DOE algorithms

Warning

Some capabilities may require the installation of GEMSEO with all its features and some others may depend on plugins.

Warning

All the features of the wrapped libraries may not be exposed through GEMSEO.

Note

The algorithm settings can be passed to a function of the form

function(..., settings_model: AlgorithmSettings | None = None, **settings: Any)

either one by one:

function(..., setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)

or using the argument name "settings_model" and the Pydantic model associated with the algorithm:

settings_model = AlgorithmSettings(setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)
function(..., settings_model=settings_model)

See also

You can find more information about this family of algorithms in the user guide.

CustomDOE

Module: gemseo.algos.doe.custom_doe.custom_doe

This samples are provided either as a file in text or csv format or as a sequence of sequences of numbers.

Pydantic model of the settings for CustomDOE

from gemseo.settings.doe import CustomDOE_Settings

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• comments : str | collections.abc.Sequence[str], optional

  The (list of) characters used to indicate the start of a comment.

  No comments if empty.

  By default it is set to #.

• delimiter : <class 'str'>, optional

  The character used to separate values.

  By default it is set to ,.

• doe_file : str | pathlib.Path

  The path to the file containing the input samples.

  If empty, use samples.

  By default it is set to .

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• samples : gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]] | collections.abc.Mapping[str, gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]] | collections.abc.Sequence[collections.abc.Mapping[str, gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]]], optional

  The input samples.

  They must be at least a 2D-array, a dictionary of 2D-arrays or a list of dictionaries of 1D-arrays. If empty, use doe_file.

  By default it is set to {}.

• skiprows : <class 'int'>, optional

  The number of first lines to skip.

  By default it is set to 0.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

DiagonalDOE

Module: gemseo.algos.doe.diagonal_doe.diagonal_doe

Diagonal design of experiments

Pydantic model of the settings for DiagonalDOE

from gemseo.settings.doe import DiagonalDOE_Settings

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The number of samples.

  The number of samples must be greater than or equal than 2.

  By default it is set to 2.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• reverse : collections.abc.Sequence[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.

  By default it is set to ().

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

Halton

Module: gemseo.algos.doe.scipy.scipy_doe

Halton sequence

Pydantic model of the settings for Halton

from gemseo.settings.doe import Halton_Settings

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• optimization : gemseo.algos.doe.scipy.settings.base_scipy_doe_settings.Optimizer | None, optional

  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.

  By default it is set to None.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• scramble : <class 'bool'>, optional

  Whether to use scrambling (Owen type).

  Only available with SciPy >= 1.10.0.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

LHS

Module: gemseo.algos.doe.scipy.scipy_doe

Latin hypercube sampling (LHS)

Pydantic model of the settings for LHS

from gemseo.settings.doe import LHS_Settings

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• optimization : gemseo.algos.doe.scipy.settings.base_scipy_doe_settings.Optimizer | None, optional

  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.

  By default it is set to None.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• scramble : <class 'bool'>, optional

  Whether to use scrambling (Owen type).

  Only available with SciPy >= 1.10.0.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• strength : <enum 'Strength'>, optional

  The strength of the LHS.

  By default it is set to 1.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

MC

Module: gemseo.algos.doe.scipy.scipy_doe

Monte Carlo sampling

Pydantic model of the settings for MC

from gemseo.settings.doe import MC_Settings

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

MorrisDOE

Module: gemseo.algos.doe.morris_doe.morris_doe

The DOE used by the Morris sensitivity analysis.

Pydantic model of the settings for MorrisDOE

from gemseo.settings.doe import MorrisDOE_Settings

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• doe_algo_name : <class 'str'>, optional

  The name of the DOE algorithm to repeat the OAT DOE.

  By default it is set to PYDOE_LHS.

• doe_algo_settings : collections.abc.Mapping[str, typing.Any], optional

  The options of the DOE algorithm.

  By default it is set to {}.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The maximum number of samples required by the user.

  If 0, deduce it from the design space dimension and n_replicates.

  By default it is set to 0.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• step : <class 'float'>, optional

  The relative step of the OAT DOE.

  By default it is set to 0.05.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OATDOE

Module: gemseo.algos.doe.oat_doe.oat_doe

The DOE used by a One-factor-at-a-Time sensitivity analysis.

Pydantic model of the settings for OATDOE

from gemseo.settings.doe import OATDOE_Settings

Required settings

• initial_point : gemseo.utils.pydantic_ndarray._NDArrayPydantic[typing.Any, numpy.dtype[+_ScalarType_co]]

  The initial point of the OAT DOE.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• step : <class 'float'>, optional

  The relative step of the OAT DOE.

  The step in the x direction is step*(max_x-min_x)`` if x+step*(max_x-min_x)<=max_x and -step*(max_x- min_x) otherwise.

  By default it is set to 0.05.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_AXIAL

Module: gemseo.algos.doe.openturns.openturns

Axial design

Pydantic model of the settings for OT_AXIAL

from gemseo.settings.doe import OT_AXIAL_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Axial.html.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• centers : collections.abc.Sequence[float] | float, optional

  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 \(]0,1[\).

  By default it is set to 0.5.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• levels : float | collections.abc.Sequence[float], optional

  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.

  By default it is set to ().

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The number of samples.

  If 0, set from the options.

  By default it is set to 0.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_COMPOSITE

Module: gemseo.algos.doe.openturns.openturns

Composite design

Pydantic model of the settings for OT_COMPOSITE

from gemseo.settings.doe import OT_COMPOSITE_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Composite.html.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• centers : collections.abc.Sequence[float] | float, optional

  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 \(]0,1[\).

  By default it is set to 0.5.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• levels : float | collections.abc.Sequence[float], optional

  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.

  By default it is set to ().

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The number of samples.

  If 0, set from the options.

  By default it is set to 0.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_FACTORIAL

Module: gemseo.algos.doe.openturns.openturns

Factorial design

Pydantic model of the settings for OT_FACTORIAL

from gemseo.settings.doe import OT_FACTORIAL_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Factorial.html.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• centers : collections.abc.Sequence[float] | float, optional

  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 \(]0,1[\).

  By default it is set to 0.5.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• levels : float | collections.abc.Sequence[float], optional

  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.

  By default it is set to ().

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The number of samples.

  If 0, set from the options.

  By default it is set to 0.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_FAURE

Module: gemseo.algos.doe.openturns.openturns

Faure sequence

Pydantic model of the settings for OT_FAURE

from gemseo.settings.doe import OT_FAURE_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.FaureSequence.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_FULLFACT

Module: gemseo.algos.doe.openturns.openturns

Full factorial design

Pydantic model of the settings for OT_FULLFACT

from gemseo.settings.doe import OT_FULLFACT_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Box.html.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• levels : typing.Union[typing.Annotated[int, Gt(gt=0)], collections.abc.Sequence[typing.Annotated[int, Gt(gt=0)]]], optional

  The number of levels per input direction.

  If scalar, this value is applied to each input direction.

  By default it is set to ().

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The number of samples.

  If 0, set from the options.

  By default it is set to 0.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_HALTON

Module: gemseo.algos.doe.openturns.openturns

Halton sequence

Pydantic model of the settings for OT_HALTON

from gemseo.settings.doe import OT_HALTON_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.HaltonSequence.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_HASELGROVE

Module: gemseo.algos.doe.openturns.openturns

Haselgrove sequence

Pydantic model of the settings for OT_HASELGROVE

from gemseo.settings.doe import OT_HASELGROVE_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.HaselgroveSequence.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_LHS

Module: gemseo.algos.doe.openturns.openturns

Latin Hypercube Sampling

Pydantic model of the settings for OT_LHS

from gemseo.settings.doe import OT_LHS_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.LHSExperiment.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_LHSC

Module: gemseo.algos.doe.openturns.openturns

Centered Latin Hypercube Sampling

Pydantic model of the settings for OT_LHSC

from gemseo.settings.doe import OT_LHSC_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.LHSExperiment.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_MONTE_CARLO

Module: gemseo.algos.doe.openturns.openturns

Monte Carlo sequence

Pydantic model of the settings for OT_MONTE_CARLO

from gemseo.settings.doe import OT_MONTE_CARLO_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Uniform.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_OPT_LHS

Module: gemseo.algos.doe.openturns.openturns

Optimal Latin Hypercube Sampling

Pydantic model of the settings for OT_OPT_LHS

from gemseo.settings.doe import OT_OPT_LHS_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SimulatedAnnealingLHS.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• annealing : <class 'bool'>, optional

  Whether to use simulated annealing to optimize the LHS.

  If False, the crude Monte Carlo method is used.

  By default it is set to True.

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• criterion : <enum 'SpaceFillingCriterion'>, optional

  The space-filling criterion.

  By default it is set to C2.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_replicates : <class 'int'>, optional

  The number of Monte Carlo replicates to optimize LHS.

  By default it is set to 1000.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• temperature : <enum 'TemperatureProfile'>, optional

  The temperature profile for simulated annealing.

  Either "Geometric" or "Linear".

  By default it is set to Geometric.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_RANDOM

Module: gemseo.algos.doe.openturns.openturns

Random sampling

Pydantic model of the settings for OT_RANDOM

from gemseo.settings.doe import OT_RANDOM_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Uniform.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_REVERSE_HALTON

Module: gemseo.algos.doe.openturns.openturns

Reverse Halton

Pydantic model of the settings for OT_REVERSE_HALTON

from gemseo.settings.doe import OT_REVERSE_HALTON_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.ReverseHaltonSequence.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_SOBOL

Module: gemseo.algos.doe.openturns.openturns

Sobol sequence

Pydantic model of the settings for OT_SOBOL

from gemseo.settings.doe import OT_SOBOL_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolSequence.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

OT_SOBOL_INDICES

Module: gemseo.algos.doe.openturns.openturns

DOE for Sobol indices

Pydantic model of the settings for OT_SOBOL_INDICES

from gemseo.settings.doe import OT_SOBOL_INDICES_Settings

More details about the algorithm and its settings on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolIndicesAlgorithm.html.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• eval_second_order : <class 'bool'>, optional

  Whether to build a DOE to evaluate also the second-order indices.

  If False, the DOE is designed for first and total-order indices only.

  By default it is set to True.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PYDOE_BBDESIGN

Module: gemseo.algos.doe.pydoe.pydoe

Box-Behnken design

Pydantic model of the settings for PYDOE_BBDESIGN

from gemseo.settings.doe import PYDOE_BBDESIGN_Settings

More details about the algorithm and its settings on https://pythonhosted.org/pyDOE/rsm.html#box-behnken.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• center : typing.Optional[typing.Annotated[int, Gt(gt=0)]], optional

  The number of center points for the Box-Behnken design.

  If None, use a pre-determined number of points.

  By default it is set to None.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PYDOE_CCDESIGN

Module: gemseo.algos.doe.pydoe.pydoe

Central Composite

Pydantic model of the settings for PYDOE_CCDESIGN

from gemseo.settings.doe import PYDOE_CCDESIGN_Settings

More details about the algorithm and its settings on https://pythonhosted.org/pyDOE/rsm.html#central-composite.

Optional settings

• alpha : <enum 'Alpha'>, optional

  A parameter to describe how the variance is distributed.

  Either "orthogonal" or "rotatable".

  By default it is set to orthogonal.

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• center : tuple[int, int], optional

  The 2-tuple of center points for the central composite design.

  By default it is set to (4, 4).

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• face : <enum 'Face'>, optional

  The relation between the start and corner (factorial) points.

  By default it is set to circumscribed.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PYDOE_FF2N

Module: gemseo.algos.doe.pydoe.pydoe

2-Level Full-Factorial

Pydantic model of the settings for PYDOE_FF2N

from gemseo.settings.doe import PYDOE_FF2N_Settings

More details about the algorithm and its settings on https://pythonhosted.org/pyDOE/factorial.html#level-full-factorial.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PYDOE_FULLFACT

Module: gemseo.algos.doe.pydoe.pydoe

Full-Factorial

Pydantic model of the settings for PYDOE_FULLFACT

from gemseo.settings.doe import PYDOE_FULLFACT_Settings

More details about the algorithm and its settings on https://pythonhosted.org/pyDOE/factorial.html#general-full-factorial.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• levels : typing.Union[collections.abc.Sequence[typing.Annotated[int, Gt(gt=0)]], typing.Annotated[int, Gt(gt=0)]], optional

  The levels.

  One must either specify n_samples or levels. The levels are inferred from the number of samples if the former is specified.

  By default it is set to ().

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• n_samples : <class 'int'>, optional

  The number of samples.

  If 0, set from the settings.

  By default it is set to 0.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PYDOE_LHS

Module: gemseo.algos.doe.pydoe.pydoe

Latin Hypercube Sampling

Pydantic model of the settings for PYDOE_LHS

from gemseo.settings.doe import PYDOE_LHS_Settings

More details about the algorithm and its settings on https://pythonhosted.org/pyDOE/randomized.html#latin-hypercube.

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• criterion : gemseo.algos.doe.pydoe.settings.pydoe_lhs.Criterion | None, optional

  The criterion to use when sampling the points.

  If None, randomize the points within the intervals.

  By default it is set to None.

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• iterations : <class 'int'>, optional

  The number of iterations in the correlation/maximin algorithms.

  By default it is set to 5.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• random_state : typing.Optional[typing.Annotated[int, Gt(gt=0)]], optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PYDOE_PBDESIGN

Module: gemseo.algos.doe.pydoe.pydoe

Plackett-Burman design

Pydantic model of the settings for PYDOE_PBDESIGN

from gemseo.settings.doe import PYDOE_PBDESIGN_Settings

More details about the algorithm and its settings on https://pythonhosted.org/pyDOE/factorial.html#plackett-burman.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

PoissonDisk

Module: gemseo.algos.doe.scipy.scipy_doe

Poisson disk sampling

Pydantic model of the settings for PoissonDisk

from gemseo.settings.doe import PoissonDisk_Settings

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• hypersphere : <enum 'Hypersphere'>, optional

  The sampling strategy to generate potential candidates.

  The candidates will be added in the final sample.

  By default it is set to volume.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• ncandidates : <class 'int'>, optional

  The number of candidates to sample per iteration.

  By default it is set to 30.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• optimization : gemseo.algos.doe.scipy.settings.base_scipy_doe_settings.Optimizer | None, optional

  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.

  By default it is set to None.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• radius : <class 'float'>, optional

  The minimal distance to keep between points when sampling new candidates.

  By default it is set to 0.05.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.

Sobol

Module: gemseo.algos.doe.scipy.scipy_doe

Engine for generating (scrambled) Sobol' sequences

Pydantic model of the settings for Sobol

from gemseo.settings.doe import Sobol_Settings

Required settings

• n_samples : <class 'int'>

  The number of samples.

Optional settings

• bits : <class 'int'>, optional

  The number of bits of the generator.

  By default it is set to 30.

• callbacks : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int, tuple[dict[str, float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]], dict[str, numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating[typing.Any]]]]]], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called after evaluating the function of interest.

  A callback must be called as callback(sample_index, (output, Jacobian)).

  By default it is set to ().

• enable_progress_bar : bool | None, optional

  Whether to enable the progress bar in the optimization log.

  If None, use the global value of enable_progress_bar (see the configure function to change it globally).

  By default it is set to None.

• eq_tolerance : <class 'float'>, optional

  The tolerance on the equality constraints.

  By default it is set to 0.01.

• eval_func : <class 'bool'>, optional

  Whether to sample the function computing the output values.

  By default it is set to True.

• eval_jac : <class 'bool'>, optional

  Whether to sample the function computing the Jacobian data.

  By default it is set to False.

• ineq_tolerance : <class 'float'>, optional

  The tolerance on the inequality constraints.

  By default it is set to 0.0001.

• log_problem : <class 'bool'>, optional

  Whether to log the definition and result of the problem.

  By default it is set to True.

• max_time : <class 'float'>, optional

  The maximum runtime in seconds, disabled if 0.

  By default it is set to 0.0.

• n_processes : <class 'int'>, optional

  The maximum number of processes to parallelize the execution.

  By default it is set to 1.

• normalize_design_space : <class 'bool'>, optional

  Whether to normalize the design space variables between 0 and 1.

  By default it is set to False.

• optimization : gemseo.algos.doe.scipy.settings.base_scipy_doe_settings.Optimizer | None, optional

  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.

  By default it is set to None.

• preprocessors : collections.abc.Sequence[typing.Annotated[collections.abc.Callable[[int], typing.Any], WithJsonSchema(json_schema={}, mode=None)]], optional

  The functions called before evaluating the function of interest.

  A preprocessor must be called as preprocessor(sample_index).

  This option is not compatible with the vectorization of functions evaluations.

  By default it is set to ().

• progress_bar_data_name : <enum 'ProgressBarDataName'>, optional

  The name of a BaseProgressBarData class to define the data of an evaluation problem to be displayed in the progress bar.

  By default it is set to ProgressBarData.

• reset_iteration_counters : <class 'bool'>, optional

  Whether to reset the iteration counters before each execution.

  By default it is set to True.

• round_ints : <class 'bool'>, optional

  Whether to round the integer variables.

  By default it is set to True.

• scramble : <class 'bool'>, optional

  Whether to use scrambling (Owen type).

  Only available with SciPy >= 1.10.0.

  By default it is set to True.

• seed : int | None, optional

  The seed used for reproducibility reasons.

  If None, use seed.

  By default it is set to None.

• store_jacobian : <class 'bool'>, optional

  Whether to store the Jacobian matrices in the database.

  This argument is ignored when the use_database option is False. If a gradient-based algorithm is used, this option cannot be set along with kkt options.

  By default it is set to True.

• use_database : <class 'bool'>, optional

  Whether to wrap the functions in the database.

  By default it is set to True.

• use_one_line_progress_bar : <class 'bool'>, optional

  Whether to log the progress bar on a single line.

  By default it is set to False.

• vectorize : <class 'bool'>, optional

  Whether to vectorize the functions evaluations.

  By default it is set to False.

• wait_time_between_samples : <class 'float'>, optional

  The time to wait between each sample evaluation, in seconds.

  By default it is set to 0.0.