Add tree learners to strategy evaluation directory

strategy_evaluation/AbstractTreeLearner.py

@@ -0,0 +1,77 @@
+import numpy as np
+
+
+class AbstractTreeLearner:
+    LEAF = -1
+    NA = -1
+
+    def author(self):
+        return 'felixm' # replace tb34 with your Georgia Tech username
+
+    def create_node(self, factor, split_value, left, right):
+        return np.array([(factor, split_value, left, right), ],
+                        dtype='|i4, f4, i4,  i4')
+
+    def query_point(self, point):
+        node_index = 0
+        while self.rel_tree[node_index][0] != self.LEAF:
+            node = self.rel_tree[node_index]
+            split_factor = node[0]
+            split_value = node[1]
+            if point[split_factor] <= split_value:
+                # Recurse into left sub-tree.
+                node_index += node[2]
+            else:
+                node_index += node[3]
+        v = self.rel_tree[node_index][1]
+        return v
+
+    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.
+        @returns the estimated values according to the saved model.
+        """
+        query_point = lambda p: self.query_point(p)
+        r = np.apply_along_axis(query_point, 1, points)
+        return r
+
+    def build_tree(self, xs, y):
+        """
+        @summary: Build a decision tree from the training data.
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        assert(xs.shape[0] == y.shape[0])
+        assert(xs.shape[0] > 0) # If this is 0 something went wrong.
+
+        if xs.shape[0] <= self.leaf_size:
+            value = np.mean(y)
+            if value < -0.3:
+                value = -1
+            elif value > 0.3:
+                value = 1
+            else:
+                value = 0
+            return self.create_node(self.LEAF, value, self.NA, self.NA)
+
+        if np.all(y[0] == y):
+            return self.create_node(self.LEAF, y[0], self.NA, self.NA)
+
+        i, split_value = self.get_i_and_split_value(xs, y)
+        select_l = xs[:, i] <= split_value
+        select_r = xs[:, i] > split_value
+        lt = self.build_tree(xs[select_l], y[select_l])
+        rt = self.build_tree(xs[select_r], y[select_r])
+        root = self.create_node(i, split_value, 1, lt.shape[0] + 1)
+        root = np.concatenate([root, lt, rt])
+        return root
+
+    def addEvidence(self, data_x, data_y):
+        """
+        @summary: Add training data to learner
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        self.rel_tree = self.build_tree(data_x, data_y)
+