gemseo.algos.doe.scipy.settings.lhs module#

Settings for the LHS DOE from the SciPy library.

Settings LHS_Settings(*, enable_progress_bar=None, eq_tolerance=0.01, ineq_tolerance=0.0001, log_problem=True, max_time=0.0, normalize_design_space=False, reset_iteration_counters=True, round_ints=True, use_database=True, use_one_line_progress_bar=False, store_jacobian=True, eval_func=True, eval_jac=False, n_processes=1, wait_time_between_samples=0.0, callbacks=(), n_samples, seed=None, scramble=True, optimization=None, strength=Strength.one, centered=False)[source]#

Bases: BaseSciPyDOESettings

The settings for the LHS DOE from the SciPy library.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:
  • enable_progress_bar (bool | None)

  • eq_tolerance (Annotated[float, Ge(ge=0)]) --

    By default it is set to 0.01.

  • ineq_tolerance (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

    By default it is set to 0.0001.

  • log_problem (bool) --

    By default it is set to True.

  • max_time (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

    By default it is set to 0.0.

  • normalize_design_space (bool) --

    By default it is set to False.

  • reset_iteration_counters (bool) --

    By default it is set to True.

  • round_ints (bool) --

    By default it is set to True.

  • use_database (bool) --

    By default it is set to True.

  • use_one_line_progress_bar (bool) --

    By default it is set to False.

  • store_jacobian (bool) --

    By default it is set to True.

  • eval_func (bool) --

    By default it is set to True.

  • eval_jac (bool) --

    By default it is set to False.

  • n_processes (Annotated[int, Gt(gt=0)]) --

    By default it is set to 1.

  • wait_time_between_samples (Annotated[float, Ge(ge=0)]) --

    By default it is set to 0.0.

  • callbacks (Sequence[Annotated[Callable[[int, tuple[dict[str, float | ndarray[Any, dtype[floating[Any]]]], dict[str, ndarray[Any, dtype[floating[Any]]]]]], Any], WithJsonSchema(json_schema={}, mode=None)]]) --

    By default it is set to ().

  • n_samples (Annotated[int, Gt(gt=0)])

  • seed (int | None)

  • scramble (bool) --

    By default it is set to True.

  • optimization (Optimizer | None)

  • strength (Strength) --

    By default it is set to 1.

  • centered (bool) --

    By default it is set to False.

Return type:

None

centered: bool = False#

Whether to center the samples within the multi-dimensional grid cells.

If SciPy >= 1.10.0, this argument is ignored; use scramble instead.

optimization: Optimizer | None = None#

The name of an optimization scheme to improve the DOE's quality.

If None, use the DOE as is. New in SciPy 1.10.0.

scramble: bool = True#

Whether to use scrambling (Owen type).

Only available with SciPy >= 1.10.0.

strength: Strength = Strength.one#

The strength of the LHS.

model_post_init(context, /)#

We need to both initialize private attributes and call the user-defined model_post_init method.

Parameters:
  • self (BaseModel)

  • context (Any)

Return type:

None