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import argparse
import pandas as pd
import re
from datasets import load_dataset
import json
from .grader import grade_answer
def extract_all_boxed_content(text):
results = []
start = 0
while True:
# Find the next occurrence of \boxed{
start = text.find(r"\boxed{", start)
if start == -1:
break # No more \boxed{ found
brace_count = 0
result = []
i = start
while i < len(text):
char = text[i]
result.append(char)
if char == '{':
brace_count += 1
elif char == '}':
brace_count -= 1
# Stop when the braces are balanced
if brace_count == 0 and result[-1] == '}':
break
i += 1
# Append the matched content
results.append(''.join(result))
start = i + 1 # Move past the current match to find the next
return results
def extract_boxed_text(solution):
strict_prediction, soft_prediction = None, None
prediction_match = extract_all_boxed_content(str(solution))
if len(prediction_match) > 0:
strict_prediction = prediction_match[-1]
if strict_prediction is not None and '\\boxed' in strict_prediction:
strict_prediction = strict_prediction.replace('\\boxed{', '')[:-1]
else:
patterns = [
r"<answer>(.*?)</answer>",
r"</answer>(.*?)</answer>",
r"<answer>(.*?)<answer>",
r"\*\*Answer:\*\* ([\d\.]+)",
# last number
r"[-+]?\d*\.\d+|\d+",
]
for pattern in patterns:
prediction_match = re.findall(pattern, str(solution))
if len(prediction_match) > 0:
break
if len(prediction_match) > 0:
soft_prediction = prediction_match[-1]
else:
soft_prediction = None
return strict_prediction, soft_prediction
def process_target(target, dataset):
if dataset == 'gsm8k':
target = int(target.split('#### ')[-1].replace(',',''))
return target
if dataset == 'math500':
return target
elif dataset == 'aime':
return target
else:
raise ValueError('Unknown dataset')
def time_evaluation(result_file):
results = []
f = open(result_file, 'r')
for line in f:
results.append(json.loads(line))
f.close()
total_time, total_token = 0, 0
total = 0
for idx, problem in enumerate(results):
total += 1
total_time += problem['time']
total_token += problem['tokens']
return {
'total': total,
'total_time': total_time,
'total_token': total_token,
'token/s': total_token / total_time,
}
def evaluation(result_file):
results = []
f = open(result_file, 'r')
for line in f:
results.append(json.loads(line))
f.close()
total, s_correct, f_correct = 0, 0, 0
total_time, total_token = 0, 0
total_steps = 0
total_experts = 0
for idx, problem in enumerate(results):
answer = problem['answer']
strict_predict_ans, flexible_predict_ans = extract_boxed_text(answer)
target = problem['task']['answer']
target = process_target(target, 'math500')
total += 1
total_time += problem['time']
total_token += problem['tokens']
total_steps += problem['steps']
total_experts += problem.get('unique_experts_count', 0)
if strict_predict_ans is not None or flexible_predict_ans is not None:
try:
if grade_answer(strict_predict_ans, target):
s_correct += 1
f_correct += 1
elif grade_answer(flexible_predict_ans, target):
f_correct += 1
except:
print('Error in extracting answers: ', strict_predict_ans, flexible_predict_ans)
pass
else:
print('No answer found: ', idx, target)
pass
print(f"Strict Match Accuracy = {s_correct}/{total} = {s_correct/total}")
print(f"Soft Match Accuracy = {f_correct}/{total} = {f_correct/total}")
return {
'strict_accuracy': s_correct / total,
'soft_accuracy': f_correct / total,
'strict_match': s_correct,
'soft_match': f_correct,
'total': total,
'total_time': total_time,
'total_token': total_token,
'token/s': total_token / total_time,
'avg_steps': total_steps / total if total > 0 else -1,
'unique_experts': total_experts / total if total > 0 else -1,
}
def passk_evaluation(result_files):
"""
Pass@k evaluation for MATH500.
A question is counted as correct if ANY of the result files answers it correctly.
result_files: list[str], each file is a JSONL result file.
"""
# Load all result files
all_results = []
for file in result_files:
with open(file, "r") as f:
results = [json.loads(line) for line in f]
all_results.append(results)
num_files = len(all_results)
num_questions = len(all_results[0])
strict_correct = 0
soft_correct = 0
for qid in range(num_questions):
# Ground truth
target = all_results[0][qid]['task']['answer']
target = process_target(target, 'math500')
strict_hit = False
soft_hit = False
# Check all result files for this question
for fidx in range(num_files):
answer = all_results[fidx][qid]['answer']
strict_ans, flexible_ans = extract_boxed_text(answer)
try:
# strict match
if grade_answer(strict_ans, target):
strict_hit = True
soft_hit = True
break
# soft match
if grade_answer(flexible_ans, target):
soft_hit = True
break
except Exception as e:
print(f"[Warning] Error grading answer in file {fidx}, qid {qid}: {e}")
continue
# Update match counters
if strict_hit:
strict_correct += 1
soft_correct += 1
elif soft_hit:
soft_correct += 1
# Final metrics
return {
"strict_accuracy": strict_correct / num_questions,
"soft_accuracy": soft_correct / num_questions,
"strict_match": strict_correct,
"soft_match": soft_correct,
"total": num_questions,
}
if __name__ == '__main__':
args = argparse.ArgumentParser()
args.add_argument('--result_file', type=str, default='results.csv')
args.add_argument('--dataset', type=str, default='math500')
args.add_argument('--split', type=str, default='test')
args.add_argument('--num-samples', type=int, default=None)
args.add_argument('--relax', action='store_true')
args = args.parse_args()
result_file = args.result_file
if args.dataset == 'gsm8k':
dataset = load_dataset('openai/gsm8k', 'main')
dataset_type = args.split
elif args.dataset =='math500':
dataset = load_dataset("HuggingFaceH4/MATH-500")
dataset_type = 'test'
elif args.dataset == 'aime':
dataset = load_dataset('AI-MO/aimo-validation-aime')
dataset_type = 'train'
else:
raise ValueError('Unknown dataset')
results = json.load(open(result_file, 'r'))
total, s_correct, f_correct = 0, 0, 0
correct_length, wrong_length, all_length = [], [], []
max_length, min_length = 0, 100000
for idx, (ans, prob) in enumerate(zip(results, dataset[dataset_type])):
if args.num_samples is not None and idx >= args.num_samples:
break
if args.dataset == 'aime' and idx < 60:
continue
strict_predict_ans, flexible_predict_ans = extract_boxed_text(ans['answer'])
target = prob['answer']
target = process_target(target, args.dataset)
total += 1
if strict_predict_ans is not None or flexible_predict_ans is not None:
try:
correct_flag = False
if grade_answer(strict_predict_ans, target):
s_correct += 1
f_correct += 1
correct_flag = True
elif grade_answer(flexible_predict_ans, target):
f_correct += 1
correct_flag = True
#print(f"Correct = {correct_flag}, \tStrict Predict = {strict_predict_ans},\t Flexible Predict = {flexible_predict_ans}, \tTarget = {target}")
except:
#print('Error in extracting answers: ', strict_predict_ans, flexible_predict_ans)
pass
else:
#print('No answer found: ', idx, target)
pass
#if total >= 224:
# break
print(f"Strict Match Accuracy = {s_correct}/{total} = {s_correct/total}")
print(f"Soft Match Accuracy = {f_correct}/{total} = {f_correct/total}")
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