From 4a45135dfa3f1aeae8b3bdf0c42422f0f60426e8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:47:29 +0200 Subject: dr +gpu numpy assert --- test/test_dr.py | 4 ++-- test/test_gpu.py | 34 +++++++++++++++++----------------- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/test/test_dr.py b/test/test_dr.py index e3d1e6b..bdb920e 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -29,7 +29,7 @@ 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)") @@ -51,4 +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 5184a6c..7ae159b 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -16,14 +16,14 @@ def test_gpu_sinkhorn(): np.random.seed(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 = np.random.rand(n_samples // 4, 100) + b = np.random.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_sinkhorn() transport.fit(a, b) @@ -34,26 +34,26 @@ 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): + 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 = np.random.rand(n_samples // 4, 100) + labels_a = np.random.randint(10, size=(n_samples // 4)) + b = np.random.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_lpl1() transport.fit(a, labels_a, b) @@ -65,9 +65,9 @@ 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) -- cgit v1.2.3