Add CV results

Makefile

@@ -86,50 +86,54 @@ MPNN_FLAG = $(if $(USE_MPNN),--use-mpnn,)
 # 例如：make finetune FREEZE_BACKBONE=1
 FREEZE_FLAG = $(if $(FREEZE_BACKBONE),--freeze-backbone,)
 
+# 设备选择：使用 DEVICE=xxx 指定设备
+# 例如：make train DEVICE=cuda:0 或 make test_cv DEVICE=mps
+DEVICE_FLAG = $(if $(DEVICE),--device $(DEVICE),)
+
 ## Pretrain on external data (delivery only)
 .PHONY: pretrain
 pretrain: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain main $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain main $(MPNN_FLAG) $(DEVICE_FLAG)
 
 ## Evaluate pretrain model (delivery metrics)
 .PHONY: test_pretrain
 test_pretrain: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain test $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain test $(MPNN_FLAG) $(DEVICE_FLAG)
 
 ## Pretrain with cross-validation (5-fold)
 .PHONY: pretrain_cv
 pretrain_cv: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv main $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv main $(MPNN_FLAG) $(DEVICE_FLAG)
 
-## Evaluate CV pretrain models on test sets
+## Evaluate CV pretrain models on test sets (auto-detects MPNN from checkpoint)
 .PHONY: test_cv
 test_cv: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv test $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv test $(DEVICE_FLAG)
 
 ## Train model (multi-task, from scratch)
 .PHONY: train
 train: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train $(MPNN_FLAG) $(DEVICE_FLAG)
 
 ## Finetune from pretrained checkpoint (use FREEZE_BACKBONE=1 to freeze backbone)
 .PHONY: finetune
 finetune: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
 
 ## Train with hyperparameter tuning
 .PHONY: tune
 tune: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --tune $(MPNN_FLAG)
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --tune $(MPNN_FLAG) $(DEVICE_FLAG)
 
 ## Run predictions
 .PHONY: predict
 predict: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict $(DEVICE_FLAG)
 
-## Test model on test set (with detailed metrics)
+## Test model on test set (with detailed metrics, auto-detects MPNN from checkpoint)
 .PHONY: test
 test: requirements
-	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict test
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict test $(DEVICE_FLAG)
 
 
 #################################################################################

data/external/all_amine_split_for_LiON/evaluation_results.json

@@ -1,5 +1,5 @@
 {
-  "data_dir": "/Users/ryde/Documents/workspaces/\u8102\u8d28\u5206\u5b50\u836f\u7269\u9012\u9001\u6027\u80fd\u9884\u6d4b/\u6700\u65b0\u6574\u7406/lnp-ml/data/external/all_amine_split_for_LiON",
+  "data_dir": "/Users/ryde/Code/lnp_ml/data/external/all_amine_split_for_LiON",
   "y_col": "quantified_delivery",
   "n_splits": 5,
   "split_results": {

data/processed/test_results.json

@@ -1,37 +1,37 @@
 {
   "loss_metrics": {
-    "loss": 2.5374555587768555,
-    "loss_size": 0.1886825958887736,
-    "loss_pdi": 0.45798932512601215,
-    "loss_ee": 0.829658567905426,
-    "loss_delivery": 0.4857304096221924,
-    "loss_biodist": 0.5346279243628184,
-    "loss_toxic": 0.04076674363265435,
-    "acc_pdi": 0.7862595419847328,
-    "acc_ee": 0.6793893129770993,
-    "acc_toxic": 0.9801980198019802
+    "loss": 2.8661977450052896,
+    "loss_size": 0.44916408757368725,
+    "loss_pdi": 0.5041926403840383,
+    "loss_ee": 0.9021427234013876,
+    "loss_delivery": 0.5761533578236898,
+    "loss_biodist": 0.4019051690896352,
+    "loss_toxic": 0.03263980595511384,
+    "acc_pdi": 0.7633587786259542,
+    "acc_ee": 0.6641221374045801,
+    "acc_toxic": 0.9702970297029703
   },
   "detailed_metrics": {
     "size": {
-      "mse": 0.1669999969286325,
-      "rmse": 0.4086563310761654,
-      "mae": 0.26111859684375066,
-      "r2": 0.2149270281561566
+      "mse": 0.41126506251447736,
+      "rmse": 0.6412995107704959,
+      "mae": 0.41415552388095633,
+      "r2": -0.9333718010891026
     },
     "delivery": {
-      "mse": 0.5193460523366603,
-      "rmse": 0.7206566813238189,
-      "mae": 0.4828052782115008,
-      "r2": 0.37299826459145
+      "mse": 0.6277965050686476,
+      "rmse": 0.7923361061245711,
+      "mae": 0.5387302115022443,
+      "r2": 0.24206702565575944
     },
     "pdi": {
-      "accuracy": 0.7862595419847328
+      "accuracy": 0.7633587786259542
     },
     "ee": {
-      "accuracy": 0.6793893129770993
+      "accuracy": 0.6641221374045801
     },
     "toxic": {
-      "accuracy": 0.9801980198019802
+      "accuracy": 0.9702970297029703
     }
   }
 }

lnp_ml/modeling/predict.py

@@ -43,7 +43,7 @@ def load_model(
     use_mpnn = config.get("use_mpnn", False)
     
     if use_mpnn:
-        # 自动查找 MPNN ensemble
+        # 总是自动查找 MPNN ensemble，避免使用 checkpoint 中的旧绝对路径（可能来自其他机器）
         logger.info("Model was trained with MPNN, auto-detecting ensemble...")
         ensemble_paths = find_mpnn_ensemble_paths()
         logger.info(f"Found {len(ensemble_paths)} MPNN models")

lnp_ml/modeling/pretrain_cv.py

@@ -491,7 +491,7 @@ def test(
     all_preds = []
     all_targets = []
     
-    for fold_dir in fold_dirs:
+    for fold_dir in tqdm(fold_dirs, desc="Evaluating folds"):
         fold_idx = int(fold_dir.name.split("_")[1])
         model_path = model_dir / f"fold_{fold_idx}" / "model.pt"
         test_path = fold_dir / "test.parquet"
@@ -509,10 +509,12 @@ def test(
         config = checkpoint["config"]
         
         use_mpnn = config.get("use_mpnn", False)
-        mpnn_paths = config.get("mpnn_ensemble_paths")
         
-        if use_mpnn and not mpnn_paths:
+        # 总是重新查找 MPNN 路径，避免使用 checkpoint 中的旧绝对路径（可能来自其他机器）
+        if use_mpnn:
             mpnn_paths = find_mpnn_ensemble_paths()
+        else:
+            mpnn_paths = None
         
         model = create_model(
             d_model=config["d_model"],
@@ -541,7 +543,8 @@ def test(
         fold_targets = []
         
         with torch.no_grad():
-            for batch in test_loader:
+            pbar = tqdm(test_loader, desc=f"Fold {fold_idx} [Test]", leave=False)
+            for batch in pbar:
                 smiles = batch["smiles"]
                 tabular = {k: v.to(device) for k, v in batch["tabular"].items()}
                 targets = batch["targets"]["delivery"].to(device)

models/pretrain_cv/test_results.json

@@ -0,0 +1,63 @@
+{
+  "fold_results": [
+    {
+      "fold_idx": 0,
+      "n_samples": 2037,
+      "mse": 0.704966583529581,
+      "rmse": 0.8396228817329724,
+      "mae": 0.6275912649623819,
+      "r2": 0.18947693925460807,
+      "correlation": 0.4894546998045728
+    },
+    {
+      "fold_idx": 1,
+      "n_samples": 1658,
+      "mse": 0.9115043728317782,
+      "rmse": 0.9547273814193129,
+      "mae": 0.7232097773077567,
+      "r2": 0.21745691549383905,
+      "correlation": 0.48734383353129934
+    },
+    {
+      "fold_idx": 2,
+      "n_samples": 1615,
+      "mse": 0.8047081385461913,
+      "rmse": 0.8970552594718963,
+      "mae": 0.6672816107495062,
+      "r2": 0.18280256744724777,
+      "correlation": 0.506874931057286
+    },
+    {
+      "fold_idx": 3,
+      "n_samples": 1754,
+      "mse": 0.8592105232097863,
+      "rmse": 0.926936094458397,
+      "mae": 0.6835461141701688,
+      "r2": 0.1746865688538688,
+      "correlation": 0.4233578915460334
+    },
+    {
+      "fold_idx": 4,
+      "n_samples": 1520,
+      "mse": 0.6832138393192553,
+      "rmse": 0.8265675043934738,
+      "mae": 0.6324079235422495,
+      "r2": 0.2766966748218629,
+      "correlation": 0.5350317708563872
+    }
+  ],
+  "summary_stats": {
+    "rmse_mean": 0.8889818242952104,
+    "rmse_std": 0.04931538867410365,
+    "r2_mean": 0.2082239331742853,
+    "r2_std": 0.03713816024862206
+  },
+  "overall": {
+    "n_samples": 8584,
+    "mse": 0.7912902048033758,
+    "rmse": 0.8895449425427452,
+    "mae": 0.6658137170204776,
+    "r2": 0.20870978896035208,
+    "correlation": 0.4812266887089009
+  }
+}