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

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')
-        view.deactivate()
+        #raw_input('Enter to continue')
+        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:
-        fig.canvas.show()
-        raw_input('Enter to continue')
+    try:
+        fig.canvas.draw()
+        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():
@@ -81,9 +84,9 @@ class BufferedAxisChangedController(AxisChangedController):
     def __init__(self, ax, plot_function, plot_limits, resolution=50, update_lim=None, **kwargs):
         """
         Buffered axis changed controller. Controls the buffer and handles update events for when the axes changed.
-        
+
         Updated plotting will be after first reload (first time will be within plot limits, after that the limits will be buffered)
-        
+
         :param plot_function:
             function to use for creating image for plotting (return ndarray-like)
             plot_function gets called with (2D!) Xtest grid if replotting required