minor nn fixes

drevalpy/models/SimpleNeuralNetwork/simple_neural_network.py

@@ -94,6 +94,10 @@ def train(
                 "ignore",
                 message=".*does not have many workers which may be a bottleneck.*",
             )
+            warnings.filterwarnings(
+                "ignore",
+                message="Starting from v1\\.9\\.0, `tensorboardX` has been removed.*",
+            )
             self.model.fit(
                 output_train=output,
                 cell_line_input=cell_line_input,

drevalpy/models/SimpleNeuralNetwork/utils.py

@@ -1,5 +1,6 @@
 """Utility functions for the simple neural network models."""
 
+import os
 import secrets
 from typing import Any
 
@@ -229,11 +230,14 @@ def fit(
         monitor = "train_loss" if (val_loader is None) else "val_loss"
 
         early_stop_callback = EarlyStopping(monitor=monitor, mode="min", patience=patience)
-        name = "version-" + "".join(
-            [secrets.choice("0123456789abcdef") for i in range(20)]
-        )  # preventing conflicts of filenames
+
+        unique_subfolder = os.path.join(model_checkpoint_dir, "run_" + secrets.token_hex(8))
+        os.makedirs(unique_subfolder, exist_ok=True)
+
+        # prevent conflicts
+        name = "version-" + "".join([secrets.choice("0123456789abcdef") for _ in range(10)])
         self.checkpoint_callback = pl.callbacks.ModelCheckpoint(
-            dirpath=model_checkpoint_dir,
+            dirpath=unique_subfolder,
             monitor=monitor,
             mode="min",
             save_top_k=1,
@@ -262,7 +266,7 @@ def fit(
 
         # load best model
         if self.checkpoint_callback.best_model_path is not None:
-            checkpoint = torch.load(self.checkpoint_callback.best_model_path)  # noqa: S614
+            checkpoint = torch.load(self.checkpoint_callback.best_model_path, weights_only=True)  # noqa: S614
             self.load_state_dict(checkpoint["state_dict"])
         else:
             print("checkpoint_callback: No best model found, using the last model.")