Make tensor dtypes np.float32 for MPS devices

numpy defaults to numpy.float64 when they should be numpy.float32

This caused training to fail on MPS devices but it works on my M1 with
this.

Author: sorenmacbeth

src/pytorch_tabular/ssl_models/common/noise_generators.py

@@ -18,7 +18,8 @@ class SwapNoiseCorrupter(nn.Module):
 
     def __init__(self, probas):
         super().__init__()
-        self.probas = torch.from_numpy(np.array(probas))
+        self.probas = torch.from_numpy(np.array(probas, dtype=np.float32))
+        print(f"666 {self.probas.dtype} 666")
 
     def forward(self, x):
         should_swap = torch.bernoulli(self.probas.to(x.device) * torch.ones(x.shape).to(x.device))

src/pytorch_tabular/tabular_datamodule.py

@@ -67,7 +67,7 @@ def __init__(
             if isinstance(target, str):
                 self.y = self.y.reshape(-1, 1)  # .astype(np.int64)
         else:
-            self.y = np.zeros((self.n, 1))  # .astype(np.int64)
+            self.y = np.zeros((self.n, 1), dtype=np.float32)  # .astype(np.int64)
 
         if task == "classification":
             self.y = self.y.astype(np.int64)
@@ -502,7 +502,7 @@ def _cache_dataset(self):
 
     def split_train_val(self, train):
         logger.debug(
-            "No validation data provided." f" Using {self.config.validation_split*100}% of train data as validation"
+            f"No validation data provided. Using {self.config.validation_split * 100}% of train data as validation"
         )
         val_idx = train.sample(
             int(self.config.validation_split * len(train)),
@@ -753,18 +753,16 @@ def _load_dataset_from_cache(self, tag: str = "train"):
             try:
                 dataset = getattr(self, f"_{tag}_dataset")
             except AttributeError:
-                raise AttributeError(
-                    f"{tag}_dataset not found in memory. Please provide the data for" f" {tag} dataloader"
-                )
+                raise AttributeError(f"{tag}_dataset not found in memory. Please provide the data for {tag} dataloader")
         elif self.cache_mode is self.CACHE_MODES.DISK:
             try:
                 dataset = torch.load(self.cache_dir / f"{tag}_dataset")
             except FileNotFoundError:
                 raise FileNotFoundError(
-                    f"{tag}_dataset not found in {self.cache_dir}. Please provide the" f" data for {tag} dataloader"
+                    f"{tag}_dataset not found in {self.cache_dir}. Please provide the data for {tag} dataloader"
                 )
         elif self.cache_mode is self.CACHE_MODES.INFERENCE:
-            raise RuntimeError("Cannot load dataset in inference mode. Use" " `prepare_inference_dataloader` instead")
+            raise RuntimeError("Cannot load dataset in inference mode. Use `prepare_inference_dataloader` instead")
         else:
             raise ValueError(f"{self.cache_mode} is not a valid cache mode")
         return dataset