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import re
import ast
from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix
from collections import Counter, defaultdict
from datetime import datetime
import numpy as np
def extract_emotion_from_output(model_output):
if not model_output:
return None, False, "empty_output"
valid_emotions = ['neutral', 'surprise', 'fear', 'sadness', 'joy', 'disgust', 'anger']
try:
if "{'emotion':" in model_output or '{"emotion":' 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 'emotion' in parsed and isinstance(parsed['emotion'], str):
emotion = parsed['emotion'].lower().strip()
if emotion in valid_emotions:
return emotion, True, None
else:
return emotion, False, "invalid_emotion_label"
cleaned_output = model_output.lower().strip()
for emotion in valid_emotions:
if cleaned_output == emotion:
return emotion, True, None
for emotion in valid_emotions:
if emotion in cleaned_output:
return emotion, False, "emotion_found_but_not_properly_formatted"
return None, False, "no_valid_emotion_found"
except Exception as e:
return None, False, f"parsing_error_{str(e)}"
def evaluate_multiparty_dialogue_emotion_recognition(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)
prediction_errors = defaultdict(list)
emotion_labels = ['neutral', 'surprise', 'fear', 'sadness', 'joy', 'disgust', 'anger']
for item in results:
item_id = item['id']
model_output = item['model_output']
gt_emotion = item['ground_truth'].lower().strip()
pred_emotion, is_valid, error_type = extract_emotion_from_output(model_output)
detailed_item = {
'id': item_id,
'model_output': model_output,
'extracted_prediction': pred_emotion,
'ground_truth': gt_emotion,
'correct': pred_emotion == gt_emotion if pred_emotion else False,
'valid': is_valid
}
detailed_results.append(detailed_item)
if not is_valid:
extraction_errors[error_type].append(item_id)
elif pred_emotion != gt_emotion:
error_pattern = f"{gt_emotion}_to_{pred_emotion}"
prediction_errors[error_pattern].append(item_id)
if is_valid:
predictions.append(pred_emotion)
ground_truths.append(gt_emotion)
if len(predictions) == 0:
return {
'error': 'No valid predictions found',
'total_samples': len(results),
'extraction_errors': dict(extraction_errors)
}
accuracy = accuracy_score(ground_truths, predictions)
weighted_f1 = f1_score(ground_truths, predictions, average='weighted')
macro_f1 = f1_score(ground_truths, predictions, average='macro')
micro_f1 = f1_score(ground_truths, predictions, average='micro')
cm = confusion_matrix(ground_truths, predictions, labels=emotion_labels)
class_report = classification_report(ground_truths, predictions,
target_names=emotion_labels,
output_dict=True,
zero_division=0)
per_class_metrics = {}
for i, label in enumerate(emotion_labels):
if label in class_report:
true_positives = cm[i, i]
false_positives = np.sum(cm[:, i]) - true_positives
false_negatives = np.sum(cm[i, :]) - true_positives
per_class_metrics[label] = {
'precision': round(class_report[label]['precision'], 4),
'recall': round(class_report[label]['recall'], 4),
'f1_score': round(class_report[label]['f1-score'], 4),
'support': int(class_report[label]['support']),
'true_positives': int(true_positives),
'false_positives': int(false_positives),
'false_negatives': int(false_negatives)
}
evaluation_result = {
'task_info': {
'task_name': 'multiparty.dialogue.emotion.recognition',
'dataset': 'MELD',
'evaluation_time': datetime.now().isoformat(),
'total_samples': len(results),
'valid_predictions': len(predictions),
'extraction_success_rate': round(len(predictions) / len(results), 4)
},
'metrics': {
'ACC': round(accuracy, 4),
'WAF': round(weighted_f1, 4),
'Macro_F1': round(macro_f1, 4),
'Micro_F1': round(micro_f1, 4)
},
'per_class_metrics': per_class_metrics,
'confusion_matrix': {
'labels': emotion_labels,
'matrix': cm.tolist(),
'normalized': (cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]).round(4).tolist()
},
'error_analysis': {
'extraction_errors': {
error_type: {
'count': len(sample_ids),
'sample_ids': sample_ids
} for error_type, sample_ids in extraction_errors.items()
},
'prediction_errors': {
error_pattern: {
'count': len(sample_ids),
'sample_ids': sample_ids
} for error_pattern, sample_ids in prediction_errors.items()
}
},
'distribution': {
'ground_truth': dict(Counter(ground_truths)),
'predictions': dict(Counter(predictions))
}
}
confusion_pairs = []
for i, label1 in enumerate(emotion_labels):
for j, label2 in enumerate(emotion_labels):
if i != j and cm[i, j] > 0:
confusion_pairs.append({
'true_emotion': label1,
'predicted_emotion': label2,
'count': int(cm[i, j]),
'percentage': round(cm[i, j] / np.sum(cm[i, :]) * 100, 2)
})
confusion_pairs.sort(key=lambda x: x['count'], reverse=True)
evaluation_result['emotion_confusion_analysis'] = {
'most_confused_pairs': confusion_pairs[:10]
}
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['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: ACC={evaluation_result['metrics']['ACC']}, WAF={evaluation_result['metrics']['WAF']}")
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")
if confusion_pairs:
print("\nMost confusable emotion pairs:")
for pair in confusion_pairs[:5]:
print(f" {pair['true_emotion']} → {pair['predicted_emotion']}: {pair['count']} times ({pair['percentage']}%)")
return evaluation_result
if __name__ == "__main__":
result_file = "model_result.json"
try:
evaluation_result = evaluate_multiparty_dialogue_emotion_recognition(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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