Merge branch 'params' of github.com:SheffieldML/GPy into params

GPy/core/parameterization/parameter_core.py

@@ -340,6 +340,10 @@ class Parameterizable(Constrainable):
         if add_self: names = map(lambda x: adjust(self.name) + "." + x, names)
         return names
     
+    @property
+    def num_params(self):
+        return len(self._parameters_)
+    
     def _add_parameter_name(self, param):
         pname = adjust_name_for_printing(param.name)
         # and makes sure to not delete programmatically added parameters

GPy/core/parameterization/variational.py

@@ -63,7 +63,7 @@ class NormalPosterior(VariationalPosterior):
         from ...plotting.matplot_dep import variational_plots
         return variational_plots.plot(self,*args)
 
-class SpikeAndSlabPosterior(VariationalPosterior):
+class SpikeAndSlab(VariationalPosterior):
     '''
     The SpikeAndSlab distribution for variational approximations.
     '''
@@ -71,7 +71,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
         """
         binary_prob : the probability of the distribution on the slab part.
         """
-        super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
+        super(SpikeAndSlab, self).__init__(means, variances, name)
         self.gamma = Param("binary_prob",binary_prob,)
         self.add_parameter(self.gamma)
 

GPy/examples/dimensionality_reduction.py

@@ -164,11 +164,10 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
     _np.random.seed(0)
     data = GPy.util.datasets.oil()
 
-    kernel = GPy.kern.RBF(Q, 1., [.1] * Q, ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
+    kernel = GPy.kern.RBF(Q, 1., _np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
     Y = data['X'][:N]
     m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
     m.data_labels = data['Y'][:N].argmax(axis=1)
-    m['.*noise.var'] = Y.var() / 100.
     
     if optimize:
         m.optimize('scg', messages=verbose, max_iters=max_iters, gtol=.05)

GPy/kern/_src/add.py

@@ -83,7 +83,7 @@ class Add(Kern):
         from white import White
         from rbf import RBF
         #from rbf_inv import RBFInv
-        #from bias import Bias
+        from bias import Bias
         from linear import Linear
         #ffrom fixed import Fixed
 
@@ -131,11 +131,11 @@ class Add(Kern):
 
 
     def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
-        from white import white
+        from white import White
-        from rbf import rbf
+        from rbf import RBF
         #from rbf_inv import rbfinv
-        #from bias import bias
+        from bias import Bias
-        from linear import linear
+        from linear import Linear
         #ffrom fixed import fixed
 
         target = np.zeros(Z.shape)
@@ -146,15 +146,15 @@ class Add(Kern):
             for p2, is2 in zip(self._parameters_, self.input_slices):
                 if p2 is p1:
                     continue
-                if isinstance(p2, white):
+                if isinstance(p2, White):
                     continue
-                elif isinstance(p2, bias):
+                elif isinstance(p2, Bias):
                     eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
                 else:
-                    eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(z[:,is2], mu[:,is2], s[:,is2]) * 2.
+                    eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
 
 
-            target += p1.gradients_z_variational(dL_dkmm, dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], s[:,is1], z[:,is1])
+            target += p1.gradients_z_variational(dL_dKmm, dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
         return target
 
     def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
@@ -195,6 +195,12 @@ class Add(Kern):
         from ..plotting.matplot_dep import kernel_plots
         kernel_plots.plot(self,*args)
 
+    def input_sensitivity(self):
+        in_sen = np.zeros((self.input_dim, self.num_params))
+        for i, [p, i_s] in enumerate(zip(self._parameters_, self.input_slices)):
+            in_sen[i_s, i] = p.input_sensitivity()
+        return in_sen
+    
     def _getstate(self):
         """
         Get the current state of the class,

GPy/kern/_src/kern.py

@@ -61,15 +61,19 @@ class Kern(Parameterized):
     def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
         raise NotImplementedError
     
-    def plot_ARD(self, *args):
+    def plot_ARD(self, *args, **kw):
-        """If an ARD kernel is present, plot a bar representation using matplotlib
+        if "matplotlib" in sys.modules:
-
+            from ...plotting.matplot_dep import kernel_plots
-        See GPy.plotting.matplot_dep.plot_ARD
+            self.plot_ARD.__doc__ += kernel_plots.plot_ARD.__doc__
-        """
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
         from ...plotting.matplot_dep import kernel_plots
-        return kernel_plots.plot_ARD(self,*args)
+        return kernel_plots.plot_ARD(self,*args,**kw)
     
+    def input_sensitivity(self):
+        """
+        Returns the sensitivity for each dimension of this kernel.
+        """
+        return np.zeros(self.input_dim)
     
     def __add__(self, other):
         """ Overloading of the '+' operator. for more control, see self.add """

GPy/kern/_src/linear.py

@@ -252,3 +252,6 @@ class Linear(Kern):
 
         return np.dot(ZA, inner).swapaxes(0, 1)  # NOTE: self.ZAinner \in [num_inducing x N x input_dim]!
 
+    def input_sensitivity(self):
+        if self.ARD: return self.variances
+        else: return self.variances.repeat(self.input_dim)

GPy/kern/_src/rbf.py

@@ -182,7 +182,7 @@ class RBF(Kern):
 
         return grad
 
-    def update_gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
+    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
         mu = posterior_variational.mean
         S = posterior_variational.variance
         self._psi_computations(Z, mu, S)
@@ -195,8 +195,7 @@ class RBF(Kern):
         grad_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * self._psi2_mudist).sum(1).sum(1)
         grad_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*self._psi2_mudist_sq - 1)).sum(1).sum(1)
 
-        posterior_variational.mean.gradient = grad_mu
+        return grad_mu, grad_S
-        posterior_variational.variance.gradient = grad_S
 
     def gradients_X(self, dL_dK, X, X2=None):
         #if self._X is None or X.base is not self._X.base or X2 is not None:
@@ -383,3 +382,7 @@ class RBF(Kern):
                      type_converters=weave.converters.blitz, **self.weave_options)
 
         return mudist, mudist_sq, psi2_exponent, psi2
+
+    def input_sensitivity(self):
+        if self.ARD: return 1./self.lengthscale
+        else: return (1./self.lengthscale).repeat(self.input_dim)

GPy/models/bayesian_gplvm.py

@@ -10,7 +10,7 @@ from ..inference.optimization import SCG
 from ..util import linalg
 from ..core.parameterization.variational import NormalPosterior, NormalPrior
 
-class BayesianGPLVM(SparseGP, GPLVM):
+class BayesianGPLVM(SparseGP):
     """
     Bayesian Gaussian Process Latent Variable Model
 
@@ -25,7 +25,8 @@ class BayesianGPLVM(SparseGP, GPLVM):
     def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
                  Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', **kwargs):
         if X == None:
-            X = self.initialise_latent(init, input_dim, Y)
+            from ..util.initialization import initialize_latent
+            X = initialize_latent(init, input_dim, Y)
         self.init = init
 
         if X_variance is None:
@@ -63,7 +64,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
         super(BayesianGPLVM, self).parameters_changed()
         self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.q)
         
-        self.kern.update_gradients_q_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
+        # TODO: This has to go into kern
+        # maybe a update_gradients_q_variational?
+        self.q.mean.gradient, self.q.variance.gradient = self.kern.gradients_q_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
         
         # update for the KL divergence
         self.variational_prior.update_gradients_KL(self.q)

GPy/models/gplvm.py

@@ -28,28 +28,20 @@ class GPLVM(GP):
         :type init: 'PCA'|'random'
         """
         if X is None:
-            X = self.initialise_latent(init, input_dim, Y)
+            from ..util.initialization import initialize_latent
+            X = initialize_latent(init, input_dim, Y)
         if kernel is None:
-            kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
+            kernel = kern.RBF(input_dim, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
 
         likelihood = Gaussian()
 
         super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
-        self.X = Param('X', X)
+        self.X = Param('latent_mean', X)
         self.add_parameter(self.X, index=0)
 
-    def initialise_latent(self, init, input_dim, Y):
-        Xr = np.random.randn(Y.shape[0], input_dim)
-        if init == 'PCA':
-            PC = PCA(Y, input_dim)[0]
-            Xr[:PC.shape[0], :PC.shape[1]] = PC
-        else:
-            pass
-        return Xr
-
     def parameters_changed(self):
-        GP.parameters_changed(self)
+        super(GPLVM, self).parameters_changed()
-        self.X.gradient = self.kern.gradients_X(self.posterior.dL_dK, self.X)
+        self.X.gradient = self.kern.gradients_X(self._dL_dK, self.X, None)
 
     def _getstate(self):
         return GP._getstate(self)
@@ -79,7 +71,8 @@ class GPLVM(GP):
         pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
 
     def plot_latent(self, *args, **kwargs):
-        return util.plot_latent.plot_latent(self, *args, **kwargs)
+        from ..plotting.matplot_dep import dim_reduction_plots
 
+        return dim_reduction_plots.plot_latent(self, *args, **kwargs)
     def plot_magnification(self, *args, **kwargs):
         return util.plot_latent.plot_magnification(self, *args, **kwargs)

GPy/plotting/matplot_dep/kernel_plots.py

@@ -9,8 +9,41 @@ from matplotlib.transforms import offset_copy
 from ...kern import Linear
 
 
+
+def add_bar_labels(fig, ax, bars, bottom=0):
+    transOffset = offset_copy(ax.transData, fig=fig,
+                              x=0., y= -2., units='points')
+    transOffsetUp = offset_copy(ax.transData, fig=fig,
+                              x=0., y=1., units='points')
+    for bar in bars:
+        for i, [patch, num] in enumerate(zip(bar.patches, np.arange(len(bar.patches)))):
+            if len(bottom) == len(bar): b = bottom[i]
+            else: b = bottom
+            height = patch.get_height() + b
+            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')
+            transform = transOffset
+            if patch.get_extents().height <= t.get_extents().height + 3:
+                va = 'bottom'
+                c = 'k'
+                transform = transOffsetUp
+            ax.text(xi, height, "${xi}$".format(xi=int(num)), color=c, rotation=0, ha='center', va=va, transform=transform)
+    
+    ax.set_xticks([])
+
+
+def plot_bars(fig, ax, x, ard_params, color, name, bottom=0):
+    from ...util.misc import param_to_array
+    return ax.bar(left=x, height=param_to_array(ard_params), width=.8, 
+                  bottom=bottom, align='center', 
+                  color=color, edgecolor='k', linewidth=1.2, 
+                  label=name.replace("_"," "))
+
 def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False):
-    """If an ARD kernel is present, plot a bar representation using matplotlib
+    """
+    If an ARD kernel is present, plot a bar representation using matplotlib
 
     :param fignum: figure number of the plot
     :param ax: matplotlib axis to plot on
@@ -24,50 +57,27 @@ def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False):
         ax = fig.add_subplot(111)
     else:
         fig = ax.figure
-    Tango.reset()
+
-    xticklabels = []
-    bars = []
-    x0 = 0
-    #for p in kernel._parameters_:
-    p = kernel
-    c = Tango.nextMedium()
-    if hasattr(p, 'ARD') and p.ARD:
     if title is None:
-            ax.set_title('ARD parameters, %s kernel' % p.name)
+        ax.set_title('ARD parameters, %s kernel' % kernel.name)
     else:
         ax.set_title(title)
-        if isinstance(p, Linear):
+    
-            ard_params = p.variances
+    Tango.reset()
-        else:
+    bars = []
-            ard_params = 1. / p.lengthscale
+    
-        x = np.arange(x0, x0 + len(ard_params))
+    ard_params = np.atleast_2d(kernel.input_sensitivity())
-        from ...util.misc import param_to_array
+    bottom = 0
-        bars.append(ax.bar(x, param_to_array(ard_params), align='center', color=c, edgecolor='k', linewidth=1.2, label=p.name.replace("_"," ")))
+    x = np.arange(kernel.input_dim)
-        xticklabels.extend([r"$\mathrm{{{name}}}\ {x}$".format(name=p.name, x=i) for i in np.arange(len(ard_params))])
+    
-        x0 += len(ard_params)
+    for i in range(ard_params.shape[-1]):
-    x = np.arange(x0)
+        c = Tango.nextMedium()
-    transOffset = offset_copy(ax.transData, fig=fig,
+        bars.append(plot_bars(fig, ax, x, ard_params[:,i], c, kernel._parameters_[i].name, bottom=bottom))
-                              x=0., y= -2., units='points')
+        bottom += ard_params[:,i]
-    transOffsetUp = offset_copy(ax.transData, fig=fig,
+    
-                              x=0., y=1., units='points')
+    ax.set_xlim(-.5, kernel.input_dim - .5)
-    for bar in bars:
+    add_bar_labels(fig, ax, [bars[-1]], bottom=bottom-ard_params[:,i])
-        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')
-            transform = transOffset
-            if patch.get_extents().height <= t.get_extents().height + 3:
-                va = 'bottom'
-                c = 'k'
-                transform = transOffsetUp
-            ax.text(xi, height, "${xi}$".format(xi=int(num)), color=c, rotation=0, ha='center', va=va, transform=transform)
-    # 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 legend:
         if title is '':
             mode = 'expand'

GPy/util/__init__.py

@@ -13,6 +13,7 @@ import classification
 import subarray_and_sorting
 import caching
 import diag
+import initialization
 
 try:
     import sympy