Merge pull request #643 from Smit-create/pytest_warn

MAINT: Fix pytest warnings

quantecon/game_theory/game_generators/tests/test_bimatrix_generators.py

@@ -14,7 +14,7 @@
 
 
 class TestBlottoGame:
-    def setup(self):
+    def setup_method(self):
         self.h, self.t = 4, 3
         rho = 0.5
         self.g = blotto_game(self.h, self.t, rho)
@@ -38,7 +38,7 @@ def test_seed(self):
 
 
 class TestRankingGame:
-    def setup(self):
+    def setup_method(self):
         self.n = 100
         self.g = ranking_game(self.n)
 
@@ -85,7 +85,7 @@ def test_sgc_game():
 
 
 class TestTournamentGame:
-    def setup(self):
+    def setup_method(self):
         self.n = 5
         self.k = 3
         self.m = comb(self.n, self.k, exact=True)
@@ -116,7 +116,7 @@ def test_raises_value_error_too_large_inputs(self):
 
 
 class TestUnitVectorGame:
-    def setup(self):
+    def setup_method(self):
         self.n = 100
         self.g = unit_vector_game(self.n)
 

quantecon/game_theory/tests/test_lemke_howson.py

@@ -7,7 +7,7 @@
 
 
 class TestLemkeHowson():
-    def setup(self):
+    def setup_method(self):
         self.game_dicts = []
 
         # From von Stengel 2007 in Algorithmic Game Theory
@@ -30,7 +30,7 @@ def test_lemke_howson(self):
 
 
 class TestLemkeHowsonDegenerate():
-    def setup(self):
+    def setup_method(self):
         self.game_dicts = []
 
         # From von Stengel 2007 in Algorithmic Game Theory

quantecon/game_theory/tests/test_mclennan_tourky.py

@@ -10,7 +10,7 @@
 
 
 class TestMclennanTourky():
-    def setup(self):
+    def setup_method(self):
         def anti_coordination(N, v):
             payoff_array = np.empty((2,)*N)
             payoff_array[0, :] = 1
@@ -56,7 +56,7 @@ def test_pure_nash(self):
 
 
 class TestMclennanTourkyInvalidInputs():
-    def setup(self):
+    def setup_method(self):
             self.bimatrix = [[(3, 3), (3, 2)],
                              [(2, 2), (5, 6)],
                              [(0, 3), (6, 1)]]
@@ -73,7 +73,7 @@ def test_mclennan_tourky_invalid_init_length(self):
 
 
 class TestEpsilonNash():
-    def setup(self):
+    def setup_method(self):
         def anti_coordination(N, v):
             payoff_array = np.empty((2,)*N)
             payoff_array[0, :] = 1

quantecon/game_theory/tests/test_normal_form_game.py

@@ -17,7 +17,7 @@
 class TestPlayer_1opponent:
     """Test the methods of Player with one opponent player"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a Player instance"""
         coordination_game_matrix = [[4, 0], [3, 2]]
         self.player = Player(coordination_game_matrix)
@@ -89,7 +89,7 @@ def test_dominated_actions(self):
 class TestPlayer_2opponents:
     """Test the methods of Player with two opponent players"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a Player instance"""
         payoffs_2opponents = [[[3, 6],
                                [4, 2]],
@@ -176,7 +176,7 @@ def test_player_corner_cases():
 class TestNormalFormGame_Sym2p:
     """Test the methods of NormalFormGame with symmetric two players"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a NormalFormGame instance"""
         coordination_game_matrix = [[4, 0], [3, 2]]
         self.g = NormalFormGame(coordination_game_matrix)
@@ -194,7 +194,7 @@ def test_is_nash_mixed(self):
 class TestNormalFormGame_Asym2p:
     """Test the methods of NormalFormGame with asymmetric two players"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a NormalFormGame instance"""
         self.BoS_bimatrix = np.array([[(3, 2), (1, 1)],
                                       [(0, 0), (2, 3)]])
@@ -234,7 +234,7 @@ def test_payoff_arrays(self):
 class TestNormalFormGame_3p:
     """Test the methods of NormalFormGame with three players"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a NormalFormGame instance"""
         payoffs_2opponents = [[[3, 6],
                                [4, 2]],
@@ -341,7 +341,7 @@ def test_normalformgame_payoff_profile_array_c_contiguous():
 class TestPlayer_0opponents:
     """Test for trivial Player with no opponent player"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a Player instance"""
         self.payoffs = [0, 1, -1]
         self.player = Player(self.payoffs)
@@ -386,7 +386,7 @@ def test_dominated_actions(self):
 class TestNormalFormGame_1p:
     """Test for trivial NormalFormGame with a single player"""
 
-    def setup(self):
+    def setup_method(self):
         """Setup a NormalFormGame instance"""
         data = [[0], [1], [1]]
         self.g = NormalFormGame(data)
@@ -444,7 +444,7 @@ def test_normalformgame_setitem_1p():
 # Trivial cases with one action #
 
 class TestPlayer_1action:
-    def setup(self):
+    def setup_method(self):
         """Setup a Player instance"""
         self.payoffs = [[0, 1]]
         self.player = Player(self.payoffs)

quantecon/game_theory/tests/test_pure_nash.py

@@ -10,7 +10,7 @@
 
 
 class TestPureNashBruteForce():
-    def setup(self):
+    def setup_method(self):
         self.game_dicts = []
 
         # Matching Pennies game with no pure nash equilibrium

quantecon/game_theory/tests/test_repeated_game.py

@@ -8,7 +8,7 @@
 
 
 class TestAS():
-    def setup(self):
+    def setup_method(self):
         self.game_dicts = []
 
         # Example from Abreu and Sannikov (2014)

quantecon/game_theory/tests/test_support_enumeration.py

@@ -29,7 +29,7 @@ def random_skew_sym(n, m=None, random_state=None):
 
 
 class TestSupportEnumeration():
-    def setup(self):
+    def setup_method(self):
         self.game_dicts = []
 
         # From von Stengel 2007 in Algorithmic Game Theory

quantecon/game_theory/tests/test_vertex_enumeration.py

@@ -10,7 +10,7 @@
 
 
 class TestVertexEnumeration:
-    def setup(self):
+    def setup_method(self):
         self.game_dicts = []
 
         # From von Stengel 2007 in Algorithmic Game Theory
@@ -71,7 +71,7 @@ def test_vertex_enumeration_invalid_g():
 
 
 class TestBestResponsePolytope:
-    def setup(self):
+    def setup_method(self):
         # From von Stengel 2007 in Algorithmic Game Theory
         bimatrix = [[(3, 3), (3, 2)],
                     [(2, 2), (5, 6)],

quantecon/markov/tests/test_approximation.py

@@ -11,7 +11,7 @@
 
 class TestTauchen:
 
-    def setup(self):
+    def setup_method(self):
         self.rho, self.sigma_u = np.random.rand(2)
         self.n = np.random.randint(3, 25)
         self.m = np.random.randint(5)
@@ -54,7 +54,7 @@ def test_states_sum_0(self):
 
 class TestRouwenhorst:
 
-    def setup(self):
+    def setup_method(self):
         self.rho, self.sigma = np.random.uniform(0, 1, size=2)
         self.n = np.random.randint(3, 26)
         self.ybar = np.random.randint(0, 11)

quantecon/markov/tests/test_core.py

@@ -191,7 +191,7 @@ class Test_markovchain_stationary_distributions_KMRMarkovMatrix2():
     p = 1/3
     TOL = 1e-2
 
-    def setup(self):
+    def setup_method(self):
         """ Setup a KMRMarkovMatrix and Compute Stationary Values """
         self.P = KMR_Markov_matrix_sequential(self.N, self.p, self.epsilon)
         self.mc = MarkovChain(self.P)
@@ -409,7 +409,7 @@ def test_mc_sample_path_lln():
 
 
 class TestMCStateValues:
-    def setup(self):
+    def setup_method(self):
         state_values = [[0, 1], [2, 3], [4, 5]]  # Pass python list
         self.state_values = np.array(state_values)
 

quantecon/markov/tests/test_ddp.py

@@ -11,7 +11,7 @@
 
 
 class TestDiscreteDP:
-    def setup(self):
+    def setup_method(self):
         # From Puterman 2005, Section 3.1
         beta = 0.95
 
@@ -194,7 +194,7 @@ def test_ddp_sorting():
 
 
 class TestFiniteHorizon:
-    def setup(self):
+    def setup_method(self):
         # From Puterman 2005, Section 3.2, Section 4.6.1
         # "single-product stochastic inventory control"
         s_indices = [0, 0, 0, 0, 1, 1, 1, 2, 2, 3]

quantecon/markov/tests/test_random.py

@@ -89,7 +89,7 @@ def test_random_stochastic_matrix_k_1():
 
 
 class TestRandomDiscreteDP:
-    def setup(self):
+    def setup_method(self):
         self.num_states, self.num_actions = 5, 4
         self.num_sa = self.num_states * self.num_actions
         self.k = 3

quantecon/random/tests/test_utilities.py

@@ -17,7 +17,7 @@
 # probvec #
 
 class TestProbvec:
-    def setup(self):
+    def setup_method(self):
         self.m, self.k = 2, 3  # m vectors of dimension k
         seed = 1234
 
@@ -68,7 +68,7 @@ def test_sample_without_replacement_value_error():
 # draw #
 
 class TestDraw:
-    def setup(self):
+    def setup_method(self):
         self.pmf = np.array([0.4, 0.1, 0.5])
         self.cdf = np.cumsum(self.pmf)
         self.n = len(self.pmf)

quantecon/tests/test_arma.py

@@ -9,7 +9,7 @@
 
 
 class TestARMA():
-    def setup(self):
+    def setup_method(self):
         # Initial Values
         phi = np.array([.95, -.4, -.4])
         theta = np.zeros(3)

quantecon/tests/test_compute_fp.py

@@ -17,7 +17,7 @@
 class TestFPLogisticEquation():
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         cls.mu_1 = 0.2  # 0 is unique fixed point forall x_0 \in [0, 1]
 
         # (4mu - 1)/(4mu) is a fixed point forall x_0 \in [0, 1]
@@ -87,7 +87,7 @@ def test_imitation_game_method(self):
 
 
 class TestComputeFPContraction():
-    def setup(self):
+    def setup_method(self):
         self.coeff = 0.5
         self.methods = ['iteration', 'imitation_game']
 

quantecon/tests/test_discrete_rv.py

@@ -12,7 +12,7 @@
 class TestDiscreteRV:
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         x = np.random.rand(10)
         x /= x.sum()
         # make sure it sums to 1

quantecon/tests/test_distributions.py

@@ -9,7 +9,7 @@
 
 
 class TestBetaBinomial:
-    def setup(self):
+    def setup_method(self):
         self.n = 100
         self.a = 5
         self.b = 5

quantecon/tests/test_dle.py

@@ -11,7 +11,7 @@
 
 class TestDLE:
 
-    def setup(self):
+    def setup_method(self):
         """
         Given LQ control is tested we will test the transformation
         to alter the problem into a form suitable to solve using LQ

quantecon/tests/test_ecdf.py

@@ -10,7 +10,7 @@
 class TestECDF:
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         cls.obs = np.random.rand(40)  # observations defining dist
         cls.ecdf = ECDF(cls.obs)
 

quantecon/tests/test_estspec.py

@@ -19,7 +19,7 @@
 class PeriodogramBase:
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         if cls is PeriodogramBase:
             raise pytest.skip("Skip PeriodogramBase tests" +
                                     " it's a base class")
@@ -47,10 +47,10 @@ def test_func_I(self):
 class TestPeriodogram(PeriodogramBase):
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         if cls is PeriodogramBase:
             raise pytest.skip("Skip BaseTest tests, it's a base class")
-        super(TestPeriodogram, cls).setup()
+        super(TestPeriodogram, cls).setup_method()
         cls.window_length = 7
         cls.w_20, cls.Iw_20 = periodogram(x_20)
         cls.w_21, cls.Iw_21 = periodogram(x_21)
@@ -60,10 +60,10 @@ def setup(cls):
 class TestArPeriodogram(PeriodogramBase):
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         if cls is PeriodogramBase:
             raise pytest.skip("Skip BaseTest tests, it's a base class")
-        super(TestArPeriodogram, cls).setup()
+        super(TestArPeriodogram, cls).setup_method()
         cls.window_length = 7
         cls.w_20, cls.Iw_20 = ar_periodogram(x_20)
         cls.w_21, cls.Iw_21 = ar_periodogram(x_21)
@@ -81,7 +81,7 @@ def test_func_Iw_shape_even_x(self):
 class TestSmooth:
 
     @classmethod
-    def setup(cls):
+    def setup_method(cls):
         cls.x_20 = np.random.rand(20)
         cls.x_21 = np.random.rand(21)
         cls.window_length = 7