def error_raised_when_noise_params_wrong(self):
env = college_admission.CollegeAdmissionsEnv(
user_params={
'noise_params':
params.BoundedGaussian(min=0, max=0.3, mu=0, sigma=0.00001),
})
with self.assertRaises(ValueError):
env._add_noise()
def test_fixed_agent_simulation_runs_successfully(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.FixedJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7)
test_util.run_test_simulation(env=env, agent=agent, stackelberg=True)
def test_agent_raises_invalid_observation_error(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.FixedJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7)
with self.assertRaises(core.InvalidObservationError):
agent.act(observation={0: 'Invalid Observation'}, done=False)
def test_robust_classifier_simulation_runs_successfully(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.RobustJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
group_cost=env.initial_params.group_cost,
burnin=10)
test_util.run_test_simulation(env=env, agent=agent, stackelberg=True)
def test_jury_successfully_initializes(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.NaiveJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7)
self.assertEqual(agent.initial_action()['threshold'], 0.7)
self.assertEqual(agent.initial_action()['epsilon_prob'], 0)
def test_assertion_raised_when_burnin_less_than_2(self):
env = college_admission.CollegeAdmissionsEnv()
with self.assertRaises(ValueError):
college_admission_jury.RobustJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
group_cost=env.initial_params.group_cost,
burnin=1)
def test_agent_produces_zero_no_epsilon_greedy(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.FixedJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7,
epsilon_greedy=False)
epsilon_probs = [
agent.initial_action()['epsilon_prob'] for _ in range(10)
]
self.assertEqual(epsilon_probs, [0] * 10)
def test_correct_robust_threshold_returned(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.RobustJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
group_cost=env.initial_params.group_cost)
agent._features = [0.1, 0.2, 0.4, 0.4, 0.5, 0.6, 0.7, 0.8]
agent._labels = [0, 0, 1, 0, 0, 1, 1, 1]
agent._train_model()
self.assertEqual(agent._threshold, 0.6)
def test_agent_produces_different_epsilon_with_epsilon_greedy(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.FixedJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7,
epsilon_greedy=True)
obs, _, done, _ = env.step(agent.initial_action())
epsilon_probs = [float(agent.initial_action()['epsilon_prob'])]
epsilon_probs.extend(
[float(agent.act(obs, done)['epsilon_prob']) for _ in range(10)])
self.assertGreater(len(set(epsilon_probs)), 1)
def test_agent_raises_episode_done_error(self):
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.FixedJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7)
with self.assertRaises(core.EpisodeDoneError):
agent.act(observation={
'threshold': np.array(0.5),
'epsilon_prob': np.array(0)
},
done=True)
def test_label_fn_returns_correct_labels(self):
"""Checks that the label function works as expected."""
observations = {
'test_scores_y': [0.2, 0.3, 0.4, 0.5, 0.6],
'selected_ground_truth': [1, 0, 2, 1, 2],
'selected_applicants': [1, 1, 0, 1, 0]
}
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.NaiveJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7)
labels = agent._label_fn(observations)
self.assertListEqual(labels, [1, 0, 1])
def test_get_default_features_returns_same_features(self):
"""Checks that the feature selection fn works as expected."""
observations = {
'test_scores_y': [0.2, 0.3, 0.4, 0.5, 0.6],
'selected_ground_truth': [1, 0, 2, 1, 2],
'selected_applicants': [1, 1, 0, 1, 0]
}
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.NaiveJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.7)
features = agent._get_default_features(observations)
self.assertListEqual(features, [0.2, 0.3, 0.5])
def test_agent_returns_same_threshold_till_burnin_learns_and_freezes(self):
"""Tests that agent returns same threshold till burnin and freezes after."""
env = college_admission.CollegeAdmissionsEnv()
agent = college_admission_jury.NaiveJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
threshold=0.3,
burnin=4,
freeze_classifier_after_burnin=True)
test_util.run_test_simulation(env=env,
agent=agent,
num_steps=10,
stackelberg=True)
actions = [float(action['threshold']) for _, action in env.history]
self.assertEqual(set(actions[:4]), {0.3})
self.assertLen(set(actions), 3)
def test_correct_max_score_change_calculated_with_subsidy(self):
"""Tests that the max gaming steps gives output as expected."""
env = college_admission.CollegeAdmissionsEnv(
user_params={
'group_cost': {
0: 2,
1: 4
},
'subsidize': True,
'subsidy_beta': 0.8,
'gaming_control': np.inf
})
agent = college_admission_jury.RobustJury(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=(lambda x: 0),
group_cost=env.initial_params.group_cost,
subsidize=env.initial_params.subsidize,
subsidy_beta=env.initial_params.subsidy_beta,
gaming_control=env.initial_params.gaming_control)
obs, _, _, _ = env.step(agent.initial_action())
max_change = agent._get_max_allowed_score_change(obs)
self.assertEqual(max_change, [0.5, 0.3125])
