File size: 11,265 Bytes
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import re
import ast
from collections import Counter, defaultdict
from datetime import datetime
from sklearn.metrics import f1_score, precision_score, recall_score
def parse_emotion_cause_pairs(output_text):
if not output_text:
return {}, False, "empty_output"
valid_emotions = ['joy', 'sadness', 'anger', 'disgust', 'fear', 'surprise', 'neutral']
try:
cleaned_output = output_text.strip()
json_match = re.search(r'\{.*\}', cleaned_output, re.DOTALL)
if not json_match:
return {}, False, "no_json_structure"
json_content = json_match.group()
parsed = None
try:
parsed = ast.literal_eval(json_content)
except (ValueError, SyntaxError) as e:
if "EOF" in str(e) or "end of string" in str(e):
last_comma_index = json_content.rfind(',')
if last_comma_index != -1:
content_after_comma = json_content[last_comma_index+1:].strip()
if content_after_comma and content_after_comma != '}':
truncated_content = json_content[:last_comma_index] + '}'
try:
parsed = ast.literal_eval(truncated_content)
except (ValueError, SyntaxError):
pass
if parsed is None:
try:
json_content_fixed = json_content.replace("'", '"')
parsed = json.loads(json_content_fixed)
except json.JSONDecodeError:
raise e
if not isinstance(parsed, dict):
return {}, False, "not_dictionary"
validated_pairs = {}
for utterance_id, pair_data in parsed.items():
if not isinstance(pair_data, dict) or 'emotion' not in pair_data or 'cause_utterance_id' not in pair_data:
continue
emotion = str(pair_data['emotion']).lower().strip()
cause_id = str(pair_data['cause_utterance_id']).strip()
if emotion not in valid_emotions:
continue
try:
int(utterance_id)
int(cause_id)
except (ValueError, TypeError):
continue
validated_pairs[str(utterance_id)] = {
'emotion': emotion,
'cause_utterance_id': cause_id
}
return validated_pairs, len(validated_pairs) > 0, None
except Exception as e:
return {}, False, f"parsing_error_{type(e).__name__}"
def calculate_pair_extraction_metrics(predictions, ground_truths):
total_tp = 0
total_fp = 0
total_fn = 0
emotion_correct_on_common = 0
cause_correct_on_common = 0
total_common_utterances = 0
all_true_emotions = []
all_pred_emotions = []
for pred_dict, true_dict in zip(predictions, ground_truths):
pred_pairs = {(str(uid), data['emotion'], str(data['cause_utterance_id'])) for uid, data in pred_dict.items()}
true_pairs = {(str(uid), data['emotion'], str(data['cause_utterance_id'])) for uid, data in true_dict.items()}
all_pred_emotions.extend([data['emotion'] for data in pred_dict.values()])
all_true_emotions.extend([data['emotion'] for data in true_dict.values()])
total_tp += len(pred_pairs.intersection(true_pairs))
total_fp += len(pred_pairs.difference(true_pairs))
total_fn += len(true_pairs.difference(pred_pairs))
common_ids = set(pred_dict.keys()) & set(true_dict.keys())
total_common_utterances += len(common_ids)
for uid in common_ids:
if pred_dict[uid]['emotion'] == true_dict[uid]['emotion']:
emotion_correct_on_common += 1
if pred_dict[uid]['cause_utterance_id'] == true_dict[uid]['cause_utterance_id']:
cause_correct_on_common += 1
micro_precision = total_tp / (total_tp + total_fp) if (total_tp + total_fp) > 0 else 0
micro_recall = total_tp / (total_tp + total_fn) if (total_tp + total_fn) > 0 else 0
micro_f1 = 2 * micro_precision * micro_recall / (micro_precision + micro_recall) if (micro_precision + micro_recall) > 0 else 0
emotion_accuracy_common = emotion_correct_on_common / total_common_utterances if total_common_utterances > 0 else 0
cause_accuracy_common = cause_correct_on_common / total_common_utterances if total_common_utterances > 0 else 0
return {
'micro_f1': micro_f1,
'micro_precision': micro_precision,
'micro_recall': micro_recall,
'emotion_accuracy_on_common_ids': emotion_accuracy_common,
'cause_accuracy_on_common_ids': cause_accuracy_common,
'total_predicted_pairs': total_tp + total_fp,
'total_ground_truth_pairs': total_tp + total_fn,
'exact_matches': total_tp,
'emotion_distribution': dict(Counter(all_true_emotions)),
'predicted_emotion_distribution': dict(Counter(all_pred_emotions))
}
def evaluate_multimodal_emotion_cause_pair_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.get('id', 'unknown')
model_output = item.get('model_output', '')
ground_truth = item.get('ground_truth', {})
pred_pairs, pred_valid, pred_error = parse_emotion_cause_pairs(model_output)
if isinstance(ground_truth, str):
gt_pairs, gt_valid, gt_error = parse_emotion_cause_pairs(ground_truth)
else:
gt_pairs, gt_valid, gt_error = parse_emotion_cause_pairs(json.dumps(ground_truth))
detailed_item = {
'id': item_id,
'model_output': model_output,
'ground_truth': ground_truth,
'extracted_prediction': pred_pairs,
'extracted_ground_truth': gt_pairs,
'prediction_valid': pred_valid,
'ground_truth_valid': gt_valid,
'predicted_pairs_count': len(pred_pairs),
'ground_truth_pairs_count': len(gt_pairs)
}
detailed_results.append(detailed_item)
if not pred_valid:
extraction_errors[pred_error].append(item_id)
if not gt_valid:
extraction_errors[f"gt_{gt_error}"].append(item_id)
if pred_valid and gt_valid:
predictions.append(pred_pairs)
ground_truths.append(gt_pairs)
if not predictions:
print("Error: No valid prediction–ground truth pairs were found for evaluation.")
print(f"Total samples: {len(results)}")
print("Parsing error stats:", json.dumps(dict(extraction_errors), indent=2))
return {
'error': 'No valid prediction and ground_truth pairs found for evaluation',
'total_samples': len(results),
'extraction_errors': dict(extraction_errors)
}
metrics = calculate_pair_extraction_metrics(predictions, ground_truths)
evaluation_result = {
'task_info': {
'task_name': 'multimodal.emotion.cause.pair.extraction',
'dataset': 'ECF',
'evaluation_time': datetime.now().isoformat(),
'total_samples': len(results),
'valid_samples_for_eval': len(predictions),
'extraction_success_rate': round(len(predictions) / len(results) if results else 0, 4)
},
'metrics': {
'Micro_F1': round(metrics['micro_f1'], 4),
'Micro_Precision': round(metrics['micro_precision'], 4),
'Micro_Recall': round(metrics['micro_recall'], 4),
'Emotion_ACC_on_Common_IDs': round(metrics['emotion_accuracy_on_common_ids'], 4),
'Cause_ACC_on_Common_IDs': round(metrics['cause_accuracy_on_common_ids'], 4),
'Total_Predicted_Pairs': metrics['total_predicted_pairs'],
'Total_Ground_Truth_Pairs': metrics['total_ground_truth_pairs'],
'Exact_Matches': metrics['exact_matches']
},
'distribution_analysis': {
'emotion_distribution': metrics['emotion_distribution'],
'predicted_emotion_distribution': metrics['predicted_emotion_distribution']
},
'error_analysis': {
'extraction_errors': {
error_type: {
'count': len(sample_ids),
'sample_ids': sample_ids[:10]
} for error_type, sample_ids in extraction_errors.items()
}
}
}
base_name = result_file_path.rsplit('.', 1)[0]
eval_output_file = f"{base_name}_evaluation.json"
detailed_output_file = f"{base_name}_detailed_results.json"
with open(eval_output_file, 'w', encoding='utf-8') as f:
json.dump(evaluation_result, f, ensure_ascii=False, indent=2)
with open(detailed_output_file, 'w', encoding='utf-8') as f:
json.dump(detailed_results, f, ensure_ascii=False, indent=2)
print("\n" + "="*50)
print("Evaluation Report")
print("="*50)
print(f"Evaluation complete: {len(results)} total samples")
print(f"Valid evaluation samples (both prediction and label parsed successfully): {len(predictions)} / {len(results)} (extraction success rate: {evaluation_result['task_info']['extraction_success_rate']:.2%})")
print("\n--- Primary Metrics ---")
print(f"Micro F1: {evaluation_result['metrics']['Micro_F1']:.4f}")
print(f"Micro Precision: {evaluation_result['metrics']['Micro_Precision']:.4f}")
print(f"Micro Recall: {evaluation_result['metrics']['Micro_Recall']:.4f}")
print("\n--- Auxiliary Metrics ---")
print(f"Emotion accuracy on common IDs (Emotion ACC): {evaluation_result['metrics']['Emotion_ACC_on_Common_IDs']:.4f}")
print(f"Cause accuracy on common IDs (Cause ACC): {evaluation_result['metrics']['Cause_ACC_on_Common_IDs']:.4f}")
print(f"Total predicted pairs: {evaluation_result['metrics']['Total_Predicted_Pairs']}")
print(f"Total ground-truth pairs: {evaluation_result['metrics']['Total_Ground_Truth_Pairs']}")
print(f"Exact matches: {evaluation_result['metrics']['Exact_Matches']}")
if extraction_errors:
print("\n--- Parsing Error Analysis ---")
for error_type, info in evaluation_result['error_analysis']['extraction_errors'].items():
print(f"- Type: {error_type}, Count: {info['count']}")
print(f"Detailed evaluation results saved to: {eval_output_file}")
print(f"Per-sample parsing details saved to: {detailed_output_file}")
return evaluation_result
if __name__ == "__main__":
result_file = "model_result.json"
try:
evaluation_result = evaluate_multimodal_emotion_cause_pair_extraction(result_file)
except FileNotFoundError:
print(f"Error: File '{result_file}' not found. Please check that the path is correct.")
except json.JSONDecodeError:
print(f"Error: File '{result_file}' is not valid JSON. Ensure the file is a JSON list (starts with '[' and ends with ']').")
except Exception as e:
print(f"An unknown error occurred during evaluation: {e}")
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