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author | Rémi Flamary <remi.flamary@gmail.com> | 2017-07-24 11:15:33 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2017-07-24 11:15:33 +0200 |
commit | 5a6b5de9b2f28c93bef1a9db2e3b94693c05ff4f (patch) | |
tree | 1f7457a028ef71253be36c44fb87c2e4131e909a /test | |
parent | 82da63f1020835a412f6174500099694a78ab6be (diff) |
add proper testing
Diffstat (limited to 'test')
-rw-r--r-- | test/test_emd_multi.py | 47 | ||||
-rw-r--r-- | test/test_gpu.py | 59 | ||||
-rw-r--r-- | test/test_gpu_sinkhorn.py | 28 | ||||
-rw-r--r-- | test/test_gpu_sinkhorn_lpl1.py | 29 | ||||
-rw-r--r-- | test/test_load_module.py | 10 | ||||
-rw-r--r-- | test/test_ot.py | 55 |
6 files changed, 114 insertions, 114 deletions
diff --git a/test/test_emd_multi.py b/test/test_emd_multi.py deleted file mode 100644 index 2eef242..0000000 --- a/test/test_emd_multi.py +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env python2 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 10 09:56:06 2017 - -@author: rflamary -""" - -import numpy as np - -import ot -from ot.datasets import get_1D_gauss as gauss -# reload(ot.lp) - -#%% parameters - -n = 5000 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std - -ls = np.arange(20, 1000, 10) -nb = len(ls) -b = np.zeros((n, nb)) -for i in range(nb): - b[:, i] = gauss(n, m=ls[i], s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -# M/=M.max() - -#%% - -print('Computing {} EMD '.format(nb)) - -# emd loss 1 proc -ot.tic() -emd_loss4 = ot.emd2(a, b, M, 1) -ot.toc('1 proc : {} s') - -# emd loss multipro proc -ot.tic() -emd_loss4 = ot.emd2(a, b, M) -ot.toc('multi proc : {} s') diff --git a/test/test_gpu.py b/test/test_gpu.py new file mode 100644 index 0000000..312a2d4 --- /dev/null +++ b/test/test_gpu.py @@ -0,0 +1,59 @@ +import ot +import numpy as np +import time +import pytest + + +@pytest.mark.skip(reason="No way to test GPU on travis yet") +def test_gpu_sinkhorn(): + import ot.gpu + + def describeRes(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 [5000]: + print(n) + a = np.random.rand(n // 4, 100) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_sinkhorn() + transport.fit(a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_sinkhorn() + transport.fit(a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) + describeRes(G2) + + +@pytest.mark.skip(reason="No way to test GPU on travis yet") +def test_gpu_sinkhorn_lpl1(): + def describeRes(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 [5000]: + print(n) + a = np.random.rand(n // 4, 100) + labels_a = np.random.randint(10, size=(n // 4)) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( + time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( + time3 - time2)) + describeRes(G2) diff --git a/test/test_gpu_sinkhorn.py b/test/test_gpu_sinkhorn.py deleted file mode 100644 index 841f062..0000000 --- a/test/test_gpu_sinkhorn.py +++ /dev/null @@ -1,28 +0,0 @@ -import ot -import numpy as np -import time -import ot.gpu - - -def describeRes(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 [5000, 10000, 15000, 20000]: - print(n) - a = np.random.rand(n // 4, 100) - b = np.random.rand(n, 100) - time1 = time.time() - transport = ot.da.OTDA_sinkhorn() - transport.fit(a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_sinkhorn() - transport.fit(a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) - describeRes(G1) - print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) diff --git a/test/test_gpu_sinkhorn_lpl1.py b/test/test_gpu_sinkhorn_lpl1.py deleted file mode 100644 index f0eb7e6..0000000 --- a/test/test_gpu_sinkhorn_lpl1.py +++ /dev/null @@ -1,29 +0,0 @@ -import ot -import numpy as np -import time -import ot.gpu - - -def describeRes(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 [5000, 10000, 15000, 20000]: - print(n) - a = np.random.rand(n // 4, 100) - labels_a = np.random.randint(10, size=(n // 4)) - b = np.random.rand(n, 100) - time1 = time.time() - transport = ot.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format(time2 - time1)) - describeRes(G1) - print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) diff --git a/test/test_load_module.py b/test/test_load_module.py deleted file mode 100644 index d77261e..0000000 --- a/test/test_load_module.py +++ /dev/null @@ -1,10 +0,0 @@ - - -import ot -import doctest - -# test lp solver -doctest.testmod(ot.lp, verbose=True) - -# test bregman solver -doctest.testmod(ot.bregman, verbose=True) diff --git a/test/test_ot.py b/test/test_ot.py new file mode 100644 index 0000000..51ee510 --- /dev/null +++ b/test/test_ot.py @@ -0,0 +1,55 @@ + + +import ot +import numpy as np + +#import pytest + + +def test_doctest(): + + import doctest + + # test lp solver + doctest.testmod(ot.lp, verbose=True) + + # test bregman solver + doctest.testmod(ot.bregman, verbose=True) + + +#@pytest.mark.skip(reason="Seems to be a conflict between pytest and multiprocessing") +def test_emd_multi(): + + from ot.datasets import get_1D_gauss as gauss + + n = 1000 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=20, s=5) # m= mean, s= std + + ls = np.arange(20, 1000, 10) + nb = len(ls) + b = np.zeros((n, nb)) + for i in range(nb): + b[:, i] = gauss(n, m=ls[i], s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + # M/=M.max() + + print('Computing {} EMD '.format(nb)) + + # emd loss 1 proc + ot.tic() + emd1 = ot.emd2(a, b, M, 1) + ot.toc('1 proc : {} s') + + # emd loss multipro proc + ot.tic() + emdn = ot.emd2(a, b, M) + ot.toc('multi proc : {} s') + + assert np.allclose(emd1, emdn) |