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Enable flagser_count capabilities from C++ flagser and have two modul…
…es to handle coeff = or != 2 (#37) * Propagate flagser count_cells binding fix * Add flagser_count bindings * Update CMakeLists for compilation of new flagser count target * Add flagser_count functions * Add tests for flagser_count functions * Remove unsupported arguments * Remove OpenMP and add Threads and add flagser_coeff * Document and add flagser coeff * Update for new pybind11 modules * Improve and document flagser-count bindings * Propagate flagser bug fix * Remove Threads as a C++ dependency * Reshape dgms output of flagser_weight * Update directed docstring Signed-off-by: Guillaume Tauzin <[email protected]> Co-authored-by: Umberto Lupo <[email protected]>
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Submodule flagser
updated
9 files
| +48 −0 | .github/workflows/build.yml | |
| +0 −23 | .travis.yml | |
| +31 −8 | CMakeLists.txt | |
| +1 −1 | include/complex/directed_flag_complex_computer.h | |
| +1 −1 | include/complex/directed_flag_complex_in_memory_computer.h | |
| +1 −0 | include/persistence.h | |
| +6 −4 | src/flagser-count.cpp | |
| +7 −1 | test/base.h | |
| +0 −4 | test/test_flagser.cpp |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,132 @@ | ||
| """Implementation of the python API for the cell count of the flagser C++ | ||
| library.""" | ||
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| import numpy as np | ||
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| from ._utils import _extract_unweighted_graph, _extract_weighted_graph | ||
| from .modules.flagser_count_pybind import compute_cell_count | ||
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| def flagser_count_unweighted(adjacency_matrix, min_dimension=0, | ||
| max_dimension=np.inf, directed=True): | ||
| """Compute the cell count per dimension of a directed/undirected unweighted | ||
| flag complex. | ||
| From an adjacency matrix construct all cells forming its associated flag | ||
| complex and compute their number per dimension. | ||
| Parameters | ||
| ---------- | ||
| adjacency_matrix : 2d ndarray or scipy.sparse matrix of shape \ | ||
| (n_vertices, n_vertices), required | ||
| Adjacency matrix of a directed/undirected unweighted graph. It is | ||
| understood as a boolean matrix. Off-diagonal, ``0`` or ``False`` values | ||
| denote abstent edges while non-``0`` or ``True`` values denote edges | ||
| which are present. Diagonal values are ignored. | ||
| directed : bool, optional, default: ``True`` | ||
| If ``True``, computes homology for the directed flad complex determined | ||
| by `adjacency_matrix`. If ``False``, computes homology for the | ||
| undirected flag complex obtained by considering all edges as | ||
| undirected, and it is therefore sufficient (but not necessary) | ||
| to pass an upper-triangular matrix. | ||
| Returns | ||
| ------- | ||
| out : list of int | ||
| Cell counts (number of simplices), per dimension greater than or equal | ||
| to `min_dimension` and less than `max_dimension`. | ||
| Notes | ||
| ----- | ||
| The input graphs cannot contain self-loops, i.e. edges that start and end | ||
| in the same vertex, therefore diagonal elements of the input adjacency | ||
| matrix will be ignored. | ||
| References | ||
| ---------- | ||
| .. [1] D. Luetgehetmann, "Documentation of the C++ flagser library"; | ||
| `GitHub: luetge/flagser <https://github.com/luetge/flagser/blob/\ | ||
| master/docs/documentation_flagser.pdf>`_. | ||
| """ | ||
| # Extract vertices and edges | ||
| vertices, edges = _extract_unweighted_graph(adjacency_matrix) | ||
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| # Call flagser_count binding | ||
| cell_count = compute_cell_count(vertices, edges, directed) | ||
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| return cell_count | ||
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| def flagser_count_weighted(adjacency_matrix, max_edge_weight=None, | ||
| directed=True): | ||
| """Compute the cell count per dimension of a directed/undirected | ||
| filtered flag complex. | ||
| From an adjacency matrix construct a filtered flag complex as a sequence of | ||
| its cells associated to their filtration values and compute the number of | ||
| cells per dimension at the end of the filtration. | ||
| Parameters | ||
| ---------- | ||
| adjacency_matrix : 2d ndarray or scipy.sparse matrix of shape \ | ||
| (n_vertices, n_vertices), required | ||
| Matrix representation of a directed/undirected weighted graph. Diagonal | ||
| elements are vertex weights. The way zero values are handled depends on | ||
| the format of the matrix. If the matrix is a dense ``numpy.ndarray``, | ||
| zero values denote zero-weighted edges. If the matrix is a sparse | ||
| ``scipy.sparse`` matrix, explicitly stored off-diagonal zeros and all | ||
| diagonal zeros denote zero-weighted edges. Off-diagonal values that | ||
| have not been explicitely stored are treated by ``scipy.sparse`` as | ||
| zeros but will be understood as infinitely-valued edges, i.e., edges | ||
| absent from the filtration. | ||
| max_edge_weight : int or float or ``None``, optional, default: ``None`` | ||
| Maximum edge weight to be considered in the filtration. All edge | ||
| weights greater than that value will be considered as | ||
| infinitely-valued, i.e., absent from the filtration. If ``None``, all | ||
| finite edge weights are considered. | ||
| directed : bool, optional, default: ``True`` | ||
| If ``True``, computes persistent homology for the directed filtered | ||
| flag complex determined by `adjacency_matrix`. If ``False``, computes | ||
| persistent homology for the undirected filtered flag complex obtained | ||
| by considering all weighted edges as undirected, and if two directed | ||
| edges corresponding to the same undirected edge are explicitly assigned | ||
| different weights and neither exceeds `max_edge_weight`, only the one | ||
| in the upper triangular part of the adjacency matrix is considered. | ||
| Therefore: | ||
| - if `max_edge_weight` is ``numpy.inf``, it is sufficient to pass a | ||
| (dense or sparse) upper-triangular matrix; | ||
| - if `max_edge_weight` is finite, it is recommended to pass either a | ||
| symmetric dense matrix, or a sparse upper-triangular matrix. | ||
| Returns | ||
| ------- | ||
| out : list of int | ||
| Cell counts (number of simplices) at filtration value | ||
| `max_edge_weight`, per dimension. | ||
| Notes | ||
| ----- | ||
| The input graphs cannot contain self-loops, i.e. edges that start and end | ||
| in the same vertex, therefore diagonal elements of the input adjacency | ||
| matrix store vertex weights. | ||
| References | ||
| ---------- | ||
| .. [1] D. Luetgehetmann, "Documentation of the C++ flagser library"; | ||
| `GitHub: luetge/flagser <https://github.com/luetge/flagser/blob/\ | ||
| master/docs/documentation_flagser.pdf>`_. | ||
| """ | ||
| # Extract vertices and edges weights | ||
| vertices, edges = _extract_weighted_graph(adjacency_matrix, | ||
| max_edge_weight) | ||
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| # Call flagser_count binding | ||
| cell_count = compute_cell_count(vertices, edges, directed) | ||
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| return cell_count |
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| Original file line number | Diff line number | Diff line change |
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| """Testing for the python bindings of the C++ flagser library.""" | ||
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| import os | ||
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| from numpy.testing import assert_almost_equal | ||
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| from pyflagser import load_unweighted_flag, load_weighted_flag, \ | ||
| flagser_count_unweighted, flagser_count_weighted | ||
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| cell_count = { | ||
| 'a.flag': [5, 7, 1], | ||
| 'b.flag': [4, 6, 3], | ||
| 'c.flag': [8, 12], | ||
| 'd.flag': [5, 9, 6], | ||
| 'e.flag': [5, 9, 7, 2], | ||
| 'f.flag': [3, 5, 3], | ||
| 'd2.flag': [2, 2], | ||
| 'd3.flag': [3, 6, 6], | ||
| 'd3-allzero.flag': [3, 6, 6], | ||
| 'double-d3.flag': [4, 10, 12], | ||
| 'double-d3-allzero.flag': [4, 10, 12], | ||
| 'd4.flag': [4, 12, 24, 24], | ||
| 'd4-allzero.flag': [4, 12, 24, 24], | ||
| 'd5.flag': [5, 20, 60, 120, 120], | ||
| 'd7.flag': [7, 42, 210, 840, 2520, 5040, 5040], | ||
| 'medium-test-data.flag': [31346, 68652, 12694, 250], | ||
| 'd10.flag': [10, 90, 720, 5040, 30240, 151200, 604800, | ||
| 1814400, 3628800, 3628800], | ||
| } | ||
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| def test_unweighted(flag_file): | ||
| adjacency_matrix = load_unweighted_flag(flag_file, fmt='dense') | ||
| cell_count_exp = cell_count[os.path.split(flag_file)[1]] | ||
| cell_count_res = flagser_count_unweighted(adjacency_matrix) | ||
| assert_almost_equal(cell_count_res, cell_count_exp) | ||
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| def test_weighted(flag_file): | ||
| adjacency_matrix = load_weighted_flag(flag_file, fmt='coo') | ||
| cell_count_exp = cell_count[os.path.split(flag_file)[1]] | ||
| cell_count_res = flagser_count_weighted(adjacency_matrix) | ||
| assert_almost_equal(cell_count_res, cell_count_exp) |
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