def _construct_models(X, Y, metric, do_mcmc, with_pending):
pending_candidates = []
if with_pending:
pending_candidates = [PendingEvaluation((0.5, 0.5)), PendingEvaluation((0.2, 0.2))]
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)
],
[],
pending_candidates
)
random_seed = 0
gpmodel = default_gpmodel(
state, random_seed=random_seed,
optimization_config=DEFAULT_OPTIMIZATION_CONFIG)
result = [GaussProcSurrogateModel(
state, 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(
GaussProcSurrogateModel(
state, metric, random_seed, gpmodel_mcmc,
fit_parameters=True,num_fantasy_samples=20))
return result
def test_gp_fit(tuning_job_state):
_set_seeds(0)
X = [
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
]
Y = [np.sum(x) * 10.0 for x in X]
# checks if fitting is running
random_seed = 0
gpmodel = default_gpmodel(tuning_job_state, random_seed,
optimization_config=DEFAULT_OPTIMIZATION_CONFIG)
model = GaussProcSurrogateModel(
tuning_job_state, DEFAULT_METRIC, random_seed, gpmodel,
fit_parameters=True, num_fantasy_samples=20)
X = [tuning_job_state.hp_ranges.to_ndarray(x) for x in X]
pred_train = model.predict(np.array(X))[0]
assert np.all(np.abs(pred_train['mean'] - Y) < 1e-1), \
"in a noiseless setting, mean of GP should coincide closely with outputs at training points"
X_test = [
(0.2, 0.2),
(0.4, 0.2),
(0.1, 0.9),
(0.5, 0.5),
]
X_test = [tuning_job_state.hp_ranges.to_ndarray(x) for x in X_test]
pred_test = model.predict(np.array(X_test))[0]
assert np.min(pred_train['std']) < np.min(pred_test['std']), \
"Standard deviation on un-observed points should be greater than at observed ones"
def test_gp_mcmc_fit(tuning_job_state):
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)], [], [])
_set_seeds(0)
def f(x):
return 0.1 * np.power(x, 3)
X = np.concatenate((np.random.uniform(-4., -1.,
10), np.random.uniform(1., 4., 10)))
Y = f(X)
X_test = np.sort(np.random.uniform(-1., 1., 10))
X = [(x, ) for x in X]
X_test = [(x, ) for x in X_test]
tuning_job_state = tuning_job_state_mcmc(X, Y)
# checks if fitting is running
random_seed = 0
gpmodel = default_gpmodel_mcmc(tuning_job_state,
random_seed,
mcmc_config=DEFAULT_MCMC_CONFIG)
model = GaussProcSurrogateModel(tuning_job_state,
DEFAULT_METRIC,
random_seed,
gpmodel,
fit_parameters=True,
num_fantasy_samples=20)
X = [tuning_job_state.hp_ranges.to_ndarray(x) for x in X]
predictions = model.predict(np.array(X))
Y_std_list = [p['std'] for p in predictions]
Y_mean_list = [p['mean'] for p in predictions]
Y_mean = np.mean(Y_mean_list, axis=0)
Y_std = np.mean(Y_std_list, axis=0)
assert np.all(np.abs(Y_mean - Y) < 1e-1), \
"in a noiseless setting, mean of GP should coincide closely with outputs at training points"
X_test = [tuning_job_state.hp_ranges.to_ndarray(x) for x in X_test]
predictions_test = model.predict(np.array(X_test))
Y_std_test_list = [p['std'] for p in predictions_test]
Y_std_test = np.mean(Y_std_test_list, axis=0)
assert np.max(Y_std) < np.min(Y_std_test), \
"Standard deviation on un-observed points should be greater than at observed ones"
def default_models() -> List[GaussProcSurrogateModel]:
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 [
GaussProcSurrogateModel(state,
DEFAULT_METRIC,
random_seed,
gpmodel,
fit_parameters=True,
num_fantasy_samples=20),
GaussProcSurrogateModel(state,
DEFAULT_METRIC,
random_seed,
gpmodel_mcmc,
fit_parameters=True,
num_fantasy_samples=20)
]
def default_models(metric, do_mcmc=True) -> List[GaussProcSurrogateModel]:
X = [
(0.0, 0.0),
(1.0, 0.0),
(0.0, 1.0),
(1.0, 1.0),
]
if metric == DEFAULT_METRIC:
Y = [dictionarize_objective(np.sum(x) * 10.0) for x in X]
elif metric == DEFAULT_COST_METRIC:
# Increasing the first hp increases cost
Y = [{metric: 1.0 + x[0] * 2.0}
for x in X]
else:
raise ValueError(f"{metric} is not a valid metric")
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 = [GaussProcSurrogateModel(
state, 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(
GaussProcSurrogateModel(
state, metric, random_seed, gpmodel_mcmc,
fit_parameters=True,num_fantasy_samples=20))
return result
def fit_predict_ours(data: dict,
random_seed: int,
optimization_config: OptimizationConfig,
test_intermediates: Optional[dict] = None) -> dict:
# Create surrogate model
num_dims = len(data['ss_limits'])
_gpmodel = GaussianProcessRegression(
kernel=Matern52(num_dims, ARD=True),
mean=ZeroMeanFunction(), # Instead of ScalarMeanFunction
optimization_config=optimization_config,
random_seed=random_seed,
test_intermediates=test_intermediates)
model = GaussProcSurrogateModel(data['state'],
DEFAULT_METRIC,
random_seed,
_gpmodel,
fit_parameters=True,
num_fantasy_samples=20)
model_params = model.get_params()
print('Hyperparameters: {}'.format(model_params))
# Prediction
predictions = model.predict(data['test_inputs'])[0]
return {'means': predictions['mean'], 'stddevs': predictions['std']}
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 = GaussProcSurrogateModel(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 = [
hp_ranges.to_ndarray(tuple(np.random.rand(2, )))
for _ in range(num_test)
]
acq_func = EIAcquisitionFunction(model)
fvals, grads = _compute_acq_with_gradient_many(acq_func, 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 = GaussProcSurrogateModel(
state2,
DEFAULT_METRIC,
random_seed,
gpmodel,
fit_parameters=False,
num_fantasy_samples=num_fantasy_samples,
normalize_targets=False)
acq_func2 = EIAcquisitionFunction(model2)
fvals_, grads_ = _compute_acq_with_gradient_many(acq_func2, 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)