class TestQlearning(TestCase):
def setUp(self):
self.real_player = RealPlayer(path=TEST_DATA_PATH, model=None)
def test_q_table_values_q_learning(self):
# given
self.real_player.model = Qlearning()
parameters = np.array([1, 0.1])
expected = [-0.1, 0, 0.1, 0, 0.1, 0]
# when
self.real_player.log_likelihood_function(params=parameters)
actual = self.real_player.model.Q_table
# then
self.assertEqual(actual, expected)
def test_q_table_values_rescorla_wagner(self):
# given
self.real_player.model = RescorlaWagner()
parameters = np.array([1, 1, 2])
expected = [-2, 0, 1, 0, 1, 0]
# when
self.real_player.log_likelihood_function(params=parameters)
actual = self.real_player.model.Q_table
# then
self.assertEqual(actual, expected)
def make_plot(dir):
model = Qlearning()
rp = RealPlayer(dir, model=model)
game = GameSession()
player = VirtualPlayer(10,
0.1,
game_skeleton=game.game_skeleton,
model=model)
game.play(player=player)
game._create_result()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
T = np.arange(-1, 1, 0.1)
print(T.shape)
alpha = np.arange(0, 1, 0.05)
print(alpha.shape)
X, Y = np.meshgrid(T, alpha)
zs = np.array([
rp.log_likelihood_function(list(x))
for x in zip(np.ravel(X), np.ravel(Y))
])
Z = zs.reshape(X.shape)
ax.plot_surface(X, Y, Z)
ax.set_xlabel('T')
ax.set_ylabel('alpha')
ax.set_zlabel('log likelihood')
def test_read_real_player_excel(self):
expected = pd.DataFrame({
'StimulusLeft': [5, 4, 1],
'StimulusRight': [6, 3, 2],
'Action': [1, 0, 1],
'Was the Action Correct?': [1, 1, 1],
'Reward': [1, 1, -1]
}).sort_index(inplace=True)
actual = RealPlayer._read_real_player_excel(TEST_DATA_PATH).sort_index(
inplace=True)
self.assertEqual(actual, expected)
def save_all_real_players_parameters_to_csv(data_dir_path, new_filename, model,
get_parameters):
# type (str, str, Qlearning, function) -> None
all_filenames = os.listdir(data_dir_path)
with open('{}.csv'.format(new_filename), 'w') as file:
writer = csv.writer(file, delimiter=',')
writer.writerow(get_header(model))
for filename in all_filenames:
if filename.endswith('xls'):
row = []
rp = RealPlayer(os.path.join(data_dir_path, filename),
get_model(model))
name = os.path.splitext(os.path.basename(filename))[0][:-8]
player_parameters, starting_points = get_parameters(
real_player=rp)
criteria = rp.model_selection()
row.append(name)
row.extend(player_parameters)
row.extend(starting_points)
row.extend(criteria)
writer.writerow(row)
def test_get_default_optimization_start_points_for_rescorla_wagner(self):
real_player = RealPlayer(TEST_DATA_PATH, model=RescorlaWagner())
assert_array_equal(real_player.start_points, np.array([1, 0.1, 0.1]))
def test_get_default_optimization_start_points_for_Q_learning(self):
real_player = RealPlayer(TEST_DATA_PATH, model=Qlearning())
assert_array_equal(real_player.start_points, np.array([1, 0.1]))
def setUp(self):
self.real_player = RealPlayer(TEST_DATA_PATH, model=Mock())
class TestRealPlayer(TestCase):
def setUp(self):
self.real_player = RealPlayer(TEST_DATA_PATH, model=Mock())
def test_read_real_player_excel(self):
expected = pd.DataFrame({
'StimulusLeft': [5, 4, 1],
'StimulusRight': [6, 3, 2],
'Action': [1, 0, 1],
'Was the Action Correct?': [1, 1, 1],
'Reward': [1, 1, -1]
}).sort_index(inplace=True)
actual = RealPlayer._read_real_player_excel(TEST_DATA_PATH).sort_index(
inplace=True)
self.assertEqual(actual, expected)
@patch('player.minimize')
def test_max_log_likelihood(self, mock_minimize):
# given
start_points = [3, 0.2]
self.real_player.model = Qlearning()
# when
self.real_player.max_log_likelihood(start_points=start_points)
# then
mock_minimize.assert_called_once_with(
self.real_player.log_likelihood_function,
x0=start_points,
method='Nelder-Mead')
@patch('player.minimize')
def test_max_log_likelihood_with_default_starting_points(
self, mock_minimize):
# given
default_start_points = np.array([1, 0.1])
self.real_player.model = Qlearning()
# when
self.real_player.max_log_likelihood()
# then
mock_minimize.assert_called_once()
assert_array_equal(mock_minimize.call_args[1]['x0'],
default_start_points)
def test_get_optimized_parameters_for_q_learning(self):
# given
self.real_player.model = Qlearning()
# when
actual = self.real_player.get_optimized_parameters()
# then
self.assertIsInstance(actual, np.ndarray)
self.assertEqual(len(actual), 2)
def test_get_optimized_parameters_for_rescorla_wagner(self):
# given
self.real_player.model = RescorlaWagner()
# when
actual = self.real_player.get_optimized_parameters()
# then
self.assertIsInstance(actual, np.ndarray)
self.assertEqual(len(actual), 3)
def test_log_likelihood_function_q_learning(self):
# given
self.real_player.model = Qlearning()
parameters = np.array([1, 0.1])
expected = -1 * (3 * np.log(1 / 2))
# when
actual = self.real_player.log_likelihood_function(params=parameters)
# then
self.assertEqual(actual, expected)
def test_log_likelihood_function_rescorla_wagner(self):
# given
self.real_player.model = RescorlaWagner()
parameters = np.array([1, 1, 2])
expected = -1 * (3 * np.log(1 / 2))
# when
actual = self.real_player.log_likelihood_function(params=parameters)
# then
self.assertEqual(actual, expected)
def test_get_default_optimization_start_points_for_Q_learning(self):
real_player = RealPlayer(TEST_DATA_PATH, model=Qlearning())
assert_array_equal(real_player.start_points, np.array([1, 0.1]))
def test_get_default_optimization_start_points_for_rescorla_wagner(self):
real_player = RealPlayer(TEST_DATA_PATH, model=RescorlaWagner())
assert_array_equal(real_player.start_points, np.array([1, 0.1, 0.1]))
def setUp(self):
self.real_player = RealPlayer(path=TEST_DATA_PATH, model=None)
from scripts.models import Qlearning, RescorlaWagner
from scripts.player import RealPlayer
if __name__ == '__main__':
model = RescorlaWagner()
rp = RealPlayer(
path=
"C:\\Users\\Marlena\\PycharmProjects\\ZPI\\ZPI\\data\\MarlenaDudalearning.xls",
model=model)
print(rp.get_optimized_parameters())
print(rp.model_selection())