def tuning_job_state_mcmc(X, Y) -> TuningJobState:
Y = [dictionarize_objective(y) for y in Y]
return TuningJobState(
HyperparameterRanges_Impl(HyperparameterRangeContinuous('x', -4., 4., LinearScaling())),
[CandidateEvaluation(x, y) for x, y in zip(X, Y)],
[], []
)
def test_compute_blacklisted_candidates(
hp_ranges: HyperparameterRanges_Impl,
candidate_evaluations: List[CandidateEvaluation],
failed_candidates: List[Candidate],
pending_evaluations: List[PendingEvaluation],
expected: Set[Candidate]):
state = TuningJobState(hp_ranges, candidate_evaluations, failed_candidates,
pending_evaluations)
actual = compute_blacklisted_candidates(state)
assert set(expected) == set(actual)
def multi_algo_state():
def _candidate_evaluations(num):
return [
CandidateEvaluation(candidate=(i, ),
metrics=dictionarize_objective(float(i)))
for i in range(num)
]
return {
'0':
TuningJobState(
hp_ranges=HyperparameterRanges_Impl(
HyperparameterRangeContinuous('a1_hp_1', -5.0, 5.0,
LinearScaling(), -5.0, 5.0)),
candidate_evaluations=_candidate_evaluations(2),
failed_candidates=[(i, ) for i in range(3)],
pending_evaluations=[PendingEvaluation((i, ))
for i in range(100)]),
'1':
TuningJobState(hp_ranges=HyperparameterRanges_Impl(),
candidate_evaluations=_candidate_evaluations(5),
failed_candidates=[],
pending_evaluations=[]),
'2':
TuningJobState(
hp_ranges=HyperparameterRanges_Impl(),
candidate_evaluations=_candidate_evaluations(3),
failed_candidates=[(i, ) for i in range(10)],
pending_evaluations=[PendingEvaluation((i, )) for i in range(1)]),
'3':
TuningJobState(hp_ranges=HyperparameterRanges_Impl(),
candidate_evaluations=_candidate_evaluations(6),
failed_candidates=[],
pending_evaluations=[]),
'4':
TuningJobState(hp_ranges=HyperparameterRanges_Impl(),
candidate_evaluations=_candidate_evaluations(120),
failed_candidates=[],
pending_evaluations=[]),
}
def default_models() -> List[GPMXNetModel]:
X = [
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
(0.0, 0.0), # same evals are added multiple times to force GP to unlearn prior
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
]
Y = [dictionarize_objective(np.sum(x) * 10.0) for x in X]
state = TuningJobState(
HyperparameterRanges_Impl(
HyperparameterRangeContinuous('x', 0.0, 1.0, LinearScaling()),
HyperparameterRangeContinuous('y', 0.0, 1.0, LinearScaling()),
),
[
CandidateEvaluation(x, y) for x, y in zip(X, Y)
],
[], [],
)
random_seed = 0
gpmodel = default_gpmodel(
state, random_seed=random_seed,
optimization_config=DEFAULT_OPTIMIZATION_CONFIG
)
gpmodel_mcmc = default_gpmodel_mcmc(
state, random_seed=random_seed,
mcmc_config=DEFAULT_MCMC_CONFIG
)
return [
GPMXNetModel(state, DEFAULT_METRIC, random_seed, gpmodel,
fit_parameters=True, num_fantasy_samples=20),
GPMXNetModel(state, DEFAULT_METRIC, random_seed, gpmodel_mcmc,
fit_parameters=True, num_fantasy_samples=20)]
def tuning_job_state() -> TuningJobState:
X = [
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
]
Y = [dictionarize_objective(np.sum(x) * 10.0) for x in X]
return TuningJobState(
HyperparameterRanges_Impl(
HyperparameterRangeContinuous('x', 0.0, 1.0, LinearScaling()),
HyperparameterRangeContinuous('y', 0.0, 1.0, LinearScaling()),
),
[
CandidateEvaluation(x, y) for x, y in zip(X, Y)
],
[], []
)
def test_get_internal_candidate_evaluations():
"""we do not test the case with no evaluations, as it is assumed
that there will be always some evaluations generated in the beginning
of the BO loop."""
candidates = [
CandidateEvaluation((2, 3.3, 'X'), dictionarize_objective(5.3)),
CandidateEvaluation((1, 9.9, 'Y'), dictionarize_objective(10.9)),
CandidateEvaluation((7, 6.1, 'X'), dictionarize_objective(13.1)),
]
state = TuningJobState(
hp_ranges=HyperparameterRanges_Impl(
HyperparameterRangeInteger('integer', 0, 10, LinearScaling()),
HyperparameterRangeContinuous('real', 0, 10, LinearScaling()),
HyperparameterRangeCategorical('categorical', ('X', 'Y')),
),
candidate_evaluations=candidates,
failed_candidates=[candidates[0].candidate
], # these should be ignored by the model
pending_evaluations=[])
result = get_internal_candidate_evaluations(state,
DEFAULT_METRIC,
normalize_targets=True,
num_fantasize_samples=20)
assert len(result.X.shape) == 2, "Input should be a matrix"
assert len(result.y.shape) == 2, "Output should be a matrix"
assert result.X.shape[0] == len(candidates)
assert result.y.shape[
-1] == 1, "Only single output value per row is suppored"
assert np.abs(np.mean(
result.y)) < 1e-8, "Mean of the normalized outputs is not 0.0"
assert np.abs(np.std(result.y) -
1.0) < 1e-8, "Std. of the normalized outputs is not 1.0"
np.testing.assert_almost_equal(result.mean, 9.766666666666666)
np.testing.assert_almost_equal(result.std, 3.283629428273267)
def default_models(do_mcmc=True) -> List[GPMXNetModel]:
X = [
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
]
Y = [dictionarize_objective(np.sum(x) * 10.0) for x in X]
state = TuningJobState(
HyperparameterRanges_Impl(
HyperparameterRangeContinuous('x', 0.0, 1.0, LinearScaling()),
HyperparameterRangeContinuous('y', 0.0, 1.0, LinearScaling()),
), [CandidateEvaluation(x, y) for x, y in zip(X, Y)], [], [])
random_seed = 0
gpmodel = default_gpmodel(state,
random_seed=random_seed,
optimization_config=DEFAULT_OPTIMIZATION_CONFIG)
result = [
GPMXNetModel(state,
DEFAULT_METRIC,
random_seed,
gpmodel,
fit_parameters=True,
num_fantasy_samples=20)
]
if do_mcmc:
gpmodel_mcmc = default_gpmodel_mcmc(state,
random_seed=random_seed,
mcmc_config=DEFAULT_MCMC_CONFIG)
result.append(
GPMXNetModel(state,
DEFAULT_METRIC,
random_seed,
gpmodel_mcmc,
fit_parameters=True,
num_fantasy_samples=20))
return result
def test_gp_fantasizing():
"""
Compare whether acquisition function evaluations (values, gradients) with
fantasizing are the same as averaging them by hand.
"""
random_seed = 4567
_set_seeds(random_seed)
num_fantasy_samples = 10
num_pending = 5
hp_ranges = HyperparameterRanges_Impl(
HyperparameterRangeContinuous('x', 0.0, 1.0, LinearScaling()),
HyperparameterRangeContinuous('y', 0.0, 1.0, LinearScaling()))
X = [
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
]
num_data = len(X)
Y = [dictionarize_objective(np.random.randn(1, 1)) for _ in range(num_data)]
# Draw fantasies. This is done for a number of fixed pending candidates
# The model parameters are fit in the first iteration, when there are
# no pending candidates
# Note: It is important to not normalize targets, because this would be
# done on the observed targets only, not the fantasized ones, so it
# would be hard to compare below.
pending_evaluations = []
for _ in range(num_pending):
pending_cand = tuple(np.random.rand(2,))
pending_evaluations.append(PendingEvaluation(pending_cand))
state = TuningJobState(
hp_ranges,
[CandidateEvaluation(x, y) for x, y in zip(X, Y)],
failed_candidates=[],
pending_evaluations=pending_evaluations)
gpmodel = default_gpmodel(
state, random_seed, optimization_config=DEFAULT_OPTIMIZATION_CONFIG)
model = GPMXNetModel(
state, DEFAULT_METRIC, random_seed, gpmodel, fit_parameters=True,
num_fantasy_samples=num_fantasy_samples, normalize_targets=False)
fantasy_samples = model.fantasy_samples
# Evaluate acquisition function and gradients with fantasizing
num_test = 50
X_test = np.vstack([hp_ranges.to_ndarray(
tuple(np.random.rand(2,))) for _ in range(num_test)])
acq_func = EIAcquisitionFunction(model)
fvals, grads = acq_func.compute_acq_with_gradients(X_test)
# Do the same computation by averaging by hand
fvals_cmp = np.empty((num_fantasy_samples,) + fvals.shape)
grads_cmp = np.empty((num_fantasy_samples,) + grads.shape)
X_full = X + state.pending_candidates
for it in range(num_fantasy_samples):
Y_full = Y + [dictionarize_objective(eval.fantasies[DEFAULT_METRIC][:, it])
for eval in fantasy_samples]
state2 = TuningJobState(
hp_ranges,
[CandidateEvaluation(x, y) for x, y in zip(X_full, Y_full)],
failed_candidates=[],
pending_evaluations=[])
# We have to skip parameter optimization here
model2 = GPMXNetModel(
state2, DEFAULT_METRIC, random_seed, gpmodel,
fit_parameters=False, num_fantasy_samples=num_fantasy_samples,
normalize_targets=False)
acq_func2 = EIAcquisitionFunction(model2)
fvals_, grads_ = acq_func2.compute_acq_with_gradients(X_test)
fvals_cmp[it, :] = fvals_
grads_cmp[it, :] = grads_
# Comparison
fvals2 = np.mean(fvals_cmp, axis=0)
grads2 = np.mean(grads_cmp, axis=0)
assert np.allclose(fvals, fvals2)
assert np.allclose(grads, grads2)