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)	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 the random walk kernel.
parse_input(X)	Parse and create features for random_walk kernel.
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)	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 the random walk kernel.
parse_input(X)	Parse and create features for random_walk kernel.
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.