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[plotting] gradient plot added

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This commit is contained in:
mzwiessele 2015-10-05 19:34:33 +01:00
parent a86d7616c9
commit 2263df0394
18 changed files with 41 additions and 23 deletions
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62
GPy/testing/plotting_tests.py
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@ -75,28 +75,21 @@ def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=1e-3)
fig.set_frameon(False)
fig.canvas.draw()
fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)))
plt.close('all')
for num, base in zip(plt.get_fignums(), baseline_images):
for ext in extensions:
#plt.close(num)
actual = os.path.join(result_dir, "{}.{}".format(base, ext))
expected = os.path.join(baseline_dir, "{}.{}".format(base, ext))
def do_test():
err = compare_images(expected, actual, tol)
try:
if not os.path.exists(expected):
raise ImageComparisonFailure(
'image does not exist: %s' % expected)
if err:
raise ImageComparisonFailure(
'images not close: {err[actual]!s} vs. {err[expected]!s} (RMS {err[rms]:.3f})'.format(err=err))
except ImageComparisonFailure:
pass
err = compare_images(expected, actual, tol, in_decorator=True)
if err:
raise ImageComparisonFailure("Error between {} and {} is {:.5f}, which is bigger then the tolerance of {:.5f}".format(actual, expected, err['rms'], tol))
yield do_test
def test_plot(self=None):
plt.close('all')
def test_plot():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
X = np.random.uniform(-2, 2, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.GPRegression(X, Y)
@ -110,9 +103,33 @@ def test_plot(self=None):
for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'samples']], extensions=extensions):
yield (do_test, )
def test_plot_sparse(self=None):
def test_twod():
np.random.seed(11111)
X = np.random.uniform(-1, 1, (40, 1))
X = np.random.uniform(-2, 2, (40, 2))
f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.GPRegression(X, Y)
m.optimize()
m.plot_data()
m.plot_mean()
for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean"]], extensions=extensions):
yield (do_test, )
def test_threed():
np.random.seed(11111)
X = np.random.uniform(-2, 2, (40, 2))
f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.GPRegression(X, Y)
m.optimize()
m.plot_data(projection='3d')
m.plot_mean(projection='3d')
for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean"]], extensions=extensions):
yield (do_test, )
def test_sparse():
np.random.seed(11111)
X = np.random.uniform(-2, 2, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.SparseGPRegression(X, Y)
@ -121,9 +138,9 @@ def test_plot_sparse(self=None):
for do_test in _image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ['inducing']], extensions=extensions):
yield (do_test, )
def test_plot_classification(self=None):
def test_classification():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
X = np.random.uniform(-2, 2, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.GPClassification(X, Y>Y.mean())
@ -136,9 +153,9 @@ def test_plot_classification(self=None):
yield (do_test, )
def test_plot_sparse_classification(self=None):
def test_sparse_classification():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
X = np.random.uniform(-2, 2, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.SparseGPClassification(X, Y>Y.mean())
@ -150,7 +167,7 @@ def test_plot_sparse_classification(self=None):
for do_test in _image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions):
yield (do_test, )
def test_gplvm_plot(self=None):
def test_gplvm():
from ..examples.dimensionality_reduction import _simulate_matern
from ..kern import RBF
from ..models import GPLVM
@ -164,10 +181,11 @@ def test_gplvm_plot(self=None):
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!!
m.plot_steepest_gradient_map(resolution=7)
plt.close('all')
m.plot_latent()
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=5)
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):
yield (do_test, )