[MRG] Sliced and 1D Wasserstein distances : backend versions (#256)

* add numpy and torch backends

* stat sets on functions

* proper import

* install recent torch on windows

* install recent torch on windows

* now testing all functions in backedn

* add jax backedn

* clenaup windowds

* proper convert for jax backedn

* pep8

* try again windows tests

* test jax conversion

* try proper widows tests

* emd fuction ses backedn

* better test partial OT

* proper tests to_numpy and teplate Backend

* pep8

* pep8 x2

* feaking sinkhorn works with torch

* sinkhorn2 compatible

* working ot.emd2

* important detach

* it should work

* jax autodiff emd

* pep8

* no tast same for jax

* new independat tests per backedn

* freaking pep8

* add tests for gradients

* deprecate ot.gpu

* worging dist function

* working dist

* dist done in backedn

* not in

* remove indexing

* change accuacy for jax

* first pull backend

* projection simplex

* projection simplex

* projection simplex

* projection simplex no ci

* projection simplex no ci

* projection simplex no ci

* pep8

* add backedn discusion to quickstart guide

* projection simplex no ci

* projection simplex no ci

* projection simplex no ci

* pep8 + better doc

* proper links

* corect doctest

* big debug documentation

* doctest again

* doctest again bis

* doctest again ter (last one or i kill myself)

* backend test + doc proj simplex

* correction test_utils

* correction test_utils

* correction cumsum

* correction flip

* correction flip v2

* more debug

* more debug

* more debug + pep8

* pep8

* argh

* proj_simplex

* backedn works for sort

* proj simplex

* jax sucks

* update doc

* Update test/test_utils.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/quickstart.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/quickstart.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/quickstart.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/readme.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update test/test_utils.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update ot/utils.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/readme.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update ot/lp/__init__.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* begin comment alex

* comment alex part 2

* optimize test gromov

* proj_simplex on vectors

* add awesome gradient decsnt example on the weights

* pep98 of course

* proof read example by alex

* pep8 again

* encoding oos in translation

* correct legend

* new backend functions for sliced

* small indent pb

* Optimized backendversion of sliced W

* error in sliced W

* after master merge

* error sliced

* error sliced

* pep8

* test_sliced pep8

* doctest + precision for sliced

* doctest

* type win test_backend gather

* type win test_backend gather

* Update sliced.py

change argument of padding pad_width

* Update backend.py

update redefinition

* Update backend.py

pep8

* Update backend.py

pep 8 again....

* pep8

* build docs

* emd2_1D example

* refectoring emd_1d and variants

* remove unused previous wasserstein_1d

* pep8

* upate example

* move stuff

* tesys should work + implemù random backend

* test random generayor functions

* correction

* better random generation

* update sliced

* update sliced

* proper tests sliced

* max sliced

* chae file nam

* add stuff

* example sliced flow and barycenter

* correct typo + update readme

* exemple sliced flow done

* pep8

* solver1d works

* pep8

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

1 files changed, 1 insertions, 56 deletions

diff --git a/test/test_ot.py b/test/test_ot.py
index 4dfc510..5bfde1d 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -8,11 +8,11 @@ import warnings
 
 import numpy as np
 import pytest
-from scipy.stats import wasserstein_distance
 
 import ot
 from ot.datasets import make_1D_gauss as gauss
 from ot.backend import torch
+from scipy.stats import wasserstein_distance
 
 
 def test_emd_dimension_and_mass_mismatch():
@@ -165,61 +165,6 @@ def test_emd_1d_emd2_1d():
         ot.emd_1d(u, v, [], [])
 
 
-def test_emd_1d_emd2_1d_with_weights():
-    # test emd1d gives similar results as emd
-    n = 20
-    m = 30
-    rng = np.random.RandomState(0)
-    u = rng.randn(n, 1)
-    v = rng.randn(m, 1)
-
-    w_u = rng.uniform(0., 1., n)
-    w_u = w_u / w_u.sum()
-
-    w_v = rng.uniform(0., 1., m)
-    w_v = w_v / w_v.sum()
-
-    M = ot.dist(u, v, metric='sqeuclidean')
-
-    G, log = ot.emd(w_u, w_v, M, log=True)
-    wass = log["cost"]
-    G_1d, log = ot.emd_1d(u, v, w_u, w_v, metric='sqeuclidean', log=True)
-    wass1d = log["cost"]
-    wass1d_emd2 = ot.emd2_1d(u, v, w_u, w_v, metric='sqeuclidean', log=False)
-    wass1d_euc = ot.emd2_1d(u, v, w_u, w_v, metric='euclidean', log=False)
-
-    # check loss is similar
-    np.testing.assert_allclose(wass, wass1d)
-    np.testing.assert_allclose(wass, wass1d_emd2)
-
-    # check loss is similar to scipy's implementation for Euclidean metric
-    wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,)), w_u, w_v)
-    np.testing.assert_allclose(wass_sp, wass1d_euc)
-
-    # check constraints
-    np.testing.assert_allclose(w_u, G.sum(1))
-    np.testing.assert_allclose(w_v, G.sum(0))
-
-
-def test_wass_1d():
-    # test emd1d gives similar results as emd
-    n = 20
-    m = 30
-    rng = np.random.RandomState(0)
-    u = rng.randn(n, 1)
-    v = rng.randn(m, 1)
-
-    M = ot.dist(u, v, metric='sqeuclidean')
-
-    G, log = ot.emd([], [], M, log=True)
-    wass = log["cost"]
-
-    wass1d = ot.wasserstein_1d(u, v, [], [], p=2.)
-
-    # check loss is similar
-    np.testing.assert_allclose(np.sqrt(wass), wass1d)
-
-
 def test_emd_empty():
     # test emd and emd2 for simple identity
     n = 100