diff options
| author | wreise <wojciech.reise@epfl.ch> | 2022-10-07 18:14:30 +0200 |
|---|---|---|
| committer | wreise <wojciech.reise@epfl.ch> | 2022-10-07 18:14:30 +0200 |
| commit | 2ec6c2457d44a06deb45f0243a9c587b284daeba (patch) | |
| tree | a04b43dcdc4eb90a2727509cad5c659dd46d14bc | |
| parent | d9dfffdb580ab865a829fce851779f33fa47e4f7 (diff) | |
| parent | 524718d63a8f633dbcc4fe7db3fe920ebd7e972c (diff) | |
Merge branch 'master' into optimize_silhouettes
43 files changed, 537 insertions, 154 deletions
diff --git a/.github/next_release.md b/.github/next_release.md index f8085513..64bda353 100644 --- a/.github/next_release.md +++ b/.github/next_release.md @@ -1,30 +1,19 @@ -We are pleased to announce the release 3.X.X of the GUDHI library. +We are pleased to announce the release 3.7.0 of the GUDHI library. As a major new feature, the GUDHI library now offers ... We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz). -Below is a list of changes made since GUDHI 3.5.0: +Below is a list of changes made since GUDHI 3.6.0: -- [Alpha complex](https://gudhi.inria.fr/python/latest/alpha_complex_user.html) - - the python weighted version for alpha complex is now available in any dimension D. - - `alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')` is deprecated, please use [read_points_from_off_file](https://gudhi.inria.fr/python/latest/point_cloud.html#gudhi.read_points_from_off_file) instead. - -- [Representations](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.vector_methods.BettiCurve) - - A more flexible Betti curve class capable of computing exact curves - -- [Simplex tree](https://gudhi.inria.fr/python/latest/simplex_tree_ref.html) - - `__deepcopy__`, `copy` and copy constructors - -- Installation - - Boost ≥ 1.66.0 is now required (was ≥ 1.56.0). - - Python >= 3.5 and cython >= 0.27 are now required. +- [Module](link) + - ... - [Module](link) - ... - Miscellaneous - - The [list of bugs that were solved since GUDHI-3.5.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.6.0+is%3Aclosed) is available on GitHub. + - The [list of bugs that were solved since GUDHI-3.6.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.7.0+is%3Aclosed) is available on GitHub. All modules are distributed under the terms of the MIT license. However, there are still GPL dependencies for many modules. We invite you to check our [license dedicated web page](https://gudhi.inria.fr/licensing/) for further details. diff --git a/.github/workflows/pip-packaging-windows.yml b/.github/workflows/pip-packaging-windows.yml index 142a114c..48a98036 100644 --- a/.github/workflows/pip-packaging-windows.yml +++ b/.github/workflows/pip-packaging-windows.yml @@ -10,7 +10,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: ['3.6', '3.7', '3.8', '3.9', '3.10'] + python-version: ['3.7', '3.8', '3.9', '3.10'] name: Build wheels for Python ${{ matrix.python-version }} steps: - uses: actions/checkout@v1 diff --git a/CMakeGUDHIVersion.txt b/CMakeGUDHIVersion.txt index b7f93799..1dab47ab 100644 --- a/CMakeGUDHIVersion.txt +++ b/CMakeGUDHIVersion.txt @@ -1,6 +1,6 @@ # Must be conform to pep440 - https://www.python.org/dev/peps/pep-0440/#pre-releases set (GUDHI_MAJOR_VERSION 3) -set (GUDHI_MINOR_VERSION 6) +set (GUDHI_MINOR_VERSION 7) # GUDHI_PATCH_VERSION can be 'ZaN' for Alpha release, 'ZbN' for Beta release, 'ZrcN' for release candidate or 'Z' for a final release. set (GUDHI_PATCH_VERSION 0a0) set(GUDHI_VERSION ${GUDHI_MAJOR_VERSION}.${GUDHI_MINOR_VERSION}.${GUDHI_PATCH_VERSION}) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index e005b3be..f54e593f 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -25,14 +25,14 @@ jobs: python -m pip install --user -r ext/gudhi-deploy/test-requirements.txt python -m pip uninstall -y pykeops brew update || true - brew install graphviz doxygen boost eigen gmp mpfr tbb cgal || true + brew install ninja graphviz doxygen boost eigen gmp mpfr tbb cgal || true displayName: 'Install build dependencies' - bash: | mkdir build cd build - cmake -DCMAKE_BUILD_TYPE:STRING=$(cmakeBuildType) -DWITH_GUDHI_EXAMPLE=ON -DWITH_GUDHI_TEST=ON -DWITH_GUDHI_UTILITIES=ON -DWITH_GUDHI_PYTHON=ON -DWITH_GUDHI_REMOTE_TEST=ON .. - make - make doxygen + cmake -DCMAKE_BUILD_TYPE:STRING=$(cmakeBuildType) -GNinja -DWITH_GUDHI_EXAMPLE=ON -DWITH_GUDHI_TEST=ON -DWITH_GUDHI_UTILITIES=ON -DWITH_GUDHI_PYTHON=ON -DWITH_GUDHI_REMOTE_TEST=ON .. + ninja + ninja doxygen ctest --output-on-failure displayName: 'Build, test and documentation generation' @@ -62,15 +62,16 @@ jobs: # No PyKeOps on windows, let's workaround this one. for /F "tokens=*" %%A in (ext\gudhi-deploy\test-requirements.txt) do python -m pip install %%A vcpkg install boost-filesystem:x64-windows boost-test:x64-windows boost-program-options:x64-windows tbb:x64-windows eigen3:x64-windows cgal:x64-windows + choco install -y ninja --force --force-dependencies displayName: 'Install build dependencies' - script: | call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64 IF %errorlevel% NEQ 0 exit /b %errorlevel% mkdir build cd build - cmake -G "Visual Studio 17 2022" -A x64 -DCMAKE_BUILD_TYPE=Release -DFORCE_EIGEN_DEFAULT_DENSE_INDEX_TYPE_TO_INT=ON $(cmakeVcpkgFlags) $(cmakeFlags) .. + cmake -DCMAKE_BUILD_TYPE=Release -G "Ninja" -DFORCE_EIGEN_DEFAULT_DENSE_INDEX_TYPE_TO_INT=ON $(cmakeVcpkgFlags) $(cmakeFlags) .. IF %errorlevel% NEQ 0 exit /b %errorlevel% - MSBuild GUDHIdev.sln /m /p:Configuration=Release /p:Platform=x64 + ninja IF %errorlevel% NEQ 0 exit /b %errorlevel% ctest --output-on-failure -C Release -E diff_files IF %errorlevel% NEQ 0 exit /b %errorlevel% diff --git a/src/Cech_complex/benchmark/cech_complex_benchmark.cpp b/src/Cech_complex/benchmark/cech_complex_benchmark.cpp index a9dc5d0d..a0e727be 100644 --- a/src/Cech_complex/benchmark/cech_complex_benchmark.cpp +++ b/src/Cech_complex/benchmark/cech_complex_benchmark.cpp @@ -40,9 +40,9 @@ Simplex_tree benchmark_cech(const std::string& off_file_points, const Filtration Points_off_reader_cgal off_reader_cgal(off_file_points); Gudhi::Clock cech_clock("Cech computation"); - Cech_complex cech_complex_from_points(off_reader_cgal.get_point_cloud(), radius); + Cech_complex cech_complex_from_points(off_reader_cgal.get_point_cloud(), radius, exact); Simplex_tree cech_stree; - cech_complex_from_points.create_complex(cech_stree, dim_max, exact); + cech_complex_from_points.create_complex(cech_stree, dim_max); // ------------------------------------------ // Display information about the Cech complex diff --git a/src/Cech_complex/include/gudhi/Cech_complex.h b/src/Cech_complex/include/gudhi/Cech_complex.h index 08b7a72f..625f7c9c 100644 --- a/src/Cech_complex/include/gudhi/Cech_complex.h +++ b/src/Cech_complex/include/gudhi/Cech_complex.h @@ -62,15 +62,18 @@ class Cech_complex { * * @param[in] points Range of points where each point is defined as `kernel::Point_d`. * @param[in] max_radius Maximal radius value. + * @param[in] exact Exact filtration values computation. Not exact if `Kernel` is not <a target="_blank" + * href="https://doc.cgal.org/latest/Kernel_d/structCGAL_1_1Epeck__d.html">CGAL::Epeck_d</a>. + * Default is false. * */ template<typename InputPointRange > - Cech_complex(const InputPointRange & points, Filtration_value max_radius) : max_radius_(max_radius) { + Cech_complex(const InputPointRange & points, Filtration_value max_radius, const bool exact = false) : max_radius_(max_radius), exact_(exact) { point_cloud_.assign(std::begin(points), std::end(points)); cech_skeleton_graph_ = Gudhi::compute_proximity_graph<SimplicialComplexForCechComplex>( - point_cloud_, max_radius_, Sphere_circumradius<Kernel, Filtration_value>()); + point_cloud_, max_radius_, Sphere_circumradius<Kernel, Filtration_value>(exact)); } /** \brief Initializes the simplicial complex from the proximity graph and expands it until a given maximal @@ -78,19 +81,17 @@ class Cech_complex { * * @param[in] complex SimplicialComplexForCech to be created. * @param[in] dim_max graph expansion until this given maximal dimension. - * @param[in] exact Exact filtration values computation. Not exact if `Kernel` is not <a target="_blank" - * href="https://doc.cgal.org/latest/Kernel_d/structCGAL_1_1Epeck__d.html">CGAL::Epeck_d</a>. * @exception std::invalid_argument In debug mode, if `complex.num_vertices()` does not return 0. * */ - void create_complex(SimplicialComplexForCechComplex& complex, int dim_max, const bool exact = false) { + void create_complex(SimplicialComplexForCechComplex& complex, int dim_max) { GUDHI_CHECK(complex.num_vertices() == 0, std::invalid_argument("Cech_complex::create_complex - simplicial complex is not empty")); // insert the proximity graph in the simplicial complex complex.insert_graph(cech_skeleton_graph_); // expand the graph until dimension dim_max - complex.expansion_with_blockers(dim_max, cech_blocker(&complex, this, exact)); + complex.expansion_with_blockers(dim_max, cech_blocker(&complex, this)); } /** @return max_radius value given at construction. */ @@ -106,11 +107,17 @@ class Cech_complex { */ std::vector<Sphere> & get_cache() { return cache_; } + /** \brief Check exact option + * @return Exact option. + */ + const bool is_exact() { return exact_; } + private: Proximity_graph cech_skeleton_graph_; Filtration_value max_radius_; Point_cloud point_cloud_; std::vector<Sphere> cache_; + const bool exact_; }; } // namespace cech_complex diff --git a/src/Cech_complex/include/gudhi/Cech_complex_blocker.h b/src/Cech_complex/include/gudhi/Cech_complex_blocker.h index 3ea82826..e78e37b7 100644 --- a/src/Cech_complex/include/gudhi/Cech_complex_blocker.h +++ b/src/Cech_complex/include/gudhi/Cech_complex_blocker.h @@ -116,7 +116,7 @@ class Cech_blocker { } Sphere sph = get_sphere(points.cbegin(), points.cend()); #if CGAL_VERSION_NR >= 1050000000 - if(exact_) CGAL::exact(sph.second); + if(cc_ptr_->is_exact()) CGAL::exact(sph.second); #endif CGAL::NT_converter<FT, Filtration_value> cast_to_fv; radius = std::sqrt(cast_to_fv(sph.second)); @@ -134,13 +134,12 @@ class Cech_blocker { } /** \internal \brief Čech complex blocker constructor. */ - Cech_blocker(SimplicialComplexForCech* sc_ptr, Cech_complex* cc_ptr, const bool exact) : sc_ptr_(sc_ptr), cc_ptr_(cc_ptr), exact_(exact) {} + Cech_blocker(SimplicialComplexForCech* sc_ptr, Cech_complex* cc_ptr) : sc_ptr_(sc_ptr), cc_ptr_(cc_ptr) {} private: SimplicialComplexForCech* sc_ptr_; Cech_complex* cc_ptr_; Kernel kernel_; - const bool exact_; }; } // namespace cech_complex diff --git a/src/Cech_complex/include/gudhi/Sphere_circumradius.h b/src/Cech_complex/include/gudhi/Sphere_circumradius.h index 790f6950..2f916c0a 100644 --- a/src/Cech_complex/include/gudhi/Sphere_circumradius.h +++ b/src/Cech_complex/include/gudhi/Sphere_circumradius.h @@ -12,6 +12,7 @@ #define SPHERE_CIRCUMRADIUS_H_ #include <CGAL/Epick_d.h> // for #include <CGAL/NT_converter.h> which is not working/compiling alone +#include <CGAL/Lazy_exact_nt.h> // for CGAL::exact #include <cmath> // for std::sqrt #include <vector> @@ -26,6 +27,7 @@ template<typename Kernel, typename Filtration_value> class Sphere_circumradius { private: Kernel kernel_; + const bool exact_; public: using FT = typename Kernel::FT; using Point = typename Kernel::Point_d; @@ -42,7 +44,9 @@ class Sphere_circumradius { * */ Filtration_value operator()(const Point& point_1, const Point& point_2) const { - return std::sqrt(cast_to_fv(kernel_.squared_distance_d_object()(point_1, point_2))) / 2.; + auto squared_dist_obj = kernel_.squared_distance_d_object()(point_1, point_2); + if(exact_) CGAL::exact(squared_dist_obj); + return std::sqrt(cast_to_fv(squared_dist_obj)) / 2.; } /** \brief Circumradius of sphere passing through point cloud using CGAL. @@ -53,9 +57,18 @@ class Sphere_circumradius { * */ Filtration_value operator()(const Point_cloud& point_cloud) const { - return std::sqrt(cast_to_fv(kernel_.compute_squared_radius_d_object()(point_cloud.begin(), point_cloud.end()))); + auto squared_radius_obj = kernel_.compute_squared_radius_d_object()(point_cloud.begin(), point_cloud.end()); + if(exact_) CGAL::exact(squared_radius_obj); + return std::sqrt(cast_to_fv(squared_radius_obj)); } + /** \brief Constructor + * @param[in] exact Option for exact filtration values computation. Not exact if `Kernel` is not <a target="_blank" + * href="https://doc.cgal.org/latest/Kernel_d/structCGAL_1_1Epeck__d.html">CGAL::Epeck_d</a>. + * Default is false. + */ + Sphere_circumradius(const bool exact = false) : exact_(exact) {} + }; } // namespace cech_complex diff --git a/src/Contraction/example/Rips_contraction.cpp b/src/Contraction/example/Rips_contraction.cpp index 42dd0910..547c290e 100644 --- a/src/Contraction/example/Rips_contraction.cpp +++ b/src/Contraction/example/Rips_contraction.cpp @@ -39,7 +39,7 @@ void build_rips(ComplexType& complex, double offset) { int main(int argc, char *argv[]) { if (argc != 3) { std::cerr << "Usage " << argv[0] << " ../../../data/meshes/SO3_10000.off 0.3 to load the file " << - "../../data/SO3_10000.off and contract the Rips complex built with paremeter 0.3.\n"; + "../../data/SO3_10000.off and contract the Rips complex built with parameter 0.3.\n"; return -1; } diff --git a/src/Contraction/include/gudhi/Edge_contraction.h b/src/Contraction/include/gudhi/Edge_contraction.h index 0b43c3b3..dff6dc14 100644 --- a/src/Contraction/include/gudhi/Edge_contraction.h +++ b/src/Contraction/include/gudhi/Edge_contraction.h @@ -48,7 +48,7 @@ Therefore, the simplification can be done without enumerating the set of simplic A typical application of this package is homology group computation. It is illustrated in the next figure where a Rips complex is built upon a set of high-dimensional points and simplified with edge contractions. -It has initially a big number of simplices (around 20 millions) but simplifying it to a much reduced form with only 15 vertices (and 714 simplices) takes only few seconds on a desktop machine (see the example bellow). +It has initially a big number of simplices (around 20 millions) but simplifying it to a much reduced form with only 15 vertices (and 714 simplices) takes only few seconds on a desktop machine (see the example below). One can then compute homology group with a simplicial complex having very few simplices instead of running the homology algorithm on the much bigger initial set of simplices which would take much more time and memory. diff --git a/src/Coxeter_triangulation/include/gudhi/Functions/Function_affine_plane_in_Rd.h b/src/Coxeter_triangulation/include/gudhi/Functions/Function_affine_plane_in_Rd.h index dc6f5f90..a9e2d507 100644 --- a/src/Coxeter_triangulation/include/gudhi/Functions/Function_affine_plane_in_Rd.h +++ b/src/Coxeter_triangulation/include/gudhi/Functions/Function_affine_plane_in_Rd.h @@ -57,7 +57,7 @@ struct Function_affine_plane_in_Rd { * The dimension of the vector should be the ambient dimension of the manifold. */ Function_affine_plane_in_Rd(const Eigen::MatrixXd& normal_matrix, const Eigen::VectorXd& offset) - : normal_matrix_(normal_matrix), d_(normal_matrix.rows()), k_(normal_matrix.cols()), m_(d_ - k_), off_(offset) { + : normal_matrix_(normal_matrix), d_(normal_matrix.rows()), k_(normal_matrix.cols()), off_(offset) { normal_matrix_.colwise().normalize(); } @@ -73,14 +73,13 @@ struct Function_affine_plane_in_Rd { : normal_matrix_(normal_matrix), d_(normal_matrix.rows()), k_(normal_matrix.cols()), - m_(d_ - k_), off_(Eigen::VectorXd::Zero(d_)) { normal_matrix_.colwise().normalize(); } private: Eigen::MatrixXd normal_matrix_; - std::size_t d_, k_, m_; + std::size_t d_, k_; Eigen::VectorXd off_; }; diff --git a/src/Coxeter_triangulation/include/gudhi/Functions/Function_moment_curve_in_Rd.h b/src/Coxeter_triangulation/include/gudhi/Functions/Function_moment_curve_in_Rd.h index 11b379f3..f315d794 100644 --- a/src/Coxeter_triangulation/include/gudhi/Functions/Function_moment_curve_in_Rd.h +++ b/src/Coxeter_triangulation/include/gudhi/Functions/Function_moment_curve_in_Rd.h @@ -46,6 +46,11 @@ struct Function_moment_curve_in_Rd { return result; } + /** @brief Returns the radius of the moment curve. */ + double get_radius() const{ + return r_; + } + /** * \brief Constructor of the function that defines an implicit moment curve * in the d-dimensional Euclidean space. @@ -53,7 +58,7 @@ struct Function_moment_curve_in_Rd { * @param[in] r Numerical parameter. * @param[in] d The ambient dimension. */ - Function_moment_curve_in_Rd(double r, std::size_t d) : m_(1), k_(d - 1), d_(d), r_(r) {} + Function_moment_curve_in_Rd(double r, std::size_t d) : k_(d - 1), d_(d), r_(r) {} /** * \brief Constructor of the function that defines an implicit moment curve @@ -64,10 +69,10 @@ struct Function_moment_curve_in_Rd { * @param[in] offset The offset of the moment curve. */ Function_moment_curve_in_Rd(double r, std::size_t d, Eigen::VectorXd& offset) - : m_(1), k_(d - 1), d_(d), r_(r), off_(offset) {} + : k_(d - 1), d_(d), r_(r), off_(offset) {} private: - std::size_t m_, k_, d_; + std::size_t k_, d_; double r_; Eigen::VectorXd off_; }; diff --git a/src/Coxeter_triangulation/include/gudhi/Permutahedral_representation/Integer_combination_iterator.h b/src/Coxeter_triangulation/include/gudhi/Permutahedral_representation/Integer_combination_iterator.h index 3ee73754..594b6fbf 100644 --- a/src/Coxeter_triangulation/include/gudhi/Permutahedral_representation/Integer_combination_iterator.h +++ b/src/Coxeter_triangulation/include/gudhi/Permutahedral_representation/Integer_combination_iterator.h @@ -68,7 +68,7 @@ class Integer_combination_iterator public: template <class Bound_range> Integer_combination_iterator(const uint& n, const uint& k, const Bound_range& bounds) - : value_(k + 2), is_end_(n == 0 || k == 0), n_(n), k_(k) { + : value_(k + 2), is_end_(n == 0 || k == 0), k_(k) { bounds_.reserve(k + 2); uint sum_radices = 0; for (auto b : bounds) { @@ -96,13 +96,12 @@ class Integer_combination_iterator } // Used for the creating an end iterator - Integer_combination_iterator() : is_end_(true), n_(0), k_(0) {} + Integer_combination_iterator() : is_end_(true), k_(0) {} private: value_t value_; // the dereference value bool is_end_; // is true when the current integer combination is the final one - uint n_; uint k_; std::vector<uint> bounds_; }; diff --git a/src/Doxyfile.in b/src/Doxyfile.in index 6e0e0333..1ec190d9 100644 --- a/src/Doxyfile.in +++ b/src/Doxyfile.in @@ -1143,6 +1143,11 @@ HTML_EXTRA_STYLESHEET = @GUDHI_DOXYGEN_COMMON_DOC_PATH@/stylesheet.css HTML_EXTRA_FILES = +# Default here is AUTO_LIGHT which means "Automatically set the mode according +# to the user preference, use light mode if no preference is set". +# Force it to LIGHT (white), as the rest of the documentation is white. +HTML_COLORSTYLE = LIGHT + # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen # will adjust the colors in the style sheet and background images according to # this color. Hue is specified as an angle on a colorwheel, see @@ -1452,17 +1457,6 @@ EXT_LINKS_IN_WINDOW = NO FORMULA_FONTSIZE = 10 -# Use the FORMULA_TRANPARENT tag to determine whether or not the images -# generated for formulas are transparent PNGs. Transparent PNGs are not -# supported properly for IE 6.0, but are supported on all modern browsers. -# -# Note that when changing this option you need to delete any form_*.png files in -# the HTML output directory before the changes have effect. -# The default value is: YES. -# This tag requires that the tag GENERATE_HTML is set to YES. - -FORMULA_TRANSPARENT = YES - # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see # http://www.mathjax.org) which uses client side Javascript for the rendering # instead of using pre-rendered bitmaps. Use this if you do not have LaTeX @@ -2127,26 +2121,38 @@ HAVE_DOT = YES DOT_NUM_THREADS = 0 -# When you want a differently looking font in the dot files that doxygen -# generates you can specify the font name using DOT_FONTNAME. You need to make -# sure dot is able to find the font, which can be done by putting it in a -# standard location or by setting the DOTFONTPATH environment variable or by -# setting DOT_FONTPATH to the directory containing the font. -# The default value is: Helvetica. +# DOT_COMMON_ATTR is common attributes for nodes, edges and labels of +# subgraphs. When you want a differently looking font in the dot files that +# doxygen generates you can specify fontname, fontcolor and fontsize attributes. +# For details please see <a href=https://graphviz.org/doc/info/attrs.html>Node, +# Edge and Graph Attributes specification</a> You need to make sure dot is able +# to find the font, which can be done by putting it in a standard location or by +# setting the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the +# directory containing the font. Default graphviz fontsize is 14. +# The default value is: fontname=Helvetica,fontsize=10. +# This tag requires that the tag HAVE_DOT is set to YES. + +DOT_COMMON_ATTR = "fontname=Helvetica,fontsize=10" + +# DOT_EDGE_ATTR is concatenated with DOT_COMMON_ATTR. For elegant style you can +# add 'arrowhead=open, arrowtail=open, arrowsize=0.5'. <a +# href=https://graphviz.org/doc/info/arrows.html>Complete documentation about +# arrows shapes.</a> +# The default value is: labelfontname=Helvetica,labelfontsize=10. # This tag requires that the tag HAVE_DOT is set to YES. -DOT_FONTNAME = Helvetica +DOT_EDGE_ATTR = "labelfontname=Helvetica,labelfontsize=10" -# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of -# dot graphs. -# Minimum value: 4, maximum value: 24, default value: 10. +# DOT_NODE_ATTR is concatenated with DOT_COMMON_ATTR. For view without boxes +# around nodes set 'shape=plain' or 'shape=plaintext' <a +# href=https://www.graphviz.org/doc/info/shapes.html>Shapes specification</a> +# The default value is: shape=box,height=0.2,width=0.4. # This tag requires that the tag HAVE_DOT is set to YES. -DOT_FONTSIZE = 10 +DOT_NODE_ATTR = "shape=box,height=0.2,width=0.4" -# By default doxygen will tell dot to use the default font as specified with -# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set -# the path where dot can find it using this tag. +# You can set the path where dot can find font specified with fontname in +# DOT_COMMON_ATTR and others dot attributes. # This tag requires that the tag HAVE_DOT is set to YES. DOT_FONTPATH = @@ -2358,18 +2364,6 @@ DOT_GRAPH_MAX_NODES = 50 MAX_DOT_GRAPH_DEPTH = 0 -# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent -# background. This is disabled by default, because dot on Windows does not seem -# to support this out of the box. -# -# Warning: Depending on the platform used, enabling this option may lead to -# badly anti-aliased labels on the edges of a graph (i.e. they become hard to -# read). -# The default value is: NO. -# This tag requires that the tag HAVE_DOT is set to YES. - -DOT_TRANSPARENT = NO - # Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) support diff --git a/src/GudhUI/view/Viewer_instructor.h b/src/GudhUI/view/Viewer_instructor.h index 58cbcd31..09ed102f 100644 --- a/src/GudhUI/view/Viewer_instructor.h +++ b/src/GudhUI/view/Viewer_instructor.h @@ -11,7 +11,7 @@ #ifndef VIEW_VIEWER_INSTRUCTOR_H_ #define VIEW_VIEWER_INSTRUCTOR_H_ -// todo do a viewer instructor that have directely a pointer to a QGLviewer and buffer ot not triangles +// todo do a viewer instructor that has directly a pointer to a QGLviewer and buffer ot not triangles #include <QFileDialog> #include <QKeyEvent> diff --git a/src/Persistence_representations/test/persistence_heat_maps_test.cpp b/src/Persistence_representations/test/persistence_heat_maps_test.cpp index b3240758..bf531773 100644 --- a/src/Persistence_representations/test/persistence_heat_maps_test.cpp +++ b/src/Persistence_representations/test/persistence_heat_maps_test.cpp @@ -78,7 +78,7 @@ BOOST_AUTO_TEST_CASE(check_compute_percentage_of_active_of_heat_maps) { to_compute_percentage_of_active.push_back(&q); to_compute_percentage_of_active.push_back(&r); Persistence_heat_maps<constant_scaling_function> percentage_of_active; - percentage_of_active.compute_percentage_of_active(to_compute_percentage_of_active, 0.1); + percentage_of_active.compute_percentage_of_active(to_compute_percentage_of_active, 0); Persistence_heat_maps<constant_scaling_function> template_percentage_of_active; template_percentage_of_active.load_from_file("data/template_percentage_of_active_of_heat_maps"); diff --git a/src/Persistence_representations/test/persistence_lanscapes_test.cpp b/src/Persistence_representations/test/persistence_lanscapes_test.cpp index 21ef18a0..59924f16 100644 --- a/src/Persistence_representations/test/persistence_lanscapes_test.cpp +++ b/src/Persistence_representations/test/persistence_lanscapes_test.cpp @@ -238,7 +238,7 @@ if ( argc != 2 ) double integral = p.compute_integral_of_landscape(); cout << "integral : " << integral <<endl; - //compute integral for each level separatelly + //compute integral for each level separately for ( size_t level = 0 ; level != p.size() ; ++level ) { cout << p.compute_integral_of_landscape( level ) << endl; diff --git a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h index 2301a66b..c00bd33d 100644 --- a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h +++ b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h @@ -723,7 +723,7 @@ class Persistent_cohomology { boost::disjoint_sets<int *, Simplex_key *> dsets_; /* The compressed annotation matrix fields.*/ Cam cam_; - /* Dictionary establishing the correspondance between the Simplex_key of + /* Dictionary establishing the correspondence between the Simplex_key of * the root vertex in the union-find ds and the Simplex_key of the vertex which * created the connected component as a 0-dimension homology feature.*/ std::map<Simplex_key, Simplex_key> zero_cocycles_; diff --git a/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp index 229ae46f..f6118fe0 100644 --- a/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp @@ -98,8 +98,16 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_copy_constructor, Simplex_tree, list_of_te BOOST_CHECK(st == st4); BOOST_CHECK(st3 == st); +#ifdef __clang__ +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wself-assign-overloaded" +#endif st = st; - print_simplex_filtration(st4, "Third self copy assignment from the default Simplex_tree"); +#ifdef __clang__ +#pragma GCC diagnostic pop +#endif + + print_simplex_filtration(st, "Third self copy assignment from the default Simplex_tree"); BOOST_CHECK(st3 == st); diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h index 5abd64d7..4c0c7dad 100644 --- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h +++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h @@ -196,10 +196,8 @@ class Skeleton_blocker_sub_complex : public ComplexType { }; /** - * @remark remarque perte de temps a creer un nouveau simplexe a chaque fois - * alors qu'on pourrait utiliser a la place de 'addresses_sigma_in_link' - * un simplex avec des valeurs sp�ciales ComplexDS::null_vertex par exemple - * pour indiquer qu'un vertex n'appartient pas au complex + * @remark waste of time to create a new simplex each time when we could use instead of addresses_sigma_in_link a + * simplex with special values (ComplexDS::null_vertex e.g.) to indicate that a vertex does not belong to the complex. */ template<typename ComplexType> bool proper_face_in_union( diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/internal/Trie.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/internal/Trie.h index 18ae6a92..116bc779 100644 --- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/internal/Trie.h +++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker/internal/Trie.h @@ -150,7 +150,7 @@ struct Trie { ++s_pos; while (s_pos != s.end() && current != 0) { bool found = false; - for (const auto child : current->childs) { + for (const auto& child : current->childs) { if (child->v == *s_pos) { ++s_pos; current = child.get(); diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_complex.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_complex.h index 8ceaa480..b4ffc756 100644 --- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_complex.h +++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_complex.h @@ -1291,7 +1291,7 @@ class Skeleton_blocker_complex { typedef boost::iterator_range<Complex_neighbors_vertices_iterator> Complex_neighbors_vertices_range; /** - * @brief Returns a Complex_edge_range over all edges of the simplicial complex that passes trough v + * @brief Returns a Complex_edge_range over all edges of the simplicial complex that passes through v */ Complex_neighbors_vertices_range vertex_range(Vertex_handle v) const { auto begin = Complex_neighbors_vertices_iterator(this, v); diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_link_complex.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_link_complex.h index a2637da3..b3bf0382 100644 --- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_link_complex.h +++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_link_complex.h @@ -164,7 +164,7 @@ ComplexType> { Vertex_handle y_parent = *parent_complex.get_address( this->get_id(*y_link)); if (parent_complex.contains_edge(x_parent, y_parent)) { - // we check that there is no blocker subset of alpha passing trough x and y + // we check that there is no blocker subset of alpha passing through x and y bool new_edge = true; for (auto blocker_parent : parent_complex.const_blocker_range( x_parent)) { diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_simplifiable_complex.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_simplifiable_complex.h index 5db9c2fd..e686aaec 100755 --- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_simplifiable_complex.h +++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker_simplifiable_complex.h @@ -267,7 +267,7 @@ void Skeleton_blocker_complex<SkeletonBlockerDS>::remove_blocker_include_in_simp template<typename SkeletonBlockerDS> void Skeleton_blocker_complex<SkeletonBlockerDS>::tip_blockers(Vertex_handle a, Vertex_handle b, std::vector<Simplex> & buffer) const { - for (auto const & blocker : this->const_blocker_range(a)) { + for (auto const blocker : this->const_blocker_range(a)) { Simplex beta = (*blocker); beta.remove_vertex(a); buffer.push_back(beta); diff --git a/src/cmake/modules/GUDHI_options.cmake b/src/cmake/modules/GUDHI_options.cmake index 5e28c87d..8379e3c6 100644 --- a/src/cmake/modules/GUDHI_options.cmake +++ b/src/cmake/modules/GUDHI_options.cmake @@ -4,7 +4,7 @@ option(WITH_GUDHI_REMOTE_TEST "Activate/deactivate datasets fetching test which option(WITH_GUDHI_PYTHON "Activate/deactivate python module compilation and installation" ON) option(WITH_GUDHI_TEST "Activate/deactivate examples compilation and installation" ON) option(WITH_GUDHI_UTILITIES "Activate/deactivate utilities compilation and installation" ON) -option(WITH_GUDHI_THIRD_PARTY "Activate/deactivate third party libraries cmake detection. When set to OFF, it is usefull for doxygen or user_version i.e." ON) +option(WITH_GUDHI_THIRD_PARTY "Activate/deactivate third party libraries cmake detection. When set to OFF, it is useful for doxygen or user_version i.e." ON) if (NOT WITH_GUDHI_THIRD_PARTY) set (WITH_GUDHI_BENCHMARK OFF) @@ -12,4 +12,4 @@ if (NOT WITH_GUDHI_THIRD_PARTY) set (WITH_GUDHI_PYTHON OFF) set (WITH_GUDHI_TEST OFF) set (WITH_GUDHI_UTILITIES OFF) -endif()
\ No newline at end of file +endif() diff --git a/src/common/doc/installation.h b/src/common/doc/installation.h index 28526498..63a37a25 100644 --- a/src/common/doc/installation.h +++ b/src/common/doc/installation.h @@ -45,7 +45,7 @@ make \endverbatim * * \subsection documentationgeneration C++ documentation * To generate the C++ documentation, the <a target="_blank" href="http://www.doxygen.org/">doxygen</a> program - * is required (version ≥ 1.9.3 is advised). Run the following command in a terminal: + * is required (version ≥ 1.9.5 is advised). Run the following command in a terminal: * \verbatim make doxygen \endverbatim * Documentation will be generated in a folder named <code>html</code>. * diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt index 65adef75..5f323935 100644 --- a/src/python/CMakeLists.txt +++ b/src/python/CMakeLists.txt @@ -298,6 +298,7 @@ if(PYTHONINTERP_FOUND) file(COPY "gudhi/dtm_rips_complex.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi") file(COPY "gudhi/hera/__init__.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/hera") file(COPY "gudhi/datasets" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi" FILES_MATCHING PATTERN "*.py") + file(COPY "gudhi/sklearn" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/") # Some files for pip package @@ -574,6 +575,11 @@ if(PYTHONINTERP_FOUND) add_gudhi_py_test(test_betti_curve_representations) endif() + # Representations preprocessing + if(SKLEARN_FOUND) + add_gudhi_py_test(test_representations_preprocessing) + endif() + # Time Delay add_gudhi_py_test(test_time_delay) @@ -603,6 +609,11 @@ if(PYTHONINTERP_FOUND) add_gudhi_py_test(test_remote_datasets) endif() + # sklearn + if(SKLEARN_FOUND) + add_gudhi_py_test(test_sklearn_cubical_persistence) + endif() + # persistence graphical tools if(MATPLOTLIB_FOUND) add_gudhi_py_test(test_persistence_graphical_tools) diff --git a/src/python/doc/cubical_complex_sklearn_itf_ref.rst b/src/python/doc/cubical_complex_sklearn_itf_ref.rst new file mode 100644 index 00000000..90ae9ccd --- /dev/null +++ b/src/python/doc/cubical_complex_sklearn_itf_ref.rst @@ -0,0 +1,102 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +Cubical complex persistence scikit-learn like interface +####################################################### + +.. list-table:: + :width: 100% + :header-rows: 0 + + * - :Since: GUDHI 3.6.0 + - :License: MIT + - :Requires: `Scikit-learn <installation.html#scikit-learn>`_ + +Cubical complex persistence 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.preprocessing.DiagramSelector` that removes non-finite persistence diagrams values +#. :class:`~gudhi.representations.vector_methods.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(homology_dimensions=0, newshape=[-1, 28, 28], n_jobs=-2)), + # Or for multiple persistence dimension computation + # ("cub_pers", CubicalPersistence(homology_dimensions=[0, 1], newshape=[-1, 28, 28])), + # ("H0_diags", DimensionSelector(index=0), # where index is the index in homology_dimensions 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(newshape=[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 + +Cubical complex persistence scikit-learn like interface reference +----------------------------------------------------------------- + +.. autoclass:: gudhi.sklearn.cubical_persistence.CubicalPersistence + :members: + :special-members: __init__ + :show-inheritance:
\ No newline at end of file diff --git a/src/python/doc/cubical_complex_sum.inc b/src/python/doc/cubical_complex_sum.inc index 90ec9fc2..b27843e5 100644 --- a/src/python/doc/cubical_complex_sum.inc +++ b/src/python/doc/cubical_complex_sum.inc @@ -1,17 +1,22 @@ .. table:: :widths: 30 40 30 - +--------------------------------------------------------------------------+----------------------------------------------------------------------+---------------------------------------------------------+ - | .. figure:: | The cubical complex represents a grid as a cell complex with | :Author: Pawel Dlotko | - | ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | cells of all dimensions. | | - | :alt: Cubical complex representation | | :Since: GUDHI 2.0.0 | - | :figclass: align-center | | | - | | | :License: MIT | - | | | | - +--------------------------------------------------------------------------+----------------------------------------------------------------------+---------------------------------------------------------+ - | * :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | - | | * :doc:`periodic_cubical_complex_ref` | - +--------------------------------------------------------------------------+----------------------------------------------------------------------+---------------------------------------------------------+ - | | * :doc:`cubical_complex_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | - | | | | - +--------------------------------------------------------------------------+----------------------------------------------------------------------+---------------------------------------------------------+ + +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ + | .. figure:: | The cubical complex represents a grid as a cell complex with | :Author: Pawel Dlotko | + | ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | cells of all dimensions. | :Since: GUDHI 2.0.0 | + | :alt: Cubical complex representation | | :License: MIT | + | :figclass: align-center | | | + +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ + | * :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | + | | * :doc:`periodic_cubical_complex_ref` | + +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ + | .. image:: | * :doc:`cubical_complex_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | + | img/tensorflow.png | | | + | :target: https://www.tensorflow.org | | | + | :height: 30 | | | + +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ + | .. image:: | * :doc:`cubical_complex_sklearn_itf_ref` | :Requires: `Scikit-learn <installation.html#scikit-learn>`_ | + | img/sklearn.png | | | + | :target: https://scikit-learn.org | | | + | :height: 30 | | | + +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index 6a211347..42a23875 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -7,14 +7,7 @@ Cubical complex user manual Definition ---------- -===================================== ===================================== ===================================== -:Author: Pawel Dlotko :Since: GUDHI PYTHON 2.0.0 :License: GPL v3 -===================================== ===================================== ===================================== - -+---------------------------------------------+----------------------------------------------------------------------+ -| :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | -| | * :doc:`periodic_cubical_complex_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. @@ -163,4 +156,4 @@ 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 +explains how to represent sublevels sets of functions using cubical complexes. diff --git a/src/python/doc/differentiation_sum.inc b/src/python/doc/differentiation_sum.inc index 3aec33df..140cf180 100644 --- a/src/python/doc/differentiation_sum.inc +++ b/src/python/doc/differentiation_sum.inc @@ -1,8 +1,8 @@ .. list-table:: - :widths: 40 30 30 + :width: 100% :header-rows: 0 - * - :Since: GUDHI 3.5.0 + * - :Since: GUDHI 3.6.0 - :License: MIT - :Requires: `TensorFlow <installation.html#tensorflow>`_ diff --git a/src/python/doc/img/sklearn.png b/src/python/doc/img/sklearn.png Binary files differnew file mode 100644 index 00000000..d1fecbbf --- /dev/null +++ b/src/python/doc/img/sklearn.png diff --git a/src/python/doc/img/tensorflow.png b/src/python/doc/img/tensorflow.png Binary files differnew file mode 100644 index 00000000..a75f3f5b --- /dev/null +++ b/src/python/doc/img/tensorflow.png diff --git a/src/python/doc/persistent_cohomology_user.rst b/src/python/doc/persistent_cohomology_user.rst index a3f294b2..39744b95 100644 --- a/src/python/doc/persistent_cohomology_user.rst +++ b/src/python/doc/persistent_cohomology_user.rst @@ -6,19 +6,24 @@ Persistent cohomology user manual ================================= Definition ---------- -===================================== ===================================== ===================================== -:Author: Clément Maria :Since: GUDHI PYTHON 2.0.0 :License: GPL v3 -===================================== ===================================== ===================================== - -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ -| :doc:`persistent_cohomology_user` | Please refer to each data structure that contains persistence | -| | feature for reference: | -| | | -| | * :doc:`simplex_tree_ref` | -| | * :doc:`cubical_complex_ref` | -| | * :doc:`periodic_cubical_complex_ref` | -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ +.. list-table:: + :width: 100% + :header-rows: 0 + + * - :Author: Clément Maria + - :Since: GUDHI 2.0.0 + - :License: MIT + +.. list-table:: + :width: 100% + :header-rows: 0 + + * - :doc:`persistent_cohomology_user` + - Please refer to each data structure that contains persistence feature for reference: + * :doc:`simplex_tree_ref` + * :doc:`cubical_complex_ref` + * :doc:`periodic_cubical_complex_ref` Computation of persistent cohomology using the algorithm of :cite:`DBLP:journals/dcg/SilvaMV11` and :cite:`DBLP:conf/compgeom/DeyFW14` and the Compressed Annotation Matrix implementation of diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index 6931ebee..2b125e54 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -12,6 +12,8 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ | * :doc:`rips_complex_user` | * :doc:`rips_complex_ref` | +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ - | | * :doc:`rips_complex_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | - | | | | + | .. image:: | * :doc:`rips_complex_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | + | img/tensorflow.png | | | + | :target: https://www.tensorflow.org | | | + | :height: 30 | | | +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index 27d218d4..c41a7803 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -7,13 +7,7 @@ Rips complex user manual Definition ---------- -================================================================================ ================================ ====================== -: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` | -+-------------------------------------------+----------------------------------------------------------------------+ +.. include:: rips_complex_sum.inc The `Rips complex <https://en.wikipedia.org/wiki/Vietoris%E2%80%93Rips_complex>`_ is a simplicial complex that generalizes proximity (:math:`\varepsilon`-ball) graphs to higher dimensions. The vertices correspond to the input diff --git a/src/python/doc/simplex_tree_sum.inc b/src/python/doc/simplex_tree_sum.inc index 3ad1292c..6b534c9e 100644 --- a/src/python/doc/simplex_tree_sum.inc +++ b/src/python/doc/simplex_tree_sum.inc @@ -11,6 +11,8 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ | * :doc:`simplex_tree_user` | * :doc:`simplex_tree_ref` | +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ - | | * :doc:`ls_simplex_tree_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | - | | | | + | .. image:: | * :doc:`ls_simplex_tree_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | + | img/tensorflow.png | | | + | :target: https://www.tensorflow.org | | | + | :height: 30 | | | +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py index a8545349..8722e162 100644 --- a/src/python/gudhi/representations/preprocessing.py +++ b/src/python/gudhi/representations/preprocessing.py @@ -1,10 +1,11 @@ # 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 Carrière +# Author(s): Mathieu Carrière, Vincent Rouvreau # # Copyright (C) 2018-2019 Inria # # Modification(s): +# - 2021/10 Vincent Rouvreau: Add DimensionSelector # - YYYY/MM Author: Description of the modification import numpy as np @@ -75,7 +76,7 @@ class Clamping(BaseEstimator, TransformerMixin): Constructor for the Clamping class. Parameters: - limit (double): clamping value (default np.inf). + limit (float): clamping value (default np.inf). """ self.minimum = minimum self.maximum = maximum @@ -234,7 +235,7 @@ class ProminentPoints(BaseEstimator, TransformerMixin): use (bool): whether to use the class or not (default False). location (string): either "upper" or "lower" (default "upper"). Whether to keep the points that are far away ("upper") or close ("lower") to the diagonal. num_pts (int): cardinality threshold (default 10). If location == "upper", keep the top **num_pts** points that are the farthest away from the diagonal. If location == "lower", keep the top **num_pts** points that are the closest to the diagonal. - threshold (double): distance-to-diagonal threshold (default -1). If location == "upper", keep the points that are at least at a distance **threshold** from the diagonal. If location == "lower", keep the points that are at most at a distance **threshold** from the diagonal. + threshold (float): distance-to-diagonal threshold (default -1). If location == "upper", keep the points that are at least at a distance **threshold** from the diagonal. If location == "lower", keep the points that are at most at a distance **threshold** from the diagonal. """ self.num_pts = num_pts self.threshold = threshold @@ -317,7 +318,7 @@ class DiagramSelector(BaseEstimator, TransformerMixin): Parameters: use (bool): whether to use the class or not (default False). - limit (double): second coordinate value that is the criterion for being an essential point (default numpy.inf). + limit (float): second coordinate value that is the criterion for being an essential point (default numpy.inf). point_type (string): either "finite" or "essential". The type of the points that are going to be extracted. """ self.use, self.limit, self.point_type = use, limit, point_type @@ -363,3 +364,51 @@ class DiagramSelector(BaseEstimator, TransformerMixin): n x 2 numpy array: extracted persistence diagram. """ return self.fit_transform([diag])[0] + + +# Mermaid sequence diagram - https://mermaid-js.github.io/mermaid-live-editor/ +# sequenceDiagram +# USER->>DimensionSelector: fit_transform(<br/>[[array( Hi(X0) ), array( Hj(X0) ), ...],<br/> [array( Hi(X1) ), array( Hj(X1) ), ...],<br/> ...]) +# DimensionSelector->>thread1: _transform([array( Hi(X0) ), array( Hj(X0) )], ...) +# DimensionSelector->>thread2: _transform([array( Hi(X1) ), array( Hj(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 from its index. + """ + + def __init__(self, index=0): + """ + Constructor for the DimensionSelector class. + + Parameters: + index (int): The returned persistence diagrams dimension index. Default value is `0`. + """ + self.index = index + + 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 tuple): List of list of persistence pairs, i.e. + `[[array( Hi(X0) ), array( Hj(X0) ), ...], [array( Hi(X1) ), array( Hj(X1) ), ...], ...]` + + Returns: + list of tuple: + Persistence diagrams in a specific dimension. i.e. if `index` was set to `m` and `Hn` is at index `m` of + the input, it returns `[array( Hn(X0) ), array( Hn(X1), ...]` + """ + + return [persistence[self.index] for persistence in X] diff --git a/src/python/gudhi/sklearn/__init__.py b/src/python/gudhi/sklearn/__init__.py new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/src/python/gudhi/sklearn/__init__.py diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py new file mode 100644 index 00000000..672af278 --- /dev/null +++ b/src/python/gudhi/sklearn/cubical_persistence.py @@ -0,0 +1,110 @@ +# 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 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 +# USER->>CubicalPersistence: fit_transform(X) +# CubicalPersistence->>thread1: _tranform(X[0]) +# CubicalPersistence->>thread2: _tranform(X[1]) +# Note right of CubicalPersistence: ... +# thread1->>CubicalPersistence: [array( H0(X[0]) ), array( H1(X[0]) )] +# thread2->>CubicalPersistence: [array( H0(X[1]) ), array( H1(X[1]) )] +# Note right of CubicalPersistence: ... +# CubicalPersistence->>USER: [[array( H0(X[0]) ), array( H1(X[0]) )],<br/> [array( H0(X[1]) ), array( H1(X[1]) )],<br/> ...] + + +class CubicalPersistence(BaseEstimator, TransformerMixin): + """ + This is a class for computing the persistence diagrams from a cubical complex. + """ + + def __init__( + self, + homology_dimensions, + newshape=None, + 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). + newshape (tuple of ints): If cells filtration values require to be reshaped + (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`), set `newshape` + to perform `numpy.reshape(X, newshape, order='C')` in + :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform` method. + 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.newshape = newshape + self.homology_coeff_field = homology_coeff_field + self.min_persistence = min_persistence + self.n_jobs = n_jobs + + 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): + cubical_complex = CubicalComplex(top_dimensional_cells=cells) + 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.homology_dimensions + ] + + def __transform_only_this_dim(self, cells): + cubical_complex = CubicalComplex(top_dimensional_cells=cells) + cubical_complex.compute_persistence( + homology_coeff_field=self.homology_coeff_field, min_persistence=self.min_persistence + ) + return cubical_complex.persistence_intervals_in_dimension(self.homology_dimensions) + + def transform(self, X, Y=None): + """Compute all the cubical complexes and their associated persistence diagrams. + + :param X: List of cells filtration values (`numpy.reshape(X, newshape, order='C'` if `newshape` is set with a tuple of ints). + :type X: list of list of float OR list of numpy.ndarray + + :return: Persistence diagrams in the format: + + - If `homology_dimensions` was set to `n`: `[array( Hn(X[0]) ), array( Hn(X[1]) ), ...]` + - If `homology_dimensions` was set to `[i, j]`: `[[array( Hi(X[0]) ), array( Hj(X[0]) )], [array( Hi(X[1]) ), array( Hj(X[1]) )], ...]` + :rtype: list of (,2) array_like or list of list of (,2) array_like + """ + if self.newshape is not None: + X = np.reshape(X, self.newshape, order='C') + + # Depends on homology_dimensions is an integer or a list of integer (else case) + if isinstance(self.homology_dimensions, 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/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py index 3304e719..5df2c370 100644 --- a/src/python/gudhi/tensorflow/cubical_layer.py +++ b/src/python/gudhi/tensorflow/cubical_layer.py @@ -18,7 +18,7 @@ def _Cubical(Xflat, Xdim, dimensions, homology_coeff_field): cc = CubicalComplex(dimensions=Xdim[::-1], top_dimensional_cells=Xflat) cc.compute_persistence(homology_coeff_field=homology_coeff_field) - # Retrieve and ouput image indices/pixels corresponding to positive and negative simplices + # Retrieve and output image indices/pixels corresponding to positive and negative simplices cof_pp = cc.cofaces_of_persistence_pairs() L_cofs = [] diff --git a/src/python/test/test_representations_preprocessing.py b/src/python/test/test_representations_preprocessing.py new file mode 100644 index 00000000..838cf30c --- /dev/null +++ b/src/python/test/test_representations_preprocessing.py @@ -0,0 +1,39 @@ +""" 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 gudhi.representations.preprocessing import DimensionSelector +import numpy as np +import pytest + +H0_0 = np.array([0.0, 0.0]) +H1_0 = np.array([1.0, 0.0]) +H0_1 = np.array([0.0, 1.0]) +H1_1 = np.array([1.0, 1.0]) +H0_2 = np.array([0.0, 2.0]) +H1_2 = np.array([1.0, 2.0]) + + +def test_dimension_selector(): + X = [[H0_0, H1_0], [H0_1, H1_1], [H0_2, H1_2]] + ds = DimensionSelector(index=0) + h0 = ds.fit_transform(X) + np.testing.assert_array_equal(h0[0], H0_0) + np.testing.assert_array_equal(h0[1], H0_1) + np.testing.assert_array_equal(h0[2], H0_2) + + ds = DimensionSelector(index=1) + h1 = ds.fit_transform(X) + np.testing.assert_array_equal(h1[0], H1_0) + np.testing.assert_array_equal(h1[1], H1_1) + np.testing.assert_array_equal(h1[2], H1_2) + + ds = DimensionSelector(index=2) + with pytest.raises(IndexError): + h2 = ds.fit_transform([[H0_0, H1_0], [H0_1, H1_1], [H0_2, H1_2]]) diff --git a/src/python/test/test_sklearn_cubical_persistence.py b/src/python/test/test_sklearn_cubical_persistence.py new file mode 100644 index 00000000..1c05a215 --- /dev/null +++ b/src/python/test/test_sklearn_cubical_persistence.py @@ -0,0 +1,59 @@ +""" 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 gudhi.sklearn.cubical_persistence import CubicalPersistence +import numpy as np +from sklearn import datasets + +CUBICAL_PERSISTENCE_H0_IMG0 = np.array([[0.0, 6.0], [0.0, 8.0], [0.0, np.inf]]) + + +def test_simple_constructor_from_top_cells(): + cells = datasets.load_digits().images[0] + cp = CubicalPersistence(homology_dimensions=0) + np.testing.assert_array_equal(cp._CubicalPersistence__transform_only_this_dim(cells), CUBICAL_PERSISTENCE_H0_IMG0) + cp = CubicalPersistence(homology_dimensions=[0, 2]) + diags = cp._CubicalPersistence__transform(cells) + assert len(diags) == 2 + np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0) + + +def test_simple_constructor_from_top_cells_list(): + digits = datasets.load_digits().images[:10] + cp = CubicalPersistence(homology_dimensions=0, n_jobs=-2) + + diags = cp.fit_transform(digits) + assert len(diags) == 10 + np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0) + + cp = CubicalPersistence(homology_dimensions=[0, 1], n_jobs=-1) + diagsH0H1 = cp.fit_transform(digits) + assert len(diagsH0H1) == 10 + for idx in range(10): + np.testing.assert_array_equal(diags[idx], diagsH0H1[idx][0]) + +def test_simple_constructor_from_flattened_cells(): + cells = datasets.load_digits().images[0] + # Not squared (extended) flatten cells + flat_cells = np.hstack((cells, np.zeros((cells.shape[0], 2)))).flatten() + + cp = CubicalPersistence(homology_dimensions=0, newshape=[-1, 8, 10]) + diags = cp.fit_transform([flat_cells]) + + np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0) + + # Not squared (extended) non-flatten cells + cells = np.hstack((cells, np.zeros((cells.shape[0], 2)))) + + # The aim of this second part of the test is to resize even if not mandatory + cp = CubicalPersistence(homology_dimensions=0, newshape=[-1, 8, 10]) + diags = cp.fit_transform([cells]) + + np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0) |
