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.
128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
"""
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MLX 기반 신경망 필터를 학습하고 저장한다.
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M4 통합 GPU(Metal)를 자동으로 사용한다.
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사용법: python scripts/train_mlx_model.py --data data/xrpusdt_1m.parquet
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import argparse
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import json
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import time
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from datetime import datetime
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import numpy as np
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import pandas as pd
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from sklearn.metrics import roc_auc_score, classification_report
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from src.dataset_builder import generate_dataset_vectorized
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from src.ml_features import FEATURE_COLS
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from src.mlx_filter import MLXFilter
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MLX_MODEL_PATH = Path("models/mlx_filter.weights")
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LOG_PATH = Path("models/training_log.json")
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def train_mlx(data_path: str, time_weight_decay: float = 2.0) -> float:
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print(f"데이터 로드: {data_path}")
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df = pd.read_parquet(data_path)
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print(f"캔들 수: {len(df)}")
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print("\n데이터셋 생성 중...")
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t0 = time.perf_counter()
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dataset = generate_dataset_vectorized(df, time_weight_decay=time_weight_decay)
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t1 = time.perf_counter()
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print(f"데이터셋 생성 완료: {t1 - t0:.1f}초, {len(dataset)}개 샘플")
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if dataset.empty or "label" not in dataset.columns:
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raise ValueError("데이터셋 생성 실패: 샘플 0개")
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print(f"학습 샘플: {len(dataset)}개 (양성={dataset['label'].sum():.0f}, 음성={(dataset['label']==0).sum():.0f})")
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if len(dataset) < 200:
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raise ValueError(f"학습 샘플 부족: {len(dataset)}개 (최소 200 필요)")
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X = dataset[FEATURE_COLS]
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y = dataset["label"]
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w = dataset["sample_weight"].values
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split = int(len(X) * 0.8)
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X_train, X_val = X.iloc[:split], X.iloc[split:]
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y_train, y_val = y.iloc[:split], y.iloc[split:]
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w_train = w[:split]
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# --- 클래스 불균형 처리: 언더샘플링 (가중치 인덱스 보존) ---
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pos_idx = np.where(y_train == 1)[0]
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neg_idx = np.where(y_train == 0)[0]
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if len(neg_idx) > len(pos_idx):
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np.random.seed(42)
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neg_idx = np.random.choice(neg_idx, size=len(pos_idx), replace=False)
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balanced_idx = np.concatenate([pos_idx, neg_idx])
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np.random.shuffle(balanced_idx)
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X_train = X_train.iloc[balanced_idx]
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y_train = y_train.iloc[balanced_idx]
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w_train = w_train[balanced_idx]
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print(f"\n언더샘플링 적용 후 학습 데이터: {len(X_train)}개 (양성={y_train.sum()}, 음성={(y_train==0).sum()})")
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# --------------------------------------
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print("\nMLX 신경망 학습 시작 (GPU)...")
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t2 = time.perf_counter()
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model = MLXFilter(
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input_dim=len(FEATURE_COLS),
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hidden_dim=128,
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lr=1e-3,
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epochs=100,
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batch_size=256,
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)
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model.fit(X_train, y_train, sample_weight=w_train)
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t3 = time.perf_counter()
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print(f"학습 완료: {t3 - t2:.1f}초")
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val_proba = model.predict_proba(X_val)
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auc = roc_auc_score(y_val, val_proba)
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print(f"\n검증 AUC: {auc:.4f}")
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print(classification_report(y_val, (val_proba >= 0.60).astype(int)))
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MLX_MODEL_PATH.parent.mkdir(exist_ok=True)
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model.save(MLX_MODEL_PATH)
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print(f"모델 저장: {MLX_MODEL_PATH}")
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log = []
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if LOG_PATH.exists():
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with open(LOG_PATH) as f:
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log = json.load(f)
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log.append({
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"date": datetime.now().isoformat(),
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"backend": "mlx",
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"auc": round(auc, 4),
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"samples": len(dataset),
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"train_sec": round(t3 - t2, 1),
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"time_weight_decay": time_weight_decay,
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"model_path": str(MLX_MODEL_PATH),
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})
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with open(LOG_PATH, "w") as f:
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json.dump(log, f, indent=2)
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return auc
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data", default="data/xrpusdt_1m.parquet")
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parser.add_argument(
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"--decay", type=float, default=2.0,
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help="시간 가중치 감쇠 강도 (0=균등, 2.0=최신이 ~7.4배 높음)",
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)
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args = parser.parse_args()
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train_mlx(args.data, time_weight_decay=args.decay)
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if __name__ == "__main__":
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main()
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