diff options
Diffstat (limited to 'src/python')
32 files changed, 413 insertions, 209 deletions
diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt index 4f26481e..5c1402a6 100644 --- a/src/python/CMakeLists.txt +++ b/src/python/CMakeLists.txt @@ -103,6 +103,9 @@ if(PYTHONINTERP_FOUND) if(EAGERPY_FOUND) add_gudhi_debug_info("EagerPy version ${EAGERPY_VERSION}") endif() + if(TENSORFLOW_FOUND) + add_gudhi_debug_info("TensorFlow version ${TENSORFLOW_VERSION}") + endif() set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_RESULT_OF_USE_DECLTYPE', ") set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_ALL_NO_LIB', ") @@ -130,6 +133,10 @@ if(PYTHONINTERP_FOUND) add_gudhi_debug_info("Eigen3 version ${EIGEN3_VERSION}") # No problem, even if no CGAL found set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_EIGEN3_ENABLED', ") + set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DGUDHI_USE_EIGEN3', ") + set(GUDHI_USE_EIGEN3 "True") + else (EIGEN3_FOUND) + set(GUDHI_USE_EIGEN3 "False") endif (EIGEN3_FOUND) set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'off_reader', ") @@ -342,29 +349,27 @@ if(PYTHONINTERP_FOUND) COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_rips_persistence_bottleneck_distance.py" -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -t 0.15 -d 3) - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - # Tangential - add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py" - --no-diagram -i 2 -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off) - - add_gudhi_py_test(test_tangential_complex) - - # Witness complex AND Subsampling - add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) - - add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) - endif() + # Tangential + add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py" + --no-diagram -i 2 -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off) + + add_gudhi_py_test(test_tangential_complex) + + # Witness complex + add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py" + --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) + + add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py" + --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) # Subsampling add_gudhi_py_test(test_subsampling) @@ -419,13 +424,11 @@ if(PYTHONINTERP_FOUND) WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_from_points_example.py") - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 0.6) - endif() + add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py" + --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 0.6) add_gudhi_py_test(test_alpha_complex) endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) @@ -442,30 +445,26 @@ if(PYTHONINTERP_FOUND) ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py" --no-barcode -f ${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt) - if(NUMPY_FOUND) - add_test(NAME random_cubical_complex_persistence_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/random_cubical_complex_persistence_example.py" - 10 10 10) - endif() + add_test(NAME random_cubical_complex_persistence_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/random_cubical_complex_persistence_example.py" + 10 10 10) add_gudhi_py_test(test_cubical_complex) # Rips - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - add_test(NAME rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3) + add_test(NAME rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py" + --no-diagram -f ${CMAKE_SOURCE_DIR}/data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3) - add_test(NAME rips_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -e 0.25 -d 3) - endif() + add_test(NAME rips_complex_diagram_persistence_from_off_file_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py + --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -e 0.25 -d 3) add_test(NAME rips_complex_from_points_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} @@ -496,14 +495,20 @@ if(PYTHONINTERP_FOUND) # Wasserstein if(OT_FOUND AND PYBIND11_FOUND) - if(TORCH_FOUND AND EAGERPY_FOUND) + # EagerPy dependency because of enable_autodiff=True + if(EAGERPY_FOUND) add_gudhi_py_test(test_wasserstein_distance) endif() add_gudhi_py_test(test_wasserstein_barycenter) endif() + if(OT_FOUND) + if(TORCH_FOUND AND TENSORFLOW_FOUND AND EAGERPY_FOUND) + add_gudhi_py_test(test_wasserstein_with_tensors) + endif() + endif() # Representations - if(SKLEARN_FOUND AND MATPLOTLIB_FOUND) + if(SKLEARN_FOUND AND MATPLOTLIB_FOUND AND OT_FOUND AND NOT CGAL_VERSION VERSION_LESS 4.11.0) add_gudhi_py_test(test_representations) endif() diff --git a/src/python/doc/bottleneck_distance_user.rst b/src/python/doc/bottleneck_distance_user.rst index 6c6e08d9..7baa76cc 100644 --- a/src/python/doc/bottleneck_distance_user.rst +++ b/src/python/doc/bottleneck_distance_user.rst @@ -47,7 +47,7 @@ The following example explains how the distance is computed: :figclass: align-center The point (0, 13) is at distance 6.5 from the diagonal and more - specifically from the point (6.5, 6.5) + specifically from the point (6.5, 6.5). Basic example @@ -72,6 +72,6 @@ The output is: .. testoutput:: - Bottleneck distance approximation = 0.81 + Bottleneck distance approximation = 0.72 Bottleneck distance value = 0.75 diff --git a/src/python/doc/conf.py b/src/python/doc/conf.py index 3cc5d1d6..b06baf9c 100755 --- a/src/python/doc/conf.py +++ b/src/python/doc/conf.py @@ -44,6 +44,8 @@ extensions = [ 'sphinx_paramlinks', ] +bibtex_bibfiles = ['../../biblio/bibliography.bib'] + todo_include_todos = True # plot option : do not show hyperlinks (Source code, png, hires.png, pdf) plot_html_show_source_link = False diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index 3fd4e27a..6a211347 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -47,8 +47,8 @@ be a set of two elements). For further details and theory of cubical complexes, please consult :cite:`kaczynski2004computational` as well as the following paper :cite:`peikert2012topological`. -Data structure. ---------------- +Data structure +-------------- The implementation of Cubical complex provides a representation of complexes that occupy a rectangular region in :math:`\mathbb{R}^n`. This extra assumption allows for a memory efficient way of storing cubical complexes in a form @@ -77,8 +77,8 @@ Knowing the sizes of the bitmap, by a series of modulo operation, we can determi present in the product that gives the cube :math:`C`. In a similar way, we can compute boundary and the coboundary of each cube. Further details can be found in the literature. -Input Format. -------------- +Input Format +------------ In the current implantation, filtration is given at the maximal cubes, and it is then extended by the lower star filtration to all cubes. There are a number of constructors that can be used to construct cubical complex by users @@ -108,8 +108,8 @@ the program output is: Cubical complex is of dimension 2 - 49 simplices. -Periodic boundary conditions. ------------------------------ +Periodic boundary conditions +---------------------------- Often one would like to impose periodic boundary conditions to the cubical complex (cf. :doc:`periodic_cubical_complex_ref`). @@ -154,7 +154,13 @@ the program output is: Periodic cubical complex is of dimension 2 - 42 simplices. -Examples. ---------- +Examples +-------- End user programs are available in python/example/ folder. + +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.
\ No newline at end of file diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 78e1af73..66efe45a 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -40,7 +40,7 @@ different, and in particular the `python/` subdirectory is actually `src/python/ there. The library uses c++14 and requires `Boost <https://www.boost.org/>`_ :math:`\geq` 1.56.0, -`CMake <https://www.cmake.org/>`_ :math:`\geq` 3.1 to generate makefiles, +`CMake <https://www.cmake.org/>`_ :math:`\geq` 3.5 to generate makefiles, `NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and `pybind11 <https://github.com/pybind/pybind11>`_ to compile the GUDHI Python module. @@ -65,7 +65,7 @@ one can build the GUDHI Python module, by running the following commands in a te cd /path-to-gudhi/ mkdir build cd build/ - cmake .. + cmake -DCMAKE_BUILD_TYPE=Release .. cd python make @@ -394,6 +394,11 @@ mathematics, science, and engineering. :class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package `SciPy <http://scipy.org>`_ as a backend if explicitly requested. +TensorFlow +---------- + +`TensorFlow <https://www.tensorflow.org>`_ is currently only used in some automatic differentiation tests. + Bug reports and contributions ***************************** diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst index 041e3247..b0477197 100644 --- a/src/python/doc/representations.rst +++ b/src/python/doc/representations.rst @@ -12,11 +12,45 @@ This module, originally available at https://github.com/MathieuCarriere/sklearn- A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. The classes in this module can handle several persistence diagrams at once. In that case, the diagrams are provided as a list of numpy arrays. Note that it is not necessary for the diagrams to have the same number of points, i.e., for the corresponding arrays to have the same number of rows: all classes can handle arrays with different shapes. -A small example is provided +Examples +-------- -.. only:: builder_html +Landscapes +^^^^^^^^^^ - * :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>` +This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation. + +.. testcode:: + + import numpy as np + from gudhi.representations import Landscape + # A single diagram with 4 points + D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]]) + diags = [D] + l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags) + print(l) + +The output is: + +.. testoutput:: + + [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304 + 2.95699199 3.08555686 2.05703791 1.02851895 0. 0.64282435 + 0. 0. 0.51425948 0. 0. 0. + 0.77138922 1.02851895]] + +Various kernels +^^^^^^^^^^^^^^^ + +This small example is also provided +:download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>` + +Machine Learning and Topological Data Analysis +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-representations.ipynb>`_ explains how to +efficiently combine machine learning and topological data analysis with the +:doc:`representations module<representations>`. Preprocessing @@ -46,27 +80,3 @@ Metrics :members: :special-members: :show-inheritance: - -Basic example -------------- - -This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation. - -.. testcode:: - - import numpy as np - from gudhi.representations import Landscape - # A single diagram with 4 points - D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]]) - diags = [D] - l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags) - print(l) - -The output is: - -.. testoutput:: - - [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304 - 2.95699199 3.08555686 2.05703791 1.02851895 0. 0.64282435 - 0. 0. 0.51425948 0. 0. 0. - 0.77138922 1.02851895]] diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index c123ea2a..2cb24990 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -1,14 +1,14 @@ .. table:: :widths: 30 40 30 - +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ - | .. figure:: | The Vietoris-Rips complex is a simplicial complex built as the | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse | - | ../../doc/Rips_complex/rips_complex_representation.png | clique-complex of a proximity graph. | | - | :figclass: align-center | | :Since: GUDHI 2.0.0 | - | | We also provide sparse approximations, to speed-up the computation | | - | | of persistent homology, and weighted versions, which are more robust | :License: MIT | - | | to outliers. | | - | | | | - +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ - | * :doc:`rips_complex_user` | * :doc:`rips_complex_ref` | - +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ + | .. figure:: | The Vietoris-Rips complex is a simplicial complex built as the | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse, Yuichi Ike | + | ../../doc/Rips_complex/rips_complex_representation.png | clique-complex of a proximity graph. | | + | :figclass: align-center | | :Since: GUDHI 2.0.0 | + | | We also provide sparse approximations, to speed-up the computation | | + | | of persistent homology, and weighted versions, which are more robust | :License: MIT | + | | to outliers. | | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ + | * :doc:`rips_complex_user` | * :doc:`rips_complex_ref` | + +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index 6048cc4e..27d218d4 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -7,9 +7,9 @@ Rips complex user manual Definition ---------- -==================================================================== ================================ ====================== -:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse :Since: GUDHI 2.0.0 :License: GPL v3 -==================================================================== ================================ ====================== +================================================================================ ================================ ====================== +:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse, Yuichi Ike :Since: GUDHI 2.0.0 :License: GPL v3 +================================================================================ ================================ ====================== +-------------------------------------------+----------------------------------------------------------------------+ | :doc:`rips_complex_user` | :doc:`rips_complex_ref` | diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 96ec7872..9ffc2759 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -175,3 +175,10 @@ The output is: [[0.27916667 0.55416667] [0.7375 0.7625 ] [0.2375 0.2625 ]] + +Tutorial +******** + +This +`notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-Barycenters-of-persistence-diagrams.ipynb>`_ +presents the concept of barycenter, or Fréchet mean, of a family of persistence diagrams.
\ No newline at end of file diff --git a/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py b/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py index 727af4fa..1e0273b3 100755 --- a/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py @@ -3,7 +3,6 @@ import argparse import errno import os -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - @@ -65,6 +64,7 @@ with open(args.file, "r") as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py index c4a71a7a..2801576e 100755 --- a/src/python/example/diagram_vectorizations_distances_kernels.py +++ b/src/python/example/diagram_vectorizations_distances_kernels.py @@ -5,11 +5,11 @@ import numpy as np from sklearn.kernel_approximation import RBFSampler from sklearn.preprocessing import MinMaxScaler -from gudhi.representations import DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ +from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ TopologicalVector, DiagramScaler, BirthPersistenceTransform,\ PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \ PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\ - SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel, WassersteinDistance + SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance) D1 = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]]) @@ -93,14 +93,21 @@ print("SW distance is " + str(sW(D1, D2))) SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.) print("SW kernel is " + str(SW(D1, D2))) -W = WassersteinDistance(order=2, internal_p=2, mode="pot") -print("Wasserstein distance (POT) is " + str(W(D1, D2))) +try: + W = WassersteinDistance(order=2, internal_p=2, mode="pot") + print("Wasserstein distance (POT) is " + str(W(D1, D2))) +except ImportError: + print("WassersteinDistance (POT) is not available, you may be missing pot.") W = WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001) print("Wasserstein distance (hera) is " + str(W(D1, D2))) -W = BottleneckDistance(epsilon=.001) -print("Bottleneck distance is " + str(W(D1, D2))) +try: + from gudhi.representations import BottleneckDistance + W = BottleneckDistance(epsilon=.001) + print("Bottleneck distance is " + str(W(D1, D2))) +except ImportError: + print("BottleneckDistance is not available, you may be missing CGAL.") PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.) print("PF kernel is " + str(PF(D1, D2))) diff --git a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py index e1e572df..4e97cfe3 100755 --- a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py @@ -3,7 +3,6 @@ import argparse import errno import os -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - @@ -82,6 +81,7 @@ with open(args.file, "r") as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: diff --git a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py index 58cb2bb5..29076c74 100755 --- a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py @@ -3,7 +3,6 @@ import argparse import errno import os -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - @@ -79,6 +78,7 @@ with open(args.file, "r") as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: diff --git a/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py b/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py index 499171df..ee3290c6 100755 --- a/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py +++ b/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py @@ -1,7 +1,6 @@ #!/usr/bin/env python import argparse -import matplotlib.pyplot as plot import errno import os import gudhi @@ -75,6 +74,7 @@ if is_file_perseus(args.file): print("betti_numbers()=") print(periodic_cubical_complex.betti_numbers()) if args.no_barcode == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_barcode(diag) plot.show() else: diff --git a/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py index 1acb187c..ea2eb7e1 100755 --- a/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py +++ b/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py @@ -2,7 +2,6 @@ import sys import argparse -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. @@ -84,5 +83,6 @@ invert_diag = [ ] if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(invert_diag, band=args.band) plot.show() diff --git a/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py index 79ccca96..236d085d 100755 --- a/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py +++ b/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py @@ -1,7 +1,6 @@ #!/usr/bin/env python import argparse -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. @@ -60,5 +59,6 @@ print("betti_numbers()=") print(simplex_tree.betti_numbers()) if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() diff --git a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py index 6f992508..e80233a9 100755 --- a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py @@ -3,7 +3,6 @@ import argparse import errno import os -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - @@ -70,6 +69,7 @@ with open(args.file, "r") as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: diff --git a/src/python/example/tangential_complex_plain_homology_from_off_file_example.py b/src/python/example/tangential_complex_plain_homology_from_off_file_example.py index 85bade4a..a4b4e9f5 100755 --- a/src/python/example/tangential_complex_plain_homology_from_off_file_example.py +++ b/src/python/example/tangential_complex_plain_homology_from_off_file_example.py @@ -3,7 +3,6 @@ import argparse import errno import os -import matplotlib.pyplot as plot import gudhi """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - @@ -62,6 +61,7 @@ with open(args.file, "r") as f: print(st.betti_numbers()) if args.no_diagram == False: + import matplotlib.pyplot as plot gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: diff --git a/src/python/gudhi/__init__.py.in b/src/python/gudhi/__init__.py.in index 79e12fbc..3043201a 100644 --- a/src/python/gudhi/__init__.py.in +++ b/src/python/gudhi/__init__.py.in @@ -23,6 +23,10 @@ __all__ = [@GUDHI_PYTHON_MODULES@ @GUDHI_PYTHON_MODULES_EXTRA@] __available_modules = '' __missing_modules = '' +# For unitary tests purpose +# could use "if 'collapse_edges' in gudhi.__all__" when collapse edges will have a python module +__GUDHI_USE_EIGEN3 = @GUDHI_USE_EIGEN3@ + # Try to import * from gudhi.__module_name for default modules. # Extra modules require an explicit import by the user (mostly because of # unusual dependencies, but also to avoid cluttering namespace gudhi and diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index 5ca127f6..cdcb1fde 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -323,22 +323,15 @@ class BettiCurve(BaseEstimator, TransformerMixin): Returns: numpy array with shape (number of diagrams) x (**resolution**): output Betti curves. """ - num_diag, Xfit = len(X), [] + Xfit = [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) step_x = x_values[1] - x_values[0] - for i in range(num_diag): - - diagram, num_pts_in_diag = X[i], X[i].shape[0] - + for diagram in X: + diagram_int = np.clip(np.ceil((diagram[:,:2] - self.sample_range[0]) / step_x), 0, self.resolution).astype(int) bc = np.zeros(self.resolution) - for j in range(num_pts_in_diag): - [px,py] = diagram[j,:2] - min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) - max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) - for k in range(min_idx, max_idx): - bc[k] += 1 - + for interval in diagram_int: + bc[interval[0]:interval[1]] += 1 Xfit.append(np.reshape(bc,[1,-1])) Xfit = np.concatenate(Xfit, 0) diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd index 75e94e0b..000323af 100644 --- a/src/python/gudhi/simplex_tree.pxd +++ b/src/python/gudhi/simplex_tree.pxd @@ -36,6 +36,12 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": Simplex_tree_skeleton_iterator operator++() nogil bint operator!=(Simplex_tree_skeleton_iterator) nogil + cdef cppclass Simplex_tree_boundary_iterator "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>::Boundary_simplex_iterator": + Simplex_tree_boundary_iterator() nogil + Simplex_tree_simplex_handle& operator*() nogil + Simplex_tree_boundary_iterator operator++() nogil + bint operator!=(Simplex_tree_boundary_iterator) nogil + cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>": Simplex_tree() nogil @@ -57,7 +63,8 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": bool make_filtration_non_decreasing() nogil void compute_extended_filtration() nogil vector[vector[pair[int, pair[double, double]]]] compute_extended_persistence_subdiagrams(vector[pair[int, pair[double, double]]] dgm, double min_persistence) nogil - Simplex_tree_interface_full_featured* collapse_edges(int nb_collapse_iteration) nogil + Simplex_tree_interface_full_featured* collapse_edges(int nb_collapse_iteration) nogil except + + void reset_filtration(double filtration, int dimension) nogil # Iterators over Simplex tree pair[vector[int], double] get_simplex_and_filtration(Simplex_tree_simplex_handle f_simplex) nogil Simplex_tree_simplices_iterator get_simplices_iterator_begin() nogil @@ -66,6 +73,7 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": vector[Simplex_tree_simplex_handle].const_iterator get_filtration_iterator_end() nogil Simplex_tree_skeleton_iterator get_skeleton_iterator_begin(int dimension) nogil Simplex_tree_skeleton_iterator get_skeleton_iterator_end(int dimension) nogil + pair[Simplex_tree_boundary_iterator, Simplex_tree_boundary_iterator] get_boundary_iterators(vector[int] simplex) nogil except + cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>": diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx index dfb1d985..d7991417 100644 --- a/src/python/gudhi/simplex_tree.pyx +++ b/src/python/gudhi/simplex_tree.pyx @@ -285,6 +285,22 @@ cdef class SimplexTree: ct.append((v, filtered_simplex.second)) return ct + def get_boundaries(self, simplex): + """This function returns a generator with the boundaries of a given N-simplex. + If you do not need the filtration values, the boundary can also be obtained as + :code:`itertools.combinations(simplex,len(simplex)-1)`. + + :param simplex: The N-simplex, represented by a list of vertex. + :type simplex: list of int. + :returns: The (simplices of the) boundary of a simplex + :rtype: generator with tuples(simplex, filtration) + """ + cdef pair[Simplex_tree_boundary_iterator, Simplex_tree_boundary_iterator] it = self.get_ptr().get_boundary_iterators(simplex) + + while it.first != it.second: + yield self.get_ptr().get_simplex_and_filtration(dereference(it.first)) + preincrement(it.first) + def remove_maximal_simplex(self, simplex): """This function removes a given maximal N-simplex from the simplicial complex. @@ -328,7 +344,7 @@ cdef class SimplexTree: return self.get_ptr().prune_above_filtration(filtration) def expansion(self, max_dim): - """Expands the Simplex_tree containing only its one skeleton + """Expands the simplex tree containing only its one skeleton until dimension max_dim. The expanded simplicial complex until dimension :math:`d` @@ -338,7 +354,7 @@ cdef class SimplexTree: The filtration value assigned to a simplex is the maximal filtration value of one of its edges. - The Simplex_tree must contain no simplex of dimension bigger than + The simplex tree must contain no simplex of dimension bigger than 1 when calling the method. :param max_dim: The maximal dimension. @@ -358,38 +374,54 @@ cdef class SimplexTree: """ return self.get_ptr().make_filtration_non_decreasing() + def reset_filtration(self, filtration, min_dim = 0): + """This function resets the filtration value of all the simplices of dimension at least min_dim. Resets all the + simplex tree when `min_dim = 0`. + `reset_filtration` may break the filtration property with `min_dim > 0`, and it is the user's responsibility to + make it a valid filtration (using a large enough `filt_value`, or calling `make_filtration_non_decreasing` + afterwards for instance). + + :param filtration: New threshold value. + :type filtration: float. + :param min_dim: The minimal dimension. Default value is 0. + :type min_dim: int. + """ + self.get_ptr().reset_filtration(filtration, min_dim) + def extend_filtration(self): - """ Extend filtration for computing extended persistence. This function only uses the - filtration values at the 0-dimensional simplices, and computes the extended persistence - diagram induced by the lower-star filtration computed with these values. + """ Extend filtration for computing extended persistence. This function only uses the filtration values at the + 0-dimensional simplices, and computes the extended persistence diagram induced by the lower-star filtration + computed with these values. .. note:: - Note that after calling this function, the filtration - values are actually modified within the Simplex_tree. - The function :func:`extended_persistence` - retrieves the original values. + Note that after calling this function, the filtration values are actually modified within the simplex tree. + The function :func:`extended_persistence` retrieves the original values. .. note:: - Note that this code creates an extra vertex internally, so you should make sure that - the Simplex_tree does not contain a vertex with the largest possible value (i.e., 4294967295). + Note that this code creates an extra vertex internally, so you should make sure that the simplex tree does + not contain a vertex with the largest possible value (i.e., 4294967295). + + This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-extended-persistence.ipynb>`_ + explains how to compute an extension of persistence called extended persistence. """ self.get_ptr().compute_extended_filtration() def extended_persistence(self, homology_coeff_field=11, min_persistence=0): - """This function retrieves good values for extended persistence, and separate the diagrams - into the Ordinary, Relative, Extended+ and Extended- subdiagrams. + """This function retrieves good values for extended persistence, and separate the diagrams into the Ordinary, + Relative, Extended+ and Extended- subdiagrams. - :param homology_coeff_field: The homology coefficient field. Must be a - prime number. Default value is 11. + :param homology_coeff_field: The homology coefficient field. Must be a prime number. Default value is 11. :type homology_coeff_field: int - :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the persistence diagram point coordinates) to take into - account (strictly greater than min_persistence). Default value is - 0.0. - Sets min_persistence to -1.0 to see all values. + :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the + persistence diagram point coordinates) to take into account (strictly greater than min_persistence). + Default value is 0.0. Sets min_persistence to -1.0 to see all values. :type min_persistence: float - :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes. + :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is + Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. + See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in + https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes. .. note:: @@ -400,6 +432,9 @@ cdef class SimplexTree: The coordinates of the persistence diagram points might be a little different than the original filtration values due to the internal transformation (scaling to [-2,-1]) that is performed on these values during the computation of extended persistence. + + This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-extended-persistence.ipynb>`_ + explains how to compute an extension of persistence called extended persistence. """ cdef vector[pair[int, pair[double, double]]] persistence_result if self.pcohptr != NULL: @@ -592,6 +627,9 @@ cdef class SimplexTree: :param nb_iterations: The number of edge collapse iterations to perform. Default is 1. :type nb_iterations: int + + :note: collapse_edges method requires `Eigen <installation.html#eigen>`_ >= 3.1.0 and an exception is thrown + if this method is not available. """ # Backup old pointer cdef Simplex_tree_interface_full_featured* ptr = self.get_ptr() diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx index f77c6f75..46f32335 100644 --- a/src/python/gudhi/subsampling.pyx +++ b/src/python/gudhi/subsampling.pyx @@ -33,7 +33,7 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi The iteration starts with the landmark `starting point`. :param points: The input point set. - :type points: Iterable[Iterable[float]]. + :type points: Iterable[Iterable[float]] Or @@ -42,14 +42,15 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi And in both cases - :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. + :param nb_points: Number of points of the subsample (the subsample may be \ + smaller if there are fewer than nb_points distinct input points) + :type nb_points: int :param starting_point: The iteration starts with the landmark `starting \ - point`,which is the index of the point to start with. If not set, this \ + point`, which is the index of the point to start with. If not set, this \ index is chosen randomly. - :type starting_point: unsigned. + :type starting_point: int :returns: The subsample point set. - :rtype: List[List[float]]. + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): @@ -76,7 +77,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): """Subsample a point set by picking random vertices. :param points: The input point set. - :type points: Iterable[Iterable[float]]. + :type points: Iterable[Iterable[float]] Or @@ -86,7 +87,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): And in both cases :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. + :type nb_points: int :returns: The subsample point set. :rtype: List[List[float]] """ @@ -104,10 +105,10 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): """Outputs a subset of the input points so that the squared distance - between any two points is greater than or equal to min_squared_dist. + between any two points is greater than min_squared_dist. :param points: The input point set. - :type points: Iterable[Iterable[float]]. + :type points: Iterable[Iterable[float]] Or @@ -118,7 +119,7 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): :param min_squared_dist: Minimum squared distance separating the output \ points. - :type min_squared_dist: float. + :type min_squared_dist: float :returns: The subsample point set. :rtype: List[List[float]] """ diff --git a/src/python/include/Alpha_complex_factory.h b/src/python/include/Alpha_complex_factory.h index d699ad9b..3405fdd6 100644 --- a/src/python/include/Alpha_complex_factory.h +++ b/src/python/include/Alpha_complex_factory.h @@ -48,11 +48,14 @@ static CgalPointType pt_cython_to_cgal(std::vector<double> const& vec) { class Abstract_alpha_complex { public: virtual std::vector<double> get_point(int vh) = 0; + virtual bool create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, bool default_filtration_value) = 0; + + virtual ~Abstract_alpha_complex() = default; }; -class Exact_Alphacomplex_dD : public Abstract_alpha_complex { +class Exact_Alphacomplex_dD final : public Abstract_alpha_complex { private: using Kernel = CGAL::Epeck_d<CGAL::Dynamic_dimension_tag>; using Point = typename Kernel::Point_d; @@ -78,7 +81,7 @@ class Exact_Alphacomplex_dD : public Abstract_alpha_complex { Alpha_complex<Kernel> alpha_complex_; }; -class Inexact_Alphacomplex_dD : public Abstract_alpha_complex { +class Inexact_Alphacomplex_dD final : public Abstract_alpha_complex { private: using Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>; using Point = typename Kernel::Point_d; @@ -104,7 +107,7 @@ class Inexact_Alphacomplex_dD : public Abstract_alpha_complex { }; template <complexity Complexity> -class Alphacomplex_3D : public Abstract_alpha_complex { +class Alphacomplex_3D final : public Abstract_alpha_complex { private: using Point = typename Alpha_complex_3d<Complexity, false, false>::Bare_point_3; diff --git a/src/python/include/Simplex_tree_interface.h b/src/python/include/Simplex_tree_interface.h index e288a8cf..629f6083 100644 --- a/src/python/include/Simplex_tree_interface.h +++ b/src/python/include/Simplex_tree_interface.h @@ -15,7 +15,9 @@ #include <gudhi/distance_functions.h> #include <gudhi/Simplex_tree.h> #include <gudhi/Points_off_io.h> +#ifdef GUDHI_USE_EIGEN3 #include <gudhi/Flag_complex_edge_collapser.h> +#endif #include <iostream> #include <vector> @@ -39,6 +41,7 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { using Skeleton_simplex_iterator = typename Base::Skeleton_simplex_iterator; using Complex_simplex_iterator = typename Base::Complex_simplex_iterator; using Extended_filtration_data = typename Base::Extended_filtration_data; + using Boundary_simplex_iterator = typename Base::Boundary_simplex_iterator; public: @@ -161,6 +164,7 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { } Simplex_tree_interface* collapse_edges(int nb_collapse_iteration) { +#ifdef GUDHI_USE_EIGEN3 using Filtered_edge = std::tuple<Vertex_handle, Vertex_handle, Filtration_value>; std::vector<Filtered_edge> edges; for (Simplex_handle sh : Base::skeleton_simplex_range(1)) { @@ -186,6 +190,9 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { collapsed_stree_ptr->insert({std::get<0>(remaining_edge), std::get<1>(remaining_edge)}, std::get<2>(remaining_edge)); } return collapsed_stree_ptr; +#else + throw std::runtime_error("Unable to collapse edges as it requires Eigen3 >= 3.1.0."); +#endif } // Iterator over the simplex tree @@ -219,6 +226,15 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { // this specific case works because the range is just a pair of iterators - won't work if range was a vector return Base::skeleton_simplex_range(dimension).end(); } + + std::pair<Boundary_simplex_iterator, Boundary_simplex_iterator> get_boundary_iterators(const Simplex& simplex) { + auto bd_sh = Base::find(simplex); + if (bd_sh == Base::null_simplex()) + throw std::runtime_error("simplex not found - cannot find boundaries"); + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + auto boundary_srange = Base::boundary_simplex_range(bd_sh); + return std::make_pair(boundary_srange.begin(), boundary_srange.end()); + } }; } // namespace Gudhi diff --git a/src/python/include/Subsampling_interface.h b/src/python/include/Subsampling_interface.h index cdda851f..6aee7231 100644 --- a/src/python/include/Subsampling_interface.h +++ b/src/python/include/Subsampling_interface.h @@ -11,6 +11,7 @@ #ifndef INCLUDE_SUBSAMPLING_INTERFACE_H_ #define INCLUDE_SUBSAMPLING_INTERFACE_H_ +#include <gudhi/distance_functions.h> #include <gudhi/choose_n_farthest_points.h> #include <gudhi/pick_n_random_points.h> #include <gudhi/sparsify_point_set.h> @@ -27,14 +28,13 @@ namespace subsampling { using Subsampling_dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; using Subsampling_point_d = Subsampling_dynamic_kernel::Point_d; -using Subsampling_ft = Subsampling_dynamic_kernel::FT; // ------ choose_n_farthest_points ------ std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, unsigned nb_points) { std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, random_starting_point, std::back_inserter(landmarks)); + choose_n_farthest_points(Euclidean_distance(), points, nb_points, + random_starting_point, std::back_inserter(landmarks)); return landmarks; } @@ -42,8 +42,8 @@ std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, unsigned nb_points, unsigned starting_point) { std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, starting_point, std::back_inserter(landmarks)); + choose_n_farthest_points(Euclidean_distance(), points, nb_points, + starting_point, std::back_inserter(landmarks)); return landmarks; } diff --git a/src/python/test/test_bottleneck_distance.py b/src/python/test/test_bottleneck_distance.py index 6915bea8..07fcc9cc 100755 --- a/src/python/test/test_bottleneck_distance.py +++ b/src/python/test/test_bottleneck_distance.py @@ -25,3 +25,15 @@ def test_basic_bottleneck(): assert gudhi.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, abs=0.1) assert gudhi.hera.bottleneck_distance(diag1, diag2, 0) == 0.75 assert gudhi.hera.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, rel=0.1) + + import numpy as np + + # Translating both diagrams along the diagonal should not affect the distance, + # checks that negative numbers are not an issue + diag1 = np.array(diag1) - 100 + diag2 = np.array(diag2) - 100 + + assert gudhi.bottleneck_distance(diag1, diag2) == 0.75 + assert gudhi.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, abs=0.1) + assert gudhi.hera.bottleneck_distance(diag1, diag2, 0) == 0.75 + assert gudhi.hera.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, rel=0.1) diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index e5c211a0..43c914f3 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -39,11 +39,11 @@ def test_multiple(): d2 = BottleneckDistance(epsilon=0.00001).fit_transform(l1) d3 = pairwise_persistence_diagram_distances(l1, l1b, e=0.00001, n_jobs=4) assert d1 == pytest.approx(d2) - assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal) + assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal) d1 = pairwise_persistence_diagram_distances(l1, l2, metric="wasserstein", order=2, internal_p=2) d2 = WassersteinDistance(order=2, internal_p=2, n_jobs=4).fit(l2).transform(l1) print(d1.shape, d2.shape) - assert d1 == pytest.approx(d2, rel=.02) + assert d1 == pytest.approx(d2, rel=0.02) def test_dummy_atol(): @@ -53,8 +53,22 @@ def test_dummy_atol(): for weighting_method in ["cloud", "iidproba"]: for contrast in ["gaussian", "laplacian", "indicator"]: - atol_vectoriser = Atol(quantiser=KMeans(n_clusters=1, random_state=202006), weighting_method=weighting_method, contrast=contrast) + atol_vectoriser = Atol( + quantiser=KMeans(n_clusters=1, random_state=202006), + weighting_method=weighting_method, + contrast=contrast, + ) atol_vectoriser.fit([a, b, c]) atol_vectoriser(a) atol_vectoriser.transform(X=[a, b, c]) + +from gudhi.representations.vector_methods import BettiCurve + + +def test_infinity(): + a = np.array([[1.0, 8.0], [2.0, np.inf], [3.0, 4.0]]) + c = BettiCurve(20, [0.0, 10.0])(a) + assert c[1] == 0 + assert c[7] == 3 + assert c[9] == 2 diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py index 83be0602..a3eacaa9 100755 --- a/src/python/test/test_simplex_tree.py +++ b/src/python/test/test_simplex_tree.py @@ -8,7 +8,7 @@ - YYYY/MM Author: Description of the modification """ -from gudhi import SimplexTree +from gudhi import SimplexTree, __GUDHI_USE_EIGEN3 import pytest __author__ = "Vincent Rouvreau" @@ -353,8 +353,54 @@ def test_collapse_edges(): assert st.num_simplices() == 10 - st.collapse_edges() - assert st.num_simplices() == 9 - assert st.find([1, 3]) == False - for simplex in st.get_skeleton(0): - assert simplex[1] == 1. + if __GUDHI_USE_EIGEN3: + st.collapse_edges() + assert st.num_simplices() == 9 + assert st.find([1, 3]) == False + for simplex in st.get_skeleton(0): + assert simplex[1] == 1. + else: + # If no Eigen3, collapse_edges throws an exception + with pytest.raises(RuntimeError): + st.collapse_edges() + +def test_reset_filtration(): + st = SimplexTree() + + assert st.insert([0, 1, 2], 3.) == True + assert st.insert([0, 3], 2.) == True + assert st.insert([3, 4, 5], 3.) == True + assert st.insert([0, 1, 6, 7], 4.) == True + + # Guaranteed by construction + for simplex in st.get_simplices(): + assert st.filtration(simplex[0]) >= 2. + + # dimension until 5 even if simplex tree is of dimension 3 to test the limits + for dimension in range(5, -1, -1): + st.reset_filtration(0., dimension) + for simplex in st.get_skeleton(3): + print(simplex) + if len(simplex[0]) < (dimension) + 1: + assert st.filtration(simplex[0]) >= 2. + else: + assert st.filtration(simplex[0]) == 0. + +def test_boundaries_iterator(): + st = SimplexTree() + + assert st.insert([0, 1, 2, 3], filtration=1.0) == True + assert st.insert([1, 2, 3, 4], filtration=2.0) == True + + assert list(st.get_boundaries([1, 2, 3])) == [([1, 2], 1.0), ([1, 3], 1.0), ([2, 3], 1.0)] + assert list(st.get_boundaries([2, 3, 4])) == [([2, 3], 1.0), ([2, 4], 2.0), ([3, 4], 2.0)] + assert list(st.get_boundaries([2])) == [] + + with pytest.raises(RuntimeError): + list(st.get_boundaries([])) + + with pytest.raises(RuntimeError): + list(st.get_boundaries([0, 4])) # (0, 4) does not exist + + with pytest.raises(RuntimeError): + list(st.get_boundaries([6])) # (6) does not exist diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py index 31f64e32..4019852e 100755 --- a/src/python/test/test_subsampling.py +++ b/src/python/test/test_subsampling.py @@ -141,12 +141,16 @@ def test_simple_sparsify_points(): # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 0.0) == [] # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 10.0) == [] assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.0) == point_set - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.0) == point_set - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.0) == [ + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.999) == point_set + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.001) == [ [0, 1], [1, 0], ] - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.01) == [[0, 1]] + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.999) == [ + [0, 1], + [1, 0], + ] + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.001) == [[0, 1]] assert ( len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0)) @@ -157,11 +161,11 @@ def test_simple_sparsify_points(): == 5 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1)) == 4 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=90.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9)) == 3 ) assert ( @@ -169,7 +173,7 @@ def test_simple_sparsify_points(): == 2 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9)) == 2 ) assert ( diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py index 90d26809..e3b521d6 100755 --- a/src/python/test/test_wasserstein_distance.py +++ b/src/python/test/test_wasserstein_distance.py @@ -97,27 +97,3 @@ def test_wasserstein_distance_pot(): def test_wasserstein_distance_hera(): _basic_wasserstein(hera_wrap(delta=1e-12), 1e-12, test_matching=False) _basic_wasserstein(hera_wrap(delta=.1), .1, test_matching=False) - -def test_wasserstein_distance_grad(): - import torch - - diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True) - diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) - diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) - assert diag1.grad is None and diag2.grad is None and diag3.grad is None - dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True) - dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True) - dist12.backward() - dist30.backward() - assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any() - diag4 = torch.tensor([[0., 10.]], requires_grad=True) - diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True) - dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) - assert dist45 == 3. - dist45.backward() - assert np.array_equal(diag4.grad, [[-1., -1.]]) - assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]]) - diag6 = torch.tensor([[5., 10.]], requires_grad=True) - pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward() - # https://github.com/jonasrauber/eagerpy/issues/6 - # assert np.array_equal(diag6.grad, [[0., 0.]]) diff --git a/src/python/test/test_wasserstein_with_tensors.py b/src/python/test/test_wasserstein_with_tensors.py new file mode 100755 index 00000000..e3f1411a --- /dev/null +++ b/src/python/test/test_wasserstein_with_tensors.py @@ -0,0 +1,47 @@ +""" 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): Mathieu Carriere + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.wasserstein import wasserstein_distance as pot +import numpy as np +import torch +import tensorflow as tf + +def test_wasserstein_distance_grad(): + diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True) + diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) + diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) + assert diag1.grad is None and diag2.grad is None and diag3.grad is None + dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True) + dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True) + dist12.backward() + dist30.backward() + assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any() + diag4 = torch.tensor([[0., 10.]], requires_grad=True) + diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True) + dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) + assert dist45 == 3. + dist45.backward() + assert np.array_equal(diag4.grad, [[-1., -1.]]) + assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]]) + diag6 = torch.tensor([[5., 10.]], requires_grad=True) + pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward() + # https://github.com/jonasrauber/eagerpy/issues/6 + # assert np.array_equal(diag6.grad, [[0., 0.]]) + +def test_wasserstein_distance_grad_tensorflow(): + with tf.GradientTape() as tape: + diag4 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[0., 10.]]), trainable=True)) + diag5 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[1., 11.], [3., 4.]]), trainable=True)) + dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) + assert dist45 == 3. + + grads = tape.gradient(dist45, [diag4, diag5]) + assert np.array_equal(grads[0].values, [[-1., -1.]]) + assert np.array_equal(grads[1].values, [[1., 1.], [-1., 1.]])
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