def test_features_manipulated_to_maximum_limit_no_control_epsilon_greedy(
self):
env = college_admission.CollegeAdmissionsEnv(
user_params={
"num_applicants": 5,
"gaming_control": np.inf,
"group_cost": {
0: 2,
1: 4,
}
})
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,
gaming_control=env.initial_params.gaming_control,
epsilon_greedy=True,
initial_epsilon_prob=0.2,
)
observations = {
"test_scores_y": np.asarray([0.2, 0.3, 0.4, 0.5, 0.4]),
"selected_applicants": np.asarray([0, 1, 0, 1, 1]),
"selected_ground_truth": np.asarray([2, 0, 2, 1, 1]),
"applicant_groups": np.asarray([0, 1, 1, 0, 1]),
}
self.assertTrue(
np.all(
np.isclose(
agent._get_maximum_manipulated_features(observations),
[0.5, 0.9, 0.6],
atol=1e-4,
)))
def test_features_manipulated_to_maximum_limit_with_no_control(self):
env = college_admission.CollegeAdmissionsEnv(
user_params={
"num_applicants": 5,
"gaming_control": np.inf,
"group_cost": {
0: 2,
1: 4
}
})
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,
)
observations = {
"test_scores_y": np.asarray([0.2, 0.3, 0.4, 0.5, 0.4]),
"selected_applicants": np.asarray([0, 1, 0, 1, 1]),
"selected_ground_truth": np.asarray([2, 0, 2, 1, 1]),
"applicant_groups": np.asarray([0, 1, 1, 0, 1]),
}
agent.act(observations, done=False)
self.assertTrue(
np.all(
np.isclose(
agent._get_maximum_manipulated_features(observations),
[0.55, 1.0, 0.65],
atol=1e-4,
)))
self.assertEqual(agent._features,
agent._get_maximum_manipulated_features(observations))
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_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_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_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])
def build_scenario(self):
"""Returns agent and env according to provided params."""
env = college_admission.CollegeAdmissionsEnv(
user_params=self.env_config)
if self.agent_type == "robust":
agent = college_admission_jury.RobustJury(
action_space=env.action_space,
reward_fn=(lambda x: 0),
observation_space=env.observation_space,
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,
epsilon_greedy=self.epsilon_greedy,
initial_epsilon_prob=self.initial_epsilon_prob,
decay_steps=self.decay_steps,
epsilon_prob_decay_rate=self.epsilon_prob_decay_rate,
burnin=self.burnin,
)
elif self.agent_type == "static":
agent = college_admission_jury.NaiveJury(
action_space=env.action_space,
reward_fn=(lambda x: 0),
observation_space=env.observation_space,
threshold=0,
epsilon_greedy=self.epsilon_greedy,
initial_epsilon_prob=self.initial_epsilon_prob,
epsilon_prob_decay_rate=self.epsilon_prob_decay_rate,
decay_steps=self.decay_steps,
freeze_classifier_after_burnin=True,
burnin=self.burnin,
)
elif self.agent_type == "continuous":
agent = college_admission_jury.NaiveJury(
action_space=env.action_space,
reward_fn=(lambda x: 0),
observation_space=env.observation_space,
threshold=0,
epsilon_greedy=self.epsilon_greedy,
initial_epsilon_prob=self.initial_epsilon_prob,
epsilon_prob_decay_rate=self.epsilon_prob_decay_rate,
freeze_classifier_after_burnin=False,
decay_steps=self.decay_steps,
burnin=self.burnin,
)
else:
agent = college_admission_jury.FixedJury(
action_space=env.action_space,
reward_fn=(lambda x: 0),
observation_space=env.observation_space,
threshold=self.agent_threshold,
epsilon_greedy=self.epsilon_greedy,
decay_steps=self.decay_steps,
initial_epsilon_prob=self.initial_epsilon_prob,
epsilon_prob_decay_rate=self.epsilon_prob_decay_rate,
)
return env, agent