Merge branch 'main' into precommit/prettier

.gitignore

@@ -151,3 +151,4 @@ compare.py
 checkpoints/
 docs/examples/basic/
 examples/test_save/
+tests/.datasets/occupancy_data.zip

src/pytorch_tabular/tabular_datamodule.py

@@ -521,7 +521,7 @@ def train_dataloader(self, batch_size: Optional[int] = None) -> DataLoader:
         )
         return DataLoader(
             dataset,
-            batch_size if batch_size is not None else self.batch_size,
+            batch_size or self.batch_size,
             shuffle=True if self.train_sampler is None else False,
             num_workers=self.config.num_workers,
             sampler=self.train_sampler,
@@ -547,7 +547,7 @@ def val_dataloader(self, batch_size: Optional[int] = None) -> DataLoader:
         )
         return DataLoader(
             dataset,
-            batch_size if batch_size is not None else self.batch_size,
+            batch_size or self.batch_size,
             shuffle=False,
             num_workers=self.config.num_workers,
             pin_memory=self.config.pin_memory,
@@ -574,7 +574,7 @@ def test_dataloader(self, batch_size: Optional[int] = None) -> DataLoader:
         )
         return DataLoader(
             dataset,
-            batch_size if batch_size is not None else self.batch_size,
+            batch_size or self.batch_size,
             shuffle=False,
             num_workers=self.config.num_workers,
             pin_memory=self.config.pin_memory,
@@ -613,7 +613,7 @@ def prepare_inference_dataloader(self, df: pd.DataFrame, batch_size: Optional[in
         )
         return DataLoader(
             dataset,
-            batch_size if batch_size is not None else self.batch_size,
+            batch_size or self.batch_size,
             shuffle=False,
             num_workers=self.config.num_workers,
         )

src/pytorch_tabular/tabular_model.py

@@ -667,8 +667,9 @@ def fit(
             assert len(metrics) == len(
                 metrics_prob_inputs
             ), "The length of `metrics` and `metrics_prob_inputs` should be equal"
-        seed = seed if seed is not None else self.config.seed
-        seed_everything(seed)
+        seed = seed or self.config.seed
+        if seed:
+            seed_everything(seed)
         if datamodule is None:
             datamodule = self.prepare_dataloader(train, validation, test, train_sampler, target_transform, seed)
         else:
@@ -738,8 +739,9 @@ def pretrain(
         assert (
             self.config.task == "ssl"
         ), f"`pretrain` is not valid for {self.config.task} task. Please use `fit` instead."
-        seed = seed if seed is not None else self.config.seed
-        seed_everything(seed)
+        seed = seed or self.config.seed
+        if seed:
+            seed_everything(seed)
         if datamodule is None:
             datamodule = self.prepare_dataloader(
                 train,
@@ -973,8 +975,9 @@ def finetune(
         assert (
             self._is_finetune_model
         ), "finetune() can only be called on a finetune model created using `TabularModel.create_finetune_model()`"
-        seed = seed if seed is not None else self.config.seed
-        seed_everything(seed)
+        seed = seed or self.config.seed
+        if seed:
+            seed_everything(seed)
         if datamodule is None:
             target_transform = self._check_and_set_target_transform(target_transform)
             self.datamodule._set_target_transform(target_transform)

src/pytorch_tabular/utils/nn_utils.py

@@ -82,7 +82,7 @@ def to_one_hot(y, depth=None):
         depth (int):  the size of the one hot dimension
     """
     y_flat = y.to(torch.int64).view(-1, 1)
-    depth = depth if depth is not None else int(torch.max(y_flat)) + 1
+    depth = depth or int(torch.max(y_flat)) + 1
     y_one_hot = torch.zeros(y_flat.size()[0], depth, device=y.device).scatter_(1, y_flat, 1)
     y_one_hot = y_one_hot.view(*(tuple(y.shape) + (-1,)))
     return y_one_hot

tests/conftest.py

@@ -15,7 +15,7 @@
     import urllib.request
 
     urllib.request.urlretrieve(
-        "https://archive.ics.uci.edu/ml/machine-learning-databases/00357/occupancy_data.zip", DATASET_ZIP_OCCUPANCY
+        "http://archive.ics.uci.edu/ml/machine-learning-databases/00357/occupancy_data.zip", DATASET_ZIP_OCCUPANCY
     )