feat: update training log and enhance ML filter functionality

- Added a new entry to the training log with detailed metrics for a LightGBM model, including AUC, precision, recall, and tuned parameters.
- Enhanced the MLFilter class to include a guard clause that prevents execution if the filter is disabled, improving robustness.

models/training_log.json

@@ -301,5 +301,30 @@
       "max_depth": 6
     },
     "weight_scale": 1.783105
+  },
+  {
+    "date": "2026-03-02T18:10:27.584046",
+    "backend": "lgbm",
+    "auc": 0.5466,
+    "best_threshold": 0.6424,
+    "best_precision": 0.426,
+    "best_recall": 0.556,
+    "samples": 535,
+    "features": 23,
+    "time_weight_decay": 0.5,
+    "model_path": "models/lgbm_filter.pkl",
+    "tuned_params_path": null,
+    "lgbm_params": {
+      "n_estimators": 434,
+      "learning_rate": 0.123659,
+      "max_depth": 6,
+      "num_leaves": 14,
+      "min_child_samples": 10,
+      "subsample": 0.929062,
+      "colsample_bytree": 0.94633,
+      "reg_alpha": 0.573971,
+      "reg_lambda": 0.000157
+    },
+    "weight_scale": 1.783105
   }
 ]

src/ml_filter.py

@@ -107,6 +107,7 @@ class MLFilter:
         모델 파일의 mtime을 확인해 변경됐으면 리로드한다.
         실제로 리로드가 일어났으면 True 반환.
         """
+        if self._disabled: return False
         onnx_changed = _mtime(self._onnx_path) != self._loaded_onnx_mtime
         lgbm_changed = _mtime(self._lgbm_path) != self._loaded_lgbm_mtime