File size: 6,930 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 | import json
import re
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 = ['weak negative', 'strong negative', 'neutral', 'strong positive', 'weak positive']
patterns = [
r"['\"]emotion['\"]:\s*['\"]([^'\"]+)['\"]",
r"emotion['\"]?\s*:\s*['\"]?([^'\"]+)['\"]?",
r"\b(weak\s+negative|strong\s+negative|neutral|strong\s+positive|weak\s+positive)\b"
]
for pattern in patterns:
match = re.search(pattern, model_output.lower(), re.IGNORECASE)
if match:
emotion = match.group(1).lower().strip()
if emotion in valid_emotions:
return emotion, True, None
if re.search(r"['\"]emotion['\"]", model_output.lower()):
return None, False, "invalid_emotion_label"
elif any(word in model_output.lower() for word in ['negative', 'positive', 'neutral']):
return None, False, "emotion_found_but_not_extracted"
else:
return None, False, "no_emotion_pattern"
def evaluate_stock_comment_emotion_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)
prediction_errors = defaultdict(list)
sentiment_labels = ['strong negative', 'weak negative', 'neutral', 'weak positive', 'strong positive']
for item in results:
item_id = item['id']
model_output = item['model_output']
gt_label = item['ground_truth'].lower()
pred_label, is_valid, error_type = extract_emotion_from_output(model_output)
detailed_item = {
'id': item_id,
'model_output': model_output,
'extracted_prediction': pred_label,
'ground_truth': gt_label,
'correct': pred_label == gt_label if pred_label else False,
'valid': is_valid
}
detailed_results.append(detailed_item)
if not is_valid:
extraction_errors[error_type].append(item_id)
elif pred_label != gt_label:
error_pattern = f"{gt_label}_to_{pred_label}"
prediction_errors[error_pattern].append(item_id)
if is_valid:
predictions.append(pred_label)
ground_truths.append(gt_label)
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')
cm = confusion_matrix(ground_truths, predictions, labels=sentiment_labels)
class_report = classification_report(ground_truths, predictions,
target_names=sentiment_labels,
output_dict=True,
zero_division=0)
evaluation_result = {
'task_info': {
'task_name': 'stock.comment.emotion.analysis',
'dataset': 'FMSA-SC',
'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)
},
'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'])
} for label in sentiment_labels if label in class_report
},
'confusion_matrix': {
'labels': sentiment_labels,
'matrix': cm.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))
}
}
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)
if problem_samples:
print(f"Problematic samples: {len(problem_samples)},see {problem_report_file}")
return evaluation_result
if __name__ == "__main__":
result_file = "model_result.json"
try:
evaluation_result = evaluate_stock_comment_emotion_analysis(result_file)
except FileNotFoundError:
print(f"Error: File not found {result_file}")
except json.JSONDecodeError:
print(f"Error: {result_file} Invalid format")
except Exception as e:
print(f"Evaluation failed: {str(e)}") |