Resolve split_value issue in DTLearner and pass all tests.

assess_learners/BagLearner.py

@@ -1,18 +1,14 @@
 import numpy as np
+from AbstractTreeLearner import AbstractTreeLearner
 
 
-class BagLearner(object):
+class BagLearner(AbstractTreeLearner):
-    def __init__(self, learner, bags=20, boost=False, verbose=False, **kwargs):
+    def __init__(self, learner, bags=9, boost=False, verbose=False, kwargs={}):
         self.learner = learner
-        self.bags = bags
-        self.boost = boost
         self.verbose = verbose
-        self.kwargs = kwargs
+        self.bags = bags
         self.learners = [learner(**kwargs) for _ in range(bags)]
 
-    def author(self):
-        return 'felixm' # replace tb34 with your Georgia Tech username
-
     def get_bag(self, data_x, data_y):
         num_items = int(data_x.shape[0] * 0.5) # 50% of samples
         bag_x, bag_y = [], []
@@ -22,7 +18,6 @@ class BagLearner(object):
             bag_y.append(data_y[i])
         return np.array(bag_x), np.array(bag_y)
 
-
     def addEvidence(self, data_x, data_y):
         """
         @summary: Add training data to learner
@@ -36,10 +31,8 @@ class BagLearner(object):
     def query(self, points):
         """
         @summary: Estimate a set of test points given the model we built.
-        @param points: should be a numpy array with each row corresponding to a specific query.
+        @param points: numpy array with each row corresponding to a query.
         @returns the estimated values according to the saved model.
         """
         return np.mean([l.query(points) for l in self.learners], axis=0)
 
-if __name__=="__main__":
-    print("the secret clue is 'zzyzx'")

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
+            if select.all():
-            # elements, but we need at least one element. Therefore, we only
+                for value in xs[:, i]:
-            # choose the index if not all elements are true. If they are we go
+                    if value < split_value:
-            # to the next smaller correlation.
+                        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
 

assess_learners/testlearner.py

@@ -48,12 +48,6 @@ if __name__=="__main__":
     trainY = data[:train_rows,-1]
     testX = data[train_rows:,0:-1]
     testY = data[train_rows:,-1]
-
-    # trainX = data[:, 0:-1]
-    # trainY = data[:, -1]
-    # testX = data[:, 0:-1]
-    # testY = data[:, -1]
-
     print(f"{testX.shape}")
     print(f"{testY.shape}")
 
@@ -85,8 +79,7 @@ if __name__=="__main__":
 
     # test_learner(lrl.LinRegLearner)
     test_learner(dtl.DTLearner, leaf_size=1)
-    test_learner(rtl.RTLearner)
+    # test_learner(rtl.RTLearner, leaf_size=6)
-    test_learner(rtl.RTLearner, leaf_size=5)
+    test_learner(bgl.BagLearner, learner=dtl.DTLearner, bags=20, kwargs = {'leaf_size': 5})
-    # test_learner(bgl.BagLearner, learner=dtl.DTLearner, bags=20)
+    test_learner(isl.InsaneLearner)
-    # learner = isl.InsaneLearner()