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[paramz] test warning catches
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mzwiessele
parent
942c134ab7
commit
97593309ef
4 changed files with 68 additions and 57 deletions
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@ -101,40 +101,42 @@ def test_figure():
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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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import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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ax, _ = pl().new_canvas(num=1)
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def test_func(x):
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return x[:, 0].reshape(3,3)
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pl().imshow_interact(ax, test_func, extent=(-1,1,-1,1), resolution=3)
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ax, _ = pl().new_canvas(num=1)
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def test_func(x):
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return x[:, 0].reshape(3,3)
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pl().imshow_interact(ax, test_func, extent=(-1,1,-1,1), resolution=3)
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ax, _ = pl().new_canvas()
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def test_func_2(x):
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y = x[:, 0].reshape(3,3)
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anno = np.argmax(x, axis=1).reshape(3,3)
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return y, anno
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pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3)
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pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3, imshow_kwargs=dict(interpolation='nearest'))
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ax, _ = pl().new_canvas()
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def test_func_2(x):
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y = x[:, 0].reshape(3,3)
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anno = np.argmax(x, axis=1).reshape(3,3)
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return y, anno
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pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3)
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pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3, imshow_kwargs=dict(interpolation='nearest'))
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ax, _ = pl().new_canvas(figsize=(4,3))
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x = np.linspace(0,1,100)
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y = [0,1,2]
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array = np.array([.4,.5])
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cmap = matplotlib.colors.LinearSegmentedColormap.from_list('WhToColor', ('r', 'b'), N=array.size)
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pl().fill_gradient(ax, x, y, facecolors=['r', 'g'], array=array, cmap=cmap)
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try:
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pl().show_canvas(ax, tight_layout=True)
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except:
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# macosx tight layout not stable
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pl().show_canvas(ax, tight_layout=False)
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ax, _ = pl().new_canvas(figsize=(4,3))
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x = np.linspace(0,1,100)
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y = [0,1,2]
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array = np.array([.4,.5])
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cmap = matplotlib.colors.LinearSegmentedColormap.from_list('WhToColor', ('r', 'b'), N=array.size)
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pl().fill_gradient(ax, x, y, facecolors=['r', 'g'], array=array, cmap=cmap)
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ax, _ = pl().new_canvas(num=4, figsize=(4,3), projection='3d', xlabel='x', ylabel='y', zlabel='z', title='awsome title', xlim=(-1,1), ylim=(-1,1), zlim=(-3,3))
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z = 2-np.abs(np.linspace(-2,2,(100)))+1
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x, y = z*np.sin(np.linspace(-2*np.pi,2*np.pi,(100))), z*np.cos(np.linspace(-np.pi,np.pi,(100)))
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pl().plot(ax, x, y, z, linewidth=2)
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for do_test in _image_comparison(
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baseline_images=['coverage_{}'.format(sub) for sub in ["imshow_interact",'annotation_interact','gradient','3d_plot',]],
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extensions=extensions):
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yield (do_test, )
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ax, _ = pl().new_canvas(num=4, figsize=(4,3), projection='3d', xlabel='x', ylabel='y', zlabel='z', title='awsome title', xlim=(-1,1), ylim=(-1,1), zlim=(-3,3))
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z = 2-np.abs(np.linspace(-2,2,(100)))+1
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x, y = z*np.sin(np.linspace(-2*np.pi,2*np.pi,(100))), z*np.cos(np.linspace(-np.pi,np.pi,(100)))
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pl().plot(ax, x, y, z, linewidth=2)
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for do_test in _image_comparison(
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baseline_images=['coverage_{}'.format(sub) for sub in ["imshow_interact",'annotation_interact','gradient','3d_plot',]],
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extensions=extensions):
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yield (do_test, )
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def test_kernel():
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@ -143,19 +145,22 @@ def test_kernel():
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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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k2 = GPy.kern.RBF(5, ARD=True) * GPy.kern.Linear(3, active_dims=[0,2,4], ARD=True) + GPy.kern.Bias(2) + GPy.kern.White(4)
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k2[:-1] = k[:]
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k2.plot_ARD(['rbf', 'linear', 'bias'], legend=True)
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k2.plot_covariance(visible_dims=[0, 3], plot_limits=(-1,3))
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k2.plot_covariance(visible_dims=[2], plot_limits=(-1, 3))
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k2.plot_covariance(visible_dims=[2, 4], plot_limits=((-1, 0), (5, 3)), projection='3d')
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k2.plot_covariance(visible_dims=[1, 4])
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for do_test in _image_comparison(
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baseline_images=['kern_{}'.format(sub) for sub in ["ARD", 'cov_2d', 'cov_1d', 'cov_3d', 'cov_no_lim']],
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extensions=extensions):
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yield (do_test, )
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import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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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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k2 = GPy.kern.RBF(5, ARD=True) * GPy.kern.Linear(3, active_dims=[0,2,4], ARD=True) + GPy.kern.Bias(2) + GPy.kern.White(4)
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k2[:-1] = k[:]
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k2.plot_ARD(['rbf', 'linear', 'bias'], legend=True)
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k2.plot_covariance(visible_dims=[0, 3], plot_limits=(-1,3))
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k2.plot_covariance(visible_dims=[2], plot_limits=(-1, 3))
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k2.plot_covariance(visible_dims=[2, 4], plot_limits=((-1, 0), (5, 3)), projection='3d')
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k2.plot_covariance(visible_dims=[1, 4])
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for do_test in _image_comparison(
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baseline_images=['kern_{}'.format(sub) for sub in ["ARD", 'cov_2d', 'cov_1d', 'cov_3d', 'cov_no_lim']],
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extensions=extensions):
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yield (do_test, )
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def test_plot():
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np.random.seed(111)
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@ -163,18 +168,21 @@ def test_plot():
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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.plot_data()
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m.plot_mean()
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m.plot_confidence()
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m.plot_density()
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m.plot_errorbars_trainset()
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m.plot_samples()
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m.plot_data_error()
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import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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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.plot_data()
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m.plot_mean()
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m.plot_confidence()
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m.plot_density()
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m.plot_errorbars_trainset()
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m.plot_samples()
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m.plot_data_error()
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for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf',
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'density',
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'out_error',
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