SparseRipsPersistence¶

class
gtda.homology.
SparseRipsPersistence
(metric='euclidean', homology_dimensions=0, 1, coeff=2, epsilon=0.1, max_edge_length=inf, infinity_values=None, reduced_homology=True, n_jobs=None)[source]¶ Persistence diagrams resulting from Sparse Vietoris–Rips filtrations.
Given a point cloud in Euclidean space, or an abstract metric space encoded by a distance matrix, information about the appearance and disappearance of topological features (technically, homology classes) of various dimensions and at different scales is summarised in the corresponding persistence diagram.
Important note:
Persistence diagrams produced by this class must be interpreted with care due to the presence of padding triples which carry no information. See
transform
for additional information.
 Parameters
metric (string or callable, optional, default:
"euclidean"
) – If set to"precomputed"
, input data is to be interpreted as a collection of distance matrices. Otherwise, input data is to be interpreted as a collection of point clouds (i.e. feature arrays), and metric determines a rule with which to calculate distances between pairs of instances (i.e. rows) in these arrays. If metric is a string, it must be one of the options allowed byscipy.spatial.distance.pdist
for its metric parameter, or a metric listed insklearn.pairwise.PAIRWISE_DISTANCE_FUNCTIONS
, including “euclidean”, “manhattan”, or “cosine”. If metric is a callable, it is called on each pair of instances and the resulting value recorded. The callable should take two arrays from the entry in X as input, and return a value indicating the distance between them.homology_dimensions (list or tuple, optional, default:
(0, 1)
) – Dimensions (nonnegative integers) of the topological features to be detected.coeff (int prime, optional, default:
2
) – Compute homology with coefficients in the prime field \(\mathbb{F}_p = \{ 0, \ldots, p  1 \}\) where \(p\) equals coeff.epsilon (float between 0. and 1., optional, default:
0.1
) – Parameter controlling the approximation to the exact Vietoris–Rips filtration. If set to 0.,SparseRipsPersistence
leads to the same results asVietorisRipsPersistence
but is slower.max_edge_length (float, optional, default:
numpy.inf
) – Maximum value of the Sparse Rips filtration parameter. Points whose distance is greater than this value will never be connected by an edge, and topological features at scales larger than this value will not be detected.infinity_values (float or None, default:
None
) – Which death value to assign to features which are still alive at filtration value max_edge_length.None
means that this death value is declared to be equal to max_edge_length.reduced_homology (bool, optional, default:
True
) – IfTrue
, the earliestborn triple in homology dimension 0 which has infinite death is discarded from each diagram computed intransform
.n_jobs (int 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.

infinity_values_
¶ Effective death value to assign to features which are still alive at filtration value max_edge_length. Set in
fit
. Type
float
See also
VietorisRipsPersistence
,WeightedRipsPersistence
,FlagserPersistence
,WeakAlphaPersistence
,EuclideanCechPersistence
,ConsistentRescaling
,ConsecutiveRescaling
Notes
GUDHI is used as a C++ backend for computing sparse Vietoris–Rips persistent homology 1. Python bindings were modified for performance.
References
 1
C. Maria, “Persistent Cohomology”, 2020; GUDHI User and Reference Manual.

__init__
(metric='euclidean', homology_dimensions=0, 1, coeff=2, epsilon=0.1, max_edge_length=inf, infinity_values=None, reduced_homology=True, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y=None)[source]¶ Calculate
infinity_values_
. Then, return the estimator.This method is here to implement the usual scikitlearn API and hence work in pipelines.
 Parameters
X (ndarray or list of length n_samples) – Input data representing a collection of point clouds if metric was not set to
"precomputed"
, and of distance matrices otherwise. Can be either a 3D ndarray whose zeroth dimension has sizen_samples
, or a list containingn_samples
2D ndarrays. Point cloud arrays have shape(n_points, n_dimensions)
, and if X is a list these shapes can vary between point clouds. If metric was set to"precomputed"
, each entry of X should be compatible with a filtration, i.e. the value at index (i, j) should be no smaller than the values at diagonal indices (i, i) and (j, j).y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
 Returns
self
 Return type
object

fit_transform
(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
X (ndarray or list of length n_samples) – Input data representing a collection of point clouds if metric was not set to
"precomputed"
, and of distance matrices otherwise. Can be either a 3D ndarray whose zeroth dimension has sizen_samples
, or a list containingn_samples
2D ndarrays. Point cloud arrays have shape(n_points, n_dimensions)
, and if X is a list these shapes can vary between point clouds. If metric was set to"precomputed"
, each entry of X should be compatible with a filtration, i.e. the value at index (i, j) should be no smaller than the values at diagonal indices (i, i) and (j, j).y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
 Returns
Xt – Array of persistence diagrams computed from the feature arrays or distance matrices in X.
n_features
equals \(\sum_q n_q\), where \(n_q\) is the maximum number of topological features in dimension \(q\) across all samples in X. Return type
ndarray of shape (n_samples, n_features, 3)

fit_transform_plot
(X, y=None, sample=0, **plot_params)¶ Fit to data, then apply
transform_plot
. Parameters
X (ndarray of shape (n_samples, ..)) – Input data.
y (ndarray of shape (n_samples,) or None) – Target values for supervised problems.
sample (int) – Sample to be plotted.
**plot_params – Optional plotting parameters.
 Returns
Xt – Transformed onesample slice from the input.
 Return type
ndarray of shape (1, ..)

get_params
(deep=True)¶ Get parameters for this estimator.
 Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
params – Parameter names mapped to their values.
 Return type
mapping of string to any

static
plot
(Xt, sample=0, homology_dimensions=None, plotly_params=None)[source]¶ Plot a sample from a collection of persistence diagrams, with homology in multiple dimensions.
 Parameters
Xt (ndarray of shape (n_samples, n_features, 3)) – Collection of persistence diagrams, such as returned by
transform
.sample (int, optional, default:
0
) – Index of the sample in Xt to be plotted.homology_dimensions (list, tuple or None, optional, default:
None
) – Which homology dimensions to include in the plot.None
means plotting all dimensions present inXt[sample]
.plotly_params (dict or None, optional, default:
None
) – Custom parameters to configure the plotly figure. Allowed keys are"traces"
and"layout"
, and the corresponding values should be dictionaries containing keyword arguments as would be fed to theupdate_traces
andupdate_layout
methods ofplotly.graph_objects.Figure
.
 Returns
fig – Plotly figure.
 Return type
plotly.graph_objects.Figure
object

set_params
(**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. Parameters
**params (dict) – Estimator parameters.
 Returns
self – Estimator instance.
 Return type
object

transform
(X, y=None)[source]¶ For each point cloud or distance matrix in X, compute the relevant persistence diagram as an array of triples [b, d, q]. Each triple represents a persistent topological feature in dimension q (belonging to homology_dimensions) which is born at b and dies at d. Only triples in which b < d are meaningful. Triples in which b and d are equal (“diagonal elements”) may be artificially introduced during the computation for padding purposes, since the number of nontrivial persistent topological features is typically not constant across samples. They carry no information and hence should be effectively ignored by any further computation.
 Parameters
X (ndarray or list of length n_samples) – Input data representing a collection of point clouds if metric was not set to
"precomputed"
, and of distance matrices otherwise. Can be either a 3D ndarray whose zeroth dimension has sizen_samples
, or a list containingn_samples
2D ndarrays. Point cloud arrays have shape(n_points, n_dimensions)
, and if X is a list these shapes can vary between point clouds. If metric was set to"precomputed"
, each entry of X should be compatible with a filtration, i.e. the value at index (i, j) should be no smaller than the values at diagonal indices (i, i) and (j, j).y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
 Returns
Xt – Array of persistence diagrams computed from the feature arrays or distance matrices in X.
n_features
equals \(\sum_q n_q\), where \(n_q\) is the maximum number of topological features in dimension \(q\) across all samples in X. Return type
ndarray of shape (n_samples, n_features, 3)

transform_plot
(X, sample=0, **plot_params)¶ Take a onesample slice from the input collection and transform it. Before returning the transformed object, plot the transformed sample.
 Parameters
X (ndarray of shape (n_samples, ..)) – Input data.
sample (int) – Sample to be plotted.
**plot_params – Optional plotting parameters.
 Returns
Xt – Transformed onesample slice from the input.
 Return type
ndarray of shape (1, ..)