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https://github.com/SheffieldML/GPy.git
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[plotting] magnification plot added
This commit is contained in:
mzwiessele
parent
61dbde7a20
commit
0610903018
70 changed files with 294 additions and 409 deletions
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@ -31,6 +31,7 @@ import numpy as np
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import GPy, os
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from nose import SkipTest
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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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try:
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from matplotlib import cbook, pyplot as plt
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@ -41,14 +42,15 @@ except:
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extensions = ['png']
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def _image_directories(func):
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def _image_directories():
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"""
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Compute the baseline and result image directories for testing *func*.
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Create the result directory if it doesn't exist.
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"""
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module_name = func.__module__
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mods = module_name.split('.')
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basedir = os.path.join(*mods)
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basedir = os.path.splitext(os.path.relpath(os.path.abspath(__file__)))[0]
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#module_name = __init__.__module__
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#mods = module_name.split('.')
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#basedir = os.path.join(*mods)
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result_dir = os.path.join(basedir, 'testresult')
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baseline_dir = os.path.join(basedir, 'baseline')
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if not os.path.exists(result_dir):
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@ -56,45 +58,44 @@ def _image_directories(func):
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return baseline_dir, result_dir
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def sequenceEqual(a, b):
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def _sequenceEqual(a, b):
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assert len(a) == len(b), "Sequences not same length"
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for i, [x, y], in enumerate(zip(a, b)):
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assert x == y, "element not matching {}".format(i)
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def notFound(path):
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def _notFound(path):
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raise IOError('File {} not in baseline')
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class test_image_comparison(object):
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def __init__(self, baseline_images=[], extensions=['pdf','svg','ong']):
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self.baseline_images = baseline_images
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self.extensions = extensions
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self.f = None
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def __call__(self, func):
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self.baseline_dir, self.result_dir = _image_directories(func)
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def test_wrap():
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func()
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for num, base in zip(plt.get_fignums(), self.baseline_images):
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for ext in self.extensions:
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fig = plt.figure(num)
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fig.axes[0].set_axis_off()
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fig.set_frameon(False)
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fig.savefig(os.path.join(self.result_dir, "{}.{}".format(base, ext)), frameon=False)
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actual = os.path.join(self.result_dir, "{}.{}".format(base, ext))
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expected = os.path.join(self.baseline_dir, "{}.{}".format(base, ext))
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yield compare_images, actual, expected, 1e-3
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plt.close('all')
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#with open(os.path.join(self.result_dir, "{}.{}".format(base, ext)), 'r') as f:
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# try:
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# with open(os.path.join(self.baseline_dir, "{}.{}".format(base, ext)), 'r') as b:
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# except:
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# yield notFound, os.path.join(self.baseline_dir, "{}.{}".format(base, ext))
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#plt.close(num)
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def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=1e-3):
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baseline_dir, result_dir = _image_directories()
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for num, base in zip(plt.get_fignums(), baseline_images):
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for ext in extensions:
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fig = plt.figure(num)
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fig.axes[0].set_axis_off()
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fig.set_frameon(False)
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fig.canvas.draw()
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fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)))
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for num, base in zip(plt.get_fignums(), baseline_images):
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for ext in extensions:
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#plt.close(num)
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actual = os.path.join(result_dir, "{}.{}".format(base, ext))
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expected = os.path.join(baseline_dir, "{}.{}".format(base, ext))
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def do_test():
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err = compare_images(actual, expected, tol)
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try:
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if not os.path.exists(expected):
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raise ImageComparisonFailure(
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'image does not exist: %s' % expected)
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if err:
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raise ImageComparisonFailure(
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'images not close: %(actual)s vs. %(expected)s '
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'(RMS %(rms).3f)'%err)
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except ImageComparisonFailure:
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pass
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yield do_test
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plt.close('all')
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return test_wrap
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@test_image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error']], extensions=extensions)
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def Plot(self=None):
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def test_plot(self=None):
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np.random.seed(11111)
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X = np.random.uniform(0, 1, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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@ -106,24 +107,22 @@ def Plot(self=None):
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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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for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'samples']], extensions=extensions):
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yield (do_test, )
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@test_image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'inducing']], extensions=extensions)
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def PlotSparse(self=None):
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def test_plot_sparse(self=None):
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np.random.seed(11111)
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X = np.random.uniform(0, 1, (40, 1))
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X = np.random.uniform(-1, 1, (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)
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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_inducing()
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for do_test in _image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ['inducing']], extensions=extensions):
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yield (do_test, )
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@test_image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
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def PlotClassification(self=None):
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def test_plot_classification(self=None):
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np.random.seed(11111)
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X = np.random.uniform(0, 1, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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@ -134,9 +133,11 @@ def PlotClassification(self=None):
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m.plot(plot_raw=True)
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m.plot(plot_raw=False, apply_link=True)
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m.plot(plot_raw=True, apply_link=True)
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for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions):
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yield (do_test, )
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@test_image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
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def PlotSparseClassification(self=None):
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def test_plot_sparse_classification(self=None):
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np.random.seed(11111)
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X = np.random.uniform(0, 1, (40, 1))
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f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
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@ -147,3 +148,29 @@ def PlotSparseClassification(self=None):
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m.plot(plot_raw=True)
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m.plot(plot_raw=False, apply_link=True)
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m.plot(plot_raw=True, apply_link=True)
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for do_test in _image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions):
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yield (do_test, )
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def test_gplvm_plot(self=None):
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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 GPLVM
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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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m.plot_prediction_fit(which_data_ycols=(0,1)) # ignore this test, as plotting is not consistent!!
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plt.close('all')
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m.plot_latent()
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m.plot_magnification(labels=labels)
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for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "magnification"]], extensions=extensions):
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yield (do_test, )
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if __name__ == '__main__':
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import nose
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nose.main()
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