db144750a3
- 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.
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
from pathlib import Path
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import joblib
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import numpy as np
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import pandas as pd
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from loguru import logger
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from src.ml_features import FEATURE_COLS
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ONNX_MODEL_PATH = Path("models/mlx_filter.weights.onnx")
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LGBM_MODEL_PATH = Path("models/lgbm_filter.pkl")
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class MLFilter:
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"""
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ML 필터. ONNX(MLX 신경망) 우선 로드, 없으면 LightGBM으로 폴백한다.
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둘 다 없으면 항상 진입을 허용한다.
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우선순위: ONNX > LightGBM > 폴백(항상 허용)
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"""
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def __init__(
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self,
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onnx_path: str = str(ONNX_MODEL_PATH),
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lgbm_path: str = str(LGBM_MODEL_PATH),
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threshold: float = 0.60,
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):
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self._onnx_path = Path(onnx_path)
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self._lgbm_path = Path(lgbm_path)
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self._threshold = threshold
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self._onnx_session = None
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self._lgbm_model = None
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self._try_load()
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def _try_load(self):
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# ONNX 우선 시도
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if self._onnx_path.exists():
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try:
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import onnxruntime as ort
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self._onnx_session = ort.InferenceSession(
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str(self._onnx_path),
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providers=["CPUExecutionProvider"],
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)
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self._lgbm_model = None
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logger.info(f"ML 필터 ONNX 모델 로드 완료: {self._onnx_path}")
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return
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except Exception as e:
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logger.warning(f"ONNX 모델 로드 실패: {e}")
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self._onnx_session = None
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# LightGBM 폴백
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if self._lgbm_path.exists():
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try:
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self._lgbm_model = joblib.load(self._lgbm_path)
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logger.info(f"ML 필터 LightGBM 모델 로드 완료: {self._lgbm_path}")
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except Exception as e:
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logger.warning(f"LightGBM 모델 로드 실패: {e}")
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self._lgbm_model = None
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def is_model_loaded(self) -> bool:
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return self._onnx_session is not None or self._lgbm_model is not None
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def should_enter(self, features: pd.Series) -> bool:
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"""
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확률 >= threshold 이면 True (진입 허용).
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모델 없으면 True 반환 (폴백).
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"""
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if not self.is_model_loaded():
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return True
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try:
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if self._onnx_session is not None:
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input_name = self._onnx_session.get_inputs()[0].name
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X = features[FEATURE_COLS].values.astype(np.float32).reshape(1, -1)
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proba = float(self._onnx_session.run(None, {input_name: X})[0][0])
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else:
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X = features.to_frame().T
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proba = float(self._lgbm_model.predict_proba(X)[0][1])
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logger.debug(f"ML 필터 확률: {proba:.3f} (임계값: {self._threshold})")
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return bool(proba >= self._threshold)
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except Exception as e:
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logger.warning(f"ML 필터 예측 오류 (폴백 허용): {e}")
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return True
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def reload_model(self):
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"""재학습 후 모델을 핫 리로드한다."""
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self._onnx_session = None
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self._lgbm_model = None
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self._try_load()
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logger.info("ML 필터 모델 리로드 완료")
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