reformat using pylint

sandguine · Mar 27, 2023 · 2397406 · 2397406
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diff --git a/open_spiel/python/algorithms/mip_nash.py b/open_spiel/python/algorithms/mip_nash.py
@@ -11,11 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-'''MIP-Nash.
+"""MIP-Nash.
 
-Based on the first formulation of https://dl.acm.org/doi/10.5555/1619410.1619413.
-Compute optimal Nash equilibrium of two-player general-sum games by solving a mixed-integer programming problem.
-'''
+Based on the first formulation of
+ https://dl.acm.org/doi/10.5555/1619410.1619413.
+Compute optimal Nash equilibrium of two-player general-sum games 
+by solving a mixed-integer programming problem.
+"""
 
 
 import numpy as np
@@ -46,8 +48,10 @@ def mip_nash(game, objective, solver='GLPK_MI'):
       for all n, b0[n] \in {0, 1},
       for all m, b1[m] \in {0, 1},
       U0, U1 are the maximum payoff differences of player 0 and 1.
-    This formulation is a basic one that may only work well for simple objective function or low-dimensional inputs.
-    To handle more complex cases, It is possible to extend this by using advanced internal solvers or piecewise linear approximation of the objective.
+    This formulation is a basic one that may only work well 
+    for simple objective function or low-dimensional inputs.
+    To handle more complex cases, It is possible to extend this by 
+    using advanced internal solvers or piecewise linear approximation of the objective.
   Args:
     game: a pyspiel matrix game object
     objective: a string representing the objective (e.g., MAX_SOCIAL_WELFARE)
@@ -94,24 +98,28 @@ def mip_nash(game, objective, solver='GLPK_MI'):
   return _simplex_projection(x.value.reshape(-1)), _simplex_projection(y.value.reshape(-1))
 
 
-
 def max_social_welfare_two_player(variables):
+  """Max social welfare objective."""
   return cp.Maximize(variables['u0'] + variables['u1'])
 
 
 def min_social_welfare_two_player(variables):
+  """Min social welfare objective."""
   return cp.Minimize(variables['u0'] + variables['u1'])
 
 
 def max_support_two_player(variables):
+  """Max support objective."""
   return cp.Minimize(cp.sum(variables['b0']) + cp.sum(variables['b1']))
 
 
 def min_support_two_player(variables):
+  """Min support objective."""
   return cp.Maximize(cp.sum(variables['b0']) + cp.sum(variables['b1']))
 
 
 def max_gini_two_player(variables):
+  """Max gini objective."""
   return cp.Minimize(cp.sum(cp.square(variables['x'])) + cp.sum(cp.square(variables['y'])))
 
 
@@ -121,4 +129,4 @@ def max_gini_two_player(variables):
     'MAX_SUPPORT': max_support_two_player,
     'MIN_SUPPORT': min_support_two_player,
     'MAX_GINI': max_gini_two_player,
-}
+}
diff --git a/open_spiel/python/algorithms/mip_nash_test.py b/open_spiel/python/algorithms/mip_nash_test.py
@@ -14,39 +14,38 @@
 """Tests for open_spiel.python.algorithms.mip_nash."""
 
 from absl.testing import absltest
-from absl.testing import parameterized
 import numpy as np
 
 from open_spiel.python.algorithms.mip_nash import mip_nash
 import pyspiel
 
 
-# prisoners' dilemma
-pd_game = pyspiel.create_matrix_game(
-    [[-2.0, -10.0], [0.0, -5.0]],
-    [[-2.0, 0.0], [-10.0, -5.0]])
+class MIPNash(absltest.TestCase):
+  def test_simple_games(self):
+    # prisoners' dilemma
+    pd_game = pyspiel.create_matrix_game(
+        [[-2.0, -10.0], [0.0, -5.0]],
+        [[-2.0, 0.0], [-10.0, -5.0]])
 
-pd_eq = (np.array([0, 1]), np.array([0, 1]))
+    pd_eq = (np.array([0, 1]), np.array([0, 1]))
 
-# stag hunt
-sh_game = pyspiel.create_matrix_game(
-    [[10.0, 1.0], [8.0, 5.0]],
-    [[10.0, 8.0], [1.0, 5.0]])
+    computed_eq = mip_nash(pd_game, objective="MAX_SOCIAL_WELFARE")
+    with self.subTest("pd"):
+      np.testing.assert_array_almost_equal(computed_eq[0], pd_eq[0])
+      np.testing.assert_array_almost_equal(computed_eq[1], pd_eq[1])
 
+    # stag hunt
+    sh_game = pyspiel.create_matrix_game(
+        [[10.0, 1.0], [8.0, 5.0]],
+        [[10.0, 8.0], [1.0, 5.0]])
 
-sh_eq = (np.array([1, 0]), np.array([1, 0]))
+    sh_eq = (np.array([1, 0]), np.array([1, 0]))
 
-class MIPNash(parameterized.TestCase):
-  @parameterized.named_parameters(
-      ("pd", pd_game, pd_eq),
-      ("sh", sh_game, sh_eq),
-  )
-  def test_simple_games(self, game, eq):
-    computed_eq = mip_nash(game, objective='MAX_SOCIAL_WELFARE')
-    with self.subTest("probability"):
-      np.testing.assert_array_almost_equal(computed_eq[0], eq[0])
-      np.testing.assert_array_almost_equal(computed_eq[1], eq[1])
+    computed_eq = mip_nash(sh_game, objective="MAX_SOCIAL_WELFARE")
+    with self.subTest("sh"):
+      np.testing.assert_array_almost_equal(computed_eq[0], sh_eq[0])
+      np.testing.assert_array_almost_equal(computed_eq[1], sh_eq[1])
 
 
 if __name__ == "__main__":
-  absltest.main()
+  absltest.main()