diff options
Diffstat (limited to 'src/python/test')
| -rwxr-xr-x | src/python/test/test_bottleneck_distance.py | 12 | ||||
| -rwxr-xr-x | src/python/test/test_representations.py | 20 | ||||
| -rwxr-xr-x | src/python/test/test_simplex_tree.py | 58 | ||||
| -rwxr-xr-x | src/python/test/test_subsampling.py | 16 | ||||
| -rwxr-xr-x | src/python/test/test_wasserstein_distance.py | 24 | ||||
| -rwxr-xr-x | src/python/test/test_wasserstein_with_tensors.py | 47 |
6 files changed, 138 insertions, 39 deletions
diff --git a/src/python/test/test_bottleneck_distance.py b/src/python/test/test_bottleneck_distance.py index 6915bea8..07fcc9cc 100755 --- a/src/python/test/test_bottleneck_distance.py +++ b/src/python/test/test_bottleneck_distance.py @@ -25,3 +25,15 @@ def test_basic_bottleneck(): assert gudhi.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, abs=0.1) assert gudhi.hera.bottleneck_distance(diag1, diag2, 0) == 0.75 assert gudhi.hera.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, rel=0.1) + + import numpy as np + + # Translating both diagrams along the diagonal should not affect the distance, + # checks that negative numbers are not an issue + diag1 = np.array(diag1) - 100 + diag2 = np.array(diag2) - 100 + + assert gudhi.bottleneck_distance(diag1, diag2) == 0.75 + assert gudhi.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, abs=0.1) + assert gudhi.hera.bottleneck_distance(diag1, diag2, 0) == 0.75 + assert gudhi.hera.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, rel=0.1) diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index e5c211a0..43c914f3 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -39,11 +39,11 @@ def test_multiple(): d2 = BottleneckDistance(epsilon=0.00001).fit_transform(l1) d3 = pairwise_persistence_diagram_distances(l1, l1b, e=0.00001, n_jobs=4) assert d1 == pytest.approx(d2) - assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal) + assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal) d1 = pairwise_persistence_diagram_distances(l1, l2, metric="wasserstein", order=2, internal_p=2) d2 = WassersteinDistance(order=2, internal_p=2, n_jobs=4).fit(l2).transform(l1) print(d1.shape, d2.shape) - assert d1 == pytest.approx(d2, rel=.02) + assert d1 == pytest.approx(d2, rel=0.02) def test_dummy_atol(): @@ -53,8 +53,22 @@ def test_dummy_atol(): for weighting_method in ["cloud", "iidproba"]: for contrast in ["gaussian", "laplacian", "indicator"]: - atol_vectoriser = Atol(quantiser=KMeans(n_clusters=1, random_state=202006), weighting_method=weighting_method, contrast=contrast) + atol_vectoriser = Atol( + quantiser=KMeans(n_clusters=1, random_state=202006), + weighting_method=weighting_method, + contrast=contrast, + ) atol_vectoriser.fit([a, b, c]) atol_vectoriser(a) atol_vectoriser.transform(X=[a, b, c]) + +from gudhi.representations.vector_methods import BettiCurve + + +def test_infinity(): + a = np.array([[1.0, 8.0], [2.0, np.inf], [3.0, 4.0]]) + c = BettiCurve(20, [0.0, 10.0])(a) + assert c[1] == 0 + assert c[7] == 3 + assert c[9] == 2 diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py index 83be0602..a3eacaa9 100755 --- a/src/python/test/test_simplex_tree.py +++ b/src/python/test/test_simplex_tree.py @@ -8,7 +8,7 @@ - YYYY/MM Author: Description of the modification """ -from gudhi import SimplexTree +from gudhi import SimplexTree, __GUDHI_USE_EIGEN3 import pytest __author__ = "Vincent Rouvreau" @@ -353,8 +353,54 @@ def test_collapse_edges(): assert st.num_simplices() == 10 - st.collapse_edges() - assert st.num_simplices() == 9 - assert st.find([1, 3]) == False - for simplex in st.get_skeleton(0): - assert simplex[1] == 1. + if __GUDHI_USE_EIGEN3: + st.collapse_edges() + assert st.num_simplices() == 9 + assert st.find([1, 3]) == False + for simplex in st.get_skeleton(0): + assert simplex[1] == 1. + else: + # If no Eigen3, collapse_edges throws an exception + with pytest.raises(RuntimeError): + st.collapse_edges() + +def test_reset_filtration(): + st = SimplexTree() + + assert st.insert([0, 1, 2], 3.) == True + assert st.insert([0, 3], 2.) == True + assert st.insert([3, 4, 5], 3.) == True + assert st.insert([0, 1, 6, 7], 4.) == True + + # Guaranteed by construction + for simplex in st.get_simplices(): + assert st.filtration(simplex[0]) >= 2. + + # dimension until 5 even if simplex tree is of dimension 3 to test the limits + for dimension in range(5, -1, -1): + st.reset_filtration(0., dimension) + for simplex in st.get_skeleton(3): + print(simplex) + if len(simplex[0]) < (dimension) + 1: + assert st.filtration(simplex[0]) >= 2. + else: + assert st.filtration(simplex[0]) == 0. + +def test_boundaries_iterator(): + st = SimplexTree() + + assert st.insert([0, 1, 2, 3], filtration=1.0) == True + assert st.insert([1, 2, 3, 4], filtration=2.0) == True + + assert list(st.get_boundaries([1, 2, 3])) == [([1, 2], 1.0), ([1, 3], 1.0), ([2, 3], 1.0)] + assert list(st.get_boundaries([2, 3, 4])) == [([2, 3], 1.0), ([2, 4], 2.0), ([3, 4], 2.0)] + assert list(st.get_boundaries([2])) == [] + + with pytest.raises(RuntimeError): + list(st.get_boundaries([])) + + with pytest.raises(RuntimeError): + list(st.get_boundaries([0, 4])) # (0, 4) does not exist + + with pytest.raises(RuntimeError): + list(st.get_boundaries([6])) # (6) does not exist diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py index 31f64e32..4019852e 100755 --- a/src/python/test/test_subsampling.py +++ b/src/python/test/test_subsampling.py @@ -141,12 +141,16 @@ def test_simple_sparsify_points(): # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 0.0) == [] # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 10.0) == [] assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.0) == point_set - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.0) == point_set - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.0) == [ + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.999) == point_set + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.001) == [ [0, 1], [1, 0], ] - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.01) == [[0, 1]] + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.999) == [ + [0, 1], + [1, 0], + ] + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.001) == [[0, 1]] assert ( len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0)) @@ -157,11 +161,11 @@ def test_simple_sparsify_points(): == 5 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1)) == 4 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=90.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9)) == 3 ) assert ( @@ -169,7 +173,7 @@ def test_simple_sparsify_points(): == 2 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9)) == 2 ) assert ( diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py index 90d26809..e3b521d6 100755 --- a/src/python/test/test_wasserstein_distance.py +++ b/src/python/test/test_wasserstein_distance.py @@ -97,27 +97,3 @@ def test_wasserstein_distance_pot(): def test_wasserstein_distance_hera(): _basic_wasserstein(hera_wrap(delta=1e-12), 1e-12, test_matching=False) _basic_wasserstein(hera_wrap(delta=.1), .1, test_matching=False) - -def test_wasserstein_distance_grad(): - import torch - - diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True) - diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) - diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) - assert diag1.grad is None and diag2.grad is None and diag3.grad is None - dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True) - dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True) - dist12.backward() - dist30.backward() - assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any() - diag4 = torch.tensor([[0., 10.]], requires_grad=True) - diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True) - dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) - assert dist45 == 3. - dist45.backward() - assert np.array_equal(diag4.grad, [[-1., -1.]]) - assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]]) - diag6 = torch.tensor([[5., 10.]], requires_grad=True) - pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward() - # https://github.com/jonasrauber/eagerpy/issues/6 - # assert np.array_equal(diag6.grad, [[0., 0.]]) diff --git a/src/python/test/test_wasserstein_with_tensors.py b/src/python/test/test_wasserstein_with_tensors.py new file mode 100755 index 00000000..e3f1411a --- /dev/null +++ b/src/python/test/test_wasserstein_with_tensors.py @@ -0,0 +1,47 @@ +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Mathieu Carriere + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.wasserstein import wasserstein_distance as pot +import numpy as np +import torch +import tensorflow as tf + +def test_wasserstein_distance_grad(): + diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True) + diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) + diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) + assert diag1.grad is None and diag2.grad is None and diag3.grad is None + dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True) + dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True) + dist12.backward() + dist30.backward() + assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any() + diag4 = torch.tensor([[0., 10.]], requires_grad=True) + diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True) + dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) + assert dist45 == 3. + dist45.backward() + assert np.array_equal(diag4.grad, [[-1., -1.]]) + assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]]) + diag6 = torch.tensor([[5., 10.]], requires_grad=True) + pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward() + # https://github.com/jonasrauber/eagerpy/issues/6 + # assert np.array_equal(diag6.grad, [[0., 0.]]) + +def test_wasserstein_distance_grad_tensorflow(): + with tf.GradientTape() as tape: + diag4 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[0., 10.]]), trainable=True)) + diag5 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[1., 11.], [3., 4.]]), trainable=True)) + dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) + assert dist45 == 3. + + grads = tape.gradient(dist45, [diag4, diag5]) + assert np.array_equal(grads[0].values, [[-1., -1.]]) + assert np.array_equal(grads[1].values, [[1., 1.], [-1., 1.]])
\ No newline at end of file |