diff --git a/_modules/graphnet/data/dataset/dataset.html b/_modules/graphnet/data/dataset/dataset.html index 0d25df931..f82fb0dff 100644 --- a/_modules/graphnet/data/dataset/dataset.html +++ b/_modules/graphnet/data/dataset/dataset.html @@ -351,7 +351,7 @@

Source code for graphn from graphnet.utilities.config import ( Configurable, DatasetConfig, - save_dataset_config, + DatasetConfigSaverABCMeta, ) from graphnet.utilities.config.parsing import traverse_and_apply from graphnet.utilities.logging import Logger @@ -420,7 +420,13 @@

Source code for graphn
[docs] -class Dataset(Logger, Configurable, torch.utils.data.Dataset, ABC): +class Dataset( + Logger, + Configurable, + torch.utils.data.Dataset, + ABC, + metaclass=DatasetConfigSaverABCMeta, +): """Base Dataset class for reading from any intermediate file format.""" # Class method(s) @@ -529,7 +535,6 @@

Source code for graphn .replace("${GRAPHNET}", GRAPHNET_ROOT_DIR) ) - @save_dataset_config def __init__( self, path: Union[str, List[str]], diff --git a/_modules/graphnet/data/dataset/sqlite/sqlite_dataset_perturbed.html b/_modules/graphnet/data/dataset/sqlite/sqlite_dataset_perturbed.html deleted file mode 100644 index 44acbb9c5..000000000 --- a/_modules/graphnet/data/dataset/sqlite/sqlite_dataset_perturbed.html +++ /dev/null @@ -1,515 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - graphnet.data.dataset.sqlite.sqlite_dataset_perturbed — graphnet documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Skip to content -
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Source code for graphnet.data.dataset.sqlite.sqlite_dataset_perturbed

-"""`Dataset` class(es) for reading perturbed data from SQLite databases."""
-
-from typing import Dict, List, Optional, Tuple, Union
-
-import numpy as np
-from numpy.random import default_rng, Generator
-import torch
-from torch_geometric.data import Data
-
-from .sqlite_dataset import SQLiteDataset
-
-
-

-[docs]
-class SQLiteDatasetPerturbed(SQLiteDataset):
-    """Pytorch dataset for reading perturbed data from SQLite databases.
-
-    This including a pre-processing step, where the input data is randomly
-    perturbed according to given per-feature "noise" levels. This is intended
-    to test the stability of a trained model under small changes to the input
-    parameters.
-    """
-
-    def __init__(
-        self,
-        path: Union[str, List[str]],
-        pulsemaps: Union[str, List[str]],
-        features: List[str],
-        truth: List[str],
-        *,
-        perturbation_dict: Dict[str, float],
-        node_truth: Optional[List[str]] = None,
-        index_column: str = "event_no",
-        truth_table: str = "truth",
-        node_truth_table: Optional[str] = None,
-        string_selection: Optional[List[int]] = None,
-        selection: Optional[List[int]] = None,
-        dtype: torch.dtype = torch.float32,
-        loss_weight_table: Optional[str] = None,
-        loss_weight_column: Optional[str] = None,
-        loss_weight_default_value: Optional[float] = None,
-        seed: Optional[Union[int, Generator]] = None,
-    ):
-        """Construct SQLiteDatasetPerturbed.
-
-        Args:
-            path: Path to the file(s) from which this `Dataset` should read.
-            pulsemaps: Name(s) of the pulse map series that should be used to
-                construct the nodes on the individual graph objects, and their
-                features. Multiple pulse series maps can be used, e.g., when
-                different DOM types are stored in different maps.
-            features: List of columns in the input files that should be used as
-                node features on the graph objects.
-            truth: List of event-level columns in the input files that should
-                be used added as attributes on the  graph objects.
-            perturbation_dict (Dict[str, float]): Dictionary mapping a feature
-                name to a standard deviation according to which the values for
-                this feature should be randomly perturbed.
-            node_truth: List of node-level columns in the input files that
-                should be used added as attributes on the graph objects.
-            index_column: Name of the column in the input files that contains
-                unique indicies to identify and map events across tables.
-            truth_table: Name of the table containing event-level truth
-                information.
-            node_truth_table: Name of the table containing node-level truth
-                information.
-            string_selection: Subset of strings for which data should be read
-                and used to construct graph objects. Defaults to None, meaning
-                all strings for which data exists are used.
-            selection: List of indicies (in `index_column`) of the events in
-                the input files that should be read. Defaults to None, meaning
-                that all events in the input files are read.
-            dtype: Type of the feature tensor on the graph objects returned.
-            loss_weight_table: Name of the table containing per-event loss
-                weights.
-            loss_weight_column: Name of the column in `loss_weight_table`
-                containing per-event loss weights. This is also the name of the
-                corresponding attribute assigned to the graph object.
-            loss_weight_default_value: Default per-event loss weight.
-                NOTE: This default value is only applied when
-                `loss_weight_table` and `loss_weight_column` are specified, and
-                in this case to events with no value in the corresponding
-                table/column. That is, if no per-event loss weight table/column
-                is provided, this value is ignored. Defaults to None.
-            seed: Optional seed for random number generation. Defaults to None.
-        """
-        # Base class constructor
-        super().__init__(
-            path=path,
-            pulsemaps=pulsemaps,
-            features=features,
-            truth=truth,
-            node_truth=node_truth,
-            index_column=index_column,
-            truth_table=truth_table,
-            node_truth_table=node_truth_table,
-            string_selection=string_selection,
-            selection=selection,
-            dtype=dtype,
-            loss_weight_table=loss_weight_table,
-            loss_weight_column=loss_weight_column,
-            loss_weight_default_value=loss_weight_default_value,
-        )
-
-        # Custom member variables
-        assert isinstance(perturbation_dict, dict)
-        assert len(set(perturbation_dict.keys())) == len(
-            perturbation_dict.keys()
-        )
-        self._perturbation_dict = perturbation_dict
-
-        self._perturbation_cols = [
-            self._features.index(key) for key in self._perturbation_dict.keys()
-        ]
-
-        if seed is not None:
-            if isinstance(seed, int):
-                self.rng = default_rng(seed)
-            elif isinstance(seed, Generator):
-                self.rng = seed
-            else:
-                raise ValueError(
-                    "Invalid seed. Must be an int or a numpy Generator."
-                )
-        else:
-            self.rng = default_rng()
-
-    def __getitem__(self, sequential_index: int) -> Data:
-        """Return graph `Data` object at `index`."""
-        if not (0 <= sequential_index < len(self)):
-            raise IndexError(
-                f"Index {sequential_index} not in range [0, {len(self) - 1}]"
-            )
-        features, truth, node_truth, loss_weight = self._query(
-            sequential_index
-        )
-        perturbed_features = self._perturb_features(features)
-        graph = self._create_graph(
-            perturbed_features, truth, node_truth, loss_weight
-        )
-        return graph
-
-    def _perturb_features(
-        self, features: List[Tuple[float, ...]]
-    ) -> List[Tuple[float, ...]]:
-        features_array = np.array(features)
-        perturbed_features = self.rng.normal(
-            loc=features_array[:, self._perturbation_cols],
-            scale=np.array(
-                list(self._perturbation_dict.values()), dtype=np.float
-            ),
-        )
-        features_array[:, self._perturbation_cols] = perturbed_features
-        return features_array.tolist()

-
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-
- © Copyright 2021-2023, GraphNeT team. - -
- Created using - Sphinx 7.2.6. - and - Material for - Sphinx -
-
-
-
- - - - \ No newline at end of file diff --git a/_modules/graphnet/models/coarsening.html b/_modules/graphnet/models/coarsening.html index 6a260f076..c9a941617 100644 --- a/_modules/graphnet/models/coarsening.html +++ b/_modules/graphnet/models/coarsening.html @@ -346,7 +346,6 @@

Source code for graphnet. std_pool_x, ) from graphnet.models import Model -from graphnet.utilities.config import save_model_config # Utility method(s) from torch_geometric.utils import degree @@ -392,7 +391,6 @@

Source code for graphnet. "sum": (sum_pool, sum_pool_x), } - @save_model_config def __init__( self, reduce: str = "avg", @@ -533,7 +531,6 @@

Source code for graphnet. class AttributeCoarsening(Coarsening): """Coarsen pulses based on specified attributes.""" - @save_model_config def __init__( self, attributes: List[str], diff --git a/_modules/graphnet/models/detector/detector.html b/_modules/graphnet/models/detector/detector.html index eef62aefc..814e04815 100644 --- a/_modules/graphnet/models/detector/detector.html +++ b/_modules/graphnet/models/detector/detector.html @@ -332,7 +332,6 @@

Source code for gr from graphnet.models import Model from graphnet.utilities.decorators import final -from graphnet.utilities.config import save_model_config
@@ -340,7 +339,6 @@

Source code for gr class Detector(Model): """Base class for all detector-specific read-ins in graphnet.""" - @save_model_config def __init__(self) -> None: """Construct `Detector`.""" # Base class constructor diff --git a/_modules/graphnet/models/gnn/convnet.html b/_modules/graphnet/models/gnn/convnet.html index ada586304..61066449f 100644 --- a/_modules/graphnet/models/gnn/convnet.html +++ b/_modules/graphnet/models/gnn/convnet.html @@ -334,7 +334,6 @@

Source code for graphnet from torch_geometric.nn import TAGConv, global_add_pool, global_max_pool from torch_geometric.data import Data -from graphnet.utilities.config import save_model_config from graphnet.models.gnn.gnn import GNN @@ -343,7 +342,6 @@

Source code for graphnet class ConvNet(GNN): """ConvNet (convolutional network) model.""" - @save_model_config def __init__( self, nb_inputs: int, diff --git a/_modules/graphnet/models/gnn/dynedge.html b/_modules/graphnet/models/gnn/dynedge.html index d882546a3..1bb966c10 100644 --- a/_modules/graphnet/models/gnn/dynedge.html +++ b/_modules/graphnet/models/gnn/dynedge.html @@ -331,7 +331,6 @@

Source code for graphnet from torch_scatter import scatter_max, scatter_mean, scatter_min, scatter_sum from graphnet.models.components.layers import DynEdgeConv -from graphnet.utilities.config import save_model_config from graphnet.models.gnn.gnn import GNN from graphnet.models.utils import calculate_xyzt_homophily @@ -348,7 +347,6 @@

Source code for graphnet class DynEdge(GNN): """DynEdge (dynamical edge convolutional) model.""" - @save_model_config def __init__( self, nb_inputs: int, diff --git a/_modules/graphnet/models/gnn/dynedge_jinst.html b/_modules/graphnet/models/gnn/dynedge_jinst.html index 125dc1399..79fbe9a81 100644 --- a/_modules/graphnet/models/gnn/dynedge_jinst.html +++ b/_modules/graphnet/models/gnn/dynedge_jinst.html @@ -334,7 +334,6 @@

Source code for gr from torch_scatter import scatter_max, scatter_mean, scatter_min, scatter_sum from graphnet.models.components.layers import DynEdgeConv -from graphnet.utilities.config import save_model_config from graphnet.models.gnn.gnn import GNN from graphnet.models.utils import calculate_xyzt_homophily @@ -344,7 +343,6 @@

Source code for gr class DynEdgeJINST(GNN): """DynEdge (dynamical edge convolutional) model used in [2209.03042].""" - @save_model_config def __init__( self, nb_inputs: int, diff --git a/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html b/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html index 8895fa50c..99b914c05 100644 --- a/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html +++ b/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html @@ -342,7 +342,6 @@

Source code from torch_scatter import scatter_max, scatter_mean, scatter_min, scatter_sum from graphnet.models.components.layers import DynTrans -from graphnet.utilities.config import save_model_config from graphnet.models.gnn.gnn import GNN from graphnet.models.utils import calculate_xyzt_homophily @@ -359,7 +358,6 @@

Source code class DynEdgeTITO(GNN): """DynEdge (dynamical edge convolutional) model.""" - @save_model_config def __init__( self, nb_inputs: int, diff --git a/_modules/graphnet/models/gnn/gnn.html b/_modules/graphnet/models/gnn/gnn.html index 7a3a22f46..1e183aaa6 100644 --- a/_modules/graphnet/models/gnn/gnn.html +++ b/_modules/graphnet/models/gnn/gnn.html @@ -330,7 +330,6 @@

Source code for graphnet.mod from torch_geometric.data import Data from graphnet.models import Model -from graphnet.utilities.config import save_model_config
@@ -338,7 +337,6 @@

Source code for graphnet.mod class GNN(Model): """Base class for all core GNN models in graphnet.""" - @save_model_config def __init__(self, nb_inputs: int, nb_outputs: int) -> None: """Construct `GNN`.""" # Base class constructor diff --git a/_modules/graphnet/models/graphs/edges/edges.html b/_modules/graphnet/models/graphs/edges/edges.html index e681629cf..294002b15 100644 --- a/_modules/graphnet/models/graphs/edges/edges.html +++ b/_modules/graphnet/models/graphs/edges/edges.html @@ -331,7 +331,6 @@

Source code for g from torch_geometric.nn import knn_graph, radius_graph from torch_geometric.data import Data -from graphnet.utilities.config import save_model_config from graphnet.models.utils import calculate_distance_matrix from graphnet.models import Model @@ -380,7 +379,6 @@

Source code for g class KNNEdges(EdgeDefinition): # pylint: disable=too-few-public-methods """Builds edges from the k-nearest neighbours.""" - @save_model_config def __init__( self, nb_nearest_neighbours: int, @@ -420,7 +418,6 @@

Source code for g class RadialEdges(EdgeDefinition): """Builds graph from a sphere of chosen radius centred at each node.""" - @save_model_config def __init__( self, radius: float, @@ -464,7 +461,6 @@

Source code for g See https://arxiv.org/pdf/1809.06166.pdf. """ - @save_model_config def __init__( self, sigma: float, diff --git a/_modules/graphnet/models/graphs/graph_definition.html b/_modules/graphnet/models/graphs/graph_definition.html index 75075b762..71dac0f62 100644 --- a/_modules/graphnet/models/graphs/graph_definition.html +++ b/_modules/graphnet/models/graphs/graph_definition.html @@ -330,16 +330,15 @@

Source code """ -from typing import Any, List, Optional, Dict, Callable +from typing import Any, List, Optional, Dict, Callable, Union import torch from torch_geometric.data import Data import numpy as np - -from graphnet.utilities.config import save_model_config +from numpy.random import default_rng, Generator from graphnet.models.detector import Detector from .edges import EdgeDefinition -from .nodes import NodeDefinition +from .nodes import NodeDefinition, NodesAsPulses from graphnet.models import Model @@ -348,14 +347,15 @@

Source code class GraphDefinition(Model): """An Abstract class to create graph definitions from.""" - @save_model_config def __init__( self, detector: Detector, - node_definition: NodeDefinition, + node_definition: NodeDefinition = NodesAsPulses(), edge_definition: Optional[EdgeDefinition] = None, node_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, ): """Construct ´GraphDefinition´. The ´detector´ holds. @@ -368,10 +368,16 @@

Source code Args: detector: The corresponding ´Detector´ representing the data. - node_definition: Definition of nodes. + 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 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 + feature should be randomly perturbed. Defaults + to None. + seed: seed or Generator used to randomly sample perturbations. + Defaults to None. """ # Base class constructor super().__init__(name=__name__, class_name=self.__class__.__name__) @@ -380,6 +386,8 @@

Source code self._detector = detector self._edge_definition = edge_definition self._node_definition = node_definition + self._perturbation_dict = perturbation_dict + if node_feature_names is None: # Assume all features in Detector is used. node_feature_names = list(self._detector.feature_map().keys()) # type: ignore @@ -395,6 +403,24 @@

Source code self.nb_inputs = len(self._node_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) + for key in self._perturbation_dict.keys() + ] + if seed is not None: + if isinstance(seed, int): + self.rng = default_rng(seed) + elif isinstance(seed, Generator): + self.rng = seed + else: + raise ValueError( + "Invalid seed. Must be an int or a numpy Generator." + ) + else: + self.rng = default_rng() +
[docs] def forward( # type: ignore @@ -415,9 +441,12 @@

Source code node_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. Defaults to None. + loss_weight_column: Name of column that holds loss weight. + Defaults to None. loss_weight: Loss weight associated with event. Defaults to None. - loss_weight_default_value: default value for loss weight. Used in instances where some events have no pre-defined loss weight. Defaults to None. + loss_weight_default_value: default value for loss weight. + Used in instances where some events have + no pre-defined loss weight. Defaults to None. data_path: Path to dataset data files. Defaults to None. Returns: @@ -428,6 +457,9 @@

Source code node_features=node_features, node_feature_names=node_feature_names ) + # Gaussian perturbation of each column if perturbation dict is given + node_features = self._perturb_input(node_features) + # Transform to pytorch tensor node_features = torch.tensor(node_features, dtype=self.dtype) @@ -445,7 +477,8 @@

Source code graph = self._edge_definition(graph) else: self.warnonce( - "No EdgeDefinition provided. Graphs will not have edges defined!" + """No EdgeDefinition provided. + Graphs will not have edges defined!""" # noqa ) # Attach data path - useful for Ensemble datasets. @@ -490,11 +523,31 @@

Source code # was instantiated with. assert len(node_feature_names) == len( self._node_feature_names - ), f"""Input features ({node_feature_names}) is not what {self.__class__.__name__} was instatiated with ({self._node_feature_names})""" + ), f"""Input features ({node_feature_names}) is not what + {self.__class__.__name__} was instatiated + with ({self._node_feature_names})""" # noqa for idx in range(len(node_feature_names)): assert ( node_feature_names[idx] == self._node_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}""" + ), f""" Order of node features in data + are not the same as expected. Got {node_feature_names} + vs. {self._node_feature_names}""" # noqa + + def _perturb_input(self, node_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], + scale=np.array( + list(self._perturbation_dict.values()), dtype=float + ), + ) + node_features[:, self._perturbation_cols] = perturbed_features + return node_features def _add_loss_weights( self, @@ -582,7 +635,8 @@

Source code graph[feature] = graph.x[:, index].detach() else: self.warnonce( - """Cannot assign graph['x']. This field is reserved for node features. Please rename your input feature.""" + """Cannot assign graph['x']. This field is reserved + for node features. Please rename your input feature.""" # noqa ) return graph diff --git a/_modules/graphnet/models/graphs/graphs.html b/_modules/graphnet/models/graphs/graphs.html index 5f3dc4be7..2e0d1f9f9 100644 --- a/_modules/graphnet/models/graphs/graphs.html +++ b/_modules/graphnet/models/graphs/graphs.html @@ -324,14 +324,14 @@

Source code for graphnet.models.graphs.graphs

 """A module containing different graph representations in GraphNeT."""
 
-from typing import List, Optional
+from typing import List, Optional, Dict, Union
 import torch
+from numpy.random import Generator
 
-from graphnet.utilities.config import save_model_config
 from .graph_definition import GraphDefinition
 from graphnet.models.detector import Detector
 from graphnet.models.graphs.edges import EdgeDefinition, KNNEdges
-from graphnet.models.graphs.nodes import NodeDefinition
+from graphnet.models.graphs.nodes import NodeDefinition, NodesAsPulses
 
 
 

@@ -339,13 +339,14 @@ 
Source code for graphn
 class KNNGraph(GraphDefinition):
     """A Graph representation where Edges are drawn to nearest neighbours."""
 
-    @save_model_config
     def __init__(
         self,
         detector: Detector,
-        node_definition: NodeDefinition,
+        node_definition: NodeDefinition = NodesAsPulses(),
         node_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,
         nb_nearest_neighbours: int = 8,
         columns: List[int] = [0, 1, 2],
     ) -> None:
@@ -356,6 +357,12 @@ 
Source code for graphn
             node_definition: Definition of nodes in the graph.
             node_feature_names: Name of node features.
             dtype: data type for node features.
+            perturbation_dict: Dictionary mapping a feature name to a standard
+                               deviation according to which the values for this
+                               feature should be randomly perturbed. Defaults
+                               to None.
+            seed: seed or Generator used to randomly sample perturbations.
+                  Defaults to None.
             nb_nearest_neighbours: Number of edges for each node. Defaults to 8.
             columns: node feature columns used for distance calculation
             . Defaults to [0, 1, 2].
@@ -370,6 +377,8 @@ 
Source code for graphn
             ),
             dtype=dtype,
             node_feature_names=node_feature_names,
+            perturbation_dict=perturbation_dict,
+            seed=seed,
         )
diff --git a/_modules/graphnet/models/graphs/nodes/nodes.html b/_modules/graphnet/models/graphs/nodes/nodes.html index 3e7040189..14aecf400 100644 --- a/_modules/graphnet/models/graphs/nodes/nodes.html +++ b/_modules/graphnet/models/graphs/nodes/nodes.html @@ -331,7 +331,6 @@

Source code for g from torch_geometric.data import Data from graphnet.utilities.decorators import final -from graphnet.utilities.config import save_model_config from graphnet.models import Model @@ -340,7 +339,6 @@

Source code for g class NodeDefinition(Model): # pylint: disable=too-few-public-methods """Base class for graph building.""" - @save_model_config def __init__(self) -> None: """Construct `Detector`.""" # Base class constructor diff --git a/_modules/graphnet/models/model.html b/_modules/graphnet/models/model.html index 9654c1e81..2d895f29e 100644 --- a/_modules/graphnet/models/model.html +++ b/_modules/graphnet/models/model.html @@ -342,13 +342,19 @@

Source code for graphnet.model from torch_geometric.data import Data from graphnet.utilities.logging import Logger -from graphnet.utilities.config import Configurable, ModelConfig +from graphnet.utilities.config import ( + Configurable, + ModelConfig, + ModelConfigSaverABC, +) from graphnet.training.callbacks import ProgressBar
[docs] -class Model(Logger, Configurable, LightningModule, ABC): +class Model( + Logger, Configurable, LightningModule, ABC, metaclass=ModelConfigSaverABC +): """Base class for all models in graphnet."""
diff --git a/_modules/graphnet/models/standard_model.html b/_modules/graphnet/models/standard_model.html index cb57cb523..29381cb6c 100644 --- a/_modules/graphnet/models/standard_model.html +++ b/_modules/graphnet/models/standard_model.html @@ -334,7 +334,6 @@

Source code for graph from torch_geometric.data import Data import pandas as pd -from graphnet.utilities.config import save_model_config from graphnet.models.graphs import GraphDefinition from graphnet.models.gnn.gnn import GNN from graphnet.models.model import Model @@ -350,7 +349,6 @@

Source code for graph model (detector read-in, GNN architecture, and task-specific read-outs). """ - @save_model_config def __init__( self, *, diff --git a/_modules/graphnet/models/task/task.html b/_modules/graphnet/models/task/task.html index c43cb2f73..f592e588a 100644 --- a/_modules/graphnet/models/task/task.html +++ b/_modules/graphnet/models/task/task.html @@ -339,7 +339,6 @@

Source code for graphnet.m from graphnet.training.loss_functions import LossFunction # type: ignore[attr-defined] from graphnet.models import Model -from graphnet.utilities.config import save_model_config from graphnet.utilities.decorators import final @@ -365,7 +364,6 @@

Source code for graphnet.m """Return default prediction labels.""" return self._default_prediction_labels - @save_model_config def __init__( self, *, @@ -605,7 +603,6 @@

Source code for graphnet.m class IdentityTask(Task): """Identity, or trivial, task.""" - @save_model_config def __init__( self, nb_outputs: int, diff --git a/_modules/graphnet/training/loss_functions.html b/_modules/graphnet/training/loss_functions.html index dd70fd272..d1b35f50a 100644 --- a/_modules/graphnet/training/loss_functions.html +++ b/_modules/graphnet/training/loss_functions.html @@ -343,7 +343,6 @@

Source code for gra softplus, ) -from graphnet.utilities.config import save_model_config from graphnet.models.model import Model from graphnet.utilities.decorators import final @@ -353,7 +352,6 @@

Source code for gra class LossFunction(Model): """Base class for loss functions in `graphnet`.""" - @save_model_config def __init__(self, **kwargs: Any) -> None: """Construct `LossFunction`, saving model config.""" super().__init__(**kwargs) @@ -461,7 +459,6 @@

Source code for gra (0, num_classes - 1). """ - @save_model_config def __init__( self, options: Union[int, List[Any], Dict[Any, int]], diff --git a/_modules/graphnet/training/utils.html b/_modules/graphnet/training/utils.html index eff87f698..787b594c7 100644 --- a/_modules/graphnet/training/utils.html +++ b/_modules/graphnet/training/utils.html @@ -348,7 +348,7 @@

Source code for graphnet.tra def collate_fn(graphs: List[Data]) -> Batch: """Remove graphs with less than two DOM hits. - Should not occur in "production. + Should not occur in "production". """ graphs = [g for g in graphs if g.n_pulses > 1] return Batch.from_data_list(graphs)

@@ -361,7 +361,7 @@

Source code for graphnet.tra def make_dataloader( db: str, pulsemaps: Union[str, List[str]], - graph_definition: Optional[GraphDefinition], + graph_definition: GraphDefinition, features: List[str], truth: List[str], *, @@ -424,7 +424,7 @@

Source code for graphnet.tra [docs] def make_train_validation_dataloader( db: str, - graph_definition: Optional[GraphDefinition], + graph_definition: GraphDefinition, selection: Optional[List[int]], pulsemaps: Union[str, List[str]], features: List[str], diff --git a/_modules/graphnet/utilities/config/dataset_config.html b/_modules/graphnet/utilities/config/dataset_config.html index 91b338109..3b7b036b5 100644 --- a/_modules/graphnet/utilities/config/dataset_config.html +++ b/_modules/graphnet/utilities/config/dataset_config.html @@ -323,7 +323,8 @@

Source code for graphnet.utilities.config.dataset_config

 """Config classes for the `graphnet.data.dataset` module."""
-
+import warnings
+from abc import ABCMeta
 from functools import wraps
 from typing import (
     TYPE_CHECKING,
@@ -512,6 +513,11 @@ 
Source code
 [docs]
 def save_dataset_config(init_fn: Callable) -> Callable:
     """Save the arguments to `__init__` functions as member `DatasetConfig`."""
+    warnings.warn(
+        "Warning: `save_dataset_config` is deprecated. Config saving "
+        "is now done automatically, for all classes inheriting from Dataset",
+        DeprecationWarning,
+    )
 
     def _replace_model_instance_with_config(
         obj: Union["Model", Any]
@@ -547,6 +553,51 @@ 
Source code
 
     return wrapper
+ + +
+[docs] +class DatasetConfigSaverMeta(type): + """Metaclass for `DatasetConfig` that saves the config after `__init__`.""" + + def __call__(cls: Any, *args: Any, **kwargs: Any) -> object: + """Catch object after construction and save config.""" + + def _replace_model_instance_with_config( + obj: Union["Model", Any] + ) -> Union[ModelConfig, Any]: + """Replace `Model` instances in `obj` with their `ModelConfig`.""" + from graphnet.models import Model + import torch + + if isinstance(obj, Model): + return obj.config + + if isinstance(obj, torch.dtype): + return obj.__str__() + else: + return obj + + # Create object + created_obj = super().__call__(*args, **kwargs) + + # Get all argument values, including defaults + cfg = get_all_argument_values(created_obj.__init__, *args, **kwargs) + cfg = traverse_and_apply(cfg, _replace_model_instance_with_config) + + # Store config in + created_obj._config = DatasetConfig(**cfg) + return created_obj
+ + + +
+[docs] +class DatasetConfigSaverABCMeta(DatasetConfigSaverMeta, ABCMeta): + """Common interface between DatasetConfigSaver and ABC Metaclasses.""" + + pass
+
diff --git a/_modules/graphnet/utilities/config/model_config.html b/_modules/graphnet/utilities/config/model_config.html index e63e73186..0aa31c78e 100644 --- a/_modules/graphnet/utilities/config/model_config.html +++ b/_modules/graphnet/utilities/config/model_config.html @@ -323,9 +323,11 @@

Source code for graphnet.utilities.config.model_config

 """Config classes for the `graphnet.models` module."""
+from abc import ABCMeta
 from functools import wraps
 import inspect
 import re
+import warnings
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -582,6 +584,11 @@ 
Source code f
 [docs]
 def save_model_config(init_fn: Callable) -> Callable:
     """Save the arguments to `__init__` functions as a member `ModelConfig`."""
+    warnings.warn(
+        "Warning: `save_model_config` is deprecated. Config saving is"
+        "now done automatically for all classes inheriting from Model",
+        DeprecationWarning,
+    )
 
     def _replace_model_instance_with_config(
         obj: Union["Model", Any]
@@ -616,6 +623,50 @@ 
Source code f
 
     return wrapper
+ + +
+[docs] +class ModelConfigSaverMeta(type): + """Metaclass for saving `ModelConfig` to `Model` instances.""" + + def __call__(cls: Any, *args: Any, **kwargs: Any) -> object: + """Catch object construction and save config after `__init__`.""" + + def _replace_model_instance_with_config( + obj: Union["Model", Any] + ) -> Union[ModelConfig, Any]: + """Replace `Model` instances in `obj` with their `ModelConfig`.""" + from graphnet.models import Model + + if isinstance(obj, Model): + return obj.config + else: + return obj + + # Create object + created_obj = super().__call__(*args, **kwargs) + + # Get all argument values, including defaults + cfg = get_all_argument_values(created_obj.__init__, *args, **kwargs) + cfg = traverse_and_apply(cfg, _replace_model_instance_with_config) + + # Store config in + created_obj._config = ModelConfig( + class_name=str(cls.__name__), + arguments=dict(**cfg), + ) + return created_obj
+ + + +
+[docs] +class ModelConfigSaverABC(ModelConfigSaverMeta, ABCMeta): + """Common interface between ModelConfigSaver and ABC Metaclasses.""" + + pass
+
diff --git a/_modules/index.html b/_modules/index.html index 120145b62..0a020631b 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -327,7 +327,6 @@

All modules for which code is available

  • graphnet.data.dataset.dataset
  • graphnet.data.dataset.parquet.parquet_dataset
  • graphnet.data.dataset.sqlite.sqlite_dataset
    • -
  • graphnet.data.dataset.sqlite.sqlite_dataset_perturbed
  • graphnet.data.extractors.i3extractor
  • graphnet.data.extractors.i3featureextractor
  • graphnet.data.extractors.i3genericextractor
    • diff --git a/_sources/api/graphnet.data.dataset.sqlite.rst.txt b/_sources/api/graphnet.data.dataset.sqlite.rst.txt index 9c6789d2d..253056886 100644 --- a/_sources/api/graphnet.data.dataset.sqlite.rst.txt +++ b/_sources/api/graphnet.data.dataset.sqlite.rst.txt @@ -16,7 +16,6 @@ sqlite :maxdepth: 2 graphnet.data.dataset.sqlite.sqlite_dataset - graphnet.data.dataset.sqlite.sqlite_dataset_perturbed diff --git a/_sources/api/graphnet.data.dataset.sqlite.sqlite_dataset_perturbed.rst.txt b/_sources/api/graphnet.data.dataset.sqlite.sqlite_dataset_perturbed.rst.txt deleted file mode 100644 index 363bb031b..000000000 --- a/_sources/api/graphnet.data.dataset.sqlite.sqlite_dataset_perturbed.rst.txt +++ /dev/null @@ -1,8 +0,0 @@ - -sqlite\_dataset\_perturbed -========================== - -.. automodule:: graphnet.data.dataset.sqlite.sqlite_dataset_perturbed - :members: - :undoc-members: - :show-inheritance: diff --git a/api/graphnet.data.dataset.dataset.html b/api/graphnet.data.dataset.dataset.html index ac7ed7ec2..88e9f8603 100644 --- a/api/graphnet.data.dataset.dataset.html +++ b/api/graphnet.data.dataset.dataset.html @@ -130,7 +130,7 @@ - + @@ -643,7 +643,7 @@
    -class graphnet.data.dataset.dataset.Dataset(path, graph_definition, pulsemaps, features, truth, *, node_truth, index_column, truth_table, node_truth_table, string_selection, selection, dtype, loss_weight_table, loss_weight_column, loss_weight_default_value, seed)[source]
    +class graphnet.data.dataset.dataset.Dataset(*args, **kwargs)[source]

    Bases: Logger, Configurable, Dataset, ABC

    Base Dataset class for reading from any intermediate file format.

    Construct Dataset.

    @@ -692,8 +692,13 @@ “10000 random events ~ event_no % 5 > 0” or “20% random events ~ event_no % 5 > 0”).

  • graph_definition (GraphDefinition) – Method that defines the graph representation.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -806,7 +811,7 @@

    Bases: Dataset

    Pytorch dataset for reading from Parquet files.

    Construct Dataset.

    @@ -566,8 +566,13 @@ “10000 random events ~ event_no % 5 > 0” or “20% random events ~ event_no % 5 > 0”).

  • graph_definition (GraphDefinition) – Method that defines the graph representation.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.data.dataset.sqlite.html b/api/graphnet.data.dataset.sqlite.html index 8ea0f5783..4111fca68 100644 --- a/api/graphnet.data.dataset.sqlite.html +++ b/api/graphnet.data.dataset.sqlite.html @@ -339,13 +339,6 @@ sqlite_dataset - -
  • - - - sqlite_dataset_perturbed - -
    • @@ -492,10 +485,6 @@
  • SQLiteDataset
    • -
  • sqlite_dataset_perturbed -
    • diff --git a/api/graphnet.data.dataset.sqlite.sqlite_dataset.html b/api/graphnet.data.dataset.sqlite.sqlite_dataset.html index 7d25db896..121d22fc0 100644 --- a/api/graphnet.data.dataset.sqlite.sqlite_dataset.html +++ b/api/graphnet.data.dataset.sqlite.sqlite_dataset.html @@ -129,7 +129,7 @@ - + @@ -365,13 +365,6 @@ - -
  • - - - sqlite_dataset_perturbed - -
    • @@ -524,7 +517,7 @@

    Dataset class(es) for reading data from SQLite databases.

    -class graphnet.data.dataset.sqlite.sqlite_dataset.SQLiteDataset(path, graph_definition, pulsemaps, features, truth, *, node_truth, index_column, truth_table, node_truth_table, string_selection, selection, dtype, loss_weight_table, loss_weight_column, loss_weight_default_value, seed)[source]
    +class graphnet.data.dataset.sqlite.sqlite_dataset.SQLiteDataset(*args, **kwargs)[source]

    Bases: Dataset

    Pytorch dataset for reading data from SQLite databases.

    Construct Dataset.

    @@ -573,8 +566,13 @@ “10000 random events ~ event_no % 5 > 0” or “20% random events ~ event_no % 5 > 0”).

  • graph_definition (GraphDefinition) – Method that defines the graph representation.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -621,12 +619,12 @@ -
    Skip to content -
    - -
    - - -
    - - - - -
    -
    - -
    -
    -
    - -
    -
    -
    -
    -
    -
    - - -
    -
    -
    - -
    -
    - -
    -

    sqlite_dataset_perturbed

    -

    Dataset class(es) for reading perturbed data from SQLite databases.

    -
    -
    -class graphnet.data.dataset.sqlite.sqlite_dataset_perturbed.SQLiteDatasetPerturbed(path, pulsemaps, features, truth, *, perturbation_dict, node_truth, index_column, truth_table, node_truth_table, string_selection, selection, dtype, loss_weight_table, loss_weight_column, loss_weight_default_value, seed)[source]
    -

    Bases: SQLiteDataset

    -

    Pytorch dataset for reading perturbed data from SQLite databases.

    -

    This including a pre-processing step, where the input data is randomly -perturbed according to given per-feature “noise” levels. This is intended -to test the stability of a trained model under small changes to the input -parameters.

    -

    Construct SQLiteDatasetPerturbed.

    -
    -
    Parameters:
    -
      -

    • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

      • -

    • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to -construct the nodes on the individual graph objects, and their -features. Multiple pulse series maps can be used, e.g., when -different DOM types are stored in different maps.

      • -

    • features (List[str]) – List of columns in the input files that should be used as -node features on the graph objects.

      • -

    • truth (List[str]) – List of event-level columns in the input files that should -be used added as attributes on the graph objects.

      • -

    • perturbation_dict (Dict[str, float]) – Dictionary mapping a feature -name to a standard deviation according to which the values for -this feature should be randomly perturbed.

      • -

    • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that -should be used added as attributes on the graph objects.

      • -

    • index_column (str, default: 'event_no') – Name of the column in the input files that contains -unique indicies to identify and map events across tables.

      • -

    • truth_table (str, default: 'truth') – Name of the table containing event-level truth -information.

      • -

    • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth -information.

      • -

    • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read -and used to construct graph objects. Defaults to None, meaning -all strings for which data exists are used.

      • -

    • selection (Optional[List[int]], default: None) – List of indicies (in index_column) of the events in -the input files that should be read. Defaults to None, meaning -that all events in the input files are read.

      • -

    • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

      • -

    • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss -weights.

      • -

    • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table -containing per-event loss weights. This is also the name of the -corresponding attribute assigned to the graph object.

      • -

    • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. -NOTE: This default value is only applied when -loss_weight_table and loss_weight_column are specified, and -in this case to events with no value in the corresponding -table/column. That is, if no per-event loss weight table/column -is provided, this value is ignored. Defaults to None.

      • -

    • seed (Union[int, Generator, None], default: None) – Optional seed for random number generation. Defaults to None.

      • -
    -
    -
    -
    -
    - - -
    -
    -
    -
    -
    - - - - - \ No newline at end of file diff --git a/api/graphnet.models.coarsening.html b/api/graphnet.models.coarsening.html index 5341555ea..4257e142b 100644 --- a/api/graphnet.models.coarsening.html +++ b/api/graphnet.models.coarsening.html @@ -572,17 +572,20 @@
    -class graphnet.models.coarsening.Coarsening(reduce, transfer_attributes)[source]
    +class graphnet.models.coarsening.Coarsening(*args, **kwargs)[source]

    Bases: Model

    Base class for coarsening operations.

    Construct Coarsening.

    Parameters:
      -

    • reduce (str) –

      • -

    • transfer_attributes (bool) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    @@ -604,68 +607,74 @@
    -class graphnet.models.coarsening.AttributeCoarsening(attributes, reduce, transfer_attributes)[source]
    +class graphnet.models.coarsening.AttributeCoarsening(*args, **kwargs)[source]

    Bases: Coarsening

    Coarsen pulses based on specified attributes.

    Construct SimpleCoarsening.

    Parameters:
      -

    • attributes (List[str]) –

      • -

    • reduce (str) –

      • -

    • transfer_attributes (bool) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    -class graphnet.models.coarsening.DOMCoarsening(reduce, transfer_attributes, keys)[source]
    +class graphnet.models.coarsening.DOMCoarsening(*args, **kwargs)[source]

    Bases: Coarsening

    Coarsen pulses to DOM-level.

    Cluster pulses on the same DOM.

    Parameters:
      -

    • reduce (str) –

      • -

    • transfer_attributes (bool) –

      • -

    • keys (List[str] | None) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    -class graphnet.models.coarsening.CustomDOMCoarsening(reduce, transfer_attributes, keys)[source]
    +class graphnet.models.coarsening.CustomDOMCoarsening(*args, **kwargs)[source]

    Bases: DOMCoarsening

    Coarsen pulses to DOM-level with additional attributes.

    Cluster pulses on the same DOM.

    Parameters:
      -

    • reduce (str) –

      • -

    • transfer_attributes (bool) –

      • -

    • keys (List[str] | None) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    -class graphnet.models.coarsening.DOMAndTimeWindowCoarsening(time_window, reduce, transfer_attributes, keys=['dom_x', 'dom_y', 'dom_z', 'rde', 'pmt_area'], time_key)[source]
    +class graphnet.models.coarsening.DOMAndTimeWindowCoarsening(*args, **kwargs)[source]

    Bases: Coarsening

    Coarsen pulses to DOM-level, with additional time-window clustering.

    Cluster pulses on the same DOM within time_window.

    Parameters:
      -

    • time_window (float) –

      • -

    • reduce (str) –

      • -

    • transfer_attributes (bool) –

      • -

    • keys (List[str]) –

      • -

    • time_key (str) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.detector.detector.html b/api/graphnet.models.detector.detector.html index 773954d8d..1ba47c47f 100644 --- a/api/graphnet.models.detector.detector.html +++ b/api/graphnet.models.detector.detector.html @@ -520,11 +520,20 @@

    Base detector-specific Model class(es).

    -class graphnet.models.detector.detector.Detector[source]
    +class graphnet.models.detector.detector.Detector(*args, **kwargs)[source]

    Bases: Model

    Base class for all detector-specific read-ins in graphnet.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.detector.icecube.html b/api/graphnet.models.detector.icecube.html index e37d2e2da..11d972fa9 100644 --- a/api/graphnet.models.detector.icecube.html +++ b/api/graphnet.models.detector.icecube.html @@ -587,11 +587,20 @@

    IceCube-specific Detector class(es).

    -class graphnet.models.detector.icecube.IceCube86[source]
    +class graphnet.models.detector.icecube.IceCube86(*args, **kwargs)[source]

    Bases: Detector

    Detector class for IceCube-86.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    @@ -606,11 +615,20 @@
    -class graphnet.models.detector.icecube.IceCubeKaggle[source]
    +class graphnet.models.detector.icecube.IceCubeKaggle(*args, **kwargs)[source]

    Bases: Detector

    Detector class for Kaggle Competition.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    @@ -625,11 +643,20 @@
    -class graphnet.models.detector.icecube.IceCubeDeepCore[source]
    +class graphnet.models.detector.icecube.IceCubeDeepCore(*args, **kwargs)[source]

    Bases: Detector

    Detector class for IceCube-DeepCore.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    @@ -644,11 +671,20 @@
    -class graphnet.models.detector.icecube.IceCubeUpgrade[source]
    +class graphnet.models.detector.icecube.IceCubeUpgrade(*args, **kwargs)[source]

    Bases: Detector

    Detector class for IceCube-Upgrade.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.detector.prometheus.html b/api/graphnet.models.detector.prometheus.html index 0c266b36a..8aa25084d 100644 --- a/api/graphnet.models.detector.prometheus.html +++ b/api/graphnet.models.detector.prometheus.html @@ -509,11 +509,20 @@

    Prometheus-specific Detector class(es).

    -class graphnet.models.detector.prometheus.Prometheus[source]
    +class graphnet.models.detector.prometheus.Prometheus(*args, **kwargs)[source]

    Bases: Detector

    Detector class for Prometheus prototype.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.gnn.convnet.html b/api/graphnet.models.gnn.convnet.html index 52d6ba1ea..9ba1f86af 100644 --- a/api/graphnet.models.gnn.convnet.html +++ b/api/graphnet.models.gnn.convnet.html @@ -524,7 +524,7 @@

    Author: Martin Ha Minh

    -class graphnet.models.gnn.convnet.ConvNet(nb_inputs, nb_outputs, nb_intermediate, dropout_ratio)[source]
    +class graphnet.models.gnn.convnet.ConvNet(*args, **kwargs)[source]

    Bases: GNN

    ConvNet (convolutional network) model.

    Construct ConvNet.

    @@ -537,8 +537,13 @@ output layer.

  • nb_intermediate (int, default: 128) – Number of nodes in intermediate layer(s).

  • dropout_ratio (float, default: 0.3) – Fraction of nodes to drop.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.gnn.dynedge.html b/api/graphnet.models.gnn.dynedge.html index 908284418..836c517ca 100644 --- a/api/graphnet.models.gnn.dynedge.html +++ b/api/graphnet.models.gnn.dynedge.html @@ -523,7 +523,7 @@

    Implementation of the DynEdge GNN model architecture.

    -class graphnet.models.gnn.dynedge.DynEdge(nb_inputs, *, nb_neighbours, features_subset, dynedge_layer_sizes, post_processing_layer_sizes, readout_layer_sizes, global_pooling_schemes, add_global_variables_after_pooling)[source]
    +class graphnet.models.gnn.dynedge.DynEdge(*args, **kwargs)[source]

    Bases: GNN

    DynEdge (dynamical edge convolutional) model.

    Construct DynEdge.

    @@ -559,8 +559,13 @@ after global pooling. The alternative is to added (distribute) them to the individual nodes before any convolutional operations.

    +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.gnn.dynedge_jinst.html b/api/graphnet.models.gnn.dynedge_jinst.html index 407d0cb08..17e28429c 100644 --- a/api/graphnet.models.gnn.dynedge_jinst.html +++ b/api/graphnet.models.gnn.dynedge_jinst.html @@ -524,7 +524,7 @@

    Author: Rasmus Oersoe

    -class graphnet.models.gnn.dynedge_jinst.DynEdgeJINST(nb_inputs, layer_size_scale)[source]
    +class graphnet.models.gnn.dynedge_jinst.DynEdgeJINST(*args, **kwargs)[source]

    Bases: GNN

    DynEdge (dynamical edge convolutional) model used in [2209.03042].

    Construct DynEdgeJINST.

    @@ -534,8 +534,13 @@

  • nb_inputs (int) – Number of input features.

  • nb_outputs – Number of output features.

  • layer_size_scale (int, default: 4) – Integer that scales the size of hidden layers.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.gnn.dynedge_kaggle_tito.html b/api/graphnet.models.gnn.dynedge_kaggle_tito.html index a27928192..82027f88a 100644 --- a/api/graphnet.models.gnn.dynedge_kaggle_tito.html +++ b/api/graphnet.models.gnn.dynedge_kaggle_tito.html @@ -529,7 +529,7 @@

    Solution by TITO.

    -class graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO(nb_inputs, features_subset(0, 4, None), dyntrans_layer_sizes, global_pooling_schemes=['max'])[source]
    +class graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO(*args, **kwargs)[source]

    Bases: GNN

    DynEdge (dynamical edge convolutional) model.

    Construct DynEdge.

    @@ -544,8 +544,13 @@ used in the DynTrans layer.

  • global_pooling_schemes (List[str], default: ['max']) – The list global pooling schemes to use. Options are: “min”, “max”, “mean”, and “sum”.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.gnn.gnn.html b/api/graphnet.models.gnn.gnn.html index 78ee2338d..537c08b73 100644 --- a/api/graphnet.models.gnn.gnn.html +++ b/api/graphnet.models.gnn.gnn.html @@ -545,17 +545,20 @@

    Base GNN-specific Model class(es).

    -class graphnet.models.gnn.gnn.GNN(nb_inputs, nb_outputs)[source]
    +class graphnet.models.gnn.gnn.GNN(*args, **kwargs)[source]

    Bases: Model

    Base class for all core GNN models in graphnet.

    Construct GNN.

    Parameters:
      -

    • nb_inputs (int) –

      • -

    • nb_outputs (int) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.graphs.edges.edges.html b/api/graphnet.models.graphs.edges.edges.html index 98b04f64a..01668c62f 100644 --- a/api/graphnet.models.graphs.edges.edges.html +++ b/api/graphnet.models.graphs.edges.edges.html @@ -557,20 +557,20 @@

    Class(es) for building/connecting graphs.

    -class graphnet.models.graphs.edges.edges.EdgeDefinition(name, class_name, level, log_folder, **kwargs)[source]
    +class graphnet.models.graphs.edges.edges.EdgeDefinition(*args, **kwargs)[source]

    Bases: Model

    Base class for graph building.

    Construct Logger.

    Parameters:
      -

    • name (str | None) –

      • -

    • class_name (str | None) –

      • -

    • level (int) –

      • -

    • log_folder (str | None) –

      • +

    • args (Any) –

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    @@ -592,7 +592,7 @@
    -class graphnet.models.graphs.edges.edges.KNNEdges(nb_nearest_neighbours, columns=[0, 1, 2])[source]
    +class graphnet.models.graphs.edges.edges.KNNEdges(*args, **kwargs)[source]

    Bases: EdgeDefinition

    Builds edges from the k-nearest neighbours.

    K-NN Edge definition.

    @@ -606,13 +606,18 @@

  • [0 (Defaults to) –

  • 1 –

  • 2]. –

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    -class graphnet.models.graphs.edges.edges.RadialEdges(radius, columns=[0, 1, 2])[source]
    +class graphnet.models.graphs.edges.edges.RadialEdges(*args, **kwargs)[source]

    Bases: EdgeDefinition

    Builds graph from a sphere of chosen radius centred at each node.

    Radial edges.

    @@ -627,13 +632,18 @@

  • [0 (Defaults to) –

  • 1 –

  • 2]. –

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    -class graphnet.models.graphs.edges.edges.EuclideanEdges(sigma, threshold, columns)[source]
    +class graphnet.models.graphs.edges.edges.EuclideanEdges(*args, **kwargs)[source]

    Bases: EdgeDefinition

    Builds edges according to Euclidean distance between nodes.

    See https://arxiv.org/pdf/1809.06166.pdf.

    @@ -641,11 +651,13 @@
    Parameters:
      -

    • sigma (float) –

      • -

    • threshold (float) –

      • -

    • columns (List[int]) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.graphs.graph_definition.html b/api/graphnet.models.graphs.graph_definition.html index 8dcade719..d4b8a4846 100644 --- a/api/graphnet.models.graphs.graph_definition.html +++ b/api/graphnet.models.graphs.graph_definition.html @@ -519,7 +519,7 @@ passed to dataloaders during training and deployment.

    -class graphnet.models.graphs.graph_definition.GraphDefinition(detector, node_definition, edge_definition, node_feature_names, dtype)[source]
    +class graphnet.models.graphs.graph_definition.GraphDefinition(*args, **kwargs)[source]

    Bases: Model

    An Abstract class to create graph definitions from.

    Construct ´GraphDefinition´. The ´detector´ holds.

    @@ -531,12 +531,23 @@
    Parameters:

    • detector (Detector) – The corresponding ´Detector´ representing the data.

      • -

    • node_definition (NodeDefinition) – Definition of nodes.

      • +

    • node_definition (NodeDefinition, default: NodesAsPulses()) – Definition of nodes. Defaults to NodesAsPulses.

    • edge_definition (Optional[EdgeDefinition], default: None) – Definition of edges. Defaults to None.

    • node_feature_names (Optional[List[str]], default: None) – Names of node feature columns. Defaults to None

    • dtype (Optional[dtype], default: torch.float32) – data type used for node features. e.g. ´torch.float´

      • +

    • perturbation_dict (Optional[Dict[str, float]], default: None) – Dictionary mapping a feature name to a standard +deviation according to which the values for this +feature should be randomly perturbed. Defaults +to None.

      • +

    • seed (Union[int, Generator, None], default: None) – seed or Generator used to randomly sample perturbations. +Defaults to None.

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    @@ -549,9 +560,12 @@

  • node_feature_names (List[str]) – name of each column. Shape ´[,d]´.

  • truth_dicts (Optional[List[Dict[str, Any]]], default: None) – Dictionary containing truth labels.

  • custom_label_functions (Optional[Dict[str, Callable[..., Any]]], default: None) – Custom label functions. See https://github.com/graphnet-team/graphnet/blob/main/GETTING_STARTED.md#adding-custom-truth-labels.

    • -

  • loss_weight_column (Optional[str], default: None) – Name of column that holds loss weight. Defaults to None.

    • +

  • loss_weight_column (Optional[str], default: None) – Name of column that holds loss weight. +Defaults to None.

  • loss_weight (Optional[float], default: None) – Loss weight associated with event. Defaults to None.

    • -

  • loss_weight_default_value (Optional[float], default: None) – default value for loss weight. Used in instances where some events have no pre-defined loss weight. Defaults to None.

    • +

  • loss_weight_default_value (Optional[float], default: None) – default value for loss weight. +Used in instances where some events have +no pre-defined loss weight. Defaults to None.

  • data_path (Optional[str], default: None) – Path to dataset data files. Defaults to None.

    • diff --git a/api/graphnet.models.graphs.graphs.html b/api/graphnet.models.graphs.graphs.html index 0d889a033..88980e719 100644 --- a/api/graphnet.models.graphs.graphs.html +++ b/api/graphnet.models.graphs.graphs.html @@ -501,7 +501,7 @@

    A module containing different graph representations in GraphNeT.

    -class graphnet.models.graphs.graphs.KNNGraph(detector, node_definition, node_feature_names, dtype, nb_nearest_neighbours, columns=[0, 1, 2])[source]
    +class graphnet.models.graphs.graphs.KNNGraph(*args, **kwargs)[source]

    Bases: GraphDefinition

    A Graph representation where Edges are drawn to nearest neighbours.

    Construct k-nn graph representation.

    @@ -509,16 +509,27 @@
    Parameters:

    • detector (Detector) – Detector that represents your data.

      • -

    • node_definition (NodeDefinition) – Definition of nodes in the graph.

      • +

    • node_definition (NodeDefinition, default: NodesAsPulses()) – Definition of nodes in the graph.

    • node_feature_names (Optional[List[str]], default: None) – Name of node features.

    • dtype (Optional[dtype], default: torch.float32) – data type for node features.

      • +

    • perturbation_dict (Optional[Dict[str, float]], default: None) – Dictionary mapping a feature name to a standard +deviation according to which the values for this +feature should be randomly perturbed. Defaults +to None.

      • +

    • seed (Union[int, Generator, None], default: None) – seed or Generator used to randomly sample perturbations. +Defaults to None.

    • nb_nearest_neighbours (int, default: 8) – Number of edges for each node. Defaults to 8.

    • columns (List[int], default: [0, 1, 2]) – node feature columns used for distance calculation

    • [0 (. Defaults to) –

    • 1 –

    • 2]. –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.graphs.nodes.nodes.html b/api/graphnet.models.graphs.nodes.nodes.html index a5ac7ba2d..f08873a7c 100644 --- a/api/graphnet.models.graphs.nodes.nodes.html +++ b/api/graphnet.models.graphs.nodes.nodes.html @@ -557,11 +557,20 @@

    Class(es) for building/connecting graphs.

    -class graphnet.models.graphs.nodes.nodes.NodeDefinition[source]
    +class graphnet.models.graphs.nodes.nodes.NodeDefinition(*args, **kwargs)[source]

    Bases: Model

    Base class for graph building.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    @@ -604,11 +613,20 @@
    -class graphnet.models.graphs.nodes.nodes.NodesAsPulses[source]
    +class graphnet.models.graphs.nodes.nodes.NodesAsPulses(*args, **kwargs)[source]

    Bases: NodeDefinition

    Represent each measured pulse of Cherenkov Radiation as a node.

    Construct Detector.

    +
    Parameters:
    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.model.html b/api/graphnet.models.model.html index 286f23f20..cee61ff8f 100644 --- a/api/graphnet.models.model.html +++ b/api/graphnet.models.model.html @@ -575,20 +575,20 @@

    Base class(es) for building models.

    -class graphnet.models.model.Model(name, class_name, level, log_folder, **kwargs)[source]
    +class graphnet.models.model.Model(*args, **kwargs)[source]

    Bases: Logger, Configurable, LightningModule, ABC

    Base class for all models in graphnet.

    Construct Logger.

    Parameters:
      -

    • name (str | None) –

      • -

    • class_name (str | None) –

      • -

    • level (int) –

      • -

    • log_folder (str | None) –

      • +

    • args (Any) –

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.standard_model.html b/api/graphnet.models.standard_model.html index b886a0725..9d0aa3ed8 100644 --- a/api/graphnet.models.standard_model.html +++ b/api/graphnet.models.standard_model.html @@ -608,7 +608,7 @@

    Standard model class(es).

    -class graphnet.models.standard_model.StandardModel(*, graph_definition, gnn, tasks, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs, scheduler_class, scheduler_kwargs, scheduler_config)[source]
    +class graphnet.models.standard_model.StandardModel(*args, **kwargs)[source]

    Bases: Model

    Main class for standard models in graphnet.

    This class chains together the different elements of a complete GNN-based @@ -617,16 +617,13 @@

    Parameters:
      -

    • graph_definition (GraphDefinition) –

      • -

    • gnn (GNN) –

      • -

    • tasks (Task | List[Task]) –

      • -

    • optimizer_class (type) –

      • -

    • optimizer_kwargs (Dict | None) –

      • -

    • scheduler_class (type | None) –

      • -

    • scheduler_kwargs (Dict | None) –

      • -

    • scheduler_config (Dict | None) –

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.task.classification.html b/api/graphnet.models.task.classification.html index 50cf9ca15..36c17b4fd 100644 --- a/api/graphnet.models.task.classification.html +++ b/api/graphnet.models.task.classification.html @@ -590,7 +590,7 @@

    Classification-specific Model class(es).

    -class graphnet.models.task.classification.MulticlassClassificationTask(nb_outputs, target_labels, *args, **kwargs)[source]
    +class graphnet.models.task.classification.MulticlassClassificationTask(*args, **kwargs)[source]

    Bases: IdentityTask

    General task for classifying any number of classes.

    Requires the same number of input features as the number of classes being @@ -602,17 +602,18 @@

    Parameters:
      -

    • nb_outputs (int) –

      • -

    • target_labels (List[str] | Any) –

    • args (Any) –

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    -class graphnet.models.task.classification.BinaryClassificationTask(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.classification.BinaryClassificationTask(*args, **kwargs)[source]

    Bases: Task

    Performs binary classification.

    Construct Task.

    @@ -652,8 +653,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -670,7 +676,7 @@
    -class graphnet.models.task.classification.BinaryClassificationTaskLogits(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.classification.BinaryClassificationTaskLogits(*args, **kwargs)[source]

    Bases: Task

    Performs binary classification form logits.

    Construct Task.

    @@ -710,8 +716,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.task.reconstruction.html b/api/graphnet.models.task.reconstruction.html index 06b772e83..d676c53b9 100644 --- a/api/graphnet.models.task.reconstruction.html +++ b/api/graphnet.models.task.reconstruction.html @@ -1059,7 +1059,7 @@

    Reconstruction-specific Model class(es).

    -class graphnet.models.task.reconstruction.AzimuthReconstructionWithKappa(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.AzimuthReconstructionWithKappa(*args, **kwargs)[source]

    Bases: Task

    Reconstructs azimuthal angle and associated kappa (1/var).

    Construct Task.

    @@ -1099,8 +1099,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1117,7 +1122,7 @@
    -class graphnet.models.task.reconstruction.AzimuthReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.AzimuthReconstruction(*args, **kwargs)[source]

    Bases: AzimuthReconstructionWithKappa

    Reconstructs azimuthal angle.

    Construct Task.

    @@ -1157,8 +1162,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1175,7 +1185,7 @@
    -class graphnet.models.task.reconstruction.DirectionReconstructionWithKappa(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.DirectionReconstructionWithKappa(*args, **kwargs)[source]

    Bases: Task

    Reconstructs direction with kappa from the 3D-vMF distribution.

    Construct Task.

    @@ -1215,8 +1225,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1233,7 +1248,7 @@
    -class graphnet.models.task.reconstruction.ZenithReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.ZenithReconstruction(*args, **kwargs)[source]

    Bases: Task

    Reconstructs zenith angle.

    Construct Task.

    @@ -1273,8 +1288,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1291,7 +1311,7 @@
    -class graphnet.models.task.reconstruction.ZenithReconstructionWithKappa(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.ZenithReconstructionWithKappa(*args, **kwargs)[source]

    Bases: ZenithReconstruction

    Reconstructs zenith angle and associated kappa (1/var).

    Construct Task.

    @@ -1331,8 +1351,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1349,7 +1374,7 @@
    -class graphnet.models.task.reconstruction.EnergyReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.EnergyReconstruction(*args, **kwargs)[source]

    Bases: Task

    Reconstructs energy using stable method.

    Construct Task.

    @@ -1389,8 +1414,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1407,7 +1437,7 @@
    -class graphnet.models.task.reconstruction.EnergyReconstructionWithPower(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.EnergyReconstructionWithPower(*args, **kwargs)[source]

    Bases: Task

    Reconstructs energy.

    Construct Task.

    @@ -1447,8 +1477,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1465,7 +1500,7 @@
    -class graphnet.models.task.reconstruction.EnergyReconstructionWithUncertainty(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.EnergyReconstructionWithUncertainty(*args, **kwargs)[source]

    Bases: EnergyReconstruction

    Reconstructs energy and associated uncertainty (log(var)).

    Construct Task.

    @@ -1505,8 +1540,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1523,7 +1563,7 @@
    -class graphnet.models.task.reconstruction.VertexReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.VertexReconstruction(*args, **kwargs)[source]

    Bases: Task

    Reconstructs vertex position and time.

    Construct Task.

    @@ -1563,8 +1603,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1581,7 +1626,7 @@
    -class graphnet.models.task.reconstruction.PositionReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.PositionReconstruction(*args, **kwargs)[source]

    Bases: Task

    Reconstructs vertex position.

    Construct Task.

    @@ -1621,8 +1666,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1639,7 +1689,7 @@
    -class graphnet.models.task.reconstruction.TimeReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.TimeReconstruction(*args, **kwargs)[source]

    Bases: Task

    Reconstructs time.

    Construct Task.

    @@ -1679,8 +1729,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -1697,7 +1752,7 @@
    -class graphnet.models.task.reconstruction.InelasticityReconstruction(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.reconstruction.InelasticityReconstruction(*args, **kwargs)[source]

    Bases: Task

    Reconstructs interaction inelasticity.

    That is, 1-(track energy / hadronic energy).

    @@ -1738,8 +1793,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.models.task.task.html b/api/graphnet.models.task.task.html index 35d5c451e..c557bedc1 100644 --- a/api/graphnet.models.task.task.html +++ b/api/graphnet.models.task.task.html @@ -623,7 +623,7 @@

    Base physics task-specific Model class(es).

    -class graphnet.models.task.task.Task(*, hidden_size, loss_function, target_labels, prediction_labels, transform_prediction_and_target, transform_target, transform_inference, transform_support, loss_weight)[source]
    +class graphnet.models.task.task.Task(*args, **kwargs)[source]

    Bases: Model

    Base class for all reconstruction and classification tasks.

    Construct Task.

    @@ -663,8 +663,13 @@ is tested on the range [-1e6, 1e6].

  • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • +

  • args (Any) –

    • +

  • kwargs (Any) –

    • +
    Return type:
    +

    object

    +
    @@ -734,7 +739,7 @@
    -class graphnet.models.task.task.IdentityTask(nb_outputs, target_labels, *args, **kwargs)[source]
    +class graphnet.models.task.task.IdentityTask(*args, **kwargs)[source]

    Bases: Task

    Identity, or trivial, task.

    Construct IdentityTask.

    @@ -743,12 +748,13 @@
    Parameters:
      -

    • nb_outputs (int) –

      • -

    • target_labels (List[str] | Any) –

    • args (Any) –

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    diff --git a/api/graphnet.training.loss_functions.html b/api/graphnet.training.loss_functions.html index feb15b1bf..0dd1cc788 100644 --- a/api/graphnet.training.loss_functions.html +++ b/api/graphnet.training.loss_functions.html @@ -634,13 +634,19 @@ handles per-event weights, etc.

    -class graphnet.training.loss_functions.LossFunction(**kwargs)[source]
    +class graphnet.training.loss_functions.LossFunction(*args, **kwargs)[source]

    Bases: Model

    Base class for loss functions in graphnet.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    @@ -669,44 +675,62 @@
    -class graphnet.training.loss_functions.MSELoss(**kwargs)[source]
    +class graphnet.training.loss_functions.MSELoss(*args, **kwargs)[source]

    Bases: LossFunction

    Mean squared error loss.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    -class graphnet.training.loss_functions.RMSELoss(**kwargs)[source]
    +class graphnet.training.loss_functions.RMSELoss(*args, **kwargs)[source]

    Bases: MSELoss

    Root mean squared error loss.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    -class graphnet.training.loss_functions.LogCoshLoss(**kwargs)[source]
    +class graphnet.training.loss_functions.LogCoshLoss(*args, **kwargs)[source]

    Bases: LossFunction

    Log-cosh loss function.

    Acts like x^2 for small x; and like |x| for large x.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    -class graphnet.training.loss_functions.CrossEntropyLoss(options, *args, **kwargs)[source]
    +class graphnet.training.loss_functions.CrossEntropyLoss(*args, **kwargs)[source]

    Bases: LossFunction

    Compute cross-entropy loss for classification tasks.

    Predictions are an [N, num_class]-matrix of logits (i.e., non-softmax’ed @@ -716,16 +740,18 @@

    Parameters:
      -

    • options (int | List[Any] | Dict[Any, int]) –

    • args (Any) –

    • kwargs (Any) –

    +
    Return type:
    +

    object

    +
    -class graphnet.training.loss_functions.BinaryCrossEntropyLoss(**kwargs)[source]
    +class graphnet.training.loss_functions.BinaryCrossEntropyLoss(*args, **kwargs)[source]

    Bases: LossFunction

    Compute binary cross entropy loss.

    Predictions are vector probabilities (i.e., values between 0 and 1), and @@ -733,7 +759,13 @@

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    @@ -800,7 +832,7 @@
    -class graphnet.training.loss_functions.VonMisesFisherLoss(**kwargs)[source]
    +class graphnet.training.loss_functions.VonMisesFisherLoss(*args, **kwargs)[source]

    Bases: LossFunction

    General class for calculating von Mises-Fisher loss.

    Requires implementation for specific dimension m in which the target and @@ -808,7 +840,13 @@

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    @@ -868,37 +906,55 @@
    -class graphnet.training.loss_functions.VonMisesFisher2DLoss(**kwargs)[source]
    +class graphnet.training.loss_functions.VonMisesFisher2DLoss(*args, **kwargs)[source]

    Bases: VonMisesFisherLoss

    von Mises-Fisher loss function vectors in the 2D plane.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    -class graphnet.training.loss_functions.EuclideanDistanceLoss(**kwargs)[source]
    +class graphnet.training.loss_functions.EuclideanDistanceLoss(*args, **kwargs)[source]

    Bases: LossFunction

    Mean squared error in three dimensions.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    -class graphnet.training.loss_functions.VonMisesFisher3DLoss(**kwargs)[source]
    +class graphnet.training.loss_functions.VonMisesFisher3DLoss(*args, **kwargs)[source]

    Bases: VonMisesFisherLoss

    von Mises-Fisher loss function vectors in the 3D plane.

    Construct LossFunction, saving model config.

    Parameters:
    -

    kwargs (Any) –

    +
      +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    diff --git a/api/graphnet.training.utils.html b/api/graphnet.training.utils.html index ac1d9919d..e8087500c 100644 --- a/api/graphnet.training.utils.html +++ b/api/graphnet.training.utils.html @@ -490,7 +490,7 @@
    graphnet.training.utils.collate_fn(graphs)[source]

    Remove graphs with less than two DOM hits.

    -

    Should not occur in “production.

    +

    Should not occur in “production”.

    Return type:

    Batch

    @@ -512,7 +512,7 @@
    @@ -697,6 +715,10 @@
  • save_dataset_config() +
    • +
  • DatasetConfigSaverMeta +
    • +
  • DatasetConfigSaverABCMeta
    • @@ -909,6 +931,18 @@
    +
    +
    +class graphnet.utilities.config.dataset_config.DatasetConfigSaverMeta[source]
    +

    Bases: type

    +

    Metaclass for DatasetConfig that saves the config after __init__.

    +
    +
    +
    +class graphnet.utilities.config.dataset_config.DatasetConfigSaverABCMeta(name, bases, namespace, **kwargs)[source]
    +

    Bases: DatasetConfigSaverMeta, ABCMeta

    +

    Common interface between DatasetConfigSaver and ABC Metaclasses.

    +
    diff --git a/api/graphnet.utilities.config.html b/api/graphnet.utilities.config.html index 59a759d23..f5cd2c868 100644 --- a/api/graphnet.utilities.config.html +++ b/api/graphnet.utilities.config.html @@ -492,11 +492,15 @@
  • dataset_config
  • model_config
  • parsing
      diff --git a/api/graphnet.utilities.config.model_config.html b/api/graphnet.utilities.config.model_config.html index 52531fc50..4ce00d05b 100644 --- a/api/graphnet.utilities.config.model_config.html +++ b/api/graphnet.utilities.config.model_config.html @@ -397,6 +397,10 @@
    • save_model_config() +
      • +
    • ModelConfigSaverMeta +
      • +
    • ModelConfigSaverABC
    • @@ -451,6 +455,20 @@ save_model_config() + +
  • + + + ModelConfigSaverMeta + + +
    • +
  • + + + ModelConfigSaverABC + +
    • @@ -554,6 +572,10 @@
  • save_model_config() +
    • +
  • ModelConfigSaverMeta +
    • +
  • ModelConfigSaverABC
    • @@ -648,6 +670,18 @@ +
    +
    +class graphnet.utilities.config.model_config.ModelConfigSaverMeta[source]
    +

    Bases: type

    +

    Metaclass for saving ModelConfig to Model instances.

    +
    +
    +
    +class graphnet.utilities.config.model_config.ModelConfigSaverABC(name, bases, namespace, **kwargs)[source]
    +

    Bases: ModelConfigSaverMeta, ABCMeta

    +

    Common interface between ModelConfigSaver and ABC Metaclasses.

    +
    diff --git a/genindex.html b/genindex.html index 7ef7dc30a..d0b6238bf 100644 --- a/genindex.html +++ b/genindex.html @@ -483,6 +483,10 @@

    D

  • Dataset (class in graphnet.data.dataset.dataset)
  • DatasetConfig (class in graphnet.utilities.config.dataset_config) +
    • +
  • DatasetConfigSaverABCMeta (class in graphnet.utilities.config.dataset_config) +
    • +
  • DatasetConfigSaverMeta (class in graphnet.utilities.config.dataset_config)
  • debug() (graphnet.utilities.logging.Logger method)
    • @@ -526,6 +530,8 @@

    D

  • (graphnet.models.task.task.Task property)
    • + + -
  • ModelConfig (class in graphnet.utilities.config.model_config) +
    • +
  • ModelConfigSaverABC (class in graphnet.utilities.config.model_config) +
    • +
  • ModelConfigSaverMeta (class in graphnet.utilities.config.model_config)
  • module @@ -1695,8 +1696,6 @@

    M

  • graphnet.data.dataset.sqlite
  • graphnet.data.dataset.sqlite.sqlite_dataset -
    • -
  • graphnet.data.dataset.sqlite.sqlite_dataset_perturbed
  • graphnet.data.extractors
    • @@ -2096,8 +2095,6 @@

    S

  • SQLiteDataConverter (class in graphnet.data.sqlite.sqlite_dataconverter)
  • SQLiteDataset (class in graphnet.data.dataset.sqlite.sqlite_dataset) -
    • -
  • SQLiteDatasetPerturbed (class in graphnet.data.dataset.sqlite.sqlite_dataset_perturbed)
  • standard_arguments (graphnet.utilities.argparse.ArgumentParser attribute)
    • diff --git a/objects.inv b/objects.inv index e2a8e9d33..15dce94cf 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html index c3e8451b2..6a97e1d01 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -395,11 +395,6 @@

    Python Module Index

        graphnet.data.dataset.sqlite.sqlite_dataset - - -     - graphnet.data.dataset.sqlite.sqlite_dataset_perturbed -     diff --git a/searchindex.js b/searchindex.js index 200c93687..1e27dc996 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["about", "api/graphnet", "api/graphnet.constants", "api/graphnet.data", "api/graphnet.data.constants", "api/graphnet.data.dataconverter", "api/graphnet.data.dataloader", "api/graphnet.data.dataset", "api/graphnet.data.dataset.dataset", "api/graphnet.data.dataset.parquet", "api/graphnet.data.dataset.parquet.parquet_dataset", "api/graphnet.data.dataset.sqlite", "api/graphnet.data.dataset.sqlite.sqlite_dataset", "api/graphnet.data.dataset.sqlite.sqlite_dataset_perturbed", "api/graphnet.data.extractors", "api/graphnet.data.extractors.i3extractor", "api/graphnet.data.extractors.i3featureextractor", "api/graphnet.data.extractors.i3genericextractor", "api/graphnet.data.extractors.i3hybridrecoextractor", 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"module-graphnet.data"]], "deepcore (graphnet.data.constants.features attribute)": [[4, "graphnet.data.constants.FEATURES.DEEPCORE"]], "deepcore (graphnet.data.constants.truth attribute)": [[4, "graphnet.data.constants.TRUTH.DEEPCORE"]], "features (class in graphnet.data.constants)": [[4, "graphnet.data.constants.FEATURES"]], "icecube86 (graphnet.data.constants.features attribute)": [[4, "graphnet.data.constants.FEATURES.ICECUBE86"]], "icecube86 (graphnet.data.constants.truth attribute)": [[4, "graphnet.data.constants.TRUTH.ICECUBE86"]], "kaggle (graphnet.data.constants.features attribute)": [[4, "graphnet.data.constants.FEATURES.KAGGLE"]], "kaggle (graphnet.data.constants.truth attribute)": [[4, "graphnet.data.constants.TRUTH.KAGGLE"]], "prometheus (graphnet.data.constants.features attribute)": [[4, "graphnet.data.constants.FEATURES.PROMETHEUS"]], "prometheus (graphnet.data.constants.truth attribute)": [[4, "graphnet.data.constants.TRUTH.PROMETHEUS"]], "truth (class in graphnet.data.constants)": [[4, "graphnet.data.constants.TRUTH"]], "upgrade (graphnet.data.constants.features attribute)": [[4, "graphnet.data.constants.FEATURES.UPGRADE"]], "upgrade (graphnet.data.constants.truth attribute)": [[4, "graphnet.data.constants.TRUTH.UPGRADE"]], "graphnet.data.constants": [[4, "module-graphnet.data.constants"]], "dataconverter (class in graphnet.data.dataconverter)": [[5, "graphnet.data.dataconverter.DataConverter"]], "fileset (class in graphnet.data.dataconverter)": [[5, "graphnet.data.dataconverter.FileSet"]], "cache_output_files() (in module graphnet.data.dataconverter)": [[5, "graphnet.data.dataconverter.cache_output_files"]], "execute() (graphnet.data.dataconverter.dataconverter method)": [[5, "graphnet.data.dataconverter.DataConverter.execute"]], "file_suffix (graphnet.data.dataconverter.dataconverter property)": [[5, "graphnet.data.dataconverter.DataConverter.file_suffix"]], "gcd_file (graphnet.data.dataconverter.fileset attribute)": [[5, 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graphnet.data.dataloader)": [[6, "graphnet.data.dataloader.do_shuffle"]], "from_dataset_config() (graphnet.data.dataloader.dataloader class method)": [[6, "graphnet.data.dataloader.DataLoader.from_dataset_config"]], "graphnet.data.dataloader": [[6, "module-graphnet.data.dataloader"]], "graphnet.data.dataset": [[7, "module-graphnet.data.dataset"]], "columnmissingexception": [[8, "graphnet.data.dataset.dataset.ColumnMissingException"]], "dataset (class in graphnet.data.dataset.dataset)": [[8, "graphnet.data.dataset.dataset.Dataset"]], "ensembledataset (class in graphnet.data.dataset.dataset)": [[8, "graphnet.data.dataset.dataset.EnsembleDataset"]], "add_label() (graphnet.data.dataset.dataset.dataset method)": [[8, "graphnet.data.dataset.dataset.Dataset.add_label"]], "concatenate() (graphnet.data.dataset.dataset.dataset class method)": [[8, "graphnet.data.dataset.dataset.Dataset.concatenate"]], "from_config() (graphnet.data.dataset.dataset.dataset class method)": [[8, "graphnet.data.dataset.dataset.Dataset.from_config"]], "graphnet.data.dataset.dataset": [[8, "module-graphnet.data.dataset.dataset"]], "load_module() (in module graphnet.data.dataset.dataset)": [[8, "graphnet.data.dataset.dataset.load_module"]], "parse_graph_definition() (in module graphnet.data.dataset.dataset)": [[8, "graphnet.data.dataset.dataset.parse_graph_definition"]], "path (graphnet.data.dataset.dataset.dataset property)": [[8, "graphnet.data.dataset.dataset.Dataset.path"]], "query_table() (graphnet.data.dataset.dataset.dataset method)": [[8, "graphnet.data.dataset.dataset.Dataset.query_table"]], "truth_table (graphnet.data.dataset.dataset.dataset property)": [[8, "graphnet.data.dataset.dataset.Dataset.truth_table"]], "graphnet.data.dataset.parquet": [[9, "module-graphnet.data.dataset.parquet"]], "parquetdataset (class in graphnet.data.dataset.parquet.parquet_dataset)": [[10, "graphnet.data.dataset.parquet.parquet_dataset.ParquetDataset"]], "graphnet.data.dataset.parquet.parquet_dataset": [[10, "module-graphnet.data.dataset.parquet.parquet_dataset"]], "query_table() (graphnet.data.dataset.parquet.parquet_dataset.parquetdataset method)": [[10, "graphnet.data.dataset.parquet.parquet_dataset.ParquetDataset.query_table"]], "graphnet.data.dataset.sqlite": [[11, "module-graphnet.data.dataset.sqlite"]], "sqlitedataset (class in graphnet.data.dataset.sqlite.sqlite_dataset)": [[12, "graphnet.data.dataset.sqlite.sqlite_dataset.SQLiteDataset"]], "graphnet.data.dataset.sqlite.sqlite_dataset": [[12, "module-graphnet.data.dataset.sqlite.sqlite_dataset"]], "query_table() (graphnet.data.dataset.sqlite.sqlite_dataset.sqlitedataset method)": [[12, "graphnet.data.dataset.sqlite.sqlite_dataset.SQLiteDataset.query_table"]], "sqlitedatasetperturbed (class in graphnet.data.dataset.sqlite.sqlite_dataset_perturbed)": [[13, "graphnet.data.dataset.sqlite.sqlite_dataset_perturbed.SQLiteDatasetPerturbed"]], "graphnet.data.dataset.sqlite.sqlite_dataset_perturbed": [[13, "module-graphnet.data.dataset.sqlite.sqlite_dataset_perturbed"]], "graphnet.data.extractors": [[14, "module-graphnet.data.extractors"]], "i3extractor (class in graphnet.data.extractors.i3extractor)": [[15, "graphnet.data.extractors.i3extractor.I3Extractor"]], "i3extractorcollection (class in graphnet.data.extractors.i3extractor)": [[15, "graphnet.data.extractors.i3extractor.I3ExtractorCollection"]], "graphnet.data.extractors.i3extractor": [[15, "module-graphnet.data.extractors.i3extractor"]], "name (graphnet.data.extractors.i3extractor.i3extractor property)": [[15, "graphnet.data.extractors.i3extractor.I3Extractor.name"]], "set_files() (graphnet.data.extractors.i3extractor.i3extractor method)": [[15, "graphnet.data.extractors.i3extractor.I3Extractor.set_files"]], "set_files() (graphnet.data.extractors.i3extractor.i3extractorcollection method)": [[15, "graphnet.data.extractors.i3extractor.I3ExtractorCollection.set_files"]], "i3featureextractor (class in graphnet.data.extractors.i3featureextractor)": [[16, "graphnet.data.extractors.i3featureextractor.I3FeatureExtractor"]], "i3featureextractoricecube86 (class in graphnet.data.extractors.i3featureextractor)": [[16, "graphnet.data.extractors.i3featureextractor.I3FeatureExtractorIceCube86"]], "i3featureextractoricecubedeepcore (class in graphnet.data.extractors.i3featureextractor)": [[16, "graphnet.data.extractors.i3featureextractor.I3FeatureExtractorIceCubeDeepCore"]], "i3featureextractoricecubeupgrade (class in graphnet.data.extractors.i3featureextractor)": [[16, "graphnet.data.extractors.i3featureextractor.I3FeatureExtractorIceCubeUpgrade"]], "i3pulsenoisetruthflagicecubeupgrade (class in graphnet.data.extractors.i3featureextractor)": [[16, "graphnet.data.extractors.i3featureextractor.I3PulseNoiseTruthFlagIceCubeUpgrade"]], "graphnet.data.extractors.i3featureextractor": [[16, "module-graphnet.data.extractors.i3featureextractor"]], "i3genericextractor (class in graphnet.data.extractors.i3genericextractor)": [[17, "graphnet.data.extractors.i3genericextractor.I3GenericExtractor"]], "graphnet.data.extractors.i3genericextractor": [[17, "module-graphnet.data.extractors.i3genericextractor"]], "i3galacticplanehybridrecoextractor (class in graphnet.data.extractors.i3hybridrecoextractor)": [[18, "graphnet.data.extractors.i3hybridrecoextractor.I3GalacticPlaneHybridRecoExtractor"]], "graphnet.data.extractors.i3hybridrecoextractor": [[18, "module-graphnet.data.extractors.i3hybridrecoextractor"]], "i3ntmuonlabelextractor (class in graphnet.data.extractors.i3ntmuonlabelsextractor)": [[19, "graphnet.data.extractors.i3ntmuonlabelsextractor.I3NTMuonLabelExtractor"]], "graphnet.data.extractors.i3ntmuonlabelsextractor": [[19, "module-graphnet.data.extractors.i3ntmuonlabelsextractor"]], "i3particleextractor (class in graphnet.data.extractors.i3particleextractor)": [[20, "graphnet.data.extractors.i3particleextractor.I3ParticleExtractor"]], "graphnet.data.extractors.i3particleextractor": [[20, "module-graphnet.data.extractors.i3particleextractor"]], "i3pisaextractor (class in graphnet.data.extractors.i3pisaextractor)": [[21, "graphnet.data.extractors.i3pisaextractor.I3PISAExtractor"]], "graphnet.data.extractors.i3pisaextractor": [[21, "module-graphnet.data.extractors.i3pisaextractor"]], "i3quesoextractor (class in graphnet.data.extractors.i3quesoextractor)": [[22, "graphnet.data.extractors.i3quesoextractor.I3QUESOExtractor"]], "graphnet.data.extractors.i3quesoextractor": [[22, "module-graphnet.data.extractors.i3quesoextractor"]], "i3retroextractor (class in graphnet.data.extractors.i3retroextractor)": [[23, "graphnet.data.extractors.i3retroextractor.I3RetroExtractor"]], "graphnet.data.extractors.i3retroextractor": [[23, "module-graphnet.data.extractors.i3retroextractor"]], "i3splinempeicextractor (class in graphnet.data.extractors.i3splinempeextractor)": [[24, "graphnet.data.extractors.i3splinempeextractor.I3SplineMPEICExtractor"]], "graphnet.data.extractors.i3splinempeextractor": [[24, "module-graphnet.data.extractors.i3splinempeextractor"]], "i3truthextractor (class in graphnet.data.extractors.i3truthextractor)": [[25, "graphnet.data.extractors.i3truthextractor.I3TruthExtractor"]], "graphnet.data.extractors.i3truthextractor": [[25, "module-graphnet.data.extractors.i3truthextractor"]], "i3tumextractor (class in graphnet.data.extractors.i3tumextractor)": [[26, "graphnet.data.extractors.i3tumextractor.I3TUMExtractor"]], "graphnet.data.extractors.i3tumextractor": [[26, "module-graphnet.data.extractors.i3tumextractor"]], "graphnet.data.extractors.utilities": [[27, "module-graphnet.data.extractors.utilities"]], "flatten_nested_dictionary() (in module graphnet.data.extractors.utilities.collections)": [[28, "graphnet.data.extractors.utilities.collections.flatten_nested_dictionary"]], "graphnet.data.extractors.utilities.collections": [[28, "module-graphnet.data.extractors.utilities.collections"]], "serialise() (in module graphnet.data.extractors.utilities.collections)": [[28, "graphnet.data.extractors.utilities.collections.serialise"]], "transpose_list_of_dicts() (in module graphnet.data.extractors.utilities.collections)": [[28, "graphnet.data.extractors.utilities.collections.transpose_list_of_dicts"]], "frame_is_montecarlo() (in module graphnet.data.extractors.utilities.frames)": [[29, "graphnet.data.extractors.utilities.frames.frame_is_montecarlo"]], "frame_is_noise() (in module graphnet.data.extractors.utilities.frames)": [[29, "graphnet.data.extractors.utilities.frames.frame_is_noise"]], "get_om_keys_and_pulseseries() (in module graphnet.data.extractors.utilities.frames)": [[29, "graphnet.data.extractors.utilities.frames.get_om_keys_and_pulseseries"]], "graphnet.data.extractors.utilities.frames": [[29, "module-graphnet.data.extractors.utilities.frames"]], "break_cyclic_recursion() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.break_cyclic_recursion"]], "cast_object_to_pure_python() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.cast_object_to_pure_python"]], "cast_pulse_series_to_pure_python() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.cast_pulse_series_to_pure_python"]], "get_member_variables() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.get_member_variables"]], "graphnet.data.extractors.utilities.types": [[30, "module-graphnet.data.extractors.utilities.types"]], "is_boost_class() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.is_boost_class"]], "is_boost_enum() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.is_boost_enum"]], "is_icecube_class() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.is_icecube_class"]], "is_method() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.is_method"]], "is_type() (in module graphnet.data.extractors.utilities.types)": [[30, "graphnet.data.extractors.utilities.types.is_type"]], "graphnet.data.parquet": [[31, "module-graphnet.data.parquet"]], "parquetdataconverter (class in graphnet.data.parquet.parquet_dataconverter)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter"]], "file_suffix (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter attribute)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.file_suffix"]], "graphnet.data.parquet.parquet_dataconverter": [[32, "module-graphnet.data.parquet.parquet_dataconverter"]], "merge_files() (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter method)": 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"file_suffix (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter attribute)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.file_suffix"]], "graphnet.data.sqlite.sqlite_dataconverter": [[35, "module-graphnet.data.sqlite.sqlite_dataconverter"]], "is_mc_tree() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.is_mc_tree"]], "is_pulse_map() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.is_pulse_map"]], "merge_files() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.merge_files"]], "save_data() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.save_data"]], "attach_index() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.attach_index"]], "create_table() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.create_table"]], "create_table_and_save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.create_table_and_save_to_sql"]], "database_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.database_exists"]], "database_table_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.database_table_exists"]], "graphnet.data.sqlite.sqlite_utilities": [[36, "module-graphnet.data.sqlite.sqlite_utilities"]], "run_sql_code() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.run_sql_code"]], "save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.save_to_sql"]], 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"graphnet.data.extractors.i3tumextractor": [[25, "module-graphnet.data.extractors.i3tumextractor"]], "graphnet.data.extractors.utilities": [[26, "module-graphnet.data.extractors.utilities"]], "flatten_nested_dictionary() (in module graphnet.data.extractors.utilities.collections)": [[27, "graphnet.data.extractors.utilities.collections.flatten_nested_dictionary"]], "graphnet.data.extractors.utilities.collections": [[27, "module-graphnet.data.extractors.utilities.collections"]], "serialise() (in module graphnet.data.extractors.utilities.collections)": [[27, "graphnet.data.extractors.utilities.collections.serialise"]], "transpose_list_of_dicts() (in module graphnet.data.extractors.utilities.collections)": [[27, "graphnet.data.extractors.utilities.collections.transpose_list_of_dicts"]], "frame_is_montecarlo() (in module graphnet.data.extractors.utilities.frames)": [[28, "graphnet.data.extractors.utilities.frames.frame_is_montecarlo"]], "frame_is_noise() (in module graphnet.data.extractors.utilities.frames)": [[28, "graphnet.data.extractors.utilities.frames.frame_is_noise"]], "get_om_keys_and_pulseseries() (in module graphnet.data.extractors.utilities.frames)": [[28, "graphnet.data.extractors.utilities.frames.get_om_keys_and_pulseseries"]], "graphnet.data.extractors.utilities.frames": [[28, "module-graphnet.data.extractors.utilities.frames"]], "break_cyclic_recursion() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.break_cyclic_recursion"]], "cast_object_to_pure_python() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.cast_object_to_pure_python"]], "cast_pulse_series_to_pure_python() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.cast_pulse_series_to_pure_python"]], "get_member_variables() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.get_member_variables"]], "graphnet.data.extractors.utilities.types": [[29, "module-graphnet.data.extractors.utilities.types"]], "is_boost_class() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_boost_class"]], "is_boost_enum() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_boost_enum"]], "is_icecube_class() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_icecube_class"]], "is_method() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_method"]], "is_type() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_type"]], "graphnet.data.parquet": [[30, "module-graphnet.data.parquet"]], "parquetdataconverter (class in graphnet.data.parquet.parquet_dataconverter)": [[31, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter"]], "file_suffix (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter attribute)": [[31, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.file_suffix"]], "graphnet.data.parquet.parquet_dataconverter": [[31, "module-graphnet.data.parquet.parquet_dataconverter"]], "merge_files() (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter method)": [[31, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.merge_files"]], "save_data() (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter method)": [[31, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.save_data"]], "insqlitepipeline (class in graphnet.data.pipeline)": [[32, "graphnet.data.pipeline.InSQLitePipeline"]], "graphnet.data.pipeline": [[32, "module-graphnet.data.pipeline"]], "graphnet.data.sqlite": [[33, "module-graphnet.data.sqlite"]], "sqlitedataconverter (class in graphnet.data.sqlite.sqlite_dataconverter)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter"]], "any_pulsemap_is_non_empty() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.any_pulsemap_is_non_empty"]], "construct_dataframe() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.construct_dataframe"]], "file_suffix (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter attribute)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.file_suffix"]], "graphnet.data.sqlite.sqlite_dataconverter": [[34, "module-graphnet.data.sqlite.sqlite_dataconverter"]], "is_mc_tree() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.is_mc_tree"]], "is_pulse_map() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.is_pulse_map"]], "merge_files() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.merge_files"]], "save_data() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[34, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.save_data"]], "attach_index() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.attach_index"]], "create_table() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.create_table"]], "create_table_and_save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.create_table_and_save_to_sql"]], "database_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.database_exists"]], "database_table_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.database_table_exists"]], "graphnet.data.sqlite.sqlite_utilities": [[35, "module-graphnet.data.sqlite.sqlite_utilities"]], "run_sql_code() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.run_sql_code"]], "save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[35, "graphnet.data.sqlite.sqlite_utilities.save_to_sql"]], "graphnet.data.utilities": [[36, "module-graphnet.data.utilities"]], "parquettosqliteconverter (class in graphnet.data.utilities.parquet_to_sqlite)": [[37, "graphnet.data.utilities.parquet_to_sqlite.ParquetToSQLiteConverter"]], "graphnet.data.utilities.parquet_to_sqlite": [[37, "module-graphnet.data.utilities.parquet_to_sqlite"]], "run() (graphnet.data.utilities.parquet_to_sqlite.parquettosqliteconverter method)": [[37, "graphnet.data.utilities.parquet_to_sqlite.ParquetToSQLiteConverter.run"]], "graphnet.data.utilities.random": [[38, "module-graphnet.data.utilities.random"]], "pairwise_shuffle() (in module graphnet.data.utilities.random)": [[38, "graphnet.data.utilities.random.pairwise_shuffle"]], "stringselectionresolver (class in graphnet.data.utilities.string_selection_resolver)": [[39, "graphnet.data.utilities.string_selection_resolver.StringSelectionResolver"]], "graphnet.data.utilities.string_selection_resolver": [[39, "module-graphnet.data.utilities.string_selection_resolver"]], "resolve() (graphnet.data.utilities.string_selection_resolver.stringselectionresolver method)": [[39, "graphnet.data.utilities.string_selection_resolver.StringSelectionResolver.resolve"]], "graphnet.deployment": [[40, "module-graphnet.deployment"]], "graphneti3module (class in graphnet.deployment.i3modules.graphnet_module)": [[43, "graphnet.deployment.i3modules.graphnet_module.GraphNeTI3Module"]], "i3inferencemodule (class in graphnet.deployment.i3modules.graphnet_module)": [[43, "graphnet.deployment.i3modules.graphnet_module.I3InferenceModule"]], "i3pulsecleanermodule (class in graphnet.deployment.i3modules.graphnet_module)": [[43, "graphnet.deployment.i3modules.graphnet_module.I3PulseCleanerModule"]], "graphnet.deployment.i3modules.graphnet_module": [[43, "module-graphnet.deployment.i3modules.graphnet_module"]], "graphnet.models": [[44, "module-graphnet.models"]], "attributecoarsening (class in graphnet.models.coarsening)": [[45, "graphnet.models.coarsening.AttributeCoarsening"]], "coarsening (class in graphnet.models.coarsening)": [[45, "graphnet.models.coarsening.Coarsening"]], "customdomcoarsening (class in graphnet.models.coarsening)": [[45, "graphnet.models.coarsening.CustomDOMCoarsening"]], "domandtimewindowcoarsening (class in graphnet.models.coarsening)": [[45, "graphnet.models.coarsening.DOMAndTimeWindowCoarsening"]], "domcoarsening (class in graphnet.models.coarsening)": [[45, "graphnet.models.coarsening.DOMCoarsening"]], "forward() (graphnet.models.coarsening.coarsening method)": [[45, "graphnet.models.coarsening.Coarsening.forward"]], "graphnet.models.coarsening": [[45, "module-graphnet.models.coarsening"]], "reduce_options (graphnet.models.coarsening.coarsening attribute)": [[45, "graphnet.models.coarsening.Coarsening.reduce_options"]], "unbatch_edge_index() (in module graphnet.models.coarsening)": [[45, "graphnet.models.coarsening.unbatch_edge_index"]], "graphnet.models.components": [[46, "module-graphnet.models.components"]], "dynedgeconv (class in graphnet.models.components.layers)": [[47, "graphnet.models.components.layers.DynEdgeConv"]], "dyntrans (class in graphnet.models.components.layers)": [[47, "graphnet.models.components.layers.DynTrans"]], "edgeconvtito (class in graphnet.models.components.layers)": [[47, "graphnet.models.components.layers.EdgeConvTito"]], "forward() (graphnet.models.components.layers.dynedgeconv method)": [[47, "graphnet.models.components.layers.DynEdgeConv.forward"]], "forward() (graphnet.models.components.layers.dyntrans method)": [[47, "graphnet.models.components.layers.DynTrans.forward"]], "forward() (graphnet.models.components.layers.edgeconvtito method)": [[47, "graphnet.models.components.layers.EdgeConvTito.forward"]], "graphnet.models.components.layers": [[47, "module-graphnet.models.components.layers"]], "message() (graphnet.models.components.layers.edgeconvtito method)": [[47, "graphnet.models.components.layers.EdgeConvTito.message"]], "reset_parameters() (graphnet.models.components.layers.edgeconvtito method)": [[47, "graphnet.models.components.layers.EdgeConvTito.reset_parameters"]], "graphnet.models.components.pool": [[48, "module-graphnet.models.components.pool"]], "group_by() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.group_by"]], "group_pulses_to_dom() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.group_pulses_to_dom"]], "group_pulses_to_pmt() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.group_pulses_to_pmt"]], "min_pool() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.min_pool"]], "min_pool_x() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.min_pool_x"]], "std_pool() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.std_pool"]], "std_pool_x() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.std_pool_x"]], "sum_pool() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.sum_pool"]], "sum_pool_and_distribute() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.sum_pool_and_distribute"]], "sum_pool_x() (in module graphnet.models.components.pool)": [[48, "graphnet.models.components.pool.sum_pool_x"]], "graphnet.models.detector": [[49, "module-graphnet.models.detector"]], "detector (class in graphnet.models.detector.detector)": [[50, "graphnet.models.detector.detector.Detector"]], "feature_map() (graphnet.models.detector.detector.detector method)": [[50, "graphnet.models.detector.detector.Detector.feature_map"]], "forward() (graphnet.models.detector.detector.detector method)": [[50, "graphnet.models.detector.detector.Detector.forward"]], "graphnet.models.detector.detector": [[50, "module-graphnet.models.detector.detector"]], "icecube86 (class in graphnet.models.detector.icecube)": [[51, "graphnet.models.detector.icecube.IceCube86"]], "icecubedeepcore (class in graphnet.models.detector.icecube)": [[51, "graphnet.models.detector.icecube.IceCubeDeepCore"]], "icecubekaggle (class in graphnet.models.detector.icecube)": [[51, "graphnet.models.detector.icecube.IceCubeKaggle"]], "icecubeupgrade (class in graphnet.models.detector.icecube)": [[51, "graphnet.models.detector.icecube.IceCubeUpgrade"]], "feature_map() (graphnet.models.detector.icecube.icecube86 method)": [[51, "graphnet.models.detector.icecube.IceCube86.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubedeepcore method)": [[51, "graphnet.models.detector.icecube.IceCubeDeepCore.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubekaggle method)": [[51, "graphnet.models.detector.icecube.IceCubeKaggle.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubeupgrade method)": [[51, "graphnet.models.detector.icecube.IceCubeUpgrade.feature_map"]], "graphnet.models.detector.icecube": [[51, "module-graphnet.models.detector.icecube"]], "prometheus (class in graphnet.models.detector.prometheus)": [[52, "graphnet.models.detector.prometheus.Prometheus"]], "feature_map() (graphnet.models.detector.prometheus.prometheus method)": [[52, "graphnet.models.detector.prometheus.Prometheus.feature_map"]], "graphnet.models.detector.prometheus": [[52, "module-graphnet.models.detector.prometheus"]], "graphnet.models.gnn": [[53, "module-graphnet.models.gnn"]], "convnet (class in graphnet.models.gnn.convnet)": [[54, "graphnet.models.gnn.convnet.ConvNet"]], "forward() (graphnet.models.gnn.convnet.convnet method)": [[54, "graphnet.models.gnn.convnet.ConvNet.forward"]], "graphnet.models.gnn.convnet": [[54, "module-graphnet.models.gnn.convnet"]], "dynedge (class in graphnet.models.gnn.dynedge)": [[55, "graphnet.models.gnn.dynedge.DynEdge"]], "forward() (graphnet.models.gnn.dynedge.dynedge method)": [[55, "graphnet.models.gnn.dynedge.DynEdge.forward"]], "graphnet.models.gnn.dynedge": [[55, "module-graphnet.models.gnn.dynedge"]], "dynedgejinst (class in graphnet.models.gnn.dynedge_jinst)": [[56, "graphnet.models.gnn.dynedge_jinst.DynEdgeJINST"]], "forward() (graphnet.models.gnn.dynedge_jinst.dynedgejinst method)": [[56, "graphnet.models.gnn.dynedge_jinst.DynEdgeJINST.forward"]], "graphnet.models.gnn.dynedge_jinst": [[56, "module-graphnet.models.gnn.dynedge_jinst"]], "dynedgetito (class in graphnet.models.gnn.dynedge_kaggle_tito)": [[57, "graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO"]], "forward() (graphnet.models.gnn.dynedge_kaggle_tito.dynedgetito method)": [[57, "graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO.forward"]], "graphnet.models.gnn.dynedge_kaggle_tito": [[57, "module-graphnet.models.gnn.dynedge_kaggle_tito"]], "gnn (class in graphnet.models.gnn.gnn)": [[58, "graphnet.models.gnn.gnn.GNN"]], "forward() (graphnet.models.gnn.gnn.gnn method)": [[58, "graphnet.models.gnn.gnn.GNN.forward"]], "graphnet.models.gnn.gnn": [[58, "module-graphnet.models.gnn.gnn"]], "nb_inputs (graphnet.models.gnn.gnn.gnn property)": [[58, "graphnet.models.gnn.gnn.GNN.nb_inputs"]], "nb_outputs (graphnet.models.gnn.gnn.gnn property)": [[58, "graphnet.models.gnn.gnn.GNN.nb_outputs"]], "graphnet.models.graphs": [[59, "module-graphnet.models.graphs"]], "graphnet.models.graphs.edges": [[60, "module-graphnet.models.graphs.edges"]], "edgedefinition (class in graphnet.models.graphs.edges.edges)": [[61, 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"module-graphnet.models.graphs.graph_definition"]], "knngraph (class in graphnet.models.graphs.graphs)": [[63, "graphnet.models.graphs.graphs.KNNGraph"]], "graphnet.models.graphs.graphs": [[63, "module-graphnet.models.graphs.graphs"]], "graphnet.models.graphs.nodes": [[64, "module-graphnet.models.graphs.nodes"]], "nodedefinition (class in graphnet.models.graphs.nodes.nodes)": [[65, "graphnet.models.graphs.nodes.nodes.NodeDefinition"]], "nodesaspulses (class in graphnet.models.graphs.nodes.nodes)": [[65, "graphnet.models.graphs.nodes.nodes.NodesAsPulses"]], "forward() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[65, "graphnet.models.graphs.nodes.nodes.NodeDefinition.forward"]], "graphnet.models.graphs.nodes.nodes": [[65, "module-graphnet.models.graphs.nodes.nodes"]], "nb_outputs (graphnet.models.graphs.nodes.nodes.nodedefinition property)": [[65, "graphnet.models.graphs.nodes.nodes.NodeDefinition.nb_outputs"]], "set_number_of_inputs() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[65, "graphnet.models.graphs.nodes.nodes.NodeDefinition.set_number_of_inputs"]], "model (class in graphnet.models.model)": [[66, "graphnet.models.model.Model"]], "fit() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.fit"]], "forward() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.forward"]], "from_config() (graphnet.models.model.model class method)": [[66, "graphnet.models.model.Model.from_config"]], "graphnet.models.model": [[66, "module-graphnet.models.model"]], "load() (graphnet.models.model.model class method)": [[66, "graphnet.models.model.Model.load"]], "load_state_dict() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.load_state_dict"]], "predict() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.predict"]], "predict_as_dataframe() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.predict_as_dataframe"]], "save() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.save"]], "save_state_dict() (graphnet.models.model.model method)": [[66, "graphnet.models.model.Model.save_state_dict"]], "standardmodel (class in graphnet.models.standard_model)": [[67, "graphnet.models.standard_model.StandardModel"]], "compute_loss() (graphnet.models.standard_model.standardmodel method)": [[67, "graphnet.models.standard_model.StandardModel.compute_loss"]], "configure_optimizers() (graphnet.models.standard_model.standardmodel method)": [[67, "graphnet.models.standard_model.StandardModel.configure_optimizers"]], "forward() (graphnet.models.standard_model.standardmodel method)": [[67, "graphnet.models.standard_model.StandardModel.forward"]], "graphnet.models.standard_model": [[67, "module-graphnet.models.standard_model"]], "inference() (graphnet.models.standard_model.standardmodel method)": [[67, 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