diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/Alpha_complex/utilities/alpha_complex_persistence.cpp | 1 | ||||
| -rw-r--r-- | src/python/doc/alpha_complex_sum.inc | 14 | ||||
| -rw-r--r-- | src/python/doc/alpha_complex_user.rst | 24 | ||||
| -rw-r--r-- | src/python/doc/cubical_complex_sum.inc | 6 | ||||
| -rw-r--r-- | src/python/doc/index.rst | 4 | ||||
| -rw-r--r-- | src/python/doc/installation.rst | 7 | ||||
| -rw-r--r-- | src/python/doc/persistent_cohomology_sum.inc | 4 | ||||
| -rw-r--r-- | src/python/doc/rips_complex_ref.rst | 4 | ||||
| -rw-r--r-- | src/python/doc/rips_complex_sum.inc | 18 | ||||
| -rw-r--r-- | src/python/doc/rips_complex_user.rst | 4 | ||||
| -rw-r--r-- | src/python/doc/tangential_complex_sum.inc | 8 | ||||
| -rw-r--r-- | src/python/gudhi/alpha_complex.pyx | 49 | ||||
| -rw-r--r-- | src/python/gudhi/bottleneck.cc | 12 | ||||
| -rw-r--r-- | src/python/gudhi/representations/kernel_methods.py | 41 | ||||
| -rw-r--r-- | src/python/gudhi/representations/metrics.py | 56 | ||||
| -rw-r--r-- | src/python/include/Alpha_complex_interface.h | 69 | ||||
| -rwxr-xr-x | src/python/test/test_alpha_complex.py | 75 | ||||
| -rwxr-xr-x | src/python/test/test_representations.py | 33 |
18 files changed, 300 insertions, 129 deletions
diff --git a/src/Alpha_complex/utilities/alpha_complex_persistence.cpp b/src/Alpha_complex/utilities/alpha_complex_persistence.cpp index 7c898dfd..e17831d9 100644 --- a/src/Alpha_complex/utilities/alpha_complex_persistence.cpp +++ b/src/Alpha_complex/utilities/alpha_complex_persistence.cpp @@ -11,6 +11,7 @@ #include <boost/program_options.hpp> #include <CGAL/Epick_d.h> +#include <CGAL/Epeck_d.h> #include <gudhi/Alpha_complex.h> #include <gudhi/Persistent_cohomology.h> diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index 3aba0d71..aeab493f 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -3,15 +3,13 @@ +----------------------------------------------------------------+-------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ | .. figure:: | Alpha complex is a simplicial complex constructed from the finite | :Author: Vincent Rouvreau | - | ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. | | - | :alt: Alpha complex representation | | :Since: GUDHI 2.0.0 | - | :figclass: align-center | The filtration value of each simplex is computed as the **square** of | | - | | the circumradius of the simplex if the circumsphere is empty (the | :License: MIT (`GPL v3 </licensing/>`_) | - | | simplex is then said to be Gabriel), and as the minimum of the | | - | | filtration values of the codimension 1 cofaces that make it not | :Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 | - | | Gabriel otherwise. | | + | ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. It has the same persistent homology | | + | :alt: Alpha complex representation | as the Čech complex and is significantly smaller. | :Since: GUDHI 2.0.0 | + | :figclass: align-center | | | + | | | :License: MIT (`GPL v3 </licensing/>`_) | + | | | | + | | | :Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 | | | | | - | | For performances reasons, it is advised to use CGAL :math:`\geq` 5.0.0. | | +----------------------------------------------------------------+-------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ | * :doc:`alpha_complex_user` | * :doc:`alpha_complex_ref` | +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst index d49f45b4..097470c1 100644 --- a/src/python/doc/alpha_complex_user.rst +++ b/src/python/doc/alpha_complex_user.rst @@ -9,15 +9,33 @@ Definition .. include:: alpha_complex_sum.inc -`AlphaComplex` is constructing a :doc:`SimplexTree <simplex_tree_ref>` using +:doc:`AlphaComplex <alpha_complex_ref>` is constructing a :doc:`SimplexTree <simplex_tree_ref>` using `Delaunay Triangulation <http://doc.cgal.org/latest/Triangulation/index.html#Chapter_Triangulations>`_ :cite:`cgal:hdj-t-19b` from the `Computational Geometry Algorithms Library <http://www.cgal.org/>`_ :cite:`cgal:eb-19b`. Remarks ^^^^^^^ -When an :math:`\alpha`-complex is constructed with an infinite value of :math:`\alpha^2`, -the complex is a Delaunay complex (with special filtration values). +* When an :math:`\alpha`-complex is constructed with an infinite value of :math:`\alpha^2`, the complex is a Delaunay + complex (with special filtration values). The Delaunay complex without filtration values is also available by + passing :code:`default_filtration_value = True` to :func:`~gudhi.AlphaComplex.create_simplex_tree`. +* For people only interested in the topology of the Alpha complex (for instance persistence), Alpha complex is + equivalent to the `Čech complex <https://gudhi.inria.fr/doc/latest/group__cech__complex.html>`_ and much smaller if + you do not bound the radii. `Čech complex <https://gudhi.inria.fr/doc/latest/group__cech__complex.html>`_ can still + make sense in higher dimension precisely because you can bound the radii. +* Using the default :code:`precision = 'safe'` makes the construction safe. + If you pass :code:`precision = 'exact'` to :func:`~gudhi.AlphaComplex.__init__`, the filtration values are the exact + ones converted to float. This can be very slow. + If you pass :code:`precision = 'safe'` (the default), the filtration values are only + guaranteed to have a small multiplicative error compared to the exact value. + A drawback, when computing persistence, is that an empty exact interval [10^12,10^12] may become a + non-empty approximate interval [10^12,10^12+10^6]. + Using :code:`precision = 'fast'` makes the computations slightly faster, and the combinatorics are still exact, but + the computation of filtration values can exceptionally be arbitrarily bad. In all cases, we still guarantee that the + output is a valid filtration (faces have a filtration value no larger than their cofaces). +* For performances reasons, it is advised to use Alpha_complex with `CGAL <installation.html#cgal>`_ :math:`\geq` 5.0.0. + +For performances reasons, it is advised to use CGAL :math:`\geq` 5.0.0. Example from points ------------------- diff --git a/src/python/doc/cubical_complex_sum.inc b/src/python/doc/cubical_complex_sum.inc index 28bf8e94..87db184d 100644 --- a/src/python/doc/cubical_complex_sum.inc +++ b/src/python/doc/cubical_complex_sum.inc @@ -2,9 +2,9 @@ :widths: 30 40 30 +--------------------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------+ - | .. figure:: | The cubical complex is an example of a structured complex useful in | :Author: Pawel Dlotko | - | ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | computational mathematics (specially rigorous numerics) and image | | - | :alt: Cubical complex representation | analysis. | :Since: GUDHI 2.0.0 | + | .. 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 | | | | | diff --git a/src/python/doc/index.rst b/src/python/doc/index.rst index 13e51047..05bc18b4 100644 --- a/src/python/doc/index.rst +++ b/src/python/doc/index.rst @@ -53,8 +53,8 @@ Tangential complex Topological descriptors computation *********************************** -Persistence cohomology -====================== +Persistent cohomology +===================== .. include:: persistent_cohomology_sum.inc diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index de09c5b3..a66e910e 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -260,6 +260,13 @@ a flag `enable_autodiff=True` is used). In order to reduce code duplication, we use `EagerPy <https://eagerpy.jonasrauber.de/>`_ which wraps arrays from PyTorch, TensorFlow and JAX in a common interface. +Joblib +------ + +`Joblib <https://joblib.readthedocs.io/>`_ is used both as a dependency of `Scikit-learn`_, +and directly for parallelism in some modules (:class:`~gudhi.point_cloud.knn.KNearestNeighbors`, +:func:`~gudhi.representations.metrics.pairwise_persistence_diagram_distances`). + Hnswlib ------- diff --git a/src/python/doc/persistent_cohomology_sum.inc b/src/python/doc/persistent_cohomology_sum.inc index a1ff2eee..58e44b8a 100644 --- a/src/python/doc/persistent_cohomology_sum.inc +++ b/src/python/doc/persistent_cohomology_sum.inc @@ -6,9 +6,7 @@ | ../../doc/Persistent_cohomology/3DTorus_poch.png | a sequence of (homology) groups, capturing global topological | | | :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | :Since: GUDHI 2.0.0 | | | homology studies the evolution -- birth, life and death -- of these | | - | Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | :License: MIT | - | Torus | theory is essentially composed of three elements: topological spaces, | | - | | their homology groups and an evolution scheme. | | + | Rips Persistent Cohomology on a 3D Torus | features when the topological space is changing. | :License: MIT | | | | | | | Computation of persistent cohomology using the algorithm of | | | | :cite:`DBLP:journals/dcg/SilvaMV11` and | | diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst index 2aa6b268..f0582d5c 100644 --- a/src/python/doc/rips_complex_ref.rst +++ b/src/python/doc/rips_complex_ref.rst @@ -13,8 +13,6 @@ Rips complex reference manual .. automethod:: gudhi.RipsComplex.__init__ -.. _weighted-rips-complex-reference-manual: - ====================================== Weighted Rips complex reference manual ====================================== @@ -26,8 +24,6 @@ Weighted Rips complex reference manual .. automethod:: gudhi.weighted_rips_complex.WeightedRipsComplex.__init__ -.. _dtm-rips-complex-reference-manual: - ================================= DTM Rips complex reference manual ================================= diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index 9cd8074b..c123ea2a 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -2,21 +2,13 @@ :widths: 30 40 30 +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ - | .. figure:: | Rips complex is a simplicial complex constructed from a one skeleton | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse | - | ../../doc/Rips_complex/rips_complex_representation.png | graph. | | + | .. 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 | - | | The filtration value of each edge is computed from a user-given | | - | | distance function and is inserted until a user-given threshold | :License: MIT | - | | value. | | + | | We also provide sparse approximations, to speed-up the computation | | + | | of persistent homology, and weighted versions, which are more robust | :License: MIT | + | | to outliers. | | | | | | - | | This complex can be built from a point cloud and a distance function, | | - | | or from a distance matrix. | | - | | | | - | | Weighted Rips complex constructs a simplicial complex from a distance | | - | | matrix and weights on vertices. | | - | | | | - | | DTM Rips complex builds a simplicial complex from a point set or | | - | | a distance matrix. | | +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ | * :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 dd2f2cc0..6048cc4e 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -400,12 +400,10 @@ The output is: [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] -.. _dtm-rips-complex: - DTM Rips Complex ---------------- -`DTMRipsComplex <rips_complex_ref.html#dtm-rips-complex-reference-manual>`_ builds a simplicial complex from a point set or a full distance matrix (in the form of ndarray), as described in the above example. +:class:`~gudhi.dtm_rips_complex.DTMRipsComplex` builds a simplicial complex from a point set or a full distance matrix (in the form of ndarray), as described in the above example. This class constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram. See `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_ for some experiments. .. testcode:: diff --git a/src/python/doc/tangential_complex_sum.inc b/src/python/doc/tangential_complex_sum.inc index 22314a2d..2f330a07 100644 --- a/src/python/doc/tangential_complex_sum.inc +++ b/src/python/doc/tangential_complex_sum.inc @@ -3,10 +3,10 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ | .. figure:: | A Tangential Delaunay complex is a simplicial complex designed to | :Author: Clément Jamin | - | ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | | - | :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | :Since: GUDHI 2.0.0 | - | | an unknown manifold. The running time depends only linearly on the | | - | | extrinsic dimension :math:`d` and exponentially on the intrinsic | :License: MIT (`GPL v3 </licensing/>`_) | + | ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in | | + | :figclass: align-center | :math:`d`-dimensional Euclidean space. The input is a point sample | :Since: GUDHI 2.0.0 | + | | coming from an unknown manifold. The running time depends only linearly| | + | | on the extrinsic dimension :math:`d` and exponentially on the intrinsic| :License: MIT (`GPL v3 </licensing/>`_) | | | dimension :math:`k`. | | | | | :Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 | +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/gudhi/alpha_complex.pyx b/src/python/gudhi/alpha_complex.pyx index d75e374a..a356384d 100644 --- a/src/python/gudhi/alpha_complex.pyx +++ b/src/python/gudhi/alpha_complex.pyx @@ -27,11 +27,11 @@ __license__ = "GPL v3" cdef extern from "Alpha_complex_interface.h" namespace "Gudhi": cdef cppclass Alpha_complex_interface "Gudhi::alpha_complex::Alpha_complex_interface": - Alpha_complex_interface(vector[vector[double]] points) nogil except + + Alpha_complex_interface(vector[vector[double]] points, bool fast_version) nogil except + # bool from_file is a workaround for cython to find the correct signature - Alpha_complex_interface(string off_file, bool from_file) nogil except + + Alpha_complex_interface(string off_file, bool fast_version, bool from_file) nogil except + vector[double] get_point(int vertex) nogil except + - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) nogil except + + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, bool exact_version, bool default_filtration_value) nogil except + # AlphaComplex python interface cdef class AlphaComplex: @@ -53,10 +53,11 @@ cdef class AlphaComplex: """ - cdef Alpha_complex_interface * thisptr + cdef Alpha_complex_interface * this_ptr + cdef bool exact # Fake constructor that does nothing but documenting the constructor - def __init__(self, points=None, off_file=''): + def __init__(self, points=None, off_file='', precision='safe'): """AlphaComplex constructor. :param points: A list of points in d-Dimension. @@ -66,15 +67,21 @@ cdef class AlphaComplex: :param off_file: An OFF file style name. :type off_file: string + + :param precision: Alpha complex precision can be 'fast', 'safe' or 'exact'. Default is 'safe'. + :type precision: string """ # The real cython constructor - def __cinit__(self, points = None, off_file = ''): + def __cinit__(self, points = None, off_file = '', precision = 'safe'): + assert precision in ['fast', 'safe', 'exact'], "Alpha complex precision can only be 'fast', 'safe' or 'exact'" + cdef bool fast = precision == 'fast' + self.exact = precision == 'exact' + cdef vector[vector[double]] pts if off_file: if os.path.isfile(off_file): - self.thisptr = new Alpha_complex_interface( - off_file.encode('utf-8'), True) + self.this_ptr = new Alpha_complex_interface(off_file.encode('utf-8'), fast, True) else: print("file " + off_file + " not found.") else: @@ -83,17 +90,16 @@ cdef class AlphaComplex: points=[] pts = points with nogil: - self.thisptr = new Alpha_complex_interface(pts) - + self.this_ptr = new Alpha_complex_interface(pts, fast) def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr + if self.this_ptr != NULL: + del self.this_ptr def __is_defined(self): """Returns true if AlphaComplex pointer is not NULL. """ - return self.thisptr != NULL + return self.this_ptr != NULL def get_point(self, vertex): """This function returns the point corresponding to a given vertex. @@ -103,21 +109,24 @@ cdef class AlphaComplex: :rtype: list of float :returns: the point. """ - return self.thisptr.get_point(vertex) + return self.this_ptr.get_point(vertex) - def create_simplex_tree(self, max_alpha_square = float('inf')): + def create_simplex_tree(self, max_alpha_square = float('inf'), default_filtration_value = False): """ - :param max_alpha_square: The maximum alpha square threshold the - simplices shall not exceed. Default is set to infinity, and - there is very little point using anything else since it does - not save time. + :param max_alpha_square: The maximum alpha square threshold the simplices shall not exceed. Default is set to + infinity, and there is very little point using anything else since it does not save time. :type max_alpha_square: float + :param default_filtration_value: Set this value to `True` if filtration values are not needed to be computed + (will be set to `NaN`). Default value is `False` (which means compute the filtration values). + :type default_filtration_value: bool :returns: A simplex tree created from the Delaunay Triangulation. :rtype: SimplexTree """ stree = SimplexTree() cdef double mas = max_alpha_square cdef intptr_t stree_int_ptr=stree.thisptr + cdef bool compute_filtration = default_filtration_value == True with nogil: - self.thisptr.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr, mas) + self.this_ptr.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr, + mas, self.exact, compute_filtration) return stree diff --git a/src/python/gudhi/bottleneck.cc b/src/python/gudhi/bottleneck.cc index 59be6088..8a3d669a 100644 --- a/src/python/gudhi/bottleneck.cc +++ b/src/python/gudhi/bottleneck.cc @@ -12,22 +12,26 @@ #include <pybind11_diagram_utils.h> -double bottleneck(Dgm d1, Dgm d2, double epsilon) +// For compatibility with older versions, we want to support e=None. +// In C++17, the recommended way is std::optional<double>. +double bottleneck(Dgm d1, Dgm d2, py::object epsilon) { + double e = (std::numeric_limits<double>::min)(); + if (!epsilon.is_none()) e = epsilon.cast<double>(); // I *think* the call to request() has to be before releasing the GIL. auto diag1 = numpy_to_range_of_pairs(d1); auto diag2 = numpy_to_range_of_pairs(d2); py::gil_scoped_release release; - return Gudhi::persistence_diagram::bottleneck_distance(diag1, diag2, epsilon); + return Gudhi::persistence_diagram::bottleneck_distance(diag1, diag2, e); } PYBIND11_MODULE(bottleneck, m) { m.attr("__license__") = "GPL v3"; m.def("bottleneck_distance", &bottleneck, py::arg("diagram_1"), py::arg("diagram_2"), - py::arg("e") = (std::numeric_limits<double>::min)(), + py::arg("e") = py::none(), R"pbdoc( Compute the Bottleneck distance between two diagrams. Points at infinity and on the diagonal are supported. @@ -43,7 +47,7 @@ PYBIND11_MODULE(bottleneck, m) { bits of the mantissa may be wrong). This version of the algorithm takes advantage of the limited precision of `double` and is usually a lot faster to compute, whatever the value of `e`. - Thus, by default, `e` is the smallest positive double. + Thus, by default (`e=None`), `e` is the smallest positive double. :type e: float :rtype: float :returns: the bottleneck distance. diff --git a/src/python/gudhi/representations/kernel_methods.py b/src/python/gudhi/representations/kernel_methods.py index 596f4f07..23fd23c7 100644 --- a/src/python/gudhi/representations/kernel_methods.py +++ b/src/python/gudhi/representations/kernel_methods.py @@ -10,7 +10,7 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin from sklearn.metrics import pairwise_distances, pairwise_kernels -from .metrics import SlicedWassersteinDistance, PersistenceFisherDistance, _sklearn_wrapper, pairwise_persistence_diagram_distances, _sliced_wasserstein_distance, _persistence_fisher_distance +from .metrics import SlicedWassersteinDistance, PersistenceFisherDistance, _sklearn_wrapper, _pairwise, pairwise_persistence_diagram_distances, _sliced_wasserstein_distance, _persistence_fisher_distance from .preprocessing import Padding ############################################# @@ -60,7 +60,7 @@ def _persistence_scale_space_kernel(D1, D2, kernel_approx=None, bandwidth=1.): weight_pss = lambda x: 1 if x[1] >= x[0] else -1 return 0.5 * _persistence_weighted_gaussian_kernel(DD1, DD2, weight=weight_pss, kernel_approx=kernel_approx, bandwidth=bandwidth) -def pairwise_persistence_diagram_kernels(X, Y=None, kernel="sliced_wasserstein", **kwargs): +def pairwise_persistence_diagram_kernels(X, Y=None, kernel="sliced_wasserstein", n_jobs=None, **kwargs): """ This function computes the kernel matrix between two lists of persistence diagrams given as numpy arrays of shape (nx2). @@ -68,38 +68,41 @@ def pairwise_persistence_diagram_kernels(X, Y=None, kernel="sliced_wasserstein", X (list of n numpy arrays of shape (numx2)): first list of persistence diagrams. Y (list of m numpy arrays of shape (numx2)): second list of persistence diagrams (optional). If None, pairwise kernel values are computed from the first list only. kernel: kernel to use. It can be either a string ("sliced_wasserstein", "persistence_scale_space", "persistence_weighted_gaussian", "persistence_fisher") or a function taking two numpy arrays of shape (nx2) and (mx2) as inputs. If it is a function, make sure that it is symmetric. + n_jobs (int): number of jobs to use for the computation. This uses joblib.Parallel(prefer="threads"), so kernels that do not release the GIL may not scale unless run inside a `joblib.parallel_backend <https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend>`_ block. **kwargs: optional keyword parameters. Any further parameters are passed directly to the kernel function. See the docs of the various kernel classes in this module. Returns: numpy array of shape (nxm): kernel matrix. """ XX = np.reshape(np.arange(len(X)), [-1,1]) - YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1]) + YY = None if Y is None or Y is X else np.reshape(np.arange(len(Y)), [-1,1]) if kernel == "sliced_wasserstein": - return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="sliced_wasserstein", num_directions=kwargs["num_directions"]) / kwargs["bandwidth"]) + return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="sliced_wasserstein", num_directions=kwargs["num_directions"], n_jobs=n_jobs) / kwargs["bandwidth"]) elif kernel == "persistence_fisher": - return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="persistence_fisher", kernel_approx=kwargs["kernel_approx"], bandwidth=kwargs["bandwidth"]) / kwargs["bandwidth_fisher"]) + return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="persistence_fisher", kernel_approx=kwargs["kernel_approx"], bandwidth=kwargs["bandwidth"], n_jobs=n_jobs) / kwargs["bandwidth_fisher"]) elif kernel == "persistence_scale_space": - return pairwise_kernels(XX, YY, metric=_sklearn_wrapper(_persistence_scale_space_kernel, X, Y, **kwargs)) + return _pairwise(pairwise_kernels, False, XX, YY, metric=_sklearn_wrapper(_persistence_scale_space_kernel, X, Y, **kwargs), n_jobs=n_jobs) elif kernel == "persistence_weighted_gaussian": - return pairwise_kernels(XX, YY, metric=_sklearn_wrapper(_persistence_weighted_gaussian_kernel, X, Y, **kwargs)) + return _pairwise(pairwise_kernels, False, XX, YY, metric=_sklearn_wrapper(_persistence_weighted_gaussian_kernel, X, Y, **kwargs), n_jobs=n_jobs) else: - return pairwise_kernels(XX, YY, metric=_sklearn_wrapper(metric, **kwargs)) + return _pairwise(pairwise_kernels, False, XX, YY, metric=_sklearn_wrapper(metric, **kwargs), n_jobs=n_jobs) class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): """ This is a class for computing the sliced Wasserstein kernel matrix from a list of persistence diagrams. The sliced Wasserstein kernel is computed by exponentiating the corresponding sliced Wasserstein distance with a Gaussian kernel. See http://proceedings.mlr.press/v70/carriere17a.html for more details. """ - def __init__(self, num_directions=10, bandwidth=1.0): + def __init__(self, num_directions=10, bandwidth=1.0, n_jobs=None): """ Constructor for the SlicedWassersteinKernel class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel applied to the sliced Wasserstein distance (default 1.). num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the kernel computation (default 10). + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_kernels` for details. """ self.bandwidth = bandwidth self.num_directions = num_directions + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -122,7 +125,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein kernel values. """ - return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="sliced_wasserstein", bandwidth=self.bandwidth, num_directions=self.num_directions) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="sliced_wasserstein", bandwidth=self.bandwidth, num_directions=self.num_directions, n_jobs=self.n_jobs) def __call__(self, diag1, diag2): """ @@ -141,7 +144,7 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): """ This is a class for computing the persistence weighted Gaussian kernel matrix from a list of persistence diagrams. The persistence weighted Gaussian kernel is computed by convolving the persistence diagram points with weighted Gaussian kernels. See http://proceedings.mlr.press/v48/kusano16.html for more details. """ - def __init__(self, bandwidth=1., weight=lambda x: 1, kernel_approx=None): + def __init__(self, bandwidth=1., weight=lambda x: 1, kernel_approx=None, n_jobs=None): """ Constructor for the PersistenceWeightedGaussianKernel class. @@ -149,9 +152,11 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved (default 1.) weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie lists or numpy arrays of the form [p_x,p_y]. kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_kernels` for details. """ self.bandwidth, self.weight = bandwidth, weight self.kernel_approx = kernel_approx + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -174,7 +179,7 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence weighted Gaussian kernel values. """ - return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_weighted_gaussian", bandwidth=self.bandwidth, weight=self.weight, kernel_approx=self.kernel_approx) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_weighted_gaussian", bandwidth=self.bandwidth, weight=self.weight, kernel_approx=self.kernel_approx, n_jobs=self.n_jobs) def __call__(self, diag1, diag2): """ @@ -193,15 +198,17 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): """ This is a class for computing the persistence scale space kernel matrix from a list of persistence diagrams. The persistence scale space kernel is computed by adding the symmetric to the diagonal of each point in each persistence diagram, with negative weight, and then convolving the points with a Gaussian kernel. See https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Reininghaus_A_Stable_Multi-Scale_2015_CVPR_paper.pdf for more details. """ - def __init__(self, bandwidth=1., kernel_approx=None): + def __init__(self, bandwidth=1., kernel_approx=None, n_jobs=None): """ Constructor for the PersistenceScaleSpaceKernel class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved (default 1.) kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_kernels` for details. """ self.bandwidth, self.kernel_approx = bandwidth, kernel_approx + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -224,7 +231,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence scale space kernel values. """ - return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_scale_space", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_scale_space", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx, n_jobs=self.n_jobs) def __call__(self, diag1, diag2): """ @@ -243,7 +250,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): """ This is a class for computing the persistence Fisher kernel matrix from a list of persistence diagrams. The persistence Fisher kernel is computed by exponentiating the corresponding persistence Fisher distance with a Gaussian kernel. See papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details. """ - def __init__(self, bandwidth_fisher=1., bandwidth=1., kernel_approx=None): + def __init__(self, bandwidth_fisher=1., bandwidth=1., kernel_approx=None, n_jobs=None): """ Constructor for the PersistenceFisherKernel class. @@ -251,9 +258,11 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): bandwidth (double): bandwidth of the Gaussian kernel applied to the persistence Fisher distance (default 1.). bandwidth_fisher (double): bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions by PersistenceFisherDistance class (default 1.). kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_kernels` for details. """ self.bandwidth = bandwidth self.bandwidth_fisher, self.kernel_approx = bandwidth_fisher, kernel_approx + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -276,7 +285,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher kernel values. """ - return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_fisher", bandwidth=self.bandwidth, bandwidth_fisher=self.bandwidth_fisher, kernel_approx=self.kernel_approx) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_fisher", bandwidth=self.bandwidth, bandwidth_fisher=self.bandwidth_fisher, kernel_approx=self.kernel_approx, n_jobs=self.n_jobs) def __call__(self, diag1, diag2): """ diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py index 8a32f7e9..cf2e0879 100644 --- a/src/python/gudhi/representations/metrics.py +++ b/src/python/gudhi/representations/metrics.py @@ -12,6 +12,7 @@ from sklearn.base import BaseEstimator, TransformerMixin from sklearn.metrics import pairwise_distances from gudhi.hera import wasserstein_distance as hera_wasserstein_distance from .preprocessing import Padding +from joblib import Parallel, delayed ############################################# # Metrics ################################### @@ -116,6 +117,20 @@ def _persistence_fisher_distance(D1, D2, kernel_approx=None, bandwidth=1.): vectorj = vectorj/vectorj_sum return np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) +def _pairwise(fallback, skipdiag, X, Y, metric, n_jobs): + if Y is not None: + return fallback(X, Y, metric=metric, n_jobs=n_jobs) + triu = np.triu_indices(len(X), k=skipdiag) + tril = (triu[1], triu[0]) + par = Parallel(n_jobs=n_jobs, prefer="threads") + d = par(delayed(metric)([triu[0][i]], [triu[1][i]]) for i in range(len(triu[0]))) + m = np.empty((len(X), len(X))) + m[triu] = d + m[tril] = d + if skipdiag: + np.fill_diagonal(m, 0) + return m + def _sklearn_wrapper(metric, X, Y, **kwargs): """ This function is a wrapper for any metric between two persistence diagrams that takes two numpy arrays of shapes (nx2) and (mx2) as arguments. @@ -134,7 +149,7 @@ PAIRWISE_DISTANCE_FUNCTIONS = { "persistence_fisher": _persistence_fisher_distance, } -def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", **kwargs): +def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", n_jobs=None, **kwargs): """ This function computes the distance matrix between two lists of persistence diagrams given as numpy arrays of shape (nx2). @@ -142,48 +157,51 @@ def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", **kwa X (list of n numpy arrays of shape (numx2)): first list of persistence diagrams. Y (list of m numpy arrays of shape (numx2)): second list of persistence diagrams (optional). If None, pairwise distances are computed from the first list only. metric: distance to use. It can be either a string ("sliced_wasserstein", "wasserstein", "hera_wasserstein" (Wasserstein distance computed with Hera---note that Hera is also used for the default option "wasserstein"), "pot_wasserstein" (Wasserstein distance computed with POT), "bottleneck", "persistence_fisher") or a function taking two numpy arrays of shape (nx2) and (mx2) as inputs. If it is a function, make sure that it is symmetric and that it outputs 0 if called on the same two arrays. + n_jobs (int): number of jobs to use for the computation. This uses joblib.Parallel(prefer="threads"), so metrics that do not release the GIL may not scale unless run inside a `joblib.parallel_backend <https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend>`_ block. **kwargs: optional keyword parameters. Any further parameters are passed directly to the distance function. See the docs of the various distance classes in this module. Returns: numpy array of shape (nxm): distance matrix """ XX = np.reshape(np.arange(len(X)), [-1,1]) - YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1]) + YY = None if Y is None or Y is X else np.reshape(np.arange(len(Y)), [-1,1]) if metric == "bottleneck": try: from .. import bottleneck_distance - return pairwise_distances(XX, YY, metric=_sklearn_wrapper(bottleneck_distance, X, Y, **kwargs)) + return _pairwise(pairwise_distances, True, XX, YY, metric=_sklearn_wrapper(bottleneck_distance, X, Y, **kwargs), n_jobs=n_jobs) except ImportError: print("Gudhi built without CGAL") raise elif metric == "pot_wasserstein": try: from gudhi.wasserstein import wasserstein_distance as pot_wasserstein_distance - return pairwise_distances(XX, YY, metric=_sklearn_wrapper(pot_wasserstein_distance, X, Y, **kwargs)) + return _pairwise(pairwise_distances, True, XX, YY, metric=_sklearn_wrapper(pot_wasserstein_distance, X, Y, **kwargs), n_jobs=n_jobs) except ImportError: print("POT (Python Optimal Transport) is not installed. Please install POT or use metric='wasserstein' or metric='hera_wasserstein'") raise elif metric == "sliced_wasserstein": Xproj = _compute_persistence_diagram_projections(X, **kwargs) Yproj = None if Y is None else _compute_persistence_diagram_projections(Y, **kwargs) - return pairwise_distances(XX, YY, metric=_sklearn_wrapper(_sliced_wasserstein_distance_on_projections, Xproj, Yproj)) + return _pairwise(pairwise_distances, True, XX, YY, metric=_sklearn_wrapper(_sliced_wasserstein_distance_on_projections, Xproj, Yproj), n_jobs=n_jobs) elif type(metric) == str: - return pairwise_distances(XX, YY, metric=_sklearn_wrapper(PAIRWISE_DISTANCE_FUNCTIONS[metric], X, Y, **kwargs)) + return _pairwise(pairwise_distances, True, XX, YY, metric=_sklearn_wrapper(PAIRWISE_DISTANCE_FUNCTIONS[metric], X, Y, **kwargs), n_jobs=n_jobs) else: - return pairwise_distances(XX, YY, metric=_sklearn_wrapper(metric, X, Y, **kwargs)) + return _pairwise(pairwise_distances, True, XX, YY, metric=_sklearn_wrapper(metric, X, Y, **kwargs), n_jobs=n_jobs) class SlicedWassersteinDistance(BaseEstimator, TransformerMixin): """ This is a class for computing the sliced Wasserstein distance matrix from a list of persistence diagrams. The Sliced Wasserstein distance is computed by projecting the persistence diagrams onto lines, comparing the projections with the 1-norm, and finally integrating over all possible lines. See http://proceedings.mlr.press/v70/carriere17a.html for more details. """ - def __init__(self, num_directions=10): + def __init__(self, num_directions=10, n_jobs=None): """ Constructor for the SlicedWassersteinDistance class. Parameters: num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the distance computation (default 10). + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_distances` for details. """ self.num_directions = num_directions + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -206,7 +224,7 @@ class SlicedWassersteinDistance(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein distances. """ - return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="sliced_wasserstein", num_directions=self.num_directions) + return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="sliced_wasserstein", num_directions=self.num_directions, n_jobs=self.n_jobs) def __call__(self, diag1, diag2): """ @@ -227,14 +245,16 @@ class BottleneckDistance(BaseEstimator, TransformerMixin): :Requires: `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 """ - def __init__(self, epsilon=None): + def __init__(self, epsilon=None, n_jobs=None): """ Constructor for the BottleneckDistance class. Parameters: epsilon (double): absolute (additive) error tolerated on the distance (default is the smallest positive float), see :func:`gudhi.bottleneck_distance`. + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_distances` for details. """ self.epsilon = epsilon + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -257,7 +277,7 @@ class BottleneckDistance(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise bottleneck distances. """ - Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric="bottleneck", e=self.epsilon) + Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric="bottleneck", e=self.epsilon, n_jobs=self.n_jobs) return Xfit def __call__(self, diag1, diag2): @@ -282,15 +302,17 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin): """ This is a class for computing the persistence Fisher distance matrix from a list of persistence diagrams. The persistence Fisher distance is obtained by computing the original Fisher distance between the probability distributions associated to the persistence diagrams given by convolving them with a Gaussian kernel. See http://papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details. """ - def __init__(self, bandwidth=1., kernel_approx=None): + def __init__(self, bandwidth=1., kernel_approx=None, n_jobs=None): """ Constructor for the PersistenceFisherDistance class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions (default 1.). kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_distances` for details. """ self.bandwidth, self.kernel_approx = bandwidth, kernel_approx + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -313,7 +335,7 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher distances. """ - return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="persistence_fisher", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx) + return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="persistence_fisher", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx, n_jobs=self.n_jobs) def __call__(self, diag1, diag2): """ @@ -332,7 +354,7 @@ class WassersteinDistance(BaseEstimator, TransformerMixin): """ This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. """ - def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01): + def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01, n_jobs=None): """ Constructor for the WassersteinDistance class. @@ -341,10 +363,12 @@ class WassersteinDistance(BaseEstimator, TransformerMixin): internal_p (int): ground metric on the (upper-half) plane (i.e. norm l_p in R^2), default value is 2 (euclidean norm), see :func:`gudhi.wasserstein.wasserstein_distance`. mode (str): method for computing Wasserstein distance. Either "pot" or "hera". delta (float): relative error 1+delta. Used only if mode == "hera". + n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_distances` for details. """ self.order, self.internal_p, self.mode = order, internal_p, mode self.metric = "pot_wasserstein" if mode == "pot" else "hera_wasserstein" self.delta = delta + self.n_jobs = n_jobs def fit(self, X, y=None): """ @@ -368,9 +392,9 @@ class WassersteinDistance(BaseEstimator, TransformerMixin): numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise Wasserstein distances. """ if self.metric == "hera_wasserstein": - Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, delta=self.delta) + Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, delta=self.delta, n_jobs=self.n_jobs) else: - Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, matching=False) + Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, matching=False, n_jobs=self.n_jobs) return Xfit def __call__(self, diag1, diag2): diff --git a/src/python/include/Alpha_complex_interface.h b/src/python/include/Alpha_complex_interface.h index 40de88f3..3ac5db1f 100644 --- a/src/python/include/Alpha_complex_interface.h +++ b/src/python/include/Alpha_complex_interface.h @@ -23,45 +23,74 @@ #include <iostream> #include <vector> #include <string> +#include <memory> // for std::unique_ptr namespace Gudhi { namespace alpha_complex { class Alpha_complex_interface { - using Dynamic_kernel = CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >; - using Point_d = Dynamic_kernel::Point_d; + private: + using Exact_kernel = CGAL::Epeck_d<CGAL::Dynamic_dimension_tag>; + using Inexact_kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>; + using Point_exact_kernel = typename Exact_kernel::Point_d; + using Point_inexact_kernel = typename Inexact_kernel::Point_d; - public: - Alpha_complex_interface(const std::vector<std::vector<double>>& points) { - auto mkpt = [](std::vector<double> const& vec){ - return Point_d(vec.size(), vec.begin(), vec.end()); - }; - alpha_complex_ = new Alpha_complex<Dynamic_kernel>(boost::adaptors::transform(points, mkpt)); + template <typename CgalPointType> + std::vector<double> pt_cgal_to_cython(CgalPointType& point) { + std::vector<double> vd; + for (auto coord = point.cartesian_begin(); coord != point.cartesian_end(); coord++) + vd.push_back(CGAL::to_double(*coord)); + return vd; } - Alpha_complex_interface(const std::string& off_file_name, bool from_file = true) { - alpha_complex_ = new Alpha_complex<Dynamic_kernel>(off_file_name); + template <typename CgalPointType> + static CgalPointType pt_cython_to_cgal(std::vector<double> const& vec) { + return CgalPointType(vec.size(), vec.begin(), vec.end()); } - ~Alpha_complex_interface() { - delete alpha_complex_; + public: + Alpha_complex_interface(const std::vector<std::vector<double>>& points, bool fast_version) + : fast_version_(fast_version) { + if (fast_version_) { + ac_inexact_ptr_ = std::make_unique<Alpha_complex<Inexact_kernel>>( + boost::adaptors::transform(points, pt_cython_to_cgal<Point_inexact_kernel>)); + } else { + ac_exact_ptr_ = std::make_unique<Alpha_complex<Exact_kernel>>( + boost::adaptors::transform(points, pt_cython_to_cgal<Point_exact_kernel>)); + } + } + + Alpha_complex_interface(const std::string& off_file_name, bool fast_version, bool from_file = true) + : fast_version_(fast_version) { + if (fast_version_) + ac_inexact_ptr_ = std::make_unique<Alpha_complex<Inexact_kernel>>(off_file_name); + else + ac_exact_ptr_ = std::make_unique<Alpha_complex<Exact_kernel>>(off_file_name); } std::vector<double> get_point(int vh) { - std::vector<double> vd; - Point_d const& ph = alpha_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord != ph.cartesian_end(); coord++) - vd.push_back(CGAL::to_double(*coord)); - return vd; + if (fast_version_) { + Point_inexact_kernel const& point = ac_inexact_ptr_->get_point(vh); + return pt_cgal_to_cython(point); + } else { + Point_exact_kernel const& point = ac_exact_ptr_->get_point(vh); + return pt_cgal_to_cython(point); + } } - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square) { - alpha_complex_->create_complex(*simplex_tree, max_alpha_square); + void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, bool exact_version, + bool default_filtration_value) { + if (fast_version_) + ac_inexact_ptr_->create_complex(*simplex_tree, max_alpha_square, exact_version, default_filtration_value); + else + ac_exact_ptr_->create_complex(*simplex_tree, max_alpha_square, exact_version, default_filtration_value); } private: - Alpha_complex<Dynamic_kernel>* alpha_complex_; + bool fast_version_; + std::unique_ptr<Alpha_complex<Exact_kernel>> ac_exact_ptr_; + std::unique_ptr<Alpha_complex<Inexact_kernel>> ac_inexact_ptr_; }; } // namespace alpha_complex diff --git a/src/python/test/test_alpha_complex.py b/src/python/test/test_alpha_complex.py index 77121302..943ad2c4 100755 --- a/src/python/test/test_alpha_complex.py +++ b/src/python/test/test_alpha_complex.py @@ -24,14 +24,18 @@ __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" -def test_empty_alpha(): - alpha_complex = AlphaComplex(points=[[0, 0]]) +def _empty_alpha(precision): + alpha_complex = AlphaComplex(points=[[0, 0]], precision = precision) assert alpha_complex.__is_defined() == True +def test_empty_alpha(): + _empty_alpha('fast') + _empty_alpha('safe') + _empty_alpha('exact') -def test_infinite_alpha(): +def _infinite_alpha(precision): point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - alpha_complex = AlphaComplex(points=point_list) + alpha_complex = AlphaComplex(points=point_list, precision = precision) assert alpha_complex.__is_defined() == True simplex_tree = alpha_complex.create_simplex_tree() @@ -79,10 +83,14 @@ def test_infinite_alpha(): else: assert False +def test_infinite_alpha(): + _infinite_alpha('fast') + _infinite_alpha('safe') + _infinite_alpha('exact') -def test_filtered_alpha(): +def _filtered_alpha(precision): point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - filtered_alpha = AlphaComplex(points=point_list) + filtered_alpha = AlphaComplex(points=point_list, precision = precision) simplex_tree = filtered_alpha.create_simplex_tree(max_alpha_square=0.25) @@ -119,7 +127,12 @@ def test_filtered_alpha(): assert simplex_tree.get_star([0]) == [([0], 0.0), ([0, 1], 0.25), ([0, 2], 0.25)] assert simplex_tree.get_cofaces([0], 1) == [([0, 1], 0.25), ([0, 2], 0.25)] -def test_safe_alpha_persistence_comparison(): +def test_filtered_alpha(): + _filtered_alpha('fast') + _filtered_alpha('safe') + _filtered_alpha('exact') + +def _safe_alpha_persistence_comparison(precision): #generate periodic signal time = np.arange(0, 10, 1) signal = [math.sin(x) for x in time] @@ -131,10 +144,10 @@ def test_safe_alpha_persistence_comparison(): embedding2 = [[signal[i], delayed[i]] for i in range(len(time))] #build alpha complex and simplex tree - alpha_complex1 = AlphaComplex(points=embedding1) + alpha_complex1 = AlphaComplex(points=embedding1, precision = precision) simplex_tree1 = alpha_complex1.create_simplex_tree() - alpha_complex2 = AlphaComplex(points=embedding2) + alpha_complex2 = AlphaComplex(points=embedding2, precision = precision) simplex_tree2 = alpha_complex2.create_simplex_tree() diag1 = simplex_tree1.persistence() @@ -143,3 +156,47 @@ def test_safe_alpha_persistence_comparison(): for (first_p, second_p) in zip_longest(diag1, diag2): assert first_p[0] == pytest.approx(second_p[0]) assert first_p[1] == pytest.approx(second_p[1]) + + +def test_safe_alpha_persistence_comparison(): + # Won't work for 'fast' version + _safe_alpha_persistence_comparison('safe') + _safe_alpha_persistence_comparison('exact') + +def _delaunay_complex(precision): + point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] + filtered_alpha = AlphaComplex(points=point_list, precision = precision) + + simplex_tree = filtered_alpha.create_simplex_tree(default_filtration_value = True) + + assert simplex_tree.num_simplices() == 11 + assert simplex_tree.num_vertices() == 4 + + assert point_list[0] == filtered_alpha.get_point(0) + assert point_list[1] == filtered_alpha.get_point(1) + assert point_list[2] == filtered_alpha.get_point(2) + assert point_list[3] == filtered_alpha.get_point(3) + try: + filtered_alpha.get_point(4) == [] + except IndexError: + pass + else: + assert False + try: + filtered_alpha.get_point(125) == [] + except IndexError: + pass + else: + assert False + + for filtered_value in simplex_tree.get_filtration(): + assert math.isnan(filtered_value[1]) + for filtered_value in simplex_tree.get_star([0]): + assert math.isnan(filtered_value[1]) + for filtered_value in simplex_tree.get_cofaces([0], 1): + assert math.isnan(filtered_value[1]) + +def test_delaunay_complex(): + _delaunay_complex('fast') + _delaunay_complex('safe') + _delaunay_complex('exact') diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index dba7f952..589cee00 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -1,12 +1,43 @@ import os import sys import matplotlib.pyplot as plt +import numpy as np +import pytest + def test_representations_examples(): # Disable graphics for testing purposes - plt.show = lambda:None + plt.show = lambda: None here = os.path.dirname(os.path.realpath(__file__)) sys.path.append(here + "/../example") import diagram_vectorizations_distances_kernels return None + + +from gudhi.representations.metrics import * +from gudhi.representations.kernel_methods import * + + +def _n_diags(n): + l = [] + for _ in range(n): + a = np.random.rand(50, 2) + a[:, 1] += a[:, 0] # So that y >= x + l.append(a) + return l + + +def test_multiple(): + l1 = _n_diags(9) + l2 = _n_diags(11) + l1b = l1.copy() + d1 = pairwise_persistence_diagram_distances(l1, e=0.00001, n_jobs=4) + 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) + 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) |