diff options
Diffstat (limited to 'src/python/test')
-rwxr-xr-x | src/python/test/test_dtm.py | 40 | ||||
-rwxr-xr-x | src/python/test/test_knn.py | 90 | ||||
-rwxr-xr-x | src/python/test/test_simplex_generators.py | 64 | ||||
-rwxr-xr-x | src/python/test/test_simplex_tree.py | 85 | ||||
-rwxr-xr-x | src/python/test/test_wasserstein_barycenter.py | 46 | ||||
-rwxr-xr-x | src/python/test/test_wasserstein_distance.py | 9 |
6 files changed, 298 insertions, 36 deletions
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py index 93b13e1a..bff4c267 100755 --- a/src/python/test/test_dtm.py +++ b/src/python/test/test_dtm.py @@ -8,43 +8,61 @@ - YYYY/MM Author: Description of the modification """ -from gudhi.point_cloud.dtm import DTM +from gudhi.point_cloud.dtm import DistanceToMeasure import numpy import pytest +import torch def test_dtm_compare_euclidean(): pts = numpy.random.rand(1000, 4) - k = 3 - dtm = DTM(k, implementation="ckdtree") + k = 6 + dtm = DistanceToMeasure(k, implementation="ckdtree") r0 = dtm.fit_transform(pts) - dtm = DTM(k, implementation="sklearn") + dtm = DistanceToMeasure(k, implementation="sklearn") r1 = dtm.fit_transform(pts) assert r1 == pytest.approx(r0) - dtm = DTM(k, implementation="sklearn", algorithm="brute") + dtm = DistanceToMeasure(k, implementation="sklearn", algorithm="brute") r2 = dtm.fit_transform(pts) assert r2 == pytest.approx(r0) - dtm = DTM(k, implementation="hnsw") + dtm = DistanceToMeasure(k, implementation="hnsw") r3 = dtm.fit_transform(pts) - assert r3 == pytest.approx(r0) + assert r3 == pytest.approx(r0, rel=0.1) from scipy.spatial.distance import cdist d = cdist(pts, pts) - dtm = DTM(k, metric="precomputed") + dtm = DistanceToMeasure(k, metric="precomputed") r4 = dtm.fit_transform(d) assert r4 == pytest.approx(r0) - dtm = DTM(k, implementation="keops") + dtm = DistanceToMeasure(k, metric="precomputed", n_jobs=2) + r4b = dtm.fit_transform(d) + assert r4b == pytest.approx(r0) + dtm = DistanceToMeasure(k, implementation="keops") r5 = dtm.fit_transform(pts) assert r5 == pytest.approx(r0) + pts2 = torch.tensor(pts, requires_grad=True) + assert pts2.grad is None + dtm = DistanceToMeasure(k, implementation="keops", enable_autodiff=True) + r6 = dtm.fit_transform(pts2) + assert r6.detach().numpy() == pytest.approx(r0) + r6.sum().backward() + assert not torch.isnan(pts2.grad).any() + pts2 = torch.tensor(pts, requires_grad=True) + assert pts2.grad is None + dtm = DistanceToMeasure(k, implementation="ckdtree", enable_autodiff=True) + r7 = dtm.fit_transform(pts2) + assert r7.detach().numpy() == pytest.approx(r0) + r7.sum().backward() + assert not torch.isnan(pts2.grad).any() def test_dtm_precomputed(): dist = numpy.array([[1.0, 3, 8], [1, 5, 5], [0, 2, 3]]) - dtm = DTM(2, q=1, metric="neighbors") + dtm = DistanceToMeasure(2, q=1, metric="neighbors") r = dtm.fit_transform(dist) assert r == pytest.approx([2.0, 3, 1]) dist = numpy.array([[2.0, 2], [0, 1], [3, 4]]) - dtm = DTM(2, q=2, metric="neighbors") + dtm = DistanceToMeasure(2, q=2, metric="neighbors") r = dtm.fit_transform(dist) assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01) diff --git a/src/python/test/test_knn.py b/src/python/test/test_knn.py index e455fb48..a87ec212 100755 --- a/src/python/test/test_knn.py +++ b/src/python/test/test_knn.py @@ -8,7 +8,7 @@ - YYYY/MM Author: Description of the modification """ -from gudhi.point_cloud.knn import KNN +from gudhi.point_cloud.knn import KNearestNeighbors import numpy as np import pytest @@ -16,39 +16,53 @@ import pytest def test_knn_explicit(): base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]]) query = np.array([[1.0, 1], [2, 2], [4, 4]]) - knn = KNN(2, metric="manhattan", return_distance=True, return_index=True) + knn = KNearestNeighbors(2, metric="manhattan", return_distance=True, return_index=True) knn.fit(base) r = knn.transform(query) assert r[0] == pytest.approx(np.array([[0, 1], [1, 0], [3, 2]])) assert r[1] == pytest.approx(np.array([[0.0, 1], [1, 2], [1, 2]])) - knn = KNN(2, metric="chebyshev", return_distance=True, return_index=False) + knn = KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False) knn.fit(base) r = knn.transform(query) assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]])) r = ( - KNN(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops") + KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops") + .fit(base) + .transform(query) + ) + assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]])) + r = ( + KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops", enable_autodiff=True) .fit(base) .transform(query) ) assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]])) - knn = KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True) + knn = KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True) knn.fit(base) r = knn.transform(query) assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]]) r = ( - KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops") + KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops") .fit(base) .transform(query) ) assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]]) dist = np.array([[0.0, 3, 8], [1, 0, 5], [1, 2, 0]]) - knn = KNN(2, metric="precomputed", return_index=True, return_distance=False) + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False) + r = knn.fit_transform(dist) + assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]]) + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, sort_results=True) + r = knn.fit_transform(dist) + assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]]) + assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]]) + # Second time in parallel + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False, n_jobs=2, sort_results=True) r = knn.fit_transform(dist) assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]]) - knn = KNN(2, metric="precomputed", return_index=True, return_distance=True) + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, n_jobs=2) r = knn.fit_transform(dist) assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]]) assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]]) @@ -57,16 +71,40 @@ def test_knn_explicit(): def test_knn_compare(): base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]]) query = np.array([[1.0, 1], [2, 2], [4, 4]]) - r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=False).fit(base).transform(query) - r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=False).fit(base).transform(query) - r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query) - r3 = KNN(2, implementation="keops", return_index=True, return_distance=False).fit(base).transform(query) + r0 = ( + KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=False) + .fit(base) + .transform(query) + ) + r1 = ( + KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=False) + .fit(base) + .transform(query) + ) + r2 = ( + KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query) + ) + r3 = ( + KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=False) + .fit(base) + .transform(query) + ) assert np.array_equal(r0, r1) and np.array_equal(r0, r2) and np.array_equal(r0, r3) - r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=True).fit(base).transform(query) - r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=True).fit(base).transform(query) - r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query) - r3 = KNN(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query) + r0 = ( + KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=True) + .fit(base) + .transform(query) + ) + r1 = ( + KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=True) + .fit(base) + .transform(query) + ) + r2 = KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query) + r3 = ( + KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query) + ) assert np.array_equal(r0[0], r1[0]) and np.array_equal(r0[0], r2[0]) and np.array_equal(r0[0], r3[0]) d0 = pytest.approx(r0[1]) assert r1[1] == d0 and r2[1] == d0 and r3[1] == d0 @@ -75,8 +113,18 @@ def test_knn_compare(): def test_knn_nop(): # This doesn't look super useful... p = np.array([[0.0]]) - assert None is KNN(k=1, return_index=False, return_distance=False, implementation="sklearn").fit_transform(p) - assert None is KNN(k=1, return_index=False, return_distance=False, implementation="ckdtree").fit_transform(p) - assert None is KNN(k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5).fit_transform(p) - assert None is KNN(k=1, return_index=False, return_distance=False, implementation="keops").fit_transform(p) - assert None is KNN(k=1, return_index=False, return_distance=False, metric="precomputed").fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="sklearn" + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="ckdtree" + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5 + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="keops" + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, metric="precomputed" + ).fit_transform(p) diff --git a/src/python/test/test_simplex_generators.py b/src/python/test/test_simplex_generators.py new file mode 100755 index 00000000..8a9b4844 --- /dev/null +++ b/src/python/test/test_simplex_generators.py @@ -0,0 +1,64 @@ +""" 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): Marc Glisse + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +import gudhi +import numpy as np + + +def test_flag_generators(): + pts = np.array([[0, 0], [0, 1.01], [1, 0], [1.02, 1.03], [100, 0], [100, 3.01], [103, 0], [103.02, 3.03]]) + r = gudhi.RipsComplex(pts, max_edge_length=4) + st = r.create_simplex_tree(max_dimension=50) + st.persistence() + g = st.flag_persistence_generators() + assert np.array_equal(g[0], [[2, 2, 0], [1, 1, 0], [3, 3, 1], [6, 6, 4], [5, 5, 4], [7, 7, 5]]) + assert len(g[1]) == 1 + assert np.array_equal(g[1][0], [[3, 2, 2, 1]]) + assert np.array_equal(g[2], [0, 4]) + assert len(g[3]) == 1 + assert np.array_equal(g[3][0], [[7, 6]]) + # Compare trivial cases (where the simplex is the generator) with persistence_pairs. + # This still makes assumptions on the order of vertices in a simplex and could be more robust. + pairs = st.persistence_pairs() + assert {tuple(i) for i in g[0]} == {(i[0][0],) + tuple(i[1]) for i in pairs if len(i[0]) == 1 and len(i[1]) != 0} + assert {(i[0], i[1]) for i in g[1][0]} == {tuple(i[0]) for i in pairs if len(i[0]) == 2 and len(i[1]) != 0} + assert set(g[2]) == {i[0][0] for i in pairs if len(i[0]) == 1 and len(i[1]) == 0} + assert {(i[0], i[1]) for i in g[3][0]} == {tuple(i[0]) for i in pairs if len(i[0]) == 2 and len(i[1]) == 0} + + +def test_lower_star_generators(): + st = gudhi.SimplexTree() + st.insert([0, 1, 2], -10) + st.insert([0, 3], -10) + st.insert([1, 3], -10) + st.assign_filtration([2], -1) + st.assign_filtration([3], 0) + st.assign_filtration([0], 1) + st.assign_filtration([1], 2) + st.make_filtration_non_decreasing() + st.persistence(min_persistence=-1) + g = st.lower_star_persistence_generators() + assert len(g[0]) == 2 + assert np.array_equal(g[0][0], [[0, 0], [3, 0], [1, 1]]) + assert np.array_equal(g[0][1], [[1, 1]]) + assert len(g[1]) == 2 + assert np.array_equal(g[1][0], [2]) + assert np.array_equal(g[1][1], [1]) + + +def test_empty(): + st = gudhi.SimplexTree() + st.persistence() + assert st.lower_star_persistence_generators() == ([], []) + g = st.flag_persistence_generators() + assert np.array_equal(g[0], np.empty((0, 3))) + assert g[1] == [] + assert np.array_equal(g[2], []) + assert g[3] == [] diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py index f7848379..2137d822 100755 --- a/src/python/test/test_simplex_tree.py +++ b/src/python/test/test_simplex_tree.py @@ -9,6 +9,7 @@ """ from gudhi import SimplexTree +import pytest __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" @@ -45,7 +46,6 @@ def test_insertion(): assert st.find([2, 3]) == False # filtration test - st.initialize_filtration() assert st.filtration([0, 1, 2]) == 4.0 assert st.filtration([0, 2]) == 4.0 assert st.filtration([1, 2]) == 4.0 @@ -92,7 +92,6 @@ def test_insertion(): assert st.find([1]) == True assert st.find([2]) == True - st.initialize_filtration() assert st.persistence(persistence_dim_max=True) == [ (1, (4.0, float("inf"))), (0, (0.0, float("inf"))), @@ -150,7 +149,6 @@ def test_expansion(): st.expansion(3) assert st.num_vertices() == 7 assert st.num_simplices() == 22 - st.initialize_filtration() assert list(st.get_filtration()) == [ ([2], 0.1), @@ -250,6 +248,87 @@ def test_make_filtration_non_decreasing(): assert st.filtration([3, 4]) == 2.0 assert st.filtration([4, 5]) == 2.0 +def test_extend_filtration(): + + # Inserted simplex: + # 5 4 + # o o + # / \ / + # o o + # /2\ /3 + # o o + # 1 0 + + st = SimplexTree() + st.insert([0,2]) + st.insert([1,2]) + st.insert([0,3]) + st.insert([2,5]) + st.insert([3,4]) + st.insert([3,5]) + st.assign_filtration([0], 1.) + st.assign_filtration([1], 2.) + st.assign_filtration([2], 3.) + st.assign_filtration([3], 4.) + st.assign_filtration([4], 5.) + st.assign_filtration([5], 6.) + + assert list(st.get_filtration()) == [ + ([0, 2], 0.0), + ([1, 2], 0.0), + ([0, 3], 0.0), + ([3, 4], 0.0), + ([2, 5], 0.0), + ([3, 5], 0.0), + ([0], 1.0), + ([1], 2.0), + ([2], 3.0), + ([3], 4.0), + ([4], 5.0), + ([5], 6.0) + ] + + st.extend_filtration() + + assert list(st.get_filtration()) == [ + ([6], -3.0), + ([0], -2.0), + ([1], -1.8), + ([2], -1.6), + ([0, 2], -1.6), + ([1, 2], -1.6), + ([3], -1.4), + ([0, 3], -1.4), + ([4], -1.2), + ([3, 4], -1.2), + ([5], -1.0), + ([2, 5], -1.0), + ([3, 5], -1.0), + ([5, 6], 1.0), + ([4, 6], 1.2), + ([3, 6], 1.4), + ([3, 4, 6], 1.4), + ([3, 5, 6], 1.4), + ([2, 6], 1.6), + ([2, 5, 6], 1.6), + ([1, 6], 1.8), + ([1, 2, 6], 1.8), + ([0, 6], 2.0), + ([0, 2, 6], 2.0), + ([0, 3, 6], 2.0) + ] + + dgms = st.extended_persistence(min_persistence=-1.) + + assert dgms[0][0][1][0] == pytest.approx(2.) + assert dgms[0][0][1][1] == pytest.approx(3.) + assert dgms[1][0][1][0] == pytest.approx(5.) + assert dgms[1][0][1][1] == pytest.approx(4.) + assert dgms[2][0][1][0] == pytest.approx(1.) + assert dgms[2][0][1][1] == pytest.approx(6.) + assert dgms[3][0][1][0] == pytest.approx(6.) + assert dgms[3][0][1][1] == pytest.approx(1.) + def test_simplices_iterator(): st = SimplexTree() diff --git a/src/python/test/test_wasserstein_barycenter.py b/src/python/test/test_wasserstein_barycenter.py new file mode 100755 index 00000000..f68c748e --- /dev/null +++ b/src/python/test/test_wasserstein_barycenter.py @@ -0,0 +1,46 @@ +from gudhi.wasserstein.barycenter import lagrangian_barycenter +import numpy as np + +""" 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): Theo Lacombe + + Copyright (C) 2019 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +__author__ = "Theo Lacombe" +__copyright__ = "Copyright (C) 2019 Inria" +__license__ = "MIT" + + +def test_lagrangian_barycenter(): + + dg1 = np.array([[0.2, 0.5]]) + dg2 = np.array([[0.2, 0.7]]) + dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]]) + dg4 = np.array([]) + dg5 = np.array([]) + dg6 = np.array([]) + res = np.array([[0.27916667, 0.55416667], [0.7375, 0.7625], [0.2375, 0.2625]]) + + dg7 = np.array([[0.1, 0.15], [0.1, 0.7], [0.2, 0.22], [0.55, 0.84], [0.11, 0.91], [0.61, 0.75], [0.33, 0.46], [0.12, 0.41], [0.32, 0.48]]) + dg8 = np.array([[0., 4.], [4, 8]]) + + # error crit. + eps = 1e-7 + + + assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg1, dg2, dg3, dg4],init=3, verbose=False) - res) < eps + assert np.array_equal(lagrangian_barycenter(pdiagset=[dg4, dg5, dg6], verbose=False), np.empty(shape=(0,2))) + assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg7], verbose=False) - dg7) < eps + Y, log = lagrangian_barycenter(pdiagset=[dg4, dg8], verbose=True) + assert np.linalg.norm(Y - np.array([[1,3], [5, 7]])) < eps + assert np.abs(log["energy"] - 2) < eps + assert np.array_equal(log["groupings"][0] , np.array([[0, -1], [1, -1]])) + assert np.array_equal(log["groupings"][1] , np.array([[0, 0], [1, 1]])) + assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg8, dg4], init=np.array([[0.2, 0.6], [0.5, 0.7]]), verbose=False) - np.array([[1, 3], [5, 7]])) < eps + assert lagrangian_barycenter(pdiagset = []) is None + diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py index 0d70e11a..1a4acc1d 100755 --- a/src/python/test/test_wasserstein_distance.py +++ b/src/python/test/test_wasserstein_distance.py @@ -8,6 +8,7 @@ - YYYY/MM Author: Description of the modification """ +from gudhi.wasserstein.wasserstein import _proj_on_diag from gudhi.wasserstein import wasserstein_distance as pot from gudhi.hera import wasserstein_distance as hera import numpy as np @@ -17,6 +18,12 @@ __author__ = "Theo Lacombe" __copyright__ = "Copyright (C) 2019 Inria" __license__ = "MIT" +def test_proj_on_diag(): + dgm = np.array([[1., 1.], [1., 2.], [3., 5.]]) + assert np.array_equal(_proj_on_diag(dgm), [[1., 1.], [1.5, 1.5], [4., 4.]]) + empty = np.empty((0, 2)) + assert np.array_equal(_proj_on_diag(empty), empty) + def _basic_wasserstein(wasserstein_distance, delta, test_infinity=True, test_matching=True): diag1 = np.array([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]]) diag2 = np.array([[2.8, 4.45], [9.5, 14.1]]) @@ -70,7 +77,7 @@ def _basic_wasserstein(wasserstein_distance, delta, test_infinity=True, test_mat assert np.array_equal(match , [[0, -1], [1, -1]]) match = wasserstein_distance(diag1, diag2, matching=True, internal_p=2., order=2.)[1] assert np.array_equal(match, [[0, 0], [1, 1], [2, -1]]) - + def hera_wrap(delta): |