fix

output/lightgbm_feature/features.txt

@@ -0,0 +1,8 @@
+bpm_avg
+bpm_change
+color_complexity
+density_avg
+density_peak
+note_count
+rhythm_complexity
+scroll_change

predict/feature/predict_lightgbm.py

@@ -14,9 +14,9 @@ warnings.filterwarnings("ignore", category=UserWarning)
 # =========================================================
 
 FEATURES_FILENAME = "features.json"
-MODEL_FILENAME = "model_lgbm.pkl"
-SCALER_FILENAME = "scaler_lgbm.pkl"
-FEATURE_NAMES_FILENAME = "features_lgbm.txt"
+MODEL_FILENAME = "model.pkl"
+SCALER_FILENAME = "scaler.pkl"
+FEATURE_NAMES_FILENAME = "features.txt"
 
 def safe_float(value):
     if value is None: return 0.0

script/compare_feature.ts

@@ -126,7 +126,7 @@ const resultData = {
     details: comparisonResults.sort((a, b) => b.error - a.error)
 };
 
-const comparePath = path.join(workingDir, "compare_feature.json");
+const comparePath = path.join(workingDir, "compare.json");
 fs.writeFileSync(comparePath, JSON.stringify(resultData, null, 2), "utf-8");
 
 console.log(`\nComparison complete! Results saved to: ${comparePath}`);

script/preprocess_feature.ts

@@ -64,3 +64,4 @@ for (const file of files) {
 
 const featurePath = path.join(workingDir, values.fileName ?? 'features.json');
 fs.writeFileSync(featurePath, JSON.stringify(features, null, 2), 'utf-8');
+console.log(`Successfully saved factors to ${featurePath}`);

train/factor/train_xgboost.py

@@ -26,9 +26,9 @@ TRAIN_SIZE = 0
 VALID_SIZE = 0
 RANDOM_STATE = 42
 
-N_ESTIMATORS = 500
-MAX_DEPTH = 6
-LEARNING_RATE = 0.05
+N_ESTIMATORS = 800
+MAX_DEPTH = 5
+LEARNING_RATE = 0.01
 CONTINUE_TRAINING = True
 ERROR_TOLERANCE = 0.1