[added testing and plotting] restructuring the plotting library

GPy/testing/plotting_tests.py

@@ -0,0 +1,124 @@
+#===============================================================================
+# Copyright (c) 2015, Max Zwiessele
+# All rights reserved.
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+# 
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# 
+# * Neither the name of GPy nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#===============================================================================
+import numpy as np
+import GPy, os, sys
+from nose import SkipTest
+try:
+    from matplotlib import cbook
+except:
+    raise SkipTest("Matplotlib not installed, not testing plots")
+
+def _image_directories(func):
+    """
+    Compute the baseline and result image directories for testing *func*.
+    Create the result directory if it doesn't exist.
+    """
+    module_name = func.__module__
+
+    path = module_name
+    
+    mods = module_name.split('.')
+    subdir = os.path.join(*mods)
+    
+    basedir = os.path.join(*mods)
+    
+    result_dir = os.path.join(basedir, 'testresult')
+    baseline_dir = os.path.join(basedir, 'baseline')
+
+    if not os.path.exists(result_dir):
+        cbook.mkdirs(result_dir)
+
+    return baseline_dir, result_dir
+
+import matplotlib.testing.decorators
+matplotlib.testing.decorators._image_directories = _image_directories
+from matplotlib.testing.decorators import image_comparison
+import matplotlib.pyplot as plt
+
+@image_comparison(baseline_images=['gp'], extensions=['pdf','png'])
+def testPlot():
+    fig, ax = plt.subplots()
+    np.random.seed(11111)
+    X = np.random.uniform(0, 1, (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.optimize()
+    m.plot_data(ax=ax)
+    m.plot_mean(ax=ax)
+    m.plot_confidence(ax=ax)
+    m.plot_density(ax=ax)
+    return ax
+
+
+@image_comparison(baseline_images=['gp_class'], extensions=['pdf','png'])
+def testPlotClassification():
+    fig, ax = plt.subplots()
+    np.random.seed(11111)
+    X = np.random.uniform(0, 1, (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.GPClassification(X, Y>Y.mean())
+    m.optimize()
+    m.plot_data(ax=ax)
+    m.plot_mean(ax=ax)
+    m.plot_confidence(ax=ax)
+    m.plot_density(ax=ax)
+    return ax
+
+@image_comparison(baseline_images=['sparse_gp_class'], extensions=['pdf','png'])
+def testPlotSparseClassification():
+    fig, ax = plt.subplots()
+    np.random.seed(11111)
+    X = np.random.uniform(0, 1, (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.SparseGPClassification(X, Y>Y.mean())
+    m.optimize()
+    m.plot_data(ax=ax)
+    m.plot_mean(ax=ax)
+    m.plot_confidence(ax=ax)
+    m.plot_density(ax=ax)
+    return ax
+
+@image_comparison(baseline_images=['sparse_gp'], extensions=['pdf','png'])
+def testPlotSparse():
+    fig, ax = plt.subplots()
+    np.random.seed(11111)
+    X = np.random.uniform(0, 1, (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.optimize()
+    m.plot_data(ax=ax)
+    m.plot_mean(ax=ax)
+    m.plot_confidence(ax=ax)
+    m.plot_density(ax=ax)
+    return ax