def test_bayesian_optimizer_optimize_raises_for_invalid_rule_keys_and_default_acquisition(
) -> None:
optimizer = BayesianOptimizer(lambda x: x[:1],
one_dimensional_range(-1, 1))
with pytest.raises(ValueError):
optimizer.optimize(3, {"foo": zero_dataset()},
{"foo": _PseudoTrainableQuadratic()})
def test_bayesian_optimizer_optimize_raises_for_negative_steps(
num_steps: int) -> None:
optimizer = BayesianOptimizer(_quadratic_observer,
one_dimensional_range(-1, 1))
with pytest.raises(ValueError, match="num_steps"):
optimizer.optimize(num_steps, {"": zero_dataset()},
{"": _PseudoTrainableQuadratic()})
def test_bayesian_optimizer_optimize_raises_for_invalid_keys(
datasets: dict[str, Dataset], models: dict[str, TrainableProbabilisticModel]
) -> None:
search_space = Box([-1], [1])
optimizer = BayesianOptimizer(lambda x: {"foo": Dataset(x, x)}, search_space)
rule = FixedAcquisitionRule([[0.0]])
with pytest.raises(ValueError):
optimizer.optimize(10, datasets, models, rule)
def test_bayesian_optimizer_optimize_raises_for_invalid_rule_keys(
datasets: Dict[str, Dataset],
model_specs: Dict[str, TrainableProbabilisticModel]) -> None:
optimizer = BayesianOptimizer(lambda x: {"foo": Dataset(x, x[:1])},
one_dimensional_range(-1, 1))
rule = FixedAcquisitionRule(tf.constant([[0.0]]))
with pytest.raises(ValueError):
optimizer.optimize(10, datasets, model_specs, rule)
def test_bayesian_optimizer_optimize_raises_for_invalid_rule_keys_and_default_acquisition(
) -> None:
optimizer = BayesianOptimizer(lambda x: x[:1], Box([-1], [1]))
data, models = {
"foo": empty_dataset([1], [1])
}, {
"foo": _PseudoTrainableQuadratic()
}
with pytest.raises(ValueError):
optimizer.optimize(3, data, models)
def test_bayesian_optimizer_optimize_raises_for_negative_steps(
num_steps: int) -> None:
optimizer = BayesianOptimizer(_quadratic_observer, Box([-1], [1]))
data, models = {
"": empty_dataset([1], [1])
}, {
"": _PseudoTrainableQuadratic()
}
with pytest.raises(ValueError, match="num_steps"):
optimizer.optimize(num_steps, data, models)
def test_bayesian_optimizer_calls_observer_once_per_iteration(steps: int) -> None:
class _CountingObserver:
call_count = 0
def __call__(self, x: tf.Tensor) -> Dataset:
self.call_count += 1
return Dataset(x, tf.reduce_sum(x ** 2, axis=-1, keepdims=True))
observer = _CountingObserver()
optimizer = BayesianOptimizer(observer, Box([-1], [1]))
data = mk_dataset([[0.5]], [[0.25]])
optimizer.optimize(steps, data, _PseudoTrainableQuadratic()).final_result.unwrap()
assert observer.call_count == steps
def test_bayesian_optimizer_optimize_for_failed_step(
observer: Observer, model: TrainableProbabilisticModel,
rule: AcquisitionRule) -> None:
optimizer = BayesianOptimizer(observer, Box([0], [1]))
data, models = {"": mk_dataset([[0.0]], [[0.0]])}, {"": model}
result, history = optimizer.optimize(3, data, models, rule).astuple()
with pytest.raises(_Whoops):
result.unwrap()
assert len(history) == 1
def test_bayesian_optimizer_optimize_returns_default_acquisition_state_of_correct_type(
) -> None:
optimizer = BayesianOptimizer(lambda x: {OBJECTIVE: Dataset(x, x[:1])},
one_dimensional_range(-1, 1))
res: OptimizationResult[None] = optimizer.optimize(
3, {OBJECTIVE: zero_dataset()},
{OBJECTIVE: _PseudoTrainableQuadratic()})
if res.error is not None:
raise res.error
assert all(logging_state.acquisition_state is None
for logging_state in res.history)
def test_bayesian_optimizer_calls_observer_once_per_iteration(
steps: int) -> None:
class _CountingObserver:
call_count = 0
def __call__(self, x: tf.Tensor) -> Dict[str, Dataset]:
self.call_count += 1
return {
OBJECTIVE: Dataset(x,
tf.reduce_sum(x**2, axis=-1, keepdims=True))
}
observer = _CountingObserver()
optimizer = BayesianOptimizer(observer, one_dimensional_range(-1, 1))
data = Dataset(tf.constant([[0.5]]), tf.constant([[0.25]]))
optimizer.optimize(steps, {
OBJECTIVE: data
}, {
OBJECTIVE: _PseudoTrainableQuadratic()
}).final_result.unwrap()
assert observer.call_count == steps
def test_bayesian_optimizer_optimize_for_failed_step(
observer: Observer, model: TrainableProbabilisticModel,
rule: AcquisitionRule) -> None:
optimizer = BayesianOptimizer(observer, one_dimensional_range(0, 1))
result, history = optimizer.optimize(3, {
"": zero_dataset()
}, {
"": model
}, rule).astuple()
with pytest.raises(_Whoops):
result.unwrap()
assert len(history) == 1