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.

scripts/train_model.py

@@ -146,7 +146,7 @@ def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFram
     return pd.DataFrame(rows)
 
 
-def train(data_path: str):
+def train(data_path: str, time_weight_decay: float = 2.0):
     print(f"데이터 로드: {data_path}")
     df_raw = pd.read_parquet(data_path)
     print(f"캔들 수: {len(df_raw)}, 컬럼: {list(df_raw.columns)}")
@@ -169,7 +169,7 @@ def train(data_path: str):
     df = df_raw[base_cols].copy()
 
     print("데이터셋 생성 중...")
-    dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df)
+    dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay)
 
     if dataset.empty or "label" not in dataset.columns:
         raise ValueError(f"데이터셋 생성 실패: 샘플 0개. 위 오류 메시지를 확인하세요.")
@@ -183,10 +183,30 @@ def train(data_path: str):
     print(f"사용 피처: {len(actual_feature_cols)}개 {actual_feature_cols}")
     X = dataset[actual_feature_cols]
     y = dataset["label"]
+    w = dataset["sample_weight"].values
 
     split = int(len(X) * 0.8)
     X_train, X_val = X.iloc[:split], X.iloc[split:]
     y_train, y_val = y.iloc[:split], y.iloc[split:]
+    w_train = w[:split]
+
+    # --- 클래스 불균형 처리: 언더샘플링 (가중치 인덱스 보존) ---
+    pos_idx = np.where(y_train == 1)[0]
+    neg_idx = np.where(y_train == 0)[0]
+
+    if len(neg_idx) > len(pos_idx):
+        np.random.seed(42)
+        neg_idx = np.random.choice(neg_idx, size=len(pos_idx), replace=False)
+
+    balanced_idx = np.concatenate([pos_idx, neg_idx])
+    np.random.shuffle(balanced_idx)
+
+    X_train = X_train.iloc[balanced_idx]
+    y_train = y_train.iloc[balanced_idx]
+    w_train = w_train[balanced_idx]
+
+    print(f"\n언더샘플링 적용 후 학습 데이터: {len(X_train)}개 (양성={y_train.sum()}, 음성={(y_train==0).sum()})")
+    # --------------------------------------
 
     model = lgb.LGBMClassifier(
         n_estimators=300,
@@ -201,6 +221,7 @@ def train(data_path: str):
     )
     model.fit(
         X_train, y_train,
+        sample_weight=w_train,
         eval_set=[(X_val, y_val)],
         callbacks=[lgb.early_stopping(30, verbose=False), lgb.log_evaluation(50)],
     )
@@ -225,9 +246,11 @@ def train(data_path: str):
             log = json.load(f)
     log.append({
         "date": datetime.now().isoformat(),
+        "backend": "lgbm",
         "auc": round(auc, 4),
         "samples": len(dataset),
         "features": len(actual_feature_cols),
+        "time_weight_decay": time_weight_decay,
         "model_path": str(MODEL_PATH),
     })
     with open(LOG_PATH, "w") as f:
@@ -239,8 +262,12 @@ def train(data_path: str):
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--data", default="data/xrpusdt_1m.parquet")
+    parser.add_argument(
+        "--decay", type=float, default=2.0,
+        help="시간 가중치 감쇠 강도 (0=균등, 2.0=최신이 ~7.4배 높음)",
+    )
     args = parser.parse_args()
-    train(args.data)
+    train(args.data, time_weight_decay=args.decay)
 
 
 if __name__ == "__main__":