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import json
import os
from collections import defaultdict
from compare_value import compare_value
from compare_sequence import is_sequence_match_ordered, is_sequence_match_unordered
from compare_str import fuzzy_string_match
from compare_multiple import multiple_choice_checker
def has_more_digits_than_other_chars(s):
if isinstance(s, (int, float)):
return True
s = s.replace('.', '1')
s = s.replace(',', '1')
s = s.replace('$', '1')
s = s.replace('B', '1')
s = s.replace('T', '1')
s = s.replace('K', '1')
digit_count = 0
other_count = 0
for char in s:
if char.isdigit():
digit_count += 1
else:
other_count += 1
return digit_count > other_count
def evaluate_answer(answer, response, qtype):
if qtype in [1, 2, 101, 102]:
if has_more_digits_than_other_chars(answer):
return "Exact Numeric", compare_value(answer, response)
else:
return "Vague String", fuzzy_string_match(answer, response)
elif qtype in [72, 54]:
return "Exact Numeric", compare_value(answer, response)
elif qtype in [10, 50, 51, 52, 110]:
return "Vague Numeric", compare_value(answer, response, eps=0.05)
elif qtype in [13, 103, 113]:
if answer.lower() in response.lower():
return "Exact String", True
return "Exact String", compare_value(answer, response)
elif qtype in [40, 41, 42, 43, 44]:
response = response.replace("\n", ",")
response = response.replace(" ", "")
answer = answer.replace(" ", "")
return "Vague Unordered Sequence", is_sequence_match_unordered(answer.split(","), response.split(","), fuzzy=True)
elif qtype in [60, 61, 70, 80, 90]:
return "Vague String", fuzzy_string_match(answer, response)
elif qtype in [71]:
response = response.replace("\n\n", "")
response = response.replace("\n", ",")
response = response.replace(" ", "")
response = response.replace("<", ",")
response = response.replace(">", ",")
if response.count(":") == 1:
response = response[response.find(':') + 1:]
answer = answer.replace(" ", "")
return "Vague Ordered Sequence", is_sequence_match_ordered(answer.split(","), response.split(","), fuzzy=True)
elif qtype in [30]:
for an in answer:
if is_sequence_match_ordered(an.split(","), response.split(","), fuzzy=True):
return "Vague Ordered Sequence", True
return "Vague Ordered Sequence", False
elif qtype in [202,1919810,1919811,1919812]:
return "Exact String", multiple_choice_checker(answer , response)
else:
print('there is no qtype',qtype)
return "Exact Numeric", compare_value(answer, response)
def process_json_data(json_data):
results = []
stats = {
'qtype_stats': defaultdict(lambda: {'correct': 0, 'total': 0}),
'figure_stats': defaultdict(lambda: {'correct': 0, 'total': 0}),
'total_correct': 0,
'total_questions': 0
}
for key, item in json_data.items():
question_id = item["question_id"]
if 'qtype' in item:
qtype = item["qtype"]
elif 'qid' in item:
qtype = item["qid"]
else:
qtype = 1
if "response" not in item or item['response'] == 'Error!':
continue
answer = str(item["answer"])
response = str(item["response"])
response = response.replace(" "," ")
figure_path = item["figure_path"]
if type(figure_path) == list:
figure_path = figure_path[0]
eval_method, score = evaluate_answer(answer, response, qtype)
results.append({
"figure_path": figure_path,
"answer": answer,
"response": response,
"question": item["question"] if "question" in item else "",
"question_id": question_id,
"qtype": qtype,
"score": score,
"eval_method": eval_method
})
stats['qtype_stats'][qtype]['correct'] += score
stats['qtype_stats'][qtype]['total'] += 1
stats['figure_stats'][figure_path]['correct'] += score
stats['figure_stats'][figure_path]['total'] += 1
stats['total_correct'] += score
stats['total_questions'] += 1
return results, stats
def calculate_accuracy(correct, total):
return round(correct / total * 100, 2) if total > 0 else 0.0
def generate_stat_report(stats):
report = {}
report['overall_accuracy'] = calculate_accuracy(
stats['total_correct'], stats['total_questions'])
qtype_report = {}
for qtype, counts in stats['qtype_stats'].items():
qtype_report[f"qtype_{qtype}"] = {
'accuracy': calculate_accuracy(counts['correct'], counts['total']),
'correct': counts['correct'],
'total': counts['total']
}
report['qtype_accuracy'] = qtype_report
figure_report = {}
for figure_path, counts in stats['figure_stats'].items():
figure_report[figure_path] = {
'accuracy': calculate_accuracy(counts['correct'], counts['total']),
'correct': counts['correct'],
'total': counts['total']
}
report['figure_accuracy'] = figure_report
return report
from copy import deepcopy
def evaluate(input_file, output_file=None, stats_file=None):
os.makedirs(os.path.dirname(output_file), exist_ok=True)
with open(input_file, 'r', encoding='utf-8') as f:
data = json.load(f)
if type(data).__name__=='list':
__ = deepcopy(data)
data = {}
for _ in __:
data[_['question_id']] = deepcopy(_)
results, stats = process_json_data(data)
report = generate_stat_report(stats)
if output_file:
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"Score save to {output_file}")
if stats_file:
with open(stats_file, 'w', encoding='utf-8') as f:
json.dump(report, f, indent=2, ensure_ascii=False)
print(f"Statis saved to {stats_file}")
print(f"Acc: {report['overall_accuracy']}% {stats['total_questions']}")
return results, report
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