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https://github.com/SheffieldML/GPy.git
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[tests] failing only on pull request, wtf?
This commit is contained in:
mzwiessele
parent
e35e0d461d
commit
d548858b42
14 changed files with 52 additions and 11 deletions
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@ -40,13 +40,9 @@ if config.get('plotting', 'library') != 'matplotlib':
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try:
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import matplotlib
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from matplotlib import cbook, pyplot as plt
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from matplotlib.testing.compare import compare_images
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from matplotlib.testing.noseclasses import ImageComparisonFailure
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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except ImportError:
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raise SkipTest("Matplotlib not installed, not testing plots")
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@ -99,6 +95,10 @@ def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=11):
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def test_kernel():
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np.random.seed(1239847)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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k = GPy.kern.RBF(5, ARD=True) + GPy.kern.Linear(3, active_dims=[0,2,4], ARD=True) + GPy.kern.Bias(2)
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k.randomize()
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k.plot_ARD(legend=True)
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@ -109,11 +109,15 @@ def test_kernel():
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def test_plot():
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np.random.seed(111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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X = np.random.uniform(-2, 2, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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Y = f+np.random.normal(0, .1, f.shape)
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m = GPy.models.SparseGPRegression(X, Y, X_variance=np.ones_like(X)*[0.06])
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m.optimize()
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#m.optimize()
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m.plot_data()
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m.plot_mean()
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m.plot_confidence()
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@ -129,11 +133,15 @@ def test_plot():
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def test_twod():
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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X = np.random.uniform(-2, 2, (40, 2))
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f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
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Y = f+np.random.normal(0, .1, f.shape)
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m = GPy.models.SparseGPRegression(X, Y, X_variance=np.ones_like(X)*[0.01, 0.2])
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m.optimize()
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#m.optimize()
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m.plot_data()
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m.plot_mean()
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m.plot_inducing()
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@ -148,6 +156,10 @@ def test_twod():
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def test_threed():
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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X = np.random.uniform(-2, 2, (40, 2))
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f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
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Y = f+np.random.normal(0, .1, f.shape)
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@ -169,11 +181,15 @@ def test_threed():
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def test_sparse():
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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X = np.random.uniform(-2, 2, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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Y = f+np.random.normal(0, .1, f.shape)
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m = GPy.models.SparseGPRegression(X, Y, X_variance=np.ones_like(X)*0.1)
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m.optimize()
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#m.optimize()
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#m.plot_inducing()
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m.plot_data()
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for do_test in _image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ['data_error']], extensions=extensions):
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@ -181,11 +197,15 @@ def test_sparse():
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def test_classification():
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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X = np.random.uniform(-2, 2, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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Y = f+np.random.normal(0, .1, f.shape)
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m = GPy.models.GPClassification(X, Y>Y.mean())
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m.optimize()
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#m.optimize()
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_, ax = plt.subplots()
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m.plot(plot_raw=False, apply_link=False, ax=ax)
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m.plot_errorbars_trainset(plot_raw=False, apply_link=False, ax=ax)
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@ -201,13 +221,19 @@ def test_classification():
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def test_sparse_classification():
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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X = np.random.uniform(-2, 2, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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Y = f+np.random.normal(0, .1, f.shape)
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m = GPy.models.SparseGPClassification(X, Y>Y.mean())
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m.optimize()
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#m.optimize()
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m.plot(plot_raw=False, apply_link=False, samples_likelihood=3)
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np.random.seed(111)
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m.plot(plot_raw=True, apply_link=False, samples=3)
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np.random.seed(111)
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m.plot(plot_raw=True, apply_link=True, samples=3)
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for do_test in _image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
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yield (do_test, )
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@ -217,17 +243,23 @@ def test_gplvm():
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from ..kern import RBF
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from ..models import GPLVM
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
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Y = Ylist[0]
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k = RBF(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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# k = kern.RBF(Q, ARD=True, lengthscale=10.)
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m = GPLVM(Y, Q, init="PCA", kernel=k)
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m.likelihood.variance = .1
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#m.optimize(messages=0)
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labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
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np.random.seed(111)
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m.plot_latent()
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np.random.seed(111)
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m.plot_scatter(projection='3d', labels=labels)
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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=7)
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for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
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@ -237,7 +269,11 @@ def test_bayesian_gplvm():
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from ..examples.dimensionality_reduction import _simulate_matern
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from ..kern import RBF
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from ..models import BayesianGPLVM
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np.random.seed(11111)
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import matplotlib
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matplotlib.rcParams.update(matplotlib.rcParamsDefault)
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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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np.random.seed(111)
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Q = 3
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_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
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Y = Ylist[0]
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@ -247,10 +283,15 @@ def test_bayesian_gplvm():
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m.likelihood.variance = .1
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#m.optimize(messages=0)
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labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
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np.random.seed(111)
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m.plot_inducing(projection='2d')
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np.random.seed(111)
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m.plot_inducing(projection='3d')
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np.random.seed(111)
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m.plot_scatter(projection='3d')
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np.random.seed(111)
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m.plot_magnification(labels=labels)
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np.random.seed(111)
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m.plot_steepest_gradient_map(resolution=7)
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for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):
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yield (do_test, )
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