import json 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_techniques_from_output(model_output): if not model_output: return [], False, "empty_output" valid_techniques = [ "Appeal to authority", "Appeal to fear/prejudice", "Black-and-white Fallacy/Dictatorship", "Causal Oversimplification", "Doubt", "Exaggeration/Minimisation", "Flag-waving", "Glittering generalities (Virtue)", "Loaded Language", "Misrepresentation of Someone's Position (Straw Man)", "Name calling/Labeling", "Obfuscation, Intentional vagueness, Confusion", "Presenting Irrelevant Data (Red Herring)", "Reductio ad hitlerum", "Repetition", "Slogans", "Smears", "Thought-terminating cliché", "Whataboutism", "Bandwagon", "Transfer", "Appeal to (Strong) Emotions" ] try: if "{'techniques':" in model_output or '{"techniques":' in model_output: cleaned_output = model_output.strip() if not cleaned_output.startswith('{'): 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 'techniques' in parsed and isinstance(parsed['techniques'], list): techniques = [tech.strip() for tech in parsed['techniques']] valid_techniques_found = [tech for tech in techniques if tech in valid_techniques] invalid_techniques = [tech for tech in techniques if tech not in valid_techniques] if invalid_techniques: return valid_techniques_found, False, "invalid_technique_labels" return valid_techniques_found, True, None found_techniques = [] for technique in valid_techniques: if technique in model_output: found_techniques.append(technique) if found_techniques: return found_techniques, False, "techniques_found_but_not_properly_formatted" return [], False, "no_techniques_pattern" except Exception as e: return [], False, f"parsing_error_{str(e)}" def calculate_multilabel_metrics(y_true_list, y_pred_list, all_labels): y_true_binary = [] y_pred_binary = [] for y_true, y_pred in zip(y_true_list, y_pred_list): true_vector = [1 if label in y_true else 0 for label in all_labels] pred_vector = [1 if label in y_pred else 0 for label in all_labels] y_true_binary.append(true_vector) y_pred_binary.append(pred_vector) y_true_binary = np.array(y_true_binary) y_pred_binary = np.array(y_pred_binary) micro_f1 = f1_score(y_true_binary, y_pred_binary, average='micro') macro_f1 = f1_score(y_true_binary, y_pred_binary, average='macro') micro_precision = precision_score(y_true_binary, y_pred_binary, average='micro') micro_recall = recall_score(y_true_binary, y_pred_binary, average='micro') per_label_f1 = f1_score(y_true_binary, y_pred_binary, average=None) per_label_precision = precision_score(y_true_binary, y_pred_binary, average=None) per_label_recall = recall_score(y_true_binary, y_pred_binary, average=None) return { 'micro_f1': micro_f1, 'macro_f1': macro_f1, 'micro_precision': micro_precision, 'micro_recall': micro_recall, 'per_label_metrics': { all_labels[i]: { 'f1': per_label_f1[i], 'precision': per_label_precision[i], 'recall': per_label_recall[i] } for i in range(len(all_labels)) } } def evaluate_persuasion_techniques_detection(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) all_techniques = [ "Appeal to authority", "Appeal to fear/prejudice", "Black-and-white Fallacy/Dictatorship", "Causal Oversimplification", "Doubt", "Exaggeration/Minimisation", "Flag-waving", "Glittering generalities (Virtue)", "Loaded Language", "Misrepresentation of Someone's Position (Straw Man)", "Name calling/Labeling", "Obfuscation, Intentional vagueness, Confusion", "Presenting Irrelevant Data (Red Herring)", "Reductio ad hitlerum", "Repetition", "Slogans", "Smears", "Thought-terminating cliché", "Whataboutism", "Bandwagon", "Transfer", "Appeal to (Strong) Emotions" ] for item in results: item_id = item['id'] model_output = item['model_output'] gt_techniques = item['ground_truth']['techniques'] if isinstance(item['ground_truth'], dict) else item['ground_truth'] pred_techniques, is_valid, error_type = extract_techniques_from_output(model_output) detailed_item = { 'id': item_id, 'model_output': model_output, 'extracted_prediction': pred_techniques, 'ground_truth': gt_techniques, 'exact_match': set(pred_techniques) == set(gt_techniques) if is_valid else False, 'valid': is_valid } detailed_results.append(detailed_item) if not is_valid: extraction_errors[error_type].append(item_id) if is_valid: predictions.append(pred_techniques) ground_truths.append(gt_techniques) if len(predictions) == 0: return { 'error': 'No valid predictions found', 'total_samples': len(results), 'extraction_errors': dict(extraction_errors) } metrics = calculate_multilabel_metrics(ground_truths, predictions, all_techniques) exact_matches = sum(1 for item in detailed_results if item['exact_match']) exact_match_accuracy = exact_matches / len([item for item in detailed_results if item['valid']]) all_true_labels = [label for labels in ground_truths for label in labels] all_pred_labels = [label for labels in predictions for label in labels] evaluation_result = { 'task_info': { 'task_name': 'detection.of.persuasion.techniques.in.memes', 'dataset': 'SemEval-2021 Task 6', 'evaluation_time': datetime.now().isoformat(), 'total_samples': len(results), 'valid_predictions': len(predictions), 'extraction_success_rate': round(len(predictions) / len(results), 4) }, '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), 'Exact_Match_Accuracy': round(exact_match_accuracy, 4) }, 'per_label_metrics': { label: { 'f1': round(metrics['per_label_metrics'][label]['f1'], 4), 'precision': round(metrics['per_label_metrics'][label]['precision'], 4), 'recall': round(metrics['per_label_metrics'][label]['recall'], 4) } for label in all_techniques }, 'error_analysis': { 'extraction_errors': { error_type: { 'count': len(sample_ids), 'sample_ids': sample_ids } for error_type, sample_ids in extraction_errors.items() } }, 'label_statistics': { 'ground_truth_distribution': dict(Counter(all_true_labels)), 'prediction_distribution': dict(Counter(all_pred_labels)), 'avg_labels_per_sample': { 'ground_truth': round(np.mean([len(labels) for labels in ground_truths]), 2), 'predictions': round(np.mean([len(labels) for labels in predictions]), 2) } } } 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['exact_match']] 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']}, Exact Match Acc={evaluation_result['metrics']['Exact_Match_Accuracy']}") 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_persuasion_techniques_detection(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)}")