apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-25 21:06:23 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[matplotlib] plot updates and testing

Browse source
This commit is contained in:
mzwiessele 2015-10-06 18:20:54 +01:00
parent 116ad8762c
commit 486def6e0c
21 changed files with 217 additions and 204 deletions
Download patch file Download diff file Expand all files Collapse all files

31
GPy/testing/plotting_tests.py
View file

@ -145,11 +145,12 @@ def test_classification():
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.GPClassification(X, Y>Y.mean())
m.optimize()
m.plot()
m.plot(plot_raw=True)
m.plot(plot_raw=False, apply_link=True)
fig, ax = plt.subplots()
m.plot(plot_raw=False, apply_link=False, ax=ax)
fig, 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 ["", "raw", 'link', 'raw_link']], extensions=extensions):
for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
yield (do_test, )
@ -160,17 +161,17 @@ def test_sparse_classification():
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.SparseGPClassification(X, Y>Y.mean())
m.optimize()
m.plot()
m.plot(plot_raw=True)
m.plot(plot_raw=False, apply_link=True)
m.plot(plot_raw=False, apply_link=False)
m.plot(plot_raw=True, apply_link=False)
m.plot(plot_raw=True, apply_link=True)
for do_test in _image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions):
for do_test in _image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
yield (do_test, )
def test_gplvm():
from ..examples.dimensionality_reduction import _simulate_matern
from ..kern import RBF
from ..models import GPLVM
np.random.seed(11111)
Q = 3
_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
Y = Ylist[0]
@ -180,18 +181,18 @@ def test_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_prediction_fit(which_data_ycols=(0,1)) # ignore this test, as plotting is not consistent!!
plt.close('all')
m.plot_latent()
m.plot_latent_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", "magnification", 'gradient']], extensions=extensions):
for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
yield (do_test, )
def test_bayesian_gplvm():
from ..examples.dimensionality_reduction import _simulate_matern
from ..kern import RBF
from ..models import BayesianGPLVM
np.random.seed(11111)
Q = 3
_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
Y = Ylist[0]
@ -201,12 +202,12 @@ 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_prediction_fit(which_data_ycols=(0,1)) # ignore this test, as plotting is not consistent!!
plt.close('all')
m.plot_latent()
m.plot_latent_inducing(projection='2d')
m.plot_latent_inducing(projection='3d')
m.plot_latent_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 ["latent", "magnification", 'gradient']], extensions=extensions):
for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):
yield (do_test, )
if __name__ == '__main__':