635 Implementing spatial tournaments

axelrod/__init__.py

@@ -12,6 +12,6 @@
 from .strategies import *
 from .deterministic_cache import DeterministicCache
 from .match_generator import *
-from .tournament import Tournament, ProbEndTournament
+from .tournament import Tournament, ProbEndTournament, SpatialTournament
 from .result_set import ResultSet, ResultSetFromFile
 from .ecosystem import Ecosystem

axelrod/match_generator.py

@@ -167,3 +167,43 @@ def estimated_size(self):
         """Rough estimate of the number of matches that will be generated."""
         size = self.__len__() * (1. / self.prob_end) * self.repetitions
         return size
+
+class SpatialMatches(RoundRobinMatches):
+    """
+    A class that generates spatially-structured matches.
+    In these matches, players interact only with their neighbors rather than the
+    entire population. This reduces to a well-mixed population when the spatial
+    graph is a complete graph.
+
+    Parameters
+    ----------
+    players : list
+        A list of axelrod.Player objects
+    turns : integer
+        The number of turns per match
+    game : axelrod.Game
+        The game object used to score the match
+    repetitions : int
+        The number of repetitions of a given match
+    edges : list
+        A list of tuples containing the existing edges
+    """
+
+    def __init__(self, players, turns, game, repetitions, edges):
+
+        player_indices = list(range(len(players)))
+        node_indices = sorted(set([node for edge in edges for node in edge]))
+        if player_indices != node_indices:
+            raise ValueError("The graph edges do not include all players.")
+
+        self.edges = edges
+        super(SpatialMatches, self).__init__(players, turns, game, repetitions)
+
+    def build_match_chunks(self):
+        for edge in self.edges:
+            match_params = self.build_single_match_params()
+            index_pair = edge
+            yield (index_pair, match_params, self.repetitions)
+
+    def __len__(self):
+        return len(self.edges)

axelrod/result_set.py

@@ -309,7 +309,7 @@ def build_payoffs(self):
                     if (player, opponent) == index_pair:
                         for interaction in repetitions:
                             utilities.append(iu.compute_final_score_per_turn(interaction)[0])
-                    if (opponent, player) == index_pair:
+                    elif (opponent, player) == index_pair:
                         for interaction in repetitions:
                             utilities.append(iu.compute_final_score_per_turn(interaction)[1])
 
@@ -460,7 +460,7 @@ def build_payoff_diffs_means(self):
                         for interaction in repetitions:
                             scores = iu.compute_final_score_per_turn(interaction)
                             diffs.append(scores[0] - scores[1])
-                    if (opponent, player) == index_pair:
+                    elif (opponent, player) == index_pair:
                         for interaction in repetitions:
                             scores = iu.compute_final_score_per_turn(interaction)
                             diffs.append(scores[1] - scores[0])
@@ -501,7 +501,7 @@ def build_cooperation(self):
                         if (player, opponent) == index_pair:
                             for interaction in repetitions:
                                 coop_count += iu.compute_cooperations(interaction)[0]
-                        if (opponent, player) == index_pair:
+                        elif (opponent, player) == index_pair:
                             for interaction in repetitions:
                                 coop_count += iu.compute_cooperations(interaction)[1]
 
@@ -625,7 +625,7 @@ def build_good_partner_matrix(self):
                                 if coops[0] >= coops[1]:
                                     good_partner_matrix[player][opponent] += 1
 
-                        if (opponent, player) == index_pair:
+                        elif (opponent, player) == index_pair:
                             for interaction in repetitions:
                                 coops = iu.compute_cooperations(interaction)
                                 if coops[0] <= coops[1]:

axelrod/tests/unit/test_match_generator.py

@@ -207,3 +207,32 @@ def test_len(self, prob_end):
             self.players, prob_end, game=None, repetitions=repetitions)
         self.assertEqual(len(rr), len(list(rr.build_match_chunks())))
         self.assertAlmostEqual(rr.estimated_size(), len(rr) * 1. / prob_end * repetitions)
+
+
+class TestSpatialMatches(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.players = [s() for s in test_strategies]
+
+    @given(repetitions=integers(min_value=1, max_value=test_repetitions),
+           turns=integers(min_value=1, max_value=test_turns))
+    @example(repetitions=test_repetitions, turns=test_turns)
+    def test_build_match_chunks(self, repetitions, turns):
+        edges = [(0, 1), (1, 2), (3, 4)]
+        sp = axelrod.SpatialMatches(
+            self.players, turns, test_game, repetitions, edges)
+        chunks = list(sp.build_match_chunks())
+        match_definitions = [tuple(list(index_pair) + [repetitions])
+                             for (index_pair, match_params, repetitions) in chunks]
+        expected_match_definitions = [(edge[0], edge[1], repetitions)
+                                      for edge in edges]
+
+        self.assertEqual(sorted(match_definitions), sorted(expected_match_definitions))
+
+    def test_len(self):
+        edges = [(0, 1), (1, 2), (3, 4)]
+        sp = axelrod.SpatialMatches(
+            self.players, test_turns, test_game, test_repetitions, edges)
+        self.assertEqual(len(sp), len(list(sp.build_match_chunks())))
+        self.assertEqual(len(sp), len(edges))

axelrod/tests/unit/test_property.py

@@ -159,6 +159,7 @@ def test_decorator_with_stochastic_strategies(self, tournament):
         for p in tournament.players:
             self.assertIn(str(p), stochastic_player_names)
 
+
 class TestGame(unittest.TestCase):
 
     def test_call(self):