def _single_credit_cluster(idx):
vec = np.zeros(feature_dim, dtype=np.int32)
vec[idx] = 1
return ApplicantDistribution(
features=distributions.Constant(mean=vec),
group_membership=distributions.Constant(group_membership),
will_default=distributions.Bernoulli(1 - success_probs[idx]))
def test_mixture_returns_components(self):
my_distribution = distributions.Mixture(components=[
distributions.Constant((0, )),
distributions.Constant((1, ))
],
weights=[0.1, 0.9])
rng = np.random.RandomState(seed=100)
samples = [my_distribution.sample(rng) for _ in range(1000)]
self.assertSetEqual(set(samples), {(0, ), (1, )})
self.assertAlmostEqual(np.mean(samples), 0.9, delta=0.1)
def _single_gmm():
"""Returns a mixture of gaussian applicant distributions."""
return distributions.Mixture(
components=[
ApplicantDistribution(
features=distributions.Gaussian(mean=mean, std=0.5),
group_membership=distributions.Constant(group),
will_default=distributions.Bernoulli(p=default_likelihoods[0])),
ApplicantDistribution(
features=distributions.Gaussian(
mean=np.array(mean) + np.array(intercluster_vec), std=0.5),
group_membership=distributions.Constant(group),
will_default=distributions.Bernoulli(p=default_likelihoods[1]))
],
weights=[0.3, 0.7])
def test_improper_distributions_raise_errors(self):
for p in [-10, -0.9, 1.3]:
with self.assertRaises(ValueError):
_ = distributions.Bernoulli(p=p)
for vec in [
[0.1, 0.3, 0.5], # Does not sum to one.
[0.5, 0.9, -0.4], # Has negative values.
]:
with self.assertRaises(ValueError):
_ = distributions.Mixture(
weights=vec,
components=[distributions.Constant(mean=(0, ))] * len(vec))
def test_constant_returns_the_same_thing(self):
my_distribution = distributions.Constant(mean=(0, 1, 2))
rng = np.random.RandomState(seed=100)
unique_samples = {my_distribution.sample(rng) for _ in range(1000)}
self.assertEqual(unique_samples, {(0, 1, 2)})
