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remove automatic imports from ot/__init__.py #36

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164ac54
remove automatic imports from ot/__init__.py
Slasnista Oct 27, 2017
7c535cc
checkups ok
Slasnista Oct 27, 2017
ac06ff5
solving pb for test with gpu
Slasnista Oct 27, 2017
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25 changes: 0 additions & 25 deletions ot/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,29 +9,4 @@
#
# License: MIT License


# All submodules and packages
from . import lp
from . import bregman
from . import optim
from . import utils
from . import datasets
from . import plot
from . import da
from . import gromov

# OT functions
from .lp import emd, emd2
from .bregman import sinkhorn, sinkhorn2, barycenter
from .da import sinkhorn_lpl1_mm
from .gromov import gromov_wasserstein, gromov_wasserstein2

# utils functions
from .utils import dist, unif, tic, toc, toq

__version__ = "0.4.0"

__all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets',
'bregman', 'lp', 'plot', 'tic', 'toc', 'toq',
'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim',
'gromov_wasserstein','gromov_wasserstein2']
62 changes: 32 additions & 30 deletions test/test_bregman.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,9 @@
# License: MIT License

import numpy as np
import ot
from ot.utils import unif, dist, dist0
from ot.bregman import sinkhorn, barycenter, unmix
from ot.datasets import get_1D_gauss


def test_sinkhorn():
Expand All @@ -14,11 +16,11 @@ def test_sinkhorn():
rng = np.random.RandomState(0)

x = rng.randn(n, 2)
u = ot.utils.unif(n)
u = unif(n)

M = ot.dist(x, x)
M = dist(x, x)

G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10)
G = sinkhorn(u, u, M, 1, stopThr=1e-10)

# check constratints
np.testing.assert_allclose(
Expand All @@ -33,22 +35,22 @@ def test_sinkhorn_empty():
rng = np.random.RandomState(0)

x = rng.randn(n, 2)
u = ot.utils.unif(n)
u = unif(n)

M = ot.dist(x, x)
M = dist(x, x)

G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True)
G, log = sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True)
# check constratints
np.testing.assert_allclose(u, G.sum(1), atol=1e-05)
np.testing.assert_allclose(u, G.sum(0), atol=1e-05)

G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10,
method='sinkhorn_stabilized', verbose=True, log=True)
G, log = sinkhorn([], [], M, 1, stopThr=1e-10,
method='sinkhorn_stabilized', verbose=True, log=True)
# check constratints
np.testing.assert_allclose(u, G.sum(1), atol=1e-05)
np.testing.assert_allclose(u, G.sum(0), atol=1e-05)

G, log = ot.sinkhorn(
G, log = sinkhorn(
[], [], M, 1, stopThr=1e-10, method='sinkhorn_epsilon_scaling',
verbose=True, log=True)
# check constratints
Expand All @@ -62,15 +64,15 @@ def test_sinkhorn_variants():
rng = np.random.RandomState(0)

x = rng.randn(n, 2)
u = ot.utils.unif(n)
u = unif(n)

M = ot.dist(x, x)
M = dist(x, x)

G0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10)
Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10)
Ges = ot.sinkhorn(
G0 = sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10)
Gs = sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10)
Ges = sinkhorn(
u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10)
Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10)
Gerr = sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10)

# check values
np.testing.assert_allclose(G0, Gs, atol=1e-05)
Expand All @@ -83,55 +85,55 @@ def test_bary():
n_bins = 100 # nb bins

# Gaussian distributions
a1 = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std
a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10)
a1 = get_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std
a2 = get_1D_gauss(n_bins, m=40, s=10)

# creating matrix A containing all distributions
A = np.vstack((a1, a2)).T

# loss matrix + normalization
M = ot.utils.dist0(n_bins)
M = dist0(n_bins)
M /= M.max()

alpha = 0.5 # 0<=alpha<=1
weights = np.array([1 - alpha, alpha])

# wasserstein
reg = 1e-3
bary_wass = ot.bregman.barycenter(A, M, reg, weights)
bary_wass = barycenter(A, M, reg, weights)

np.testing.assert_allclose(1, np.sum(bary_wass))

ot.bregman.barycenter(A, M, reg, log=True, verbose=True)
barycenter(A, M, reg, log=True, verbose=True)


def test_unmix():

n_bins = 50 # nb bins

# Gaussian distributions
a1 = ot.datasets.get_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std
a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10)
a1 = get_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std
a2 = get_1D_gauss(n_bins, m=40, s=10)

a = ot.datasets.get_1D_gauss(n_bins, m=30, s=10)
a = get_1D_gauss(n_bins, m=30, s=10)

# creating matrix A containing all distributions
D = np.vstack((a1, a2)).T

# loss matrix + normalization
M = ot.utils.dist0(n_bins)
M = dist0(n_bins)
M /= M.max()

M0 = ot.utils.dist0(2)
M0 = dist0(2)
M0 /= M0.max()
h0 = ot.unif(2)
h0 = unif(2)

# wasserstein
reg = 1e-3
um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,)
um = unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,)

np.testing.assert_allclose(1, np.sum(um), rtol=1e-03, atol=1e-03)
np.testing.assert_allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03)

ot.bregman.unmix(a, D, M, M0, h0, reg,
1, alpha=0.01, log=True, verbose=True)
unmix(a, D, M, M0, h0, reg,
1, alpha=0.01, log=True, verbose=True)
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