lightgbm

.gemini/notes.md

@@ -2,16 +2,20 @@
 
 ## Technical Stack
 - **Runtime**: [Bun](https://bun.sh/)
-- **Language**: TypeScript
-- **Library**: [tja](https://www.npmjs.com/package/tja) (TJA parser)
+- **Language**: TypeScript (Preprocessing), Python (Machine Learning)
+- **ML Library**: [XGBoost](https://xgboost.readthedocs.io/), [scikit-learn](https://scikit-learn.org/)
+- **TJA Parser**: [tja-parser](https://www.npmjs.com/package/tja-parser)
 
-## Key Documents
-- `.gemini/tja-spec.md`: Rigorous TJA format specification.
-- `tja-format.mediawiki`: Original source document.
-- `measure.csv`: Dataset with columns `상수`, `songno`, `diff`.
+## Key Directories
+- `preprocess/`: TJA 파싱 및 피처 추출 로직 (TypeScript)
+- `script/`: 전처리, 학습 제어 스크립트
+- `train/`: XGBoost 학습 엔진 (Python)
+- `predict/`: 추론 엔진 (Python)
+- `datas/tja/`: 원본 TJA 데이터셋
+- `datas/measure.csv`: 정답지 (상수 데이터)
+- `test/`: 학습 결과물 (model.pkl, scaler.pkl, features.json)
 
-## Models (model/)
-- `constant_predictor.py`: DNN 기반 상수 예측 모델.
-
-## Library Usage (tja)
-...
+## Data Flow
+1. `datas/tja/*.tja` → `script/preprocess.ts` → `test/features.json`
+2. `test/features.json` + `datas/measure.csv` → `train/train_xgboost.py` → `test/model.pkl`
+3. `test/model.pkl` + `test/features.json` → `predict/predict_xgboost.py` → Result