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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): Theo Lacombe, Marc Glisse
Copyright (C) 2019 Inria
Modification(s):
- 2020/07 Théo Lacombe: Added tests about handling essential parts in diagrams.
- YYYY/MM Author: Description of the modification
"""
from gudhi.wasserstein.wasserstein import _proj_on_diag, _finite_part, _handle_essential_parts, _get_essential_parts
from gudhi.wasserstein.wasserstein import _warn_infty
from gudhi.wasserstein import wasserstein_distance as pot
from gudhi.hera import wasserstein_distance as hera
import numpy as np
import pytest
__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 test_finite_part():
diag = np.array([[0, 1], [3, 5], [2, np.inf], [3, np.inf], [-np.inf, 8], [-np.inf, 12], [-np.inf, -np.inf],
[np.inf, np.inf], [-np.inf, np.inf], [-np.inf, np.inf]])
assert np.array_equal(_finite_part(diag), [[0, 1], [3, 5]])
def test_handle_essential_parts():
diag1 = np.array([[0, 1], [3, 5],
[2, np.inf], [3, np.inf],
[-np.inf, 8], [-np.inf, 12],
[-np.inf, -np.inf],
[np.inf, np.inf],
[-np.inf, np.inf], [-np.inf, np.inf]])
diag2 = np.array([[0, 2], [3, 5],
[2, np.inf], [4, np.inf],
[-np.inf, 8], [-np.inf, 11],
[-np.inf, -np.inf],
[np.inf, np.inf],
[-np.inf, np.inf], [-np.inf, np.inf]])
diag3 = np.array([[0, 2], [3, 5],
[2, np.inf], [4, np.inf], [6, np.inf],
[-np.inf, 8], [-np.inf, 11],
[-np.inf, -np.inf],
[np.inf, np.inf],
[-np.inf, np.inf], [-np.inf, np.inf]])
c, m = _handle_essential_parts(diag1, diag2, order=1)
assert c == pytest.approx(2, 0.0001) # Note: here c is only the cost due to essential part (thus 2, not 3)
# Similarly, the matching only corresponds to essential parts.
# Note that (-inf,-inf) and (+inf,+inf) coordinates are matched to the diagonal.
assert np.array_equal(m, [[4, 4], [5, 5], [2, 2], [3, 3], [8, 8], [9, 9], [6, -1], [7, -1], [-1, 6], [-1, 7]])
c, m = _handle_essential_parts(diag1, diag3, order=1)
assert c == np.inf
assert (m is None)
def test_get_essential_parts():
diag1 = np.array([[0, 1], [3, 5], [2, np.inf], [3, np.inf], [-np.inf, 8], [-np.inf, 12], [-np.inf, -np.inf],
[np.inf, np.inf], [-np.inf, np.inf], [-np.inf, np.inf]])
diag2 = np.array([[0, 1], [3, 5], [2, np.inf], [3, np.inf]])
res = _get_essential_parts(diag1)
res2 = _get_essential_parts(diag2)
assert np.array_equal(res[0], [4, 5])
assert np.array_equal(res[1], [2, 3])
assert np.array_equal(res[2], [8, 9])
assert np.array_equal(res[3], [6] )
assert np.array_equal(res[4], [7] )
assert np.array_equal(res2[0], [] )
assert np.array_equal(res2[1], [2, 3])
assert np.array_equal(res2[2], [] )
assert np.array_equal(res2[3], [] )
assert np.array_equal(res2[4], [] )
def test_warn_infty():
with pytest.warns(UserWarning):
assert _warn_infty(matching=False)==np.inf
c, m = _warn_infty(matching=True)
assert (c == np.inf)
assert (m is None)
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]])
diag3 = np.array([[0, 2], [4, 6]])
diag4 = np.array([[0, 3], [4, 8]])
emptydiag = np.array([])
# We just need to handle positive numbers here
def approx(x):
return pytest.approx(x, rel=delta)
assert wasserstein_distance(emptydiag, emptydiag, internal_p=2., order=1.) == 0.
assert wasserstein_distance(emptydiag, emptydiag, internal_p=np.inf, order=1.) == 0.
assert wasserstein_distance(emptydiag, emptydiag, internal_p=np.inf, order=2.) == 0.
assert wasserstein_distance(emptydiag, emptydiag, internal_p=2., order=2.) == 0.
assert wasserstein_distance(diag3, emptydiag, internal_p=np.inf, order=1.) == approx(2.)
assert wasserstein_distance(diag3, emptydiag, internal_p=1., order=1.) == approx(4.)
assert wasserstein_distance(diag4, emptydiag, internal_p=1., order=2.) == approx(5.) # thank you Pythagorician triplets
assert wasserstein_distance(diag4, emptydiag, internal_p=np.inf, order=2.) == approx(2.5)
assert wasserstein_distance(diag4, emptydiag, internal_p=2., order=2.) == approx(3.5355339059327378)
assert wasserstein_distance(diag1, diag2, internal_p=2., order=1.) == approx(1.4453593023967701)
assert wasserstein_distance(diag1, diag2, internal_p=2.35, order=1.74) == approx(0.9772734057168739)
assert wasserstein_distance(diag1, emptydiag, internal_p=2.35, order=1.7863) == approx(3.141592214572228)
assert wasserstein_distance(diag3, diag4, internal_p=1., order=1.) == approx(3.)
assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=1.) == approx(3.) # no diag matching here
assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=2.) == approx(np.sqrt(5))
assert wasserstein_distance(diag3, diag4, internal_p=1., order=2.) == approx(np.sqrt(5))
assert wasserstein_distance(diag3, diag4, internal_p=4.5, order=2.) == approx(np.sqrt(5))
if test_infinity:
diag5 = np.array([[0, 3], [4, np.inf]])
diag6 = np.array([[7, 8], [4, 6], [3, np.inf]])
assert wasserstein_distance(diag4, diag5) == np.inf
assert wasserstein_distance(diag5, diag6, order=1, internal_p=np.inf) == approx(4.)
assert wasserstein_distance(diag5, emptydiag) == np.inf
if test_matching:
match = wasserstein_distance(emptydiag, emptydiag, matching=True, internal_p=1., order=2)[1]
assert np.array_equal(match, [])
match = wasserstein_distance(emptydiag, emptydiag, matching=True, internal_p=np.inf, order=2.24)[1]
assert np.array_equal(match, [])
match = wasserstein_distance(emptydiag, diag2, matching=True, internal_p=np.inf, order=2.)[1]
assert np.array_equal(match , [[-1, 0], [-1, 1]])
match = wasserstein_distance(diag2, emptydiag, matching=True, internal_p=np.inf, order=2.24)[1]
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]])
if test_matching and test_infinity:
diag7 = np.array([[0, 3], [4, np.inf], [5, np.inf]])
diag8 = np.array([[0,1], [0, np.inf], [-np.inf, -np.inf], [np.inf, np.inf]])
diag9 = np.array([[-np.inf, -np.inf], [np.inf, np.inf]])
diag10 = np.array([[0,1], [-np.inf, -np.inf], [np.inf, np.inf]])
match = wasserstein_distance(diag5, diag6, matching=True, internal_p=2., order=2.)[1]
assert np.array_equal(match, [[0, -1], [-1,0], [-1, 1], [1, 2]])
match = wasserstein_distance(diag5, diag7, matching=True, internal_p=2., order=2.)[1]
assert (match is None)
cost, match = wasserstein_distance(diag7, emptydiag, matching=True, internal_p=2., order=2.3)
assert (cost == np.inf)
assert (match is None)
cost, match = wasserstein_distance(emptydiag, diag7, matching=True, internal_p=2.42, order=2.)
assert (cost == np.inf)
assert (match is None)
cost, match = wasserstein_distance(diag8, diag9, matching=True, internal_p=2., order=2.)
assert (cost == np.inf)
assert (match is None)
cost, match = wasserstein_distance(diag9, diag10, matching=True, internal_p=1., order=1.)
assert (cost == 1)
assert (match == [[0, -1],[1, -1],[-1, 0], [-1, 1], [-1, 2]]) # type 4 and 5 are match to the diag anyway.
cost, match = wasserstein_distance(diag9, emptydiag, matching=True, internal_p=2., order=2.)
assert (cost == 0.)
assert (match == [[0, -1], [1, -1]])
def hera_wrap(**extra):
def fun(*kargs,**kwargs):
return hera(*kargs,**kwargs,**extra)
return fun
def pot_wrap(**extra):
def fun(*kargs,**kwargs):
return pot(*kargs,**kwargs,**extra)
return fun
def test_wasserstein_distance_pot():
_basic_wasserstein(pot, 1e-15, test_infinity=False, test_matching=True) # pot with its standard args
_basic_wasserstein(pot_wrap(enable_autodiff=True, keep_essential_parts=False), 1e-15, test_infinity=False, test_matching=False)
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)
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