diff options
| -rw-r--r-- | test/test_bregman.py | 65 | ||||
| -rw-r--r-- | test/test_da.py | 32 | ||||
| -rw-r--r-- | test/test_dr.py | 34 | ||||
| -rw-r--r-- | test/test_gpu.py | 42 | ||||
| -rw-r--r-- | test/test_optim.py | 32 | ||||
| -rw-r--r-- | test/test_ot.py | 4 |
6 files changed, 100 insertions, 109 deletions
diff --git a/test/test_bregman.py b/test/test_bregman.py index 025568c..1638ef6 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,17 +1,14 @@ - -import ot import numpy as np - -# import pytest +import ot def test_sinkhorn(): # test sinkhorn n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) @@ -19,45 +16,47 @@ def test_sinkhorn(): G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn def test_sinkhorn_empty(): # test sinkhorn n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + 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) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + 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_epsilon_scaling', verbose=True, log=True) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(1), atol=1e-05) + np.testing.assert_allclose(u, G.sum(0), atol=1e-05) def test_sinkhorn_variants(): # test sinkhorn n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) @@ -69,24 +68,24 @@ def test_sinkhorn_variants(): Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) # check values - assert np.allclose(G0, Gs, atol=1e-05) - assert np.allclose(G0, Ges, atol=1e-05) - assert np.allclose(G0, Gerr) + np.testing.assert_allclose(G0, Gs, atol=1e-05) + np.testing.assert_allclose(G0, Ges, atol=1e-05) + np.testing.assert_allclose(G0, Gerr) def test_bary(): - n = 100 # nb bins + n_bins = 100 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n, m=30, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) + 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) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T # loss matrix + normalization - M = ot.utils.dist0(n) + M = ot.utils.dist0(n_bins) M /= M.max() alpha = 0.5 # 0<=alpha<=1 @@ -96,26 +95,26 @@ def test_bary(): reg = 1e-3 bary_wass = ot.bregman.barycenter(A, M, reg, weights) - assert np.allclose(1, np.sum(bary_wass)) + np.testing.assert_allclose(1, np.sum(bary_wass)) ot.bregman.barycenter(A, M, reg, log=True, verbose=True) def test_unmix(): - n = 50 # nb bins + n_bins = 50 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n, m=20, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) + 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) - a = ot.datasets.get_1D_gauss(n, m=30, s=10) + a = ot.datasets.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) + M = ot.utils.dist0(n_bins) M /= M.max() M0 = ot.utils.dist0(2) @@ -126,8 +125,8 @@ def test_unmix(): reg = 1e-3 um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,) - assert np.allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) - assert np.allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) + 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) diff --git a/test/test_da.py b/test/test_da.py index 50d3aba..a38390f 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -1,19 +1,17 @@ - -import ot import numpy as np - -# import pytest +import ot -def test_OTDA(): +def test_otda(): - n = 150 # nb bins + n_samples = 150 # nb samples + np.random.seed(0) - xs, ys = ot.datasets.get_data_classif('3gauss', n) - xt, yt = ot.datasets.get_data_classif('3gauss2', n) + xs, ys = ot.datasets.get_data_classif('3gauss', n_samples) + xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples) - a, b = ot.unif(n), ot.unif(n) + a, b = ot.unif(n_samples), ot.unif(n_samples) # LP problem da_emd = ot.da.OTDA() # init class @@ -21,8 +19,8 @@ def test_OTDA(): da_emd.interp() # interpolation of source samples da_emd.predict(xs) # interpolation of source samples - assert np.allclose(a, np.sum(da_emd.G, 1)) - assert np.allclose(b, np.sum(da_emd.G, 0)) + np.testing.assert_allclose(a, np.sum(da_emd.G, 1)) + np.testing.assert_allclose(b, np.sum(da_emd.G, 0)) # sinkhorn regularization lambd = 1e-1 @@ -31,8 +29,8 @@ def test_OTDA(): da_entrop.interp() da_entrop.predict(xs) - assert np.allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) - assert np.allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) # non-convex Group lasso regularization reg = 1e-1 @@ -42,8 +40,8 @@ def test_OTDA(): da_lpl1.interp() da_lpl1.predict(xs) - assert np.allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) - assert np.allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) # True Group lasso regularization reg = 1e-1 @@ -53,8 +51,8 @@ def test_OTDA(): da_l1l2.interp() da_l1l2.predict(xs) - assert np.allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) - assert np.allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) # linear mapping da_emd = ot.da.OTDA_mapping_linear() # init class diff --git a/test/test_dr.py b/test/test_dr.py index 3da7705..bdb920e 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -1,8 +1,9 @@ -import ot + import numpy as np +import ot import pytest -try: # test if cudamat installed +try: # test if autograd and pymanopt are installed import ot.dr nogo = False except ImportError: @@ -12,15 +13,15 @@ except ImportError: @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_fda(): - n = 90 # nb samples in source and target datasets + n_samples = 90 # nb samples in source and target datasets np.random.seed(0) - # generate circle dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n) + # generate gaussian dataset + xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) - nbnoise = 8 + n_features_noise = 8 - xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise))) p = 1 @@ -28,26 +29,21 @@ def test_fda(): projfda(xs) - assert np.allclose(np.sum(Pfda**2, 0), np.ones(p)) + np.testing.assert_allclose(np.sum(Pfda**2, 0), np.ones(p)) @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_wda(): - n = 100 # nb samples in source and target datasets - nz = 0.2 + n_samples = 100 # nb samples in source and target datasets np.random.seed(0) - # generate circle dataset - t = np.random.rand(n) * 2 * np.pi - ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 - xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) - xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + # generate gaussian dataset + xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) - nbnoise = 8 + n_features_noise = 8 - xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise))) p = 2 @@ -55,4 +51,4 @@ def test_wda(): projwda(xs) - assert np.allclose(np.sum(Pwda**2, 0), np.ones(p)) + np.testing.assert_allclose(np.sum(Pwda**2, 0), np.ones(p)) diff --git a/test/test_gpu.py b/test/test_gpu.py index 24797f2..98f59f7 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -1,5 +1,6 @@ -import ot + import numpy as np +import ot import time import pytest @@ -13,16 +14,16 @@ except ImportError: @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn(): - np.random.seed(0) + rng = np.random.RandomState(0) - def describeRes(r): + def describe_res(r): print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [50, 100, 500, 1000]: - print(n) - a = np.random.rand(n // 4, 100) - b = np.random.rand(n, 100) + for n_samples in [50, 100, 500, 1000]: + print(n_samples) + a = rng.rand(n_samples // 4, 100) + b = rng.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_sinkhorn() transport.fit(a, b) @@ -33,26 +34,27 @@ def test_gpu_sinkhorn(): G2 = transport.G time3 = time.time() print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) - describeRes(G1) + describe_res(G1) print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) + describe_res(G2) - assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) + np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn_lpl1(): - np.random.seed(0) - def describeRes(r): + rng = np.random.RandomState(0) + + def describe_res(r): print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" .format(np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [50, 100, 500]: - print(n) - a = np.random.rand(n // 4, 100) - labels_a = np.random.randint(10, size=(n // 4)) - b = np.random.rand(n, 100) + for n_samples in [50, 100, 500]: + print(n_samples) + a = rng.rand(n_samples // 4, 100) + labels_a = np.random.randint(10, size=(n_samples // 4)) + b = rng.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_lpl1() transport.fit(a, labels_a, b) @@ -64,9 +66,9 @@ def test_gpu_sinkhorn_lpl1(): time3 = time.time() print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( time2 - time1)) - describeRes(G1) + describe_res(G1) print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( time3 - time2)) - describeRes(G2) + describe_res(G2) - assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) + np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) diff --git a/test/test_optim.py b/test/test_optim.py index a77a37c..2840cad 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -1,24 +1,22 @@ - -import ot import numpy as np +import ot -# import pytest def test_conditional_gradient(): - n = 100 # nb bins + n_bins = 100 # nb bins np.random.seed(0) # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() def f(G): @@ -31,23 +29,23 @@ def test_conditional_gradient(): G, log = ot.optim.cg(a, b, M, reg, f, df, verbose=True, log=True) - assert np.allclose(a, G.sum(1)) - assert np.allclose(b, G.sum(0)) + np.testing.assert_allclose(a, G.sum(1)) + np.testing.assert_allclose(b, G.sum(0)) def test_generalized_conditional_gradient(): - n = 100 # nb bins + n_bins = 100 # nb bins np.random.seed(0) # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() def f(G): @@ -61,5 +59,5 @@ def test_generalized_conditional_gradient(): G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True) - assert np.allclose(a, G.sum(1), atol=1e-05) - assert np.allclose(b, G.sum(0), atol=1e-05) + np.testing.assert_allclose(a, G.sum(1), atol=1e-05) + np.testing.assert_allclose(b, G.sum(0), atol=1e-05) diff --git a/test/test_ot.py b/test/test_ot.py index 5bf65c6..9c0acab 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -1,9 +1,7 @@ - -import ot import numpy as np +import ot -# import pytest def test_doctest(): |