[magnification] mostly plotting and some model corrections for _predictive_variable

GPy/core/gp.py

@@ -405,9 +405,10 @@ class GP(Model):
 
         for each point N in Xnew
         """
-        from ..util.linalg import jitchol
         G = self.predict_wishard_embedding(Xnew, kern)
-        return np.array([2*np.sqrt(np.exp(np.sum(np.log(np.diag(jitchol(G[n, :, :])))))) for n in range(Xnew.shape[0])])
+        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])])
 
     def posterior_samples_f(self,X,size=10, full_cov=True):
         """
@@ -564,6 +565,22 @@ class GP(Model):
                                      plot_raw=plot_raw, Y_metadata=Y_metadata,
                                      data_symbol=data_symbol, predict_kw=predict_kw, **kw)
 
+    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 input_sensitivity(self, summarize=True):
         """
         Returns the sensitivity for each dimension of this model

GPy/core/sparse_gp.py

@@ -181,3 +181,18 @@ 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/core/verbose_optimization.py

@@ -24,7 +24,6 @@ class VerboseOptimization(object):
             self.model.add_observer(self, self.print_status)
             self.status = 'running'
             self.clear = clear_after_finish
-            self.deltat = .2
 
             self.update()
 
@@ -80,6 +79,7 @@ class VerboseOptimization(object):
 
     def __enter__(self):
         self.start = time.time()
+        self._time = self.start
         return self
 
     def print_out(self, seconds):
@@ -143,12 +143,12 @@ class VerboseOptimization(object):
     def print_status(self, me, which=None):
         self.update()
 
-        seconds = time.time()-self.start
+        t = time.time()
+        seconds = t-self.start
         #sys.stdout.write(" "*len(self.message))
-        self.deltat += seconds
-        if self.deltat > .3 or seconds < .3:
+        if t-self._time > .3 or seconds < .3:
             self.print_out(seconds)
-            self.deltat = 0
+            self._time = t
 
         self.iteration += 1