grakel.WeisfeilerLehmanOptimalAssignment¶
- class grakel.WeisfeilerLehmanOptimalAssignment(n_jobs=None, verbose=False, normalize=False, n_iter=5, sparse=False)[source][source]¶
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_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_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_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_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.