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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
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