[plotting&kern] bugfixes in plotting and kernel size

GPy/core/gp.py

@@ -365,13 +365,14 @@ class GP(Model):
             mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable)
 
         dK_dXnew_full = np.empty((self._predictive_variable.shape[0], Xnew.shape[0], Xnew.shape[1]))
+        one = np.ones((1,1))
         for i in range(self._predictive_variable.shape[0]):
-            dK_dXnew_full[i] = kern.gradients_X([[1.]], Xnew, self._predictive_variable[[i]])
+            dK_dXnew_full[i] = kern.gradients_X(one, Xnew, self._predictive_variable[[i]])
 
         if full_cov:
-            dK2_dXdX = kern.gradients_XX([[1.]], Xnew)
+            dK2_dXdX = kern.gradients_XX(one, Xnew)
         else:
-            dK2_dXdX = kern.gradients_XX_diag([[1.]], Xnew)
+            dK2_dXdX = kern.gradients_XX_diag(one, Xnew)
 
         def compute_cov_inner(wi):
             if full_cov:
@@ -458,7 +459,7 @@ class GP(Model):
         m, v = self._raw_predict(X,  full_cov=full_cov, **predict_kwargs)
         if self.normalizer is not None:
             m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
-        
+
         def sim_one_dim(m, v):
             if not full_cov:
                 return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T

GPy/kern/src/kern.py

@@ -61,12 +61,12 @@ class Kern(Parameterized):
         self.psicomp = PSICOMP_GH()
 
     def __setstate__(self, state):
-        self._all_dims_active = range(0, max(state['active_dims'])+1)
+        self._all_dims_active = np.arange(0, max(state['active_dims'])+1)
         super(Kern, self).__setstate__(state)
 
     @property
     def _effective_input_dim(self):
-        return self._all_dims_active.size
+        return np.size(self._all_dims_active)
 
     @Cache_this(limit=20)
     def _slice_X(self, X):

GPy/kern/src/stationary.py

@@ -97,7 +97,7 @@ class Stationary(Kern):
         r = self._scaled_dist(X, X2)
         return self.K_of_r(r)
 
-    @Cache_this(limit=20, ignore_args=())
+    @Cache_this(limit=3, ignore_args=())
     def dK_dr_via_X(self, X, X2):
         #a convenience function, so we can cache dK_dr
         return self.dK_dr(self._scaled_dist(X, X2))
@@ -127,7 +127,7 @@ class Stationary(Kern):
             r2 = np.clip(r2, 0, np.inf)
             return np.sqrt(r2)
 
-    @Cache_this(limit=20, ignore_args=())
+    @Cache_this(limit=3, ignore_args=())
     def _scaled_dist(self, X, X2=None):
         """
         Efficiently compute the scaled distance, r.

GPy/plotting/gpy_plot/gp_plots.py

@@ -46,7 +46,7 @@ def plot_mean(self, plot_limits=None, fixed_inputs=None,
     """
     Plot the mean of the GP.
 
-    You can deactivate the legend for this one plot by supplying None to label. 
+    You can deactivate the legend for this one plot by supplying None to label.
 
     Give the Y_metadata in the predict_kw if you need it.
 
@@ -116,7 +116,7 @@ def plot_confidence(self, lower=2.5, upper=97.5, plot_limits=None, fixed_inputs=
     E.g. the 95% confidence interval is $2.5, 97.5$.
     Note: Only implemented for one dimension!
 
-    You can deactivate the legend for this one plot by supplying None to label. 
+    You can deactivate the legend for this one plot by supplying None to label.
 
     Give the Y_metadata in the predict_kw if you need it.
 
@@ -170,7 +170,7 @@ def plot_samples(self, plot_limits=None, fixed_inputs=None,
     """
     Plot the mean of the GP.
 
-    You can deactivate the legend for this one plot by supplying None to label. 
+    You can deactivate the legend for this one plot by supplying None to label.
 
     Give the Y_metadata in the predict_kw if you need it.
 
@@ -231,7 +231,7 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
     E.g. the 95% confidence interval is $2.5, 97.5$.
     Note: Only implemented for one dimension!
 
-    You can deactivate the legend for this one plot by supplying None to label. 
+    You can deactivate the legend for this one plot by supplying None to label.
 
     Give the Y_metadata in the predict_kw if you need it.
 
@@ -288,7 +288,7 @@ def plot(self, plot_limits=None, fixed_inputs=None,
     """
     Convenience function for plotting the fit of a GP.
 
-    You can deactivate the legend for this one plot by supplying None to label. 
+    You can deactivate the legend for this one plot by supplying None to label.
 
     Give the Y_metadata in the predict_kw if you need it.
 
@@ -330,6 +330,8 @@ def plot(self, plot_limits=None, fixed_inputs=None,
         # It does not make sense to plot the data (which lives not in the latent function space) into latent function space.
         plot_data = False
     plots = {}
+    if hasattr(self, 'Z') and plot_inducing:
+        plots.update(_plot_inducing(self, canvas, visible_dims, projection, 'Inducing'))
     if plot_data:
         plots.update(_plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data"))
         plots.update(_plot_data_error(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data Error"))
@@ -340,8 +342,6 @@ def plot(self, plot_limits=None, fixed_inputs=None,
                                       get_which_data_ycols(self, which_data_ycols),
                                       predict_kw, samples_likelihood)
         plots.update(_plot_samples(canvas, helper_data, helper_prediction, projection, "Lik Samples"))
-    if hasattr(self, 'Z') and plot_inducing:
-        plots.update(_plot_inducing(self, canvas, visible_dims, projection, 'Inducing'))
     return pl().add_to_canvas(canvas, plots, legend=legend)
 
 
@@ -362,7 +362,7 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
 
     If you want fine graned control use the specific plotting functions supplied in the model.
 
-    You can deactivate the legend for this one plot by supplying None to label. 
+    You can deactivate the legend for this one plot by supplying None to label.
 
     Give the Y_metadata in the predict_kw if you need it.
 

GPy/plotting/gpy_plot/plot_util.py

@@ -285,7 +285,10 @@ def get_x_y_var(model):
         X = model.X.mean.values
         X_variance = model.X.variance.values
     else:
-        X = model.X.values
+        try:
+            X = model.X.values
+        except AttributeError:
+            X = model.X
         X_variance = None
     try:
         Y = model.Y.values