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import re
import ast
from sklearn.metrics import f1_score, precision_score, recall_score, classification_report
from collections import Counter, defaultdict
from datetime import datetime
import numpy as np
def extract_targets_from_prompt(prompt):
targets_match = re.search(r"Targets:\s*([^\n]+)", prompt)
if targets_match:
targets_text = targets_match.group(1).strip()
targets = [target.strip() for target in re.split(r'[,;]', targets_text)]
return targets
return []
def extract_sentiment_dict_from_output(model_output):
if not model_output:
return {}, False, "empty_output"
valid_sentiments = ['positive', 'neutral', 'negative']
try:
if "{" in model_output and "}" in model_output:
cleaned_output = model_output.strip()
json_match = re.search(r'\{[^}]*\}', cleaned_output)
if json_match:
cleaned_output = json_match.group()
try:
parsed = ast.literal_eval(cleaned_output)
except:
parsed = json.loads(cleaned_output)
if isinstance(parsed, dict):
sentiment_dict = {}
all_valid = True
for target, sentiment in parsed.items():
if isinstance(sentiment, str) and sentiment.lower() in valid_sentiments:
sentiment_dict[target] = sentiment.lower()
else:
all_valid = False
if all_valid and len(sentiment_dict) > 0:
return sentiment_dict, True, None
else:
return sentiment_dict, False, "invalid_sentiment_labels"
pairs = re.findall(r"([^:{},'\"]+):\s*['\"]?(positive|neutral|negative)['\"]?", model_output.lower())
if pairs:
sentiment_dict = {}
for target, sentiment in pairs:
target = target.strip(' \'"')
sentiment_dict[target] = sentiment
return sentiment_dict, False, "extracted_from_text_patterns"
return {}, False, "no_sentiment_pattern"
except Exception as e:
return {}, False, f"parsing_error_{str(e)}"
def parse_ground_truth_dict(ground_truth):
if isinstance(ground_truth, dict):
return {k: v.lower() for k, v in ground_truth.items()}
elif isinstance(ground_truth, str):
try:
parsed = ast.literal_eval(ground_truth)
if isinstance(parsed, dict):
return {k: v.lower() for k, v in parsed.items()}
except:
pass
return {}
def calculate_multimodal_absa_metrics(predictions, ground_truths):
all_pred_sentiments = []
all_true_sentiments = []
for pred_pairs, true_pairs in zip(predictions, ground_truths):
pred_dict = dict(pred_pairs)
true_dict = dict(true_pairs)
common_targets = set(pred_dict.keys()) & set(true_dict.keys())
for target in common_targets:
all_pred_sentiments.append(pred_dict[target])
all_true_sentiments.append(true_dict[target])
if len(all_pred_sentiments) == 0:
return {
'micro_f1': 0.0,
'macro_f1': 0.0,
'micro_precision': 0.0,
'micro_recall': 0.0,
'per_class_metrics': {},
'valid_pairs': 0
}
labels = ['positive', 'neutral', 'negative']
micro_f1 = f1_score(all_true_sentiments, all_pred_sentiments, average='micro')
macro_f1 = f1_score(all_true_sentiments, all_pred_sentiments, average='macro')
micro_precision = precision_score(all_true_sentiments, all_pred_sentiments, average='micro')
micro_recall = recall_score(all_true_sentiments, all_pred_sentiments, average='micro')
class_report = classification_report(all_true_sentiments, all_pred_sentiments,
target_names=labels,
output_dict=True, zero_division=0)
per_class_metrics = {}
for label in labels:
if label in class_report:
per_class_metrics[label] = {
'precision': class_report[label]['precision'],
'recall': class_report[label]['recall'],
'f1_score': class_report[label]['f1-score'],
'support': int(class_report[label]['support'])
}
return {
'micro_f1': micro_f1,
'macro_f1': macro_f1,
'micro_precision': micro_precision,
'micro_recall': micro_recall,
'per_class_metrics': per_class_metrics,
'valid_pairs': len(all_pred_sentiments)
}
def evaluate_multimodal_aspect_based_sentiment_analysis(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)
target_level_errors = defaultdict(list)
for item in results:
item_id = item['id']
prompt = item['prompt']
model_output = item['model_output']
gt_dict = parse_ground_truth_dict(item['ground_truth'])
targets = extract_targets_from_prompt(prompt)
pred_dict, is_valid, error_type = extract_sentiment_dict_from_output(model_output)
pred_pairs = []
true_pairs = []
target_results = {}
for target in targets:
if target in gt_dict:
true_sentiment = gt_dict[target]
true_pairs.append((target, true_sentiment))
if target in pred_dict:
pred_sentiment = pred_dict[target]
pred_pairs.append((target, pred_sentiment))
target_results[target] = {
'predicted': pred_sentiment,
'ground_truth': true_sentiment,
'correct': pred_sentiment == true_sentiment
}
if pred_sentiment != true_sentiment:
error_pattern = f"{true_sentiment}_to_{pred_sentiment}"
target_level_errors[error_pattern].append(f"{item_id}_{target}")
else:
target_results[target] = {
'predicted': None,
'ground_truth': true_sentiment,
'correct': False
}
detailed_item = {
'id': item_id,
'targets': targets,
'model_output': model_output,
'extracted_prediction': pred_dict,
'ground_truth': gt_dict,
'target_results': target_results,
'all_targets_correct': all(result['correct'] for result in target_results.values()),
'valid': is_valid
}
detailed_results.append(detailed_item)
if not is_valid:
extraction_errors[error_type].append(item_id)
if len(pred_pairs) > 0:
predictions.append(pred_pairs)
ground_truths.append(true_pairs)
if len(predictions) == 0:
return {
'error': 'No valid predictions found',
'total_samples': len(results),
'extraction_errors': dict(extraction_errors)
}
metrics = calculate_multimodal_absa_metrics(predictions, ground_truths)
all_correct_samples = sum(1 for item in detailed_results if item['all_targets_correct'])
sample_level_accuracy = all_correct_samples / len(detailed_results)
all_true_sentiments = []
all_pred_sentiments = []
for item in detailed_results:
for target, result in item['target_results'].items():
if result['ground_truth']:
all_true_sentiments.append(result['ground_truth'])
if result['predicted']:
all_pred_sentiments.append(result['predicted'])
evaluation_result = {
'task_info': {
'task_name': 'multimodal.aspect.based.sentiment.analysis',
'dataset': 'Twitter2015/2017',
'evaluation_time': datetime.now().isoformat(),
'total_samples': len(results),
'valid_samples': len(predictions),
'extraction_success_rate': round(len(predictions) / len(results), 4),
'total_target_pairs': metrics['valid_pairs']
},
'metrics': {
'Micro_F1': round(metrics['micro_f1'], 4),
'Macro_F1': round(metrics['macro_f1'], 4),
'Micro_Precision': round(metrics['micro_precision'], 4),
'Micro_Recall': round(metrics['micro_recall'], 4),
'Sample_Level_Accuracy': round(sample_level_accuracy, 4)
},
'per_class_metrics': {
label: {
'precision': round(metrics['per_class_metrics'][label]['precision'], 4),
'recall': round(metrics['per_class_metrics'][label]['recall'], 4),
'f1_score': round(metrics['per_class_metrics'][label]['f1_score'], 4),
'support': metrics['per_class_metrics'][label]['support']
} for label in metrics['per_class_metrics']
},
'error_analysis': {
'extraction_errors': {
error_type: {
'count': len(sample_ids),
'sample_ids': sample_ids
} for error_type, sample_ids in extraction_errors.items()
},
'target_level_errors': {
error_pattern: {
'count': len(target_ids),
'target_ids': target_ids
} for error_pattern, target_ids in target_level_errors.items()
}
},
'distribution': {
'ground_truth_sentiments': dict(Counter(all_true_sentiments)),
'predicted_sentiments': 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 not item['all_targets_correct']]
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 metrics: Micro F1={evaluation_result['metrics']['Micro_F1']}, Sample-level Accuracy={evaluation_result['metrics']['Sample_Level_Accuracy']}")
print(f"Number of target pairs: {evaluation_result['task_info']['total_target_pairs']}")
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_aspect_based_sentiment_analysis(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)}")
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