Merge pull request #351 from marcharper/win_plot

Plot for distribution of head-to-head wins

Author: marcharper

axelrod/plot.py

@@ -1,3 +1,5 @@
+import numpy
+
 matplotlib_installed = True
 try:
     import matplotlib.pyplot as plt
@@ -11,7 +13,6 @@ class Plot(object):
 
     def __init__(self, result_set):
         self.result_set = result_set
-        # self._nplayers = self.result_set.nplayers
         self.matplotlib_installed = matplotlib_installed
 
     @property
@@ -55,6 +56,44 @@ def boxplot(self):
         plt.title(self._boxplot_title)
         return figure
 
+    @property
+    def _winplot_dataset(self):
+        # Sort wins by median
+        wins = self.result_set.wins
+        players = self.result_set.players
+        medians = map(numpy.median, wins)
+        medians = sorted([(m, i) for (i, m) in enumerate(medians)], reverse=True)
+        # Reorder and grab names
+        wins = [wins[x[1]] for x in medians]
+        ranked_names = [str(players[x[1]]) for x in medians]
+        return wins, ranked_names
+
+    @property
+    def _winplot_title(self):
+        return ("Distributions of wins:"
+                " {} turns repeated {} times ({} strategies)").format(
+            self.result_set.turns,
+            self.result_set.repetitions,
+            len(self.result_set.ranking))
+
+    def winplot(self):
+        if not self.matplotlib_installed:
+            return None
+
+        wins, ranked_names = self._winplot_dataset
+        maximum = max(max(w) for w in wins)
+
+        figure = plt.figure()
+        plt.boxplot(wins)
+        plt.xticks(
+            self._boxplot_xticks_locations,
+            ranked_names,
+            rotation=90)
+        plt.tick_params(axis='both', which='both', labelsize=7)
+        plt.title(self._winplot_title)
+        plt.ylim(-0.5, 0.5 + maximum)
+        return figure
+
     def payoff(self):
 
         if not self.matplotlib_installed:

axelrod/tests/unit/test_plot.py

@@ -26,11 +26,13 @@ def setUpClass(cls):
         cls.expected_boxplot_xticks_labels = ['Player3', 'Player1', 'Player2']
         cls.expected_boxplot_title = ('Mean score per stage game over 5 turns'
                                       ' repeated 2 times (3 strategies)')
-
         cls.expected_payoff_dataset = [
             [0.0, 3.2, 3.2],
             [4.2, 0.0, 2.0],
             [3.6, 1.8, 0.0]]
+        cls.expected_winplot_dataset = ([[2, 4], [0, 2], [0, 0]],
+                                        ['Player1', 'Player2', 'Player3'])
+        cls.expected_winplot_title = "Distributions of wins: 5 turns repeated 2 times (3 strategies)"
 
     def test_init(self):
         result_set = self.test_result_set
@@ -67,6 +69,23 @@ def test_boxplot(self):
         else:
             self.skipTest('matplotlib not installed')
 
+    def test_winplot_dataset(self):
+        plot = axelrod.Plot(self.test_result_set)
+        self.assertSequenceEqual(
+            plot._winplot_dataset,
+            self.expected_winplot_dataset)
+
+    def test_winplot_title(self):
+        plot = axelrod.Plot(self.test_result_set)
+        self.assertEqual(plot._winplot_title, self.expected_winplot_title)
+
+    def test_winplot(self):
+        if matplotlib_installed:
+            plot = axelrod.Plot(self.test_result_set)
+            self.assertIsInstance(plot.winplot(), matplotlib.pyplot.Figure)
+        else:
+            self.skipTest('matplotlib not installed')
+
     def test_payoff_dataset(self):
         plot = axelrod.Plot(self.test_result_set)
         self.assertSequenceEqual(

axelrod/tournament_manager.py

@@ -126,7 +126,7 @@ def _save_plots(self, tournament, ecosystem=None, image_format="svg"):
             self._logger.error('The matplotlib library is not installed. '
                             'No plots will be produced')
             return
-        for plot_type in ('boxplot', 'payoff'):
+        for plot_type in ('boxplot', 'payoff', 'winplot'):
             figure = getattr(plot, plot_type)()
             file_name = self._output_file_path(
                 tournament.name + '_' + plot_type, image_format)

docs/usage.rst

@@ -303,6 +303,27 @@ The resulting wins matrix is::
 
 which shows, for example, that Cooperator had 0 wins, Defector won 8 times in each repetition and Random won 4 times in the first repetition and twice in the second.
 
+For any tournament, a plot of the distribution of wins is created, much like the
+plot for the distribution of mean scores. For the demo strategies we create the
+plot as follows::
+
+    import axelrod
+    strategies = [s() for s in axelrod.demo_strategies]
+    tournament = axelrod.Tournament(strategies, game=Game(30, 0, 50, 10))
+    results = tournament.play()
+    plot = axelrod.Plot(results)
+    p = plot.winplot()
+    p.show()
+
+This produces:
+
+.. image:: _static/usage/demo_strategies_winplot.svg
+   :width: 50%
+   :align: center
+
+In this case most of the strategies are deterministic, so there is not much
+variation in the distributions. See below for a more complex example.
+
 Noisy Tournaments
 ^^^^^^^^^^^^^^^^^
 
@@ -365,6 +386,23 @@ and accordingly to the ranking of strategies overall:
 | |boxplot_no_noise| | |boxplot_5_noise| |
 +--------------------+-------------------+
 
+as well as the distributions of wins:
+
+.. |winplot_no_noise| image:: http://axelrod-python.github.io/tournament/assets/strategies_winplot.svg
+   :width: 75%
+   :align: middle
+   :alt: Strategy performance without noise
+
+.. |winplot_5_noise| image:: http://axelrod-python.github.io/tournament/assets/strategies_winplot_noise_5.svg
+   :width: 75%
+   :align: middle
+   :alt: Strategy performance with 5% noise
+
++--------------------+-------------------+
+| |winplot_no_noise| | |winplot_5_noise| |
++--------------------+-------------------+
+
+
 To run a noisy tournament, just use the keyword argument `noise` when
 creating tournaments. Both `run_axelrod` and the utility function
 `run_tournaments` accept and passthrough the noise argument. To run the
@@ -528,6 +566,13 @@ The results from the tournament itself (ordered by median score):
    :width: 50%
    :align: center
 
+The distributions of head-to-head wins for each strategy:
+
+.. image:: http://axelrod-python.github.io/tournament/assets/strategies_winplot.svg
+   :width: 50%
+   :align: center
+
+
 The payoff matrix from that tournament:
 
 .. image:: http://axelrod-python.github.io/tournament/assets/strategies_payoff.svg