def scenario_builder(self):
"""Returns an agent and environment pair."""
env_params = lending_params.DelayedImpactParams(
applicant_distribution=lending_params.two_group_credit_clusters(
cluster_probabilities=self.cluster_probabilities,
group_likelihoods=[self.group_0_prob, 1 - self.group_0_prob]),
bank_starting_cash=self.bank_starting_cash,
interest_rate=self.interest_rate,
cluster_shift_increment=self.cluster_shift_increment,
)
env = lending.DelayedImpactEnv(env_params)
agent_params = classifier_agents.ScoringAgentParams(
feature_keys=['applicant_features'],
group_key='group',
default_action_fn=(lambda: 1),
burnin=self.burnin,
convert_one_hot_to_integer=True,
threshold_policy=self.threshold_policy,
skip_retraining_fn=lambda action, observation: action == 0,
cost_matrix=params.CostMatrix(
fn=0, fp=-1, tp=env_params.interest_rate, tn=0))
agent = oracle_lending_agent.OracleThresholdAgent(
action_space=env.action_space,
reward_fn=rewards.BinarizedScalarDeltaReward(
'bank_cash', baseline=env.initial_params.bank_starting_cash),
observation_space=env.observation_space,
params=agent_params,
env=env)
agent.seed(100)
return env, agent
def test_oracle_maxutil_classifier_is_stable(self):
env = lending.DelayedImpactEnv()
agent_params = classifier_agents.ScoringAgentParams(
feature_keys=['applicant_features'],
group_key='group',
default_action_fn=(lambda: 1),
burnin=1,
threshold_policy=threshold_policies.ThresholdPolicy.SINGLE_THRESHOLD,
convert_one_hot_to_integer=True,
cost_matrix=params.CostMatrix(
fn=0, fp=-1, tp=env.initial_params.interest_rate, tn=0))
agent = oracle_lending_agent.OracleThresholdAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.BinarizedScalarDeltaReward('bank_cash'),
params=agent_params,
env=env)
test_util.run_test_simulation(env=env, agent=agent)
# Drop 0 threshold associated with burn-in.
first_nonzero_threshold = None
for thresh in agent.global_threshold_history:
if thresh > 0:
if first_nonzero_threshold is None:
first_nonzero_threshold = thresh
self.assertAlmostEqual(first_nonzero_threshold, thresh)
# Make sure there is at least one non-zero threshold.
self.assertIsNotNone(first_nonzero_threshold)
def test_final_credit_distribution_metric_can_interact_with_lending(self):
env = lending.DelayedImpactEnv()
env.set_scalar_reward(rewards.NullReward())
# Use step=-1 to get the final credit distribution.
final_distribution = lending_metrics.CreditDistribution(env, step=-1)
initial_distribution = lending_metrics.CreditDistribution(env, step=0)
test_util.run_test_simulation(
env=env, metric=[final_distribution, initial_distribution])
def test_cumulative_count(self):
env = lending.DelayedImpactEnv()
metric = lending_metrics.CumulativeLoans(env)
env.seed(100)
_ = env.reset()
for _ in range(10):
env.step(np.asarray(1))
result = metric.measure(env)
self.assertEqual(result.shape, (2, 10))
# On the first step, the combined number of loans given out should be 1.
self.assertEqual(result[:, 0].sum(), 1)
# On the last step, the combined number of loans given out should be 10.
self.assertEqual(result[:, -1].sum(), 10)
def test_no_loans_to_group_zero(self):
env = lending.DelayedImpactEnv()
metric = lending_metrics.CumulativeLoans(env)
env.seed(100)
obs = env.reset()
for _ in range(10):
# action is 0 for group 0 and 1 for group 1.
action = np.argmax(obs['group'])
obs, _, _, _ = env.step(action)
result = metric.measure(env)
self.assertEqual(result.shape, (2, 10))
# Group 0 gets no loans.
self.assertEqual(result[0, -1], 0)
# Group 1 gets at least 1 loan.
self.assertGreater(result[1, -1], 0)
def test_higher_credit_scores_default_less(self):
env = lending.DelayedImpactEnv()
high_scores = []
low_scores = []
rng = np.random.RandomState()
rng.seed(100)
for _ in range(1000):
applicant = env.initial_params.applicant_distribution.sample(rng)
if np.argmax(applicant.features) > 4:
high_scores.append(applicant)
else:
low_scores.append(applicant)
self.assertNotEmpty(high_scores)
self.assertNotEmpty(low_scores)
self.assertLess(
np.mean([applicant.will_default for applicant in high_scores]),
np.mean([applicant.will_default for applicant in low_scores]))
def test_oracle_lending_agent_interacts(self):
env = lending.DelayedImpactEnv()
agent_params = classifier_agents.ScoringAgentParams(
feature_keys=['applicant_features'],
group_key='group',
default_action_fn=(lambda: 1),
burnin=1,
convert_one_hot_to_integer=True,
cost_matrix=params.CostMatrix(
fn=0, fp=-1, tp=env.initial_params.interest_rate, tn=0))
agent = oracle_lending_agent.OracleThresholdAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.BinarizedScalarDeltaReward('bank_cash'),
params=agent_params,
env=env)
test_util.run_test_simulation(env=env, agent=agent)
def test_measure_distribution_change_measurement(self):
# The lower cluster has a 100% success rate and the upper cluster has a 0%
# success rate. This causes applicants to move constantly between clusters.
clusters = distributions.Mixture(
components=[
lending_params._credit_cluster_builder(
group_membership=[1, 0],
cluster_probs=[0.1, 0.9],
success_probs=[1., 0.])(),
lending_params._credit_cluster_builder(
group_membership=[0, 1],
cluster_probs=[0.8, 0.2],
success_probs=[1., 0.])(),
],
weights=(0.5, 0.5))
env = lending.DelayedImpactEnv(
lending_params.DelayedImpactParams(applicant_distribution=clusters))
initial_distribution = lending_metrics.CreditDistribution(env, 0)
final_distribution = lending_metrics.CreditDistribution(env, -1)
# Giving a loan should change the distribution.
env.step(np.asarray(1))
# Take another step to move current state into history. This step does not
# change the distribution because the loan is rejected.
env.step(np.asarray(0))
self.assertEqual({
'0': [0.1, 0.9],
'1': [0.8, 0.2]
}, initial_distribution.measure(env))
self.assertNotEqual({
'0': [0.1, 0.9],
'1': [0.8, 0.2]
}, final_distribution.measure(env))
def test_credit_score_distributions_change(self):
env = lending.DelayedImpactEnv()
state = env._get_state()
# Candidate is in group 0
state.group = [1, 0]
state.group_id = 0
# Candidate will default.
state.will_default = 1
# Should move probability mass from clusters 3 to 2.
state.applicant_features = [0] * 7
state.applicant_features[3] = 1
lending._CreditShift().update(state, lending.LoanDecision.ACCEPT)
def get_cluster_probs(params):
return params.applicant_distribution.components[0].weights
self.assertLess(
get_cluster_probs(state.params)[3],
get_cluster_probs(env.initial_params)[3])
self.assertGreater(
get_cluster_probs(state.params)[2],
get_cluster_probs(env.initial_params)[2])
def test_metric_can_interact_with_lending(self):
env = lending.DelayedImpactEnv()
metric = value_tracking_metrics.ValueChange(env, 'bank_cash')
test_util.run_test_simulation(env=env, metric=metric)
def test_delayed_impact_env_has_multinomial_observation_space(self):
env = lending.DelayedImpactEnv()
for _ in range(10):
features = env.observation_space.sample()['applicant_features']
self.assertEqual(features.sum(), 1)
self.assertSameElements(features, {0, 1})
def test_render_fails_for_high_dimensional_environments(self):
env = lending.DelayedImpactEnv()
test_util.run_test_simulation(env=env)
with self.assertRaises(NotImplementedError):
env.render()
