update visualisation and returned evidence

update plot gp to mask non-finite evidence so that the robust model does not need to filter these out

elfi/methods/bo/gpy_regression.py

@@ -570,23 +570,23 @@ def optimize(self):
                 logger.warning("Numerical error in GP optimization. Stopping optimization")
 
     @property
-    def n_evidence_all(self):
-        """Return the number of observed samples with failed simulations included."""
+    def n_evidence(self):
+        """Return the number of observed samples."""
         return len(self._Y)
 
     @property
-    def failed(self):
-        """Return inputs that resulted in failed simulations."""
-        return self._X[~np.isfinite(self._Y).reshape(-1)]
+    def n_valid_evidence(self):
+        """Return the number of valid observed samples."""
+        return np.sum(np.isfinite(self._Y))
 
     @property
-    def X_all(self):
-        """Return all inputs."""
+    def X(self):
+        """Return input evidence."""
         return self._X
 
     @property
-    def Y_all(self):
-        """Return all outputs."""
+    def Y(self):
+        """Return output evidence."""
         Y = self._Y.copy()
         Y[~np.isfinite(Y)] = self.FAILED_OUTPUT
         return Y

elfi/methods/inference/bolfi.py

@@ -287,7 +287,7 @@ def _allow_submit(self, batch_index):
         return True
 
     def _should_optimize(self):
-        current = self.state['n_evidence']
+        current = self.target_model.n_evidence + self.batch_size
         next_update = self.state['last_GP_update'] + self.update_interval
         return current >= self.n_initial_evidence and current >= next_update
 

elfi/visualization/visualization.py

@@ -450,8 +450,10 @@ def plot_gp(gp, parameter_names, axes=None, resol=50,
     shape = (n_plots, n_plots)
     axes, kwargs = _create_axes(axes, shape, **kwargs)
 
-    x_evidence = gp.X
-    y_evidence = gp.Y
+    valid_inds = np.isfinite(gp.Y).squeeze()
+    x_evidence = gp.X[valid_inds]
+    y_evidence = gp.Y[valid_inds]
+    x_evidence_failed = gp.X[~valid_inds]
     if const is None:
         const = x_evidence[np.argmin(y_evidence), :]
     bounds = bounds or gp.bounds
@@ -488,6 +490,13 @@ def plot_gp(gp, parameter_names, axes=None, resol=50,
                                      color="red",
                                      alpha=0.7,
                                      s=5)
+                if len(x_evidence_failed) > 0:
+                    axes[jy, ix].scatter(x_evidence_failed[:, ix],
+                                         x_evidence_failed[:, jy],
+                                         marker="^",
+                                         color="blue",
+                                         alpha=0.5,
+                                         s=7)
 
                 if true_params is not None:
                     axes[jy, ix].plot([true_params[parameter_names[ix]],