diff --git a/.gemini/checkpoint-final.md b/.gemini/checkpoint-final.md index d7a3ac4..4999e5f 100644 --- a/.gemini/checkpoint-final.md +++ b/.gemini/checkpoint-final.md @@ -1,21 +1,20 @@ -# fumen-analyze 최종 진행 리포트 (Final) +# fumen-analyze 프로젝트 상태 보고 (Current) -## 1. 모델 성능 요약 -- **최종 MAE**: **0.1758** -- **모델 유형**: `GradientBoostingRegressor` (Warm Start + Precision Tuning) -- **최종 Estimators**: 1,600개 -- **성과**: 30개 이상의 피처를 기반으로 채보의 미세한 난이도 차이를 성공적으로 학습. 이상치(에러 > 1.0)를 배제한 정밀 튜닝으로 안정성 확보. +## 1. 모델 아키텍처 전환 +- **이전**: `GradientBoostingRegressor` (scikit-learn) +- **현재**: `XGBRegressor` (XGBoost) +- **변경 사유**: 대규모 데이터셋에 대한 학습 속도 향상 및 하이퍼파라미터 튜닝의 유연성 확보. -## 2. 핵심 피처 가중치 (추정) -- **밀도**: Global NPS, Peak NPS (1s/2s) -- **리듬**: 엇박 비율(Triplets), 리듬 표준편차, 가속도 -- **패턴**: 손 배치 전환(Hand-switching), 3노트 단위 패턴 복잡도 -- **기믹**: SV/BPM 변화 빈도 및 변동성 +## 2. 성능 지표 (목표치) +- **목표 MAE**: **0.15 이하** (현재 약 0.17~0.18 추정) +- **허용 오차 범위**: ±0.1 (상수 단위 기준) +- **성과**: 리듬 복잡도 및 순간 밀도 피처를 도입하여 10성급 고난이도 채보 간의 미세한 서열(상수 11.0~12.0)을 유의미하게 구분 중. -## 3. 사용 안내 -- **학습 업데이트**: `./run_pipeline.sh` (현재 정밀 튜닝 모드 설정됨) -- **상수 예측**: `./run_predict.sh [diff]` -- **문서 참조**: `GUIDE.md` +## 3. 핵심 업데이트 사항 +- **BPM/Scroll 변화 감지**: `#BPMCHANGE` 뿐만 아니라 `#SCROLL` 변화를 결합한 시각적 속도 변화 피처 추가. +- **색상 복잡도 가중치**: 노트 간 간격이 좁을수록 더 높은 난이도 가중치를 부여하는 $1/\Delta t^2$ 로직 적용. +- **지속 학습 지원**: `CONTINUE_TRAINING` 옵션을 통해 기존 모델에 추가 데이터를 점진적으로 학습 가능. -## 4. 최종 결론 -현재 모델은 10성급 고난이도 곡들 사이의 미세한 '상수' 서열을 0.1~0.2 오차 범위 내에서 예측할 수 있는 수준에 도달했습니다. 추가 데이터 확보 시 0.1 미만으로의 진입이 충분히 가능합니다. +## 4. 향후 과제 +- `rhythm_complexity` 로직의 정교화 (비정형 박자 감지 강화). +- `measure.csv` 데이터셋 확충을 통한 과적합 방지 및 일반화 성능 향상. diff --git a/.gemini/difficulty-factors.md b/.gemini/difficulty-factors.md index 7573a45..e2b65ce 100644 --- a/.gemini/difficulty-factors.md +++ b/.gemini/difficulty-factors.md @@ -1,28 +1,23 @@ -# TJA 난이도 산정 핵심 요소 (Technical Factors) +# TJA 난이도 산정 피처 (Features) -학습 모델(DifficultyNet)에 입력되는 8가지 핵심 지표입니다. (spec.md 업데이트 반영) +`preprocess/featurize.ts`에서 추출되는 8가지 핵심 피처입니다. -## 1. 물리적 밀도 -- **density_avg**: 평균 노트 밀도 (총 노트 수 / 곡 길이) -- **density_peak**: 1초당 최대 밀도 (가장 촘촘한 구간의 노트 수) +## 1. 양적 지표 +- **note_count**: 총 노트 수. 곡의 길이에 따른 절대적인 노트 양. -## 2. BPM 및 속도 변화 -- **bpm_avg**: 곡의 평균 BPM -- **bpm_var**: BPM 변화율 (BPM 변속의 다양성) +## 2. 밀도 지표 +- **density_avg**: 평균 NPS. `(총 노트 수 / 곡의 총 소요 시간) * 1000`. +- **density_peak**: 순간 최대 밀도. 임의의 노트를 기준으로 1초 이내에 포함된 최대 노트 수. -## 3. 구조적 특징 -- **note_count**: 총 노트 수 -- **long_note_duration**: 전체 대비 긴 노트(지속형) 점유율 +## 3. 속도 변화 지표 +- **bpm_avg**: 노트별 BPM의 단순 산술 평균. +- **bpm_change**: BPM 변동 횟수. 인접한 두 노트 간 BPM 차이가 1.5 이상일 때 카운트. +- **scroll_change**: 시각적 속도(`BPM * Scroll`) 변동 횟수. 차이가 1.5 이상일 때 카운트. -## 4. 패턴 및 연타 -- **complex_ratio**: 복합 패턴(큰 북/작은 북 혼합) 비율 -- **roll_count**: 연타(Roll) 노트의 총 개수 +## 4. 구조적 복잡도 +- **rhythm_complexity**: 리듬의 정규성. 인접한 노트 간 시간 간격의 비율이 2의 거듭제곱(1, 0.5, 2 등)에 가까운 횟수를 카운트. + - *참고: 현재 코드는 정규 리듬일 때 증가하며, 문서와 실제 로직의 방향성이 반대일 수 있음.* +- **color_complexity**: 색상 배치 복잡도. 인접 노트의 색상(Don/Ka)이 바뀔 때, 그 간격의 제곱의 역수($1/\Delta t^2$)를 누적. 간격이 좁을수록 수치가 급격히 상승. -## 계산 로직 -- **밀도 계열**: 노트 간 시간 간격(`delta`)의 역수(`1/delta`)로 순시 밀도를 계산하여 `avg`/`peak` 도출. -- **BPM 계열**: `#BPM` 및 `#STOP` 명령을 기반으로 시간 축 전개 후 평균 및 표준편차(`var`) 계산. -- **패턴 계열**: 슬라이딩 윈도우(4~8개 노트)에서 노트 타입 변화(`d↔k`) 횟수를 측정하여 비율 산정. -- **연타 계열**: `#ROLL` 명령 및 입력 밀집 구간 카운트. - -## 중요 주의사항 -- 모든 Factor는 모델 학습 전 `StandardScaler` 또는 `RobustScaler`를 통해 반드시 이상치를 처리해야 합니다. +## 주의사항 +- `color_complexity`는 수치의 범위가 매우 클 수 있으므로 학습 시 `StandardScaler`를 통한 정규화가 필수적입니다. diff --git a/.gemini/notes.md b/.gemini/notes.md index 9283ebb..ec921e8 100644 --- a/.gemini/notes.md +++ b/.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 diff --git a/.gemini/pipeline-design.md b/.gemini/pipeline-design.md index a93285a..75f75ff 100644 --- a/.gemini/pipeline-design.md +++ b/.gemini/pipeline-design.md @@ -1,13 +1,35 @@ -# 파이프라인 실행 규칙 +# 파이프라인 실행 가이드 -모든 경로는 실행 시점에 인자로 지정하여 관리합니다. +모든 실행은 프로젝트 루트 디렉토리에서 수행합니다. -## 1. 학습 파이프라인 (`run_train.sh`) -- **인자 1 (TJA_DIR)**: 학습용 TJA 채보가 저장된 폴더 (예: `sample/training`) -- **인자 2 (MODEL_PATH)**: 모델이 저장될 경로 (예: `output/model/v2_constant`) -- **인자 3 (DATASET_DIR)**: 데이터셋이 저장될 폴더 (예: `output/dataset`) +## 1. 전처리 (Feature Extraction) +TJA 파일들로부터 피처를 추출하여 `features.json`을 생성합니다. +```bash +bun run script/preprocess.ts --workingDir ./test --dataDir ./datas +``` +- **--workingDir**: 결과물(`features.json`)이 저장될 폴더. +- **--dataDir**: 원본 데이터(`tja/` 폴더)가 위치한 폴더. -## 2. 예측 파이프라인 (`run_predict.sh`) -- **인자 1 (MODEL_PATH)**: 추론에 사용할 모델 경로 -- **인자 2 (TJA_DIR)**: 예측할 TJA 채보가 모여있는 폴더 -- **인자 3 (OUTPUT_DIR)**: 결과가 저장될 폴더 +## 2. 모델 학습 (Training) +추출된 피처와 상수 데이터를 사용하여 XGBoost 모델을 학습시킵니다. +```bash +python3 train/train_xgboost.py \ + --workingDir ./test \ + --dataDir ./datas \ + --trainSize 1000 \ + --validSize 200 +``` +- **--trainSize / --validSize**: 학습 및 검증에 사용할 데이터 수. + +## 3. 난이도 예측 (Inference) +특정 곡 번호(`songno`)에 대한 난이도 상수를 예측합니다. +```bash +python3 predict/predict_xgboost.py --workingDir ./test --songno 1000 +``` +- **--songno**: 예측할 곡의 번호 (파일명 기준). + +## 주요 산출물 (in `workingDir`) +- `features.json`: 추출된 피처 데이터셋. +- `model.pkl`: 학습된 XGBoost 모델 파일. +- `scaler.pkl`: 피처 정규화를 위한 Scaler 객체. +- `features.txt`: 학습에 사용된 피처 이름 목록. diff --git a/docs/3-1.xgboost.md b/docs/3-1.xgboost.md new file mode 100644 index 0000000..86d8494 --- /dev/null +++ b/docs/3-1.xgboost.md @@ -0,0 +1,56 @@ +# XGBoost 기반 난이도 상수 예측 모델 + +이 문서는 프로젝트의 핵심 학습 엔진인 XGBoost 모델의 구조, 하이퍼파라미터 및 파이프라인 과정을 설명합니다. + +## 1. 모델 개요 +본 프로젝트는 태고의 달인 채보의 미세한 난이도 차이(상수)를 예측하기 위해 **XGBoost (Extreme Gradient Boosting)** 회귀 모델을 사용합니다. 8가지 주요 피처를 입력으로 받아 0.1 단위의 정밀한 난이도 상수를 출력하는 것을 목표로 합니다. + +## 2. 하이퍼파라미터 설정 +`train/train_xgboost.py`에 정의된 주요 설정값은 다음과 같습니다. + +| 파라미터 | 설정값 | 설명 | +| :--- | :--- | :--- | +| `N_ESTIMATORS` | 500 | 결정 트리의 개수 | +| `MAX_DEPTH` | 6 | 각 트리의 최대 깊이 | +| `LEARNING_RATE` | 0.05 | 학습률 (경사 하강 단계의 크기) | +| `SUBSAMPLE` | 0.8 | 각 트리 학습 시 사용할 데이터 샘플 비율 (과적합 방지) | +| `COLSAMPLE_BYTREE` | 0.8 | 각 트리 학습 시 사용할 피처 비율 | +| `RANDOM_STATE` | 42 | 결과 재현성을 위한 난수 시드 | +| `Objective` | `reg:squarederror` | 평균 제곱 오차 최소화를 목표로 함 | + +## 3. 학습 프로세스 (Training Pipeline) + +### A. 데이터 로드 및 매칭 +- `features.json`: 전처리 단계에서 추출된 채보별 피처 데이터. +- `measure.csv`: 각 곡의 `songno`, `diff`에 대응하는 실제 난이도 상수(Label). +- 두 데이터를 `(songno, difficulty)` 키를 기준으로 매칭하여 학습 데이터셋을 구성합니다. + +### B. 전처리 및 정규화 +- **Feature Selection**: `songno`, `difficulty`를 제외한 모든 수치형 데이터를 피처로 사용합니다. +- **Scaling**: `sklearn.preprocessing.StandardScaler`를 사용하여 피처의 평균을 0, 분산을 1로 정규화합니다. 이는 `color_complexity`와 같이 수치 범위가 큰 피처가 모델에 과도한 영향을 주는 것을 방지합니다. + +### C. 모델 학습 및 지속 학습 (Warm Start) +- `CONTINUE_TRAINING` 옵션이 활성화된 경우, 기존에 저장된 `model.pkl`과 `scaler.pkl`을 로드하여 이전 상태를 유지하며 추가 학습을 진행합니다. + +## 4. 평가 지표 +- **MAE (Mean Absolute Error)**: 실제 상수와 예측값 간의 절대 오차 평균. +- **Accuracy (±0.1)**: 예측값이 실제 값과 0.1 이내로 일치하는 비율을 측정하여 실질적인 예측 성능을 평가합니다. +- **Feature Importance**: 학습 완료 후 각 피처가 예측에 기여한 중요도를 출력하여 모델의 판단 근거를 확인합니다. + +## 5. 예측 프로세스 (Inference) +`predict/predict_xgboost.py`를 통해 다음 과정을 거쳐 결과를 도출합니다. + +1. 학습 시 저장된 `model.pkl`, `scaler.pkl`, `features.txt` 로드. +2. 입력받은 `songno`에 해당하는 피처를 `features.json`에서 탐색. +3. 학습 시와 동일한 순서로 피처 벡터를 구성하고 Scaler를 적용. +4. 모델을 통해 예측을 수행하고 결과를 JSON 배열 형태로 출력. + +```json +[ + { + "songno": "1000", + "diff": "oni", + "predicted": 11.2345 + } +] +``` diff --git a/output/.DS_Store b/output/.DS_Store new file mode 100644 index 0000000..1500622 Binary files /dev/null and b/output/.DS_Store differ diff --git a/output/lightgbm/compare.json b/output/lightgbm/compare.json new file mode 100644 index 0000000..72c8fec --- /dev/null +++ b/output/lightgbm/compare.json @@ -0,0 +1,9824 @@ +{ + "summary": { + "total_compared": 1402, + "average_absolute_error": 0.2209804564907278, + "timestamp": "2026-04-25T05:04:52.416Z", + "script_used": "predict/predict_lightgbm.py" + }, + "details": [ + { + "songno": "43", + "diff": "oni", + "actual": 8.1, + "predicted": 4.8798, + "error": 3.2201999999999993 + }, + { + "songno": 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0000000..c75e8a9 Binary files /dev/null and b/output/lightgbm/model_lgbm.pkl differ diff --git a/test/scaler.pkl b/output/lightgbm/scaler_lgbm.pkl similarity index 100% rename from test/scaler.pkl rename to output/lightgbm/scaler_lgbm.pkl diff --git a/output/lightgbm/temp.json b/output/lightgbm/temp.json new file mode 100644 index 0000000..f62e775 --- /dev/null +++ b/output/lightgbm/temp.json @@ -0,0 +1,17594 @@ +[ + { + "songno": "449", + "difficulty": "oni", + "note_count": 784, + "density_avg": 5.587527839643652, + "density_peak": 12, + "bpm_avg": 280.46938775510205, + "bpm_change": 2, + "scroll_change": 22, + "rhythm_complexity": 670, + "color_complexity": 0.006692674014408041 + }, + { + "songno": "307", + "difficulty": "oni", + "note_count": 604, + "density_avg": 4.531287297527706, + "density_peak": 9, + "bpm_avg": 176, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 575, + "color_complexity": 0.004356983446119197 + }, + { + "songno": "461", + "difficulty": "oni", 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@@ -0,0 +1,8 @@ +bpm_avg +bpm_change +color_complexity +density_avg +density_peak +note_count +rhythm_complexity +scroll_change diff --git a/output/lightgbm2/model_lgbm.pkl b/output/lightgbm2/model_lgbm.pkl new file mode 100644 index 0000000..9c39f4e Binary files /dev/null and b/output/lightgbm2/model_lgbm.pkl differ diff --git a/output/lightgbm2/scaler_lgbm.pkl b/output/lightgbm2/scaler_lgbm.pkl new file mode 100644 index 0000000..04d9232 Binary files /dev/null and b/output/lightgbm2/scaler_lgbm.pkl differ diff --git a/output/lightgbm2/temp.json b/output/lightgbm2/temp.json new file mode 100644 index 0000000..f62e775 --- /dev/null +++ b/output/lightgbm2/temp.json @@ -0,0 +1,17594 @@ +[ + { + "songno": "449", + "difficulty": "oni", + "note_count": 784, + "density_avg": 5.587527839643652, + "density_peak": 12, + "bpm_avg": 280.46938775510205, + "bpm_change": 2, + "scroll_change": 22, + "rhythm_complexity": 670, + "color_complexity": 0.006692674014408041 + }, + { + "songno": "307", + 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and b/output/lightgbm3/model_lgbm.pkl differ diff --git a/output/lightgbm3/scaler_lgbm.pkl b/output/lightgbm3/scaler_lgbm.pkl new file mode 100644 index 0000000..4c5344f Binary files /dev/null and b/output/lightgbm3/scaler_lgbm.pkl differ diff --git a/output/lightgbm3/temp.json b/output/lightgbm3/temp.json new file mode 100644 index 0000000..f62e775 --- /dev/null +++ b/output/lightgbm3/temp.json @@ -0,0 +1,17594 @@ +[ + { + "songno": "449", + "difficulty": "oni", + "note_count": 784, + "density_avg": 5.587527839643652, + "density_peak": 12, + "bpm_avg": 280.46938775510205, + "bpm_change": 2, + "scroll_change": 22, + "rhythm_complexity": 670, + "color_complexity": 0.006692674014408041 + }, + { + "songno": "307", + "difficulty": "oni", + "note_count": 604, + "density_avg": 4.531287297527706, + "density_peak": 9, + "bpm_avg": 176, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 575, + "color_complexity": 0.004356983446119197 + }, + { + "songno": "461", + "difficulty": 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+ "songno": "1067", + "difficulty": "oni", + "note_count": 693, + "density_avg": 5.974137931034483, + "density_peak": 17, + "bpm_avg": 240, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 598, + "color_complexity": 0.006562313074548445 + }, + { + "songno": "1067", + "difficulty": "ura", + "note_count": 518, + "density_avg": 4.441586280814576, + "density_peak": 12, + "bpm_avg": 240, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 359, + "color_complexity": 0.00247671069063878 + }, + { + "songno": "518", + "difficulty": "oni", + "note_count": 987, + "density_avg": 8.803048502151556, + "density_peak": 20, + "bpm_avg": 242.59473150962512, + "bpm_change": 23, + "scroll_change": 201, + "rhythm_complexity": 899, + "color_complexity": 0.018626343258294577 + }, + { + "songno": "1098", + "difficulty": "oni", + "note_count": 354, + "density_avg": 3.4555314533622563, + "density_peak": 10, + "bpm_avg": 135, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 117, + "color_complexity": 0.0015401502067561236 + }, + { + "songno": "242", + "difficulty": "oni", + "note_count": 377, + "density_avg": 3.3248131539611356, + "density_peak": 6, + "bpm_avg": 118, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 364, + "color_complexity": 0.0009504215126543182 + }, + { + "songno": "524", + "difficulty": "oni", + "note_count": 725, + "density_avg": 6.220714018022406, + "density_peak": 13, + "bpm_avg": 177.29444137930983, + "bpm_change": 66, + "scroll_change": 0, + "rhythm_complexity": 584, + "color_complexity": 0.00586770711563485 + }, + { + "songno": "530", + "difficulty": "oni", + "note_count": 465, + "density_avg": 4.465020576131686, + "density_peak": 10, + "bpm_avg": 140, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 440, + "color_complexity": 0.0020684337247432526 + }, + { + "songno": "256", + "difficulty": "oni", + "note_count": 302, + "density_avg": 3.751552795031056, + "density_peak": 9, + "bpm_avg": 147.41721854304635, + "bpm_change": 2, + "scroll_change": 2, + "rhythm_complexity": 262, + "color_complexity": 0.0016181926166362723 + }, + { + "songno": "256", + "difficulty": "ura", + "note_count": 416, + "density_avg": 5.143740340030912, + "density_peak": 10, + "bpm_avg": 150.8653846153846, + "bpm_change": 2, + "scroll_change": 2, + "rhythm_complexity": 354, + "color_complexity": 0.0024673533825034562 + }, + { + "songno": "1259", + "difficulty": "oni", + "note_count": 723, + "density_avg": 5.503605769230769, + "density_peak": 10, + "bpm_avg": 190, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 617, + "color_complexity": 0.00663277520754722 + }, + { + "songno": "915", + "difficulty": "oni", + "note_count": 319, + "density_avg": 3.3522033898305086, + "density_peak": 7, + "bpm_avg": 93, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 185, + "color_complexity": 0.0008326252898563732 + }, + { + "songno": "1271", + "difficulty": "oni", + "note_count": 395, + "density_avg": 3.6056453883996116, + "density_peak": 7, + "bpm_avg": 104.6088101265824, + "bpm_change": 19, + "scroll_change": 2, + "rhythm_complexity": 342, + "color_complexity": 0.0016478852131883524 + }, + { + "songno": "1271", + "difficulty": "ura", + "note_count": 705, + "density_avg": 6.42036702121103, + "density_peak": 12, + "bpm_avg": 103.91668085106386, + "bpm_change": 25, + "scroll_change": 2, + "rhythm_complexity": 621, + "color_complexity": 0.003912253740360649 + }, + { + "songno": "929", + "difficulty": "oni", + "note_count": 762, + "density_avg": 6.489709013484741, + "density_peak": 15, + "bpm_avg": 180, + "bpm_change": 0, + "scroll_change": 17, + "rhythm_complexity": 717, + "color_complexity": 0.008077239489795926 + }, + { + "songno": "726", + "difficulty": "oni", + "note_count": 496, + "density_avg": 4.781094527363184, + "density_peak": 10, + "bpm_avg": 155, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 454, + "color_complexity": 0.0026125022149435817 + }, + { + "songno": "732", + "difficulty": "oni", + "note_count": 327, + "density_avg": 3.4112489803198, + "density_peak": 6, + "bpm_avg": 128.002874617737, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 312, + "color_complexity": 0.0008848248717018 + }, + { + "songno": "875", + "difficulty": "oni", + "note_count": 650, + "density_avg": 5.645539906103287, + "density_peak": 13, + "bpm_avg": 185, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 634, + "color_complexity": 0.004144921585630589 + }, + { + "songno": "35", + "difficulty": "oni", + "note_count": 315, + "density_avg": 3.23855421686747, + "density_peak": 7, + "bpm_avg": 128, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 240, + "color_complexity": 0.0006133637138780425 + }, + { + "songno": "1339", + "difficulty": "oni", + "note_count": 433, + "density_avg": 4.926052332195677, + "density_peak": 10, + "bpm_avg": 150, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 375, + "color_complexity": 0.002240042845895285 + }, + { + "songno": "861", + "difficulty": "oni", + "note_count": 416, + "density_avg": 5.10063694267516, + "density_peak": 9, + "bpm_avg": 154, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 352, + "color_complexity": 0.001895326770581583 + }, + { + "songno": "21", + "difficulty": "oni", + "note_count": 835, + "density_avg": 7.665267282395746, + "density_peak": 12, + "bpm_avg": 164, + "bpm_change": 0, + "scroll_change": 17, + "rhythm_complexity": 791, + "color_complexity": 0.010350692863008938 + }, + { + "songno": "849", + "difficulty": "oni", + "note_count": 1210, + "density_avg": 8.016327577363652, + "density_peak": 21, + "bpm_avg": 293.9834710743802, + "bpm_change": 9, + "scroll_change": 15, + "rhythm_complexity": 1082, + "color_complexity": 0.032368213694464 + }, + { + "songno": "1305", + "difficulty": "oni", + "note_count": 643, + "density_avg": 4.909838472834068, + "density_peak": 11, + "bpm_avg": 130, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 530, + "color_complexity": 0.003986833247174614 + }, + { + "songno": "685", + "difficulty": "oni", + "note_count": 885, + "density_avg": 7.6991813655950425, + "density_peak": 19, + "bpm_avg": 217.13902372881356, + "bpm_change": 18, + "scroll_change": 2, + "rhythm_complexity": 821, + "color_complexity": 0.011592309992935527 + }, + { + "songno": "1311", + "difficulty": "oni", + "note_count": 523, + "density_avg": 4.347980997624703, + "density_peak": 15, + "bpm_avg": 210, + "bpm_change": 0, + "scroll_change": 7, + "rhythm_complexity": 380, + "color_complexity": 0.0023727944962687662 + }, + { + "songno": "1311", + "difficulty": "ura", + "note_count": 897, + "density_avg": 7.463153724247228, + "density_peak": 27, + "bpm_avg": 210, + "bpm_change": 0, + "scroll_change": 8, + "rhythm_complexity": 763, + "color_complexity": 0.0067704685133135835 + }, + { + "songno": "108", + "difficulty": "oni", + "note_count": 882, + "density_avg": 6.468000000000001, + "density_peak": 12, + "bpm_avg": 176, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 838, + "color_complexity": 0.009115015090832046 + }, + { + "songno": "652", + "difficulty": "oni", + "note_count": 527, + "density_avg": 4.469015003261578, + "density_peak": 9, + "bpm_avg": 130, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 515, + "color_complexity": 0.002473152164784899 + }, + { + "songno": "652", + "difficulty": "ura", + "note_count": 614, + "density_avg": 5.206784083496412, + "density_peak": 9, + "bpm_avg": 130, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 531, + "color_complexity": 0.002866896583522302 + }, + { + "songno": "134", + "difficulty": "oni", + "note_count": 635, + "density_avg": 6.1092246745897, + "density_peak": 12, + "bpm_avg": 170, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 577, + "color_complexity": 0.005163154842975053 + }, + { + "songno": "120", + "difficulty": "oni", + "note_count": 765, + "density_avg": 6.216357950447204, + "density_peak": 20, + "bpm_avg": 206.61401307189553, + "bpm_change": 15, + "scroll_change": 224, + "rhythm_complexity": 543, + "color_complexity": 0.004980770586693651 + }, + { + "songno": "646", + "difficulty": "oni", + "note_count": 456, + "density_avg": 3.5800807537012114, + "density_peak": 8, + "bpm_avg": 175, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 408, + "color_complexity": 0.0015286650613049817 + }, + { + "songno": "646", + "difficulty": "ura", + "note_count": 756, + "density_avg": 5.935397039030955, + "density_peak": 12, + "bpm_avg": 175, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 717, + "color_complexity": 0.008138518238598654 + }, + { + "songno": "493", + "difficulty": "oni", + "note_count": 552, + "density_avg": 3.9239193083573487, + "density_peak": 10, + "bpm_avg": 148, + "bpm_change": 0, + "scroll_change": 1, + "rhythm_complexity": 450, + "color_complexity": 0.0017604967403042494 + }, + { + "songno": "493", + "difficulty": "ura", + "note_count": 1035, + "density_avg": 7.357348703170029, + "density_peak": 15, + "bpm_avg": 148, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 953, + "color_complexity": 0.012013295995585432 + }, + { + "songno": "1107", + "difficulty": "oni", + "note_count": 850, + "density_avg": 6.535869996036465, + "density_peak": 13, + "bpm_avg": 194, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 814, + "color_complexity": 0.007554047567047632 + }, + { + "songno": "1113", + "difficulty": "oni", + "note_count": 561, + "density_avg": 4.46087786259542, + "density_peak": 9, + "bpm_avg": 125, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 553, + "color_complexity": 0.0028368573020750966 + }, + { + "songno": "487", + "difficulty": "oni", + "note_count": 556, + "density_avg": 4.5315763787213275, + "density_peak": 12, + "bpm_avg": 167, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 522, + "color_complexity": 0.0027883777378602532 + }, + { + "songno": "487", + "difficulty": "ura", + "note_count": 806, + "density_avg": 6.569155685700342, + "density_peak": 12, + "bpm_avg": 167, + "bpm_change": 0, + "scroll_change": 12, + "rhythm_complexity": 784, + "color_complexity": 0.007836578406899344 + }, + { + "songno": "478", + "difficulty": "oni", + "note_count": 555, + "density_avg": 5.020559210526316, + "density_peak": 12, + "bpm_avg": 165, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 532, + "color_complexity": 0.0026770483889597546 + }, + { + "songno": "336", + "difficulty": "oni", + "note_count": 1304, + "density_avg": 11.808920081503283, + "density_peak": 21, + "bpm_avg": 291.5184049079755, + "bpm_change": 1, + "scroll_change": 1, + "rhythm_complexity": 1255, + "color_complexity": 0.03172641728093539 + }, + { + "songno": "450", + "difficulty": "oni", + "note_count": 950, + "density_avg": 6.918238993710691, + "density_peak": 15, + "bpm_avg": 220, + "bpm_change": 0, + "scroll_change": 0, + "rhythm_complexity": 910, + "color_complexity": 0.01523599996929097 + }, + { + "songno": "322", + "difficulty": "oni", + "note_count": 607, + "density_avg": 4.979395030714651, + "density_peak": 13, + "bpm_avg": 149.54920922570017, + "bpm_change": 15, + "scroll_change": 15, + "rhythm_complexity": 560, + "color_complexity": 0.005248271768336951 + } +] \ No newline at end of file diff --git a/predict/predict_lightgbm.py b/predict/predict_lightgbm.py new file mode 100644 index 0000000..fac933a --- /dev/null +++ b/predict/predict_lightgbm.py @@ -0,0 +1,70 @@ +import argparse +import json +import math +import os +import joblib +import numpy as np +import warnings + +# 경고 무시 (Feature name 관련 경고 제거) +warnings.filterwarnings("ignore", category=UserWarning) + +# ========================================================= +# 파일명 +# ========================================================= + +FEATURES_FILENAME = "features.json" +MODEL_FILENAME = "model_lgbm.pkl" +SCALER_FILENAME = "scaler_lgbm.pkl" +FEATURE_NAMES_FILENAME = "features_lgbm.txt" + +def safe_float(value): + if value is None: return 0.0 + x = float(value) + return x if math.isfinite(x) else 0.0 + +def predict(working_dir: str, songno: str, feature: str = None): + features_path = os.path.join(working_dir, FEATURES_FILENAME) if feature is None else feature + 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) + + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model not found at {model_path}") + + model = joblib.load(model_path) + scaler = joblib.load(scaler_path) + + with open(feature_names_path, "r", encoding="utf-8") as f: + feature_names = [line.strip() for line in f.readlines() if line.strip()] + + with open(features_path, "r", encoding="utf-8") as f: + data = json.load(f) + + targets = [item for item in data if str(item["songno"]) == str(songno)] + if len(targets) == 0: + raise ValueError(f"Chart not found: songno={songno}") + + results = [] + for target in targets: + row = [safe_float(target.get(k, 0)) for k in feature_names] + X = np.array([row], dtype=np.float32) + X = scaler.transform(X) + pred = model.predict(X)[0] + + results.append({ + "songno": str(songno), + "diff": target.get("difficulty", "unknown"), + "predicted": round(float(pred), 4) + }) + + print(json.dumps(results, indent=2, ensure_ascii=False)) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--workingDir", required=True) + parser.add_argument("--feature", required=False) + parser.add_argument("--songno", required=True) + args = parser.parse_args() + + predict(args.workingDir, args.songno, args.feature) diff --git a/predict/predict_xgboost.py b/predict/predict_xgboost.py index f6d044b..3617794 100644 --- a/predict/predict_xgboost.py +++ b/predict/predict_xgboost.py @@ -109,7 +109,7 @@ def predict( # feature vector 생성 # ===================================================== - row = [] + results = [] for target in targets: row = [] @@ -124,12 +124,13 @@ def predict( pred = model.predict(X)[0] - diff = target.get("difficulty", "unknown") + results.append({ + "songno": str(songno), + "diff": target.get("difficulty", "unknown"), + "predicted": round(float(pred), 4) + }) - print( - f"{diff:10} " - f"{pred:.1f}" - ) + print(json.dumps(results, indent=2, ensure_ascii=False)) # ========================================================= diff --git a/script/compare.ts b/script/compare.ts index a34ef85..326f0e1 100644 --- a/script/compare.ts +++ b/script/compare.ts @@ -1,25 +1,148 @@ import Bun from 'bun'; +import path from 'node:path'; import { parseArgs } from 'node:util'; +import fs from 'node:fs'; const { values } = parseArgs({ args: Bun.argv, options: { - workingDir: { - type: "string" - }, - tja: { - type: "string" - }, - predictScript: { - type: "string" - }, + workingDir: { type: "string" }, + dataDir: { type: "string" }, + script: { type: "string" }, }, + strict: true, allowPositionals: true, }); -if (!values.tja || !values.workingDir || !values.predictScript) { - console.error("--workingDir --dataDir --trainDir"); +if (!values.workingDir || !values.dataDir || !values.script) { + console.error("Usage: bun run script/compare.ts --workingDir --dataDir --script "); process.exit(1); } -const songno = "temp"; +const workingDir = values.workingDir; +const dataDir = values.dataDir; +const predictScript = values.script; +const tempFileName = "temp.json"; +const tempFilePath = path.join(workingDir, tempFileName); + +// 1. 전처리 실행 (temp.json 생성하여 기존 features.json 보존) +console.log("Step 1: Running preprocessing to temp.json..."); +const preprocessResult = Bun.spawnSync([ + "bun", "run", "script/preprocess.ts", + "--workingDir", workingDir, + "--dataDir", dataDir, + "--fileName", tempFileName +]); + +if (!preprocessResult.success) { + console.error("Preprocessing failed"); + console.error(preprocessResult.stderr.toString()); + process.exit(1); +} + +// 2. measure.csv 로드 +console.log("Step 2: Loading measure.csv..."); +const measurePath = path.join(dataDir, "measure.csv"); +const measureContent = fs.readFileSync(measurePath, "utf-8"); +const measureMap = new Map(); + +measureContent.split("\n").forEach((line, index) => { + if (index === 0 || !line.trim()) return; + const parts = line.split(","); + if (parts.length >= 3) { + const constant = parts[0]; + const songno = parts[1]; + const diff = parts[2]; + measureMap.set(`${songno.trim()}_${diff.trim()}`, parseFloat(constant)); + } +}); + +// 3. temp.json 로드하여 대상 곡 목록 추출 +const features = JSON.parse(fs.readFileSync(tempFilePath, "utf-8")); +const uniqueSongnos = Array.from(new Set(features.map((f: any) => f.songno))); + +// 4. 예측 및 비교 +console.log(`Step 3: Predicting and comparing ${uniqueSongnos.length} songs...`); +const comparisonResults: any[] = []; +let processedCount = 0; + +for (const songno of uniqueSongnos) { + try { + const predictProcess = Bun.spawnSync([ + "python3", predictScript, + "--workingDir", workingDir, + "--songno", songno as string, + "--feature", tempFilePath + ]); + + if (!predictProcess.success) { + console.error(`\n[ERROR] Failed to predict songno ${songno}`); + console.error(predictProcess.stderr.toString()); + processedCount++; + continue; + } + + const output = predictProcess.stdout.toString().trim(); + // JSON 부분만 추출 (경고문 등이 섞여있을 경우 대비) + const jsonStart = output.indexOf('['); + const jsonEnd = output.lastIndexOf(']') + 1; + + if (jsonStart === -1 || jsonEnd === 0) { + console.error(`\n[ERROR] Invalid output format for songno ${songno}`); + processedCount++; + continue; + } + + const predictions = JSON.parse(output.substring(jsonStart, jsonEnd)); + + predictions.forEach((pred: any) => { + const key = `${pred.songno}_${pred.diff}`; + const actual = measureMap.get(key); + + if (actual !== undefined) { + comparisonResults.push({ + songno: pred.songno, + diff: pred.diff, + actual: actual, + predicted: pred.predicted, + error: Math.abs(actual - pred.predicted) + }); + } + }); + + processedCount++; + if (processedCount % 10 === 0 || processedCount === uniqueSongnos.length) { + const percent = ((processedCount / uniqueSongnos.length) * 100).toFixed(1); + process.stdout.write(`\rProgress: ${processedCount}/${uniqueSongnos.length} (${percent}%) `); + } + } catch (err) { + console.error(`\nError processing songno ${songno}:`, err); + processedCount++; + } +} +console.log("\nPrediction finished."); + +// 5. 결과 분석 및 저장 +if (comparisonResults.length === 0) { + console.error("No comparison results were generated."); + process.exit(1); +} + +const avgError = comparisonResults.reduce((acc, curr) => acc + curr.error, 0) / comparisonResults.length; +const resultData = { + summary: { + total_compared: comparisonResults.length, + average_absolute_error: avgError, + timestamp: new Date().toISOString(), + script_used: predictScript + }, + details: comparisonResults.sort((a, b) => b.error - a.error) +}; + +const comparePath = path.join(workingDir, "compare.json"); +fs.writeFileSync(comparePath, JSON.stringify(resultData, null, 2), "utf-8"); + +console.log(`\nComparison complete!`); +console.log(`Total compared: ${comparisonResults.length}`); +console.log(`Average Error: ${avgError.toFixed(4)}`); +console.log(`Results saved to: ${comparePath}`); diff --git a/script/preprocess.ts b/script/preprocess.ts index 2603661..3dfdcf6 100644 --- a/script/preprocess.ts +++ b/script/preprocess.ts @@ -13,9 +13,11 @@ const { values } = parseArgs({ }, dataDir: { type: "string" + }, + fileName: { + type: "string" } }, - strict: true, allowPositionals: true, }) @@ -60,5 +62,5 @@ for (const file of files) { } } -const featurePath = path.join(workingDir, 'features.json'); +const featurePath = path.join(workingDir, values.fileName ?? 'features.json'); fs.writeFileSync(featurePath, JSON.stringify(features, null, 2), 'utf-8'); \ No newline at end of file diff --git a/test/model.pkl b/test/model.pkl deleted file mode 100644 index c24f08c..0000000 Binary files a/test/model.pkl and /dev/null differ diff --git a/train/train_lightgbm.py b/train/train_lightgbm.py new file mode 100644 index 0000000..c96675b --- /dev/null +++ b/train/train_lightgbm.py @@ -0,0 +1,152 @@ +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) diff --git a/train/train_xgboost_full.py b/train/train_xgboost_full.py deleted file mode 100644 index 57242e4..0000000 --- a/train/train_xgboost_full.py +++ /dev/null @@ -1,313 +0,0 @@ -import argparse -import csv -import json -import math -import os -import joblib -import numpy as np - -from xgboost import XGBRegressor -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import mean_absolute_error - -# ========================================================= -# Hyper Parameters -# ========================================================= - -TEST_SIZE = 0.2 -RANDOM_STATE = 42 - -N_ESTIMATORS = 500 -MAX_DEPTH = 6 -LEARNING_RATE = 0.05 -SUBSAMPLE = 0.8 -COLSAMPLE_BYTREE = 0.8 - -CONTINUE_TRAINING = True - -# 예측 성공으로 간주할 허용 오차 -ERROR_TOLERANCE = 0.1 - -# ========================================================= -# 파일명 -# ========================================================= - -FEATURES_FILENAME = "features.json" -MEASURE_FILENAME = "measure.csv" - -MODEL_FILENAME = "model.pkl" -SCALER_FILENAME = "scaler.pkl" -FEATURE_NAMES_FILENAME = "features.txt" - -# ========================================================= -# 무시할 key -# ========================================================= - -IGNORE_KEYS = { - "songno", - "difficulty" -} - - -def safe_float(value): - if value is None: - return 0.0 - - x = float(value) - - if not math.isfinite(x): - return 0.0 - - return x - - -def train_model( - working_dir: str, - data_dir: str -): - # ===================================================== - # path - # ===================================================== - - 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 - ) - - # ===================================================== - # features.json - # ===================================================== - - 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 - # ===================================================== - - feature_map = {} - - for item in feature_data: - key = ( - str(item["songno"]), - str(item["difficulty"]) - ) - - feature_map[key] = item - - # ===================================================== - # feature names - # ===================================================== - - feature_names = sorted([ - k for k in feature_data[0].keys() - if k not in IGNORE_KEYS - ]) - - # ===================================================== - # measure.csv - # ===================================================== - - X = [] - y = [] - - 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 = safe_float(row[0]) - songno = str(row[1]) - diff = str(row[2]) - - key = (songno, diff) - - if key not in feature_map: - print( - f"[WARN] feature not found: " - f"{songno} {diff}" - ) - continue - - feature_item = feature_map[key] - - features = [ - safe_float(feature_item.get(k, 0)) - for k in feature_names - ] - - X.append(features) - y.append(measure) - - if len(X) == 0: - raise ValueError("No training data") - - X = np.array(X, dtype=np.float32) - y = np.array(y, dtype=np.float32) - - print(f"Dataset Size: {len(X)}") - print(f"Feature Count: {len(feature_names)}") - - # ===================================================== - # split - # ===================================================== - - X_train, X_valid, y_train, y_valid = train_test_split( - X, - y, - test_size=TEST_SIZE, - random_state=RANDOM_STATE - ) - - # ===================================================== - # scaler - # ===================================================== - - 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) - - # ===================================================== - # model - # ===================================================== - - if CONTINUE_TRAINING and os.path.exists(model_path): - print("Loading existing model...") - - model = joblib.load(model_path) - - previous_booster = model.get_booster() - - model.fit( - X_train, - y_train, - xgb_model=previous_booster - ) - - else: - print("Creating new model...") - - model = XGBRegressor( - n_estimators=N_ESTIMATORS, - max_depth=MAX_DEPTH, - learning_rate=LEARNING_RATE, - subsample=SUBSAMPLE, - colsample_bytree=COLSAMPLE_BYTREE, - objective="reg:squarederror", - random_state=RANDOM_STATE - ) - - model.fit(X_train, y_train) - - # ===================================================== - # evaluate - # ===================================================== - - pred = model.predict(X_valid) - - mae = mean_absolute_error(y_valid, pred) - - correct = np.sum( - np.abs(pred - y_valid) <= ERROR_TOLERANCE - ) - - accuracy = correct / len(y_valid) - - print(f"\nMAE: {mae:.4f}") - print( - f"Accuracy " - f"(±{ERROR_TOLERANCE}): " - f"{accuracy:.4f}" - ) - - # ===================================================== - # feature importance - # ===================================================== - - print("\nFeature Importance:") - - importance = model.feature_importances_ - - pairs = list(zip(feature_names, importance)) - pairs.sort(key=lambda x: x[1], reverse=True) - - for name, score in pairs: - print(f"{name:25} {score:.6f}") - - # ===================================================== - # save - # ===================================================== - - 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("\nSaved:") - print(model_path) - print(scaler_path) - print(feature_names_path) - - -# ========================================================= -# main -# ========================================================= - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--workingDir", - required=True - ) - - parser.add_argument( - "--dataDir", - required=True - ) - - args = parser.parse_args() - - train_model( - args.workingDir, - args.dataDir - ) \ No newline at end of file