diff --git a/_modules/graphnet/models/components/embedding.html b/_modules/graphnet/models/components/embedding.html
index f73cb60b1..06128b82d 100644
--- a/_modules/graphnet/models/components/embedding.html
+++ b/_modules/graphnet/models/components/embedding.html
@@ -452,6 +452,7 @@ Source code for
f " { n_features } features."
)
elif n_features >= 6 :
+
hidden_dim = 6 * seq_length
else :
hidden_dim = int (( n_features + 0.5 ) * seq_length )
diff --git a/_modules/graphnet/models/graphs/graph_definition.html b/_modules/graphnet/models/graphs/graph_definition.html
index 5b13ae1db..595450b28 100644
--- a/_modules/graphnet/models/graphs/graph_definition.html
+++ b/_modules/graphnet/models/graphs/graph_definition.html
@@ -377,7 +377,7 @@ Source code
def __init__ (
self ,
detector : Detector ,
- node_definition : NodeDefinition = NodesAsPulses (),
+ node_definition : NodeDefinition = None ,
edge_definition : Optional [ EdgeDefinition ] = None ,
input_feature_names : Optional [ List [ str ]] = None ,
dtype : Optional [ torch . dtype ] = torch . float ,
@@ -422,6 +422,9 @@ Source code
# Base class constructor
super () . __init__ ( name = __name__ , class_name = self . __class__ . __name__ )
+ if node_definition is None :
+ node_definition = NodesAsPulses ()
+
# Member Variables
self . _detector = detector
self . _edge_definition = edge_definition
diff --git a/_modules/graphnet/models/graphs/graphs.html b/_modules/graphnet/models/graphs/graphs.html
index 55767ca84..b7d2da420 100644
--- a/_modules/graphnet/models/graphs/graphs.html
+++ b/_modules/graphnet/models/graphs/graphs.html
@@ -408,6 +408,50 @@ Source code for graphn
seed = seed ,
)
+
+
+
+
[docs]
+
class EdgelessGraph ( GraphDefinition ):
+
"""A Data representation without edge assignment.
+
+
I.e the resulting representation is created without an EdgeDefinition.
+
"""
+
+
def __init__ (
+
self ,
+
detector : Detector ,
+
node_definition : NodeDefinition = None ,
+
input_feature_names : Optional [ List [ str ]] = None ,
+
dtype : Optional [ torch . dtype ] = torch . float ,
+
perturbation_dict : Optional [ Dict [ str , float ]] = None ,
+
seed : Optional [ Union [ int , Generator ]] = None ,
+
) -> None :
+
"""Construct isolated nodes graph representation.
+
+
Args:
+
detector: Detector that represents your data.
+
node_definition: Definition of nodes in the graph.
+
input_feature_names: Name of input feature columns.
+
dtype: data type for node features.
+
perturbation_dict: Dictionary mapping a feature name to a standard
+
deviation according to which the values for this
+
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__ (
+
detector = detector ,
+
node_definition = node_definition or NodesAsPulses (),
+
edge_definition = None ,
+
dtype = dtype ,
+
input_feature_names = input_feature_names ,
+
perturbation_dict = perturbation_dict ,
+
seed = seed ,
+
) Source code for gra
"""Implement loss calculation."""
# Check(s)
assert prediction . dim () == 2
+ if target . dim () != prediction . dim ():
+ target = target . squeeze ( 1 )
assert prediction . size () == target . size ()
elements = torch . mean (( prediction - target ) ** 2 , dim =- 1 )
@@ -845,6 +847,102 @@ Source code for gra
p = kappa . unsqueeze ( 1 ) * prediction [:, [ 0 , 1 , 2 ]]
return self . _evaluate ( p , target )
+
+
+
+
[docs]
+
class EnsembleLoss ( LossFunction ):
+
"""Chain multiple loss functions together."""
+
+
def __init__ (
+
self ,
+
loss_functions : List [ LossFunction ],
+
loss_factors : List [ float ] = None ,
+
prediction_keys : Optional [ List [ List [ int ]]] = None ,
+
* args : Any ,
+
** kwargs : Any ,
+
) -> None :
+
"""Chain multiple loss functions together.
+
+
Optionally apply a weight to each loss function contribution.
+
+
E.g. Loss = RMSE*0.5 + LogCoshLoss*1.5
+
+
Args:
+
loss_functions: A list of loss functions to use.
+
Each loss function contributes a term to the overall loss.
+
loss_factors: An optional list of factors that will be mulitplied
+
to each loss function contribution. Must be ordered according
+
to `loss_functions`. If not given, the weights default to 1.
+
prediction_keys: An optional list of lists of indices for which
+
prediction columns to use for each loss function. If not
+
given, all columns are used for all loss functions.
+
"""
+
if loss_factors is None :
+
# add weight of 1 - i.e no discrimination
+
loss_factors = np . repeat ( 1 , len ( loss_functions )) . tolist ()
+
+
assert len ( loss_functions ) == len ( loss_factors )
+
self . _factors = loss_factors
+
self . _loss_functions = loss_functions
+
+
if prediction_keys is not None :
+
self . _prediction_keys : Optional [ List [ List [ int ]]] = prediction_keys
+
else :
+
self . _prediction_keys = None
+
super () . __init__ ( * args , ** kwargs )
+
+
def _forward ( self , prediction : Tensor , target : Tensor ) -> Tensor :
+
"""Calculate loss using multiple loss functions.
+
+
Args:
+
prediction: Output of the model.
+
target: Target tensor, extracted from graph object.
+
+
Returns:
+
Elementwise loss terms. Shape [N,]
+
"""
+
if self . _prediction_keys is None :
+
prediction_keys = [ list ( range ( prediction . size ( 1 )))] * len (
+
self . _loss_functions
+
)
+
else :
+
prediction_keys = self . _prediction_keys
+
for k , ( loss_function , prediction_key ) in enumerate (
+
zip ( self . _loss_functions , prediction_keys )
+
):
+
if k == 0 :
+
elements = self . _factors [ k ] * loss_function . _forward (
+
prediction = prediction [:, prediction_key ], target = target
+
)
+
else :
+
elements += self . _factors [ k ] * loss_function . _forward (
+
prediction = prediction [:, prediction_key ], target = target
+
)
+
return elements
+
[docs]
+
class RMSEVonMisesFisher3DLoss ( EnsembleLoss ):
+
"""Combine the VonMisesFisher3DLoss with RMSELoss."""
+
+
def __init__ ( self , vmfs_factor : float = 0.05 ) -> None :
+
"""VonMisesFisher3DLoss with a RMSE penality term.
+
+
The VonMisesFisher3DLoss will be weighted with `vmfs_factor`.
+
+
Args:
+
vmfs_factor: A factor applied to the VonMisesFisher3DLoss term.
+
Defaults ot 0.05.
+
"""
+
super () . __init__ (
+
loss_functions = [ RMSELoss (), VonMisesFisher3DLoss ()],
+
loss_factors = [ 1 , vmfs_factor ],
+
prediction_keys = [[ 0 , 1 , 2 ], [ 0 , 1 , 2 , 3 ]],
+
) Parameters:
detector (Detector
node_definition (NodeDefinition NodesAsPulses(
- NodesAsPulses(
- {
- 'arguments': {
- 'input_feature_names': None,
- },
- })
-)
node_definition (Optional NodeDefinition None
edge_definition (Optional EdgeDefinition None
input_feature_names (Optional List str None
graphs
KNNGraph
+
+
+ EdgelessGraph
@@ -553,6 +562,8 @@
graphs
+
+class graphnet.models.graphs.graphs. EdgelessGraph ( * args ** kwargs ) [source]
+Bases: GraphDefinition
+A Data representation without edge assignment.
+I.e the resulting representation is created without an EdgeDefinition.
+Construct isolated nodes graph representation.
+
+Parameters:
+
+detector (Detector
node_definition (Optional NodeDefinition None
input_feature_names (Optional List str None
dtype (Optional dtype torch.float32
perturbation_dict (Optional Dict str float None
seed (Union int Generator None None
args (Any )
kwargs (Any )
+Return type:
+object
+
+
diff --git a/api/graphnet.models.graphs.html b/api/graphnet.models.graphs.html
index c515a008c..1aa55999d 100644
--- a/api/graphnet.models.graphs.html
+++ b/api/graphnet.models.graphs.html
@@ -572,6 +572,7 @@
graphs
utils
utils
VonMisesFisher3DLoss
+
+
+ EnsembleLoss
+
+
+
+ RMSEVonMisesFisher3DLoss
@@ -649,6 +667,10 @@
EuclideanDistanceLoss VonMisesFisher3DLoss EnsembleLoss RMSEVonMisesFisher3DLoss
+
+class graphnet.training.loss_functions. EnsembleLoss ( * args ** kwargs ) [source]
+Bases: LossFunction
+Chain multiple loss functions together.
+Chain multiple loss functions together.
+
+Optionally apply a weight to each loss function contribution.
+
E.g. Loss = RMSE*0.5 + LogCoshLoss*1.5
+
+
+Parameters:
+
+loss_functions (List LossFunction
loss_factors (Optional List float None
according (to each loss function contribution. Must be ordered )
given (to loss_functions. If not )
1. (the weights default to )
prediction_keys (Optional List List int None
args (Any )
kwargs (Any )
+Return type:
+object
+
+
+
+
+class graphnet.training.loss_functions. RMSEVonMisesFisher3DLoss ( * args ** kwargs ) [source]
+Bases: EnsembleLoss
+Combine the VonMisesFisher3DLoss with RMSELoss.
+VonMisesFisher3DLoss with a RMSE penality term.
+
+The VonMisesFisher3DLoss will be weighted with vmfs_factor .
+
+
+Parameters:
+
+
+Return type:
+object
+
+
diff --git a/genindex.html b/genindex.html
index 2994875e1..1a626ee27 100644
--- a/genindex.html
+++ b/genindex.html
@@ -681,6 +681,8 @@ E
EdgeConvTito (class in graphnet.models.components.layers)
EdgeDefinition (class in graphnet.models.graphs.edges.edges)
+EdgelessGraph (class in graphnet.models.graphs.graphs)
EnergyReconstruction (class in graphnet.models.task.reconstruction)
E
EnsembleDataset (class in graphnet.data.dataset.dataset)
eps_like() (in module graphnet.utilities.maths)
+ EnsembleLoss (class in graphnet.training.loss_functions)
ERDAHostedDataset (class in graphnet.data.curated_datamodule)
+ eps_like() (in module graphnet.utilities.maths)
resolve() (graphnet.data.utilities.string_selection_resolver.StringSelectionResolver method)
RMSELoss (class in graphnet.training.loss_functions)
+RMSEVonMisesFisher3DLoss (class in graphnet.training.loss_functions)
RNN_TITO (class in graphnet.models.gnn.RNN_tito)