giotto.diagrams
.Filtering¶

class
giotto.diagrams.
Filtering
(homology_dimensions=None, epsilon=0.01)¶ Filtering of persistence diagrams.
Filtering a diagram means removing all points whose distance from the diagonal is less than or equal to a certain cutoff value which can be interpreted as (\(1/\sqrt{2}\) times) the “minimum amount of persistence” required from points in the filtered diagram.
 Parameters
 homology_dimensionsiterable or None, optional, default:
None
When set to
None
, subdiagrams corresponding to all homology dimensions seen infit
will be filtered. Otherwise, it contains the homology dimensions at which filtering should occur. epsilonfloat, optional, default:
0.01
The cutoff value controlling the amount of filtering.
 homology_dimensionsiterable or None, optional, default:
 Attributes
 homology_dimensions_list
If homology_dimensions is set to
None
, then this is the list of homology dimensions seen infit
, sorted in ascending order. Otherwise, it is a similarly sorted version of homology_dimensions.
See also
Methods
fit
(self, X[, y])Store relevant homology dimensions in
homology_dimensions_
.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])Filter all relevant persistence subdiagrams.

__init__
(self, homology_dimensions=None, epsilon=0.01)¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y=None)¶ Store relevant homology dimensions in
homology_dimensions_
. Then, return the estimator.This method is there to implement the usual scikitlearn 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)¶ Filter all relevant persistence subdiagrams.
 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_features, 3)
Filtered persistence diagrams. Only the subdiagrams corresponding to dimensions in
homology_dimensions_
are filtered. Discarded points are replaced by points on the diagonal.