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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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for field, acc in accuracies.items():
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print(f"Field '{field}' accuracy: {acc:.4f}")
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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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evaluate_scraping(args.output, args.groundtruth, args.threshold, args.result)
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if __name__ == "__main__":
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main() |
