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Trying to fix dL_dytil gradient
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Alan Saul
parent
48d693791e
commit
146d7e2458
2 changed files with 84 additions and 48 deletions
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@ -79,16 +79,29 @@ class Laplace(likelihood):
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return (self._Kgradients(dL_d_K_Sigma, dK_dthetaK), self._gradients(dL_d_K_Sigma))
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def _shared_gradients_components(self):
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dL_dytil = -np.dot(self.Y.T, inv(self.K+self.Sigma_tilde)) #or *0.5? Shouldn't this be -y*R
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Ki, _, _, _ = pdinv(self.K)
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#Y__KS_i = np.dot(self.Y.T, inv(self.K+self.Sigma_tilde))
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#dL_dytil = -0.5*Y__KS_i #or *0.5? Shouldn't this be -y*R
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#dL_dytil = -0.5*np.trace(np.dot(inv(self.K+self.Sigma_tilde), (np.dot(self.Y, self.Y.T) + self.Y.T)))
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#dL_dytil_simple_term = -0.5*np.dot(inv(self.K+self.Sigma_tilde),
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#dL_dytil_simple_term = -np.dot(self.Y.T, inv(self.K+self.Sigma_tilde), self.Y)
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c = inv(self.K+self.Sigma_tilde)
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dL_dytil_simple_term = -0.5*np.diag(np.dot(c, self.Y) + np.dot(self.Y.T, c))
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P = np.diagflat(1/np.dot(Ki, self.f_hat))
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K_Wi_i = inv(self.K+self.Sigma_tilde)
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dL_dytil_difficult_term = np.diag(( -0.5*(np.dot(self.K + self.Sigma_tilde, P))
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+0.5*mdot(K_Wi_i, self.Y, self.Y.T, K_Wi_i, P)
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) * np.eye(self.N))
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dL_dytil = dL_dytil_simple_term + dL_dytil_difficult_term
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d3likelihood_d3fhat = self.likelihood_function.d3link(self.data, self.f_hat, self.extra_data)
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Wi = np.diagonal(self.Sigma_tilde) #Convenience
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#Can just hadamard product as diagonal matricies multiplied are just multiplying elements
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dWi_dfhat = np.diagflat(-1*Wi*(-1*d3likelihood_d3fhat)*Wi)
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Ki, _, _, _ = pdinv(self.K)
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#dytil_dfhat_implicit = np.dot(dWi_dfhat, Ki) + np.eye(self.N)
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#dytil_dfhat = np.dot(dWi_dfhat, Ki) + np.eye(self.N)
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#Wi(Ki + W) = Wi__Ki_W using the last K prior given to fit_full
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#dytil_dfhat_explicit = self.Wi__Ki_W
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@ -97,6 +110,8 @@ class Laplace(likelihood):
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dytil_dfhat = - np.diagflat(np.dot(dWi_dfhat, np.dot(Ki, self.f_hat))) + np.dot(self.Sigma_tilde, Ki) + np.eye(self.N)
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self.dytil_dfhat = dytil_dfhat
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#dytil_dfhat = np.eye(dytil_dfhat.shape[0])
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self.dL_dfhat = np.dot(dL_dytil, dytil_dfhat) #FIXME: Purely for checkgradding....
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return dL_dytil, dytil_dfhat
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def _Kgradients(self, dL_d_K_Sigma, dK_dthetaK):
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@ -1,26 +1,29 @@
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import unittest
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import numpy as np
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np.random.seed(82)
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import GPy
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from GPy.models import GP
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from GPy.util.linalg import pdinv, tdot
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from scipy import linalg
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class LikelihoodGradParam(GP):
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def __init__(self, X, likelihood_function, kernel, param_name=None, function=None, **kwargs):
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super(LikelihoodGradParam, self).__init__(X, likelihood_function, kernel)
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class LikelihoodParamGrad(GP):
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def __init__(self, X=None, likelihood_function=None, kernel=None, param_name=None, function=None, dparam_name=None, **kwargs):
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self.param_name = param_name
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self.dparam_name = dparam_name
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self.func = function
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super(LikelihoodParamGrad, self).__init__(X, likelihood_function, kernel)
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#self.func_params = kwargs
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#self.parameter = self.likelihood.__getattribute__(self.param_name)
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def _get_param_names(self):
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f_hats = ["f_{}".format(i) for i in range(len(self.likelihood.f_hat))]
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return f_hats
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params = getattr(self.likelihood, self.dparam_name)
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params_names = ["{}_{}".format(self.dparam_name, i) for i in range(len(params))]
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return params_names
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def _get_params(self):
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return np.hstack([np.squeeze(self.likelihood.f_hat)])
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#return np.hstack([self.likelihood.__getattribute__(self.param_name)])
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params = getattr(self.likelihood, self.dparam_name)
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return np.hstack([params])
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def hack_dL_dK(self):
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self.K = self.kern.K(self.X)
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@ -38,29 +41,56 @@ class LikelihoodGradParam(GP):
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self.dL_dK = 0.5 * (tmp - self.D * self.Ki)
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def _set_params(self, x):
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self.likelihood.f_hat = x.reshape(self.N, 1)
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raise NotImplementedError
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def log_likelihood(self):
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raise NotImplementedError
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def _log_likelihood_gradients(self):
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raise NotImplementedError
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class Likelihood_F_Grad(LikelihoodParamGrad):
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def __init__(self, **kwargs):
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super(Likelihood_F_Grad, self).__init__(**kwargs)
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def _set_params(self, x):
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params = getattr(self.likelihood, self.dparam_name)
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setattr(self.likelihood, self.dparam_name, x.reshape(*params.shape))
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self.likelihood._compute_likelihood_variables()
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self.hack_dL_dK()
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def log_likelihood(self):
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return self.func(self.likelihood)[0, 0]
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ll = self.func(self)
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if self.param_name == "dL_dfhat_":
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import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
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if len(ll.shape) == 0 or len(ll.shape) == 1:
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return ll.sum()
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elif len(ll.shape) == 2:
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#print "Only checking first likelihood"
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return ll[0, 0]
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else:
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raise ValueError('Not implemented for larger matricies yet')
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return ll
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def _log_likelihood_gradients(self):
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#gradient = self.likelihood.__getattribute__(self.param_name)
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self.likelihood._compute_likelihood_variables()
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self.likelihood._gradients(partial=np.diag(self.dL_dK))
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gradient = getattr(self.likelihood, self.param_name)
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#Need to sum over fhats? For dytil_dfhat...
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#gradient = np.flatten(gradient, axis=0)
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#return gradient[:, 0]
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return gradient[0, :]
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if len(gradient.shape) == 1:
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return gradient
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elif len(gradient.shape) == 2:
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#print "Only checking first gradients"
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return gradient[0,: ]
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else:
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raise ValueError('Not implemented for larger matricies yet')
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class LaplaceTests(unittest.TestCase):
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def setUp(self):
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real_var = 0.1
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#Start a function, any function
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self.X = np.linspace(0.0, 10.0, 30)[:, None]
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self.X = np.linspace(0.0, 10.0, 4)[:, None]
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#self.X = np.random.randn(,1)
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#self.X = np.ones((10,1))
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Y = np.sin(self.X) + np.random.randn(*self.X.shape)*real_var
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@ -74,49 +104,40 @@ class LaplaceTests(unittest.TestCase):
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t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=initial_sd_guess)
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self.stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, rasm=True)
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self.stu_t_likelihood.fit_full(self.kernel.K(self.X))
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self.m = LikelihoodGradParam(self.X, self.stu_t_likelihood, self.kernel, None, None)
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self.m.constrain_fixed('rbf_v', 1.0898)
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self.m.constrain_fixed('rbf_l', 1.8651)
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def tearDown(self):
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self.m = None
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def test_dy_dfhat(self):
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def ytil(likelihood):
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Sigma_tilde = likelihood.Sigma_tilde
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K = likelihood.K
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def ytil(self):
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Sigma_tilde = self.likelihood.Sigma_tilde
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K = self.likelihood.K
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Ki, _, _, _ = pdinv(K)
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f_hat = likelihood.f_hat
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f_hat = self.likelihood.f_hat
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Sigma, _, _, _ = pdinv(Sigma_tilde)
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return np.dot(np.dot(Sigma_tilde, (Ki + Sigma)), f_hat)
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self.m.func = ytil
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self.m.param_name = 'dytil_dfhat'
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self.m = Likelihood_F_Grad(X=self.X, likelihood_function=self.stu_t_likelihood,
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kernel=self.kernel, param_name='dytil_dfhat',
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function=ytil, dparam_name='f_hat')
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#self.m.constrain_fixed('rbf_v', 1.0898)
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#self.m.constrain_fixed('rbf_l', 1.8651)
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self.m.randomize()
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#try:
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self.m.checkgrad(verbose=1)
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assert self.m.checkgrad()
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#except:
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#import ipdb;ipdb.set_trace()
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def test_dL_dfhat(self):
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def L(self):
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return np.array(-0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z)
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#def test_dL_dytil(self):
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#def L(likelihood):
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##-0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z
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#Sigma_tilde = likelihood.Sigma_tilde
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#Ki = likelihood.K
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#f_hat = likelihood.f_hat
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#Sigma, _, _, _ = pdinv(Sigma_tilde)
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#return np.dot(np.dot(Sigma_tilde, (Ki + Sigma)), f_hat)
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#self.m.func = L
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#self.m.param_name = 'dL_dytil'
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#m.randomize()
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##try:
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#m.checkgrad(verbose=1)
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#assert m.checkgrad()
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#except:
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#import ipdb;ipdb.set_trace()
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self.m = Likelihood_F_Grad(X=self.X, likelihood_function=self.stu_t_likelihood,
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kernel=self.kernel, param_name='dL_dfhat',
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function=L, dparam_name='f_hat')
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self.m.constrain_fixed('rbf_v', 1.0898)
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self.m.constrain_fixed('rbf_l', 1.8651)
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self.m.randomize()
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self.m.checkgrad(verbose=1)
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assert self.m.checkgrad()
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if __name__ == "__main__":
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unittest.main()
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