grakel.VertexHistogram

class grakel.VertexHistogram(n_jobs=None, normalize=False, verbose=False, sparse='auto')[source][source]

Vertex Histogram kernel as found in [SB15].

Parameters
sparsebool, or ‘auto’, default=’auto’

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. If ‘auto’, uses a sparse matrix when the number of zeros is more than the half of the matrix size. In all cases if the dense matrix doesn’t fit system memory, I sparse approach will be tried.

Attributes
None.

Methods

diagonal(self)

Calculate the kernel matrix diagonal of the fitted 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 initialisation.

pairwise_operation(self, x, y)

Calculate a pairwise kernel between two elements.

parse_input(self, X)

Parse and check the given input for VH kernel.

set_params(self, \*\*params)

Call the parent method.

transform(self, X)

Calculate the kernel matrix, between given and fitted dataset.

Initialise a vertex histogram kernel.

Attributes
X

Methods

diagonal(self)

Calculate the kernel matrix diagonal of the fitted 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 initialisation.

pairwise_operation(self, x, y)

Calculate a pairwise kernel between two elements.

parse_input(self, X)

Parse and check the given input for VH 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, sparse='auto')[source][source]

Initialise a vertex histogram kernel.

diagonal(self)[source][source]

Calculate the kernel matrix diagonal of the fitted data.

Parameters
None.
Returns
X_diagnp.array

The diagonal of the kernel matrix, of the fitted. 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]

Initialize all transformer arguments, needing initialisation.

pairwise_operation(self, x, y)[source]

Calculate a pairwise kernel between two elements.

Parameters
x, yObject

Objects as occur from parse_input.

Returns
kernelnumber

The kernel value.

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

Parse and check the given input for VH 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 fitting the given graph format).

Returns
outnp.array, shape=(len(X), n_labels)

A np.array for frequency (cols) histograms for all Graphs (rows).

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

SB15

Mahito Sugiyama and Karsten M. Borgwardt. Halting in Random Walk Kernels. In Advances in Neural Information Processing Systems, 1639–1647. 2015.