feat: add YAML archiving for run parameters and results with timestamped directories

README.md

@@ -252,6 +252,19 @@ save_to_xlsx(project_name=projet_name, file_name='cmn', data=cmn)
 save_to_xlsx(project_name=projet_name, file_name='acc_and_loss', data=epoch_data)
 ```
 
+### 3.6 每次运行参数与结果 YAML 存档
+
+当前 `main.py` 会为每次运行创建时间戳目录：
+
+- `Result/<project_name>/<YYYYMMDD_HHMMSS>/`
+
+并在目录下自动保存：
+
+- `run_params.yaml`：本次运行参数快照。
+- `run_result.yaml`：本次运行结果摘要（最佳轮次与最后轮次指标）。
+
+这样可以直接对比不同运行的参数与结果变化。
+
 ## 4. load_data 参数说明
 
 | 参数 | 类型 | 默认值 | 说明 |

main.py

@@ -1,22 +1,97 @@
+import datetime
+from pathlib import Path
+
 from Qtorch.Models.Qmlp import Qmlp
 from Qfunctions.loadData import load_data
 from Qfunctions.saveToXlsx import save_to_xlsx as save_to_xlsx
 
+
+def _to_builtin(value):
+  if isinstance(value, dict):
+    return {str(k): _to_builtin(v) for k, v in value.items()}
+  if isinstance(value, (list, tuple)):
+    return [_to_builtin(v) for v in value]
+  if hasattr(value, 'item'):
+    try:
+      return value.item()
+    except Exception:
+      return str(value)
+  return value
+
+
+def _yaml_scalar(value):
+  if value is None:
+    return 'null'
+  if isinstance(value, bool):
+    return 'true' if value else 'false'
+  if isinstance(value, (int, float)):
+    return str(value)
+  text = str(value).replace('"', '\\"')
+  return f'"{text}"'
+
+
+def _yaml_lines(value, indent=0):
+  space = ' ' * indent
+
+  if isinstance(value, dict):
+    if not value:
+      return [space + '{}']
+    lines = []
+    for k, v in value.items():
+      key = str(k)
+      if isinstance(v, (dict, list, tuple)):
+        lines.append(f'{space}{key}:')
+        lines.extend(_yaml_lines(v, indent + 2))
+      else:
+        lines.append(f'{space}{key}: {_yaml_scalar(v)}')
+    return lines
+
+  if isinstance(value, (list, tuple)):
+    if not value:
+      return [space + '[]']
+    lines = []
+    for item in value:
+      if isinstance(item, (dict, list, tuple)):
+        lines.append(f'{space}-')
+        lines.extend(_yaml_lines(item, indent + 2))
+      else:
+        lines.append(f'{space}- {_yaml_scalar(item)}')
+    return lines
+
+  return [space + _yaml_scalar(value)]
+
+
+def _save_yaml(file_path, data):
+  built_data = _to_builtin(data)
+  text = '\n'.join(_yaml_lines(built_data)) + '\n'
+  with open(file_path, 'w', encoding='utf-8') as f:
+    f.write(text)
+
 def main():
   # 输入元数据文件夹名称
-  projet_name = '20260319Numbers'                                                                
+  projet_name = '20260409 grap'
   # 请在[]内输入每一个分类的名称
   # label_names 是一个列表里面按顺序包含了小写的‘a'到‘z’
-  label_names = list(range(10))
+  label_names = list(range(1, 10))
+  hidden_layers = [256, 128, 128, 128]
+  test_size = 0.5
+  dropout_rate = 0
+  epochs = 300
+
+  run_id = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+  run_project_name = f'{projet_name}/{run_id}'
+  run_dir = Path('Result') / projet_name / run_id
+  run_dir.mkdir(parents=True, exist_ok=True)
+
   print(label_names)
   data = load_data(projet_name, label_names)
   
   model = Qmlp(
     data=data,
     labels=label_names,
-    hidden_layers = [128, 256, 128],
-    test_size=0.3,
-    dropout_rate=0      
+    hidden_layers=hidden_layers,
+    test_size=test_size,
+    dropout_rate=dropout_rate
     )
   # model = QCNN(
   #    data=data,
@@ -30,19 +105,71 @@ def main():
   
   pca_2d, pca_3d = model.get_PCA()
 
-  model.fit(300)
+  model.fit(epochs)
   
   cm = model.get_cm()
   cmn = model.get_cmn()
   epoch_data = model.get_epoch_data()
 
-  save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
-  save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
-  save_to_xlsx(project_name=projet_name, file_name="cm", data=cm)
-  save_to_xlsx(project_name=projet_name, file_name="cmn", data=cmn)
-  save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
+  save_to_xlsx(project_name=run_project_name, file_name='pca_2d', data=pca_2d)
+  save_to_xlsx(project_name=run_project_name, file_name='pca_3d', data=pca_3d)
+  save_to_xlsx(project_name=run_project_name, file_name='cm', data=cm)
+  save_to_xlsx(project_name=run_project_name, file_name='cmn', data=cmn)
+  save_to_xlsx(project_name=run_project_name, file_name='acc_and_loss', data=epoch_data)
 
-  print("Done")
+  run_params = {
+    'run_id': run_id,
+    'project_name': projet_name,
+    'label_names': label_names,
+    'model': {
+      'type': 'Qmlp',
+      'hidden_layers': hidden_layers,
+      'dropout_rate': dropout_rate,
+    },
+    'train': {
+      'epochs': epochs,
+      'test_size': test_size,
+    },
+    'created_at': datetime.datetime.now().isoformat(timespec='seconds'),
+  }
+
+  if not epoch_data.empty:
+    best_idx = int(epoch_data['test_accuracy'].idxmax())
+    best_row = epoch_data.loc[best_idx].to_dict()
+    last_row = epoch_data.iloc[-1].to_dict()
+    run_result = {
+      'run_id': run_id,
+      'result_dir': str(run_dir),
+      'best_epoch': int(best_row.get('epoch', 0)),
+      'best_metrics': best_row,
+      'last_epoch_metrics': last_row,
+      'generated_files': [
+        'pca_2d.xlsx',
+        'pca_2d.png',
+        'pca_3d.xlsx',
+        'pca_3d.png',
+        'cm.xlsx',
+        'cm.png',
+        'cmn.xlsx',
+        'cmn.png',
+        'acc_and_loss.xlsx',
+        'acc_and_loss_epoch.png',
+        'acc_and_loss_last_epoch_bar.png',
+      ],
+      'created_at': datetime.datetime.now().isoformat(timespec='seconds'),
+    }
+  else:
+    run_result = {
+      'run_id': run_id,
+      'result_dir': str(run_dir),
+      'message': 'No epoch data generated.',
+      'created_at': datetime.datetime.now().isoformat(timespec='seconds'),
+    }
+
+  _save_yaml(run_dir / 'run_params.yaml', run_params)
+  _save_yaml(run_dir / 'run_result.yaml', run_result)
+
+  print(f'Done. Result saved in: {run_dir}')
   
 if __name__ == '__main__':
   main()