grakel.NeighborhoodSubgraphPairwiseDistance

class grakel.NeighborhoodSubgraphPairwiseDistance(n_jobs=None, normalize=False, verbose=False, r=3, d=4)

The Neighborhood subgraph pairwise distance kernel.

See [CDG10].

Parameters

rint, default=3

The maximum considered radius between vertices.

dint, default=4

Neighborhood depth.

Attributes

_ngxint

The number of graphs upon fit.

_ngyint

The number of graphs upon transform.

_fit_keysdict

A dictionary with keys from 0 to _d+1, constructed upon fit holding an enumeration of all the found (in the fit dataset) tuples of two hashes and a radius in this certain level.

_X_level_norm_factordict

A dictionary with keys from 0 to _d+1, that holds the self calculated kernel [krg(X_i, X_i) for i=1:ngraphs_X] for all levels.

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 initialization.
pairwise_operation(self, x, y)	Calculate a pairwise kernel between two elements.
parse_input(self, X)	Parse and create features for the NSPD kernel.
set_params(self, \*\*params)	Call the parent method.
transform(self, X[, y])	Calculate the kernel matrix, between given and fitted dataset.

Initialize an NSPD 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 initialization.
pairwise_operation(self, x, y)	Calculate a pairwise kernel between two elements.
parse_input(self, X)	Parse and create features for the NSPD kernel.
set_params(self, \*\*params)	Call the parent method.
transform(self, X[, y])	Calculate the kernel matrix, between given and fitted dataset.

__init__(self, n_jobs=None, normalize=False, verbose=False, r=3, d=4)

Initialize an NSPD kernel.

diagonal(self)

Calculate the kernel matrix diagonal of the fitted data.

Static. Added for completeness.

Parameters

None.

Returns

X_diagint

Always equal with r*d.

Y_diagint

Always equal with r*d.

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.

Returns

Knumpy array, shape = [n_input_graphs, 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 and create features for the NSPD 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

Mdict

A dictionary with keys all the distances from 0 to self.d and values the the np.arrays with rows corresponding to the non-null input graphs and columns to the enumerations of tuples consisting of pairs of hash values and radius, from all the given graphs of the input (plus the fitted one’s on transform).

set_params(self, **params)

Call the parent method.

transform(self, X, y=None)

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).

yObject, default=None

Ignored argument, added for the pipeline.

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

CDG10

Fabrizio Costa and Kurt De Grave. Fast Neighborhood Subgraph Pairwise Distance Kernel. In Proceedings of the 26th International Conference on Machine Learning, 255–262. 2010.