gemseo / algos / doe

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lib_pydoe module

PyDOE algorithms wrapper.

class gemseo.algos.doe.lib_pydoe.PyDOE[source]

Bases: DOELibrary

PyDOE optimization library interface See DOELibrary.

ALGO_LIST: ClassVar[list[str]] = ['fullfact', 'ff2n', 'pbdesign', 'bbdesign', 'ccdesign', 'lhs']
CENTER_BB_KEYWORD = 'center_bb'
CENTER_CC_KEYWORD = 'center_cc'
DESC_LIST: ClassVar[list[str]] = ['Full-Factorial implemented in pyDOE', '2-Level Full-Factorial implemented in pyDOE', 'Plackett-Burman design implemented in pyDOE', 'Box-Behnken design implemented in pyDOE', 'Central Composite implemented in pyDOE', 'Latin Hypercube Sampling implemented in pyDOE']
ITERATION_KEYWORD = 'iterations'
LIBRARY_NAME: ClassVar[str | None] = 'PyDOE'

The name of the interfaced library.

PYDOE_2LEVELFACT_DESC = '2-Level Full-Factorial implemented in pyDOE'
PYDOE_BBDESIGN = 'bbdesign'
PYDOE_BBDESIGN_DESC = 'Box-Behnken design implemented in pyDOE'
PYDOE_CCDESIGN = 'ccdesign'
PYDOE_CCDESIGN_DESC = 'Central Composite implemented in pyDOE'
PYDOE_FULLFACT = 'fullfact'
PYDOE_FULLFACT_DESC = 'Full-Factorial implemented in pyDOE'
PYDOE_LHS = 'lhs'
PYDOE_LHS_DESC = 'Latin Hypercube Sampling implemented in pyDOE'
PYDOE_PBDESIGN = 'pbdesign'
PYDOE_PBDESIGN_DESC = 'Plackett-Burman design implemented in pyDOE'
WEB_LIST: ClassVar[list[str]] = ['', '', '', '', '', '']
algo_name: str | None

The name of the algorithm used currently.

descriptions: dict[str, AlgorithmDescription]

The description of the algorithms contained in the library.

eval_jac: bool

Whether to evaluate the Jacobian.

internal_algo_name: str | None

The internal name of the algorithm used currently.

It typically corresponds to the name of the algorithm in the wrapped library if any.

opt_grammar: JSONGrammar | None

The grammar defining the options of the current algorithm.

problem: OptimizationProblem

The optimization problem the driver library is bonded to.

samples: ndarray

The input samples with the design space variable types stored as dtype metadata.

seed: int

The seed to be used for reproducibility reasons.

This seed is initialized at 0 and each call to execute() increments it before using it.

unit_samples: ndarray

The input samples transformed in \([0,1]\).