snake_case

src/graphnet/models/gnn/RNN_tito.py

@@ -26,9 +26,9 @@ def __init__(
         nb_inputs: int,
         *,
         nb_neighbours: int = 8,
-        RNN_layers: int = 2,
-        RNN_hidden_size: int = 64,
-        RNN_dropout: float = 0.5,
+        rnn_layers: int = 2,
+        rnn_hidden_size: int = 64,
+        rnn_dropout: float = 0.5,
         features_subset: Optional[List[int]] = None,
         dyntrans_layer_sizes: Optional[List[Tuple[int, ...]]] = None,
         post_processing_layer_sizes: Optional[List[int]] = None,
@@ -45,11 +45,11 @@ def __init__(
             nb_inputs (int): Number of input features.
             nb_neighbours (int, optional): Number of neighbours to consider.
                 Defaults to 8.
-            RNN_layers (int, optional): Number of RNN layers.
+            rnn_layers (int, optional): Number of RNN layers.
                 Defaults to 1.
-            RNN_hidden_size (int, optional): Size of the hidden state of the RNN. Also determines the size of the output of the RNN.
+            rnn_hidden_size (int, optional): Size of the hidden state of the RNN. Also determines the size of the output of the RNN.
                 Defaults to 64.
-            RNN_dropout (float, optional): Dropout to use in the RNN. Defaults  to 0.5.
+            rnn_dropout (float, optional): Dropout to use in the RNN. Defaults  to 0.5.
             features_subset (List[int], optional): The subset of latent
                 features on each node that are used as metric dimensions when performing the k-nearest neighbours clustering. Defaults to [0,1,2,3]
             dyntrans_layer_sizes (List[Tuple[int, ...]], optional): List of tuples representing the sizes of the hidden layers of the DynTrans model.
@@ -63,9 +63,9 @@ def __init__(
         """
         self._nb_neighbours = nb_neighbours
         self._nb_inputs = nb_inputs
-        self._RNN_layers = RNN_layers
-        self._RNN_hidden_size = RNN_hidden_size  # RNN_hidden_size
-        self._RNN_dropout = RNN_dropout
+        self._rnn_layers = rnn_layers
+        self._rnn_hidden_size = rnn_hidden_size
+        self._rnn_dropout = rnn_dropout
         self._embedding_dim = embedding_dim
         self._n_head = n_head
         self._use_global_features = use_global_features
@@ -97,15 +97,15 @@ def __init__(
         super().__init__(nb_inputs, self._readout_layer_sizes[-1])
 
         self._rnn = Node_RNN(
-            num_layers=self._RNN_layers,
+            num_layers=self._rnn_layers,
             nb_inputs=2,
-            hidden_size=self._RNN_hidden_size,
-            RNN_dropout=self._RNN_dropout,
+            hidden_size=self._rnn_hidden_size,
+            rnn_dropout=self._rnn_dropout,
             embedding_dim=self._embedding_dim,
         )
 
         self._dynedge_tito = DynEdgeTITO(
-            nb_inputs=self._RNN_hidden_size + 5,
+            nb_inputs=self._rnn_hidden_size + 5,
             dyntrans_layer_sizes=self._dyntrans_layer_sizes,
             features_subset=self._features_subset,
             global_pooling_schemes=self._global_pooling_schemes,