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Merge branch 'params' of github.com:SheffieldML/GPy into params
Conflicts: GPy/core/parameterization/param.py
This commit is contained in:
Max Zwiessele
commit
a13d6ca894
8 changed files with 69 additions and 61 deletions
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@ -153,23 +153,6 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
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def _collect_gradient(self, target):
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target[:] = self.gradient.flat
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#===========================================================================
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# Fixing Parameters:
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#===========================================================================
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def constrain_fixed(self, warning=True):
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"""
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Constrain this paramter to be fixed to the current value it carries.
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:param warning: print a warning for overwriting constraints.
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"""
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self._highest_parent_._fix(self, warning)
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fix = constrain_fixed
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def unconstrain_fixed(self):
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"""
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This parameter will no longer be fixed.
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"""
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self._highest_parent_._unfix(self)
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unfix = unconstrain_fixed
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#===========================================================================
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# Tying operations -> bugged, TODO
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#===========================================================================
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def tie_to(self, param):
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@ -94,6 +94,25 @@ class Constrainable(Nameable):
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def __init__(self, name):
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super(Constrainable,self).__init__(name)
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#===========================================================================
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# Fixing Parameters:
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#===========================================================================
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def constrain_fixed(self, value=None, warning=True):
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"""
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Constrain this paramter to be fixed to the current value it carries.
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:param warning: print a warning for overwriting constraints.
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"""
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if value is not None:
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self[:] = value
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self._highest_parent_._fix(self,warning)
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fix = constrain_fixed
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def unconstrain_fixed(self):
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"""
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This parameter will no longer be fixed.
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"""
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self._highest_parent_._unfix(self)
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unfix = unconstrain_fixed
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#===========================================================================
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# Constrain operations -> done
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#===========================================================================
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def constrain(self, transform, warning=True, update=True):
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@ -37,39 +37,43 @@ def student_t_approx(optimize=True, plot=True):
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# Kernel object
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kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel2 = kernel1.copy()
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kernel3 = kernel1.copy()
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kernel4 = kernel1.copy()
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kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel3 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel4 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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#Gaussian GP model on clean data
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m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
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# optimize
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m1.ensure_default_constraints()
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m1.constrain_fixed('white', 1e-5)
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m1['white'] = 1e-5
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m1['white'].constrain_fixed('white')
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m1.randomize()
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#Gaussian GP model on corrupt data
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m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
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m2.ensure_default_constraints()
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m2.constrain_fixed('white', 1e-5)
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m1['white'] = 1e-5
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m1['white'].constrain_fixed('white')
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m2.randomize()
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#Student t GP model on clean data
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t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution)
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m3 = GPy.models.GPRegression(X, Y.copy(), kernel3, likelihood=stu_t_likelihood)
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t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
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laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
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m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
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m3.ensure_default_constraints()
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m3.constrain_bounded('t_noise', 1e-6, 10.)
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m3.constrain_fixed('white', 1e-5)
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m3['t_noise'].constrain_bounded(1e-6, 10.)
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m3['white'] = 1e-5
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m3['white'].constrain_fixed()
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m3.randomize()
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#Student t GP model on corrupt data
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t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
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corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution)
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m4 = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
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t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
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laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
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m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
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m4.ensure_default_constraints()
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m4.constrain_bounded('t_noise', 1e-6, 10.)
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m4.constrain_fixed('white', 1e-5)
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m4['t_noise'].constrain_bounded(1e-6, 10.)
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m4['white'] = 1e-5
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m4['white'].constrain_fixed()
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m4.randomize()
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if optimize:
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@ -281,11 +281,12 @@ def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
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f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
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Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
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noise_model = GPy.likelihoods.poisson()
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likelihood = GPy.likelihoods.Laplace(Y,noise_model)
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kern = GPy.kern.rbf(1)
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poisson_lik = GPy.likelihoods.Poisson()
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laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
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# create simple GP Model
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m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
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m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf)
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if optimize:
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m.optimize(optimizer)
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@ -11,9 +11,8 @@
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#http://gaussianprocess.org/gpml/code.
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import numpy as np
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from ...util.linalg import mdot, jitchol, pddet, dpotrs, dtrtrs, dpotri, symmetrify
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from ...util.linalg import mdot, jitchol, dpotrs, dtrtrs, dpotri, symmetrify
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from ...util.misc import param_to_array
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from functools import partial as partial_func
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from posterior import Posterior
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import warnings
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from scipy import optimize
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@ -85,7 +84,6 @@ class LaplaceInference(object):
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Ki_f = Ki_f_init.copy()
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f = np.dot(K, Ki_f)
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#define the objective function (to be maximised)
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def obj(Ki_f, f):
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return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + likelihood.logpdf(f, Y, extra_data=Y_metadata)
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@ -205,14 +203,6 @@ class LaplaceInference(object):
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return log_marginal, woodbury_vector, K_Wi_i, dL_dK, dL_dthetaL
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#def likelihood_gradients(self, f_hat, K, Y, Ki_W_i, dL_dfhat, I_KW_i, likelihood, Y_metadata):
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#"""
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#Gradients with respect to likelihood parameters (dL_dthetaL)
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#:rtype: array of derivatives (1 x num_likelihood_params)
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#"""
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def _compute_B_statistics(self, K, W, log_concave):
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"""
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Rasmussen suggests the use of a numerically stable positive definite matrix B
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@ -245,6 +235,5 @@ class LaplaceInference(object):
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#K_Wi_i_2 , _= dpotri(L2)
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#symmetrify(K_Wi_i_2)
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return K_Wi_i, L, LiW12
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@ -19,8 +19,11 @@ class Poisson(Likelihood):
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.. Note::
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Y is expected to take values in {0,1,2,...}
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"""
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def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
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super(Poisson, self).__init__(gp_link,analytical_mean,analytical_variance)
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def __init__(self, gp_link=None):
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if gp_link is None:
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gp_link = link_functions.Log_ex_1()
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super(Poisson, self).__init__(gp_link, name='Poisson')
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def _preprocess_values(self,Y):
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return Y
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@ -244,7 +244,7 @@ class StudentT(Likelihood):
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d2logpdf_dlink2_dv = np.zeros_like(d2logpdf_dlink2_dvar) #FIXME: Not done yet
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return np.hstack((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
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def _predictive_variance_analytical(self, mu, sigma, predictive_mean=None):
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def predictive_variance(self, mu, sigma, predictive_mean=None):
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"""
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Compute predictive variance of student_t*normal p(y*|f*)p(f*)
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@ -264,7 +264,7 @@ class StudentT(Likelihood):
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return true_var
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def _predictive_mean_analytical(self, mu, sigma):
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def predictive_mean(self, mu, sigma):
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"""
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Compute mean of the prediction
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"""
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@ -86,7 +86,7 @@ class TestNoiseModels(object):
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Generic model checker
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"""
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def setUp(self):
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self.N = 5
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self.N = 15
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self.D = 3
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self.X = np.random.rand(self.N, self.D)*10
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@ -104,7 +104,7 @@ class TestNoiseModels(object):
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self.var = np.random.rand(1)
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#Make a bigger step as lower bound can be quite curved
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self.step = 1e-3
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self.step = 1e-4
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def tearDown(self):
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self.Y = None
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@ -165,11 +165,20 @@ class TestNoiseModels(object):
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},
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"laplace": True
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},
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"Student_t_small_deg_free": {
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"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [self.var],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Student_t_small_var": {
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"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [0.01],
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"vals": [0.0001],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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},
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"laplace": True
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