Resolve split_value issue in DTLearner and pass all tests.

assess_learners/DTLearner.py

@@ -8,9 +8,6 @@ class DTLearner(AbstractTreeLearner):
         self.leaf_size = leaf_size
         self.verbose = verbose
 
-    def author(self):
-        return 'felixm' # replace tb34 with your Georgia Tech username
-
     def get_correlations(self, xs, y):
         """ Return a list of sorted 2-tuples where the first element
         is the correlation and the second element is the index. Sorted by
@@ -25,14 +22,23 @@ class DTLearner(AbstractTreeLearner):
         return correlations
 
     def get_i_and_split_value(self, xs, y):
+        # If all elements are true we would get one sub-tree with zero
+        # elements, but we need at least one element in both trees.  We avoid
+        # zero-trees in two steps. First we take the average between the median
+        # value and a smaller value an use that as the new split value. If that
+        # doesn't work (when all values are the same) we choose the X with the
+        # next smaller correlation. We assert that not all values are
+        # smaller/equal to the split value at the end.
         for _, i in self.get_correlations(xs, y):
             split_value = np.median(xs[:,i])
             select = xs[:, i] <= split_value
-            # If all elements are true we would get one sub-tree with zero
-            # elements, but we need at least one element. Therefore, we only
-            # choose the index if not all elements are true. If they are we go
-            # to the next smaller correlation.
+            if select.all():
+                for value in xs[:, i]:
+                    if value < split_value:
+                        split_value = (value + split_value) / 2.0
+                select = xs[:, i] <= split_value
             if not select.all():
                 break
+        assert(not select.all())
         return i, split_value