grakel.MultiscaleLaplacian

class grakel.MultiscaleLaplacian(n_jobs=None, normalize=False, verbose=False, L=3, gamma=0.01, heta=0.01)[source][source]

Laplacian Graph Kernel as proposed in [KP16].

Parameters
Lint, default=3

The number of neighborhoods.

gammaReal, default=0.01

A small softening parameter of float value.

hetafloat, default=0.01

A smoothing parameter of float value.

Attributes
Lint

The number of neighborhoods.

gammaReal

A smoothing parameter for calculation of S matrices.

hetafloat

A smoothing parameter for calculation of S matrices.

Methods

diagonal(self)

Calculate the kernel matrix diagonal of the fit/transformed data.

fit(self, X[, y])

Fit a dataset, for a transformer.

fit_transform(self, X)

Fit and transform, on the same dataset.

get_params(self[, deep])

Get parameters for this estimator.

initialize(self)

Initialize all transformer arguments, needing initialization.

pairwise_operation(self, x, y)

ML kernel as proposed in [KP16]..

parse_input(self, X)

Parse and create features for multiscale_laplacian kernel.

set_params(self, \*\*params)

Call the parent method.

transform(self, X)

Calculate the kernel matrix, between given and fitted dataset.

Initialise a multiscale_laplacian kernel.

Attributes
X

Methods

diagonal(self)

Calculate the kernel matrix diagonal of the fit/transformed data.

fit(self, X[, y])

Fit a dataset, for a transformer.

fit_transform(self, X)

Fit and transform, on the same dataset.

get_params(self[, deep])

Get parameters for this estimator.

initialize(self)

Initialize all transformer arguments, needing initialization.

pairwise_operation(self, x, y)

ML kernel as proposed in [KP16]..

parse_input(self, X)

Parse and create features for multiscale_laplacian kernel.

set_params(self, \*\*params)

Call the parent method.

transform(self, X)

Calculate the kernel matrix, between given and fitted dataset.
__init__(self, n_jobs=None, normalize=False, verbose=False, L=3, gamma=0.01, heta=0.01)[source][source]

Initialise a multiscale_laplacian kernel.

diagonal(self)[source]

Calculate the kernel matrix diagonal of the fit/transformed data.

Parameters
None.
Returns
X_diagnp.array

The diagonal of the kernel matrix between the fitted data. This consists of each element calculated with itself.

Y_diagnp.array

The diagonal of the kernel matrix, of the transform. This consists of each element calculated with itself.

fit(self, X, y=None)[source]

Fit a dataset, for a transformer.

Parameters
Xiterable

Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that fitting the given graph format). The train samples.

yNone

There is no need of a target in a transformer, yet the pipeline API requires this parameter.

Returns
selfobject
Returns self.
fit_transform(self, X)[source]

Fit and transform, on the same dataset.

Parameters
Xiterable

Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that fitting the given graph format). If None the kernel matrix is calculated upon fit data. The test samples.

yNone

There is no need of a target in a transformer, yet the pipeline API requires this parameter.

Returns
Knumpy array, shape = [n_targets, n_input_graphs]

corresponding to the kernel matrix, a calculation between all pairs of graphs between target an features

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

initialize(self)[source][source]

Initialize all transformer arguments, needing initialization.

pairwise_operation(self, x, y)[source][source]

ML kernel as proposed in [KP16]..

Parameters
x, ytuple

Tuple consisting of A, phi, neighborhoods up to self.L and the laplacian of A.

Returns
kernelnumber

The kernel value.

parse_input(self, X)[source][source]

Parse and create features for multiscale_laplacian kernel.

Parameters
Xiterable

For the input to pass the test, we must have: Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that correspond to the given graph format). A valid input also consists of graph type objects.

Returns
outlist

Tuples consisting of the Adjacency matrix, phi, phi_outer dictionary of neihborhood indexes and inverse laplacians up to level self.L and the inverse Laplacian of A.

set_params(self, **params)[source]

Call the parent method.

transform(self, X)[source]

Calculate the kernel matrix, between given and fitted dataset.

Parameters
Xiterable

Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that fitting the given graph format). If None the kernel matrix is calculated upon fit data. The test samples.

Returns
Knumpy array, shape = [n_targets, n_input_graphs]

corresponding to the kernel matrix, a calculation between all pairs of graphs between target an features

Bibliography

KP16(1,2,3,4)

Risi Kondor and Horace Pan. The Multiscale Laplacian Graph Kernel. In Advances in Neural Information Processing Systems, 2990–2998. 2016.