Faster node representations

matdeeplearn/preprocessor/helpers.py

@@ -439,43 +439,19 @@ def add_selfloop(
     return edge_indices, edge_weights, distance_matrix_masked
 
 
-def load_node_representation(node_representation="onehot"):
-    node_rep_path = Path(__file__).parent
-    default_reps = {"onehot": str(node_rep_path / "./node_representations/onehot.csv")}
+def one_hot_node_rep(Z, device):
+    return F.one_hot(Z - 1, num_classes = 100)
 
-    rep_file_path = node_representation
-    if node_representation in default_reps:
-        rep_file_path = default_reps[node_representation]
-
-    file_type = rep_file_path.split(".")[-1]
-    loaded_rep = None
-
-    if file_type == "csv":
-        loaded_rep = np.genfromtxt(rep_file_path, delimiter=",")
-        # TODO: need to check if typecasting to integer is needed
-        loaded_rep = loaded_rep.astype(int)
-
-    elif file_type == "json":
-        # TODO
-        pass
-
-    return loaded_rep
-
-
-def generate_node_features(input_data, n_neighbors, device, use_degree=False):
-    node_reps = load_node_representation()
-    node_reps = torch.from_numpy(node_reps).to(device)
-    n_elements, n_features = node_reps.shape
-
+def generate_node_features(input_data, n_neighbors, device, use_degree=False, node_rep_func = one_hot_node_rep):
     if isinstance(input_data, Data):
-        input_data.x = node_reps[input_data.z - 1].view(-1, n_features)
+        input_data.x = node_rep_func(input_data.z, device = device)
         if use_degree:
             return one_hot_degree(input_data, n_neighbors)
         return input_data
 
     for i, data in enumerate(input_data):
         # minus 1 as the reps are 0-indexed but atomic number starts from 1
-        data.x = node_reps[data.z - 1].view(-1, n_features).float()
+        data.x = node_rep_func(data.z, device = device).float()
 
     #for i, data in enumerate(input_data):
         #input_data[i] = one_hot_degree(data, n_neighbors)