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""" 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
import numpy
import pytest
import torch
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)
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