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[testing] more restructuring, almost ready to ship, added some tests for testing with travis

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mzwiessele 2015-10-04 16:25:15 +01:00
parent 1b9fba0cc6
commit e1a9d276a3
1 changed files with 6 additions and 4 deletions
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10
GPy/testing/plotting_tests.py
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@ -38,6 +38,8 @@ try:
except:
raise SkipTest("Matplotlib not installed, not testing plots")
extensions = ['pdf']
def _image_directories(func):
"""
Compute the baseline and result image directories for testing *func*.
@ -65,7 +67,7 @@ matplotlib.testing.decorators._image_directories = _image_directories
from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt
@image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error']], extensions=['pdf','png'])
@image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error']], extensions=extensions)
def testPlot():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
@ -79,7 +81,7 @@ def testPlot():
m.plot_density()
m.plot_errorbars_trainset()
@image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'inducing']], extensions=['pdf','png'])
@image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'inducing']], extensions=extensions)
def testPlotSparse():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
@ -94,7 +96,7 @@ def testPlotSparse():
m.plot_errorbars_trainset()
m.plot_inducing()
@image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=['pdf','png'])
@image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
def testPlotClassification():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
@ -107,7 +109,7 @@ def testPlotClassification():
m.plot(plot_raw=False, apply_link=True)
m.plot(plot_raw=True, apply_link=True)
@image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=['pdf','png'])
@image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
def testPlotSparseClassification():
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))