def _pairs_of_shapes(
*, excluding_ranks: Container[int] = ()
) -> FrozenSet[Tuple[ShapeLike, ShapeLike]]:
shapes = various_shapes(excluding_ranks=excluding_ranks)
return frozenset(itertools.product(shapes, shapes))
space = Integers(3)
with pytest.raises(ValueError):
space**exponent
def test_search_space___pow___multiplies_correct_number_of_search_spaces(
) -> None:
assert (Integers(5)**7).limit == 5**7
def _points_in_2D_search_space() -> tf.Tensor:
return tf.constant([[-1.0, 0.4], [-1.0, 0.6], [0.0, 0.4], [0.0, 0.6],
[1.0, 0.4], [1.0, 0.6]])
@pytest.mark.parametrize("shape", various_shapes(excluding_ranks=[2]))
def test_discrete_search_space_raises_for_invalid_shapes(
shape: ShapeLike) -> None:
with pytest.raises(ValueError):
DiscreteSearchSpace(tf.random.uniform(shape))
def test_discrete_search_space_points() -> None:
space = DiscreteSearchSpace(_points_in_2D_search_space())
npt.assert_array_equal(space.points, _points_in_2D_search_space())
@pytest.mark.parametrize("point", list(_points_in_2D_search_space()))
def test_discrete_search_space_contains_all_its_points(
point: tf.Tensor) -> None:
assert point in DiscreteSearchSpace(_points_in_2D_search_space())
return t + u
assert add(tf.constant(1.), tf.constant(2.)) == tf.constant(3.)
@pytest.mark.parametrize('apply', [True, False])
@pytest.mark.parametrize('kwargs', [{}, {'autograph': False}])
def test_jit_compiles_function(apply: bool, kwargs: Any) -> None:
@jit(apply, **kwargs)
def one() -> tf.Tensor:
return tf.constant(0)
tf_function_type = type(tf.function(lambda x: x))
assert isinstance(one, tf_function_type) == apply
@pytest.mark.parametrize('this_shape', various_shapes())
@pytest.mark.parametrize('that_shape', various_shapes())
def test_shapes_equal(this_shape: ShapeLike, that_shape: ShapeLike) -> None:
assert shapes_equal(tf.ones(this_shape),
tf.ones(that_shape)) == (this_shape == that_shape)
@pytest.mark.parametrize(
't, expected',
[(tf.constant(0), np.array(0)),
(np.arange(12).reshape(3, -1), np.arange(12).reshape(3, -1)),
(tf.reshape(tf.range(12), [3, -1]), np.arange(12).reshape(3, -1))])
def test_to_numpy(t: TensorType, expected: np.ndarray) -> None:
npt.assert_array_equal(to_numpy(t), expected)
)
def test_probability_of_feasibility(threshold: float, at: tf.Tensor, expected: float) -> None:
actual = probability_of_feasibility(QuadraticMeanAndRBFKernel(), threshold, at)
npt.assert_allclose(actual, expected, rtol=1e-4)
@pytest.mark.parametrize("at", [tf.constant([[0.0]]), tf.constant([[-3.4]]), tf.constant([[0.2]])])
@pytest.mark.parametrize("threshold", [-2.3, 0.2])
def test_probability_of_feasibility_builder_builds_pof(threshold: float, at: tf.Tensor) -> None:
builder = ProbabilityOfFeasibility(threshold)
acq = builder.prepare_acquisition_function(zero_dataset(), QuadraticMeanAndRBFKernel())
expected = probability_of_feasibility(QuadraticMeanAndRBFKernel(), threshold, at)
npt.assert_allclose(acq(at), expected)
@pytest.mark.parametrize("shape", various_shapes() - {()})
def test_probability_of_feasibility_raises_on_non_scalar_threshold(shape: ShapeLike) -> None:
threshold = tf.ones(shape)
with pytest.raises(ValueError):
probability_of_feasibility(QuadraticMeanAndRBFKernel(), threshold, tf.constant([[0.0]]))
@pytest.mark.parametrize("shape", [[], [0], [2]])
def test_probability_of_feasibility_raises_on_incorrect_at_shape(shape: ShapeLike) -> None:
at = tf.ones(shape)
with pytest.raises(ValueError):
probability_of_feasibility(QuadraticMeanAndRBFKernel(), 0.0, at)
@pytest.mark.parametrize("shape", various_shapes() - {()})
def test_probability_of_feasibility_builder_raises_on_non_scalar_threshold(
@pytest.mark.parametrize(
'at', [tf.constant([[0.0]]),
tf.constant([[-3.4]]),
tf.constant([[0.2]])])
@pytest.mark.parametrize('threshold', [-2.3, 0.2])
def test_probability_of_feasibility_builder_builds_pof(threshold: float,
at: tf.Tensor) -> None:
builder = ProbabilityOfFeasibility(threshold)
acq = builder.prepare_acquisition_function(zero_dataset(),
QuadraticWithUnitVariance())
expected = probability_of_feasibility(QuadraticWithUnitVariance(),
threshold, at)
npt.assert_allclose(acq(at), expected)
@pytest.mark.parametrize('shape', various_shapes() - {()})
def test_probability_of_feasibility_raises_on_non_scalar_threshold(
shape: ShapeLike) -> None:
threshold = tf.ones(shape)
with pytest.raises(ValueError):
probability_of_feasibility(QuadraticWithUnitVariance(), threshold,
tf.constant([[0.0]]))
@pytest.mark.parametrize('shape', [[], [0], [2]])
def test_probability_of_feasibility_raises_on_incorrect_at_shape(
shape: ShapeLike) -> None:
at = tf.ones(shape)
with pytest.raises(ValueError):
probability_of_feasibility(QuadraticWithUnitVariance(), 0.0, at)