refactor: refactor scar dataloader

scar/main/_scar.py

@@ -300,8 +300,8 @@ def __init__(
                     ambient_profile[batch_id, :] = subset.X.sum(axis=0) / subset.X.sum()
 
                 # add a mapper to locate the batch id
-                self.batch_id = torch.from_numpy(batch_id_per_cell).int().to(self.device)
-                self.n_batch = np.unique(batch_id_per_cell).size
+                self.batch_id = batch_id_per_cell
+                self.n_batch = len(np.unique(batch_id_per_cell))
 
             # get ambient profile from AnnData.uns
             elif (ambient_profile is None) and ("ambient_profile_all" in raw_count.uns):
@@ -322,16 +322,15 @@ def __init__(
                 f"Expecting str or np.array or pd.DataFrame object, but get a {type(raw_count)}"
             )
 
-        raw_count = raw_count.fillna(0)  # missing vals -> zeros
-
         # Loading numpy to tensor on GPU
+        raw_count = raw_count.fillna(0)  # missing vals -> zeros raw_count if isinstance(raw_count, ad.AnnData) else 
         self.raw_count = raw_count.values
         """raw_count : np.ndarray, raw count matrix.
         """
         self.n_features = raw_count.shape[1]
 
-        self.cell_id = list(raw_count.index)
-        self.feature_names = list(raw_count.columns)
+        self.cell_id = raw_count.index.to_list()
+        self.feature_names = raw_count.columns.to_list()
 
         if isinstance(ambient_profile, str):
             ambient_profile = pd.read_pickle(ambient_profile)
@@ -355,7 +354,7 @@ def __init__(
                 .reshape(1, -1)
             )
             # add a mapper to locate the artificial batch id
-            self.batch_id = torch.zeros(raw_count.shape[0]).int().to(self.device)
+            self.batch_id = np.zeros(raw_count.shape[0], dtype=int)#.reshape(-1, 1)
             self.n_batch = 1
 
         self.ambient_profile = ambient_profile
@@ -710,10 +709,10 @@ class UMIDataset(torch.utils.data.Dataset):
     def __init__(self, raw_count, ambient_profile, batch_id, device, list_ids=None):
         """Initialization"""
         self.device = device
-        self.raw_count = torch.from_numpy(raw_count).int().to(device)
+        self.raw_count = torch.from_numpy(raw_count).int().to(self.device)
         self.ambient_profile = torch.from_numpy(ambient_profile).float().to(device)
-        self.batch_id = batch_id.to(torch.int64).to(device)
-        self.batch_onehot = self._onehot()
+        self.batch_id = torch.from_numpy(batch_id).to(torch.int64)
+        self.batch_onehot = torch.from_numpy(np.eye(len(np.unique(batch_id)))).to(torch.int64).to(self.device)
 
         if list_ids:
             self.list_ids = list_ids
@@ -731,11 +730,4 @@ def __getitem__(self, index):
         sc_count = self.raw_count[sc_id, :]
         sc_ambient = self.ambient_profile[self.batch_id[sc_id], :]
         sc_batch_id_onehot = self.batch_onehot[self.batch_id[sc_id], :]
-        return sc_count, sc_ambient, sc_batch_id_onehot
-
-    def _onehot(self):
-        """One-hot encoding"""
-        n_batch = self.batch_id.unique().size()[0]
-        x_onehot = torch.zeros(n_batch, n_batch).to(self.device)
-        x_onehot.scatter_(1, self.batch_id.unique().unsqueeze(1), 1)
-        return x_onehot
+        return sc_count, sc_ambient, sc_batch_id_onehot