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(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)	Lovasz theta kernel as proposed in [JJDB14].
parse_input(self, X)	Parse and create features for svm_theta kernel.
set_params(self, \*\*params)	Call the parent method.
transform(self, X)	Calculate the kernel matrix, between given and fitted dataset.

Initialise a lovasz_theta 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)	Lovasz theta kernel as proposed in [JJDB14].
parse_input(self, X)	Parse and create features for svm_theta 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, random_state=None, n_samples=50, subsets_size_range=(2, 8), metric=<function _inner at 0x7f183e4b8d90>)

Initialise a lovasz_theta kernel.

diagonal(self)

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)

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)

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)

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)

Initialize all transformer arguments, needing initialization.

pairwise_operation(self, x, y)

Lovasz theta kernel as proposed in [JJDB14].

Parameters

x, ydict

Subgraph samples metric dictionaries for all levels.

Returns

kernelnumber

The kernel value.

parse_input(self, X)

Parse and create features for svm_theta 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

The lovasz metrics for the given input.

set_params(self, **params)

Call the parent method.

transform(self, X)

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

JJDB14(1,2,3,4)

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.