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[MRG] Fix bug in regularized OTDA l1lp with log #413

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Merged
merged 5 commits into from
Dec 6, 2022
Merged

[MRG] Fix bug in regularized OTDA l1lp with log #413

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c9a7735
correct bug in DA l1lp with log
rflamary Dec 6, 2022
d2fd0a2
better tests and speedup with smaller dataset size
rflamary Dec 6, 2022
9a4293e
remove jax for log test
rflamary Dec 6, 2022
4f2c92f
remove trndorflow for log test
rflamary Dec 6, 2022
c5c6380
pep8!
rflamary Dec 6, 2022
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3 changes: 2 additions & 1 deletion RELEASES.md
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,8 @@ roughly 2^31) (PR #381)
- Fixed an issue where the doc could not be built due to some changes in matplotlib's API (Issue #403, PR #402)
- Replaced Numpy C Compiler with Setuptools C Compiler due to deprecation issues (Issue #408, PR #409)
- Fixed weak optimal transport docstring (Issue #404, PR #410)

- Fixed error whith parameter `log=True`for `SinkhornLpl1Transport` (Issue #412,
PR #413)

## 0.8.2

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13 changes: 10 additions & 3 deletions ot/da.py
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Expand Up @@ -126,8 +126,12 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
W = nx.zeros(M.shape, type_as=M)
for cpt in range(numItermax):
Mreg = M + eta * W
transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
stopThr=stopInnerThr)
if log:
transp, log = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
stopThr=stopInnerThr, log=True)
else:
transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
stopThr=stopInnerThr)
# the transport has been computed. Check if classes are really
# separated
W = nx.ones(M.shape, type_as=M)
Expand All @@ -136,7 +140,10 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
majs = p * ((majs + epsilon) ** (p - 1))
W[indices_labels[i]] = majs

return transp
if log:
return transp, log
else:
return transp


def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
Expand Down
62 changes: 41 additions & 21 deletions test/test_da.py
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Expand Up @@ -42,14 +42,34 @@ def test_class_jax_tf():
otda.fit(Xs=Xs, ys=ys, Xt=Xt)


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
@pytest.mark.parametrize("class_to_test", [ot.da.EMDTransport, ot.da.SinkhornTransport, ot.da.SinkhornLpl1Transport, ot.da.SinkhornL1l2Transport, ot.da.SinkhornL1l2Transport])
def test_log_da(nx, class_to_test):

ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)

Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

otda = class_to_test(log=True)

# test its computed
otda.fit(Xs=Xs, ys=ys, Xt=Xt)
assert hasattr(otda, "log_")


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_sinkhorn_lpl1_transport_class(nx):
"""test_sinkhorn_transport
"""

ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down Expand Up @@ -136,7 +156,7 @@ def test_sinkhorn_l1l2_transport_class(nx):
"""

ns = 50
nt = 100
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down Expand Up @@ -230,8 +250,8 @@ def test_sinkhorn_transport_class(nx):
"""test_sinkhorn_transport
"""

ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down Expand Up @@ -323,8 +343,8 @@ def test_unbalanced_sinkhorn_transport_class(nx):
"""test_sinkhorn_transport
"""

ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down Expand Up @@ -402,8 +422,8 @@ def test_emd_transport_class(nx):
"""test_sinkhorn_transport
"""

ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down Expand Up @@ -558,8 +578,8 @@ def test_mapping_transport_class_specific_seed(nx):
@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_linear_mapping(nx):
ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand All @@ -579,8 +599,8 @@ def test_linear_mapping(nx):
@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_linear_mapping_class(nx):
ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down Expand Up @@ -609,9 +629,9 @@ def test_jcpot_transport_class(nx):
"""test_jcpot_transport
"""

ns1 = 150
ns2 = 150
nt = 200
ns1 = 50
ns2 = 50
nt = 50

Xs1, ys1 = make_data_classif('3gauss', ns1)
Xs2, ys2 = make_data_classif('3gauss', ns2)
Expand Down Expand Up @@ -681,9 +701,9 @@ def test_jcpot_barycenter(nx):
"""test_jcpot_barycenter
"""

ns1 = 150
ns2 = 150
nt = 200
ns1 = 50
ns2 = 50
nt = 50

sigma = 0.1
np.random.seed(1985)
Expand Down Expand Up @@ -713,8 +733,8 @@ def test_jcpot_barycenter(nx):
def test_emd_laplace_class(nx):
"""test_emd_laplace_transport
"""
ns = 150
nt = 200
ns = 50
nt = 50

Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Expand Down