gemseo.algos.doe.diagonal_doe.settings.diagonal_doe_settings module#
Settings of the diagonal DOE for scalable model construction.
- Settings DiagonalDOE_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_jac=False, n_processes=1, wait_time_between_samples=0.0, callbacks=(), n_samples=2, reverse=None)[source]#
Bases:
BaseNSamplesBasedDOESettings
The settings of the diagonal DOE for scalable model construction.
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)]) --
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.
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_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)]) --
By default it is set to 2.
- Return type:
None
- n_samples: int = 2#
The number of samples.
The number of samples must be greater than or equal than 2.
- Constraints:
ge = 2
- reverse: list[str] [Optional]#
The dimensions or variables to sample from upper to lower bounds.
If empty, every dimension will be sampled from lower to upper bounds.
- 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