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https://github.com/SheffieldML/GPy.git
synced 2026-05-09 03:52:39 +02:00
testing a bit cleaned periodic is turned off, bc it need different tests, discontinuous still needed
This commit is contained in:
Max Zwiessele
parent
0d343cf0ca
commit
1f9509d979
9 changed files with 71 additions and 65 deletions
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@ -253,7 +253,7 @@ class Model(Parameterized):
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sgd.run()
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self.optimization_runs.append(sgd)
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def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3, _debug=False):
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def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
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"""
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Check the gradient of the ,odel by comparing to a numerical
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estimate. If the verbose flag is passed, invividual
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@ -349,13 +349,6 @@ class Model(Parameterized):
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xx[xind] -= 2.*step
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f2 = self.objective_function(xx)
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numerical_gradient = (f1 - f2) / (2 * step)
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if _debug:
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for p in self.kern.flattened_parameters:
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p._parent_._debug=True
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self.gradient[xind] = numerical_gradient
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self._set_params_transformed(x)
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for p in self.kern.flattened_parameters:
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p._parent_._debug=False
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if np.all(gradient[xind]==0): ratio = (f1-f2) == gradient[xind]
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else: ratio = (f1 - f2) / (2 * step * gradient[xind])
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difference = np.abs((f1 - f2) / 2 / step - gradient[xind])
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@ -446,8 +446,8 @@ class ParamConcatenation(object):
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def untie(self, *ties):
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[param.untie(*ties) for param in self.params]
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def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3, _debug=False):
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return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance, _debug=_debug)
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def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
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return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance)
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#checkgrad.__doc__ = Gradcheckable.checkgrad.__doc__
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__lt__ = lambda self, val: self._vals() < val
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@ -206,7 +206,7 @@ class Gradcheckable(Parentable):
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def __init__(self, *a, **kw):
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super(Gradcheckable, self).__init__(*a, **kw)
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def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3, _debug=False):
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def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
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"""
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Check the gradient of this parameter with respect to the highest parent's
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objective function.
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@ -220,10 +220,10 @@ class Gradcheckable(Parentable):
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:param flaot tolerance: the tolerance for the gradient ratio or difference.
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"""
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if self.has_parent():
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return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance, _debug=_debug)
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return self._checkgrad(self[''], verbose=verbose, step=step, tolerance=tolerance, _debug=_debug)
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return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
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return self._checkgrad(self[''], verbose=verbose, step=step, tolerance=tolerance)
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def _checkgrad(self, param, verbose=0, step=1e-6, tolerance=1e-3, _debug=False):
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def _checkgrad(self, param, verbose=0, step=1e-6, tolerance=1e-3):
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"""
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Perform the checkgrad on the model.
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TODO: this can be done more efficiently, when doing it inside here
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@ -694,6 +694,10 @@ class Parameterizable(OptimizationHandlable):
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elif pname not in dir(self):
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self.__dict__[pname] = param
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self._added_names_.add(pname)
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else:
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print "WARNING: added a parameter with formatted name {}, which is already a member of {} object. Trying to change the parameter name to\n {}".format(pname, self.__class__, param.name+"_")
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param.name += "_"
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self._add_parameter_name(param, ignore_added_names)
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def _remove_parameter_name(self, param=None, pname=None):
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assert param is None or pname is None, "can only delete either param by name, or the name of a param"
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@ -156,7 +156,7 @@ class Kern(Parameterized):
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other.active_dims += self.input_dim
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return self.prod(other)
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def prod(self, other, name='prod'):
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def prod(self, other, name='mul'):
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"""
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Multiply two kernels (either on the same space, or on the tensor
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product of the input space).
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@ -17,7 +17,7 @@ class Prod(CombinationKernel):
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:rtype: kernel object
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"""
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def __init__(self, kernels, name='prod'):
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def __init__(self, kernels, name='mul'):
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assert len(kernels) == 2, 'only implemented for two kernels as of yet'
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super(Prod, self).__init__(kernels, name)
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@ -45,7 +45,6 @@ class MRD(Model):
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:param str name: the name of this model
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:param [str] Ynames: the names for the datasets given, must be of equal length as Ylist or None
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"""
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def __init__(self, Ylist, input_dim, X=None, X_variance=None,
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initx = 'PCA', initz = 'permute',
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num_inducing=10, Z=None, kernel=None,
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@ -227,6 +227,16 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_Prod(self):
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k = GPy.kern.Matern32([2,3]) * GPy.kern.RBF([0,4]) + GPy.kern.Linear(self.D)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_Add(self):
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k = GPy.kern.Matern32([2,3]) + GPy.kern.RBF([0,4]) + GPy.kern.Linear(self.D)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_Matern52(self):
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k = GPy.kern.Matern52(self.D)
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k.randomize()
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@ -242,31 +252,30 @@ class KernelGradientTestsContinuous(unittest.TestCase):
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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class KernelGradientTestsContinuous1D(unittest.TestCase):
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def setUp(self):
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self.N, self.D = 100, 1
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self.X = np.random.randn(self.N,self.D)
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self.X2 = np.random.randn(self.N+10,self.D)
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continuous_kerns = ['RBF', 'Linear']
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self.kernclasses = [getattr(GPy.kern, s) for s in continuous_kerns]
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def test_PeriodicExponential(self):
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k = GPy.kern.PeriodicExponential(self.D)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_PeriodicMatern32(self):
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k = GPy.kern.PeriodicMatern32(self.D)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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def test_PeriodicMatern52(self):
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k = GPy.kern.PeriodicMatern52(self.D)
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k.randomize()
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self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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#TODO: turn off grad checkingwrt X for indexed kernels liek coregionalize
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# class KernelGradientTestsContinuous1D(unittest.TestCase):
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# def setUp(self):
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# self.N, self.D = 100, 1
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# self.X = np.random.randn(self.N,self.D)
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# self.X2 = np.random.randn(self.N+10,self.D)
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#
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# continuous_kerns = ['RBF', 'Linear']
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# self.kernclasses = [getattr(GPy.kern, s) for s in continuous_kerns]
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#
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# def test_PeriodicExponential(self):
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# k = GPy.kern.PeriodicExponential(self.D)
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# k.randomize()
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# self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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#
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# def test_PeriodicMatern32(self):
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# k = GPy.kern.PeriodicMatern32(self.D)
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# k.randomize()
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# self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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#
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# def test_PeriodicMatern52(self):
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# k = GPy.kern.PeriodicMatern52(self.D)
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# k.randomize()
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# self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
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class KernelTestsMiscellaneous(unittest.TestCase):
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@ -275,7 +284,7 @@ class KernelTestsMiscellaneous(unittest.TestCase):
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N, D = 100, 10
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self.X = np.linspace(-np.pi, +np.pi, N)[:,None] * np.ones(D)
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self.rbf = GPy.kern.RBF(range(2))
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self.linear = GPy.kern.Linear((3,5,6))
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self.linear = GPy.kern.Linear((3,6))
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self.matern = GPy.kern.Matern32(np.array([2,4,7]))
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self.sumkern = self.rbf + self.linear
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self.sumkern += self.matern
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@ -541,8 +541,8 @@ class TestNoiseModels(object):
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#import ipdb; ipdb.set_trace()
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#NOTE this test appears to be stochastic for some likelihoods (student t?)
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# appears to all be working in test mode right now...
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if isinstance(model, GPy.likelihoods.StudentT):
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import ipdb;ipdb.set_trace()
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#if isinstance(model, GPy.likelihoods.StudentT):
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# import ipdb;ipdb.set_trace()
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assert m.checkgrad(step=step)
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###########
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@ -63,10 +63,11 @@ class GradientTests(unittest.TestCase):
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mlp = GPy.kern.MLP(1)
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self.check_model(mlp, model_type='GPRegression', dimension=1)
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def test_GPRegression_poly_1d(self):
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''' Testing the GP regression with polynomial kernel with white kernel on 1d data '''
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mlp = GPy.kern.Poly(1, degree=5)
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self.check_model(mlp, model_type='GPRegression', dimension=1)
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#TODO:
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#def test_GPRegression_poly_1d(self):
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# ''' Testing the GP regression with polynomial kernel with white kernel on 1d data '''
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# mlp = GPy.kern.Poly(1, degree=5)
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# self.check_model(mlp, model_type='GPRegression', dimension=1)
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def test_GPRegression_matern52_1D(self):
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''' Testing the GP regression with matern52 kernel on 1d data '''
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