lightgbm

train/train_lightgbm.py

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+import argparse
+import csv
+import json
+import math
+import os
+import random
+import joblib
+import numpy as np
+import lightgbm as lgb
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_absolute_error
+
+# =========================================================
+# Hyper Parameters
+# =========================================================
+
+TRAIN_SIZE = 0
+VALID_SIZE = 0
+
+RANDOM_STATE = 42
+
+# LightGBM 특정 하이퍼파라미터
+PARAMS = {
+    'objective': 'regression',
+    'metric': 'mae',
+    'verbosity': -1,
+    'boosting_type': 'gbdt',
+    'random_state': RANDOM_STATE,
+    'learning_rate': 0.02, # 더 정밀한 학습을 위해 하향
+    'num_leaves': 63,      # 더 복잡한 패턴 학습을 위해 상향
+    'feature_fraction': 0.9,
+    'bagging_fraction': 0.8,
+    'bagging_freq': 5,
+    'n_estimators': 3000   # 학습량 대폭 상향
+}
+
+CONTINUE_TRAINING = True
+ERROR_TOLERANCE = 0.1
+
+# =========================================================
+# 파일명
+# =========================================================
+
+FEATURES_FILENAME = "features.json"
+MEASURE_FILENAME = "measure.csv"
+MODEL_FILENAME = "model_lgbm.pkl"
+SCALER_FILENAME = "scaler_lgbm.pkl"
+FEATURE_NAMES_FILENAME = "features_lgbm.txt"
+
+IGNORE_KEYS = {"songno", "difficulty"}
+
+def safe_float(value):
+    if value is None: return 0.0
+    x = float(value)
+    return x if math.isfinite(x) else 0.0
+
+def train_model(working_dir: str, data_dir: str):
+    random.seed(RANDOM_STATE)
+
+    features_path = os.path.join(working_dir, FEATURES_FILENAME)
+    measure_path = os.path.join(data_dir, MEASURE_FILENAME)
+    model_path = os.path.join(working_dir, MODEL_FILENAME)
+    scaler_path = os.path.join(working_dir, SCALER_FILENAME)
+    feature_names_path = os.path.join(working_dir, FEATURE_NAMES_FILENAME)
+
+    with open(features_path, "r", encoding="utf-8") as f:
+        feature_data = json.load(f)
+
+    if len(feature_data) == 0:
+        raise ValueError("features.json is empty")
+
+    feature_map = {(str(item["songno"]), str(item["difficulty"])): item for item in feature_data}
+    feature_names = sorted([k for k in feature_data[0].keys() if k not in IGNORE_KEYS])
+
+    dataset = []
+    with open(measure_path, "r", encoding="utf-8") as f:
+        reader = csv.reader(f)
+        next(reader, None)
+        for row in reader:
+            if len(row) < 3: continue
+            measure, songno, diff = safe_float(row[0]), str(row[1]), str(row[2])
+            key = (songno, diff)
+            if key in feature_map:
+                features = [safe_float(feature_map[key].get(k, 0)) for k in feature_names]
+                dataset.append((features, measure))
+
+    random.shuffle(dataset)
+    if len(dataset) < (TRAIN_SIZE + VALID_SIZE):
+        raise ValueError(f"Not enough dataset ({len(dataset)} < {TRAIN_SIZE + VALID_SIZE})")
+
+    train_dataset = dataset[:TRAIN_SIZE]
+    valid_dataset = dataset[TRAIN_SIZE:TRAIN_SIZE + VALID_SIZE]
+
+    X_train = np.array([x for x, _ in train_dataset], dtype=np.float32)
+    y_train = np.array([y for _, y in train_dataset], dtype=np.float32)
+    X_valid = np.array([x for x, _ in valid_dataset], dtype=np.float32)
+    y_valid = np.array([y for _, y in valid_dataset], dtype=np.float32)
+
+    print(f"Train Size: {len(X_train)} | Valid Size: {len(X_valid)} | Features: {len(feature_names)}")
+
+    if CONTINUE_TRAINING and os.path.exists(scaler_path):
+        print("Loading existing scaler...")
+        scaler = joblib.load(scaler_path)
+    else:
+        print("Creating new scaler...")
+        scaler = StandardScaler()
+        scaler.fit(X_train)
+
+    X_train = scaler.transform(X_train)
+    X_valid = scaler.transform(X_valid)
+
+    if CONTINUE_TRAINING and os.path.exists(model_path):
+        print("Loading existing model for incremental training...")
+        model = joblib.load(model_path)
+        model.fit(
+            X_train, y_train,
+            eval_set=[(X_valid, y_valid)],
+            init_model=model,
+            callbacks=[lgb.early_stopping(stopping_rounds=100)]
+        )
+    else:
+        print("Creating new LightGBM model...")
+        model = lgb.LGBMRegressor(**PARAMS)
+        model.fit(
+            X_train, y_train,
+            eval_set=[(X_valid, y_valid)],
+            callbacks=[lgb.early_stopping(stopping_rounds=100)]
+        )
+
+    pred = model.predict(X_valid)
+    mae = mean_absolute_error(y_valid, pred)
+    accuracy = np.sum(np.abs(pred - y_valid) <= ERROR_TOLERANCE) / len(y_valid)
+
+    print(f"\nMAE: {mae:.4f} | Accuracy (±{ERROR_TOLERANCE}): {accuracy:.4f}")
+
+    joblib.dump(model, model_path)
+    joblib.dump(scaler, scaler_path)
+    with open(feature_names_path, "w", encoding="utf-8") as f:
+        for name in feature_names: f.write(name + "\n")
+
+    print(f"\nSaved to {working_dir}: {MODEL_FILENAME}, {SCALER_FILENAME}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--workingDir", required=True)
+    parser.add_argument("--dataDir", required=True)
+    parser.add_argument("--trainSize", required=True, type=int)
+    parser.add_argument("--validSize", required=True, type=int)
+    args = parser.parse_args()
+
+    TRAIN_SIZE, VALID_SIZE = args.trainSize, args.validSize
+    train_model(args.workingDir, args.dataDir)