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import argparse
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import json
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import os
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from datetime import datetime
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from difflib import SequenceMatcher
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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 load_json_or_jsonl(file_path: str):
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with open(file_path, "r", encoding="utf-8") as f:
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content = f.read().strip()
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if not content:
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return []
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try:
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data = json.loads(content)
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if isinstance(data, list):
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return data
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except json.JSONDecodeError:
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pass
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lines = []
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with open(file_path, "r", encoding="utf-8") as f:
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for i, line in enumerate(f, 1):
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line = line.strip()
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if not line:
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continue
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try:
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item = json.loads(line)
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if isinstance(item, list):
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lines.extend(item)
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else:
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lines.append(item)
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except Exception as e:
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print(f"❌ Line {i} JSON parse failed: {line}")
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raise e
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return lines
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def normalized_similarity(a: str, b: str) -> float:
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return SequenceMatcher(None, a.strip(), b.strip()).ratio()
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def evaluate_scrapy_output(pred_path: str, truth_path: str, result_path: str = None) -> bool:
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threshold = 0.95
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process_success = check_file_valid(pred_path) and check_file_valid(truth_path)
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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_path}, truth={truth_path}"
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}
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if result_path:
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with open(result_path, "a", encoding="utf-8") as f:
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f.write(json.dumps(result, ensure_ascii=False) + "\n")
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return False
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try:
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pred_lines = load_json_or_jsonl(pred_path)
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true_lines = load_json_or_jsonl(truth_path)
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if len(pred_lines) != len(true_lines):
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print(f"⚠️ Crawl results count mismatch (predicted {len(pred_lines)}, truth {len(true_lines)})")
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total_fields = 0
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total_similarity = 0
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for pred, true in zip(pred_lines, true_lines):
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for field in ["author", "text"]:
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pred_val = str(pred.get(field, ""))
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true_val = str(true.get(field, ""))
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sim = normalized_similarity(pred_val, true_val)
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total_similarity += sim
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total_fields += 1
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avg_similarity = total_similarity / total_fields if total_fields else 0
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result_passed = avg_similarity >= threshold
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print(f"📊 Average field similarity (edit distance): {avg_similarity:.2%}")
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print("✅ Extraction valid, similarity >= 95%" if result_passed else "❌ Extraction failed")
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if result_path:
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result = {
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"Process": True,
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"Result": result_passed,
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"TimePoint": datetime.now().isoformat(),
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"comments": f"Average field similarity: {avg_similarity:.4f}, {'meets' if result_passed else 'does not meet'} 95% threshold"
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}
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with open(result_path, "a", encoding="utf-8") as f:
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f.write(json.dumps(result, ensure_ascii=False) + "\n")
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return result_passed
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except Exception as e:
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print(f"❌ Runtime error: {e}")
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if result_path:
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result = {
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"Process": True,
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"Result": False,
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"TimePoint": datetime.now().isoformat(),
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"comments": f"Runtime error: {str(e)}"
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}
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with open(result_path, "a", encoding="utf-8") as f:
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f.write(json.dumps(result, ensure_ascii=False) + "\n")
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return False
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Evaluate field-level similarity of Scrapy crawl results")
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parser.add_argument("--output", type=str, required=True, help="Prediction results (JSON/JSONL) path")
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parser.add_argument("--groundtruth", type=str, required=True, help="Ground truth (JSON/JSONL) path")
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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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success = evaluate_scrapy_output(args.output, args.groundtruth, args.result) |
