PercentileCluster

configs/datasets/dev_lvl7_robustness_muon_neutrino_0000.yml

@@ -10,7 +10,7 @@ graph_definition:
     node_definition:
       arguments: {}
       class_name: NodesAsPulses
-    node_feature_names: [dom_x, dom_y, dom_z, dom_time, charge, rde, pmt_area]
+    input_feature_names: [dom_x, dom_y, dom_z, dom_time, charge, rde, pmt_area]
   class_name: KNNGraph
 pulsemaps:
   - SRTTWOfflinePulsesDC

configs/datasets/test_data_sqlite.yml

@@ -10,7 +10,7 @@ graph_definition:
     node_definition:
       arguments: {}
       class_name: NodesAsPulses
-    node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+    input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
   class_name: KNNGraph
 index_column: event_no
 loss_weight_column: null

configs/datasets/training_classification_example_data_sqlite.yml

@@ -10,7 +10,7 @@ graph_definition:
     node_definition:
       arguments: {}
       class_name: NodesAsPulses
-    node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+    input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
   class_name: KNNGraph
 pulsemaps:
   - total

configs/datasets/training_example_data_parquet.yml

@@ -10,7 +10,7 @@ graph_definition:
     node_definition:
       arguments: {}
       class_name: NodesAsPulses
-    node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+    input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
   class_name: KNNGraph
 pulsemaps:
   - total

configs/datasets/training_example_data_sqlite.yml

@@ -10,7 +10,7 @@ graph_definition:
     node_definition:
       arguments: {}
       class_name: NodesAsPulses
-    node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+    input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
   class_name: KNNGraph
 pulsemaps:
   - total

configs/models/dynedge_PID_classification_example.yml

@@ -25,7 +25,7 @@ arguments:
           ModelConfig:
             arguments: {}
             class_name: NodesAsPulses
-        node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+        input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
       class_name: KNNGraph
   optimizer_class: '!class torch.optim.adam Adam'
   optimizer_kwargs: {eps: 0.001, lr: 0.001}

configs/models/dynedge_position_custom_scaling_example.yml

@@ -17,7 +17,7 @@ arguments:
           ModelConfig:
             arguments: {}
             class_name: NodesAsPulses
-        node_feature_names: null
+        input_feature_names: null
       class_name: KNNGraph
   gnn:
     ModelConfig:

configs/models/example_direction_reconstruction_model.yml

@@ -13,7 +13,7 @@ arguments:
           ModelConfig:
             arguments: {}
             class_name: NodesAsPulses
-        node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+        input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
       class_name: KNNGraph
   gnn:
     ModelConfig:

configs/models/example_energy_reconstruction_model.yml

@@ -25,7 +25,7 @@ arguments:
           ModelConfig:
             arguments: {}
             class_name: NodesAsPulses
-        node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+        input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
       class_name: KNNGraph
   optimizer_class: '!class torch.optim.adam Adam'
   optimizer_kwargs: {eps: 0.001, lr: 0.001}

configs/models/example_vertex_position_reconstruction_model.yml

@@ -25,7 +25,7 @@ arguments:
           ModelConfig:
             arguments: {}
             class_name: NodesAsPulses
-        node_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
+        input_feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t]
       class_name: KNNGraph
   optimizer_class: '!class torch.optim.adam Adam'
   optimizer_kwargs: {eps: 0.001, lr: 0.001}

examples/02_data/04_ensemble_dataset.py

@@ -24,7 +24,7 @@
     detector=IceCubeDeepCore(),
     node_definition=NodesAsPulses(),
     nb_nearest_neighbours=8,
-    node_feature_names=features,
+    input_feature_names=features,
 )
 
 

examples/05_pisa/02_make_pipeline_database.py

@@ -65,7 +65,7 @@ def main() -> None:
         detector=IceCubeDeepCore(),
         node_definition=NodesAsPulses(),
         nb_nearest_neighbours=8,
-        node_feature_names=FEATURES.DEEPCORE,
+        input_feature_names=FEATURES.DEEPCORE,
     )
 
     # Remove `interaction_time` if it exists

src/graphnet/data/dataset/dataset.py

@@ -629,8 +629,8 @@ def _create_graph(
         # Construct graph data object
         assert self._graph_definition is not None
         graph = self._graph_definition(
-            node_features=node_features,
-            node_feature_names=self._features[
+            input_features=node_features,
+            input_feature_names=self._features[
                 1:
             ],  # first entry is index column
             truth_dicts=truth_dicts,

src/graphnet/deployment/i3modules/graphnet_module.py

@@ -84,12 +84,12 @@ def _make_graph(
     ) -> Data:  # py-l-i-n-t-:- -d-i-s-able=invalid-name
         """Process Physics I3Frame into graph."""
         # Extract features
-        node_features = self._extract_feature_array_from_frame(frame)
+        input_features = self._extract_feature_array_from_frame(frame)
         # Prepare graph data
-        if len(node_features) > 0:
+        if len(input_features) > 0:
             data = self._graph_definition(
-                node_features=node_features,
-                node_feature_names=self._features,
+                input_features=input_features,
+                input_feature_names=self._features,
             )
             return Batch.from_data_list([data])
         else:

src/graphnet/models/graphs/graph_definition.py

@@ -26,7 +26,7 @@ def __init__(
         detector: Detector,
         node_definition: NodeDefinition = NodesAsPulses(),
         edge_definition: Optional[EdgeDefinition] = None,
-        node_feature_names: Optional[List[str]] = None,
+        input_feature_names: Optional[List[str]] = None,
         dtype: Optional[torch.dtype] = torch.float,
         perturbation_dict: Optional[Dict[str, float]] = None,
         seed: Optional[Union[int, Generator]] = None,
@@ -44,7 +44,10 @@ def __init__(
             detector: The corresponding ´Detector´ representing the data.
             node_definition: Definition of nodes. Defaults to NodesAsPulses.
             edge_definition: Definition of edges. Defaults to None.
-            node_feature_names: Names of node feature columns. Defaults to None
+            input_feature_names: Names of each column in expected input data
+                that will be built into a graph. If not provided,
+                it is automatically assumed that all features in `Detector` is
+                used.
             dtype: data type used for node features. e.g. ´torch.float´
             perturbation_dict: Dictionary mapping a feature name to a standard
                                deviation according to which the values for this
@@ -62,25 +65,30 @@ def __init__(
         self._node_definition = node_definition
         self._perturbation_dict = perturbation_dict
 
-        if node_feature_names is None:
+        if input_feature_names is None:
             # Assume all features in Detector is used.
-            node_feature_names = list(self._detector.feature_map().keys())  # type: ignore
-        self._node_feature_names = node_feature_names
+            input_feature_names = list(self._detector.feature_map().keys())  # type: ignore
+        self._input_feature_names = input_feature_names
+
+        # Set input data column names for node definition
+        self._node_definition.set_output_feature_names(
+            self._input_feature_names
+        )
 
         # Set data type
         self.to(dtype)
 
         # Set Input / Output dimensions
         self._node_definition.set_number_of_inputs(
-            node_feature_names=node_feature_names
+            input_feature_names=input_feature_names
         )
-        self.nb_inputs = len(self._node_feature_names)
+        self.nb_inputs = len(self._input_feature_names)
         self.nb_outputs = self._node_definition.nb_outputs
 
         # Set perturbation_cols if needed
         if isinstance(self._perturbation_dict, dict):
             self._perturbation_cols = [
-                self._node_feature_names.index(key)
+                self._input_feature_names.index(key)
                 for key in self._perturbation_dict.keys()
             ]
         if seed is not None:
@@ -97,8 +105,8 @@ def __init__(
 
     def forward(  # type: ignore
         self,
-        node_features: np.ndarray,
-        node_feature_names: List[str],
+        input_features: np.ndarray,
+        input_feature_names: List[str],
         truth_dicts: Optional[List[Dict[str, Any]]] = None,
         custom_label_functions: Optional[Dict[str, Callable[..., Any]]] = None,
         loss_weight_column: Optional[str] = None,
@@ -109,8 +117,8 @@ def forward(  # type: ignore
         """Construct graph as ´Data´ object.
 
         Args:
-            node_features: node features for graph. Shape ´[num_nodes, d]´
-            node_feature_names: name of each column. Shape ´[,d]´.
+            input_features: Input features for graph construction. Shape ´[num_rows, d]´
+            input_feature_names: name of each column. Shape ´[,d]´.
             truth_dicts: Dictionary containing truth labels.
             custom_label_functions: Custom label functions. See https://github.com/graphnet-team/graphnet/blob/main/GETTING_STARTED.md#adding-custom-truth-labels.
             loss_weight_column: Name of column that holds loss weight.
@@ -126,23 +134,27 @@ def forward(  # type: ignore
         """
         # Checks
         self._validate_input(
-            node_features=node_features, node_feature_names=node_feature_names
+            input_features=input_features,
+            input_feature_names=input_feature_names,
         )
 
         # Gaussian perturbation of each column if perturbation dict is given
-        node_features = self._perturb_input(node_features)
+        input_features = self._perturb_input(input_features)
 
         # Transform to pytorch tensor
-        node_features = torch.tensor(node_features, dtype=self.dtype)
+        input_features = torch.tensor(input_features, dtype=self.dtype)
 
         # Standardize / Scale  node features
-        node_features = self._detector(node_features, node_feature_names)
+        input_features = self._detector(input_features, input_feature_names)
+
+        # Create graph & get new node feature names
+        graph, node_feature_names = self._node_definition(input_features)
 
-        # Create graph
-        graph = self._node_definition(node_features)
+        # Enforce dtype
+        graph.x = graph.x.type(self.dtype)
 
         # Attach number of pulses as static attribute.
-        graph.n_pulses = torch.tensor(len(node_features), dtype=torch.int32)
+        graph.n_pulses = torch.tensor(len(input_features), dtype=torch.int32)
 
         # Assign edges
         if self._edge_definition is not None:
@@ -186,40 +198,40 @@ def forward(  # type: ignore
         return graph
 
     def _validate_input(
-        self, node_features: np.array, node_feature_names: List[str]
+        self, input_features: np.array, input_feature_names: List[str]
     ) -> None:
         # node feature matrix dimension check
-        assert node_features.shape[1] == len(node_feature_names)
+        assert input_features.shape[1] == len(input_feature_names)
 
         # check that provided features for input is the same that the ´Graph´
         # was instantiated with.
-        assert len(node_feature_names) == len(
-            self._node_feature_names
-        ), f"""Input features ({node_feature_names}) is not what 
+        assert len(input_feature_names) == len(
+            self._input_feature_names
+        ), f"""Input features ({input_feature_names}) is not what 
                {self.__class__.__name__} was instatiated
-               with ({self._node_feature_names})"""  # noqa
-        for idx in range(len(node_feature_names)):
+               with ({self._input_feature_names})"""  # noqa
+        for idx in range(len(input_feature_names)):
             assert (
-                node_feature_names[idx] == self._node_feature_names[idx]
+                input_feature_names[idx] == self._input_feature_names[idx]
             ), f""" Order of node features in data
-                    are not the same as expected. Got {node_feature_names} 
-                    vs. {self._node_feature_names}"""  # noqa
+                    are not the same as expected. Got {input_feature_names} 
+                    vs. {self._input_feature_names}"""  # noqa
 
-    def _perturb_input(self, node_features: np.ndarray) -> np.ndarray:
+    def _perturb_input(self, input_features: np.ndarray) -> np.ndarray:
         if isinstance(self._perturbation_dict, dict):
             self.warning_once(
                 f"""Will randomly perturb
                 {list(self._perturbation_dict.keys())}
                 using stds {self._perturbation_dict.values()}"""  # noqa
             )
             perturbed_features = self.rng.normal(
-                loc=node_features[:, self._perturbation_cols],
+                loc=input_features[:, self._perturbation_cols],
                 scale=np.array(
                     list(self._perturbation_dict.values()), dtype=float
                 ),
             )
-            node_features[:, self._perturbation_cols] = perturbed_features
-        return node_features
+            input_features[:, self._perturbation_cols] = perturbed_features
+        return input_features
 
     def _add_loss_weights(
         self,

src/graphnet/models/graphs/graphs.py

@@ -17,7 +17,7 @@ def __init__(
         self,
         detector: Detector,
         node_definition: NodeDefinition = NodesAsPulses(),
-        node_feature_names: Optional[List[str]] = None,
+        input_feature_names: Optional[List[str]] = None,
         dtype: Optional[torch.dtype] = torch.float,
         perturbation_dict: Optional[Dict[str, float]] = None,
         seed: Optional[Union[int, Generator]] = None,
@@ -29,7 +29,7 @@ def __init__(
         Args:
             detector: Detector that represents your data.
             node_definition: Definition of nodes in the graph.
-            node_feature_names: Name of node features.
+            input_feature_names: Name of input feature columns.
             dtype: data type for node features.
             perturbation_dict: Dictionary mapping a feature name to a standard
                                deviation according to which the values for this
@@ -50,7 +50,7 @@ def __init__(
                 columns=columns,
             ),
             dtype=dtype,
-            node_feature_names=node_feature_names,
+            input_feature_names=input_feature_names,
             perturbation_dict=perturbation_dict,
             seed=seed,
         )

src/graphnet/models/graphs/nodes/__init__.py

@@ -5,4 +5,4 @@
 and their features.
 """
 
-from .nodes import NodeDefinition, NodesAsPulses
+from .nodes import NodeDefinition, NodesAsPulses, PercentileClusters