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

Settings for the optimized LHS DOE from the OpenTURNS library.

Settings OT_OPT_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, temperature=TemperatureProfile.GEOMETRIC, criterion=SpaceFillingCriterion.C2, annealing=True, n_replicates=1000)[source]#

Bases: BaseOpenTURNSSettings

The settings for the optimized LHS DOE 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), Ge(ge=0), 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, Ge(ge=2), Gt(gt=0)])

  • seed (int | None)

  • temperature (TemperatureProfile) --

    By default it is set to "Geometric".

  • criterion (SpaceFillingCriterion) --

    By default it is set to "C2".

  • annealing (bool) --

    By default it is set to True.

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

    By default it is set to 1000.

Return type:

None

annealing: bool = True#

Whether to use simulated annealing to optimize the LHS.

If False, the crude Monte Carlo method is used.

criterion: SpaceFillingCriterion = SpaceFillingCriterion.C2#

The space-filling criterion.

n_replicates: PositiveInt = 1000#

The number of Monte Carlo replicates to optimize LHS.

Constraints:

  • gt = 0

n_samples: PositiveInt [Required]#

The number of samples.

Constraints:

  • ge = 2

  • gt = 0

temperature: TemperatureProfile = TemperatureProfile.GEOMETRIC#

The temperature profile for simulated annealing.

Either "Geometric" or "Linear".

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) -- The BaseModel instance.

  • context (Any) -- The context.

Return type:

None