removing pre_init

Author: sevmag

src/graphnet/models/data_representation/data_representation.py

@@ -25,7 +25,6 @@ def __init__(
         sensor_mask: Optional[List[int]] = None,
         string_mask: Optional[List[int]] = None,
         repeat_labels: bool = False,
-        **kwargs: Any,
     ):
         """Construct´DataRepresentation´. The ´detector´ holds.
 
@@ -59,8 +58,6 @@ def __init__(
         # Base class constructor
         super().__init__(name=__name__, class_name=self.__class__.__name__)
 
-        self._pre_init(**kwargs)
-
         # Member Variables
         self._detector = detector
         self._perturbation_dict = perturbation_dict
@@ -78,18 +75,9 @@ def __init__(
             )  # noqa: E501 # type: ignore
         self._input_feature_names = input_feature_names
 
-        # Set final data column names
-        self.output_feature_names = self._set_output_feature_names(
-            self._input_feature_names
-        )
-
         # Set data type
         self.to(dtype)
 
-        # Set Input / Output dimensions
-        self.nb_inputs = len(self._input_feature_names)
-        self.nb_outputs = len(self.output_feature_names)
-
         # Set perturbation_cols if needed
         if isinstance(self._perturbation_dict, dict):
             self._perturbation_cols = [
@@ -108,6 +96,25 @@ def __init__(
         else:
             self.rng = default_rng()
 
+    @property
+    def nb_inputs(self) -> int:
+        """Return the number of input features."""
+        return len(self._input_feature_names)
+
+    @property
+    def nb_outputs(self) -> int:
+        """Return the number of output features."""
+        return len(self.output_feature_names)
+
+    @property
+    def output_feature_names(self) -> List[str]:
+        """Initialize / return the names of output features."""
+        if not hasattr(self, "_output_feature_names"):
+            self._output_feature_names = self._set_output_feature_names(
+                self._input_feature_names
+            )
+        return self._output_feature_names
+
     def forward(  # type: ignore
         self,
         input_features: np.ndarray,
@@ -411,15 +418,6 @@ def _set_output_feature_names(
         """Set the final data output feature names."""
         raise NotImplementedError
 
-    @abstractmethod
-    def _pre_init(self, **kwargs: Any) -> None:
-        """Assign member varibales within `__init__()`.
-
-        E.g. Necessary member varibales for the `_set_output_feature_names()`
-        call in `__init__()`.
-        """
-        raise NotImplementedError
-
     @abstractmethod
     def _create_data(self, input_features: torch.Tensor) -> Data:
         """Create data from input features.

src/graphnet/models/data_representation/graphs/graph_definition.py

@@ -20,13 +20,6 @@
 class GraphDefinition(DataRepresentation):
     """An Abstract class to create graph definitions from."""
 
-    def _pre_init(  # type: ignore[override]
-        self, node_definition: NodeDefinition
-    ) -> None:
-        """Pre-initialization steps."""
-        # Pre-initialization steps
-        self._node_definition = node_definition
-
     def __init__(
         self,
         detector: Detector,
@@ -81,9 +74,6 @@ def __init__(
             add_static_features: If True, the original features will be
                 added as static attributes to the graph. Defaults to True.
         """
-        if node_definition is None:
-            node_definition = NodesAsPulses()
-
         # Base class constructor
         super().__init__(
             detector=detector,
@@ -95,9 +85,11 @@ def __init__(
             sensor_mask=sensor_mask,
             string_mask=string_mask,
             repeat_labels=repeat_labels,
-            node_definition=node_definition,  # -> kwargs
         )
 
+        if node_definition is None:
+            node_definition = NodesAsPulses()
+        self._node_definition = node_definition
         self._edge_definition = edge_definition
         if self._edge_definition is None:
             self.warning_once(