feat: enhance model training and deployment scripts with time-weighted sampling

- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training.
- Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance.
- Adjusted deployment scripts to support new backend options and improved error handling for model file transfers.
- Added new entries to the training log for better tracking of model performance metrics over time.
- Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.

src/ml_filter.py

@@ -1,32 +1,63 @@
 from pathlib import Path
 import joblib
+import numpy as np
 import pandas as pd
 from loguru import logger
 
+from src.ml_features import FEATURE_COLS
+
+ONNX_MODEL_PATH = Path("models/mlx_filter.weights.onnx")
+LGBM_MODEL_PATH = Path("models/lgbm_filter.pkl")
+
 
 class MLFilter:
     """
-    LightGBM 모델을 로드하고 진입 여부를 판단한다.
-    모델 파일이 없으면 항상 진입을 허용한다 (폴백).
+    ML 필터. ONNX(MLX 신경망) 우선 로드, 없으면 LightGBM으로 폴백한다.
+    둘 다 없으면 항상 진입을 허용한다.
+
+    우선순위: ONNX > LightGBM > 폴백(항상 허용)
     """
 
-    def __init__(self, model_path: str = "models/lgbm_filter.pkl", threshold: float = 0.60):
-        self._model_path = Path(model_path)
+    def __init__(
+        self,
+        onnx_path: str = str(ONNX_MODEL_PATH),
+        lgbm_path: str = str(LGBM_MODEL_PATH),
+        threshold: float = 0.60,
+    ):
+        self._onnx_path = Path(onnx_path)
+        self._lgbm_path = Path(lgbm_path)
         self._threshold = threshold
-        self._model = None
+        self._onnx_session = None
+        self._lgbm_model = None
         self._try_load()
 
     def _try_load(self):
-        if self._model_path.exists():
+        # ONNX 우선 시도
+        if self._onnx_path.exists():
             try:
-                self._model = joblib.load(self._model_path)
-                logger.info(f"ML 필터 모델 로드 완료: {self._model_path}")
+                import onnxruntime as ort
+                self._onnx_session = ort.InferenceSession(
+                    str(self._onnx_path),
+                    providers=["CPUExecutionProvider"],
+                )
+                self._lgbm_model = None
+                logger.info(f"ML 필터 ONNX 모델 로드 완료: {self._onnx_path}")
+                return
             except Exception as e:
-                logger.warning(f"ML 필터 모델 로드 실패: {e}")
-                self._model = None
+                logger.warning(f"ONNX 모델 로드 실패: {e}")
+                self._onnx_session = None
+
+        # LightGBM 폴백
+        if self._lgbm_path.exists():
+            try:
+                self._lgbm_model = joblib.load(self._lgbm_path)
+                logger.info(f"ML 필터 LightGBM 모델 로드 완료: {self._lgbm_path}")
+            except Exception as e:
+                logger.warning(f"LightGBM 모델 로드 실패: {e}")
+                self._lgbm_model = None
 
     def is_model_loaded(self) -> bool:
-        return self._model is not None
+        return self._onnx_session is not None or self._lgbm_model is not None
 
     def should_enter(self, features: pd.Series) -> bool:
         """
@@ -36,8 +67,13 @@ class MLFilter:
         if not self.is_model_loaded():
             return True
         try:
-            X = features.to_frame().T
-            proba = self._model.predict_proba(X)[0][1]
+            if self._onnx_session is not None:
+                input_name = self._onnx_session.get_inputs()[0].name
+                X = features[FEATURE_COLS].values.astype(np.float32).reshape(1, -1)
+                proba = float(self._onnx_session.run(None, {input_name: X})[0][0])
+            else:
+                X = features.to_frame().T
+                proba = float(self._lgbm_model.predict_proba(X)[0][1])
             logger.debug(f"ML 필터 확률: {proba:.3f} (임계값: {self._threshold})")
             return bool(proba >= self._threshold)
         except Exception as e:
@@ -46,5 +82,7 @@ class MLFilter:
 
     def reload_model(self):
         """재학습 후 모델을 핫 리로드한다."""
+        self._onnx_session = None
+        self._lgbm_model = None
         self._try_load()
         logger.info("ML 필터 모델 리로드 완료")