# giotto.graphs.GraphGeodesicDistance¶

class giotto.graphs.GraphGeodesicDistance(n_jobs=None)

Distance matrices arising from geodesic distances on graphs.

For each (possibly weighted and/or directed) graph in a collection, this transformer calculates the length of the shortest (directed or undirected) path between any two of its vertices, setting it to numpy.inf when two vertices cannot be connected by a path.

The graphs are encoded as sparse adjacency matrices, while the outputs are dense distance matrices of variable size.

Parameters
n_jobsint or None, optional, default: None

The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

Examples

>>> import numpy as np
>>> from giotto.graphs import TransitionGraph, GraphGeodesicDistance
>>> X = np.arange(4).reshape(1, -1, 1)
>>> tg = TransitionGraph(func=None).fit_transform(X)
>>> print(tg[0].toarray())
[[False  True False False]
[ True False  True False]
[False  True False  True]
[False False  True False]]
>>> ggd = GraphGeodesicDistance().fit_transform(tg)
>>> print(ggd[0])
[[0. 1. 2. 3.]
[1. 0. 1. 2.]
[2. 1. 0. 1.]
[3. 2. 1. 0.]]


Methods

 fit(self, X[, y]) Do nothing and return the estimator unchanged. fit_transform(self, X[, y]) Fit to data, then transform it. get_params(self[, deep]) Get parameters for this estimator. set_params(self, \*\*params) Set the parameters of this estimator. transform(self, X[, y]) Use sklearn.utils.graph_shortest_path.graph_shortest_path to compute the lengths of graph shortest paths between any two vertices.
__init__(self, n_jobs=None)

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)

Do nothing and return the estimator unchanged.

This method is there to implement the usual scikit-learn API and hence work in pipelines.

Parameters
Xndarray of sparse or dense arrays, shape (n_samples,)

Input data, i.e. a collection of adjacency matrices of graphs.

yNone

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

Returns
selfobject
fit_transform(self, X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

Returns
X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters
deepboolean, optional

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.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns
self
transform(self, X, y=None)

Use sklearn.utils.graph_shortest_path.graph_shortest_path to compute the lengths of graph shortest paths between any two vertices.

Parameters
Xndarray of sparse or dense arrays, shape (n_samples,)

Input data, i.e. a collection of adjacency matrices of graphs.

yNone

Ignored.

Returns
Xtndarray, shape (n_samples,) or (n_samples, n_vertices, n_vertices)

Array of distance matrices. If the distance matrices have variable size across samples, Xt is a one-dimensional array of dense arrays.