def setUp(self):
self.obsd1 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([0.0, 0.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd2 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([1.0, 5.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd3 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([2.0, 25.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd4 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([3.0, 125.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd_mid = ObservationData(
metric_names=["m1", "m2"],
means=np.array([1.5, 50.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd_extreme = ObservationData(
metric_names=["m1", "m2"],
means=np.array([-1.0, 126.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.t = PercentileY(
search_space=None,
observation_features=None,
observation_data=[
deepcopy(self.obsd1),
deepcopy(self.obsd2),
deepcopy(self.obsd3),
deepcopy(self.obsd4),
],
)
self.t_with_winsorization = PercentileY(
search_space=None,
observation_features=None,
observation_data=[
deepcopy(self.obsd1),
deepcopy(self.obsd2),
deepcopy(self.obsd3),
deepcopy(self.obsd4),
],
config={"winsorize": True},
)
def testInit(self):
with self.assertRaises(ValueError):
PercentileY(search_space=None,
observation_features=[],
observation_data=[])
class PercentileYTransformTest(TestCase):
def setUp(self):
self.obsd1 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([0.0, 0.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd2 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([1.0, 5.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd3 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([2.0, 25.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd4 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([3.0, 125.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd_mid = ObservationData(
metric_names=["m1", "m2"],
means=np.array([1.5, 50.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd_extreme = ObservationData(
metric_names=["m1", "m2"],
means=np.array([-1.0, 126.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.t = PercentileY(
search_space=None,
observation_features=None,
observation_data=[
deepcopy(self.obsd1),
deepcopy(self.obsd2),
deepcopy(self.obsd3),
deepcopy(self.obsd4),
],
)
self.t_with_winsorization = PercentileY(
search_space=None,
observation_features=None,
observation_data=[
deepcopy(self.obsd1),
deepcopy(self.obsd2),
deepcopy(self.obsd3),
deepcopy(self.obsd4),
],
config={"winsorize": True},
)
def testInit(self):
with self.assertRaises(ValueError):
PercentileY(search_space=None,
observation_features=[],
observation_data=[])
def testTransformObservations(self):
self.assertListEqual(self.t.percentiles["m1"], [0.0, 1.0, 2.0, 3.0])
self.assertListEqual(self.t.percentiles["m2"], [0.0, 5.0, 25.0, 125.0])
transformed_obsd_mid = self.t.transform_observation_data(
[deepcopy(self.obsd_mid)], [])[0]
self.assertListEqual(list(transformed_obsd_mid.means), [0.5, 0.75])
transformed_obsd_extreme = self.t.transform_observation_data(
[deepcopy(self.obsd_extreme)], [])[0]
self.assertListEqual(list(transformed_obsd_extreme.means), [0.0, 1.0])
def testTransformObservationsWithWinsorization(self):
self.assertListEqual(self.t.percentiles["m1"], [0.0, 1.0, 2.0, 3.0])
self.assertListEqual(self.t.percentiles["m2"], [0.0, 5.0, 25.0, 125.0])
transformed_obsd_mid = self.t_with_winsorization.transform_observation_data(
[deepcopy(self.obsd_mid)], [])[0]
self.assertListEqual(list(transformed_obsd_mid.means), [0.5, 0.75])
transformed_obsd_extreme = self.t_with_winsorization.transform_observation_data(
[deepcopy(self.obsd_extreme)], [])[0]
# Approximate assertion for winsorization
results = np.array(list(transformed_obsd_extreme.means))
self.assertTrue(
all(np.isclose(results, np.array([0.0847075, 0.9152924]))))
class PercentileYTransformTest(TestCase):
def setUp(self):
self.obsd1 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([0.0, 0.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd2 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([1.0, 5.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd3 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([2.0, 25.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd4 = ObservationData(
metric_names=["m1", "m2"],
means=np.array([3.0, 125.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd_mid = ObservationData(
metric_names=["m1", "m2"],
means=np.array([1.5, 50.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.obsd_extreme = ObservationData(
metric_names=["m1", "m2"],
means=np.array([-1.0, 126.0]),
covariance=np.array([[1.0, 0.0], [0.0, 1.0]]),
)
self.t = PercentileY(
search_space=None,
observation_features=None,
observation_data=[
deepcopy(self.obsd1),
deepcopy(self.obsd2),
deepcopy(self.obsd3),
deepcopy(self.obsd4),
],
)
self.t_with_winsorization = PercentileY(
search_space=None,
observation_features=None,
observation_data=[
deepcopy(self.obsd1),
deepcopy(self.obsd2),
deepcopy(self.obsd3),
deepcopy(self.obsd4),
],
config={"winsorize": True},
)
def testInit(self):
with self.assertRaises(ValueError):
PercentileY(search_space=None, observation_features=[], observation_data=[])
def testTransformObservations(self):
self.assertListEqual(self.t.percentiles["m1"], [0.0, 1.0, 2.0, 3.0])
self.assertListEqual(self.t.percentiles["m2"], [0.0, 5.0, 25.0, 125.0])
# Mid-range value transformation
transformed_obsd_mid = self.t.transform_observation_data(
[deepcopy(self.obsd_mid)], []
)[0]
mean_results = np.array(list(transformed_obsd_mid.means))
expected = np.array([0.5, 0.75])
self.assertTrue(
np.allclose(mean_results, expected),
msg=f"Unexpected mean Results: {mean_results}. Expected: {expected}.",
)
cov_results = np.array(transformed_obsd_mid.covariance)
self.assertTrue(
np.all(np.isnan(cov_results)),
msg=f"Unexpected covariance Result: {cov_results}. Expected all nans.",
)
# Extreme value transformation
transformed_obsd_extreme = self.t.transform_observation_data(
[deepcopy(self.obsd_extreme)], []
)[0]
mean_results = np.array(list(transformed_obsd_extreme.means))
expected = np.array([0.0, 1.0])
self.assertTrue(
np.allclose(mean_results, expected),
msg=f"Unexpected mean Results: {mean_results}. Expected: {expected}.",
)
cov_results = np.array(transformed_obsd_extreme.covariance)
self.assertTrue(
np.all(np.isnan(cov_results)),
msg=f"Unexpected covariance Result: {cov_results}. Expected all nans.",
)
def testTransformObservationsWithWinsorization(self):
self.assertListEqual(self.t.percentiles["m1"], [0.0, 1.0, 2.0, 3.0])
self.assertListEqual(self.t.percentiles["m2"], [0.0, 5.0, 25.0, 125.0])
transformed_obsd_mid = self.t_with_winsorization.transform_observation_data(
[deepcopy(self.obsd_mid)], []
)[0]
mean_results = np.array(list(transformed_obsd_mid.means))
expected = np.array([0.5, 0.75])
self.assertTrue(
np.allclose(mean_results, expected),
msg=f"Unexpected mean Results: {mean_results}. Expected: {expected}.",
)
transformed_obsd_extreme = self.t_with_winsorization.transform_observation_data(
[deepcopy(self.obsd_extreme)], []
)[0]
mean_results = np.array(list(transformed_obsd_extreme.means))
expected = np.array([0.0847075, 0.9152924])
self.assertTrue(
np.allclose(mean_results, expected),
msg=f"Unexpected mean Results: {mean_results}. Expected: {expected}.",
)