add seq position to foldcomp dataset, minor tweaks to fc->pyg conversion

graphein/ml/datasets/foldcomp_dataset.py

@@ -180,7 +180,9 @@ def __init__(
         self._get_indices()
 
         super().__init__(
-            root=self.root, transform=self.transform, pre_transform=None  # type: ignore
+            root=self.root,
+            transform=self.transform,
+            pre_transform=None,  # type: ignore
         )
 
     @property
@@ -202,9 +204,7 @@ def processed_file_names(self):
     def download(self):
         """Downloads foldcomp database if not already downloaded."""
 
-        if not all(
-            os.path.exists(self.root / f) for f in self._database_files
-        ):
+        if not all(os.path.exists(self.root / f) for f in self._database_files):
             log.info(f"Downloading FoldComp dataset {self.database}...")
             curr_dir = os.getcwd()
             os.chdir(self.root)
@@ -249,9 +249,7 @@ def _get_indices(self):
             ]
         # Sub sample
         log.info(f"Sampling fraction: {self.fraction}...")
-        accessions = random.sample(
-            accessions, int(len(accessions) * self.fraction)
-        )
+        accessions = random.sample(accessions, int(len(accessions) * self.fraction))
         self.ids = accessions
         log.info("Creating index...")
         indices = dict(enumerate(accessions))
@@ -264,7 +262,9 @@ def process(self):
         # Open the database
         log.info("Opening database...")
         if self.ids is not None:
-            self.db = foldcomp.open(self.root / self.database, ids=self.ids, decompress=False)  # type: ignore
+            self.db = foldcomp.open(
+                self.root / self.database, ids=self.ids, decompress=False
+            )  # type: ignore
         else:
             self.db = foldcomp.open(self.root / self.database, decompress=False)  # type: ignore
 
@@ -275,9 +275,10 @@ def fc_to_pyg(data: Dict[str, Any], name: Optional[str] = None) -> Protein:
         residue_type = torch.tensor(
             [STANDARD_AMINO_ACIDS.index(res) for res in data["residues"]],
         )
+        n_res = len(res)
 
         # Get residue numbers
-        res_num = [i for i, _ in enumerate(res)]
+        res_num = np.arange(n_res)
 
         # Get list of atom types
         atom_types = []
@@ -292,7 +293,7 @@ def fc_to_pyg(data: Dict[str, Any], name: Optional[str] = None) -> Protein:
         atom_idx = np.array([ATOM_NUMBERING[atm] for atm in atom_types])
 
         # Initialize coordinates
-        coords = np.ones((len(res), 37, 3)) * 1e-5
+        coords = np.ones((n_res, 37, 3)) * 1e-5
 
         res_idx = np.repeat(res_num, atom_counts)
         coords[res_idx, atom_idx, :] = np.array(data["coordinates"])
@@ -302,11 +303,12 @@ def fc_to_pyg(data: Dict[str, Any], name: Optional[str] = None) -> Protein:
             coords=torch.from_numpy(coords).float(),
             residues=res,
             residue_id=[f"A:{m}:{str(n)}" for m, n in zip(res, res_num)],
-            chains=torch.zeros(len(res)),
+            chains=torch.zeros(n_res),
             residue_type=residue_type.long(),
             b_factor=torch.from_numpy(b_factor).float(),
             id=name,
-            x=torch.zeros(len(res)),
+            x=torch.zeros(n_res),
+            seq_pos=torch.from_numpy(res_num).unsqueeze(-1),
         )
 
     def len(self) -> int:
@@ -380,9 +382,7 @@ def __init__(
         self.val_split = val_split
         self.test_split = test_split
         self.transform = (
-            self._compose_transforms(transform)
-            if transform is not None
-            else None
+            self._compose_transforms(transform) if transform is not None else None
         )
 
         if (
@@ -421,9 +421,7 @@ def _get_indices(self):
         self.ids = ds.ids
         ds.db.close()
 
-    def _split_data(
-        self, train_split: float, val_split: float, test_split: float
-    ):
+    def _split_data(self, train_split: float, val_split: float, test_split: float):
         """Split the database into non-overlapping train, validation and test"""
         if not hasattr(self, "ids"):
             self._get_indices()