grakel.CoreFramework

class grakel.CoreFramework(n_jobs=None, verbose=False, normalize=False, min_core=-1, base_graph_kernel=None)

The core kernel framework, as proposed in [NMLV18].

Parameters

base_graph_kernelgrakel.kernels.kernel or tuple, default=None

If tuple it must consist of a valid kernel object and a dictionary of parameters. General parameters concerning normalization, concurrency, .. will be ignored, and the ones of given on __init__ will be passed in case it is needed. Default base_graph_kernel is VertexHistogram.

min_coreint, default=-1

Core numbers bigger than min_core will only be considered.

Attributes

base_graph_kernel_function

A void function that initializes a base kernel object.

Methods

diagonal(self)	Calculate the kernel matrix diagonal for fitted data.
fit(self, X[, y])	Fit a dataset, for a transformer.
fit_transform(self, X[, y])	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)	Calculate a pairwise kernel between two elements.
parse_input(self, X)	Parse input and create features, while initializing and/or calculating sub-kernels.
set_params(self, \*\*params)	Call the parent method.
transform(self, X)	Calculate the kernel matrix, between given and fitted dataset.

Initialise a hadamard_code kernel.

Attributes

X

Methods

diagonal(self)	Calculate the kernel matrix diagonal for fitted data.
fit(self, X[, y])	Fit a dataset, for a transformer.
fit_transform(self, X[, y])	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)	Calculate a pairwise kernel between two elements.
parse_input(self, X)	Parse input and create features, while initializing and/or calculating sub-kernels.
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, verbose=False, normalize=False, min_core=-1, base_graph_kernel=None)

Initialise a hadamard_code kernel.

diagonal(self)

Calculate the kernel matrix diagonal for fitted data.

A funtion called on transform on a seperate dataset to apply normalization on the exterior.

Parameters

None.

Returns

X_diagnp.array

The diagonal of the kernel matrix, of the fitted data. This consists of kernel calculation for each element with itself.

Y_diagnp.array

The diagonal of the kernel matrix, of the transformed data. This consists of kernel calculation for each element 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, y=None)

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)

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)

Parse input and create features, while initializing and/or calculating sub-kernels.

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

base_graph_kernelobject

Returns base_graph_kernel. Only if called from fit or fit_transform.

Knp.array

Returns the kernel matrix. Only if called from transform or fit_transform.

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

NMLV18

Giannis Nikolentzos, Polykarpos Meladianos, Stratis Limnios, and Michalis Vazirgiannis. A Degeneracy Framework for Graph Similarity. In 27th International Joint Conference on Artificial Intelligence. 2018.