giotto.diagrams
.PersistenceLandscape¶
-
class
giotto.diagrams.
PersistenceLandscape
(n_layers=1, n_values=100, n_jobs=None)¶ Persistence landscapes of persistence diagrams.
Given a persistence diagram consisting of birth-death-dimension triples [b, d, q], subdiagrams corresponding to distinct homology dimensions are considered separately, and layers of their respective persistence landscapes are obtained by evenly sampling the filtration parameter.
- Parameters
- n_layersint, optional, default:
1
How many layers to consider in the persistence landscape.
- n_valuesint, optional, default:
100
The number of filtration parameter values, per available homology dimension, to sample during
fit
.- 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_layersint, optional, default:
- Attributes
See also
Notes
The samplings in
samplings_
are in general different between different homology dimensions. This means that the j-th entry of the k-layer of a persistence landscape in homology dimension q typically arises from a different parameter value to the j-th entry of a k-layer in dimension q’.Methods
fit
(self, X[, y])Store all observed homology dimensions in
homology_dimensions_
and, for each dimension separately, store evenly sample filtration parameter values insamplings_
.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])Compute the persistence landscapes of diagrams in X.
-
__init__
(self, n_layers=1, n_values=100, n_jobs=None)¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(self, X, y=None)¶ Store all observed homology dimensions in
homology_dimensions_
and, for each dimension separately, store evenly sample filtration parameter values insamplings_
. Then, return the estimator.This method is there to implement the usual scikit-learn API and hence work in pipelines.
- Parameters
- Xndarray, shape (n_samples, n_features, 3)
Input data. Array of persistence diagrams, each a collection of triples [b, d, q] representing persistent topological features through their birth (b), death (d) and homology dimension (q).
- 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)¶ Compute the persistence landscapes of diagrams in X.
- Parameters
- Xndarray, shape (n_samples, n_features, 3)
Input data. Array of persistence diagrams, each a collection of triples [b, d, q] representing persistent topological features through their birth (b), death (d) and homology dimension (q).
- yNone
There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
- Xtndarray, shape (n_samples, n_homology_dimensions, n_layers, n_values)
Persistence lanscapes: one landscape (represented as a two-dimensional array) per sample and per homology dimension seen in
fit
. Each landscape contains a number n_layers of layers. Index i along axis 1 corresponds to the i-th homology dimension inhomology_dimensions_
.