File size: 12,484 Bytes
ba1d61a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 | import json
import re
import ast
from sklearn.metrics import f1_score, precision_score, recall_score
from collections import Counter, defaultdict
from datetime import datetime
import numpy as np
def parse_quintuple_list(output_text):
if not output_text:
return [], False, "empty_output"
try:
cleaned_output = output_text.strip()
list_match = re.search(r'\[(.*)\]', cleaned_output, re.DOTALL)
if not list_match:
return [], False, "no_list_structure"
list_content = list_match.group(1).strip()
if not list_content:
return [], True, None
try:
full_list = ast.literal_eval('[' + list_content + ']')
except:
fixed_content = list_content
fixed_content = re.sub(r'"([^"]*)"', r"'\1'", fixed_content)
try:
full_list = ast.literal_eval('[' + fixed_content + ']')
except:
return parse_tuples_manually(list_content)
quintuples = []
for item in full_list:
if isinstance(item, (tuple, list)) and len(item) >= 5:
quintuple = tuple(str(element).strip() for element in item[:5])
quintuples.append(quintuple)
else:
return [], False, "invalid_tuple_structure"
return quintuples, True, None
except Exception as e:
return [], False, f"parsing_error_{str(e)}"
def parse_tuples_manually(list_content):
try:
tuple_pattern = r'\(\s*([^)]+)\s*\)'
matches = re.findall(tuple_pattern, list_content)
quintuples = []
for match in matches:
elements = []
current_element = ""
in_quotes = False
quote_char = None
i = 0
while i < len(match):
char = match[i]
if char in ['"', "'"] and (i == 0 or match[i-1] != '\\'):
if not in_quotes:
in_quotes = True
quote_char = char
elif char == quote_char:
in_quotes = False
quote_char = None
elif char == ',' and not in_quotes:
elements.append(current_element.strip().strip('"\''))
current_element = ""
i += 1
continue
current_element += char
i += 1
if current_element:
elements.append(current_element.strip().strip('"\''))
if len(elements) >= 5:
quintuple = tuple(elements[:5])
quintuples.append(quintuple)
return quintuples, len(quintuples) > 0, "manual_parsing" if len(quintuples) > 0 else "manual_parsing_failed"
except Exception as e:
return [], False, f"manual_parsing_error_{str(e)}"
def normalize_quintuple(quintuple):
holder, target, aspect, opinion, sentiment = quintuple
sentiment_lower = sentiment.lower().strip()
if sentiment_lower in ['positive', 'pos']:
sentiment = 'positive'
elif sentiment_lower in ['negative', 'neg']:
sentiment = 'negative'
elif sentiment_lower in ['neutral', 'neu']:
sentiment = 'neutral'
else:
sentiment = sentiment_lower
holder = holder.strip()
target = target.strip()
aspect = aspect.strip()
opinion = opinion.strip()
return (holder, target, aspect, opinion, sentiment)
def calculate_quintuple_metrics(predictions, ground_truths):
total_pred = 0
total_true = 0
total_correct = 0
exact_matches = 0
partial_matches = {'holder': 0, 'target': 0, 'aspect': 0, 'opinion': 0, 'sentiment': 0}
for pred_list, true_list in zip(predictions, ground_truths):
pred_normalized = [normalize_quintuple(q) for q in pred_list]
true_normalized = [normalize_quintuple(q) for q in true_list]
total_pred += len(pred_normalized)
total_true += len(true_normalized)
pred_set = set(pred_normalized)
true_set = set(true_normalized)
exact_matches += len(pred_set & true_set)
total_correct += len(pred_set & true_set)
for pred_q in pred_normalized:
for true_q in true_normalized:
if pred_q == true_q:
continue
for i, (field_name, pred_field, true_field) in enumerate(
zip(['holder', 'target', 'aspect', 'opinion', 'sentiment'], pred_q, true_q)):
if pred_field.lower() == true_field.lower():
partial_matches[field_name] += 1
precision = total_correct / total_pred if total_pred > 0 else 0
recall = total_correct / total_true if total_true > 0 else 0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
return {
'micro_f1': f1,
'micro_precision': precision,
'micro_recall': recall,
'exact_matches': exact_matches,
'total_predicted': total_pred,
'total_ground_truth': total_true,
'partial_matches': partial_matches
}
def evaluate_multimodal_quintuple_extraction(result_file_path):
with open(result_file_path, 'r', encoding='utf-8') as f:
results = json.load(f)
predictions = []
ground_truths = []
detailed_results = []
extraction_errors = defaultdict(list)
for item in results:
item_id = item['id']
model_output = item['model_output']
if isinstance(item['ground_truth'], str):
gt_quintuples, gt_valid, gt_error = parse_quintuple_list(item['ground_truth'])
elif isinstance(item['ground_truth'], list):
gt_quintuples = []
for gt_item in item['ground_truth']:
if isinstance(gt_item, (tuple, list)) and len(gt_item) >= 5:
gt_quintuples.append(tuple(str(element).strip() for element in gt_item[:5]))
gt_valid = True
gt_error = None
else:
gt_quintuples = []
gt_valid = False
gt_error = "invalid_ground_truth_format"
pred_quintuples, pred_valid, pred_error = parse_quintuple_list(model_output)
detailed_item = {
'id': item_id,
'model_output': model_output,
'extracted_prediction': pred_quintuples,
'ground_truth': gt_quintuples,
'prediction_count': len(pred_quintuples),
'ground_truth_count': len(gt_quintuples),
'exact_matches': len(set(pred_quintuples) & set(gt_quintuples)) if pred_valid and gt_valid else 0,
'valid': pred_valid and gt_valid
}
detailed_results.append(detailed_item)
if not pred_valid:
extraction_errors[pred_error].append(item_id)
elif not gt_valid:
extraction_errors[f"gt_{gt_error}"].append(item_id)
if pred_valid and gt_valid:
predictions.append(pred_quintuples)
ground_truths.append(gt_quintuples)
if len(predictions) == 0:
return {
'error': 'No valid predictions found',
'total_samples': len(results),
'extraction_errors': dict(extraction_errors)
}
metrics = calculate_quintuple_metrics(predictions, ground_truths)
sample_with_predictions = sum(1 for item in detailed_results if item['prediction_count'] > 0)
sample_with_correct_predictions = sum(1 for item in detailed_results if item['exact_matches'] > 0)
all_pred_holders = []
all_pred_targets = []
all_pred_sentiments = []
all_true_holders = []
all_true_targets = []
all_true_sentiments = []
for pred_list, true_list in zip(predictions, ground_truths):
for quintuple in pred_list:
all_pred_holders.append(quintuple[0])
all_pred_targets.append(quintuple[1])
all_pred_sentiments.append(quintuple[4])
for quintuple in true_list:
all_true_holders.append(quintuple[0])
all_true_targets.append(quintuple[1])
all_true_sentiments.append(quintuple[4])
evaluation_result = {
'task_info': {
'task_name': 'multimodal.quintuple.extraction',
'dataset': 'PanoSent',
'evaluation_time': datetime.now().isoformat(),
'total_samples': len(results),
'valid_samples': len(predictions),
'extraction_success_rate': round(len(predictions) / len(results), 4)
},
'metrics': {
'Micro_F1': round(metrics['micro_f1'], 4),
'Micro_Precision': round(metrics['micro_precision'], 4),
'Micro_Recall': round(metrics['micro_recall'], 4),
'Exact_Matches': metrics['exact_matches'],
'Total_Predicted': metrics['total_predicted'],
'Total_Ground_Truth': metrics['total_ground_truth']
},
'sample_level_stats': {
'samples_with_predictions': sample_with_predictions,
'samples_with_correct_predictions': sample_with_correct_predictions,
'avg_predictions_per_sample': round(metrics['total_predicted'] / len(predictions), 2) if len(predictions) > 0 else 0,
'avg_ground_truth_per_sample': round(metrics['total_ground_truth'] / len(predictions), 2) if len(predictions) > 0 else 0
},
'partial_match_analysis': {
field: {
'matches': metrics['partial_matches'][field],
'rate': round(metrics['partial_matches'][field] / max(metrics['total_predicted'], 1), 4)
} for field in ['holder', 'target', 'aspect', 'opinion', 'sentiment']
},
'error_analysis': {
'extraction_errors': {
error_type: {
'count': len(sample_ids),
'sample_ids': sample_ids
} for error_type, sample_ids in extraction_errors.items()
}
},
'distribution': {
'holders': {
'ground_truth': dict(Counter(all_true_holders)),
'predictions': dict(Counter(all_pred_holders))
},
'targets': {
'ground_truth': dict(Counter(all_true_targets)),
'predictions': dict(Counter(all_pred_targets))
},
'sentiments': {
'ground_truth': dict(Counter(all_true_sentiments)),
'predictions': dict(Counter(all_pred_sentiments))
}
}
}
base_name = result_file_path.replace('.json', '')
eval_output_file = f"{base_name}_evaluation.json"
with open(eval_output_file, 'w', encoding='utf-8') as f:
json.dump(evaluation_result, f, ensure_ascii=False, indent=2)
detailed_output_file = f"{base_name}_detailed_results.json"
with open(detailed_output_file, 'w', encoding='utf-8') as f:
json.dump(detailed_results, f, ensure_ascii=False, indent=2)
problem_samples = [item for item in detailed_results if item['exact_matches'] == 0 and item['ground_truth_count'] > 0]
if problem_samples:
problem_report_file = f"{base_name}_problem_samples.json"
with open(problem_report_file, 'w', encoding='utf-8') as f:
json.dump(problem_samples, f, ensure_ascii=False, indent=2)
print(f"Evaluation complete: {len(results)} samples")
print(f"Key metric: Micro F1={evaluation_result['metrics']['Micro_F1']}")
print(f"Exact matches: {evaluation_result['metrics']['Exact_Matches']}/{evaluation_result['metrics']['Total_Ground_Truth']}")
print(f"Average predictions per sample: {evaluation_result['sample_level_stats']['avg_predictions_per_sample']}")
print(f"Extraction success rate: {evaluation_result['task_info']['extraction_success_rate']}")
print(f"Results saved to: {eval_output_file}")
if problem_samples:
print(f"Problematic samples: {len(problem_samples)}; see {problem_report_file} for details")
return evaluation_result
if __name__ == "__main__":
result_file = "model_result.json"
try:
evaluation_result = evaluate_multimodal_quintuple_extraction(result_file)
except FileNotFoundError:
print(f"Error: file not found {result_file}")
except json.JSONDecodeError:
print(f"Error: invalid format for {result_file}")
except Exception as e:
print(f"Evaluation failed: {str(e)}")
|