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[test] coverage increased

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mzwiessele 2015-10-10 16:39:37 +01:00
parent 2e4be065d1
commit 844c24247b
26 changed files with 63 additions and 46 deletions
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39
GPy/testing/plotting_tests.py
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@ -112,7 +112,7 @@ def test_plot():
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)
m = GPy.models.SparseGPRegression(X, Y, X_variance=np.ones_like(X)*[0.06])
m.optimize()
m.plot_data()
m.plot_mean()
@ -120,7 +120,11 @@ def test_plot():
m.plot_density()
m.plot_errorbars_trainset()
m.plot_samples()
for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'samples']], extensions=extensions):
m.plot_data_error()
for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf',
'density',
'out_error',
'samples', 'in_error']], extensions=extensions):
yield (do_test, )
def test_twod():
@ -128,11 +132,18 @@ def test_twod():
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 = GPy.models.SparseGPRegression(X, Y, X_variance=np.ones_like(X)*[0.01, 0.2])
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):
m.plot_inducing()
#m.plot_errorbars_trainset()
m.plot_data_error()
for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",
'inducing',
#'out_error',
'in_error',
]], extensions=extensions):
yield (do_test, )
def test_threed():
@ -140,7 +151,7 @@ def test_threed():
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 = GPy.models.SparseGPRegression(X, Y)
m.likelihood.variance = .1
#m.optimize()
m.plot_samples(projection='3d', samples=1)
@ -148,7 +159,10 @@ def test_threed():
plt.close('all')
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",
m.plot_inducing(projection='3d')
#m.plot_errorbars_trainset(projection='3d')
for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", 'inducing',
#'error',
#"samples", "samples_lik"
]], extensions=extensions):
yield (do_test, )
@ -158,10 +172,11 @@ def test_sparse():
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)
m = GPy.models.SparseGPRegression(X, Y, X_variance=np.ones_like(X)*0.1)
m.optimize()
m.plot_inducing()
for do_test in _image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ['inducing']], extensions=extensions):
#m.plot_inducing()
m.plot_data()
for do_test in _image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ['data_error']], extensions=extensions):
yield (do_test, )
def test_classification():
@ -173,9 +188,13 @@ def test_classification():
m.optimize()
_, ax = plt.subplots()
m.plot(plot_raw=False, apply_link=False, ax=ax)
m.plot_errorbars_trainset(plot_raw=False, apply_link=False, ax=ax)
_, ax = plt.subplots()
m.plot(plot_raw=True, apply_link=False, ax=ax)
m.plot(plot_raw=True, apply_link=True)
m.plot_errorbars_trainset(plot_raw=True, apply_link=False, ax=ax)
_, ax = plt.subplots()
m.plot(plot_raw=True, apply_link=True, ax=ax)
m.plot_errorbars_trainset(plot_raw=True, apply_link=True, ax=ax)
for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
yield (do_test, )