grakel.WeisfeilerLehmanOptimalAssignment

class grakel.WeisfeilerLehmanOptimalAssignment(n_jobs=None, verbose=False, normalize=False, n_iter=5, sparse=False)

Compute the Weisfeiler Lehman Optimal Assignment Kernel.

See [KGW16].

Parameters:

n_iterint, default=5

The number of iterations.

Attributes:

Xdict

Holds a list of fitted subkernel modules.

sparsebool

Defines if the data will be stored in a sparse format. Sparse format is slower, but less memory consuming and in some cases the only solution.

_nxnumber

Holds the number of inputs.

_n_iterint

Holds the number, of iterations.

_hierarchydict

A hierarchy produced by the WL relabeling procedure.

_inv_labelsdict

An inverse dictionary, used for relabeling on each iteration.

Methods

diagonal()	Calculate the kernel matrix diagonal for fitted data.
fit(X[, y])	Fit a dataset, for a transformer.
fit_transform(X[, y])	Fit and transform, on the same dataset.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
initialize()	Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)	Calculate a pairwise kernel between two elements.
parse_input(X)	Parse input for weisfeiler lehman optimal assignment.
set_output(*[, transform])	Set output container.
set_params(**params)	Call the parent method.
transform(X)	Calculate the kernel matrix, between given and fitted dataset.

Initialise a weisfeiler_lehman kernel.

Attributes:

X

Methods

diagonal()	Calculate the kernel matrix diagonal for fitted data.
fit(X[, y])	Fit a dataset, for a transformer.
fit_transform(X[, y])	Fit and transform, on the same dataset.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
initialize()	Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)	Calculate a pairwise kernel between two elements.
parse_input(X)	Parse input for weisfeiler lehman optimal assignment.
set_output(*[, transform])	Set output container.
set_params(**params)	Call the parent method.
transform(X)	Calculate the kernel matrix, between given and fitted dataset.

__init__(n_jobs=None, verbose=False, normalize=False, n_iter=5, sparse=False)

Initialise a weisfeiler_lehman kernel.

Bibliography

[KGW16]

Nils M Kriege, Pierre-Louis Giscard, and Richard Wilson. On Valid Optimal Assignment Kernels and Applications to Graph Classification. In Advances in Neural Information Processing Systems, 1623–1631. 2016.