change doc according to proposal

1 files changed, 1 insertions, 94 deletions

diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst
index e62a4395..42a23875 100644
--- a/src/python/doc/cubical_complex_user.rst
+++ b/src/python/doc/cubical_complex_user.rst
@@ -7,19 +7,7 @@ Cubical complex user manual
 Definition
 ----------
 
-.. list-table::
-   :widths: 25 50 25
-   :header-rows: 0
-
-   * - :Author: Pawel Dlotko
-     - :Since: GUDHI 2.0.0
-     - :License: MIT
-   * - :doc:`cubical_complex_user`
-     - * :doc:`cubical_complex_ref`
-       * :doc:`periodic_cubical_complex_ref`
-       * :doc:`cubical_complex_sklearn_itf_ref`
-     -
-
+.. include:: cubical_complex_sum.inc
 
 The cubical complex is an example of a structured complex useful in computational mathematics (specially rigorous
 numerics) and image analysis.
@@ -169,84 +157,3 @@ Tutorial
 
 This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-cubical-complexes.ipynb>`_
 explains how to represent sublevels sets of functions using cubical complexes.
-
-Scikit-learn like interface example
------------------------------------
-
-In this example, hand written digits are used as an input.
-a TDA scikit-learn pipeline is constructed and is composed of:
-
-#. :class:`~gudhi.sklearn.cubical_persistence.CubicalPersistence` that builds a cubical complex from the inputs and
-   returns its persistence diagrams
-#. :class:`~gudhi.representations.DiagramSelector` that removes non-finite persistence diagrams values
-#. :class:`~gudhi.representations.PersistenceImage` that builds the persistence images from persistence diagrams
-#. `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_ which is a scikit-learn support
-   vector classifier.
-
-This ML pipeline is trained to detect if the hand written digit is an '8' or not, thanks to the fact that an '8' has
-two holes in :math:`\mathbf{H}_1`, or, like in this example, three connected components in :math:`\mathbf{H}_0`.
-
-.. code-block:: python
-
-    # Standard scientific Python imports
-    import numpy as np
-    
-    # Standard scikit-learn imports
-    from sklearn.datasets import fetch_openml
-    from sklearn.pipeline import Pipeline
-    from sklearn.model_selection import train_test_split
-    from sklearn.svm import SVC
-    from sklearn import metrics
-    
-    # Import TDA pipeline requirements
-    from gudhi.sklearn.cubical_persistence import CubicalPersistence
-    from gudhi.representations import PersistenceImage, DiagramSelector
-    
-    X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False)
-    
-    # Target is: "is an eight ?"
-    y = (y == "8") * 1
-    print("There are", np.sum(y), "eights out of", len(y), "numbers.")
-    
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
-    pipe = Pipeline(
-        [
-            ("cub_pers", CubicalPersistence(persistence_dimension=0, dimensions=[28, 28], n_jobs=-2)),
-            # Or for multiple persistence dimension computation
-            # ("cub_pers", CubicalPersistence(persistence_dimension=[0, 1], dimensions=[28, 28], n_jobs=-2)),
-            # ("H0_diags", DimensionSelector(index=0), # where index is the index in persistence_dimension array
-            ("finite_diags", DiagramSelector(use=True, point_type="finite")),
-            (
-                "pers_img",
-                PersistenceImage(bandwidth=50, weight=lambda x: x[1] ** 2, im_range=[0, 256, 0, 256], resolution=[20, 20]),
-            ),
-            ("svc", SVC()),
-        ]
-    )
-    
-    # Learn from the train subset
-    pipe.fit(X_train, y_train)
-    # Predict from the test subset
-    predicted = pipe.predict(X_test)
-    
-    print(f"Classification report for TDA pipeline {pipe}:\n" f"{metrics.classification_report(y_test, predicted)}\n")
-
-
-.. code-block:: none
-
-    There are 6825 eights out of 70000 numbers.
-    Classification report for TDA pipeline Pipeline(steps=[('cub_pers',
-                     CubicalPersistence(dimensions=[28, 28], n_jobs=-2)),
-                    ('finite_diags', DiagramSelector(use=True)),
-                    ('pers_img',
-                     PersistenceImage(bandwidth=50, im_range=[0, 256, 0, 256],
-                                      weight=<function <lambda> at 0x7f3e54137ae8>)),
-                    ('svc', SVC())]):
-                  precision    recall  f1-score   support
-    
-               0       0.97      0.99      0.98     25284
-               1       0.92      0.68      0.78      2716
-    
-        accuracy                           0.96     28000
-       macro avg       0.94      0.84      0.88     28000
-    weighted avg       0.96      0.96      0.96     28000
\ No newline at end of file