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 test_serial_fingerprint(self):
strategy = axl.TitForTat()
tf = TransitiveFingerprint(strategy)
tf.fingerprint(repetitions=1,
progress_bar=False,
filename="test_outputs/tran_fin.csv")
self.assertEqual(tf.data.shape, (50, 50))
def test_serial_fingerprint(self):
strategy = axl.TitForTat()
tf = TransitiveFingerprint(strategy)
tf.fingerprint(
repetitions=1, progress_bar=False, filename="test_outputs/tran_fin.csv"
)
self.assertEqual(tf.data.shape, (50, 50))
def test_analyse_cooperation_ratio(self):
tf = TransitiveFingerprint(axl.TitForTat)
filename = "test_outputs/test_fingerprint.csv"
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 test_fingerprint_with_filename(self):
filename = "test_outputs/test_fingerprint.csv"
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_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_fingerprint_with_filename(self):
filename = "test_outputs/test_fingerprint.csv"
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")), 50 + 1)
def test_analyse_cooperation_ratio(self):
tf = TransitiveFingerprint(axl.TitForTat)
filename = "test_outputs/test_fingerprint.csv"
with open(filename, "w") as f:
f.write("""0,1,Player0,Player1,CCC,DDD
0,1,Player0,Player1,CCC,DDD
0,2,Player0,Player2,CCD,DDD
0,2,Player0,Player2,CCC,DDD
0,3,Player0,Player3,CCD,DDD
0,3,Player0,Player3,DCC,DDD
0,4,Player0,Player3,DDD,DDD
0,4,Player0,Player3,DDD,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 test_plot(self):
"""
Test that plot is created with various arguments.
"""
tf = TransitiveFingerprint(axl.TitForTat)
tf.fingerprint(turns=10, repetitions=2, progress_bar=False)
p = tf.plot()
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(cmap="jet")
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(interpolation="bicubic")
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(title="Title")
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(colorbar=False)
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(labels=False)
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(display_names=True)
self.assertIsInstance(p, matplotlib.pyplot.Figure)
def test_plot_with_axis(self):
fig, axarr = matplotlib.pyplot.subplots(2, 2)
tf = TransitiveFingerprint(axl.TitForTat)
tf.fingerprint(turns=10, repetitions=2, progress_bar=False)
p = tf.plot(ax=axarr[0, 0])
self.assertIsInstance(p, matplotlib.pyplot.Figure)
def test_plot(self):
"""
Test that plot is created with various arguments.
"""
tf = TransitiveFingerprint(axl.TitForTat)
tf.fingerprint(turns=10, repetitions=2, progress_bar=False)
p = tf.plot()
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(cmap="jet")
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(interpolation="bicubic")
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(title="Title")
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(colorbar=False)
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(labels=False)
self.assertIsInstance(p, matplotlib.pyplot.Figure)
p = tf.plot(display_names=True)
self.assertIsInstance(p, matplotlib.pyplot.Figure)
def test_parallel_fingerprint(self):
strategy = axl.TitForTat()
tf = TransitiveFingerprint(strategy)
tf.fingerprint(repetitions=1, progress_bar=False, processes=2)
self.assertEqual(tf.data.shape, (50, 50))
def test_plot_with_axis(self):
fig, axarr = matplotlib.pyplot.subplots(2, 2)
tf = TransitiveFingerprint(axl.TitForTat)
tf.fingerprint(turns=10, repetitions=2, progress_bar=False)
p = tf.plot(ax=axarr[0, 0])
self.assertIsInstance(p, matplotlib.pyplot.Figure)
def test_parallel_fingerprint(self):
strategy = axl.TitForTat()
tf = TransitiveFingerprint(strategy)
tf.fingerprint(repetitions=1, progress_bar=False, processes=2)
self.assertEqual(tf.data.shape, (50, 50))