Remove/adjust more tests.

Author: drvinceknight

axelrod/_strategy_utils.py

@@ -77,8 +77,7 @@ def _limited_simulate_play(player_1, player_2, h1):
     for both players. Note that player_1's move is an argument.
 
     If you need a more complete simulation, see `simulate_play` in
-    player.py. This function is specifically designed for the needs
-    of MindReader.
+    player.py. 
 
     Parameters
     ----------
@@ -94,24 +93,6 @@ def _limited_simulate_play(player_1, player_2, h1):
     player_2.update_history(h2, h1)
 
 
-def simulate_match(player_1, player_2, strategy, rounds=10):
-    """Simulates a number of rounds with a constant strategy.
-
-    Parameters
-    ----------
-    player_1: Player
-        The player that will have a constant strategy.
-    player_2: Player
-        The player we want to simulate.
-    strategy: Action
-        The constant strategy to use for first player.
-    rounds: int
-        The number of rounds to play.
-    """
-    for match in range(rounds):
-        _limited_simulate_play(player_1, player_2, strategy)
-
-
 def _calculate_scores(p1, p2, game):
     """Calculates the scores for two players based their history.
 

axelrod/tests/strategies/test_darwin.py

@@ -61,19 +61,6 @@ def test_strategy(self):
         expected_actions = [(C, C)] + [(D, C)] * 3 + [(C, C)] * 2
         self.versus_test(axl.Cooperator(), expected_actions)
 
-    def test_against_geller_and_mindreader(self):
-        self.versus_test(
-            axl.GellerCooperator(),
-            expected_actions=[(C, C)] * 2,
-            attrs={"genome": [C, C]},
-        )
-
-        self.versus_test(
-            axl.MindReader(),
-            expected_actions=[(C, D)] * 2,
-            attrs={"genome": [D, C]},
-        )
-
     def test_reset_history_and_attributes(self):
         # Overwrite this method because Darwin does not reset
         self.versus_test(

axelrod/tests/strategies/test_player.py

@@ -467,13 +467,7 @@ def test_reset_clone(self):
     def test_clone_reproducible_play(self, seed, turns, noise):
         # Test that the cloned player produces identical play
         player = self.player()
-        if player.name in [
-            "Darwin",
-            "Human",
-            "Mind Bender",
-            "Mind Controller",
-            "Mind Warper",
-        ]:
+        if player.name in ["Darwin", "Human"]:
             # Known exceptions
             return
 

axelrod/tests/unit/test_classification.py

@@ -162,9 +162,6 @@ def test_obey_axelrod(self):
         """A test that verifies if the obey_axl function works correctly"""
         known_cheaters = [
             axl.Darwin,
-            axl.Geller,
-            axl.GellerCooperator,
-            axl.GellerDefector,
         ]
 
         known_basic = [
@@ -208,9 +205,6 @@ def test_is_basic(self):
         """A test that verifies if the is_basic function works correctly"""
         known_cheaters = [
             axl.Darwin,
-            axl.Geller,
-            axl.GellerCooperator,
-            axl.GellerDefector,
         ]
 
         known_basic = [

axelrod/tests/unit/test_moran.py

@@ -302,9 +302,8 @@ def test_four_players(self):
 
     @given(strategies=strategy_lists(min_size=2, max_size=4))
     @settings(max_examples=5, deadline=None)
-    # Two specific examples relating to cloning of strategies
-    @example(strategies=[axl.BackStabber, axl.MindReader])
-    @example(strategies=[axl.ThueMorse, axl.MindReader])
+    # A specific example relating to cloning of strategies
+    @example(strategies=[axl.ThueMorse, axl.BackStabber])
     def test_property_players(self, strategies):
         """Hypothesis test that randomly checks players"""
         players = [s() for s in strategies]

axelrod/tests/unit/test_strategy_utils.py

@@ -7,7 +7,6 @@
     inspect_strategy,
     look_ahead,
     recursive_thue_morse,
-    simulate_match,
     thue_morse_generator,
 )
 from hypothesis import given, settings
@@ -81,54 +80,6 @@ def test_strategies_without_countermeasures_return_their_strategy(self):
         self.assertEqual(inspect_strategy(inspector=inspector, opponent=tft), D)
         self.assertEqual(tft.strategy(inspector), D)
 
-    def test_strategies_with_countermeasures_return_their_countermeasures(self):
-        d_geller = axl.GellerDefector()
-        inspector = axl.Cooperator()
-        match = axl.Match((d_geller, inspector), turns=1)
-        match.play()
-        self.assertEqual(
-            inspect_strategy(inspector=inspector, opponent=d_geller), D
-        )
-        self.assertEqual(d_geller.strategy(inspector), C)
-
-
-class TestSimulateMatch(unittest.TestCase):
-    def test_tft_reacts_to_cooperation(self):
-        tft = axl.TitForTat()
-        inspector = axl.Alternator()
-
-        simulate_match(inspector, tft, C, 5)
-        self.assertEqual(inspector.history, [C, C, C, C, C])
-        self.assertEqual(tft.history, [C, C, C, C, C])
-
-    def test_tft_reacts_to_defection(self):
-        tft = axl.TitForTat()
-        inspector = axl.Alternator()
-
-        simulate_match(inspector, tft, D, 5)
-        self.assertEqual(inspector.history, [D, D, D, D, D])
-        self.assertEqual(tft.history, [C, D, D, D, D])
-
-
-class TestLookAhead(unittest.TestCase):
-    def setUp(self):
-        self.inspector = axl.Player()
-        self.game = axl.Game()
-
-    def test_cooperator(self):
-        tft = axl.Cooperator()
-        # It always makes sense to defect here.
-        self.assertEqual(look_ahead(self.inspector, tft, self.game, 1), D)
-        self.assertEqual(look_ahead(self.inspector, tft, self.game, 2), D)
-        self.assertEqual(look_ahead(self.inspector, tft, self.game, 5), D)
-
-    def test_tit_for_tat(self):
-        tft = axl.TitForTat()
-        # Cooperation should be chosen if we look ahead further than one move.
-        self.assertEqual(look_ahead(self.inspector, tft, self.game, 1), D)
-        self.assertEqual(look_ahead(self.inspector, tft, self.game, 2), C)
-        self.assertEqual(look_ahead(self.inspector, tft, self.game, 5), C)
-
 
 class TestRecursiveThueMorse(unittest.TestCase):
     def test_initial_values(self):