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[tests working now?]

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mzwiessele 2015-10-07 00:52:47 +01:00
parent 5290e4bf0e
commit 7ebdc698f6
34 changed files with 42 additions and 33 deletions
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14
GPy/testing/plotting_tests.py
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@ -74,7 +74,7 @@ def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=10):
fig.axes[0].set_axis_off()
fig.set_frameon(False)
fig.canvas.draw()
fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)))
fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)), transparent=True, edgecolor='none', facecolor='none')
for num, base in zip(plt.get_fignums(), baseline_images):
for ext in extensions:
#plt.close(num)
@ -145,9 +145,9 @@ def test_classification():
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.GPClassification(X, Y>Y.mean())
m.optimize()
fig, ax = plt.subplots()
_, ax = plt.subplots()
m.plot(plot_raw=False, apply_link=False, ax=ax)
fig, ax = plt.subplots()
_, ax = plt.subplots()
m.plot(plot_raw=True, apply_link=False, ax=ax)
m.plot(plot_raw=True, apply_link=True)
for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
@ -182,7 +182,7 @@ def test_gplvm():
#m.optimize(messages=0)
labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
m.plot_latent()
m.plot_latent_scatter(projection='3d', labels=labels)
m.plot_scatter(projection='3d', labels=labels)
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=7)
for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
@ -202,9 +202,9 @@ def test_bayesian_gplvm():
m.likelihood.variance = .1
#m.optimize(messages=0)
labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
m.plot_latent_inducing(projection='2d')
m.plot_latent_inducing(projection='3d')
m.plot_latent_scatter(projection='3d')
m.plot_inducing(projection='2d')
m.plot_inducing(projection='3d')
m.plot_scatter(projection='3d')
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=7)
for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):