Linted efr_test

Jamesflynn1 · Nov 15, 2023 · 3654a8d · 3654a8d
1 parent 65d82f2
commit 3654a8d
Showing 1 changed file with 20 additions and 19 deletions.
diff --git a/open_spiel/python/algorithms/efr_test.py b/open_spiel/python/algorithms/efr_test.py
@@ -29,41 +29,43 @@
 class EFRTest(parameterized.TestCase, absltest.TestCase):
 
   def setUp(self):
-    self._KUHN_GAME = pyspiel.load_game("kuhn_poker")
-    self._LEDUC_GAME = pyspiel.load_game("leduc_poker")
-    self._KUHN_3P_GAME = pyspiel.load_game("kuhn_poker(players=3)")
-    self._SHERIFF_GAME = pyspiel.load_game("sheriff")
+    self.kuhn_game = pyspiel.load_game("kuhn_poker")
+    self.leduc_game = pyspiel.load_game("leduc_poker")
+    self.kuhn_3p_game = pyspiel.load_game("kuhn_poker(players=3)")
+    self.sheriff_game = pyspiel.load_game("sheriff")
 
-    self._KUHN_UNIFORM_POLICY = policy.TabularPolicy(self._KUHN_GAME)
-    self._LEDUC_UNIFORM_POLICY = policy.TabularPolicy(self._LEDUC_GAME)
+    self.kuhn_uniform_policy = policy.TabularPolicy(self.kuhn_game)
+    self.leduc_uniform_policy = policy.TabularPolicy(self.leduc_game)
 
-  @parameterized.parameters(["blind action", "informed action", "blind cf", "informed cf", "bps", "cfps", "csps", "tips", "bhv"])
+  @parameterized.parameters(["blind action", "informed action", "blind cf",
+                             "informed cf","bps", "cfps", "csps",
+                             "tips", "bhv"])
   def test_policy_zero_is_uniform(self, deviations_name):
     # We use Leduc and not Kuhn, because Leduc has illegal actions and Kuhn does
     # not.
     cfr_solver = efr.EFRSolver(
-        game=self._LEDUC_GAME,
+        game=self.leduc_game,
         deviations_name=deviations_name
         )
     np.testing.assert_array_equal(
-        self._LEDUC_UNIFORM_POLICY.action_probability_array,
+        self.leduc_uniform_policy.action_probability_array,
         cfr_solver.current_policy().action_probability_array)
     np.testing.assert_array_equal(
-        self._LEDUC_UNIFORM_POLICY.action_probability_array,
+        self.leduc_uniform_policy.action_probability_array,
         cfr_solver.average_policy().action_probability_array)
 
   @parameterized.parameters(
       ["blind cf", "informed cf", "bps", "cfps", "csps", "tips", "bhv"])
   def test_efr_kuhn_poker(self, deviations_name):
     efr_solver = efr.EFRSolver(
-        game=self._KUHN_GAME,
+        game=self.kuhn_game,
         deviations_name=deviations_name
         )
     for _ in range(300):
       efr_solver.evaluate_and_update_policy()
     average_policy = efr_solver.average_policy()
     average_policy_values = expected_game_score.policy_value(
-        self._KUHN_GAME.new_initial_state(), [average_policy] * 2)
+        self.kuhn_game.new_initial_state(), [average_policy] * 2)
     # 1/18 is the Nash value. See https://en.wikipedia.org/wiki/Kuhn_poker
     np.testing.assert_allclose(
         average_policy_values, [-1 / 18, 1 / 18], atol=1e-3)
@@ -72,7 +74,7 @@ def test_efr_kuhn_poker(self, deviations_name):
       ["blind cf", "informed cf", "bps", "cfps", "csps", "tips", "bhv"])
   def test_efr_kuhn_poker_3p(self, deviations_name):
     efr_solver = efr.EFRSolver(
-        game=self._KUHN_3P_GAME,
+        game=self.kuhn_3p_game,
         deviations_name=deviations_name
     )
     strategies = []
@@ -84,26 +86,25 @@ def test_efr_kuhn_poker_3p(self, deviations_name):
       strategies.append(policy.python_policy_to_pyspiel_policy(
           efr_solver.current_policy()))
       corr_dev = pyspiel.uniform_correlation_device(strategies)
-      cce_dist_info = pyspiel.cce_dist(self._KUHN_3P_GAME, corr_dev)
+      cce_dist_info = pyspiel.cce_dist(self.kuhn_3p_game, corr_dev)
       corr_dist_values.append(cce_dist_info.dist_value)
     self.assertLess(corr_dist_values[-1], corr_dist_values[0])
-
 
   @parameterized.parameters(
       ["blind cf", "bps", "tips"])
   def test_efr_cce_dist_sheriff(self, deviations_name):
     efr_solver = efr.EFRSolver(
-        game=self._SHERIFF_GAME,
+        game=self.sheriff_game,
         deviations_name=deviations_name
-    )   
+    )
     strategies = []
     corr_dist_values = []
     for _ in range(5):
       efr_solver.evaluate_and_update_policy()
       strategies.append(policy.python_policy_to_pyspiel_policy(
-          efr_solver.current_policy()))      
+          efr_solver.current_policy()))
       corr_dev = pyspiel.uniform_correlation_device(strategies)
-      cce_dist_info = pyspiel.cce_dist(self._SHERIFF_GAME, corr_dev)
+      cce_dist_info = pyspiel.cce_dist(self.sheriff_game, corr_dev)
       corr_dist_values.append(cce_dist_info.dist_value)
     self.assertLess(corr_dist_values[-1], corr_dist_values[0])
 if __name__ == "__main__":