tests package

Submodules

tests.gosac_benchmark module

class tests.gosac_benchmark.Problem(objf: Callable[[ndarray], ndarray], gfun: Callable[[ndarray], ndarray], iindex: tuple[int, ...], bounds: tuple[tuple[float, float], ...], xmin: tuple[float, ...] | None = None, fmin: float | None = None) → None

Bases: object

A class to represent a problem for the GOSAC benchmark.

Attributes

objfCallable[[np.ndarray], np.ndarray]: The objective function. Receives a 2D array of shape (n, dim) and returns a 1D array of shape (n,).
gfunCallable[[np.ndarray], np.ndarray]: The constraint function. Receives a 2D array of shape (n, dim) and returns a 2D array of shape (n, gdim).
iindextuple[int, …]: The indices of the integer variables.
boundstuple[tuple[float, float], …]: The bounds of the variables.
xmintuple[float, …] | None: The known minimum of the objective function. If None, the minimum is unknown.
fminfloat | None: The value of the objective function at the known minimum. If None, the minimum is unknown.

bounds: tuple[tuple[float, float], ...]

fmin: Optional[float] = None

gfun: Callable[[ndarray], ndarray]

iindex: tuple[int, ...]

objf: Callable[[ndarray], ndarray]

xmin: Optional[tuple[float, ...]] = None

tests.gosac_benchmark.fRana(x: ndarray) → ndarray

Return type:: ndarray

tests.gosac_benchmark.fWeierstrass(x: ndarray) → ndarray

Return type:: ndarray

tests.test_acquisition module

Test the acquisition functions.

tests.test_gosac_bench module

tests.test_gosac_bench.test_benchmark(problem: Problem) → None

Return type:: None

tests.test_gp module

Test the Gaussian Process model and helpers.

tests.test_gp.test_X(n: int, copy_X_train: bool)

tests.test_gp.test_expected_improvement()

tests.test_optimize module

Test the optimization routines.

tests.test_optimize.test_batched_sampling()

tests.test_optimize.test_callback(minimize)

tests.test_optimize.test_multiple_calls(minimize)

tests.test_rbf module

Test the RBF model.

class tests.test_rbf.TestRbfModel