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https://github.com/SheffieldML/GPy.git
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351 lines
15 KiB
Python
351 lines
15 KiB
Python
#===============================================================================
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# Copyright (c) 2015, Max Zwiessele
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of GPy nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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import numpy as np
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import GPy, os
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from nose import SkipTest
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from ..util.config import config
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from ..plotting import change_plotting_library
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import unittest
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change_plotting_library('matplotlib')
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if config.get('plotting', 'library') != 'matplotlib':
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raise SkipTest("Matplotlib not installed, not testing plots")
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try:
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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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except ImportError:
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raise SkipTest("Matplotlib not installed, not testing plots")
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extensions = ['png']
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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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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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cbook.mkdirs(result_dir)
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return baseline_dir, result_dir
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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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raise IOError('File {} not in baseline')
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def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=11):
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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)), transparent=True, edgecolor='none', facecolor='none')
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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(expected, actual, tol, in_decorator=True)
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if err:
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raise ImageComparisonFailure("Error between {} and {} is {:.5f}, which is bigger then the tolerance of {:.5f}".format(actual, expected, err['rms'], tol))
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yield do_test
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plt.close('all')
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def test_figure():
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np.random.seed(1239847)
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from GPy.plotting import plotting_library as pl
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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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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(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(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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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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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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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.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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'samples', 'in_error']], extensions=extensions):
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yield (do_test, )
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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.plot_data()
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m.plot_mean()
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m.plot_inducing()
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#m.plot_errorbars_trainset()
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m.plot_data_error()
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for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",
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'inducing',
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#'out_error',
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'in_error',
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]], extensions=extensions):
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yield (do_test, )
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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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m = GPy.models.SparseGPRegression(X, Y)
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m.likelihood.variance = .1
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#m.optimize()
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m.plot_samples(projection='3d', samples=1)
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m.plot_samples(projection='3d', plot_raw=False, samples=1)
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plt.close('all')
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m.plot_data(projection='3d')
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m.plot_mean(projection='3d')
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m.plot_inducing(projection='3d')
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#m.plot_errorbars_trainset(projection='3d')
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for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", 'inducing',
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#'error',
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#"samples", "samples_lik"
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]], extensions=extensions):
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yield (do_test, )
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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.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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yield (do_test, )
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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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_, 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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_, ax = plt.subplots()
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m.plot(plot_raw=True, apply_link=False, ax=ax)
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m.plot_errorbars_trainset(plot_raw=True, apply_link=False, ax=ax)
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_, ax = plt.subplots()
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m.plot(plot_raw=True, apply_link=True, ax=ax)
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m.plot_errorbars_trainset(plot_raw=True, apply_link=True, ax=ax)
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for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
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yield (do_test, )
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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.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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def test_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 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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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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yield (do_test, )
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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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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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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 = BayesianGPLVM(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_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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if __name__ == '__main__':
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import nose
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nose.main()
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