dimensionality reduction merge

GPy/core/gp.py

@@ -96,8 +96,7 @@ class GP(GPBase):
         model for a new variable Y* = v_tilde/tau_tilde, with a covariance
         matrix K* = K + diag(1./tau_tilde) plus a normalization term.
         """
-        return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -
+        return - 0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) - 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z
-            0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)
 
 
     def _log_likelihood_gradients(self):

GPy/core/sparse_gp.py

@@ -108,7 +108,7 @@ class SparseGP(GPBase):
         self.B = np.eye(self.num_inducing) + self.A
         self.LB = jitchol(self.B)
 
-        # VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
+        #VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
         self.psi1Vf = np.dot(self.psi1.T, self.likelihood.VVT_factor)
 
         # back substutue C into psi1Vf
@@ -163,7 +163,7 @@ class SparseGP(GPBase):
     def log_likelihood(self):
         """ Compute the (lower bound on the) log marginal likelihood """
         if self.likelihood.is_heteroscedastic:
-            A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
+            A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V*self.likelihood.Y)
             B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
         else:
             A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
@@ -246,7 +246,7 @@ class SparseGP(GPBase):
         Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.num_inducing) - Bi)
 
         if self.Cpsi1V is None:
-            psi1V = np.dot(self.psi1.T, self.likelihood.V)
+            psi1V = np.dot(self.psi1.T,self.likelihood.V)
             tmp, _ = dtrtrs(self.Lm, np.asfortranarray(psi1V), lower=1, trans=0)
             tmp, _ = dpotrs(self.LB, tmp, lower=1)
             self.Cpsi1V, _ = dtrtrs(self.Lm, tmp, lower=1, trans=1)

GPy/core/svigp.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pylab as pb
 from .. import kern
-from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, backsub_both_sides
+from ..util.linalg import linalg, pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, backsub_both_sides
 from ..likelihoods import EP
 from gp_base import GPBase
 from model import Model
@@ -269,7 +269,6 @@ class SVIGP(GPBase):
     def optimize(self, iterations, print_interval=10, callback=lambda:None, callback_interval=5):
 
         param_step = 0.
-
         #Iterate!
         for i in range(iterations):
 
@@ -288,6 +287,7 @@ class SVIGP(GPBase):
             #compute the steps in all parameters
             vb_step = self.vb_steplength*natgrads[0]
             if (self.epochs>=1):#only move the parameters after the first epoch
+                # print "it {} ep {} par {}".format(self.iterations, self.epochs, param_step)
                 param_step = self.momentum*param_step + self.param_steplength*grads
             else:
                 param_step = 0.
@@ -295,8 +295,8 @@ class SVIGP(GPBase):
             self.set_vb_param(self.get_vb_param() + vb_step)
             #Note: don't recompute everything here, wait until the next iteration when we have a new batch
             self._set_params(self._untransform_params(self._get_params_transformed() + param_step), computations=False)
-
             #print messages if desired
+            
             if i and (not i%print_interval):
                 print i, np.mean(self._ll_trace[-print_interval:]) #, self.log_likelihood()
                 print np.round(np.mean(self._grad_trace[-print_interval:],0),3)

GPy/examples/dimensionality_reduction.py

@@ -24,7 +24,7 @@ def BGPLVM(seed=default_seed):
     Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
     lik = Gaussian(Y, normalize=True)
 
-    k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
+    k = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
     # k = GPy.kern.rbf(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
 
@@ -145,6 +145,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
 
     # create simple GP model
     kernel = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
+
     Y = data['X'][:N]
     Yn = Y - Y.mean(0)
     Yn /= Yn.std(0)
@@ -375,11 +376,12 @@ def stick():
 def stick_bgplvm(model=None):
     data = GPy.util.datasets.stick()
     Q = 6
-    kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
+    kernel = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
-    m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
+    m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=35,kernel=kernel)
     # optimize
     m.ensure_default_constraints()
-    m.optimize(messages=1, max_f_eval=3000, xtol=1e-300, ftol=1e-300)
+    m.constrain_bounded('.*rbf_inv',1e-5, 100)
+    m.optimize(messages=1, max_iters=3000,xtol=1e-300,ftol=1e-300)
     m._set_params(m._get_params())
     plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
     plt.sca(latent_axes)

GPy/examples/regression.py

@@ -67,8 +67,8 @@ def toy_ARD(optim_iters=1000, kernel_type='linear', N=300, D=4):
     X4 = np.log(np.sort(np.random.rand(N,1),0))
     X = np.hstack((X1, X2, X3, X4))
 
-    Y1 = np.asarray(2*X[:,0]+3).T
+    Y1 = np.asmatrix(2*X[:,0]+3).T
-    Y2 = np.asarray(4*(X[:,2]-1.5*X[:,0])).T
+    Y2 = np.asmatrix(4*(X[:,2]-1.5*X[:,0])).T
     Y = np.hstack((Y1, Y2))
 
     Y = np.dot(Y, np.random.rand(2,D));

GPy/kern/kern.py

@@ -66,26 +66,12 @@ class kern(Parameterized):
         Parameterized.setstate(self, state)
 
 
-    def plot_ARD(self, fignum=None, ax=None, title='', legend=False):
+    def plot_ARD(self, fignum=None, ax=None, title=None):
-        """If an ARD kernel is present, it bar-plots the ARD parameters,
+        """If an ARD kernel is present, it bar-plots the ARD parameters"""
-        :param fignum: figure number of the plot
-        :param ax: matplotlib axis to plot on
-        :param title: 
-            title of the plot, 
-            pass '' to not print a title
-            pass None for a generic title
-        """
         if ax is None:
             fig = pb.figure(fignum)
             ax = fig.add_subplot(111)
-        from GPy.util import Tango
-        from matplotlib.textpath import TextPath
-        Tango.reset()
-        xticklabels = []
-        bars = []
-        x0 = 0
         for p in self.parts:
-            c = Tango.nextMedium()
             if hasattr(p, 'ARD') and p.ARD:
                 if title is None:
                     ax.set_title('ARD parameters, %s kernel' % p.name)
@@ -96,32 +82,10 @@ class kern(Parameterized):
                 else:
                     ard_params = 1. / p.lengthscale
 
-                x = np.arange(x0, x0 + len(ard_params))
+                x = np.arange(len(ard_params))
-                bars.append(ax.bar(x, ard_params, align='center', color=c, edgecolor='k', linewidth=1.2, label=p.name))
+                ax.bar(x - 0.4, ard_params)
-                xticklabels.extend([r"$\mathrm{{{name}}}\ {x}$".format(name=p.name, x=i) for i in np.arange(len(ard_params))])
+                ax.set_xticks(x)
-                x0 += len(ard_params)
+                ax.set_xticklabels([r"${}$".format(i) for i in x])
-        x = np.arange(x0)
-        for bar in bars:
-            for patch, num in zip(bar.patches, np.arange(len(bar.patches))):
-                height = patch.get_height()
-                xi = patch.get_x() + patch.get_width() / 2.
-                va = 'top'
-                c = 'w'
-                t = TextPath((0, 0), "${xi}$".format(xi=xi), rotation=0, usetex=True, ha='center')
-                if patch.get_extents().height <= t.get_extents().height + 2:
-                    va = 'bottom'
-                    c = 'k'
-                ax.text(xi, height, "${xi}$".format(xi=int(num)), color=c, rotation=0, ha='center', va=va)
-        # for xi, t in zip(x, xticklabels):
-        #    ax.text(xi, maxi / 2, t, rotation=90, ha='center', va='center')
-        # ax.set_xticklabels(xticklabels, rotation=17)
-        ax.set_xticks([])
-        ax.set_xlim(-.5, x0 - .5)
-        if title is '':
-            ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
-                      ncol=len(bars), mode="expand", borderaxespad=0.)
-        else:
-            ax.legend()
         return ax
 
     def _transform_gradients(self, g):
@@ -397,7 +361,6 @@ class kern(Parameterized):
 
         # compute the "cross" terms
         # TODO: better looping, input_slices
-
         for i1, i2 in itertools.permutations(range(len(self.parts)), 2):
             p1, p2 = self.parts[i1], self.parts[i2]
 #             ipsl1, ipsl2 = self.input_slices[i1], self.input_slices[i2]

GPy/models/bayesian_gplvm.py

@@ -115,8 +115,8 @@ class BayesianGPLVM(SparseGP, GPLVM):
         self.dbound_dZtheta = SparseGP._log_likelihood_gradients(self)
         return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
 
-    def plot_latent(self, plot_inducing=True, *args, **kwargs):
+    def plot_latent(self, *args, **kwargs):
-        return plot_latent.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs)
+        return plot_latent.plot_latent(self, *args, **kwargs)
 
     def do_test_latents(self, Y):
         """

GPy/models/gplvm.py

@@ -36,10 +36,10 @@ class GPLVM(GP):
         self.ensure_default_constraints()
 
     def initialise_latent(self, init, input_dim, Y):
-        Xr = np.random.randn(Y.shape[0], input_dim)
         if init == 'PCA':
-            Xr[:, :Y.shape[1]] = PCA(Y, input_dim)[0]
+            return PCA(Y, input_dim)[0]
-        return Xr
+        else:
+            return np.random.randn(Y.shape[0], input_dim)
 
     def getstate(self):
         return GP.getstate(self)

GPy/testing/unit_tests.py

@@ -23,7 +23,7 @@ class GradientTests(unittest.TestCase):
         self.X2D = np.random.uniform(-3., 3., (40, 2))
         self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05
 
-    def check_model_with_white(self, kern, model_type='GPRegression', dimension=1, uncertain_inputs=False):
+    def check_model_with_white(self, kern, model_type='GPRegression', dimension=1):
         # Get the correct gradients
         if dimension == 1:
             X = self.X1D
@@ -36,10 +36,7 @@ class GradientTests(unittest.TestCase):
 
         noise = GPy.kern.white(dimension)
         kern = kern + noise
-        if uncertain_inputs:
+        m = model_fit(X, Y, kernel=kern)
-            m = model_fit(X, Y, kernel=kern, X_variance=np.random.rand(X.shape[0], X.shape[1]))
-        else:
-            m = model_fit(X, Y, kernel=kern)
         m.randomize()
         # contrain all parameters to be positive
         self.assertTrue(m.checkgrad())
@@ -144,26 +141,6 @@ class GradientTests(unittest.TestCase):
         rbf = GPy.kern.rbf(2)
         self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=2)
 
-    def test_SparseGPRegression_rbf_linear_white_kern_1D(self):
-        ''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
-        rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
-        self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=1)
-
-    def test_SparseGPRegression_rbf_linear_white_kern_2D(self):
-        ''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
-        rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
-        self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=2)
-
-    def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
-        ''' Testing the sparse GP regression with rbf, linear and white kernel on 2d data with uncertain inputs'''
-        rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
-        self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
-
-    def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
-        ''' Testing the sparse GP regression with rbf, linear and white kernel on 1d data with uncertain inputs'''
-        rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
-        self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
-
     def test_GPLVM_rbf_bias_white_kern_2D(self):
         """ Testing GPLVM with rbf + bias and white kernel """
         N, input_dim, D = 50, 1, 2