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_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))