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pcikle tests added
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Max Zwiessele
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206
GPy/testing/pickle_tests.py
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206
GPy/testing/pickle_tests.py
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'''
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Created on 13 Mar 2014
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@author: maxz
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'''
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import unittest, itertools
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import cPickle as pickle
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import numpy as np
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from GPy.core.parameterization.index_operations import ParameterIndexOperations,\
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ParameterIndexOperationsView
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import tempfile
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from GPy.core.parameterization.param import Param
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from GPy.core.parameterization.array_core import ObsAr
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from GPy.core.parameterization.priors import Gaussian
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from GPy.kern._src.rbf import RBF
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from GPy.kern._src.linear import Linear
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from GPy.kern._src.static import Bias, White
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from GPy.examples.dimensionality_reduction import mrd_simulation,\
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bgplvm_simulation
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from GPy.examples.regression import toy_rbf_1d_50
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from GPy.core.parameterization.variational import NormalPosterior
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from GPy.models.gp_regression import GPRegression
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class ListDictTestCase(unittest.TestCase):
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def assertListDictEquals(self, d1, d2, msg=None):
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for k,v in d1.iteritems():
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self.assertListEqual(list(v), list(d2[k]), msg)
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def assertArrayListEquals(self, l1, l2):
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for a1, a2 in itertools.izip(l1,l2):
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np.testing.assert_array_equal(a1, a2)
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class Test(ListDictTestCase):
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def test_parameter_index_operations(self):
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pio = ParameterIndexOperations(dict(test1=np.array([4,3,1,6,4]), test2=np.r_[2:130]))
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piov = ParameterIndexOperationsView(pio, 20, 250)
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self.assertListDictEquals(dict(piov.items()), dict(piov.copy().iteritems()))
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self.assertListDictEquals(dict(pio.iteritems()), dict(pio.copy().items()))
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self.assertArrayListEquals(pio.copy().indices(), pio.indices())
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self.assertArrayListEquals(piov.copy().indices(), piov.indices())
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with tempfile.TemporaryFile('w+b') as f:
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pickle.dump(pio, f)
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f.seek(0)
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pio2 = pickle.load(f)
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self.assertListDictEquals(pio._properties, pio2._properties)
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with tempfile.TemporaryFile('w+b') as f:
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pickle.dump(piov, f)
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f.seek(0)
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pio2 = pickle.load(f)
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self.assertListDictEquals(dict(piov.items()), dict(pio2.iteritems()))
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def test_param(self):
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param = Param('test', np.arange(4*2).reshape(4,2))
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param[0].constrain_positive()
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param[1].fix()
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param[2].set_prior(Gaussian(0,1))
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pcopy = param.copy()
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self.assertListEqual(param.tolist(), pcopy.tolist())
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self.assertListEqual(str(param).split('\n'), str(pcopy).split('\n'))
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self.assertIsNot(param, pcopy)
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with tempfile.TemporaryFile('w+b') as f:
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pickle.dump(param, f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(param.tolist(), pcopy.tolist())
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self.assertSequenceEqual(str(param), str(pcopy))
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def test_observable_array(self):
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obs = ObsAr(np.arange(4*2).reshape(4,2))
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pcopy = obs.copy()
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self.assertListEqual(obs.tolist(), pcopy.tolist())
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with tempfile.TemporaryFile('w+b') as f:
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pickle.dump(obs, f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(obs.tolist(), pcopy.tolist())
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self.assertSequenceEqual(str(obs), str(pcopy))
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def test_parameterized(self):
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par = RBF(1, active_dims=[1]) + Linear(2, active_dims=[0,2]) + Bias(3) + White(3)
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par.gradient = 10
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par.randomize()
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pcopy = par.copy()
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self.assertIsInstance(pcopy.constraints, ParameterIndexOperations)
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self.assertIsInstance(pcopy.rbf.constraints, ParameterIndexOperationsView)
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self.assertIs(pcopy.constraints, pcopy.rbf.constraints._param_index_ops)
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self.assertIs(pcopy.constraints, pcopy.rbf.lengthscale.constraints._param_index_ops)
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self.assertIs(pcopy.constraints, pcopy.linear.constraints._param_index_ops)
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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self.assertListEqual(par.full_gradient.tolist(), pcopy.full_gradient.tolist())
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assertIsNot(par.param_array, pcopy.param_array)
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self.assertIsNot(par.full_gradient, pcopy.full_gradient)
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with tempfile.TemporaryFile('w+b') as f:
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par.pickle(f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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pcopy.gradient = 10
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np.testing.assert_allclose(par.linear.full_gradient, pcopy.linear.full_gradient)
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np.testing.assert_allclose(pcopy.linear.full_gradient, 10)
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self.assertSequenceEqual(str(par), str(pcopy))
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def test_model(self):
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par = toy_rbf_1d_50(optimize=0, plot=0)
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pcopy = par.copy()
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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self.assertListEqual(par.full_gradient.tolist(), pcopy.full_gradient.tolist())
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assertIsNot(par.param_array, pcopy.param_array)
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self.assertIsNot(par.full_gradient, pcopy.full_gradient)
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self.assertTrue(pcopy.checkgrad())
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self.assert_(np.any(pcopy.gradient!=0.0))
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with tempfile.TemporaryFile('w+b') as f:
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par.pickle(f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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np.testing.assert_allclose(par.full_gradient, pcopy.full_gradient)
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assert_(pcopy.checkgrad())
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def test_modelrecreation(self):
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par = toy_rbf_1d_50(optimize=0, plot=0)
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pcopy = GPRegression(par.X.copy(), par.Y.copy(), kernel=par.kern.copy())
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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self.assertListEqual(par.full_gradient.tolist(), pcopy.full_gradient.tolist())
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assertIsNot(par.param_array, pcopy.param_array)
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self.assertIsNot(par.full_gradient, pcopy.full_gradient)
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self.assertTrue(pcopy.checkgrad())
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self.assert_(np.any(pcopy.gradient!=0.0))
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with tempfile.TemporaryFile('w+b') as f:
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par.pickle(f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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np.testing.assert_allclose(par.full_gradient, pcopy.full_gradient)
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assert_(pcopy.checkgrad())
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def test_posterior(self):
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X = np.random.randn(3,5)
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Xv = np.random.rand(*X.shape)
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par = NormalPosterior(X,Xv)
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par.gradient = 10
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pcopy = par.copy()
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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self.assertListEqual(par.full_gradient.tolist(), pcopy.full_gradient.tolist())
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assertIsNot(par.param_array, pcopy.param_array)
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self.assertIsNot(par.full_gradient, pcopy.full_gradient)
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with tempfile.TemporaryFile('w+b') as f:
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par.pickle(f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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pcopy.gradient = 10
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np.testing.assert_allclose(par.full_gradient, pcopy.full_gradient)
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np.testing.assert_allclose(pcopy.mean.full_gradient, 10)
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self.assertSequenceEqual(str(par), str(pcopy))
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def test_model_concat(self):
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par = mrd_simulation(optimize=0, plot=0, plot_sim=0)
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par.randomize()
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pcopy = par.copy()
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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self.assertListEqual(par.full_gradient.tolist(), pcopy.full_gradient.tolist())
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assertIsNot(par.param_array, pcopy.param_array)
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self.assertIsNot(par.full_gradient, pcopy.full_gradient)
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self.assertTrue(pcopy.checkgrad())
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self.assert_(np.any(pcopy.gradient!=0.0))
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with tempfile.TemporaryFile('w+b') as f:
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par.pickle(f)
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f.seek(0)
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pcopy = pickle.load(f)
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self.assertListEqual(par.param_array.tolist(), pcopy.param_array.tolist())
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np.testing.assert_allclose(par.full_gradient, pcopy.full_gradient)
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self.assertSequenceEqual(str(par), str(pcopy))
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self.assert_(pcopy.checkgrad())
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def _callback(self, what, which):
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what.count += 1
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def test_add_observer(self):
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par = toy_rbf_1d_50(optimize=0, plot=0)
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par.name = "original"
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par.count = 0
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par.add_observer(self, self._callback, 1)
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pcopy = GPRegression(par.X.copy(), par.Y.copy(), kernel=par.kern.copy())
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self.assertNotIn(par._observer_callables_[0], pcopy._observer_callables_)
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pcopy = par.copy()
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pcopy.name = "copy"
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self.assertTrue(par.checkgrad())
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self.assertTrue(pcopy.checkgrad())
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self.assertTrue(pcopy.kern.checkgrad())
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self.assertIn(par._observer_callables_[0], pcopy._observer_callables_)
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self.assertEqual(par.count, 3)
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self.assertEqual(pcopy.count, 6) # 3 of each call to checkgrad
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if __name__ == "__main__":
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#import sys;sys.argv = ['', 'Test.test_parameter_index_operations']
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unittest.main()
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