adds test to increase coverage, in particular coverage of ShouldDemote mecanism

Author: edouardArgenson

axelrod/strategies/dbs.py

@@ -346,8 +346,16 @@ def minimax_tree_search(begin_node, policy, max_depth):
         # depth is < max_depth
         siblings = begin_node.get_siblings()
         # The stochastic node value is the expected values of siblings
-        node_value = (begin_node.pC * minimax_tree_search(siblings[0], policy, max_depth) 
-            + (1 - begin_node.pC) * minimax_tree_search(siblings[1], policy, max_depth))
+        node_value = (
+            begin_node.pC * minimax_tree_search(
+                                            siblings[0], 
+                                            policy, 
+                                            max_depth) 
+            + (1 - begin_node.pC) * minimax_tree_search(
+                                            siblings[1], 
+                                            policy, 
+                                            max_depth)
+            )
         return node_value
     else:   # determinist node
         if begin_node.depth == max_depth:
@@ -358,8 +366,10 @@ def minimax_tree_search(begin_node, policy, max_depth):
             # this returns the two max expected values, for choice C or D,
             # as a tuple
             return (
-                minimax_tree_search(siblings[0], policy, max_depth) + begin_node.get_value(),
-                minimax_tree_search(siblings[1], policy, max_depth) + begin_node.get_value()
+                minimax_tree_search(siblings[0], policy, max_depth) 
+                    + begin_node.get_value(),
+                minimax_tree_search(siblings[1], policy, max_depth) 
+                    + begin_node.get_value()
                 )
         elif begin_node.depth < max_depth:
             siblings = begin_node.get_siblings(policy)

axelrod/tests/strategies/.test_dbs.py.swp

@@ -69,4 +69,24 @@ def test_strategy(self):
                 expected_actions=exp_actions, 
                 init_kwargs = init_kwargs_2
                 )
+        
+        # check that ShouldDemote mecanism works.
+        # We play against Alternator during 12 turns to make the 
+        # algorithm learn Alternator's strategy, then at turn 13 we
+        # change opponent to Defector, hence trigging ShouldDemote
+        # mecanism
+        # For this test we use violation_threshold=3
+        init_kwargs_3 = {
+                'discount_factor':.75, 'promotion_threshold':3, 
+                'violation_threshold':3, 'reject_threshold':3,
+                'tree_depth':5
+                }
+        exp_actions = [(C, C), (C, D)] * 3 + [(D, C), (C, D)] * 3 
+        exp_actions += [(D, D), (C, D)] * 3 + [(D, D)]
+        mock_actions = [C, D, C, D, C, D, C, D, C, D, C, D, D, D, D, D, D, D, D]
+        self.versus_test(
+                opponent=axelrod.MockPlayer(actions=mock_actions),
+                expected_actions=exp_actions, 
+                init_kwargs = init_kwargs_3
+                )