cleanup

Author: mkumar73

docs/examples/embedding.rst

@@ -21,7 +21,6 @@
 
    examples/classification
    examples/regression
-   examples/embedding
    examples/distributional
 
 .. toctree::

docs/index.rst

@@ -1,6 +1,6 @@
+import numpy as np
 import torch
 from torch.utils.data import Dataset
-import numpy as np
 
 
 class MambularDataset(Dataset):
@@ -49,7 +49,8 @@ def __getitem__(self, idx):
             feature_tensor[idx] for feature_tensor in self.cat_features_list
         ]
         num_features = [
-            torch.as_tensor(feature_tensor[idx]).clone().detach().to(torch.float32)
+            torch.as_tensor(feature_tensor[idx]).clone(
+            ).detach().to(torch.float32)
             for feature_tensor in self.num_features_list
         ]
         label = self.labels[idx]
@@ -62,57 +63,3 @@ def __getitem__(self, idx):
 
         # Keep categorical and numerical features separate
         return cat_features, num_features, label
-
-
-class EmbeddingMambularDataset(Dataset):
-    """
-    A specialized version of MambularDataset intended for datasets related to protein studies, maintaining the
-    same structure and functionality.
-
-    This class is designed to handle structured data with separate categorical and numerical features, suitable
-    for both regression and classification tasks within the context of protein studies.
-
-    Parameters:
-        cat_features_list (list of Tensors): A list of tensors representing the categorical features.
-        num_features_list (list of Tensors): A list of tensors representing the numerical features.
-        labels (Tensor): A tensor of labels.
-        regression (bool, optional): A flag indicating if the dataset is for a regression task. Defaults to True.
-    """
-
-    def __init__(self, cat_features_list, num_features_list, labels, regression=True):
-        self.cat_features_list = cat_features_list  # Categorical features tensors
-        self.num_features_list = num_features_list  # Numerical features tensors
-        self.labels = labels
-        self.regression = regression
-
-    def __len__(self):
-        return len(self.labels)
-
-    def __getitem__(self, idx):
-        """
-        Retrieves the features and label for a given index in the context of protein studies.
-
-        Parameters:
-            idx (int): The index of the data point.
-
-        Returns:
-            tuple: A tuple containing two lists of tensors (one for categorical features and one for numerical
-            features) and a single label (float if regression is True), specifically designed for protein data.
-        """
-        cat_features = [
-            feature_tensor[idx] for feature_tensor in self.cat_features_list
-        ]
-        num_features = [
-            torch.tensor(feature_tensor[idx], dtype=torch.float32)
-            for feature_tensor in self.num_features_list
-        ]
-        label = self.labels[idx]
-        if self.regression:
-            # Convert the label to float for regression tasks
-            # label = float(label)
-            label = torch.tensor(label, dtype=torch.float32)
-        else:
-            label = torch.tensor(label, dtype=torch.long)
-
-        # Keep categorical and numerical features separate
-        return cat_features, num_features, label