a6697e7cca
- Added a new markdown document outlining the plan to enhance the LightGBM model's AUC from 0.54 to 0.57+ through feature normalization, strong time weighting, and walk-forward validation. - Implemented rolling z-score normalization for absolute value features in `src/dataset_builder.py` to improve model robustness against regime changes. - Introduced a walk-forward validation function in `scripts/train_model.py` to accurately measure future prediction performance. - Updated training log to include new model performance metrics and added ONNX model export functionality for compatibility. - Adjusted model training parameters for better performance and included detailed validation results in the training log.
377 lines
13 KiB
Python
377 lines
13 KiB
Python
"""
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과거 캔들 데이터로 LightGBM 필터 모델을 학습하고 저장한다.
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사용법: python scripts/train_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 math
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from datetime import datetime
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from multiprocessing import Pool, cpu_count
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from pathlib import Path
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import joblib
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import lightgbm as lgb
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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.indicators import Indicators
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from src.ml_features import build_features, FEATURE_COLS
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from src.label_builder import build_labels
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from src.dataset_builder import generate_dataset_vectorized
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def _cgroup_cpu_count() -> int:
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"""cgroup v1/v2 쿼터를 읽어 실제 할당된 CPU 수를 반환한다.
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LXC/컨테이너 환경에서 cpu_count()가 호스트 전체 코어를 반환하는 문제를 방지한다.
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쿼터를 읽을 수 없으면 cpu_count()를 그대로 사용한다.
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"""
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# cgroup v2
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try:
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quota_path = Path("/sys/fs/cgroup/cpu.max")
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if quota_path.exists():
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parts = quota_path.read_text().split()
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if parts[0] != "max":
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quota = int(parts[0])
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period = int(parts[1])
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return max(1, math.floor(quota / period))
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except Exception:
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pass
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# cgroup v1
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try:
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quota = int(Path("/sys/fs/cgroup/cpu/cpu.cfs_quota_us").read_text())
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period = int(Path("/sys/fs/cgroup/cpu/cpu.cfs_period_us").read_text())
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if quota > 0:
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return max(1, math.floor(quota / period))
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except Exception:
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pass
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return cpu_count()
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LOOKAHEAD = 60
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ATR_SL_MULT = 1.5
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ATR_TP_MULT = 3.0
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MODEL_PATH = Path("models/lgbm_filter.pkl")
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PREV_MODEL_PATH = Path("models/lgbm_filter_prev.pkl")
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LOG_PATH = Path("models/training_log.json")
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def _process_index(args: tuple) -> dict | None:
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"""단일 인덱스에 대해 피처+레이블을 계산한다. Pool worker 함수."""
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i, df_values, df_columns = args
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df = pd.DataFrame(df_values, columns=df_columns)
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window = df.iloc[i - 60: i + 1].copy()
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ind = Indicators(window)
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df_ind = ind.calculate_all()
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if df_ind.iloc[-1].isna().any():
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return None
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signal = ind.get_signal(df_ind)
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if signal == "HOLD":
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return None
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entry_price = float(df_ind["close"].iloc[-1])
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atr = float(df_ind["atr"].iloc[-1])
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if atr <= 0:
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return None
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stop_loss = entry_price - atr * ATR_SL_MULT if signal == "LONG" else entry_price + atr * ATR_SL_MULT
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take_profit = entry_price + atr * ATR_TP_MULT if signal == "LONG" else entry_price - atr * ATR_TP_MULT
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future = df.iloc[i + 1: i + 1 + LOOKAHEAD]
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label = build_labels(
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future_closes=future["close"].tolist(),
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future_highs=future["high"].tolist(),
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future_lows=future["low"].tolist(),
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take_profit=take_profit,
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stop_loss=stop_loss,
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side=signal,
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)
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if label is None:
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return None
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features = build_features(df_ind, signal)
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row = features.to_dict()
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row["label"] = label
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return row
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def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFrame:
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"""신호 발생 시점마다 피처와 레이블을 병렬로 생성한다."""
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total = len(df)
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indices = range(60, total - LOOKAHEAD)
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# M4 mini: 10코어(P4+E6). 너무 많은 worker는 IPC 오버헤드를 늘리므로 8로 제한
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workers = n_jobs or min(max(1, _cgroup_cpu_count() - 1), 8)
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print(f" 병렬 처리: {workers}코어 사용 (총 {len(indices):,}개 인덱스)")
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# DataFrame을 numpy로 변환해서 worker 간 전달 비용 최소화
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df_values = df.values
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df_columns = list(df.columns)
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task_args = [(i, df_values, df_columns) for i in indices]
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rows = []
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errors = []
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# chunksize를 크게 잡아 IPC 직렬화 횟수를 줄임
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chunk = max(100, len(task_args) // workers)
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with Pool(processes=workers) as pool:
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for idx, result in enumerate(pool.imap(_process_index, task_args, chunksize=chunk)):
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if isinstance(result, dict):
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rows.append(result)
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elif result is not None:
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errors.append(result)
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if (idx + 1) % 10000 == 0:
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print(f" 진행: {idx + 1:,}/{len(task_args):,} | 샘플: {len(rows):,}개")
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if errors:
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print(f" [경고] worker 오류 {len(errors)}건: {errors[0]}")
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if not rows:
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print(" [오류] 생성된 샘플이 없습니다. worker 예외 여부를 확인합니다...")
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# 단일 프로세스로 첫 번째 인덱스를 직접 실행해서 예외 확인
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try:
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test_result = _process_index(task_args[0])
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print(f" 단일 실행 결과: {test_result}")
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except Exception as e:
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import traceback
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print(f" 단일 실행 예외:\n{traceback.format_exc()}")
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return pd.DataFrame(rows)
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def train(data_path: str, time_weight_decay: float = 2.0):
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print(f"데이터 로드: {data_path}")
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df_raw = pd.read_parquet(data_path)
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print(f"캔들 수: {len(df_raw)}, 컬럼: {list(df_raw.columns)}")
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# 병합 데이터셋 여부 판별
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btc_df = None
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eth_df = None
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base_cols = ["open", "high", "low", "close", "volume"]
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if "close_btc" in df_raw.columns:
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btc_df = df_raw[[c + "_btc" for c in base_cols]].copy()
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btc_df.columns = base_cols
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print("BTC 피처 활성화")
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if "close_eth" in df_raw.columns:
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eth_df = df_raw[[c + "_eth" for c in base_cols]].copy()
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eth_df.columns = base_cols
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print("ETH 피처 활성화")
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df = df_raw[base_cols].copy()
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print("데이터셋 생성 중...")
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dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay)
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if dataset.empty or "label" not in dataset.columns:
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raise ValueError(f"데이터셋 생성 실패: 샘플 0개. 위 오류 메시지를 확인하세요.")
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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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actual_feature_cols = [c for c in FEATURE_COLS if c in dataset.columns]
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print(f"사용 피처: {len(actual_feature_cols)}개 {actual_feature_cols}")
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X = dataset[actual_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 (시간 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.sort(np.concatenate([pos_idx, neg_idx])) # 시간 순서 유지
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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 후 학습 데이터: {len(X_train)}개 (양성={y_train.sum()}, 음성={(y_train==0).sum()})")
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print(f"검증 데이터: {len(X_val)}개 (양성={int(y_val.sum())}, 음성={int((y_val==0).sum())})")
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# ---------------------------------------------------------------
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model = lgb.LGBMClassifier(
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n_estimators=500,
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learning_rate=0.05,
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num_leaves=31,
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min_child_samples=15,
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subsample=0.8,
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colsample_bytree=0.8,
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reg_alpha=0.05,
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reg_lambda=0.1,
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random_state=42,
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verbose=-1,
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)
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model.fit(
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X_train, y_train,
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sample_weight=w_train,
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eval_set=[(X_val, y_val)],
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eval_metric="auc",
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callbacks=[
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lgb.early_stopping(80, first_metric_only=True, verbose=False),
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lgb.log_evaluation(50),
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],
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)
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val_proba = model.predict_proba(X_val)[:, 1]
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auc = roc_auc_score(y_val, val_proba)
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# 최적 임계값 탐색 (F1 기준)
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thresholds = np.arange(0.40, 0.70, 0.05)
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best_thr, best_f1 = 0.50, 0.0
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for thr in thresholds:
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pred = (val_proba >= thr).astype(int)
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from sklearn.metrics import f1_score
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f1 = f1_score(y_val, pred, zero_division=0)
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if f1 > best_f1:
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best_f1, best_thr = f1, thr
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print(f"\n검증 AUC: {auc:.4f} | 최적 임계값: {best_thr:.2f} (F1={best_f1:.3f})")
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print(classification_report(y_val, (val_proba >= best_thr).astype(int), zero_division=0))
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if MODEL_PATH.exists():
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import shutil
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shutil.copy(MODEL_PATH, PREV_MODEL_PATH)
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print(f"기존 모델 백업: {PREV_MODEL_PATH}")
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MODEL_PATH.parent.mkdir(exist_ok=True)
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joblib.dump(model, MODEL_PATH)
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print(f"모델 저장: {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": "lgbm",
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"auc": round(auc, 4),
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"samples": len(dataset),
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"features": len(actual_feature_cols),
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"time_weight_decay": time_weight_decay,
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"model_path": str(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 walk_forward_auc(
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data_path: str,
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time_weight_decay: float = 2.0,
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n_splits: int = 5,
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train_ratio: float = 0.6,
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) -> None:
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"""Walk-Forward 검증: 슬라이딩 윈도우로 n_splits번 학습/검증 반복.
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시계열 순서를 지키면서 매 폴드마다 학습 구간을 늘려가며 검증한다.
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실제 미래 예측력의 평균 AUC를 측정하는 데 사용한다.
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"""
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import warnings
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print(f"\n=== Walk-Forward 검증 ({n_splits}폴드, decay={time_weight_decay}) ===")
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df_raw = pd.read_parquet(data_path)
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base_cols = ["open", "high", "low", "close", "volume"]
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btc_df = eth_df = None
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if "close_btc" in df_raw.columns:
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btc_df = df_raw[[c + "_btc" for c in base_cols]].copy()
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btc_df.columns = base_cols
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if "close_eth" in df_raw.columns:
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eth_df = df_raw[[c + "_eth" for c in base_cols]].copy()
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eth_df.columns = base_cols
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df = df_raw[base_cols].copy()
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dataset = generate_dataset_vectorized(
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df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay
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)
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actual_feature_cols = [c for c in FEATURE_COLS if c in dataset.columns]
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X = dataset[actual_feature_cols].values
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y = dataset["label"].values
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w = dataset["sample_weight"].values
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n = len(dataset)
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step = max(1, int(n * (1 - train_ratio) / n_splits))
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train_end_start = int(n * train_ratio)
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aucs = []
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for i in range(n_splits):
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tr_end = train_end_start + i * step
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val_end = tr_end + step
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if val_end > n:
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break
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X_tr, y_tr, w_tr = X[:tr_end], y[:tr_end], w[:tr_end]
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X_val, y_val = X[tr_end:val_end], y[tr_end:val_end]
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pos_idx = np.where(y_tr == 1)[0]
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neg_idx = np.where(y_tr == 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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idx = np.sort(np.concatenate([pos_idx, neg_idx]))
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model = lgb.LGBMClassifier(
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n_estimators=500,
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learning_rate=0.05,
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num_leaves=31,
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min_child_samples=15,
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subsample=0.8,
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colsample_bytree=0.8,
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reg_alpha=0.05,
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reg_lambda=0.1,
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random_state=42,
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verbose=-1,
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)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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model.fit(X_tr[idx], y_tr[idx], sample_weight=w_tr[idx])
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proba = model.predict_proba(X_val)[:, 1]
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auc = roc_auc_score(y_val, proba) if len(np.unique(y_val)) > 1 else 0.5
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aucs.append(auc)
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print(
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f" 폴드 {i+1}/{n_splits}: 학습={tr_end}개, "
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f"검증={tr_end}~{val_end} ({step}개), AUC={auc:.4f}"
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)
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print(f"\n Walk-Forward 평균 AUC: {np.mean(aucs):.4f} ± {np.std(aucs):.4f}")
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print(f" 폴드별: {[round(a, 4) for a in aucs]}")
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data", default="data/combined_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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parser.add_argument("--wf", action="store_true", help="Walk-Forward 검증 실행")
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parser.add_argument("--wf-splits", type=int, default=5, help="Walk-Forward 폴드 수")
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args = parser.parse_args()
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if args.wf:
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walk_forward_auc(args.data, time_weight_decay=args.decay, n_splits=args.wf_splits)
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else:
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train(args.data, time_weight_decay=args.decay)
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if __name__ == "__main__":
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main()
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