fix: samples tests and plotting, multioutput

GPy/core/gp.py

@@ -578,7 +578,7 @@ class GP(Model):
                 mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))
         return mag
 
-      def posterior_samples_f(self,X, size=10, **predict_kwargs):
+    def posterior_samples_f(self,X, size=10, **predict_kwargs):
         """
         Samples the posterior GP at the points X.
 
@@ -589,7 +589,8 @@ class GP(Model):
         :returns: set of simulations
         :rtype: np.ndarray (Nnew x D x samples) 
         """
-        m, v = self._raw_predict(X,  full_cov=True, **predict_kwargs)
+        predict_kwargs["full_cov"] = True  # Always use the full covariance for posterior samples. 
+        m, v = self._raw_predict(X,  **predict_kwargs)
         if self.normalizer is not None:
             m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
 

GPy/plotting/gpy_plot/gp_plots.py

@@ -208,10 +208,10 @@ def _plot_samples(self, canvas, helper_data, helper_prediction, projection,
         if len(free_dims)==1:
             # 1D plotting:
             update_not_existing_kwargs(kwargs, pl().defaults.samples_1d)  # @UndefinedVariable
-            plots = [pl().plot(canvas, Xgrid[:, free_dims], samples[:, s], label=label if s==0 else None, **kwargs) for s in range(samples.shape[-1])]
+            plots = [pl().plot(canvas, Xgrid[:, free_dims], samples[:, :, s], label=label if s==0 else None, **kwargs) for s in range(samples.shape[-1])]
         elif len(free_dims)==2 and projection=='3d':
             update_not_existing_kwargs(kwargs, pl().defaults.samples_3d)  # @UndefinedVariable
-            plots = [pl().surface(canvas, x, y, samples[:, s].reshape(resolution, resolution), **kwargs) for s in range(samples.shape[-1])]
+            plots = [pl().surface(canvas, x, y, samples[:, :, s].reshape(resolution, resolution), **kwargs) for s in range(samples.shape[-1])]
         else:
             pass # Nothing to plot!
         return dict(gpmean=plots)

GPy/plotting/gpy_plot/plot_util.py

@@ -81,8 +81,8 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
     else: percentiles = []
 
     if samples > 0:
-        fsamples = self.posterior_samples(Xgrid, full_cov=True, size=samples, **predict_kw)
-        fsamples = fsamples[which_data_ycols] if fsamples.ndim == 3 else fsamples
+        fsamples = self.posterior_samples(Xgrid, size=samples, **predict_kw)
+        fsamples = fsamples[:, which_data_ycols, :]
     else:
         fsamples = None
 
@@ -95,12 +95,9 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
             retmu[:, [i]] = self.likelihood.gp_link.transf(mu[:, [i]])
             for perc in percs:
                 perc[:, [i]] = self.likelihood.gp_link.transf(perc[:, [i]])
-            if fsamples is not None and fsamples.ndim == 3:
+            if fsamples is not None:
                 for s in range(fsamples.shape[-1]):
-                    fsamples[i, :, s] = self.likelihood.gp_link.transf(fsamples[i, :, s])
-            elif fsamples is not None:
-                for s in range(fsamples.shape[-1]):
-                    fsamples[:, s] = self.likelihood.gp_link.transf(fsamples[:, s])
+                    fsamples[:, i, s] = self.likelihood.gp_link.transf(fsamples[:, i, s])
     return retmu, percs, fsamples
 
 def helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resolution):

GPy/testing/plotting_tests.py

@@ -38,7 +38,7 @@ from nose import SkipTest
 
 try:
     import matplotlib
-    matplotlib.use('agg')
+    # matplotlib.use('agg')
 except ImportError:
     # matplotlib not installed
     from nose import SkipTest
@@ -367,13 +367,13 @@ def test_classification():
     m = GPy.models.GPClassification(X, Y>Y.mean())
     #m.optimize()
     _, ax = plt.subplots()
-    m.plot(plot_raw=False, apply_link=False, ax=ax)
+    m.plot(plot_raw=False, apply_link=False, ax=ax, samples=3)
     m.plot_errorbars_trainset(plot_raw=False, apply_link=False, ax=ax)
     _, ax = plt.subplots()
-    m.plot(plot_raw=True, apply_link=False, ax=ax)
+    m.plot(plot_raw=True, apply_link=False, ax=ax, samples=3)
     m.plot_errorbars_trainset(plot_raw=True, apply_link=False, ax=ax)
     _, ax = plt.subplots()
-    m.plot(plot_raw=True, apply_link=True, ax=ax)
+    m.plot(plot_raw=True, apply_link=True, ax=ax, samples=3)
     m.plot_errorbars_trainset(plot_raw=True, apply_link=True, ax=ax)
     for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
         yield (do_test, )