--- a
+++ b/Tuning_hyperparameters/Best_fold_info.py
@@ -0,0 +1,100 @@
+import json
+import re
+from pathlib import Path
+from typing import Dict, List, Optional
+'''
+This script is used to deal with the problem that the best parameter json file and the running log are difficult to correspond.
+You only need to change the path of the last line to your directory, and the script will automatically find the lowest directory, 
+automatically read the unique json file and the.log file inside, and update the best parameter fold information to the json file.
+'''
+def parse_parameters(line: str) -> Optional[Dict]:
+    """Parsing of parameters"""
+    param_pattern = {
+        "batch": r"batch: (.+?)\s",
+        "lr": r"lr: (.+?)\s",
+        "patience": r"patience: (.+?)\s",
+        "dropout1": r"dropout1: (.+?)\s",
+        "dropout2": r"dropout2: (.+?)\s",
+        "earlystopping": r"earlystopping: (.+?)\s",
+        "tsv_file": r"tsv_file: (\S+\.tsv)"
+    }
+    
+    params = {}
+    for key, pattern in param_pattern.items():
+        match = re.search(pattern, line)
+        if not match:
+            return None
+        params[key] = match.group(1).strip()
+    return params
+
+def find_closest_statistics(log_lines: List[str], param_line_num: int) -> List[str]:
+    """Statistical value extraction"""
+    for i in range(param_line_num, len(log_lines)):
+        if "Statistic values for all folds" in log_lines[i]:
+            values_str = log_lines[i].split("[")[1].split("]")[0]
+            return [v.strip() for v in values_str.split(",")]
+    return []
+
+def match_string_parameters(config_params: Dict, log_params: Dict) -> bool:
+    """String matching"""
+    keys_to_match = ["batch", "lr", "patience", 
+                    "dropout1", "dropout2", "earlystopping"]
+    return all(
+        config_params.get(k, "") == log_params.get(k, "")
+        for k in keys_to_match
+    )
+
+def process_directory(dir_path: Path):
+    try:
+        json_file = next(dir_path.glob("*.json"))
+        log_file = next(dir_path.glob("*.log"))
+    except StopIteration:
+        return
+
+    with open(json_file, 'r+') as f:
+        config = json.load(f)
+        log_lines = open(log_file, 'r').readlines()
+        
+        param_white_list = {}
+        for tsv_name, entries in config.items():
+            param_white_list[tsv_name] = {
+                "batch": str(entries[1]["batch_size"]),
+                "lr": str(entries[1]["lr"]),
+                "patience": str(entries[1]["patience"]),
+                "dropout1": str(entries[1]["dropout1"]),
+                "dropout2": str(entries[1]["dropout2"]),
+                "earlystopping": str(entries[1]["earlystopping"])
+            }
+        
+        matched_stats = {}
+        for line_num, line in enumerate(log_lines):
+            params = parse_parameters(line)
+            if not params:
+                continue
+            
+            tsv_name = params["tsv_file"]
+            if tsv_name not in param_white_list:
+                continue
+            
+            if match_string_parameters(param_white_list[tsv_name], params):
+                stats = find_closest_statistics(log_lines, line_num)
+                if stats:
+                    matched_stats[tsv_name] = stats
+        
+        for tsv_name, stats in matched_stats.items():
+            try:
+                config[tsv_name][0] = [float(v) for v in stats]
+            except ValueError:
+                config[tsv_name][0] = []
+        
+        f.seek(0)
+        json.dump(config, f, indent=4)
+        f.truncate()
+
+def main(root_dir: Path):
+    for dir_path in root_dir.rglob('*'):
+        if dir_path.is_dir():
+            process_directory(dir_path)
+
+if __name__ == "__main__":
+    main(Path(r"Your/path/"))