grakel.RandomWalk

class grakel.RandomWalk(n_jobs=None, normalize=False, verbose=False, lamda=0.1, method_type='fast', kernel_type='geometric', p=None)

The random walk kernel class.

See [KTI03], [GartnerFW03] and [VBS07].

Parameters:

lambdafloat

A lambda factor concerning summation.

method_typestr, valid_values={“baseline”, “fast”}

The method to use for calculating random walk kernel:

• “baseline” Complexity: \(O(|V|^6)\) (see [KTI03], [GartnerFW03])

• “fast” Complexity: \(O((|E|+|V|)|V||M|)\) (see [VBS07])

kernel_typestr, valid_values={“geometric”, “exponential”}

Defines how inner summation will be applied.

pint or None

If initialised defines the number of steps.

Attributes:

mu_list

List of coefficients concerning a finite sum, in case p is not None.

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)	Calculate the random walk kernel.
parse_input(X)	Parse and create features for random_walk 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 random_walk 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)	Calculate the random walk kernel.
parse_input(X)	Parse and create features for random_walk 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, lamda=0.1, method_type='fast', kernel_type='geometric', p=None)

Initialise a random_walk kernel.

Bibliography

[GartnerFW03] (1,2)

Thomas Gärtner, Peter Flach, and Stefan Wrobel. On Graph Kernels: Hardness Results and Efficient Alternatives. In Learning Theory and Kernel Machines, 129–143. 2003.

[KTI03] (1,2)

Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi. Marginalized Kernels Between Labeled Graphs. In Proceedings of the 20th Conference in Machine Learning, 321–328. 2003.

[VBS07] (1,2)

S.V.N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph. Fast Computation of Graph Kernels. In Advances in Neural Information Processing Systems, 1449–1456. 2007.