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Added test generator (not quite finished yet)
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3 changed files with 58 additions and 26 deletions
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@ -194,7 +194,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
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# Remove the mean (no bias kernel to ensure signal/noise is in RBF/white)
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data['Y'] = data['Y'] - np.mean(data['Y'])
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lls = GPy.examples.regression.contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
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lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
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pb.contour(length_scales, log_SNRs, np.exp(lls), 20)
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ax = pb.gca()
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pb.xlabel('length scale')
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@ -229,7 +229,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
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ax.set_ylim(ylim)
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return (models, lls)
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def contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf):
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def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf):
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"""Evaluate the GP objective function for a given data set for a range of signal to noise ratios and a range of lengthscales.
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:data_set: A data set from the utils.datasets director.
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@ -6,14 +6,14 @@
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Code of Tutorials
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"""
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import pylab as pb
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pb.ion()
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import numpy as np
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import GPy
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def tuto_GP_regression():
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"""The detailed explanations of the commands used in this file can be found in the tutorial section"""
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import pylab as pb
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pb.ion()
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import numpy as np
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import GPy
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X = np.random.uniform(-3.,3.,(20,1))
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Y = np.sin(X) + np.random.randn(20,1)*0.05
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@ -39,11 +39,6 @@ def tuto_GP_regression():
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# 2-dimensional example #
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###########################
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import pylab as pb
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pb.ion()
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import numpy as np
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import GPy
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# sample inputs and outputs
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X = np.random.uniform(-3.,3.,(50,2))
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Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(50,1)*0.05
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@ -67,9 +62,6 @@ def tuto_GP_regression():
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def tuto_kernel_overview():
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"""The detailed explanations of the commands used in this file can be found in the tutorial section"""
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import pylab as pb
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import numpy as np
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import GPy
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pb.ion()
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ker1 = GPy.kern.rbf(1) # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
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@ -4,23 +4,63 @@
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import unittest
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import numpy as np
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import GPy
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import inspect
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import pkgutil
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import os
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class ExamplesTests(unittest.TestCase):
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def test_check_model_returned(self):
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pass
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def _checkgrad(self, model):
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self.assertTrue(model.checkgrad())
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def test_model_checkgrads(self):
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pass
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def _model_instance(self, model):
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self.assertTrue(isinstance(model, GPy.models))
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def test_all_examples(self):
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examples_module = __import__("GPy").examples
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#Load models
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"""
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def model_instance_generator(model):
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def check_model_returned(self):
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self._model_instance(model)
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return check_model_returned
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#Loop through models
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#for model in models:
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#self.assertTrue(m.checkgrad())
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def checkgrads_generator(model):
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def model_checkgrads(self):
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self._checkgrad(model)
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return model_checkgrads
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"""
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def model_checkgrads(model):
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assert model.checkgrad() is True
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def model_instance(model):
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assert model.checkgrad() is True
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def test_models():
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examples_path = os.path.dirname(GPy.examples.__file__)
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#Load modules
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for loader, module_name, is_pkg in pkgutil.iter_modules([examples_path]):
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#Load examples
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module_examples = loader.find_module(module_name).load_module(module_name)
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functions = [ func for func in [inspect.getmembers(module_examples, predicate=inspect.isfunction)[0]] if func[0].startswith('_') is False ]
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for example in functions:
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print "Testing example: ", example[0]
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#Generate model
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model = example[1]()
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print model
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#Create tests for instance check
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"""
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test = model_instance_generator(model)
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test.__name__ = 'test_instance_%s' % example[0]
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setattr(ExamplesTests, test.__name__, test)
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#Create tests for checkgrads check
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test = checkgrads_generator(model)
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test.__name__ = 'test_checkgrads_%s' % example[0]
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setattr(ExamplesTests, test.__name__, test)
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"""
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model_checkgrads.description = 'test_checkgrads_%s' % example[0]
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yield model_checkgrads, model
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model_instance.description = 'test_checkgrads_%s' % example[0]
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yield model_instance, model
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if __name__ == "__main__":
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print "Running unit tests, please be (very) patient..."
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unittest.main()
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