Gradients almost there for dytil_dfhat, diagonal terms are right

GPy/likelihoods/Laplace.py

@@ -96,7 +96,10 @@ class Laplace(likelihood):
         #dytil_dfhat1 = np.dot(self.Sigma_tilde, Ki) + np.eye(self.N) # or self.Wi__Ki_W? Theyre the same basically
 
         a = mdot(dWi_dfhat, Ki, self.f_hat)
-        dytil_dfhat = mdot(dWi_dfhat, Ki, self.f_hat) + np.dot(self.Sigma_tilde, Ki) + np.eye(self.N)
+        b = np.dot(self.Sigma_tilde, Ki)
+        dytil_dfhat = - np.dot(dWi_dfhat, np.dot(Ki, self.f_hat)) + np.dot(self.Sigma_tilde, Ki) + np.eye(self.N)
+        #dytil_dfhat = - (np.dot(dWi_dfhat, Ki)*self.f_hat[:, None] + np.dot(self.Sigma_tilde, Ki)).sum(-1) + np.eye(self.N)
+        self.dytil_dfhat = dytil_dfhat
         return dL_dytil, dytil_dfhat
 
     def _Kgradients(self, dL_d_K_Sigma, dK_dthetaK):
@@ -330,19 +333,25 @@ class Laplace(likelihood):
 
     def fit_full(self, K):
         """
-        The laplace approximation algorithm
+        The laplace approximation algorithm, find K and expand hessian
         For nomenclature see Rasmussen & Williams 2006 - modified for numerical stability
         :K: Covariance matrix
         """
         self.K = K.copy()
-        #assert np.all(self.K.T == self.K)
+
-        #self.K_safe = K.copy()
+        #Find mode
         if self.rasm:
             self.f_hat = self.rasm_mode(K)
         else:
             self.f_hat = self.ncg_mode(K)
 
+        #Compute hessian and other variables at mode
+        self._compute_likelihood_variables()
+
+    def _compute_likelihood_variables(self):
         #At this point get the hessian matrix
+        #print "Data: ", self.data
+        #print "fhat: ", self.f_hat
         self.W = -np.diag(self.likelihood_function.link_hess(self.data, self.f_hat, extra_data=self.extra_data))
 
         if not self.likelihood_function.log_concave:
@@ -352,14 +361,14 @@ class Laplace(likelihood):
                                        #This is a property only held by non-log-concave likelihoods
 
         #TODO: Could save on computation when using rasm by returning these, means it isn't just a "mode finder" though
-        self.B, self.B_chol, self.W_12 = self._compute_B_statistics(K, self.W)
+        self.B, self.B_chol, self.W_12 = self._compute_B_statistics(self.K, self.W)
         self.Bi, _, _, B_det = pdinv(self.B)
 
         Ki_W_i = self.K - mdot(self.K, self.W_12, self.Bi, self.W_12, self.K)
         self.ln_Ki_W_i_det = np.linalg.det(Ki_W_i)
 
         b = np.dot(self.W, self.f_hat) + self.likelihood_function.link_grad(self.data, self.f_hat, extra_data=self.extra_data)[:, None]
-        solve_chol = cho_solve((self.B_chol, True), mdot(self.W_12, (K, b)))
+        solve_chol = cho_solve((self.B_chol, True), mdot(self.W_12, (self.K, b)))
         a = b - mdot(self.W_12, solve_chol)
         self.f_Ki_f = np.dot(self.f_hat.T, a)
         self.ln_K_det = pddet(self.K)

GPy/likelihoods/likelihood_functions.py

@@ -10,8 +10,7 @@ from scipy.special import gammaln, gamma
 from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
 
 class likelihood_function:
-    """
+    """ Likelihood class for doing Expectation propagation
-    Likelihood class for doing Expectation propagation
 
     :param Y: observed output (Nx1 numpy.darray)
     ..Note:: Y values allowed depend on the likelihood_function used
@@ -241,6 +240,7 @@ class student_t(likelihood_function):
         y = np.squeeze(y)
         f = np.squeeze(f)
         assert y.shape == f.shape
+
         e = y - f
         hess = ((self.v + 1)*(e**2 - self.v*(self.sigma**2))) / ((((self.sigma**2)*self.v) + e**2)**2)
         return np.squeeze(hess)

GPy/testing/laplace_approx.tests.py

@@ -0,0 +1,123 @@
+import unittest
+import numpy as np
+
+import GPy
+from GPy.models import GP
+from GPy.util.linalg import pdinv, tdot
+from scipy import linalg
+
+class LikelihoodGradParam(GP):
+    def __init__(self, X, likelihood_function, kernel, param_name=None, function=None, **kwargs):
+        super(LikelihoodGradParam, self).__init__(X, likelihood_function, kernel)
+        self.param_name = param_name
+        self.func = function
+        #self.func_params = kwargs
+        #self.parameter = self.likelihood.__getattribute__(self.param_name)
+
+    def _get_param_names(self):
+        f_hats = ["f_{}".format(i) for i in range(len(self.likelihood.f_hat))]
+        return f_hats
+
+    def _get_params(self):
+        return np.hstack([np.squeeze(self.likelihood.f_hat)])
+        #return np.hstack([self.likelihood.__getattribute__(self.param_name)])
+
+    def hack_dL_dK(self):
+        self.K = self.kern.K(self.X)
+        self.K += self.likelihood.covariance_matrix
+
+        self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
+
+        # the gradient of the likelihood wrt the covariance matrix
+        if self.likelihood.YYT is None:
+            alpha, _ = linalg.lapack.flapack.dpotrs(self.L, self.likelihood.Y, lower=1)
+            self.dL_dK = 0.5 * (tdot(alpha) - self.D * self.Ki)
+        else:
+            tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
+            tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
+            self.dL_dK = 0.5 * (tmp - self.D * self.Ki)
+
+    def _set_params(self, x):
+        self.likelihood.f_hat = x.reshape(self.N, 1)
+        self.likelihood._compute_likelihood_variables()
+        self.hack_dL_dK()
+
+    def log_likelihood(self):
+        return self.func(self.likelihood)[0, 0]
+
+    def _log_likelihood_gradients(self):
+        #gradient = self.likelihood.__getattribute__(self.param_name)
+        self.likelihood._compute_likelihood_variables()
+        self.likelihood._gradients(partial=np.diag(self.dL_dK))
+        gradient = getattr(self.likelihood, self.param_name)
+        #Need to sum over fhats? For dytil_dfhat...
+        #gradient = np.flatten(gradient, axis=0)
+        #return gradient[:, 0]
+        return gradient[0, :]
+
+
+class LaplaceTests(unittest.TestCase):
+    def setUp(self):
+        real_var = 0.1
+        #Start a function, any function
+        #self.X = np.linspace(0.0, 10.0, 30)[:, None]
+        self.X = np.random.randn(2,1)
+        #self.X = np.ones((10,1))
+        Y = np.sin(self.X) + np.random.randn(*self.X.shape)*real_var
+        self.Y = Y/Y.max()
+        self.kernel = GPy.kern.rbf(self.X.shape[1])
+
+        deg_free = 10000
+        real_sd = np.sqrt(real_var)
+        initial_sd_guess = 1
+
+        t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=initial_sd_guess)
+        self.stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, rasm=True)
+        self.stu_t_likelihood.fit_full(self.kernel.K(self.X))
+        self.m = LikelihoodGradParam(self.X, self.stu_t_likelihood, self.kernel, None, None)
+        self.m.constrain_fixed('rbf_v', 1.0898)
+        self.m.constrain_fixed('rbf_l', 1.8651)
+
+    def tearDown(self):
+        self.m = None
+
+    def test_dy_dfhat(self):
+        def ytil(likelihood):
+            Sigma_tilde = likelihood.Sigma_tilde
+            K = likelihood.K
+            Ki, _, _, _ = pdinv(K)
+            f_hat = likelihood.f_hat
+            Sigma, _, _, _ = pdinv(Sigma_tilde)
+            return np.dot(np.dot(Sigma_tilde, (Ki + Sigma)), f_hat)
+
+        self.m.func = ytil
+        self.m.param_name = 'dytil_dfhat'
+        self.m.randomize()
+        #try:
+        self.m.checkgrad(verbose=1)
+        assert self.m.checkgrad()
+        #except:
+            #import ipdb;ipdb.set_trace()
+
+
+    #def test_dL_dytil(self):
+        #def L(likelihood):
+            ##-0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z
+            #Sigma_tilde = likelihood.Sigma_tilde
+            #Ki = likelihood.K
+            #f_hat = likelihood.f_hat
+            #Sigma, _, _, _ = pdinv(Sigma_tilde)
+            #return np.dot(np.dot(Sigma_tilde, (Ki + Sigma)), f_hat)
+
+        #self.m.func = L
+        #self.m.param_name = 'dL_dytil'
+        #m.randomize()
+        ##try:
+        #m.checkgrad(verbose=1)
+        #assert m.checkgrad()
+        #except:
+            #import ipdb;ipdb.set_trace()
+
+if __name__ == "__main__":
+    unittest.main()
+