[magnification] plot_magnification expanded

GPy/core/gp.py

@@ -371,7 +371,7 @@ class GP(Model):
             var_jac = compute_cov_inner(self.posterior.woodbury_inv)
         return mean_jac, var_jac
 
-    def predict_wishard_embedding(self, Xnew, kern=None):
+    def predict_wishard_embedding(self, Xnew, kern=None, mean=True, covariance=True):
         """
         Predict the wishard embedding G of the GP. This is the density of the
         input of the GP defined by the probabilistic function mapping f.
@@ -391,13 +391,16 @@ class GP(Model):
         mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
         if var_jac.ndim == 3:
             Sigma = np.einsum('iqd,ipd->iqp', var_jac, var_jac)
-            G = mumuT + Sigma
         else:
-            Sigma = np.einsum('iq,ip->iqp', var_jac, var_jac)
+            Sigma = self.output_dim*np.einsum('iq,ip->iqp', var_jac, var_jac)
-            G = mumuT + self.output_dim*Sigma
+        G = 0.
+        if mean:
+            G += mumuT
+        if covariance:
+            G += Sigma
         return G
 
-    def predict_magnification(self, Xnew, kern=None):
+    def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True):
         """
         Predict the magnification factor as
 
@@ -405,7 +408,7 @@ class GP(Model):
 
         for each point N in Xnew
         """
-        G = self.predict_wishard_embedding(Xnew, kern)
+        G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
         from ..util.linalg import jitchol
         return np.array([np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :])))))) for n in range(Xnew.shape[0])])
         #return np.array([np.sqrt(np.linalg.det(G[n, :, :])) for n in range(Xnew.shape[0])])
@@ -569,7 +572,7 @@ class GP(Model):
                 resolution=50, ax=None, marker='o', s=40,
                 fignum=None, legend=True,
                 plot_limits=None,
-                aspect='auto', updates=False, **kwargs):
+                aspect='auto', updates=False, plot_inducing=True, kern=None, **kwargs):
 
         import sys
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
@@ -577,7 +580,7 @@ class GP(Model):
 
         return dim_reduction_plots.plot_magnification(self, labels, which_indices,
                 resolution, ax, marker, s,
-                fignum, False, legend,
+                fignum, plot_inducing, legend,
                 plot_limits, aspect, updates, **kwargs)
 
 

GPy/core/sparse_gp.py

@@ -181,18 +181,3 @@ class SparseGP(GP):
                     var[i] = np.diag(var_)+p0-t2
 
         return mu, var
-
-    def plot_magnification(self, labels=None, which_indices=None,
-                resolution=50, ax=None, marker='o', s=40,
-                fignum=None, legend=True,
-                plot_limits=None,
-                aspect='auto', updates=False, plot_inducing=True, **kwargs):
-
-        import sys
-        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
-        from ..plotting.matplot_dep import dim_reduction_plots
-
-        return dim_reduction_plots.plot_magnification(self, labels, which_indices,
-                resolution, ax, marker, s,
-                fignum, plot_inducing, legend,
-                plot_limits, aspect, updates, **kwargs)

GPy/kern/_src/add.py

@@ -14,7 +14,7 @@ class Add(CombinationKernel):
 
     This kernel will take over the active dims of it's subkernels passed in.
     """
-    def __init__(self, subkerns, name='add'):
+    def __init__(self, subkerns, name='sum'):
         for i, kern in enumerate(subkerns[:]):
             if isinstance(kern, Add):
                 del subkerns[i]
@@ -72,6 +72,16 @@ class Add(CombinationKernel):
         [target.__iadd__(p.gradients_X_diag(dL_dKdiag, X)) for p in self.parts]
         return target
 
+    def gradients_XX(self, dL_dK, X, X2):
+        target = 0.
+        [target.__iadd__(p.gradients_XX(dL_dK, X, X2)) for p in self.parts]
+        return target
+
+    def gradients_XX_diag(self, dL_dKdiag, X):
+        target = np.zeros(X.shape)
+        [target.__iadd__(p.gradients_XX_diag(dL_dKdiag, X)) for p in self.parts]
+        return target
+
     @Cache_this(limit=2, force_kwargs=['which_parts'])
     def psi0(self, Z, variational_posterior):
         return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))

GPy/kern/_src/kern.py

@@ -102,7 +102,7 @@ class Kern(Parameterized):
         raise NotImplementedError
     def gradients_XX(self, dL_dK, X, X2):
         raise(NotImplementedError, "This is the second derivative of K wrt X and X2, and not implemented for this kernel")
-    def gradients_XX_diag(self, dL_dK, X, X2):
+    def gradients_XX_diag(self, dL_dKdiag, X):
         raise(NotImplementedError, "This is the diagonal of the second derivative of K wrt X and X2, and not implemented for this kernel")
     def gradients_X_diag(self, dL_dKdiag, X):
         raise NotImplementedError

GPy/kern/_src/static.py

@@ -24,6 +24,11 @@ class Static(Kern):
     def gradients_X_diag(self, dL_dKdiag, X):
         return np.zeros(X.shape)
 
+    def gradients_XX(self, dL_dK, X, X2):
+        return np.zeros((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)
+    def gradients_XX_diag(self, dL_dKdiag, X):
+        return np.zeros(X.shape)
+
     def gradients_Z_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         return np.zeros(Z.shape)
 

GPy/models/bayesian_gplvm.py

@@ -137,20 +137,6 @@ class BayesianGPLVM(SparseGP_MPI):
                 fignum, plot_inducing, legend,
                 plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
 
-    def plot_magnification(self, labels=None, which_indices=None,
-                resolution=50, ax=None, marker='o', s=40,
-                fignum=None, legend=True,
-                plot_limits=None,
-                aspect='auto', updates=False, **kwargs):
-        import sys
-        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
-        from ..plotting.matplot_dep import dim_reduction_plots
-
-        return dim_reduction_plots.plot_magnification(self, labels, which_indices,
-                resolution, ax, marker, s,
-                fignum, False, legend,
-                plot_limits, aspect, updates, **kwargs)
-
     def do_test_latents(self, Y):
         """
         Compute the latent representation for a set of new points Y

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -9,7 +9,8 @@ import itertools
 try:
     import Tango
     from matplotlib.cm import get_cmap
-    import pylab as pb
+    from matplotlib import pyplot as pb
+    from matplotlib import cm
 except:
     pass
 
@@ -137,7 +138,7 @@ def plot_latent(model, labels=None, which_indices=None,
     view = ImshowController(ax, plot_function,
                             (xmin, ymin, xmax, ymax),
                             resolution, aspect=aspect, interpolation='bilinear',
-                            cmap=pb.cm.binary, **imshow_kwargs)
+                            cmap=cm.binary, **imshow_kwargs)
 
     # make sure labels are in order of input:
     labels = np.asarray(labels)
@@ -192,18 +193,18 @@ def plot_latent(model, labels=None, which_indices=None,
 
     if updates:
         try:
-            ax.figure.canvas.show()
+            fig.canvas.show()
         except Exception as e:
             print("Could not invoke show: {}".format(e))
-        raw_input('Enter to continue')
+        #raw_input('Enter to continue')
-        view.deactivate()
+        return view
     return ax
 
 def plot_magnification(model, labels=None, which_indices=None,
                 resolution=60, ax=None, marker='o', s=40,
                 fignum=None, plot_inducing=False, legend=True,
                 plot_limits=None,
-                aspect='auto', updates=False):
+                aspect='auto', updates=False, mean=True, covariance=True, kern=None):
     """
     :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
     :param resolution: the resolution of the grid on which to evaluate the predictive variance
@@ -211,6 +212,8 @@ def plot_magnification(model, labels=None, which_indices=None,
     if ax is None:
         fig = pb.figure(num=fignum)
         ax = fig.add_subplot(111)
+    else:
+        fig = ax.figure
     Tango.reset()
 
     if labels is None:
@@ -295,13 +298,13 @@ def plot_magnification(model, labels=None, which_indices=None,
     def plot_function(x):
         Xtest_full = np.zeros((x.shape[0], X.shape[1]))
         Xtest_full[:, [input_1, input_2]] = x
-        mf = model.predict_magnification(Xtest_full)
+        mf = model.predict_magnification(Xtest_full, kern=kern, mean=mean, covariance=covariance)
         return mf
 
     view = ImshowController(ax, plot_function,
                             (xmin, ymin, xmax, ymax),
                             resolution, aspect=aspect, interpolation='bilinear',
-                            cmap=pb.cm.gray)
+                            cmap=cm.gray)
 
     # make sure labels are in order of input:
     ulabels = []
@@ -317,7 +320,7 @@ def plot_magnification(model, labels=None, which_indices=None,
         elif type(ul) is np.int64:
             this_label = 'class %i' % ul
         else:
-            this_label = 'class %i' % i
+            this_label = unicode(ul)
         m = marker.next()
 
         index = np.nonzero(labels == ul)[0]
@@ -327,7 +330,7 @@ def plot_magnification(model, labels=None, which_indices=None,
         else:
             x = X[index, input_1]
             y = X[index, input_2]
-        ax.scatter(x, y, marker=m, s=s, color=Tango.nextMedium(), label=this_label)
+        ax.scatter(x, y, marker=m, s=s, c=Tango.nextMedium(), label=this_label, linewidth=.2, edgecolor='k', alpha=.9)
 
     ax.set_xlabel('latent dimension %i' % input_1)
     ax.set_ylabel('latent dimension %i' % input_2)
@@ -337,18 +340,27 @@ def plot_magnification(model, labels=None, which_indices=None,
 
     ax.set_xlim((xmin, xmax))
     ax.set_ylim((ymin, ymax))
-    ax.grid(b=False) # remove the grid if present, it doesn't look good
-    ax.set_aspect('auto') # set a nice aspect ratio
 
-    if plot_inducing:
+    if plot_inducing and hasattr(model, 'Z'):
         Z = model.Z
         ax.scatter(Z[:, input_1], Z[:, input_2], c='w', s=18, marker="^", edgecolor='k', linewidth=.3, alpha=.7)
 
-    if updates:
+    try:
-        fig.canvas.show()
+        fig.canvas.draw()
-        raw_input('Enter to continue')
+        fig.tight_layout()
+        fig.canvas.draw()
+    except Exception as e:
+        print("Could not invoke tight layout: {}".format(e))
+        pass
 
-    pb.title('Magnification Factor')
+    if updates:
+        try:
+            fig.canvas.draw()
+            fig.canvas.show()
+        except Exception as e:
+            print("Could not invoke show: {}".format(e))
+        #raw_input('Enter to continue')
+        return view
     return ax
 
 

GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.py

@@ -9,6 +9,9 @@ class AxisEventController(object):
     def __init__(self, ax):
         self.ax = ax
         self.activate()
+    def __del__(self):
+        self.deactivate()
+        return self
     def deactivate(self):
         for cb_class in self.ax.callbacks.callbacks.values():
             for cb_num in cb_class.keys():