_contain_score / completed
pbar.set_postfix({
'LLM_Acc': f'{current_llm_acc:.3f}',
'Contain_Acc': f'{current_contain_acc:.3f}'
})
pbar.update(1)
pbar.close()
llm_accuracy = sum(llm_scores) / len(llm_scores)
contain_accuracy = sum(contain_scores) / len(contain_scores)
logger.info(f"Evaluation Results:")
logger.info(f" LLM Accuracy: {llm_accuracy:.4f} ({sum(llm_scores)}/{len(llm_scores)})")
logger.info(f" Contain Accuracy: {contain_accuracy:.4f} ({sum(contain_scores)}/{len(contain_scores)})")
with open(self.predictions_path, "w", encoding="utf-8") as f:
json.dump(self.prediction_results, f, ensure_ascii=False, indent=4)
with open(os.path.join(os.path.dirname(self.predictions_path), "evaluation_results.json"), "w", encoding="utf-8") as f:
json.dump({"llm_accuracy": llm_accuracy, "contain_accuracy": contain_accuracy}, f, ensure_ascii=False, indent=4)
return llm_accuracy, contain_accuracy
|
