整合与baseline比对的相关脚本

.dockerignore

@@ -67,7 +67,7 @@ logs/
 # Models (will be mounted as volume or copied explicitly)
 # Note: models/final/ is copied in Dockerfile
 models/finetune_cv/
-models/pretrain_cv/
+models/benchmark/
 models/mpnn/
 models/*.pt
 models/*.json

Makefile

@@ -76,10 +76,10 @@ data_final: requirements
 data_pretrain: requirements
 	$(PYTHON_INTERPRETER) scripts/process_external.py
 
-## Process CV data for cross-validation pretrain (external/all_amine_split_for_LiON -> processed/cv)
+## Process baseline CV data for benchmark (external/all_amine_split_for_LiON -> processed/benchmark)
-.PHONY: data_pretrain_cv
+.PHONY: data_benchmark
-data_pretrain_cv: requirements
+data_benchmark: requirements
-	$(PYTHON_INTERPRETER) scripts/process_external_cv.py
+	$(PYTHON_INTERPRETER) scripts/process_benchmark_data.py
 
 ## Process internal data with CV splitting (interim -> processed/cv)
 ## Use SCAFFOLD_SPLIT=1 to enable amine-based scaffold splitting (default: random shuffle)
@@ -96,10 +96,11 @@ data_cv: requirements
 pretrain: requirements
 	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain main $(MPNN_FLAG) $(DEVICE_FLAG)
 
-## Pretrain with cross-validation (5-fold)
+## Benchmark on baseline CV data: 5-fold train + test (delivery only)
-.PHONY: pretrain_cv
+.PHONY: benchmark
-pretrain_cv: requirements
+benchmark: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv main $(MPNN_FLAG) $(DEVICE_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.benchmark main $(MPNN_FLAG) $(DEVICE_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.benchmark test $(DEVICE_FLAG)
 
 ## Train model (multi-task, from scratch)
 .PHONY: train
@@ -140,11 +141,6 @@ finetune_cv: requirements
 test_pretrain: requirements
 	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain test $(MPNN_FLAG) $(DEVICE_FLAG)
 
-## Evaluate CV pretrain models on test sets (auto-detects MPNN from checkpoint)
-.PHONY: test_pretrain_cv
-test_pretrain_cv: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv test $(DEVICE_FLAG)
-
 ## Evaluate CV finetuned models on test sets (auto-detects MPNN from checkpoint)
 .PHONY: test_cv
 test_cv: requirements

lnp_ml/modeling/benchmark.py

@@ -1,4 +1,4 @@
-"""基于 Cross-Validation 的预训练脚本"""
+"""Benchmark 脚本：在 baseline 论文公开的 CV 划分上评估模型（仅 delivery 任务）"""
 
 import json
 from pathlib import Path
@@ -232,7 +232,7 @@ def train_fold(
     plot_loss_curves(
         history=history,
         output_path=loss_plot_path,
-        title=f"Pretrain Fold {fold_idx} Loss Curves",
+        title=f"Benchmark Fold {fold_idx} Loss Curves",
     )
     logger.info(f"Saved fold {fold_idx} loss curves to {loss_plot_path}")
     
@@ -281,8 +281,8 @@ def create_model(
 
 @app.command()
 def main(
-    data_dir: Path = PROCESSED_DATA_DIR / "pretrain_cv",
+    data_dir: Path = PROCESSED_DATA_DIR / "benchmark",
-    output_dir: Path = MODELS_DIR / "pretrain_cv",
+    output_dir: Path = MODELS_DIR / "benchmark",
     # 模型参数
     d_model: int = 256,
     num_heads: int = 8,
@@ -305,7 +305,7 @@ def main(
     device: str = "cuda" if torch.cuda.is_available() else "cpu",
 ):
     """
-    基于 5-fold Cross-Validation 预训练 LNP 模型（仅 delivery 任务）。
+    在 baseline 论文公开的 5-fold CV 划分上训练模型（仅 delivery 任务）。
     
     每个 fold 单独训练一个模型，保存到 output_dir/fold_x/model.pt。
     使用 --use-mpnn 启用 MPNN encoder。
@@ -332,7 +332,7 @@ def main(
     
     if not fold_dirs:
         logger.error(f"No fold_* directories found in {data_dir}")
-        logger.info("Please run 'make data_pretrain_cv' first to process CV data.")
+        logger.info("Please run 'make data_benchmark' first to process benchmark CV data.")
         raise typer.Exit(1)
     
     logger.info(f"Found {len(fold_dirs)} folds: {[d.name for d in fold_dirs]}")
@@ -430,7 +430,7 @@ def main(
     
     # 汇总结果
     logger.info("\n" + "=" * 60)
-    logger.info("CROSS-VALIDATION TRAINING COMPLETE")
+    logger.info("BENCHMARK CV TRAINING COMPLETE")
     logger.info("=" * 60)
     
     val_losses = [r["best_val_loss"] for r in fold_results]
@@ -474,16 +474,16 @@ def main(
 
 @app.command()
 def test(
-    data_dir: Path = PROCESSED_DATA_DIR / "pretrain_cv",
+    data_dir: Path = PROCESSED_DATA_DIR / "benchmark",
-    model_dir: Path = MODELS_DIR / "pretrain_cv",
+    model_dir: Path = MODELS_DIR / "benchmark",
-    output_path: Path = MODELS_DIR / "pretrain_cv" / "test_results.json",
+    output_path: Path = MODELS_DIR / "benchmark" / "test_results.json",
     batch_size: int = 64,
     device: str = "cuda" if torch.cuda.is_available() else "cpu",
 ):
     """
-    在测试集上评估 CV 预训练模型。
+    在 baseline CV 测试集上评估 benchmark 模型。
     
-    使用每个 fold 的模型在对应的测试集上评估。
+    使用每个 fold 训练的模型在对应的测试集上评估，汇总跨 fold 结果。
     """
     logger.info(f"Using device: {device}")
     device = torch.device(device)
@@ -609,7 +609,7 @@ def test(
     r2s = [r["r2"] for r in fold_results]
     
     logger.info("\n" + "=" * 60)
-    logger.info("CV TEST EVALUATION RESULTS")
+    logger.info("BENCHMARK TEST EVALUATION RESULTS")
     logger.info("=" * 60)
     
     logger.info(f"\n[Summary Statistics (across {len(fold_results)} folds)]")

scripts/process_benchmark_data.py

@@ -1,4 +1,4 @@
-"""处理 cross-validation 数据脚本：将 CV splits 转换为模型所需的 parquet 格式"""
+"""处理 benchmark 数据脚本：将 baseline 论文公开的 CV splits 转换为模型所需的 parquet 格式"""
 
 from pathlib import Path
 from typing import Dict, List, Tuple
@@ -151,18 +151,18 @@ def get_feature_columns() -> List[str]:
 @app.command()
 def main(
     data_dir: Path = EXTERNAL_DATA_DIR / "all_amine_split_for_LiON",
-    output_dir: Path = PROCESSED_DATA_DIR / "pretrain_cv",
+    output_dir: Path = PROCESSED_DATA_DIR / "benchmark",
     n_folds: int = 5,
 ):
     """
-    处理 cross-validation 数据，生成模型所需的 parquet 文件。
+    处理 baseline 论文公开的 CV 划分数据，生成 benchmark 所需的 parquet 文件。
     
     输出结构:
-        - processed/pretrain_cv/fold_0/train.parquet
+        - processed/benchmark/fold_0/train.parquet
-        - processed/pretrain_cv/fold_0/valid.parquet
+        - processed/benchmark/fold_0/valid.parquet
-        - processed/pretrain_cv/fold_0/test.parquet
+        - processed/benchmark/fold_0/test.parquet
-        - processed/pretrain_cv/fold_1/...
+        - processed/benchmark/fold_1/...
-        - processed/pretrain_cv/feature_columns.txt
+        - processed/benchmark/feature_columns.txt
     """
     logger.info(f"Processing CV data from {data_dir}")
     
@@ -223,7 +223,7 @@ def main(
     logger.success(f"Saved feature columns to {cols_path}")
     
     logger.info("\n" + "=" * 60)
-    logger.info("CV DATA PROCESSING COMPLETE")
+    logger.info("BENCHMARK DATA PROCESSING COMPLETE")
     logger.info("=" * 60)
     logger.info(f"Output directory: {output_dir}")
     logger.info(f"Number of folds: {len(cv_dirs)}")