gemseo / algos / doe

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lib_custom module

Design of experiments from custom data.

class gemseo.algos.doe.lib_custom.CustomDOE[source]

Bases: DOELibrary

A design of experiments from samples provided as a file or an array.

The samples are provided either as a file in text or csv format or as a sequence of sequences of numbers, e.g. a 2D numpy array.

A csv file format is assumed to have a header whereas a text file (extension .txt) does not.

Constructor Abstract class.

read_file(doe_file, delimiter=',', comments='#', skiprows=0)[source]

Read a file containing several samples (one per line) and return them.

Parameters:

  • doe_file (str | Path | TextIO) – Either the file, the filename, or the generator to read.

  • delimiter (str | None) –

    The character used to separate values. If None, use whitespace.

    By default it is set to “,”.

  • comments (str | Sequence[str] | None) –

    The characters or list of characters used to indicate the start of a comment. None implies no comments.

    By default it is set to “#”.

  • skiprows (int) –

    Skip the first skiprows lines.

    By default it is set to 0.

Returns:

The samples.

Return type:

ndarray

COMMENTS_KEYWORD: ClassVar[str] = 'comments'

The name given to the string indicating a comment line.

DELIMITER_KEYWORD: ClassVar[str] = 'delimiter'

The name given to the string separating two fields.

DOE_FILE: ClassVar[str] = 'doe_file'

The name given to the DOE file.

LIBRARY_NAME: ClassVar[str | None] = 'GEMSEO'

The name of the interfaced library.

SAMPLES: ClassVar[str] = 'samples'

The name given to the samples.

SKIPROWS_KEYWORD: ClassVar[str] = 'skiprows'

The name given to the number of skipped rows in the DOE file.

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: Any | None

The problem to be solved.

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 \([0,1]\).