OWTreeViewer: Use tree adapter for regression (missing variance)

Author: pavlin-policar

Orange/widgets/visualize/owtreeviewer.py

@@ -359,9 +359,9 @@ def update_node_info_cls(self, node):
     def update_node_info_reg(self, node):
         """Update the printed contents of the node for regression trees"""
         node_inst = node.node_inst
-        print(self.tree_adapter.get_distribution(node_inst)[0])
-        mean, var = node_inst.value
-        insts = len(node_inst.subset)
+        # TODO calculate variance in tree adapter
+        mean, var = self.tree_adapter.get_distribution(node_inst)[0][0], 0.
+        insts = len(self.tree_adapter.get_instances_in_nodes(self.dataset, [node_inst]))
         text = "{:.1f} ± {:.1f}<br/>".format(mean, var)
         text += "{} instances".format(insts)
         text = self._update_node_info_attr_name(node, text)
@@ -393,20 +393,24 @@ def toggle_node_color_reg(self):
             for node in self.scene.nodes():
                 node.backgroundBrush = brush
         elif self.regression_colors == self.COL_INSTANCE:
-            max_insts = len(self.model.instances)
+            max_insts = len(self.tree_adapter.get_instances_in_nodes(
+                self.dataset, [self.tree_adapter.root]))
             for node in self.scene.nodes():
+                node_insts = len(self.tree_adapter.get_instances_in_nodes(
+                    self.dataset, [node.node_inst]))
                 node.backgroundBrush = QBrush(def_color.lighter(
-                    120 - 20 * len(node.node_inst.subset) / max_insts))
+                    120 - 20 * node_insts / max_insts))
         elif self.regression_colors == self.COL_MEAN:
             minv = np.nanmin(self.dataset.Y)
             maxv = np.nanmax(self.dataset.Y)
             fact = 1 / (maxv - minv) if minv != maxv else 1
             colors = self.scene.colors
             for node in self.scene.nodes():
-                node.backgroundBrush = QBrush(
-                    colors[fact * (node.node_inst.value[0] - minv)])
+                node_mean = self.tree_adapter.get_distribution(node.node_inst)[0][0]
+                node.backgroundBrush = QBrush(colors[fact * (node_mean - minv)])
         else:
             nodes = list(self.scene.nodes())
+            # TODO Get variance from tree adapter
             variances = [node.node_inst.value[1] for node in nodes]
             max_var = max(variances)
             for node, var in zip(nodes, variances):
@@ -428,9 +432,10 @@ def test():
     from Orange.regression.tree import TreeLearner, SklTreeRegressionLearner
     a = QApplication(sys.argv)
     ow = OWTreeGraph()
-    data = Table("iris")
-    # data = Table("housing")
-    clf = SklTreeLearner()(data)
+    # data = Table("iris")
+    data = Table("housing")
+    clf = SklTreeRegressionLearner()(data)
+    # clf = TreeLearner()(data)
     clf.instances = data
 
     ow.ctree(clf)