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Diffstat (limited to 'src/python/test/test_dtm.py')
-rwxr-xr-x | src/python/test/test_dtm.py | 101 |
1 files changed, 101 insertions, 0 deletions
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py new file mode 100755 index 00000000..b276f041 --- /dev/null +++ b/src/python/test/test_dtm.py @@ -0,0 +1,101 @@ +""" 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 +""" + +from gudhi.point_cloud.dtm import DistanceToMeasure, DTMDensity +import numpy +import pytest +import torch +import math +import warnings + + +def test_dtm_compare_euclidean(): + pts = numpy.random.rand(1000, 4) + k = 6 + dtm = DistanceToMeasure(k, implementation="ckdtree") + r0 = dtm.fit_transform(pts) + dtm = DistanceToMeasure(k, implementation="sklearn") + r1 = dtm.fit_transform(pts) + assert r1 == pytest.approx(r0) + dtm = DistanceToMeasure(k, implementation="sklearn", algorithm="brute") + r2 = dtm.fit_transform(pts) + assert r2 == pytest.approx(r0) + dtm = DistanceToMeasure(k, implementation="hnsw") + r3 = dtm.fit_transform(pts) + assert r3 == pytest.approx(r0, rel=0.1) + from scipy.spatial.distance import cdist + + d = cdist(pts, pts) + dtm = DistanceToMeasure(k, metric="precomputed") + r4 = dtm.fit_transform(d) + assert r4 == pytest.approx(r0) + 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 = 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 = DistanceToMeasure(2, q=2, metric="neighbors") + r = dtm.fit_transform(dist) + assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01) + + +def test_density_normalized(): + sample = numpy.random.normal(0, 1, (1000000, 2)) + queries = numpy.array([[0.0, 0.0], [-0.5, 0.7], [0.4, 1.7]]) + expected = numpy.exp(-(queries ** 2).sum(-1) / 2) / (2 * math.pi) + estimated = DTMDensity(k=150, normalize=True).fit(sample).transform(queries) + assert estimated == pytest.approx(expected, rel=0.4) + + +def test_density(): + distances = [[0, 1, 10], [2, 0, 30], [1, 3, 5]] + density = DTMDensity(k=2, metric="neighbors", dim=1).fit_transform(distances) + expected = numpy.array([2.0, 1.0, 0.5]) + assert density == pytest.approx(expected) + distances = [[0, 1], [2, 0], [1, 3]] + density = DTMDensity(metric="neighbors", dim=1).fit_transform(distances) + assert density == pytest.approx(expected) + density = DTMDensity(weights=[0.5, 0.5], metric="neighbors", dim=1).fit_transform(distances) + assert density == pytest.approx(expected) + +def test_dtm_overflow_warnings(): + pts = numpy.array([[10., 100000000000000000000000000000.], [1000., 100000000000000000000000000.]]) + impl_warn = ["keops", "hnsw"] + for impl in impl_warn: + with warnings.catch_warnings(record=True) as w: + dtm = DistanceToMeasure(2, implementation=impl) + r = dtm.fit_transform(pts) + assert len(w) == 1 + assert issubclass(w[0].category, RuntimeWarning) + assert "Overflow" in str(w[0].message) |