review: modification proposed from EB + HM comments fix

2 files changed, 19 insertions, 78 deletions

diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py
index 329c9435..454cdd07 100644
--- a/src/python/gudhi/sklearn/cubical_persistence.py
+++ b/src/python/gudhi/sklearn/cubical_persistence.py
@@ -33,8 +33,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
     def __init__(
         self,
         dimensions=None,
-        max_persistence_dimension=0,
-        only_this_dim=-1,
+        persistence_dimension=-1,
         homology_coeff_field=11,
         min_persistence=0.0,
         n_jobs=None,
@@ -45,20 +44,16 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         Parameters:
             dimensions (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`)
-            max_persistence_dimension (int): The returned persistence diagrams maximal dimension. Default value is `0`.
-                Ignored if `only_this_dim` is set.
-            only_this_dim (int): The returned persistence diagrams dimension. If `only_this_dim` is set,
-                `max_persistence_dimension` will be ignored. 
-                Short circuit the use of :class:`~gudhi.sklearn.post_processing.DimensionSelector` when only one
-                dimension matters.
+            persistence_dimension (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).
             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`. Sets `min_persistence` to `-1.0` to see all values.
+                `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.dimensions = dimensions
-        self.max_persistence_dimension = max_persistence_dimension
-        self.only_this_dim = only_this_dim
+        self.persistence_dimension = persistence_dimension
         self.homology_coeff_field = homology_coeff_field
         self.min_persistence = min_persistence
         self.n_jobs = n_jobs
@@ -75,7 +70,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
             homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence
         )
         return [
-            cubical_complex.persistence_intervals_in_dimension(dim) for dim in range(self.max_persistence_dimension + 1)
+            cubical_complex.persistence_intervals_in_dimension(dim) for dim in self.persistence_dimension
         ]
 
     def __transform_only_this_dim(self, cells):
@@ -83,28 +78,31 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         cubical_complex.compute_persistence(
             homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence
         )
-        return cubical_complex.persistence_intervals_in_dimension(self.only_this_dim)
+        return cubical_complex.persistence_intervals_in_dimension(self.persistence_dimension)
 
     def transform(self, X, Y=None):
         """
         Compute all the cubical complexes and their associated persistence diagrams.
 
         Parameters:
-            X (list of list of double OR list of numpy.ndarray): List of cells filtration values that can be flatten if
-                `dimensions` is set in the constructor, or already with the correct shape in a numpy.ndarray (and
+            X (list of list of double OR list of numpy.ndarray): List of cells filtration values that should be flatten
+                if `dimensions` is set in the constructor, or already with the correct shape in a numpy.ndarray (and
                 `dimensions` must not be set).
 
         Returns:
+            list of pairs or list of list of pairs:
             Persistence diagrams in the format:
-            - If `only_this_dim` was set to `n`: `[array( Hn(X[0]) ), array( Hn(X[1]) ), ...]` 
-            - else: `[[array( H0(X[0]) ), array( H1(X[0]) ), ...], [array( H0(X[1]) ), array( H1(X[1]) ), ...], ...]` 
+              - 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 self.only_this_dim == -1:
-            # threads is preferred as cubical construction and persistence computation releases the GIL
-            return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform)(cells) for cells in X)
-        else:
+        # Depends on persistence_dimension is an integer or a list of integer (else case)
+        if isinstance(self.persistence_dimension, 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
             )
+        else:
+            # threads is preferred as cubical construction and persistence computation releases the GIL
+            return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform)(cells) for cells in X)
+
diff --git a/src/python/gudhi/sklearn/post_processing.py b/src/python/gudhi/sklearn/post_processing.py
deleted file mode 100644
index 3b12466b..00000000
--- a/src/python/gudhi/sklearn/post_processing.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# 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 sklearn.base import BaseEstimator, TransformerMixin
-
-# Mermaid sequence diagram - https://mermaid-js.github.io/mermaid-live-editor/
-# sequenceDiagram
-#     USER->>DimensionSelector: fit_transform(<br/>[[array( H0(X0) ), array( H1(X0) ), ...],<br/> [array( H0(X1) ), array( H1(X1) ), ...],<br/> ...])
-#     DimensionSelector->>thread1: _transform([array( H0(X0) ), array( H1(X0) )], ...)
-#     DimensionSelector->>thread2: _transform([array( H0(X1) ), array( H1(X1) )], ...)
-#     Note right of DimensionSelector: ...
-#     thread1->>DimensionSelector: array( Hn(X0) )
-#     thread2->>DimensionSelector: array( Hn(X1) )
-#     Note right of DimensionSelector: ...
-#     DimensionSelector->>USER: [array( Hn(X0) ), <br/> array( Hn(X1) ), <br/> ...]
-
-
-class DimensionSelector(BaseEstimator, TransformerMixin):
-    """
-    This is a class to select persistence diagrams in a specific dimension.
-    """
-
-    def __init__(self, persistence_dimension=0):
-        """
-        Constructor for the DimensionSelector class.
-
-        Parameters:
-            persistence_dimension (int): The returned persistence diagrams dimension. Default value is `0`.
-        """
-        self.persistence_dimension = persistence_dimension
-
-    def fit(self, X, Y=None):
-        """
-        Nothing to be done, but useful when included in a scikit-learn Pipeline.
-        """
-        return self
-
-    def transform(self, X, Y=None):
-        """
-        Select persistence diagrams from its dimension.
-
-        Parameters:
-            X (list of list of pairs): List of list of persistence pairs, i.e.
-            `[[array( H0(X0) ), array( H1(X0) ), ...], [array( H0(X1) ), array( H1(X1) ), ...], ...]` 
-
-        Returns:
-            Persistence diagrams in a specific dimension, i.e.
-            `[array( Hn(X0) ), array( Hn(X1), ...]`
-        """
-
-        return [persistence[self.persistence_dimension] for persistence in X]