def testInit(self):
self.assertEqual(self.t.Ymean, {"m1": 1.0, "m2": 1.5})
self.assertEqual(self.t.Ystd, {"m1": 1.0, "m2": 0.5})
with self.assertRaises(ValueError):
StandardizeY(search_space=None,
observation_features=None,
observation_data=[])
def setUp(self):
self.obsd1 = ObservationData(
metric_names=["m1", "m2", "m2"],
means=np.array([1.0, 2.0, 1.0]),
covariance=np.array([[1.0, 0.2, 0.4], [0.2, 2.0, 0.8],
[0.4, 0.8, 3.0]]),
)
self.obsd2 = ObservationData(
metric_names=["m1", "m1", "m2", "m2"],
means=np.array([1.0, 1.0, 2.0, 1.0]),
covariance=np.array([
[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.2, 0.4],
[0.0, 0.2, 2.0, 0.8],
[0.0, 0.4, 0.8, 3.0],
]),
)
self.t = StandardizeY(
search_space=None,
observation_features=None,
observation_data=[self.obsd1, self.obsd2],
)
class StandardizeYTransformTest(TestCase):
def setUp(self):
self.obsd1 = ObservationData(
metric_names=["m1", "m2", "m2"],
means=np.array([1.0, 2.0, 1.0]),
covariance=np.array([[1.0, 0.2, 0.4], [0.2, 2.0, 0.8],
[0.4, 0.8, 3.0]]),
)
self.obsd2 = ObservationData(
metric_names=["m1", "m1", "m2", "m2"],
means=np.array([1.0, 1.0, 2.0, 1.0]),
covariance=np.array([
[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.2, 0.4],
[0.0, 0.2, 2.0, 0.8],
[0.0, 0.4, 0.8, 3.0],
]),
)
self.t = StandardizeY(
search_space=None,
observation_features=None,
observation_data=[self.obsd1, self.obsd2],
)
def testInit(self):
self.assertEqual(self.t.Ymean, {"m1": 1.0, "m2": 1.5})
self.assertEqual(self.t.Ystd, {"m1": 1.0, "m2": 0.5})
with self.assertRaises(ValueError):
StandardizeY(search_space=None,
observation_features=None,
observation_data=[])
def testTransformObservations(self):
obsd1_t = ObservationData(
metric_names=["m1", "m2", "m2"],
means=np.array([0.0, 1.0, -1.0]),
covariance=np.array([[1.0, 0.4, 0.8], [0.4, 8.0, 3.2],
[0.8, 3.2, 12.0]]),
)
obsd2 = [deepcopy(self.obsd1)]
obsd2 = self.t.transform_observation_data(obsd2, [])
self.assertTrue(obsd2[0] == obsd1_t)
obsd2 = self.t.untransform_observation_data(obsd2, [])
self.assertTrue(obsd2[0] == self.obsd1)
def testTransformOptimizationConfig(self):
m1 = Metric(name="m1")
m2 = Metric(name="m2")
m3 = Metric(name="m3")
objective = Objective(metric=m3, minimize=False)
cons = [
OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=2.0,
relative=False),
OutcomeConstraint(metric=m2,
op=ComparisonOp.LEQ,
bound=3.5,
relative=False),
]
oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
oc = self.t.transform_optimization_config(oc, None, None)
cons_t = [
OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=1.0,
relative=False),
OutcomeConstraint(metric=m2,
op=ComparisonOp.LEQ,
bound=4.0,
relative=False),
]
self.assertTrue(oc.outcome_constraints == cons_t)
self.assertTrue(oc.objective == objective)
# Check fail with relative
con = OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=2.0,
relative=True)
oc = OptimizationConfig(objective=objective, outcome_constraints=[con])
with self.assertRaises(ValueError):
oc = self.t.transform_optimization_config(oc, None, None)
class StandardizeYTransformTest(TestCase):
def setUp(self):
self.obsd1 = ObservationData(
metric_names=["m1", "m2", "m2"],
means=np.array([1.0, 2.0, 1.0]),
covariance=np.array([[1.0, 0.2, 0.4], [0.2, 2.0, 0.8],
[0.4, 0.8, 3.0]]),
)
self.obsd2 = ObservationData(
metric_names=["m1", "m1", "m2", "m2"],
means=np.array([1.0, 1.0, 2.0, 1.0]),
covariance=np.array([
[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.2, 0.4],
[0.0, 0.2, 2.0, 0.8],
[0.0, 0.4, 0.8, 3.0],
]),
)
self.t = StandardizeY(
search_space=None,
observation_features=None,
observation_data=[self.obsd1, self.obsd2],
)
def testInit(self):
self.assertEqual(self.t.Ymean, {"m1": 1.0, "m2": 1.5})
self.assertEqual(self.t.Ystd, {"m1": 1.0, "m2": sqrt(1 / 3)})
with self.assertRaises(ValueError):
StandardizeY(search_space=None,
observation_features=None,
observation_data=[])
def testTransformObservations(self):
obsd1_t = ObservationData(
metric_names=["m1", "m2", "m2"],
means=np.array([0.0, sqrt(3 / 4), -sqrt(3 / 4)]),
covariance=np.array([
[1.0, 0.2 * sqrt(3), 0.4 * sqrt(3)],
[0.2 * sqrt(3), 6.0, 2.4],
[0.4 * sqrt(3), 2.4, 9.0],
], ),
)
obsd2 = [deepcopy(self.obsd1)]
obsd2 = self.t.transform_observation_data(obsd2, [])
self.assertTrue(osd_allclose(obsd2[0], obsd1_t))
obsd2 = self.t.untransform_observation_data(obsd2, [])
self.assertTrue(osd_allclose(obsd2[0], self.obsd1))
def testTransformOptimizationConfig(self):
m1 = Metric(name="m1")
m2 = Metric(name="m2")
m3 = Metric(name="m3")
objective = Objective(metric=m3, minimize=False)
cons = [
OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=2.0,
relative=False),
OutcomeConstraint(metric=m2,
op=ComparisonOp.LEQ,
bound=3.5,
relative=False),
ScalarizedOutcomeConstraint(
metrics=[m1, m2],
weights=[0.5, 0.5],
op=ComparisonOp.LEQ,
bound=3.5,
relative=False,
),
]
oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
oc = self.t.transform_optimization_config(oc, None, None)
cons_t = [
OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=1.0,
relative=False),
OutcomeConstraint(
metric=m2,
op=ComparisonOp.LEQ,
bound=2.0 * sqrt(3), # (3.5 - 1.5) / sqrt(1/3)
relative=False,
),
ScalarizedOutcomeConstraint(
metrics=[m1, m2],
weights=[0.5 * 1.0, 0.5 * sqrt(1 / 3)],
op=ComparisonOp.LEQ,
bound=2.25, # 3.5 - (0.5 * 1.0 + 0.5 * 1.5)
relative=False,
),
]
self.assertTrue(oc.outcome_constraints == cons_t)
self.assertTrue(oc.objective == objective)
# Check fail with relative
con = OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=2.0,
relative=True)
oc = OptimizationConfig(objective=objective, outcome_constraints=[con])
with self.assertRaises(ValueError):
oc = self.t.transform_optimization_config(oc, None, None)