gemseo.algos.doe.openturns.settings.base_ot_stratified_doe module#

Settings for the stratified DOEs from the OpenTURNS library.

Settings BaseOTStratifiedDOESettings(*, enable_progress_bar=None, eq_tolerance=0.01, ineq_tolerance=0.0001, log_problem=True, max_time=0.0, normalize_design_space=False, progress_bar_data_name='ProgressBarData', reset_iteration_counters=True, round_ints=True, store_jacobian=True, use_database=True, use_one_line_progress_bar=False, eval_func=True, eval_jac=False, n_processes=1, wait_time_between_samples=0.0, callbacks=(), preprocessors=(), vectorize=False, n_samples=0, seed=None, levels=(), centers=0.5)[source]#

Bases: BaseOpenTURNSSettings

The settings for the stratified DOEs from the OpenTURNS 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)]) --

    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)]) --

    By default it is set to 0.0.

  • normalize_design_space (bool) --

    By default it is set to False.

  • progress_bar_data_name (ProgressBarDataName) --

    By default it is set to "ProgressBarData".

  • reset_iteration_counters (bool) --

    By default it is set to True.

  • round_ints (bool) --

    By default it is set to True.

  • store_jacobian (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.

  • 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[tuple[Any, ...], dtype[floating[Any]]]], dict[str, ndarray[tuple[Any, ...], dtype[floating[Any]]]]]], Any], WithJsonSchema(json_schema={}, mode=None)]]) --

    By default it is set to ().

  • preprocessors (Sequence[Annotated[Callable[[int], Any], WithJsonSchema(json_schema={}, mode=None)]]) --

    By default it is set to ().

  • vectorize (bool) --

    By default it is set to False.

  • n_samples (Annotated[int, Ge(ge=0)]) --

    By default it is set to 0.

  • seed (int | None)

  • levels (float | Sequence[float]) --

    By default it is set to ().

  • centers (Sequence[float] | float) --

    By default it is set to 0.5.

Return type:

None

centers: Sequence[float] | float = 0.5#

The center of DOE in the unit hypercube.

This option is available for the axial, composite and factorial DOE algorithm. If scalar, this value is applied to each direction of the hypercube; the values must be in \(]0,1[\).

levels: float | Sequence[float] = ()#

The levels.

In the case of axial, composite and factorial DOEs, the positions of the levels relative to the center; the levels will be equispaced and symmetrical relative to the center; e.g. [0.2, 0.8] in dimension 1 will generate the samples [0.15, 0.6, 0.75, 0.8, 0.95, 1] for an axial DOE; the values must be in \(]0,1]\).

In the case of a full-factorial DOE, the number of levels per input direction; if scalar, this value is applied to each input direction.

n_samples: NonNegativeInt = 0#

The number of samples.

If 0, set from the options.

Constraints:

  • ge = 0