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 ajoblib.parallel_backend
context.1
means using all processors.
 n_jobsint or None, optional, default:
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 scikitlearn 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 onedimensional array of dense arrays.