def setUpClass(cls):
cls.test_key = (axl.TitForTat(), axl.Defector())
cls.test_value = [(C, D), (D, D), (D, D)]
save_path = pathlib.Path("test_outputs/test_cache_save.txt")
cls.test_save_file = axl_filename(save_path)
load_path = pathlib.Path("test_outputs/test_cache_load.txt")
cls.test_load_file = axl_filename(load_path)
test_data_to_pickle = {("Tit For Tat", "Defector"): [(C, D), (D, D), (D, D)]}
cls.test_pickle = pickle.dumps(test_data_to_pickle)
with open(cls.test_load_file, "wb") as f:
f.write(cls.test_pickle)
def test_analyse_cooperation_ratio(self):
tf = TransitiveFingerprint(axl.TitForTat)
path = pathlib.Path("test_outputs/test_fingerprint.csv")
filename = axl_filename(path)
with open(filename, "w") as f:
f.write(
"""Interaction index,Player index,Opponent index,Repetition,Player name,Opponent name,Actions
0,0,1,0,Player0,Player1,CCC
0,1,0,0,Player1,Player0,DDD
1,0,1,1,Player0,Player1,CCC
1,1,0,1,Player1,Player0,DDD
2,0,2,0,Player0,Player2,CCD
2,2,0,0,Player2,Player0,DDD
3,0,2,1,Player0,Player2,CCC
3,2,0,1,Player2,Player0,DDD
4,0,3,0,Player0,Player3,CCD
4,3,0,0,Player3,Player0,DDD
5,0,3,1,Player0,Player3,DCC
5,3,0,1,Player3,Player0,DDD
6,0,4,2,Player0,Player4,DDD
6,4,0,2,Player4,Player0,DDD
7,0,4,3,Player0,Player4,DDD
7,4,0,3,Player4,Player0,DDD""")
data = tf.analyse_cooperation_ratio(filename)
expected_data = np.array([[1, 1, 1], [1, 1, 1 / 2], [1 / 2, 1, 1 / 2],
[0, 0, 0]])
self.assertTrue(np.array_equal(data, expected_data))
def setUpClass(cls):
cls.game = axl.Game()
cls.players = [s() for s in test_strategies]
cls.test_name = "test"
cls.test_repetitions = test_repetitions
cls.test_turns = test_turns
cls.expected_payoff = [
[600, 600, 0, 600, 600],
[600, 600, 199, 600, 600],
[1000, 204, 200, 204, 204],
[600, 600, 199, 600, 600],
[600, 600, 199, 600, 600],
]
cls.expected_cooperation = [
[200, 200, 200, 200, 200],
[200, 200, 1, 200, 200],
[0, 0, 0, 0, 0],
[200, 200, 1, 200, 200],
[200, 200, 1, 200, 200],
]
path = pathlib.Path("test_outputs/test_tournament.csv")
cls.filename = axl_filename(path)
def test_repeat_tournament_stochastic(self):
"""
A test to check that tournament gives same results when setting seed.
"""
files = []
for _ in range(2):
axl.seed(0)
stochastic_players = [
s() for s in axl.short_run_time_strategies
if axl.Classifiers["stochastic"](s())
]
tournament = axl.Tournament(
name="test",
players=stochastic_players,
game=self.game,
turns=2,
repetitions=2,
)
path = pathlib.Path(
"test_outputs/stochastic_tournament_{}.csv".format(_))
files.append(axl_filename(path))
tournament.play(progress_bar=False,
filename=files[-1],
build_results=False)
self.assertTrue(filecmp.cmp(files[0], files[1]))
def test_load_error_for_inccorect_format(self):
path = pathlib.Path("test_outputs/test.cache")
filename = axl_filename(path)
with open(filename, "wb") as io:
pickle.dump(range(5), io)
with self.assertRaises(ValueError):
self.cache.load(filename)
def test_axl_filename(self):
path = pathlib.Path("axelrod/strategies/titfortat.py")
actual_fn = axl_filename(path)
# First go from "unit" up to "tests", then up to "axelrod"
dirname = os.path.dirname(__file__)
expected_fn = os.path.join(dirname, "../../strategies/titfortat.py")
self.assertTrue(os.path.samefile(actual_fn, expected_fn))
def test_big_tournaments(self, tournament):
"""A test to check that tournament runs with a sample of non-cheating
strategies."""
path = pathlib.Path("test_outputs/test_tournament.csv")
filename = axl_filename(path)
self.assertIsNone(
tournament.play(progress_bar=False,
filename=filename,
build_results=False))
def test_fingerprint_with_filename(self):
path = pathlib.Path("test_outputs/test_fingerprint.csv")
filename = axl_filename(path)
strategy = axl.TitForTat()
tf = TransitiveFingerprint(strategy)
tf.fingerprint(turns=1, repetitions=1, progress_bar=False, filename=filename)
with open(filename, "r") as out:
data = out.read()
self.assertEqual(len(data.split("\n")), 102)
def test_serial_fingerprint(self):
strategy = axl.TitForTat()
tf = TransitiveFingerprint(strategy)
path = pathlib.Path("test_outputs/test_fingerprint.csv")
tf.fingerprint(
repetitions=1,
progress_bar=False,
filename=axl_filename(path),
)
self.assertEqual(tf.data.shape, (50, 50))
def test_fingerprint_with_filename(self):
path = pathlib.Path("test_outputs/test_fingerprint.csv")
filename = axl_filename(path)
af = AshlockFingerprint(axl.TitForTat)
af.fingerprint(
turns=1, repetitions=1, step=0.5, progress_bar=False, filename=filename
)
with open(filename, "r") as out:
data = out.read()
self.assertEqual(len(data.split("\n")), 20)
def test_write_to_csv_with_results(self):
tournament = axl.Tournament(
name=self.test_name,
players=self.players,
game=self.game,
turns=2,
repetitions=2,
)
tournament.play(filename=self.filename, progress_bar=False)
df = pd.read_csv(self.filename)
path = pathlib.Path("test_outputs/expected_test_tournament.csv")
expected_df = pd.read_csv(axl_filename(path))
self.assertTrue(df.equals(expected_df))
def test_temp_file_creation(self):
RecordedMksTemp.reset_record()
af = AshlockFingerprint(axl.TitForTat)
path = pathlib.Path("test_outputs/test_fingerprint.csv")
filename = axl_filename(path)
self.assertEqual(RecordedMksTemp.record, [])
# Temp file is created and destroyed.
af.fingerprint(
turns=1, repetitions=1, step=0.5, progress_bar=False, filename=None
)
self.assertEqual(len(RecordedMksTemp.record), 1)
filename = RecordedMksTemp.record[0][1]
self.assertIsInstance(filename, str)
self.assertNotEqual(filename, "")
self.assertFalse(os.path.isfile(filename))
def setUpClass(cls):
path = pathlib.Path("test_outputs/test_results.csv")
cls.filename = axl_filename(path)
cls.players = [axl.Alternator(), axl.TitForTat(), axl.Defector()]
cls.repetitions = 3
cls.turns = 5
cls.test_result_set = axl.ResultSet(cls.filename,
cls.players,
cls.repetitions,
progress_bar=False)
cls.test_result_set = axl.ResultSet(cls.filename,
cls.players,
cls.repetitions,
progress_bar=False)
cls.expected_boxplot_dataset = [
[(17 / 5 + 9 / 5) / 2 for _ in range(3)],
[(13 / 5 + 4 / 5) / 2 for _ in range(3)],
[3 / 2 for _ in range(3)],
]
cls.expected_boxplot_xticks_locations = [1, 2, 3, 4]
cls.expected_boxplot_xticks_labels = [
"Defector", "Tit For Tat", "Alternator"
]
cls.expected_lengthplot_dataset = [
[cls.turns for _ in range(3)],
[cls.turns for _ in range(3)],
[cls.turns for _ in range(3)],
]
cls.expected_payoff_dataset = [
[
0,
mean([9 / 5 for _ in range(3)]),
mean([17 / 5 for _ in range(3)])
],
[
mean([4 / 5 for _ in range(3)]), 0,
mean([13 / 5 for _ in range(3)])
],
[
mean([2 / 5 for _ in range(3)]),
mean([13 / 5 for _ in range(3)]), 0
],
]
cls.expected_winplot_dataset = (
[[2, 2, 2], [0, 0, 0], [0, 0, 0]],
["Defector", "Tit For Tat", "Alternator"],
)
cls.expected_sdvplot_dataset = (
[
[3, 3, 3, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, -1, -1, -1],
[0, 0, 0, 0, 0, 0, -3, -3, -3],
],
["Defector", "Tit For Tat", "Alternator"],
)
