def test_exploit(self):
cacb = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
)
# lowest cost exists
costs_per_action = {
10.0: 100.0,
11.0: 100.0,
12.0: 90.0,
13.0: 100.0,
14.0: 100.0,
15.0: 100.0,
}
epsilon = 0.10
action, prob = cacb._exploit(costs_per_action, epsilon)
self.assertEqual(action, 12.0)
self.assertEqual(prob, 0.90)
# no clear winner => should choose the first of the best
costs_per_action = {
10.0: 100.0,
11.0: 90.0,
12.0: 100.0,
13.0: 90.0,
14.0: 90.0,
15.0: 100.0,
}
epsilon = 0.10
action, prob = cacb._exploit(costs_per_action, epsilon)
self.assertEqual(action, 11.0)
self.assertEqual(prob, 0.90)
def test_get_previous_move(self):
cacb = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
data_file=mock_file,
)
self.assertEqual(cacb._get_previous_move(0.1), (True, 10.0, 2.0))
def test_existing_data(self):
cacb = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
data_file=mock_file,
)
self.assertEqual(cacb.logged_data.shape[0], 5)
cacb.learn(np.array([0, 0, 1]), 0, 100, 0.90)
log_file_data = pd.read_csv(mock_file,
header=None).values # type: ignore
self.assertEqual(log_file_data.shape[0], 6)
def test_get_actions_one_hot(self):
actions_one_hot = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
)._get_actions_one_hot(12)
assert_array_equal(actions_one_hot, np.array([0, 0, 1, 0, 0, 0]))
def test_get_actions(self):
actions1 = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
)._get_actions()
self.assertListEqual(actions1, [10, 11, 12, 13, 14, 15])
actions2 = ContinuousActionContextualBanditModel(
min_value=-10,
max_value=-5,
action_width=1,
)._get_actions()
self.assertListEqual(actions2, [-10, -9, -8, -7, -6, -5])
actions3 = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=2,
)._get_actions()
self.assertListEqual(actions3, [10, 12, 14])
def test_log_example(self):
context = np.array([1, 2, 3])
action = 11
cost = 100
prob = 0.75
# uncategorized actions
cacb_1 = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
categorize_actions=False)
cacb_1._log_example(context, action, cost, prob)
assert_array_equal(cacb_1.logged_data,
np.array([0.75, 100, 11, 1, 2, 3]).reshape(1, -1))
# categorized actions
cacb_2 = ContinuousActionContextualBanditModel(min_value=10,
max_value=15,
action_width=1,
categorize_actions=True)
cacb_2._log_example(context, action, cost, prob)
assert_array_equal(
cacb_2.logged_data,
np.array([0.75, 100, 0, 1, 0, 0, 0, 0, 1, 2, 3]).reshape(1, -1),
)
# log another example
cacb_2._log_example(context, 10, 90, 0.90)
assert_array_equal(
cacb_2.logged_data,
np.array([
np.array([0.75, 100, 0, 1, 0, 0, 0, 0, 1, 2, 3]),
np.array([0.90, 90, 1, 0, 0, 0, 0, 0, 1, 2, 3]),
]),
)
def test_existing_data_and_memory(self):
cacb = ContinuousActionContextualBanditModel(min_value=10,
max_value=15,
action_width=1,
data_file=mock_file,
memory=10)
self.assertEqual(cacb.logged_data.shape[0], 5)
cacb = ContinuousActionContextualBanditModel(min_value=10,
max_value=15,
action_width=1,
data_file=mock_file,
memory=3)
self.assertEqual(cacb.logged_data.shape[0], 3)
self.assertListEqual(list(cacb.logged_data[-1]),
[0.0333, 120, 4, 1, 1, 0])
cacb._log_example(np.array([0, 1, 1]), 1, 105, 0.0333)
self.assertEqual(cacb.logged_data.shape[0], 3)
self.assertListEqual(list(cacb.logged_data[-1]),
[0.0333, 105, 1, 0, 1, 1])
def test_explore(self):
cacb = ContinuousActionContextualBanditModel(
min_value=10,
max_value=15,
action_width=1,
)
# the best action is 12
costs_per_action = {
10.0: 100.0,
11.0: 100.0,
12.0: 90.0,
13.0: 100.0,
14.0: 100.0,
15.0: 100.0,
}
# exploration width = 1
epsilon = 0.10
exploration_width = 1
action, prob = cacb._explore(costs_per_action, epsilon,
exploration_width)
self.assertEqual(prob, 0.05)
self.assertIn(action, [11, 13])
# exploration direction = left
epsilon = 0.10
exploration_width = 1
direction = "left"
action, prob = cacb._explore(costs_per_action, epsilon,
exploration_width, direction)
self.assertEqual(prob, 0.10)
self.assertEqual(action, 11)
# exploration width = 2
epsilon = 0.10
exploration_width = 2
action, prob = cacb._explore(costs_per_action, epsilon,
exploration_width)
self.assertEqual(prob, 0.025)
self.assertIn(action, [10, 11, 13, 14])
# exploration width = 1, optimum in the end
costs_per_action = {
10.0: 90.0,
11.0: 100.0,
12.0: 100.0,
13.0: 100.0,
14.0: 100.0,
15.0: 100.0,
}
epsilon = 0.10
exploration_width = 1
action, prob = cacb._explore(costs_per_action, epsilon,
exploration_width)
self.assertEqual(prob, 0.10)
self.assertEqual(action, 11)
# exploration width = 1, optimum in the left end, left direction
costs_per_action = {
10.0: 90.0,
11.0: 100.0,
12.0: 100.0,
13.0: 100.0,
14.0: 100.0,
15.0: 100.0,
}
epsilon = 0.10
exploration_width = 1
action, prob = cacb._explore(costs_per_action,
epsilon,
exploration_width,
direction="left")
self.assertEqual(prob, 0.10)
self.assertEqual(action, 10)