lib_custom module¶
Design of experiments from custom data.
- class gemseo.algos.doe.lib_custom.CustomDOE[source]¶
Bases:
gemseo.algos.doe.doe_lib.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.
- Return type
None
- compute_doe(variables_space, size=None, unit_sampling=False, **options)¶
Compute a design of experiments (DOE) in a variables space.
- Parameters
variables_space (DesignSpace) – The variables space to be sampled.
size (int | None) –
The size of the DOE. If
None
, the size is deduced from theoptions
.By default it is set to None.
unit_sampling (bool) –
Whether to sample in the unit hypercube.
By default it is set to False.
**options (DOELibraryOptionType) – The options of the DOE algorithm.
- Returns
The design of experiments whose rows are the samples and columns the variables.
- Return type
ndarray
- static compute_phip_criteria(samples, power=10.0)¶
Compute the \(\phi^p\) space-filling criterion.
See Morris & Mitchell, Exploratory designs for computational experiments, 1995.
- Parameters
samples (numpy.ndarray) – The samples of the input variables.
power (float) –
The power \(p\) of the \(\phi^p\) criterion.
By default it is set to 10.0.
- Returns
The \(\phi^p\) space-filling criterion.
- Return type
- deactivate_progress_bar()¶
Deactivate the progress bar.
- Return type
None
- driver_has_option(option_name)¶
Check the existence of an option.
- ensure_bounds(orig_func, normalize=True)¶
Project the design vector onto the design space before execution.
- Parameters
orig_func – The original function.
normalize –
Whether to use the normalized design space.
By default it is set to True.
- Returns
A function calling the original function with the input data projected onto the design space.
- evaluate_samples(eval_jac=False, n_processes=1, wait_time_between_samples=0.0)¶
Evaluate all the functions of the optimization problem at the samples.
- Parameters
eval_jac (bool) –
Whether to evaluate the Jacobian.
By default it is set to False.
n_processes (int) –
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
wait_time_between_samples (float) –
The time to wait between each sample evaluation, in seconds.
By default it is set to 0.0.
Warning
This class relies on multiprocessing features when
n_processes > 1
, it is therefore necessary to protect its execution with anif __name__ == '__main__':
statement when working on Windows.
- execute(problem, algo_name=None, eval_obs_jac=False, skip_int_check=False, **options)¶
Execute the driver.
- Parameters
problem (OptimizationProblem) – The problem to be solved.
algo_name (str | None) –
The name of the algorithm. If None, use the algo_name attribute which may have been set by the factory.
By default it is set to None.
eval_obs_jac (bool) –
Whether to evaluate the Jacobian of the observables.
By default it is set to False.
skip_int_check (bool) –
Whether to skip the integer variable handling check of the selected algorithm.
By default it is set to False.
**options (DriverLibOptionType) – The options for the algorithm.
- Returns
The optimization result.
- Raises
ValueError – If algo_name was not either set by the factory or given as an argument.
- Return type
- export_samples(doe_output_file)¶
Export the samples generated by DOE library to a CSV file.
- Parameters
doe_output_file (Path | str) – The path to the output file.
- Return type
None
- filter_adapted_algorithms(problem)¶
Filter the algorithms capable of solving the problem.
- finalize_iter_observer()¶
Finalize the iteration observer.
- Return type
None
- get_optimum_from_database(message=None, status=None)¶
Retrieves the optimum from the database and builds an optimization result object from it.
- get_x0_and_bounds_vects(normalize_ds)¶
Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.
- Parameters
normalize_ds – Whether to normalize the input variables that are not integers, according to the normalization policy of the design space.
- Returns
The current value, the lower bounds and the upper bounds.
- init_iter_observer(max_iter, message)¶
Initialize the iteration observer.
It will handle the stopping criterion and the logging of the progress bar.
- Parameters
- Raises
ValueError – If the max_iter is not greater than or equal to one.
- Return type
None
- init_options_grammar(algo_name)¶
Initialize the options grammar.
- Parameters
algo_name (str) – The name of the algorithm.
- Return type
- is_algo_requires_grad(algo_name)¶
Returns True if the algorithm requires a gradient evaluation.
- Parameters
algo_name – The name of the algorithm.
- static is_algorithm_suited(algorithm_description, problem)¶
Check if the algorithm is suited to the problem according to its description.
- Parameters
algorithm_description (gemseo.algos.doe.doe_lib.DOEAlgorithmDescription) – The description of the algorithm.
problem (gemseo.algos.opt_problem.OptimizationProblem) – The problem to be solved.
- Returns
Whether the algorithm is suited to the problem.
- Return type
- new_iteration_callback(x_vect=None)¶
Callback called at each new iteration, i.e. every time a design vector that is not already in the database is proposed by the optimizer.
Iterate the progress bar, implement the stop criteria.
- Parameters
x_vect (ndarray | None) –
The design variables values. If None, use the values of the last iteration.
By default it is set to None.
- Raises
MaxTimeReached – If the elapsed time is greater than the maximum execution time.
- Return type
None
- 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.
- COMPLEX_STEP_METHOD = 'complex_step'¶
- DESIGN_ALGO_NAME = 'Design algorithm'¶
- DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']¶
- DIMENSION = 'dimension'¶
- EQ_TOLERANCE = 'eq_tolerance'¶
- EVAL_JAC = 'eval_jac'¶
- EVAL_OBS_JAC_OPTION = 'eval_obs_jac'¶
- FINITE_DIFF_METHOD = 'finite_differences'¶
- INEQ_TOLERANCE = 'ineq_tolerance'¶
- LEVEL_KEYWORD = 'levels'¶
- MAX_DS_SIZE_PRINT = 40¶
- MAX_TIME = 'max_time'¶
- NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'¶
- N_PROCESSES = 'n_processes'¶
- N_SAMPLES = 'n_samples'¶
- OPTIONS_DIR: Final[str] = 'options'¶
The name of the directory containing the files of the grammars of the options.
- OPTIONS_MAP: dict[str, str] = {}¶
The names of the options in GEMSEO mapping to those in the wrapped library.
- PHIP_CRITERIA = 'phi^p'¶
- ROUND_INTS_OPTION = 'round_ints'¶
- SAMPLES_TAG = 'samples'¶
- SEED = 'seed'¶
- SKIPROWS_KEYWORD: ClassVar[str] = 'skiprows'¶
The name given to the number of skipped rows in the DOE file.
- USER_DEFINED_GRADIENT = 'user'¶
- USE_DATABASE_OPTION = 'use_database'¶
- WAIT_TIME_BETWEEN_SAMPLES = 'wait_time_between_samples'¶
- activate_progress_bar: ClassVar[bool] = True¶
Whether to activate the progress bar in the optimization log.
- descriptions: dict[str, AlgorithmDescription]¶
The description of the algorithms contained in the library.
- 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.