diff options
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/sklearn/cubical_persistence.py | 38 |
1 files changed, 21 insertions, 17 deletions
diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py index dc7be7f5..06e9128b 100644 --- a/src/python/gudhi/sklearn/cubical_persistence.py +++ b/src/python/gudhi/sklearn/cubical_persistence.py @@ -10,6 +10,7 @@ from .. import CubicalComplex from sklearn.base import BaseEstimator, TransformerMixin +import numpy as np # joblib is required by scikit-learn from joblib import Parallel, delayed @@ -33,7 +34,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): def __init__( self, newshape=None, - persistence_dimension=-1, + homology_dimensions=-1, homology_coeff_field=11, min_persistence=0.0, n_jobs=None, @@ -42,18 +43,20 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): Constructor for the CubicalPersistence class. Parameters: - newshape (list of int): A list of number of top dimensional cells if cells filtration values will require - to be reshaped (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`) - persistence_dimension (int or list of int): The returned persistence diagrams dimension(s). + newshape (tuple of ints): If cells filtration values require to be reshaped + (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`), set `newshape` + to perform `numpy.reshape(X, newshape, order='C'` in + :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform` method. + homology_dimensions (int or list of int): The returned persistence diagrams dimension(s). Short circuit the use of :class:`~gudhi.representations.preprocessing.DimensionSelector` when only one - dimension matters (in other words, when `persistence_dimension` is an int). + dimension matters (in other words, when `homology_dimensions` is an int). homology_coeff_field (int): The homology coefficient field. Must be a prime number. Default value is 11. min_persistence (float): The minimum persistence value to take into account (strictly greater than `min_persistence`). Default value is `0.0`. Set `min_persistence` to `-1.0` to see all values. n_jobs (int): cf. https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html """ self.newshape = newshape - self.persistence_dimension = persistence_dimension + self.homology_dimensions = homology_dimensions self.homology_coeff_field = homology_coeff_field self.min_persistence = min_persistence self.n_jobs = n_jobs @@ -65,37 +68,38 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): return self def __transform(self, cells): - cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.newshape) + cubical_complex = CubicalComplex(top_dimensional_cells=cells) cubical_complex.compute_persistence( homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence ) return [ - cubical_complex.persistence_intervals_in_dimension(dim) for dim in self.persistence_dimension + cubical_complex.persistence_intervals_in_dimension(dim) for dim in self.homology_dimensions ] def __transform_only_this_dim(self, cells): - cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.newshape) + cubical_complex = CubicalComplex(top_dimensional_cells=cells) cubical_complex.compute_persistence( homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence ) - return cubical_complex.persistence_intervals_in_dimension(self.persistence_dimension) + return cubical_complex.persistence_intervals_in_dimension(self.homology_dimensions) def transform(self, X, Y=None): """Compute all the cubical complexes and their associated persistence diagrams. - :param X: List of cells filtration values that should be flatten if `newshape` is set in the constructor, or - already with the correct shape in a numpy.ndarray (and `newshape` must not be set). + :param X: List of cells filtration values (`numpy.reshape(X, newshape, order='C'` if `newshape` is set with a tuple of ints). :type X: list of list of float OR list of numpy.ndarray :return: Persistence diagrams in the format: - - If `persistence_dimension` was set to `n`: `[array( Hn(X[0]) ), array( Hn(X[1]) ), ...]` - - If `persistence_dimension` was set to `[i, j]`: `[[array( Hi(X[0]) ), array( Hj(X[0]) )], [array( Hi(X[1]) ), array( Hj(X[1]) )], ...]` + - If `homology_dimensions` was set to `n`: `[array( Hn(X[0]) ), array( Hn(X[1]) ), ...]` + - If `homology_dimensions` was set to `[i, j]`: `[[array( Hi(X[0]) ), array( Hj(X[0]) )], [array( Hi(X[1]) ), array( Hj(X[1]) )], ...]` :rtype: list of tuple or list of list of tuple """ - - # Depends on persistence_dimension is an integer or a list of integer (else case) - if isinstance(self.persistence_dimension, int): + if self.newshape is not None: + X = np.reshape(X, self.newshape, order='C') + + # Depends on homology_dimensions is an integer or a list of integer (else case) + if isinstance(self.homology_dimensions, int): # threads is preferred as cubical construction and persistence computation releases the GIL return Parallel(n_jobs=self.n_jobs, prefer="threads")( delayed(self.__transform_only_this_dim)(cells) for cells in X |