def test_logistic_epsilon_abnormal_epsilon():
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=2, epsilon=1.3)
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=2, epsilon=-0.3)
def test_logistic_epsilon_normal_epsilon():
policy1 = LogisticEpsilonGreedy(n_actions=2, dim=2)
assert 0 <= policy1.epsilon <= 1
policy2 = LogisticEpsilonGreedy(n_actions=2, dim=2, epsilon=0.5)
assert policy2.epsilon == 0.5
def test_logistic_epsilon_select_action_exploration():
trial_num = 50
policy = LogisticEpsilonGreedy(n_actions=2, dim=2, epsilon=1.0)
context = np.array([1.0, 1.0]).reshape(1, -1)
policy.update_params(action=0, reward=1.0, context=context)
policy.update_params(action=0, reward=1.0, context=context)
policy.update_params(action=1, reward=1.0, context=context)
policy.update_params(action=1, reward=0.0, context=context)
selected_action = [policy.select_action(context=context) for _ in range(trial_num)]
assert 0 < sum(selected_action)[0] < trial_num
def test_logistic_base_exception():
# invalid dim
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=-3)
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=0)
with pytest.raises(TypeError):
LogisticEpsilonGreedy(n_actions=2, dim="3")
# invalid n_actions
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=-3, dim=2)
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=1, dim=2)
with pytest.raises(TypeError):
LogisticEpsilonGreedy(n_actions="2", dim=2)
# invalid len_list
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=2, len_list=-3)
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=2, len_list=0)
with pytest.raises(TypeError):
LogisticEpsilonGreedy(n_actions=2, dim=2, len_list="3")
# invalid batch_size
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=2, batch_size=-2)
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, dim=2, batch_size=0)
with pytest.raises(TypeError):
LogisticEpsilonGreedy(n_actions=2, dim=2, batch_size="10")
# invalid relationship between n_actions and len_list
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=5, len_list=10, dim=2)
with pytest.raises(ValueError):
LogisticEpsilonGreedy(n_actions=2, len_list=3, dim=2)
def test_logistic_epsilon_each_action_model():
n_actions = 3
policy = LogisticEpsilonGreedy(n_actions=n_actions, dim=2, epsilon=0.5)
for i in range(n_actions):
assert isinstance(policy.model_list[i], MiniBatchLogisticRegression)