grakel.SvmTheta

class grakel.SvmTheta(n_jobs=None, normalize=False, verbose=False, random_state=None, n_samples=50, subsets_size_range=(2, 8), metric=<function _inner>)

Calculate the SVM theta kernel.

See [JJDB14].

Parameters:

X,Yvalid-graph-format

The pair of graphs on which the kernel is applied.

n_samplesint, default=50

Number of samples.

subsets_size_rangetuple, len=2, default=(2,8)

(min, max) size of the vertex set of sampled subgraphs.

metricfunction (number, number -> number), default=:math:f(x,y)=x*y

The applied metric between the svm_theta numbers of the two graphs.

random_stateRandomState or int, default=None

A random number generator instance or an int to initialize a RandomState as a seed.

Attributes:

random_state_RandomState

A RandomState object handling all randomness of the class.

Methods

diagonal()	Calculate the kernel matrix diagonal of the fit/transformed 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)	Lovasz theta kernel as proposed in [JJDB14].
parse_input(X)	Parse and create features for svm_theta kernel.
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 lovasz_theta kernel.

Attributes:

X

Methods

diagonal()	Calculate the kernel matrix diagonal of the fit/transformed 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)	Lovasz theta kernel as proposed in [JJDB14].
parse_input(X)	Parse and create features for svm_theta kernel.
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, normalize=False, verbose=False, random_state=None, n_samples=50, subsets_size_range=(2, 8), metric=<function _inner>)

Initialise a lovasz_theta kernel.

Bibliography

[JJDB14] (1,2,3,4,5)

Fredrik Johansson, Vinay Jethava, Devdatt Dubhashi, and Chiranjib Bhattacharyya. Global graph kernels using geometric embeddings. In Proceedings of the 31st International Conference on Machine Learning, 694–702. 2014.