# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
# Author(s): Vincent Rouvreau
#
# Copyright (C) 2021 Inria
#
# Modification(s):
# - YYYY/MM Author: Description of the modification
from .. import CubicalComplex
from .._pers_cub_low_dim import _persistence_on_a_line, _persistence_on_rectangle_from_top_cells
from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np
# joblib is required by scikit-learn
from joblib import Parallel, delayed
# Mermaid sequence diagram - https://mermaid-js.github.io/mermaid-live-editor/
# sequenceDiagram
# participant USER
# participant CP as CubicalPersistence
# USER->>CP: fit_transform(X)
# CP->>thread1: _tranform(X[0])
# CP->>thread2: _tranform(X[1])
# Note right of CP: ...
# thread1->>CP: [array( H0(X[0]) ), array( H1(X[0]) )]
# thread2->>CP: [array( H0(X[1]) ), array( H1(X[1]) )]
# Note right of CP: ...
# CP->>USER: [[array( H0(X[0]) ), array( H1(X[0]) )],<br/> [array( H0(X[1]) ), array( H1(X[1]) )],<br/> ...]
[docs]
class CubicalPersistence(BaseEstimator, TransformerMixin):
"""
This is a class for computing the persistence diagrams from a cubical complex.
"""
[docs]
def __init__(
self,
homology_dimensions,
input_type='top_dimensional_cells',
homology_coeff_field=11,
min_persistence=0.0,
n_jobs=None,
):
"""
Constructor for the CubicalPersistence class.
Parameters:
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 `homology_dimensions` is an int).
input_type (str): 'top_dimensional_cells' if the filtration values passed to `transform()` are those of the
top-dimensional cells, 'vertices' if they correspond to the vertices.
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.homology_dimensions = homology_dimensions
self.input_type = input_type
self.homology_coeff_field = homology_coeff_field
self.min_persistence = min_persistence
self.n_jobs = n_jobs
[docs]
def fit(self, X, Y=None):
"""
Nothing to be done, but useful when included in a scikit-learn Pipeline.
"""
return self
def __transform(self, cells):
cells = np.asarray(cells)
if len(cells.shape) == 1 and self.min_persistence >= 0:
res = _persistence_on_a_line(cells)
if self.min_persistence > 0:
# It would be more efficient inside _persistence_on_a_line, but not worth it?
res = res[res[:, 1] - res[:, 0] > self.min_persistence]
# Wasteful if dim_list_ does not contain 0, but that seems unlikely.
return [res if i == 0 else np.empty((0,2)) for i in self.dim_list_]
if len(cells.shape) == 2 and self.input_type == 'top_dimensional_cells' and self.min_persistence >= 0:
if cells.size == 0:
diags = [np.empty((0,2)), np.empty((0,2))]
elif cells.shape[0] == 1 or cells.shape[1] == 1:
diags = [_persistence_on_a_line(cells.reshape(-1)), np.empty((0,2))]
elif cells.shape[0] == 2:
diags = [_persistence_on_a_line(cells.min(0)), np.empty((0,2))]
elif cells.shape[1] == 2:
diags = [_persistence_on_a_line(cells.min(1)), np.empty((0,2))]
else:
diags = _persistence_on_rectangle_from_top_cells(cells, self.min_persistence)
return [diags[i] if i in (0, 1) else np.empty((0,2)) for i in self.dim_list_]
if self.input_type == 'top_dimensional_cells':
cubical_complex = CubicalComplex(top_dimensional_cells=cells)
elif self.input_type == 'vertices':
cubical_complex = CubicalComplex(vertices=cells)
else:
raise ValueError("input_type can only be 'top_dimensional_cells' or 'vertices'")
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.dim_list_
]
