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feat: connect Optuna tuning results to train_model.py via --tuned-params

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- _load_lgbm_params() 헬퍼 추가: 기본 파라미터 반환, JSON 주어지면 덮어씀
- train(): tuned_params_path 인자 추가, weight_scale 적용
- walk_forward_auc(): tuned_params_path 인자 추가, weight_scale 적용
- main(): --tuned-params argparse 인자 추가, 두 함수에 전달
- training_log.json에 tuned_params_path, lgbm_params, weight_scale 기록

Made-with: Cursor
This commit is contained in:
21in7
2026-03-02 14:45:15 +09:00
parent caaa81f5f9
commit 4afc7506d7
1 changed files with 57 additions and 30 deletions
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87
scripts/train_model.py
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@@ -146,7 +146,36 @@ def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFram
return pd.DataFrame(rows)
def train(data_path: str, time_weight_decay: float = 2.0):
def _load_lgbm_params(tuned_params_path: str | None) -> tuple[dict, float]:
"""기본 LightGBM 파라미터를 반환하고, 튜닝 JSON이 주어지면 덮어쓴다.
반환: (lgbm_params, weight_scale)
"""
lgbm_params: dict = {
"n_estimators": 500,
"learning_rate": 0.05,
"num_leaves": 31,
"min_child_samples": 15,
"subsample": 0.8,
"colsample_bytree": 0.8,
"reg_alpha": 0.05,
"reg_lambda": 0.1,
}
weight_scale = 1.0
if tuned_params_path:
with open(tuned_params_path, "r", encoding="utf-8") as f:
tune_data = json.load(f)
best_params = dict(tune_data["best_trial"]["params"])
weight_scale = float(best_params.pop("weight_scale", 1.0))
lgbm_params.update(best_params)
print(f"\n[Optuna] 튜닝 파라미터 로드: {tuned_params_path}")
print(f"[Optuna] 적용 파라미터: {lgbm_params}")
print(f"[Optuna] weight_scale: {weight_scale}\n")
return lgbm_params, weight_scale
def train(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None):
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)}")
@@ -188,7 +217,10 @@ def train(data_path: str, time_weight_decay: float = 2.0):
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]
# 튜닝 파라미터 로드 (없으면 기본값 사용)
lgbm_params, weight_scale = _load_lgbm_params(tuned_params_path)
w_train = (w[:split] * weight_scale).astype(np.float32)
# --- 클래스 불균형 처리: 언더샘플링 (시간 가중치 인덱스 보존) ---
pos_idx = np.where(y_train == 1)[0]
@@ -208,18 +240,7 @@ def train(data_path: str, time_weight_decay: float = 2.0):
print(f"검증 데이터: {len(X_val)}개 (양성={int(y_val.sum())}, 음성={int((y_val==0).sum())})")
# ---------------------------------------------------------------
model = lgb.LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
num_leaves=31,
min_child_samples=15,
subsample=0.8,
colsample_bytree=0.8,
reg_alpha=0.05,
reg_lambda=0.1,
random_state=42,
verbose=-1,
)
model = lgb.LGBMClassifier(**lgbm_params, random_state=42, verbose=-1)
model.fit(
X_train, y_train,
sample_weight=w_train,
@@ -268,7 +289,7 @@ def train(data_path: str, time_weight_decay: float = 2.0):
if LOG_PATH.exists():
with open(LOG_PATH) as f:
log = json.load(f)
log.append({
log_entry: dict = {
"date": datetime.now().isoformat(),
"backend": "lgbm",
"auc": round(auc, 4),
@@ -279,7 +300,11 @@ def train(data_path: str, time_weight_decay: float = 2.0):
"features": len(actual_feature_cols),
"time_weight_decay": time_weight_decay,
"model_path": str(MODEL_PATH),
})
"tuned_params_path": tuned_params_path,
"lgbm_params": lgbm_params,
"weight_scale": weight_scale,
}
log.append(log_entry)
with open(LOG_PATH, "w") as f:
json.dump(log, f, indent=2)
@@ -291,6 +316,7 @@ def walk_forward_auc(
time_weight_decay: float = 2.0,
n_splits: int = 5,
train_ratio: float = 0.6,
tuned_params_path: str | None = None,
) -> None:
"""Walk-Forward 검증: 슬라이딩 윈도우로 n_splits번 학습/검증 반복.
@@ -320,6 +346,9 @@ def walk_forward_auc(
w = dataset["sample_weight"].values
n = len(dataset)
lgbm_params, weight_scale = _load_lgbm_params(tuned_params_path)
w = (w * weight_scale).astype(np.float32)
step = max(1, int(n * (1 - train_ratio) / n_splits))
train_end_start = int(n * train_ratio)
@@ -340,18 +369,7 @@ def walk_forward_auc(
neg_idx = np.random.choice(neg_idx, size=len(pos_idx), replace=False)
idx = np.sort(np.concatenate([pos_idx, neg_idx]))
model = lgb.LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
num_leaves=31,
min_child_samples=15,
subsample=0.8,
colsample_bytree=0.8,
reg_alpha=0.05,
reg_lambda=0.1,
random_state=42,
verbose=-1,
)
model = lgb.LGBMClassifier(**lgbm_params, random_state=42, verbose=-1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.fit(X_tr[idx], y_tr[idx], sample_weight=w_tr[idx])
@@ -377,12 +395,21 @@ def main():
)
parser.add_argument("--wf", action="store_true", help="Walk-Forward 검증 실행")
parser.add_argument("--wf-splits", type=int, default=5, help="Walk-Forward 폴드 수")
parser.add_argument(
"--tuned-params", type=str, default=None,
help="Optuna 튜닝 결과 JSON 경로 (지정 시 기본 파라미터를 덮어씀)",
)
args = parser.parse_args()
if args.wf:
walk_forward_auc(args.data, time_weight_decay=args.decay, n_splits=args.wf_splits)
walk_forward_auc(
args.data,
time_weight_decay=args.decay,
n_splits=args.wf_splits,
tuned_params_path=args.tuned_params,
)
else:
train(args.data, time_weight_decay=args.decay)
train(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params)
if __name__ == "__main__":