From 767171593f2a98a26b9a39bf110a45085e3b982e Mon Sep 17 00:00:00 2001 From: Nathan Cassereau <84033440+ncassereau-idris@users.noreply.github.com> Date: Thu, 24 Mar 2022 10:53:47 +0100 Subject: [MRG] Domain adaptation and unbalanced solvers with backend support (#343) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * First draft * Add matrix inverse and square root to backend * Eigen decomposition for older versions of pytorch (1.8.1 and older) * Corrected eigen decomposition for pytorch 1.8.1 and older * Spectral theorem is a thing * Optimization * small optimization * More functions converted * pep8 * remove a warning and prepare torch meshgrid for future torch release (which will change default indexing) * dots and pep8 * Meshgrid corrected for older version and prepared for future versions changes * New backend functions * Base transport * LinearTransport * All transport classes + pep8 * PR added to release file * Jcpot barycenter test * unbalanced with backend * pep8 * bug solve * test of domain adaptation with backends * solve bug for tic toc & macos * solving scipy deprecation warning * solving scipy deprecation warning attempt2 * solving scipy deprecation warning attempt3 * A warning is triggered when a float->int conversion is detected * bug solve * docs * release file updated * Better handling of float->int conversion in EMD * Corrected test for is_floating_point * docs * release file updated * cupy does not allow implicit cast * fromnumpy * added test * test da tf jax * test unbalanced with no provided histogram * using type_as argument in unif function correctly * pep8 * transport plan cast in emd changed behaviour, now trying to cast as histogram's dtype, defaulting to cost matrix Co-authored-by: Rémi Flamary --- test/test_1d_solver.py | 28 ++--- test/test_backend.py | 66 ++++++++++- test/test_bregman.py | 81 ++++--------- test/test_da.py | 307 ++++++++++++++++++++++++------------------------ test/test_gromov.py | 147 ++++++++--------------- test/test_optim.py | 17 ++- test/test_ot.py | 19 ++- test/test_sliced.py | 32 ++--- test/test_unbalanced.py | 157 +++++++++++++++---------- test/test_weak.py | 4 +- 10 files changed, 414 insertions(+), 444 deletions(-) (limited to 'test') diff --git a/test/test_1d_solver.py b/test/test_1d_solver.py index 6a42cfe..20f307a 100644 --- a/test/test_1d_solver.py +++ b/test/test_1d_solver.py @@ -66,9 +66,7 @@ def test_wasserstein_1d(nx): rho_v = np.abs(rng.randn(n)) rho_v /= rho_v.sum() - xb = nx.from_numpy(x) - rho_ub = nx.from_numpy(rho_u) - rho_vb = nx.from_numpy(rho_v) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v) # test 1 : wasserstein_1d should be close to scipy W_1 implementation np.testing.assert_almost_equal(wasserstein_1d(xb, xb, rho_ub, rho_vb, p=1), @@ -98,9 +96,7 @@ def test_wasserstein_1d_type_devices(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - xb = nx.from_numpy(x, type_as=tp) - rho_ub = nx.from_numpy(rho_u, type_as=tp) - rho_vb = nx.from_numpy(rho_v, type_as=tp) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v, type_as=tp) res = wasserstein_1d(xb, xb, rho_ub, rho_vb, p=1) @@ -122,17 +118,13 @@ def test_wasserstein_1d_device_tf(): # Check that everything stays on the CPU with tf.device("/CPU:0"): - xb = nx.from_numpy(x) - rho_ub = nx.from_numpy(rho_u) - rho_vb = nx.from_numpy(rho_v) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v) res = wasserstein_1d(xb, xb, rho_ub, rho_vb, p=1) nx.assert_same_dtype_device(xb, res) if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - xb = nx.from_numpy(x) - rho_ub = nx.from_numpy(rho_u) - rho_vb = nx.from_numpy(rho_v) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v) res = wasserstein_1d(xb, xb, rho_ub, rho_vb, p=1) nx.assert_same_dtype_device(xb, res) assert nx.dtype_device(res)[1].startswith("GPU") @@ -190,9 +182,7 @@ def test_emd1d_type_devices(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - xb = nx.from_numpy(x, type_as=tp) - rho_ub = nx.from_numpy(rho_u, type_as=tp) - rho_vb = nx.from_numpy(rho_v, type_as=tp) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v, type_as=tp) emd = ot.emd_1d(xb, xb, rho_ub, rho_vb) emd2 = ot.emd2_1d(xb, xb, rho_ub, rho_vb) @@ -214,9 +204,7 @@ def test_emd1d_device_tf(): # Check that everything stays on the CPU with tf.device("/CPU:0"): - xb = nx.from_numpy(x) - rho_ub = nx.from_numpy(rho_u) - rho_vb = nx.from_numpy(rho_v) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v) emd = ot.emd_1d(xb, xb, rho_ub, rho_vb) emd2 = ot.emd2_1d(xb, xb, rho_ub, rho_vb) nx.assert_same_dtype_device(xb, emd) @@ -224,9 +212,7 @@ def test_emd1d_device_tf(): if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - xb = nx.from_numpy(x) - rho_ub = nx.from_numpy(rho_u) - rho_vb = nx.from_numpy(rho_v) + xb, rho_ub, rho_vb = nx.from_numpy(x, rho_u, rho_v) emd = ot.emd_1d(xb, xb, rho_ub, rho_vb) emd2 = ot.emd2_1d(xb, xb, rho_ub, rho_vb) nx.assert_same_dtype_device(xb, emd) diff --git a/test/test_backend.py b/test/test_backend.py index 027c4cd..311c075 100644 --- a/test/test_backend.py +++ b/test/test_backend.py @@ -217,6 +217,8 @@ def test_empty_backend(): nx.zero_pad(M, v) with pytest.raises(NotImplementedError): nx.argmax(M) + with pytest.raises(NotImplementedError): + nx.argmin(M) with pytest.raises(NotImplementedError): nx.mean(M) with pytest.raises(NotImplementedError): @@ -264,12 +266,27 @@ def test_empty_backend(): nx.device_type(M) with pytest.raises(NotImplementedError): nx._bench(lambda x: x, M, n_runs=1) + with pytest.raises(NotImplementedError): + nx.solve(M, v) + with pytest.raises(NotImplementedError): + nx.trace(M) + with pytest.raises(NotImplementedError): + nx.inv(M) + with pytest.raises(NotImplementedError): + nx.sqrtm(M) + with pytest.raises(NotImplementedError): + nx.isfinite(M) + with pytest.raises(NotImplementedError): + nx.array_equal(M, M) + with pytest.raises(NotImplementedError): + nx.is_floating_point(M) def test_func_backends(nx): rnd = np.random.RandomState(0) M = rnd.randn(10, 3) + SquareM = rnd.randn(10, 10) v = rnd.randn(3) val = np.array([1.0]) @@ -288,6 +305,7 @@ def test_func_backends(nx): lst_name = [] Mb = nx.from_numpy(M) + SquareMb = nx.from_numpy(SquareM) vb = nx.from_numpy(v) val = nx.from_numpy(val) @@ -467,6 +485,10 @@ def test_func_backends(nx): lst_b.append(nx.to_numpy(A)) lst_name.append('argmax') + A = nx.argmin(Mb) + lst_b.append(nx.to_numpy(A)) + lst_name.append('argmin') + A = nx.mean(Mb) lst_b.append(nx.to_numpy(A)) lst_name.append('mean') @@ -529,7 +551,11 @@ def test_func_backends(nx): A = nx.where(Mb >= nx.stack([nx.linspace(0, 1, 10)] * 3, axis=1), Mb, 0.0) lst_b.append(nx.to_numpy(A)) - lst_name.append('where') + lst_name.append('where (cond, x, y)') + + A = nx.where(nx.from_numpy(np.array([True, False]))) + lst_b.append(nx.to_numpy(nx.stack(A))) + lst_name.append('where (cond)') A = nx.copy(Mb) lst_b.append(nx.to_numpy(A)) @@ -550,15 +576,47 @@ def test_func_backends(nx): nx._bench(lambda x: x, M, n_runs=1) + A = nx.solve(SquareMb, Mb) + lst_b.append(nx.to_numpy(A)) + lst_name.append('solve') + + A = nx.trace(SquareMb) + lst_b.append(nx.to_numpy(A)) + lst_name.append('trace') + + A = nx.inv(SquareMb) + lst_b.append(nx.to_numpy(A)) + lst_name.append('matrix inverse') + + A = nx.sqrtm(SquareMb.T @ SquareMb) + lst_b.append(nx.to_numpy(A)) + lst_name.append("matrix square root") + + A = nx.concatenate([vb, nx.from_numpy(np.array([np.inf, np.nan]))], axis=0) + A = nx.isfinite(A) + lst_b.append(nx.to_numpy(A)) + lst_name.append("isfinite") + + assert not nx.array_equal(Mb, vb), "array_equal (shape)" + assert nx.array_equal(Mb, Mb), "array_equal (elements) - expected true" + assert not nx.array_equal( + Mb, Mb + nx.eye(*list(Mb.shape)) + ), "array_equal (elements) - expected false" + + assert nx.is_floating_point(Mb), "is_floating_point - expected true" + assert not nx.is_floating_point( + nx.from_numpy(np.array([0, 1, 2], dtype=int)) + ), "is_floating_point - expected false" + lst_tot.append(lst_b) lst_np = lst_tot[0] lst_b = lst_tot[1] for a1, a2, name in zip(lst_np, lst_b, lst_name): - if not np.allclose(a1, a2): - print('Assert fail on: ', name) - assert np.allclose(a1, a2, atol=1e-7) + np.testing.assert_allclose( + a2, a1, atol=1e-7, err_msg=f'ASSERT FAILED ON: {name}' + ) def test_random_backends(nx): diff --git a/test/test_bregman.py b/test/test_bregman.py index 1419f9b..6c37984 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -155,8 +155,7 @@ def test_sinkhorn_backends(nx): G = ot.sinkhorn(a, a, M, 1) - ab = nx.from_numpy(a) - M_nx = nx.from_numpy(M) + ab, M_nx = nx.from_numpy(a, M) Gb = ot.sinkhorn(ab, ab, M_nx, 1) @@ -176,8 +175,7 @@ def test_sinkhorn2_backends(nx): G = ot.sinkhorn(a, a, M, 1) - ab = nx.from_numpy(a) - M_nx = nx.from_numpy(M) + ab, M_nx = nx.from_numpy(a, M) Gb = ot.sinkhorn2(ab, ab, M_nx, 1) @@ -260,8 +258,7 @@ def test_sinkhorn_variants(nx): M = ot.dist(x, x) - ub = nx.from_numpy(u) - M_nx = nx.from_numpy(M) + ub, M_nx = nx.from_numpy(u, M) G = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10) Gl = nx.to_numpy(ot.sinkhorn(ub, ub, M_nx, 1, method='sinkhorn_log', stopThr=1e-10)) @@ -298,8 +295,7 @@ def test_sinkhorn_variants_dtype_device(nx, method): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - ub = nx.from_numpy(u, type_as=tp) - Mb = nx.from_numpy(M, type_as=tp) + ub, Mb = nx.from_numpy(u, M, type_as=tp) Gb = ot.sinkhorn(ub, ub, Mb, 1, method=method, stopThr=1e-10) @@ -318,8 +314,7 @@ def test_sinkhorn2_variants_dtype_device(nx, method): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - ub = nx.from_numpy(u, type_as=tp) - Mb = nx.from_numpy(M, type_as=tp) + ub, Mb = nx.from_numpy(u, M, type_as=tp) lossb = ot.sinkhorn2(ub, ub, Mb, 1, method=method, stopThr=1e-10) @@ -337,8 +332,7 @@ def test_sinkhorn2_variants_device_tf(method): # Check that everything stays on the CPU with tf.device("/CPU:0"): - ub = nx.from_numpy(u) - Mb = nx.from_numpy(M) + ub, Mb = nx.from_numpy(u, M) Gb = ot.sinkhorn(ub, ub, Mb, 1, method=method, stopThr=1e-10) lossb = ot.sinkhorn2(ub, ub, Mb, 1, method=method, stopThr=1e-10) nx.assert_same_dtype_device(Mb, Gb) @@ -346,8 +340,7 @@ def test_sinkhorn2_variants_device_tf(method): if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - ub = nx.from_numpy(u) - Mb = nx.from_numpy(M) + ub, Mb = nx.from_numpy(u, M) Gb = ot.sinkhorn(ub, ub, Mb, 1, method=method, stopThr=1e-10) lossb = ot.sinkhorn2(ub, ub, Mb, 1, method=method, stopThr=1e-10) nx.assert_same_dtype_device(Mb, Gb) @@ -370,9 +363,7 @@ def test_sinkhorn_variants_multi_b(nx): M = ot.dist(x, x) - ub = nx.from_numpy(u) - bb = nx.from_numpy(b) - M_nx = nx.from_numpy(M) + ub, bb, M_nx = nx.from_numpy(u, b, M) G = ot.sinkhorn(u, b, M, 1, method='sinkhorn', stopThr=1e-10) Gl = nx.to_numpy(ot.sinkhorn(ub, bb, M_nx, 1, method='sinkhorn_log', stopThr=1e-10)) @@ -400,9 +391,7 @@ def test_sinkhorn2_variants_multi_b(nx): M = ot.dist(x, x) - ub = nx.from_numpy(u) - bb = nx.from_numpy(b) - M_nx = nx.from_numpy(M) + ub, bb, M_nx = nx.from_numpy(u, b, M) G = ot.sinkhorn2(u, b, M, 1, method='sinkhorn', stopThr=1e-10) Gl = nx.to_numpy(ot.sinkhorn2(ub, bb, M_nx, 1, method='sinkhorn_log', stopThr=1e-10)) @@ -483,9 +472,7 @@ def test_barycenter(nx, method, verbose, warn): alpha = 0.5 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) - A_nx = nx.from_numpy(A) - M_nx = nx.from_numpy(M) - weights_nx = nx.from_numpy(weights) + A_nx, M_nx, weights_nx = nx.from_numpy(A, M, weights) reg = 1e-2 if nx.__name__ in ("jax", "tf") and method == "sinkhorn_log": @@ -523,9 +510,7 @@ def test_barycenter_debiased(nx, method, verbose, warn): alpha = 0.5 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) - A_nx = nx.from_numpy(A) - M_nx = nx.from_numpy(M) - weights_nx = nx.from_numpy(weights) + A_nx, M_nx, weights_nx = nx.from_numpy(A, M, weights) # wasserstein reg = 1e-2 @@ -594,9 +579,7 @@ def test_barycenter_stabilization(nx): alpha = 0.5 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) - A_nx = nx.from_numpy(A) - M_nx = nx.from_numpy(M) - weights_b = nx.from_numpy(weights) + A_nx, M_nx, weights_b = nx.from_numpy(A, M, weights) # wasserstein reg = 1e-2 @@ -697,11 +680,7 @@ def test_unmix(nx): M0 /= M0.max() h0 = ot.unif(2) - ab = nx.from_numpy(a) - Db = nx.from_numpy(D) - M_nx = nx.from_numpy(M) - M0b = nx.from_numpy(M0) - h0b = nx.from_numpy(h0) + ab, Db, M_nx, M0b, h0b = nx.from_numpy(a, D, M, M0, h0) # wasserstein reg = 1e-3 @@ -727,12 +706,7 @@ def test_empirical_sinkhorn(nx): M = ot.dist(X_s, X_t) M_m = ot.dist(X_s, X_t, metric='euclidean') - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - X_sb = nx.from_numpy(X_s) - X_tb = nx.from_numpy(X_t) - M_nx = nx.from_numpy(M, type_as=ab) - M_mb = nx.from_numpy(M_m, type_as=ab) + ab, bb, X_sb, X_tb, M_nx, M_mb = nx.from_numpy(a, b, X_s, X_t, M, M_m) G_sqe = nx.to_numpy(ot.bregman.empirical_sinkhorn(X_sb, X_tb, 1)) sinkhorn_sqe = nx.to_numpy(ot.sinkhorn(ab, bb, M_nx, 1)) @@ -776,12 +750,7 @@ def test_lazy_empirical_sinkhorn(nx): M = ot.dist(X_s, X_t) M_m = ot.dist(X_s, X_t, metric='euclidean') - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - X_sb = nx.from_numpy(X_s) - X_tb = nx.from_numpy(X_t) - M_nx = nx.from_numpy(M, type_as=ab) - M_mb = nx.from_numpy(M_m, type_as=ab) + ab, bb, X_sb, X_tb, M_nx, M_mb = nx.from_numpy(a, b, X_s, X_t, M, M_m) f, g = ot.bregman.empirical_sinkhorn(X_sb, X_tb, 1, numIterMax=numIterMax, isLazy=True, batchSize=(1, 3), verbose=True) f, g = nx.to_numpy(f), nx.to_numpy(g) @@ -825,19 +794,13 @@ def test_empirical_sinkhorn_divergence(nx): a = np.linspace(1, n, n) a /= a.sum() b = ot.unif(n) - X_s = np.reshape(np.arange(n), (n, 1)) - X_t = np.reshape(np.arange(0, n * 2, 2), (n, 1)) + X_s = np.reshape(np.arange(n, dtype=np.float64), (n, 1)) + X_t = np.reshape(np.arange(0, n * 2, 2, dtype=np.float64), (n, 1)) M = ot.dist(X_s, X_t) M_s = ot.dist(X_s, X_s) M_t = ot.dist(X_t, X_t) - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - X_sb = nx.from_numpy(X_s) - X_tb = nx.from_numpy(X_t) - M_nx = nx.from_numpy(M, type_as=ab) - M_sb = nx.from_numpy(M_s, type_as=ab) - M_tb = nx.from_numpy(M_t, type_as=ab) + ab, bb, X_sb, X_tb, M_nx, M_sb, M_tb = nx.from_numpy(a, b, X_s, X_t, M, M_s, M_t) emp_sinkhorn_div = nx.to_numpy(ot.bregman.empirical_sinkhorn_divergence(X_sb, X_tb, 1, a=ab, b=bb)) sinkhorn_div = nx.to_numpy( @@ -872,9 +835,7 @@ def test_stabilized_vs_sinkhorn_multidim(nx): M /= np.median(M) epsilon = 0.1 - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - M_nx = nx.from_numpy(M, type_as=ab) + ab, bb, M_nx = nx.from_numpy(a, b, M) G_np, _ = ot.bregman.sinkhorn(a, b, M, reg=epsilon, method="sinkhorn", log=True) G, log = ot.bregman.sinkhorn(ab, bb, M_nx, reg=epsilon, @@ -936,9 +897,7 @@ def test_screenkhorn(nx): x = rng.randn(n, 2) M = ot.dist(x, x) - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - M_nx = nx.from_numpy(M, type_as=ab) + ab, bb, M_nx = nx.from_numpy(a, b, M) # sinkhorn G_sink = nx.to_numpy(ot.sinkhorn(ab, bb, M_nx, 1e-1)) diff --git a/test/test_da.py b/test/test_da.py index 9f2bb50..4bf0ab1 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -19,7 +19,32 @@ except ImportError: nosklearn = True -def test_sinkhorn_lpl1_transport_class(): +def test_class_jax_tf(): + backends = [] + from ot.backend import jax, tf + if jax: + backends.append(ot.backend.JaxBackend()) + if tf: + backends.append(ot.backend.TensorflowBackend()) + + for nx in backends: + ns = 150 + nt = 200 + + 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 = ot.da.SinkhornLpl1Transport() + + with pytest.raises(TypeError): + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + + +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_sinkhorn_lpl1_transport_class(nx): """test_sinkhorn_transport """ @@ -29,6 +54,8 @@ def test_sinkhorn_lpl1_transport_class(): 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 = ot.da.SinkhornLpl1Transport() # test its computed @@ -44,15 +71,15 @@ def test_sinkhorn_lpl1_transport_class(): mu_s = unif(ns) mu_t = unif(nt) assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3) assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -62,7 +89,7 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) + Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0]) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -85,24 +112,26 @@ def test_sinkhorn_lpl1_transport_class(): # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornLpl1Transport() otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(otda_unsup.cost_) + n_unsup = nx.sum(otda_unsup.cost_) otda_semi = ot.da.SinkhornLpl1Transport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(otda_semi.cost_) + n_semisup = nx.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" # check that the coupling forbids mass transport between labeled source # and labeled target samples - mass_semi = np.sum( + mass_semi = nx.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) assert mass_semi == 0, "semisupervised mode not working" -def test_sinkhorn_l1l2_transport_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_sinkhorn_l1l2_transport_class(nx): """test_sinkhorn_transport """ @@ -112,6 +141,8 @@ def test_sinkhorn_l1l2_transport_class(): 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 = ot.da.SinkhornL1l2Transport() # test its computed @@ -128,15 +159,15 @@ def test_sinkhorn_l1l2_transport_class(): mu_s = unif(ns) mu_t = unif(nt) assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3) assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -156,7 +187,7 @@ def test_sinkhorn_l1l2_transport_class(): assert_equal(transp_ys.shape[0], ys.shape[0]) assert_equal(transp_ys.shape[1], len(np.unique(yt))) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) + Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0]) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -169,22 +200,22 @@ def test_sinkhorn_l1l2_transport_class(): # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornL1l2Transport() otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(otda_unsup.cost_) + n_unsup = nx.sum(otda_unsup.cost_) otda_semi = ot.da.SinkhornL1l2Transport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(otda_semi.cost_) + n_semisup = nx.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" # check that the coupling forbids mass transport between labeled source # and labeled target samples - mass_semi = np.sum( + mass_semi = nx.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max] - assert_allclose(mass_semi, np.zeros_like(mass_semi), + assert_allclose(nx.to_numpy(mass_semi), np.zeros(list(mass_semi.shape)), rtol=1e-9, atol=1e-9) # check everything runs well with log=True @@ -193,7 +224,9 @@ def test_sinkhorn_l1l2_transport_class(): assert len(otda.log_.keys()) != 0 -def test_sinkhorn_transport_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_sinkhorn_transport_class(nx): """test_sinkhorn_transport """ @@ -203,6 +236,8 @@ def test_sinkhorn_transport_class(): 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 = ot.da.SinkhornTransport() # test its computed @@ -219,15 +254,15 @@ def test_sinkhorn_transport_class(): mu_s = unif(ns) mu_t = unif(nt) assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3) assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -247,7 +282,7 @@ def test_sinkhorn_transport_class(): assert_equal(transp_ys.shape[0], ys.shape[0]) assert_equal(transp_ys.shape[1], len(np.unique(yt))) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) + Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0]) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -260,19 +295,19 @@ def test_sinkhorn_transport_class(): # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornTransport() otda_unsup.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(otda_unsup.cost_) + n_unsup = nx.sum(otda_unsup.cost_) otda_semi = ot.da.SinkhornTransport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(otda_semi.cost_) + n_semisup = nx.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" # check that the coupling forbids mass transport between labeled source # and labeled target samples - mass_semi = np.sum( + mass_semi = nx.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) assert mass_semi == 0, "semisupervised mode not working" @@ -282,7 +317,9 @@ def test_sinkhorn_transport_class(): assert len(otda.log_.keys()) != 0 -def test_unbalanced_sinkhorn_transport_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_unbalanced_sinkhorn_transport_class(nx): """test_sinkhorn_transport """ @@ -292,6 +329,8 @@ def test_unbalanced_sinkhorn_transport_class(): 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 = ot.da.UnbalancedSinkhornTransport() # test its computed @@ -318,7 +357,7 @@ def test_unbalanced_sinkhorn_transport_class(): assert_equal(transp_ys.shape[0], ys.shape[0]) assert_equal(transp_ys.shape[1], len(np.unique(yt))) - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -328,7 +367,7 @@ def test_unbalanced_sinkhorn_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) + Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0]) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -341,12 +380,12 @@ def test_unbalanced_sinkhorn_transport_class(): # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornTransport() otda_unsup.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(otda_unsup.cost_) + n_unsup = nx.sum(otda_unsup.cost_) otda_semi = ot.da.SinkhornTransport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(otda_semi.cost_) + n_semisup = nx.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" @@ -357,7 +396,9 @@ def test_unbalanced_sinkhorn_transport_class(): assert len(otda.log_.keys()) != 0 -def test_emd_transport_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_emd_transport_class(nx): """test_sinkhorn_transport """ @@ -367,6 +408,8 @@ def test_emd_transport_class(): 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 = ot.da.EMDTransport() # test its computed @@ -382,15 +425,15 @@ def test_emd_transport_class(): mu_s = unif(ns) mu_t = unif(nt) assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3) assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -410,7 +453,7 @@ def test_emd_transport_class(): assert_equal(transp_ys.shape[0], ys.shape[0]) assert_equal(transp_ys.shape[1], len(np.unique(yt))) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) + Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0]) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -423,28 +466,32 @@ def test_emd_transport_class(): # test unsupervised vs semi-supervised mode otda_unsup = ot.da.EMDTransport() otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(otda_unsup.cost_) + n_unsup = nx.sum(otda_unsup.cost_) otda_semi = ot.da.EMDTransport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(otda_semi.cost_) + n_semisup = nx.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" # check that the coupling forbids mass transport between labeled source # and labeled target samples - mass_semi = np.sum( + mass_semi = nx.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max] # we need to use a small tolerance here, otherwise the test breaks - assert_allclose(mass_semi, np.zeros_like(mass_semi), + assert_allclose(nx.to_numpy(mass_semi), np.zeros(list(mass_semi.shape)), rtol=1e-2, atol=1e-2) -def test_mapping_transport_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +@pytest.mark.parametrize("kernel", ["linear", "gaussian"]) +@pytest.mark.parametrize("bias", ["unbiased", "biased"]) +def test_mapping_transport_class(nx, kernel, bias): """test_mapping_transport """ @@ -455,101 +502,29 @@ def test_mapping_transport_class(): Xt, yt = make_data_classif('3gauss2', nt) Xs_new, _ = make_data_classif('3gauss', ns + 1) - ########################################################################## - # kernel == linear mapping tests - ########################################################################## + Xs, Xt, Xs_new = nx.from_numpy(Xs, Xt, Xs_new) - # check computation and dimensions if bias == False - otda = ot.da.MappingTransport(kernel="linear", bias=False) + # Mapping tests + bias = bias == "biased" + otda = ot.da.MappingTransport(kernel=kernel, bias=bias) otda.fit(Xs=Xs, Xt=Xt) assert hasattr(otda, "coupling_") assert hasattr(otda, "mapping_") assert hasattr(otda, "log_") assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(otda.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) + S = Xs.shape[0] if kernel == "gaussian" else Xs.shape[1] # if linear + if bias: + S += 1 + assert_equal(otda.mapping_.shape, ((S, Xt.shape[1]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3) assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) - - # test transform - transp_Xs = otda.transform(Xs=Xs) - assert_equal(transp_Xs.shape, Xs.shape) - - transp_Xs_new = otda.transform(Xs_new) - - # check that the oos method is working - assert_equal(transp_Xs_new.shape, Xs_new.shape) - - # check computation and dimensions if bias == True - otda = ot.da.MappingTransport(kernel="linear", bias=True) - otda.fit(Xs=Xs, Xt=Xt) - assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(otda.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1]))) - - # test margin constraints - mu_s = unif(ns) - mu_t = unif(nt) - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) - - # test transform - transp_Xs = otda.transform(Xs=Xs) - assert_equal(transp_Xs.shape, Xs.shape) - - transp_Xs_new = otda.transform(Xs_new) - - # check that the oos method is working - assert_equal(transp_Xs_new.shape, Xs_new.shape) - - ########################################################################## - # kernel == gaussian mapping tests - ########################################################################## - - # check computation and dimensions if bias == False - otda = ot.da.MappingTransport(kernel="gaussian", bias=False) - otda.fit(Xs=Xs, Xt=Xt) - - assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(otda.mapping_.shape, ((Xs.shape[0], Xt.shape[1]))) - - # test margin constraints - mu_s = unif(ns) - mu_t = unif(nt) - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) - - # test transform - transp_Xs = otda.transform(Xs=Xs) - assert_equal(transp_Xs.shape, Xs.shape) - - transp_Xs_new = otda.transform(Xs_new) - - # check that the oos method is working - assert_equal(transp_Xs_new.shape, Xs_new.shape) - - # check computation and dimensions if bias == True - otda = ot.da.MappingTransport(kernel="gaussian", bias=True) - otda.fit(Xs=Xs, Xt=Xt) - assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(otda.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1]))) - - # test margin constraints - mu_s = unif(ns) - mu_t = unif(nt) - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -561,29 +536,39 @@ def test_mapping_transport_class(): assert_equal(transp_Xs_new.shape, Xs_new.shape) # check everything runs well with log=True - otda = ot.da.MappingTransport(kernel="gaussian", log=True) + otda = ot.da.MappingTransport(kernel=kernel, bias=bias, log=True) otda.fit(Xs=Xs, Xt=Xt) assert len(otda.log_.keys()) != 0 + +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_mapping_transport_class_specific_seed(nx): # check that it does not crash when derphi is very close to 0 + ns = 20 + nt = 30 np.random.seed(39) Xs, ys = make_data_classif('3gauss', ns) Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.MappingTransport(kernel="gaussian", bias=False) - otda.fit(Xs=Xs, Xt=Xt) + otda.fit(Xs=nx.from_numpy(Xs), Xt=nx.from_numpy(Xt)) np.random.seed(None) -def test_linear_mapping(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_linear_mapping(nx): ns = 150 nt = 200 Xs, ys = make_data_classif('3gauss', ns) Xt, yt = make_data_classif('3gauss2', nt) - A, b = ot.da.OT_mapping_linear(Xs, Xt) + Xsb, Xtb = nx.from_numpy(Xs, Xt) - Xst = Xs.dot(A) + b + A, b = ot.da.OT_mapping_linear(Xsb, Xtb) + + Xst = nx.to_numpy(nx.dot(Xsb, A) + b) Ct = np.cov(Xt.T) Cst = np.cov(Xst.T) @@ -591,22 +576,26 @@ def test_linear_mapping(): np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) -def test_linear_mapping_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_linear_mapping_class(nx): ns = 150 nt = 200 Xs, ys = make_data_classif('3gauss', ns) Xt, yt = make_data_classif('3gauss2', nt) + Xsb, Xtb = nx.from_numpy(Xs, Xt) + otmap = ot.da.LinearTransport() - otmap.fit(Xs=Xs, Xt=Xt) + otmap.fit(Xs=Xsb, Xt=Xtb) assert hasattr(otmap, "A_") assert hasattr(otmap, "B_") assert hasattr(otmap, "A1_") assert hasattr(otmap, "B1_") - Xst = otmap.transform(Xs=Xs) + Xst = nx.to_numpy(otmap.transform(Xs=Xsb)) Ct = np.cov(Xt.T) Cst = np.cov(Xst.T) @@ -614,7 +603,9 @@ def test_linear_mapping_class(): np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) -def test_jcpot_transport_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_jcpot_transport_class(nx): """test_jcpot_transport """ @@ -627,6 +618,8 @@ def test_jcpot_transport_class(): Xt, yt = make_data_classif('3gauss2', nt) + Xs1, ys1, Xs2, ys2, Xt, yt = nx.from_numpy(Xs1, ys1, Xs2, ys2, Xt, yt) + Xs = [Xs1, Xs2] ys = [ys1, ys2] @@ -649,19 +642,24 @@ def test_jcpot_transport_class(): for i in range(len(Xs)): # test margin constraints w.r.t. uniform target weights for each coupling matrix assert_allclose( - np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_[i], axis=0)), mu_t, rtol=1e-3, atol=1e-3) # test margin constraints w.r.t. modified source weights for each source domain assert_allclose( - np.dot(otda.log_['D1'][i], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, - atol=1e-3) + nx.to_numpy( + nx.dot(otda.log_['D1'][i], nx.sum(otda.coupling_[i], axis=1)) + ), + nx.to_numpy(otda.proportions_), + rtol=1e-3, + atol=1e-3 + ) # test transform transp_Xs = otda.transform(Xs=Xs) [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - Xs_new, _ = make_data_classif('3gauss', ns1 + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns1 + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -670,15 +668,16 @@ def test_jcpot_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) - assert_equal(transp_yt.shape[1], len(np.unique(ys))) + assert_equal(transp_yt.shape[1], len(np.unique(nx.to_numpy(*ys)))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) - [assert_equal(x.shape[0], y.shape[0]) for x, y in zip(transp_ys, ys)] - [assert_equal(x.shape[1], len(np.unique(y))) for x, y in zip(transp_ys, ys)] + for x, y in zip(transp_ys, ys): + assert_equal(x.shape[0], y.shape[0]) + assert_equal(x.shape[1], len(np.unique(nx.to_numpy(y)))) -def test_jcpot_barycenter(): +def test_jcpot_barycenter(nx): """test_jcpot_barycenter """ @@ -695,19 +694,23 @@ def test_jcpot_barycenter(): Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1) Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2) - Xt, yt = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt) + Xt, _ = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt) - Xs = [Xs1, Xs2] - ys = [ys1, ys2] + Xs1b, ys1b, Xs2b, ys2b, Xtb = nx.from_numpy(Xs1, ys1, Xs2, ys2, Xt) - prop = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', + Xsb = [Xs1b, Xs2b] + ysb = [ys1b, ys2b] + + prop = ot.bregman.jcpot_barycenter(Xsb, ysb, Xtb, reg=.5, metric='sqeuclidean', numItermax=10000, stopThr=1e-9, verbose=False, log=False) - np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(nx.to_numpy(prop), [1 - pt, pt], rtol=1e-3, atol=1e-3) @pytest.mark.skipif(nosklearn, reason="No sklearn available") -def test_emd_laplace_class(): +@pytest.skip_backend("jax") +@pytest.skip_backend("tf") +def test_emd_laplace_class(nx): """test_emd_laplace_transport """ ns = 150 @@ -716,6 +719,8 @@ def test_emd_laplace_class(): 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 = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) # test its computed @@ -732,15 +737,15 @@ def test_emd_laplace_class(): mu_t = unif(nt) assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3) assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0]) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -750,7 +755,7 @@ def test_emd_laplace_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) + Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0]) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -763,9 +768,9 @@ def test_emd_laplace_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) - assert_equal(transp_yt.shape[1], len(np.unique(ys))) + assert_equal(transp_yt.shape[1], len(np.unique(nx.to_numpy(ys)))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) assert_equal(transp_ys.shape[0], ys.shape[0]) - assert_equal(transp_ys.shape[1], len(np.unique(yt))) + assert_equal(transp_ys.shape[1], len(np.unique(nx.to_numpy(yt)))) diff --git a/test/test_gromov.py b/test/test_gromov.py index 0dcf2da..12fd2b9 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -35,11 +35,7 @@ def test_gromov(nx): C1 /= C1.max() C2 /= C2.max() - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) - G0b = nx.from_numpy(G0) + C1b, C2b, pb, qb, G0b = nx.from_numpy(C1, C2, p, q, G0) G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', G0=G0, verbose=True) Gb = nx.to_numpy(ot.gromov.gromov_wasserstein(C1b, C2b, pb, qb, 'square_loss', G0=G0b, verbose=True)) @@ -105,11 +101,7 @@ def test_gromov_dtype_device(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - C1b = nx.from_numpy(C1, type_as=tp) - C2b = nx.from_numpy(C2, type_as=tp) - pb = nx.from_numpy(p, type_as=tp) - qb = nx.from_numpy(q, type_as=tp) - G0b = nx.from_numpy(G0, type_as=tp) + C1b, C2b, pb, qb, G0b = nx.from_numpy(C1, C2, p, q, G0, type_as=tp) Gb = ot.gromov.gromov_wasserstein(C1b, C2b, pb, qb, 'square_loss', G0=G0b, verbose=True) gw_valb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', G0=G0b, log=False) @@ -136,11 +128,7 @@ def test_gromov_device_tf(): # Check that everything stays on the CPU with tf.device("/CPU:0"): - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) - G0b = nx.from_numpy(G0) + C1b, C2b, pb, qb, G0b = nx.from_numpy(C1, C2, p, q, G0) Gb = ot.gromov.gromov_wasserstein(C1b, C2b, pb, qb, 'square_loss', G0=G0b, verbose=True) gw_valb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', G0=G0b, log=False) nx.assert_same_dtype_device(C1b, Gb) @@ -148,11 +136,7 @@ def test_gromov_device_tf(): if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) - G0b = nx.from_numpy(G0b) + C1b, C2b, pb, qb, G0b = nx.from_numpy(C1, C2, p, q, G0) Gb = ot.gromov.gromov_wasserstein(C1b, C2b, pb, qb, 'square_loss', verbose=True) gw_valb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', log=False) nx.assert_same_dtype_device(C1b, Gb) @@ -222,10 +206,7 @@ def test_entropic_gromov(nx): C1 /= C1.max() C2 /= C2.max() - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) + C1b, C2b, pb, qb = nx.from_numpy(C1, C2, p, q) G = ot.gromov.entropic_gromov_wasserstein( C1, C2, p, q, 'square_loss', epsilon=5e-4, verbose=True) @@ -285,10 +266,7 @@ def test_entropic_gromov_dtype_device(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - C1b = nx.from_numpy(C1, type_as=tp) - C2b = nx.from_numpy(C2, type_as=tp) - pb = nx.from_numpy(p, type_as=tp) - qb = nx.from_numpy(q, type_as=tp) + C1b, C2b, pb, qb = nx.from_numpy(C1, C2, p, q, type_as=tp) Gb = ot.gromov.entropic_gromov_wasserstein( C1b, C2b, pb, qb, 'square_loss', epsilon=5e-4, verbose=True @@ -320,10 +298,7 @@ def test_pointwise_gromov(nx): C1 /= C1.max() C2 /= C2.max() - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) + C1b, C2b, pb, qb = nx.from_numpy(C1, C2, p, q) def loss(x, y): return np.abs(x - y) @@ -381,10 +356,7 @@ def test_sampled_gromov(nx): C1 /= C1.max() C2 /= C2.max() - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) + C1b, C2b, pb, qb = nx.from_numpy(C1, C2, p, q) def loss(x, y): return np.abs(x - y) @@ -423,19 +395,15 @@ def test_gromov_barycenter(nx): n_samples = 3 p = ot.unif(n_samples) - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - p1b = nx.from_numpy(p1) - p2b = nx.from_numpy(p2) - pb = nx.from_numpy(p) + C1b, C2b, p1b, p2b, pb = nx.from_numpy(C1, C2, p1, p2, p) Cb = ot.gromov.gromov_barycenters( n_samples, [C1, C2], [p1, p2], p, [.5, .5], - 'square_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42 + 'square_loss', max_iter=100, tol=1e-3, verbose=False, random_state=42 ) Cbb = nx.to_numpy(ot.gromov.gromov_barycenters( n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5], - 'square_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42 + 'square_loss', max_iter=100, tol=1e-3, verbose=False, random_state=42 )) np.testing.assert_allclose(Cb, Cbb, atol=1e-06) np.testing.assert_allclose(Cbb.shape, (n_samples, n_samples)) @@ -443,15 +411,15 @@ def test_gromov_barycenter(nx): # test of gromov_barycenters with `log` on Cb_, err_ = ot.gromov.gromov_barycenters( n_samples, [C1, C2], [p1, p2], p, [.5, .5], - 'square_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True + 'square_loss', max_iter=100, tol=1e-3, verbose=False, random_state=42, log=True ) Cbb_, errb_ = ot.gromov.gromov_barycenters( n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5], - 'square_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True + 'square_loss', max_iter=100, tol=1e-3, verbose=False, random_state=42, log=True ) Cbb_ = nx.to_numpy(Cbb_) np.testing.assert_allclose(Cb_, Cbb_, atol=1e-06) - np.testing.assert_array_almost_equal(err_['err'], errb_['err']) + np.testing.assert_array_almost_equal(err_['err'], nx.to_numpy(*errb_['err'])) np.testing.assert_allclose(Cbb_.shape, (n_samples, n_samples)) Cb2 = ot.gromov.gromov_barycenters( @@ -468,15 +436,15 @@ def test_gromov_barycenter(nx): # test of gromov_barycenters with `log` on Cb2_, err2_ = ot.gromov.gromov_barycenters( n_samples, [C1, C2], [p1, p2], p, [.5, .5], - 'kl_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True + 'kl_loss', max_iter=100, tol=1e-3, verbose=False, random_state=42, log=True ) Cb2b_, err2b_ = ot.gromov.gromov_barycenters( n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5], - 'kl_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True + 'kl_loss', max_iter=100, tol=1e-3, verbose=False, random_state=42, log=True ) Cb2b_ = nx.to_numpy(Cb2b_) np.testing.assert_allclose(Cb2_, Cb2b_, atol=1e-06) - np.testing.assert_array_almost_equal(err2_['err'], err2_['err']) + np.testing.assert_array_almost_equal(err2_['err'], nx.to_numpy(*err2b_['err'])) np.testing.assert_allclose(Cb2b_.shape, (n_samples, n_samples)) @@ -495,11 +463,7 @@ def test_gromov_entropic_barycenter(nx): n_samples = 2 p = ot.unif(n_samples) - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - p1b = nx.from_numpy(p1) - p2b = nx.from_numpy(p2) - pb = nx.from_numpy(p) + C1b, C2b, p1b, p2b, pb = nx.from_numpy(C1, C2, p1, p2, p) Cb = ot.gromov.entropic_gromov_barycenters( n_samples, [C1, C2], [p1, p2], p, [.5, .5], @@ -523,7 +487,7 @@ def test_gromov_entropic_barycenter(nx): ) Cbb_ = nx.to_numpy(Cbb_) np.testing.assert_allclose(Cb_, Cbb_, atol=1e-06) - np.testing.assert_array_almost_equal(err_['err'], errb_['err']) + np.testing.assert_array_almost_equal(err_['err'], nx.to_numpy(*errb_['err'])) np.testing.assert_allclose(Cbb_.shape, (n_samples, n_samples)) Cb2 = ot.gromov.entropic_gromov_barycenters( @@ -548,7 +512,7 @@ def test_gromov_entropic_barycenter(nx): ) Cb2b_ = nx.to_numpy(Cb2b_) np.testing.assert_allclose(Cb2_, Cb2b_, atol=1e-06) - np.testing.assert_array_almost_equal(err2_['err'], err2_['err']) + np.testing.assert_array_almost_equal(err2_['err'], nx.to_numpy(*err2b_['err'])) np.testing.assert_allclose(Cb2b_.shape, (n_samples, n_samples)) @@ -578,12 +542,7 @@ def test_fgw(nx): M = ot.dist(ys, yt) M /= M.max() - Mb = nx.from_numpy(M) - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - pb = nx.from_numpy(p) - qb = nx.from_numpy(q) - G0b = nx.from_numpy(G0) + Mb, C1b, C2b, pb, qb, G0b = nx.from_numpy(M, C1, C2, p, q, G0) G, log = ot.gromov.fused_gromov_wasserstein(M, C1, C2, p, q, 'square_loss', alpha=0.5, G0=G0, log=True) Gb, logb = ot.gromov.fused_gromov_wasserstein(Mb, C1b, C2b, pb, qb, 'square_loss', alpha=0.5, G0=G0b, log=True) @@ -681,13 +640,7 @@ def test_fgw_barycenter(nx): n_samples = 3 p = ot.unif(n_samples) - ysb = nx.from_numpy(ys) - ytb = nx.from_numpy(yt) - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - p1b = nx.from_numpy(p1) - p2b = nx.from_numpy(p2) - pb = nx.from_numpy(p) + ysb, ytb, C1b, C2b, p1b, p2b, pb = nx.from_numpy(ys, yt, C1, C2, p1, p2, p) Xb, Cb = ot.gromov.fgw_barycenters( n_samples, [ysb, ytb], [C1b, C2b], [p1b, p2b], [.5, .5], 0.5, fixed_structure=False, @@ -731,10 +684,8 @@ def test_gromov_wasserstein_linear_unmixing(nx): Cdict = np.stack([C1, C2]) p = ot.unif(n) - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - Cdictb = nx.from_numpy(Cdict) - pb = nx.from_numpy(p) + C1b, C2b, Cdictb, pb = nx.from_numpy(C1, C2, Cdict, p) + tol = 10**(-5) # Tests without regularization reg = 0. @@ -764,8 +715,8 @@ def test_gromov_wasserstein_linear_unmixing(nx): np.testing.assert_allclose(unmixing2, [0., 1.], atol=1e-01) np.testing.assert_allclose(C1_emb, nx.to_numpy(C1b_emb), atol=1e-06) np.testing.assert_allclose(C2_emb, nx.to_numpy(C2b_emb), atol=1e-06) - np.testing.assert_allclose(reconstruction1, reconstruction1b, atol=1e-06) - np.testing.assert_allclose(reconstruction2, reconstruction2b, atol=1e-06) + np.testing.assert_allclose(reconstruction1, nx.to_numpy(reconstruction1b), atol=1e-06) + np.testing.assert_allclose(reconstruction2, nx.to_numpy(reconstruction2b), atol=1e-06) np.testing.assert_allclose(C1b_emb.shape, (n, n)) np.testing.assert_allclose(C2b_emb.shape, (n, n)) @@ -798,8 +749,8 @@ def test_gromov_wasserstein_linear_unmixing(nx): np.testing.assert_allclose(unmixing2, [0., 1.], atol=1e-01) np.testing.assert_allclose(C1_emb, nx.to_numpy(C1b_emb), atol=1e-06) np.testing.assert_allclose(C2_emb, nx.to_numpy(C2b_emb), atol=1e-06) - np.testing.assert_allclose(reconstruction1, reconstruction1b, atol=1e-06) - np.testing.assert_allclose(reconstruction2, reconstruction2b, atol=1e-06) + np.testing.assert_allclose(reconstruction1, nx.to_numpy(reconstruction1b), atol=1e-06) + np.testing.assert_allclose(reconstruction2, nx.to_numpy(reconstruction2b), atol=1e-06) np.testing.assert_allclose(C1b_emb.shape, (n, n)) np.testing.assert_allclose(C2b_emb.shape, (n, n)) @@ -824,13 +775,14 @@ def test_gromov_wasserstein_dictionary_learning(nx): dataset_means = [C.mean() for C in Cs] np.random.seed(0) Cdict_init = np.random.normal(loc=np.mean(dataset_means), scale=np.std(dataset_means), size=(n_atoms, shape, shape)) + if projection == 'nonnegative_symmetric': Cdict_init = 0.5 * (Cdict_init + Cdict_init.transpose((0, 2, 1))) Cdict_init[Cdict_init < 0.] = 0. - Csb = [nx.from_numpy(C) for C in Cs] - psb = [nx.from_numpy(p) for p in ps] - qb = nx.from_numpy(q) - Cdict_initb = nx.from_numpy(Cdict_init) + + Csb = nx.from_numpy(*Cs) + psb = nx.from_numpy(*ps) + qb, Cdict_initb = nx.from_numpy(q, Cdict_init) # Test: compare reconstruction error using initial dictionary and dictionary learned using this initialization # > Compute initial reconstruction of samples on this random dictionary without backend @@ -882,6 +834,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): ) total_reconstruction_b += reconstruction + total_reconstruction_b = nx.to_numpy(total_reconstruction_b) np.testing.assert_array_less(total_reconstruction_b, initial_total_reconstruction) np.testing.assert_allclose(total_reconstruction_b, total_reconstruction, atol=1e-05) np.testing.assert_allclose(total_reconstruction_b, total_reconstruction, atol=1e-05) @@ -924,6 +877,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): ) total_reconstruction_b_bis += reconstruction + total_reconstruction_b_bis = nx.to_numpy(total_reconstruction_b_bis) np.testing.assert_allclose(total_reconstruction_b_bis, total_reconstruction_b, atol=1e-05) np.testing.assert_allclose(Cdict_bis, nx.to_numpy(Cdictb_bis), atol=1e-03) @@ -969,6 +923,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): ) total_reconstruction_b_bis2 += reconstruction + total_reconstruction_b_bis2 = nx.to_numpy(total_reconstruction_b_bis2) np.testing.assert_allclose(total_reconstruction_b_bis2, total_reconstruction_bis2, atol=1e-05) @@ -985,12 +940,8 @@ def test_fused_gromov_wasserstein_linear_unmixing(nx): Ydict = np.stack([F, F]) p = ot.unif(n) - C1b = nx.from_numpy(C1) - C2b = nx.from_numpy(C2) - Fb = nx.from_numpy(F) - Cdictb = nx.from_numpy(Cdict) - Ydictb = nx.from_numpy(Ydict) - pb = nx.from_numpy(p) + C1b, C2b, Fb, Cdictb, Ydictb, pb = nx.from_numpy(C1, C2, F, Cdict, Ydict, p) + # Tests without regularization reg = 0. @@ -1022,8 +973,8 @@ def test_fused_gromov_wasserstein_linear_unmixing(nx): np.testing.assert_allclose(C2_emb, nx.to_numpy(C2b_emb), atol=1e-03) np.testing.assert_allclose(Y1_emb, nx.to_numpy(Y1b_emb), atol=1e-03) np.testing.assert_allclose(Y2_emb, nx.to_numpy(Y2b_emb), atol=1e-03) - np.testing.assert_allclose(reconstruction1, reconstruction1b, atol=1e-06) - np.testing.assert_allclose(reconstruction2, reconstruction2b, atol=1e-06) + np.testing.assert_allclose(reconstruction1, nx.to_numpy(reconstruction1b), atol=1e-06) + np.testing.assert_allclose(reconstruction2, nx.to_numpy(reconstruction2b), atol=1e-06) np.testing.assert_allclose(C1b_emb.shape, (n, n)) np.testing.assert_allclose(C2b_emb.shape, (n, n)) @@ -1058,8 +1009,8 @@ def test_fused_gromov_wasserstein_linear_unmixing(nx): np.testing.assert_allclose(C2_emb, nx.to_numpy(C2b_emb), atol=1e-03) np.testing.assert_allclose(Y1_emb, nx.to_numpy(Y1b_emb), atol=1e-03) np.testing.assert_allclose(Y2_emb, nx.to_numpy(Y2b_emb), atol=1e-03) - np.testing.assert_allclose(reconstruction1, reconstruction1b, atol=1e-06) - np.testing.assert_allclose(reconstruction2, reconstruction2b, atol=1e-06) + np.testing.assert_allclose(reconstruction1, nx.to_numpy(reconstruction1b), atol=1e-06) + np.testing.assert_allclose(reconstruction2, nx.to_numpy(reconstruction2b), atol=1e-06) np.testing.assert_allclose(C1b_emb.shape, (n, n)) np.testing.assert_allclose(C2b_emb.shape, (n, n)) @@ -1093,12 +1044,10 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): dataset_feature_means = np.stack([Y.mean(axis=0) for Y in Ys]) Ydict_init = np.random.normal(loc=dataset_feature_means.mean(axis=0), scale=dataset_feature_means.std(axis=0), size=(n_atoms, shape, 2)) - Csb = [nx.from_numpy(C) for C in Cs] - Ysb = [nx.from_numpy(Y) for Y in Ys] - psb = [nx.from_numpy(p) for p in ps] - qb = nx.from_numpy(q) - Cdict_initb = nx.from_numpy(Cdict_init) - Ydict_initb = nx.from_numpy(Ydict_init) + Csb = nx.from_numpy(*Cs) + Ysb = nx.from_numpy(*Ys) + psb = nx.from_numpy(*ps) + qb, Cdict_initb, Ydict_initb = nx.from_numpy(q, Cdict_init, Ydict_init) # Test: Compute initial reconstruction of samples on this random dictionary alpha = 0.5 @@ -1151,6 +1100,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): ) total_reconstruction_b += reconstruction + total_reconstruction_b = nx.to_numpy(total_reconstruction_b) np.testing.assert_array_less(total_reconstruction_b, initial_total_reconstruction) np.testing.assert_allclose(total_reconstruction_b, total_reconstruction, atol=1e-05) np.testing.assert_allclose(Cdict, nx.to_numpy(Cdictb), atol=1e-03) @@ -1192,6 +1142,8 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200 ) total_reconstruction_b_bis += reconstruction + + total_reconstruction_b_bis = nx.to_numpy(total_reconstruction_b_bis) np.testing.assert_allclose(total_reconstruction_b_bis, total_reconstruction_b, atol=1e-05) # Test: without using adam optimizer, with log and verbose set to True @@ -1237,4 +1189,5 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): total_reconstruction_b_bis2 += reconstruction # > Compare results with/without backend + total_reconstruction_b_bis2 = nx.to_numpy(total_reconstruction_b_bis2) np.testing.assert_allclose(total_reconstruction_bis2, total_reconstruction_b_bis2, atol=1e-05) diff --git a/test/test_optim.py b/test/test_optim.py index 41f9cbe..67e9d13 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -32,9 +32,7 @@ def test_conditional_gradient(nx): def fb(G): return 0.5 * nx.sum(G ** 2) - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - Mb = nx.from_numpy(M, type_as=ab) + ab, bb, Mb = nx.from_numpy(a, b, M) reg = 1e-1 @@ -74,9 +72,7 @@ def test_conditional_gradient_itermax(nx): def fb(G): return 0.5 * nx.sum(G ** 2) - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - Mb = nx.from_numpy(M, type_as=ab) + ab, bb, Mb = nx.from_numpy(a, b, M) reg = 1e-1 @@ -118,9 +114,7 @@ def test_generalized_conditional_gradient(nx): reg1 = 1e-3 reg2 = 1e-1 - ab = nx.from_numpy(a) - bb = nx.from_numpy(b) - Mb = nx.from_numpy(M, type_as=ab) + ab, bb, Mb = nx.from_numpy(a, b, M) G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True) Gb, log = ot.optim.gcg(ab, bb, Mb, reg1, reg2, fb, df, verbose=True, log=True) @@ -142,9 +136,12 @@ def test_line_search_armijo(nx): pk = np.array([[-0.25, 0.25], [0.25, -0.25]]) gfk = np.array([[23.04273441, 23.0449082], [23.04273441, 23.0449082]]) old_fval = -123 + + xkb, pkb, gfkb = nx.from_numpy(xk, pk, gfk) + # Should not throw an exception and return 0. for alpha alpha, a, b = ot.optim.line_search_armijo( - lambda x: 1, nx.from_numpy(xk), nx.from_numpy(pk), nx.from_numpy(gfk), old_fval + lambda x: 1, xkb, pkb, gfkb, old_fval ) alpha_np, anp, bnp = ot.optim.line_search_armijo( lambda x: 1, xk, pk, gfk, old_fval diff --git a/test/test_ot.py b/test/test_ot.py index 3e2d845..bb258e2 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -47,8 +47,7 @@ def test_emd_backends(nx): G = ot.emd(a, a, M) - ab = nx.from_numpy(a) - Mb = nx.from_numpy(M) + ab, Mb = nx.from_numpy(a, M) Gb = ot.emd(ab, ab, Mb) @@ -68,8 +67,7 @@ def test_emd2_backends(nx): val = ot.emd2(a, a, M) - ab = nx.from_numpy(a) - Mb = nx.from_numpy(M) + ab, Mb = nx.from_numpy(a, M) valb = ot.emd2(ab, ab, Mb) @@ -90,8 +88,7 @@ def test_emd_emd2_types_devices(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - ab = nx.from_numpy(a, type_as=tp) - Mb = nx.from_numpy(M, type_as=tp) + ab, Mb = nx.from_numpy(a, M, type_as=tp) Gb = ot.emd(ab, ab, Mb) @@ -117,8 +114,7 @@ def test_emd_emd2_devices_tf(): # Check that everything stays on the CPU with tf.device("/CPU:0"): - ab = nx.from_numpy(a) - Mb = nx.from_numpy(M) + ab, Mb = nx.from_numpy(a, M) Gb = ot.emd(ab, ab, Mb) w = ot.emd2(ab, ab, Mb) nx.assert_same_dtype_device(Mb, Gb) @@ -126,8 +122,7 @@ def test_emd_emd2_devices_tf(): if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - ab = nx.from_numpy(a) - Mb = nx.from_numpy(M) + ab, Mb = nx.from_numpy(a, M) Gb = ot.emd(ab, ab, Mb) w = ot.emd2(ab, ab, Mb) nx.assert_same_dtype_device(Mb, Gb) @@ -310,8 +305,8 @@ def test_free_support_barycenter_backends(nx): X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init) - measures_locations2 = [nx.from_numpy(x) for x in measures_locations] - measures_weights2 = [nx.from_numpy(x) for x in measures_weights] + measures_locations2 = nx.from_numpy(*measures_locations) + measures_weights2 = nx.from_numpy(*measures_weights) X_init2 = nx.from_numpy(X_init) X2 = ot.lp.free_support_barycenter(measures_locations2, measures_weights2, X_init2) diff --git a/test/test_sliced.py b/test/test_sliced.py index 91e0961..08ab4fb 100644 --- a/test/test_sliced.py +++ b/test/test_sliced.py @@ -123,9 +123,7 @@ def test_sliced_backend(nx): n_projections = 20 - xb = nx.from_numpy(x) - yb = nx.from_numpy(y) - Pb = nx.from_numpy(P) + xb, yb, Pb = nx.from_numpy(x, y, P) val0 = ot.sliced_wasserstein_distance(x, y, projections=P) @@ -153,9 +151,7 @@ def test_sliced_backend_type_devices(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - xb = nx.from_numpy(x, type_as=tp) - yb = nx.from_numpy(y, type_as=tp) - Pb = nx.from_numpy(P, type_as=tp) + xb, yb, Pb = nx.from_numpy(x, y, P, type_as=tp) valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb) @@ -174,17 +170,13 @@ def test_sliced_backend_device_tf(): # Check that everything stays on the CPU with tf.device("/CPU:0"): - xb = nx.from_numpy(x) - yb = nx.from_numpy(y) - Pb = nx.from_numpy(P) + xb, yb, Pb = nx.from_numpy(x, y, P) valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb) nx.assert_same_dtype_device(xb, valb) if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - xb = nx.from_numpy(x) - yb = nx.from_numpy(y) - Pb = nx.from_numpy(P) + xb, yb, Pb = nx.from_numpy(x, y, P) valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb) nx.assert_same_dtype_device(xb, valb) assert nx.dtype_device(valb)[1].startswith("GPU") @@ -203,9 +195,7 @@ def test_max_sliced_backend(nx): n_projections = 20 - xb = nx.from_numpy(x) - yb = nx.from_numpy(y) - Pb = nx.from_numpy(P) + xb, yb, Pb = nx.from_numpy(x, y, P) val0 = ot.max_sliced_wasserstein_distance(x, y, projections=P) @@ -233,9 +223,7 @@ def test_max_sliced_backend_type_devices(nx): for tp in nx.__type_list__: print(nx.dtype_device(tp)) - xb = nx.from_numpy(x, type_as=tp) - yb = nx.from_numpy(y, type_as=tp) - Pb = nx.from_numpy(P, type_as=tp) + xb, yb, Pb = nx.from_numpy(x, y, P, type_as=tp) valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb) @@ -254,17 +242,13 @@ def test_max_sliced_backend_device_tf(): # Check that everything stays on the CPU with tf.device("/CPU:0"): - xb = nx.from_numpy(x) - yb = nx.from_numpy(y) - Pb = nx.from_numpy(P) + xb, yb, Pb = nx.from_numpy(x, y, P) valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb) nx.assert_same_dtype_device(xb, valb) if len(tf.config.list_physical_devices('GPU')) > 0: # Check that everything happens on the GPU - xb = nx.from_numpy(x) - yb = nx.from_numpy(y) - Pb = nx.from_numpy(P) + xb, yb, Pb = nx.from_numpy(x, y, P) valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb) nx.assert_same_dtype_device(xb, valb) assert nx.dtype_device(valb)[1].startswith("GPU") diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index e8349d1..db59504 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -9,11 +9,9 @@ import ot import pytest from ot.unbalanced import barycenter_unbalanced -from scipy.special import logsumexp - @pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) -def test_unbalanced_convergence(method): +def test_unbalanced_convergence(nx, method): # test generalized sinkhorn for unbalanced OT n = 100 rng = np.random.RandomState(42) @@ -28,36 +26,51 @@ def test_unbalanced_convergence(method): epsilon = 1. reg_m = 1. + a, b, M = nx.from_numpy(a, b, M) + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, reg_m=reg_m, method=method, log=True, verbose=True) - loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, - method=method, - verbose=True) + loss = nx.to_numpy(ot.unbalanced.sinkhorn_unbalanced2( + a, b, M, epsilon, reg_m, method=method, verbose=True + )) # check fixed point equations # in log-domain fi = reg_m / (reg_m + epsilon) - logb = np.log(b + 1e-16) - loga = np.log(a + 1e-16) - logKtu = logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) - logKv = logsumexp(log["logv"][None, :] - M / epsilon, axis=1) + logb = nx.log(b + 1e-16) + loga = nx.log(a + 1e-16) + logKtu = nx.logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) + logKv = nx.logsumexp(log["logv"][None, :] - M / epsilon, axis=1) v_final = fi * (logb - logKtu) u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["logu"], atol=1e-05) + nx.to_numpy(u_final), nx.to_numpy(log["logu"]), atol=1e-05) np.testing.assert_allclose( - v_final, log["logv"], atol=1e-05) + nx.to_numpy(v_final), nx.to_numpy(log["logv"]), atol=1e-05) # check if sinkhorn_unbalanced2 returns the correct loss - np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) + np.testing.assert_allclose(nx.to_numpy(nx.sum(G * M)), loss, atol=1e-5) + + # check in case no histogram is provided + M_np = nx.to_numpy(M) + a_np, b_np = np.array([]), np.array([]) + a, b = nx.from_numpy(a_np, b_np) + + G = ot.unbalanced.sinkhorn_unbalanced( + a, b, M, reg=epsilon, reg_m=reg_m, method=method, verbose=True + ) + G_np = ot.unbalanced.sinkhorn_unbalanced( + a_np, b_np, M_np, reg=epsilon, reg_m=reg_m, method=method, verbose=True + ) + np.testing.assert_allclose(G_np, nx.to_numpy(G)) @pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) -def test_unbalanced_multiple_inputs(method): +def test_unbalanced_multiple_inputs(nx, method): # test generalized sinkhorn for unbalanced OT n = 100 rng = np.random.RandomState(42) @@ -72,6 +85,8 @@ def test_unbalanced_multiple_inputs(method): epsilon = 1. reg_m = 1. + a, b, M = nx.from_numpy(a, b, M) + loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, reg_m=reg_m, method=method, @@ -80,23 +95,24 @@ def test_unbalanced_multiple_inputs(method): # check fixed point equations # in log-domain fi = reg_m / (reg_m + epsilon) - logb = np.log(b + 1e-16) - loga = np.log(a + 1e-16)[:, None] - logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, - axis=0) - logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + logb = nx.log(b + 1e-16) + loga = nx.log(a + 1e-16)[:, None] + logKtu = nx.logsumexp( + log["logu"][:, None, :] - M[:, :, None] / epsilon, axis=0 + ) + logKv = nx.logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) v_final = fi * (logb - logKtu) u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["logu"], atol=1e-05) + nx.to_numpy(u_final), nx.to_numpy(log["logu"]), atol=1e-05) np.testing.assert_allclose( - v_final, log["logv"], atol=1e-05) + nx.to_numpy(v_final), nx.to_numpy(log["logv"]), atol=1e-05) assert len(loss) == b.shape[1] -def test_stabilized_vs_sinkhorn(): +def test_stabilized_vs_sinkhorn(nx): # test if stable version matches sinkhorn n = 100 @@ -112,19 +128,27 @@ def test_stabilized_vs_sinkhorn(): M /= np.median(M) epsilon = 0.1 reg_m = 1. - G, log = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, reg=epsilon, - method="sinkhorn_stabilized", - reg_m=reg_m, - log=True, - verbose=True) - G2, log2 = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, - method="sinkhorn", log=True) + + ab, bb, Mb = nx.from_numpy(a, b, M) + + G, _ = ot.unbalanced.sinkhorn_unbalanced2( + ab, bb, Mb, epsilon, reg_m, method="sinkhorn_stabilized", log=True + ) + G2, _ = ot.unbalanced.sinkhorn_unbalanced2( + ab, bb, Mb, epsilon, reg_m, method="sinkhorn", log=True + ) + G2_np, _ = ot.unbalanced.sinkhorn_unbalanced2( + a, b, M, epsilon, reg_m, method="sinkhorn", log=True + ) + G = nx.to_numpy(G) + G2 = nx.to_numpy(G2) np.testing.assert_allclose(G, G2, atol=1e-5) + np.testing.assert_allclose(G2, G2_np, atol=1e-5) @pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) -def test_unbalanced_barycenter(method): +def test_unbalanced_barycenter(nx, method): # test generalized sinkhorn for unbalanced OT barycenter n = 100 rng = np.random.RandomState(42) @@ -138,25 +162,29 @@ def test_unbalanced_barycenter(method): epsilon = 1. reg_m = 1. - q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, - method=method, log=True, verbose=True) + A, M = nx.from_numpy(A, M) + + q, log = barycenter_unbalanced( + A, M, reg=epsilon, reg_m=reg_m, method=method, log=True, verbose=True + ) # check fixed point equations fi = reg_m / (reg_m + epsilon) - logA = np.log(A + 1e-16) - logq = np.log(q + 1e-16)[:, None] - logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, - axis=0) - logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + logA = nx.log(A + 1e-16) + logq = nx.log(q + 1e-16)[:, None] + logKtu = nx.logsumexp( + log["logu"][:, None, :] - M[:, :, None] / epsilon, axis=0 + ) + logKv = nx.logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) v_final = fi * (logq - logKtu) u_final = fi * (logA - logKv) np.testing.assert_allclose( - u_final, log["logu"], atol=1e-05) + nx.to_numpy(u_final), nx.to_numpy(log["logu"]), atol=1e-05) np.testing.assert_allclose( - v_final, log["logv"], atol=1e-05) + nx.to_numpy(v_final), nx.to_numpy(log["logv"]), atol=1e-05) -def test_barycenter_stabilized_vs_sinkhorn(): +def test_barycenter_stabilized_vs_sinkhorn(nx): # test generalized sinkhorn for unbalanced OT barycenter n = 100 rng = np.random.RandomState(42) @@ -170,21 +198,24 @@ def test_barycenter_stabilized_vs_sinkhorn(): epsilon = 0.5 reg_m = 10 - qstable, log = barycenter_unbalanced(A, M, reg=epsilon, - reg_m=reg_m, log=True, - tau=100, - method="sinkhorn_stabilized", - verbose=True - ) - q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, - method="sinkhorn", - log=True) + Ab, Mb = nx.from_numpy(A, M) - np.testing.assert_allclose( - q, qstable, atol=1e-05) + qstable, _ = barycenter_unbalanced( + Ab, Mb, reg=epsilon, reg_m=reg_m, log=True, tau=100, + method="sinkhorn_stabilized", verbose=True + ) + q, _ = barycenter_unbalanced( + Ab, Mb, reg=epsilon, reg_m=reg_m, method="sinkhorn", log=True + ) + q_np, _ = barycenter_unbalanced( + A, M, reg=epsilon, reg_m=reg_m, method="sinkhorn", log=True + ) + q, qstable = nx.to_numpy(q, qstable) + np.testing.assert_allclose(q, qstable, atol=1e-05) + np.testing.assert_allclose(q, q_np, atol=1e-05) -def test_wrong_method(): +def test_wrong_method(nx): n = 10 rng = np.random.RandomState(42) @@ -199,19 +230,20 @@ def test_wrong_method(): epsilon = 1. reg_m = 1. + a, b, M = nx.from_numpy(a, b, M) + with pytest.raises(ValueError): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, - reg_m=reg_m, - method='badmethod', - log=True, - verbose=True) + ot.unbalanced.sinkhorn_unbalanced( + a, b, M, reg=epsilon, reg_m=reg_m, method='badmethod', + log=True, verbose=True + ) with pytest.raises(ValueError): - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, - method='badmethod', - verbose=True) + ot.unbalanced.sinkhorn_unbalanced2( + a, b, M, epsilon, reg_m, method='badmethod', verbose=True + ) -def test_implemented_methods(): +def test_implemented_methods(nx): IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_reg_scaling'] NOT_VALID_TOKENS = ['foo'] @@ -228,6 +260,9 @@ def test_implemented_methods(): M = ot.dist(x, x) epsilon = 1. reg_m = 1. + + a, b, M, A = nx.from_numpy(a, b, M, A) + for method in IMPLEMENTED_METHODS: ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) diff --git a/test/test_weak.py b/test/test_weak.py index c4c3278..945efb1 100644 --- a/test/test_weak.py +++ b/test/test_weak.py @@ -45,9 +45,7 @@ def test_weak_ot_bakends(nx): G = ot.weak_optimal_transport(xs, xt, u, u) - xs2 = nx.from_numpy(xs) - xt2 = nx.from_numpy(xt) - u2 = nx.from_numpy(u) + xs2, xt2, u2 = nx.from_numpy(xs, xt, u) G2 = ot.weak_optimal_transport(xs2, xt2, u2, u2) -- cgit v1.2.3