| import argparse
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| import csv
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| import os
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| import json
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| from datetime import datetime
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|
|
|
|
| def check_file_valid(file_path: str) -> bool:
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| if not os.path.isfile(file_path):
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| print(f"❌ File does not exist: {file_path}")
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| return False
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| if os.path.getsize(file_path) == 0:
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| print(f"❌ File is empty: {file_path}")
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| return False
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| return True
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|
|
|
|
| def evaluate_scraping(pred_file: str, gt_file: str, threshold: float = 0.95, result_file: str = None):
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| process_success = check_file_valid(pred_file) and check_file_valid(gt_file)
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|
|
| if not process_success:
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| result = {
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| "Process": False,
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| "Result": False,
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| "TimePoint": datetime.now().isoformat(),
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| "comments": f"❌ File does not exist or is empty: pred={pred_file}, gt={gt_file}"
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| }
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| if result_file:
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| with open(result_file, "a", encoding="utf-8") as f:
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| f.write(json.dumps(result, ensure_ascii=False, default=str) + "\n")
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| return False
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|
|
|
|
| preds = []
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| with open(pred_file, 'r', encoding='utf-8') as f:
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| reader = csv.DictReader(f)
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| for row in reader:
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| preds.append(row)
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|
|
|
|
| gts = []
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| with open(gt_file, 'r', encoding='utf-8') as f:
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| reader = csv.DictReader(f)
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| for row in reader:
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| gts.append(row)
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|
|
| if len(preds) != len(gts):
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| print(
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| f"⚠️ Prediction and ground truth counts mismatch (predicted {len(preds)}, truth {len(gts)}), comparing minimum count.")
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|
|
| num_samples = min(len(preds), len(gts))
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|
|
| fields = preds[0].keys()
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| correct_counts = {field: 0 for field in fields}
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|
|
|
|
| for i in range(num_samples):
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| for field in fields:
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| if preds[i][field] == gts[i][field]:
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| correct_counts[field] += 1
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|
|
| accuracies = {field: correct_counts[field] / num_samples for field in fields}
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|
|
|
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| for field, acc in accuracies.items():
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| print(f"Field '{field}' accuracy: {acc:.4f}")
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|
|
|
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| success = all(acc >= threshold for acc in accuracies.values())
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|
|
| if success:
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| print("✅ Validation passed: All columns accuracy >95%")
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| else:
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| print("❌ Validation failed: Some columns accuracy <95%")
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|
|
|
|
| if result_file:
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| result = {
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| "Process": True,
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| "Result": success,
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| "TimePoint": datetime.now().isoformat(),
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| "comments": f"Field-level accuracy: {accuracies}, {'meets' if success else 'does not meet'} 95% threshold"
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| }
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| with open(result_file, "a", encoding="utf-8") as f:
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| f.write(json.dumps(result, ensure_ascii=False, default=str) + "\n")
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|
|
| return accuracies, success
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|
|
|
|
| def main():
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| parser = argparse.ArgumentParser(description="Evaluate field-level accuracy of Scrapy crawl results")
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| parser.add_argument('--output', type=str, required=True, help="Prediction results (CSV) path")
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| parser.add_argument('--groundtruth', type=str, required=True, help="Ground truth data (CSV) path")
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| parser.add_argument('--threshold', type=float, default=0.95, help="Field accuracy threshold")
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| parser.add_argument('--result', type=str, required=False, help="Output JSONL file path for results")
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|
|
| args = parser.parse_args()
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|
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| evaluate_scraping(args.output, args.groundtruth, args.threshold, args.result)
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|
|
|
|
| if __name__ == "__main__":
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| main() |
