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185 lines
6.8 KiB
Python
185 lines
6.8 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, sys
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from nose import SkipTest
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raise SkipTest('Not Testing plotting yet, will be later')
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try:
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from matplotlib import cbook
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except:
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raise SkipTest("Matplotlib not installed, not testing plots")
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def _image_directories(func):
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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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path = module_name
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mods = module_name.split('.')
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subdir = os.path.join(*mods)
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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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import matplotlib.testing.decorators
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matplotlib.testing.decorators._image_directories = _image_directories
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from matplotlib.testing.decorators import image_comparison
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import matplotlib.pyplot as plt
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@image_comparison(baseline_images=['gp'], extensions=['pdf','png'])
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def testPlot():
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fig, ax = plt.subplots()
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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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Y = f+np.random.normal(0, .1, f.shape)
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m = GPy.models.GPRegression(X, Y)
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m.optimize()
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m.plot_data(ax=ax)
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m.plot_mean(ax=ax)
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m.plot_mean(ax=ax, plot_raw=True)
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m.plot_mean(ax=ax, apply_link=True)
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m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
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m.plot_confidence(ax=ax)
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m.plot_density(ax=ax)
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return ax
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@image_comparison(baseline_images=['gp_class'], extensions=['pdf','png'])
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def testPlotClassification():
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fig, ax = plt.subplots()
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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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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.plot_data(ax=ax)
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m.plot_mean(ax=ax)
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m.plot_mean(ax=ax, plot_raw=True)
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m.plot_mean(ax=ax, apply_link=True)
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m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
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m.plot_confidence(ax=ax)
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m.plot_confidence(ax=ax, plot_raw=True)
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m.plot_confidence(ax=ax, apply_link=True)
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m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
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m.plot_density(ax=ax)
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m.plot_density(ax=ax, plot_raw=True)
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m.plot_density(ax=ax, apply_link=True)
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m.plot_density(ax=ax, plot_raw=True, apply_link=True)
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return ax
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@image_comparison(baseline_images=['sparse_gp_class'], extensions=['pdf','png'])
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def testPlotSparseClassification():
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fig, ax = plt.subplots()
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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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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_data(ax=ax)
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m.plot_mean(ax=ax)
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m.plot_mean(ax=ax, plot_raw=True)
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m.plot_mean(ax=ax, apply_link=True)
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m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
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m.plot_confidence(ax=ax)
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m.plot_confidence(ax=ax, plot_raw=True)
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m.plot_confidence(ax=ax, apply_link=True)
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m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
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m.plot_density(ax=ax)
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m.plot_density(ax=ax, plot_raw=True)
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m.plot_density(ax=ax, apply_link=True)
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m.plot_density(ax=ax, plot_raw=True, apply_link=True)
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m.plot_inducing(ax=ax)
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return ax
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@image_comparison(baseline_images=['sparse_gp'], extensions=['pdf','png'])
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def testPlotSparse():
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fig, ax = plt.subplots()
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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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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(ax=ax)
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m.plot_mean(ax=ax)
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m.plot_mean(ax=ax, plot_raw=True)
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m.plot_mean(ax=ax, apply_link=True)
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m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
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m.plot_confidence(ax=ax)
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m.plot_confidence(ax=ax, plot_raw=True)
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m.plot_confidence(ax=ax, apply_link=True)
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m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
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m.plot_density(ax=ax)
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m.plot_density(ax=ax, plot_raw=True)
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m.plot_density(ax=ax, apply_link=True)
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m.plot_density(ax=ax, plot_raw=True, apply_link=True)
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m.plot_inducing(ax=ax)
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return ax
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@image_comparison(baseline_images=['sparse_latent'], extensions=['pdf','png'])
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def testPlotSparse():
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fig, ax = plt.subplots()
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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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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(ax=ax)
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m.plot_mean(ax=ax)
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m.plot_mean(ax=ax, plot_raw=True)
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m.plot_mean(ax=ax, apply_link=True)
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m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
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m.plot_confidence(ax=ax)
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m.plot_confidence(ax=ax, plot_raw=True)
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m.plot_confidence(ax=ax, apply_link=True)
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m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
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m.plot_density(ax=ax)
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m.plot_density(ax=ax, plot_raw=True)
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m.plot_density(ax=ax, apply_link=True)
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m.plot_density(ax=ax, plot_raw=True, apply_link=True)
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m.plot_inducing(ax=ax)
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return ax
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