Merge pull request #797 from Axelrod-Python/789

Add initial play summary to the results set.

Author: meatballs

axelrod/result_set.py

@@ -383,6 +383,7 @@ def _build_empty_metrics(self, keep_interactions=False):
         self.cooperation = [[0 for opponent in plist] for player in plist]
         self.normalised_cooperation = [[[] for opponent in plist]
                                        for player in plist]
+        self.initial_cooperation_count = [0 for player in plist]
         self.state_distribution = [[Counter() for opponent in plist]
                                    for player in plist]
         self.good_partner_matrix = [[0 for opponent in plist]
@@ -532,6 +533,24 @@ def _update_cooperation(self, p1, p2, cooperations):
         self.cooperation[p1][p2] += cooperations[0]
         self.cooperation[p2][p1] += cooperations[1]
 
+    def _update_initial_cooperation_count(self, p1, p2, initial_cooperations):
+        """
+        During a read of the data, update the initial cooperation count
+        attribute
+
+        Parameters
+        ----------
+
+            p1, p2 : int
+                The indices of the first and second player
+            initial_cooperations : tuple
+                A 2 tuple with a 0 or 1 indicating if the initial move of each
+                player was Cooperation (0) or Defection (1), e.g. (0, 1) for a
+                round (C, D)
+        """
+        self.initial_cooperation_count[p1] += initial_cooperations[0]
+        self.initial_cooperation_count[p2] += initial_cooperations[1]
+
     def _update_state_distribution(self, p1, p2, counter):
         """
         During a read of the data, update the state_distribution attribute
@@ -606,6 +625,16 @@ def _build_good_partner_rating(self):
                 max(1, float(self.total_interactions[player]))
                 for player in range(self.nplayers)]
 
+    @update_progress_bar
+    def _build_initial_cooperation_rate(self):
+        """
+        At the end of a read of the data, build the normalised initial
+        cooperation rate attribute
+        """
+        return [self.initial_cooperation_count[player] /
+                max(1, float(self.total_interactions[player]))
+                for player in range(self.nplayers)]
+
     def _build_score_related_metrics(self, progress_bar=False,
                                      keep_interactions=False):
         """
@@ -653,11 +682,14 @@ def _build_score_related_metrics(self, progress_bar=False,
                     self._update_normalised_scores(repetition, p1, p2,
                                                    scores_per_turn)
                     self._update_cooperation(p1, p2, cooperations)
+                    initial_coops = iu.compute_cooperations(interaction[:1])
+                    self._update_initial_cooperation_count(p1, p2,
+                                                           initial_coops)
                     self._update_state_distribution(p1, p2, state_counter)
                     self._update_good_partner_matrix(p1, p2, cooperations)
 
         if progress_bar:
-            self.progress_bar = tqdm.tqdm(total=11 + 2 * self.nplayers,
+            self.progress_bar = tqdm.tqdm(total=12 + 2 * self.nplayers,
                                           desc="Finishing")
         self._summarise_normalised_scores()
         self._summarise_normalised_cooperation()
@@ -670,6 +702,7 @@ def _build_score_related_metrics(self, progress_bar=False,
         self.payoff_diffs_means = self._build_payoff_diffs_means()
         self.vengeful_cooperation = self._build_vengeful_cooperation()
         self.cooperating_rating = self._build_cooperating_rating()
+        self.initial_cooperation_rate = self._build_initial_cooperation_rate()
         self.good_partner_rating = self._build_good_partner_rating()
         self.eigenjesus_rating = self._build_eigenjesus_rating()
         self.eigenmoses_rating = self._build_eigenmoses_rating()
@@ -739,8 +772,8 @@ def summarise(self):
 
         self.player = namedtuple("Player", ["Rank", "Name", "Median_score",
                                             "Cooperation_rating", "Wins",
-                                            "CC_rate", "CD_rate", "DC_rate",
-                                            "DD_rate"])
+                                            "Initial_C_rate", "CC_rate",
+                                            "CD_rate", "DC_rate", "DD_rate"])
 
         states = [(C, C), (C, D), (D, C), (D, D)]
         state_prob = []
@@ -756,7 +789,8 @@ def summarise(self):
             state_prob.append(counts)
 
         summary_measures = list(zip(self.players, median_scores,
-                                    self.cooperating_rating, median_wins))
+                                    self.cooperating_rating, median_wins,
+                                    self.initial_cooperation_rate))
 
         summary_data = []
         for rank, i in enumerate(self.ranking):
@@ -769,7 +803,7 @@ def write_summary(self, filename):
         """
         Write a csv file containing summary data of the results of the form:
 
-            "Rank", "Name", "Median-score-per-turn", "Cooperation-rating", "Wins", "CC-Rate", "CD-Rate", "DC-Rate", "DD-rate"
+            "Rank", "Name", "Median-score-per-turn", "Cooperation-rating", "Initial_C_Rate", "Wins", "CC-Rate", "CD-Rate", "DC-Rate", "DD-rate"
 
         Parameters
         ----------

axelrod/tests/unit/test_resultset.py

@@ -120,6 +120,9 @@ def setUpClass(cls):
                 [0, 0, 0],
             ]
 
+        cls.expected_initial_cooperation_count = [6, 6, 0]
+        cls.expected_initial_cooperation_rate = [1, 1, 0]
+
         cls.expected_state_distribution = [
                 [], [], []
             ]
@@ -223,13 +226,13 @@ def test_init_with_different_game(self):
     def test_with_progress_bar(self):
         rs = axelrod.ResultSet(self.players, self.interactions)
         self.assertTrue(rs.progress_bar)
-        self.assertEqual(rs.progress_bar.total, 11 + 2 * rs.nplayers)
+        self.assertEqual(rs.progress_bar.total, 12 + 2 * rs.nplayers)
         self.assertEqual(rs.progress_bar.n, rs.progress_bar.total)
 
         rs = axelrod.ResultSet(self.players, self.interactions,
                                progress_bar=True)
         self.assertTrue(rs.progress_bar)
-        self.assertEqual(rs.progress_bar.total, 11 + 2 * rs.nplayers)
+        self.assertEqual(rs.progress_bar.total, 12 + 2 * rs.nplayers)
         self.assertEqual(rs.progress_bar.n, rs.progress_bar.total)
 
     def test_match_lengths(self):
@@ -345,6 +348,14 @@ def test_cooperation(self):
         self.assertEqual(len(rs.cooperation), rs.nplayers)
         self.assertEqual(rs.cooperation, self.expected_cooperation)
 
+    def test_initial_cooperation_count(self):
+        rs = axelrod.ResultSet(self.players, self.interactions,
+                               progress_bar=False)
+        self.assertIsInstance(rs.initial_cooperation_count, list)
+        self.assertEqual(len(rs.initial_cooperation_count), rs.nplayers)
+        self.assertEqual(rs.initial_cooperation_count,
+                         self.expected_initial_cooperation_count)
+
     def test_normalised_cooperation(self):
         rs = axelrod.ResultSet(self.players, self.interactions,
                                progress_bar=False)
@@ -353,6 +364,14 @@ def test_normalised_cooperation(self):
         self.assertEqual(rs.normalised_cooperation,
                          self.expected_normalised_cooperation)
 
+    def test_initial_cooperation_rate(self):
+        rs = axelrod.ResultSet(self.players, self.interactions,
+                               progress_bar=False)
+        self.assertIsInstance(rs.initial_cooperation_rate, list)
+        self.assertEqual(len(rs.initial_cooperation_rate), rs.nplayers)
+        self.assertEqual(rs.initial_cooperation_rate,
+                         self.expected_initial_cooperation_rate)
+
     def test_state_distribution(self):
         rs = axelrod.ResultSet(self.players, self.interactions,
                                progress_bar=False)
@@ -459,6 +478,12 @@ def test_summarise(self):
         self.assertEqual([float(player.Wins) for player in sd],
                          ranked_median_wins)
 
+        ranked_initial_coop_rates = [self.expected_initial_cooperation_rate[i]
+                                     for i in rs.ranking]
+        self.assertEqual([float(player.Initial_C_rate)
+                          for player in sd],
+                         ranked_initial_coop_rates)
+
         for player in sd:
             self.assertEqual(player.CC_rate + player.CD_rate + player.DC_rate + player.DD_rate, 1)
 
@@ -471,7 +496,7 @@ def test_write_summary(self):
             csvreader = csv.reader(csvfile)
             for row in csvreader:
                 ranked_names.append(row[1])
-                self.assertEqual(len(row), 9)
+                self.assertEqual(len(row), 10)
         self.assertEqual(ranked_names[0], "Name")
         self.assertEqual(ranked_names[1:], rs.ranked_names)
 
@@ -809,6 +834,9 @@ def setUpClass(cls):
             [0, 0, 0],
             ]
 
+        cls.expected_initial_cooperation_count = [6, 3, 0]
+        cls.expected_initial_cooperation_rate = [1, 1, 0]
+
         cls.expected_vengeful_cooperation = [[2 * element - 1 for element in row]
                                    for row in cls.expected_normalised_cooperation]
 
@@ -1026,6 +1054,9 @@ def setUpClass(cls):
                 [0.0, 0.0, mean([5 / 5.0 for _ in range(3)]), 0.0]
                 ]
 
+        cls.expected_initial_cooperation_count = [3.0, 3.0, 0, 3.0]
+        cls.expected_initial_cooperation_rate = [1.0, 1.0, 0, 1.0]
+
         cls.expected_vengeful_cooperation = [[2 * element - 1 for element in row]
                                    for row in cls.expected_normalised_cooperation]
 
@@ -1188,6 +1219,9 @@ def setUpClass(cls):
                 [0.0, 0.0, 0.0, mean([5 / 5.0 for _ in range(3)])]
                 ]
 
+        cls.expected_initial_cooperation_count = [0, 0, 0, 0]
+        cls.expected_initial_cooperation_rate = [0, 0, 0, 0]
+
         cls.expected_vengeful_cooperation = [[2 * element - 1 for element in row]
                                    for row in cls.expected_normalised_cooperation]
 
@@ -1265,3 +1299,4 @@ def test_summarise_without_failure(self, tournament):
             total_rate = round(player.CC_rate + player.CD_rate +
                                player.DC_rate + player.DD_rate, 3)
             self.assertTrue(total_rate in [0, 1])
+            self.assertTrue(0 <= player.Initial_C_rate <= 1)

docs/tutorials/getting_started/tournament_results.rst

@@ -24,6 +24,8 @@ This tutorial will show you how to access the various results of a tournament:
 - State distribution: the count of each type of state of a match
 - Normalised state distribution: the normalised count of each type of state of a
   match
+- Initial cooperation count: the count of initial cooperation by each player.
+- Initial cooperation rate: the rate of initial cooperation by each player.
 - Cooperation rating: cooperation rating of each player
 - Vengeful cooperation: a morality metric from the literature (see
   :ref:`morality-metrics`).
@@ -63,8 +65,8 @@ This gives the length of the matches played by each player::
      [[10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10]],
      [[10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10]]]
 
-Every player plays 200 turns against every other player for every repetition of
-the tournament.
+Every player plays 10 turns against every other player (including themselves)
+for every repetition of the tournament.
 
 Scores
 ------
@@ -267,6 +269,28 @@ the second the action of the opponent::
       Counter({('C', 'C'): 1.0}),
       Counter()]]
 
+
+Initial cooperation counts
+--------------------------
+
+This gives the count of cooperations made by each player during the first turn
+of every match::
+
+    >>> results.initial_cooperation_count
+    [9, 0, 9, 9]
+
+Each player plays an opponent a total of 9 times (3 opponents and 3
+repetitions). Apart from the :code:`Defector`, they all cooperate on the first
+turn.
+
+Initial cooperation rates
+-------------------------
+
+This gives the rate of which a strategy cooperates during the first turn::
+
+    >>> results.initial_cooperation_rate
+    [1.0, 0.0, 1.0, 1.0]
+
 Morality Metrics
 ----------------
 
@@ -299,10 +323,10 @@ that summarises the results of the tournament::
 
     >>> summary = results.summarise()
     >>> pprint.pprint(summary)
-    [Player(Rank=0, Name='Defector', Median_score=2.6..., Cooperation_rating=0.0, Wins=3.0, CC_rate=...),
-     Player(Rank=1, Name='Tit For Tat', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, CC_rate=...),
-     Player(Rank=2, Name='Grudger', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, CC_rate=...),
-     Player(Rank=3, Name='Cooperator', Median_score=2.0..., Cooperation_rating=1.0, Wins=0.0, CC_rate=...)]
+    [Player(Rank=0, Name='Defector', Median_score=2.6..., Cooperation_rating=0.0, Wins=3.0, Initial_C_rate=0.0, CC_rate=...),
+     Player(Rank=1, Name='Tit For Tat', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, Initial_C_rate=1.0, CC_rate=...),
+     Player(Rank=2, Name='Grudger', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, Initial_C_rate=1.0, CC_rate=...),
+     Player(Rank=3, Name='Cooperator', Median_score=2.0..., Cooperation_rating=1.0, Wins=0.0, Initial_C_rate=1.0, CC_rate=...)]
 
 It is also possible to write this data directly to a csv file using the
 `write_summary` method::
@@ -313,8 +337,8 @@ It is also possible to write this data directly to a csv file using the
     ...     csvreader = csv.reader(outfile)
     ...     for row in csvreader:
     ...         print(row)
-    ['Rank', 'Name', 'Median_score', 'Cooperation_rating', 'Wins', 'CC_rate', 'CD_rate', 'DC_rate', 'DD_rate']
-    ['0', 'Defector', '2.6...', '0.0', '3.0', '0.0', '0.0', '0.4...', '0.6...']
-    ['1', 'Tit For Tat', '2.3...', '0.7', '0.0', '0.66...', '0.03...', '0.0', '0.3...']
-    ['2', 'Grudger', '2.3...', '0.7', '0.0', '0.66...', '0.03...', '0.0', '0.3...']
-    ['3', 'Cooperator', '2.0...', '1.0', '0.0', '0.66...', '0.33...', '0.0', '0.0']
+    ['Rank', 'Name', 'Median_score', 'Cooperation_rating', 'Wins', 'Initial_C_rate', 'CC_rate', 'CD_rate', 'DC_rate', 'DD_rate']
+    ['0', 'Defector', '2.6...', '0.0', '3.0', '0.0', '0.0', '0.0', '0.4...', '0.6...']
+    ['1', 'Tit For Tat', '2.3...', '0.7', '0.0', '1.0', '0.66...', '0.03...', '0.0', '0.3...']
+    ['2', 'Grudger', '2.3...', '0.7', '0.0', '1.0', '0.66...', '0.03...', '0.0', '0.3...']
+    ['3', 'Cooperator', '2.0...', '1.0', '0.0', '1.0', '0.66...', '0.33...', '0.0', '0.0']