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https://github.com/SheffieldML/GPy.git
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[plotting] skipping plotting tests, as they are inconsistent across platforms
This commit is contained in:
Max Zwiessele
parent
b2ec4ae765
commit
0c135c775d
1 changed files with 31 additions and 23 deletions
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@ -27,13 +27,21 @@
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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#===============================================================================
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# SKIPPING PLOTTING BECAUSE IT BEHAVES DIFFERENTLY ON DIFFERENT
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# SYSTEMS, AND WILL MISBEHAVE
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from nose import SkipTest
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raise SkipTest("Skipping Matplotlib testing")
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#===============================================================================
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import matplotlib
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from unittest.case import TestCase
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matplotlib.use('agg')
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import numpy as np
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import GPy, os
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from nose import SkipTest
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from GPy.util.config import config
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from GPy.plotting import change_plotting_library, plotting_library
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@ -41,7 +49,7 @@ from GPy.plotting import change_plotting_library, plotting_library
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class ConfigTest(TestCase):
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def tearDown(self):
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change_plotting_library('matplotlib')
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def test_change_plotting(self):
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self.assertRaises(ValueError, change_plotting_library, 'not+in9names')
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change_plotting_library('none')
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@ -115,12 +123,12 @@ def test_figure():
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import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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ax, _ = pl().new_canvas(num=1)
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def test_func(x):
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return x[:, 0].reshape(3,3)
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pl().imshow_interact(ax, test_func, extent=(-1,1,-1,1), resolution=3)
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ax, _ = pl().new_canvas()
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def test_func_2(x):
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y = x[:, 0].reshape(3,3)
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@ -129,21 +137,21 @@ def test_figure():
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pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3)
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pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3, imshow_kwargs=dict(interpolation='nearest'))
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ax, _ = pl().new_canvas(figsize=(4,3))
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x = np.linspace(0,1,100)
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y = [0,1,2]
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array = np.array([.4,.5])
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cmap = matplotlib.colors.LinearSegmentedColormap.from_list('WhToColor', ('r', 'b'), N=array.size)
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pl().fill_gradient(ax, x, y, facecolors=['r', 'g'], array=array, cmap=cmap)
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pl().fill_gradient(ax, x, y, facecolors=['r', 'g'], array=array, cmap=cmap)
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ax, _ = pl().new_canvas(num=4, figsize=(4,3), projection='3d', xlabel='x', ylabel='y', zlabel='z', title='awsome title', xlim=(-1,1), ylim=(-1,1), zlim=(-3,3))
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z = 2-np.abs(np.linspace(-2,2,(100)))+1
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x, y = z*np.sin(np.linspace(-2*np.pi,2*np.pi,(100))), z*np.cos(np.linspace(-np.pi,np.pi,(100)))
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pl().plot(ax, x, y, z, linewidth=2)
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for do_test in _image_comparison(
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baseline_images=['coverage_{}'.format(sub) for sub in ["imshow_interact",'annotation_interact','gradient','3d_plot',]],
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extensions=extensions):
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@ -194,9 +202,9 @@ def test_plot():
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m.plot_errorbars_trainset()
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m.plot_samples()
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m.plot_data_error()
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for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf',
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'density',
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'out_error',
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for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf',
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'density',
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'out_error',
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'samples', 'in_error']], extensions=extensions):
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yield (do_test, )
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@ -216,9 +224,9 @@ def test_twod():
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m.plot_inducing()
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#m.plot_errorbars_trainset()
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m.plot_data_error()
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for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",
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'inducing',
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#'out_error',
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for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",
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'inducing',
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#'out_error',
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'in_error',
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]], extensions=extensions):
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yield (do_test, )
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@ -242,7 +250,7 @@ def test_threed():
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m.plot_mean(projection='3d')
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m.plot_inducing(projection='3d')
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#m.plot_errorbars_trainset(projection='3d')
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for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", 'inducing',
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for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", 'inducing',
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#'error',
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#"samples", "samples_lik"
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]], extensions=extensions):
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@ -316,7 +324,7 @@ def test_gplvm():
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matplotlib.rcParams[u'figure.figsize'] = (4,3)
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matplotlib.rcParams[u'text.usetex'] = False
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Q = 3
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# Define dataset
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# Define dataset
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N = 10
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k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
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k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
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@ -325,10 +333,10 @@ def test_gplvm():
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A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
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B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
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C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
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Y = np.vstack((A,B,C))
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labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
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k = RBF(Q, ARD=True, lengthscale=2) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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m = GPLVM(Y, Q, init="PCA", kernel=k)
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m.kern.lengthscale[:] = [1./.3, 1./.1, 1./.7]
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@ -341,7 +349,7 @@ def test_gplvm():
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np.random.seed(111)
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m.plot_magnification(labels=labels)
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m.plot_steepest_gradient_map(resolution=10, data_labels=labels)
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for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']],
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for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']],
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extensions=extensions,
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tol=12):
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yield (do_test, )
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@ -355,7 +363,7 @@ def test_bayesian_gplvm():
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matplotlib.rcParams[u'figure.figsize'] = (4,3)
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matplotlib.rcParams[u'text.usetex'] = False
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Q = 3
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# Define dataset
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# Define dataset
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N = 10
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k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
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k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
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@ -364,10 +372,10 @@ def test_bayesian_gplvm():
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A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
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B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
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C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
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Y = np.vstack((A,B,C))
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labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
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k = RBF(Q, ARD=True, lengthscale=2) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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m = BayesianGPLVM(Y, Q, init="PCA", kernel=k)
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m.kern.lengthscale[:] = [1./.3, 1./.1, 1./.7]
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