gemseo / algos / doe

lib_openturns module¶

OpenTUNRS DOE algorithms wrapper¶

Classes:

OpenTURNS()

OpenTURNS library of DOE algorithms wrapping.

class gemseo.algos.doe.lib_openturns.OpenTURNS[source]¶

Bases: gemseo.algos.doe.doe_lib.DOELibrary

OpenTURNS library of DOE algorithms wrapping.

Constructor Unless mentioned, DOE are normalized between [0,1]

Attributes:

`ALGO_LIST`
`ANNEALING`
`CENTER_KEYWORD`
`COMPLEX_STEP_METHOD`
`CRITERIA`
`CRITERION`
`DESCRIPTION`
`DESC_LIST`
`DESIGN_ALGO_NAME`
`DIFFERENTIATION_METHODS`
`DIMENSION`
`DISTRIBUTION_DEFAULT`
`DISTRIBUTION_KEYWORD`
`DISTRIBUTION_LIST`
`DOE_SETTINGS_OPTIONS`
`END_KEYWORD`
`EQ_TOLERANCE`
`EVAL_JAC`
`FINITE_DIFF_METHOD`
`HANDLE_EQ_CONS`
`HANDLE_INEQ_CONS`
`INEQ_TOLERANCE`
`INTERNAL_NAME`
`LEVEL_KEYWORD`
`LIB`
`MAX_DS_SIZE_PRINT`
`MAX_TIME`
`MEAN_KEYWORD`
`MIN_DIMS`
`NORMALIZE_DESIGN_SPACE_OPTION`
`N_PROCESSES`
`N_REPLICATES`
`N_SAMPLES`
`OPTIONS_DIR`
`OPTIONS_MAP`
`OT_ARCSINE`
`OT_AXIAL`
`OT_AXIAL_DESC`
`OT_AXIAL_WEB`
`OT_BETA`
`OT_COMPOSITE`
`OT_COMPOSITE_DESC`
`OT_COMPOSITE_WEB`
`OT_DIRICHLET`
`OT_DOC`
`OT_FACTORIAL`
`OT_FACTORIAL_DESC`
`OT_FACTORIAL_WEB`
`OT_FAURE`
`OT_FAURE_DESC`
`OT_FAURE_WEB`
`OT_FULLFACT`
`OT_FULLFACT_DESC`
`OT_FULLFACT_WEB`
`OT_HALTON`
`OT_HALTON_DESC`
`OT_HALTON_WEB`
`OT_HASEL`
`OT_HASEL_DESC`
`OT_HASEL_WEB`
`OT_LHS`
`OT_LHSC`
`OT_LHSC_DESC`
`OT_LHSC_WEB`
`OT_LHSO`
`OT_LHSO_DESC`
`OT_LHSO_WEB`
`OT_LHS_DESC`
`OT_LHS_WEB`
`OT_MC`
`OT_MC_DESC`
`OT_MC_WEB`
`OT_NORMAL`
`OT_RANDOM`
`OT_RANDOM_DESC`
`OT_RANDOM_WEB`
`OT_REVERSE_HALTON`
`OT_REVERSE_HALTON_DESC`
`OT_REVERSE_HALTON_WEB`
`OT_SOBOL`
`OT_SOBOL_DESC`
`OT_SOBOL_INDICES`
`OT_SOBOL_INDICES_DESC`
`OT_SOBOL_INDICES_WEB`
`OT_SOBOL_WEB`
`OT_TRAPEZOIDAL`
`OT_TRIANGULAR`
`OT_TRUNCNORMAL`
`OT_UNIFORM`
`PHIP_CRITERIA`
`POSITIVE_CONSTRAINTS`
`PROBLEM_TYPE`
`REQUIRE_GRAD`
`ROUND_INTS_OPTION`
`SAMPLES_TAG`
`SEED`
`START_KEYWORD`
`STD_KEYWORD`
`TEMPERATURE`
`TEMPERATURES`
`USER_DEFINED_GRADIENT`
`USE_DATABASE_OPTION`
`WAIT_TIME_BETWEEN_SAMPLES`
`WEBSITE`
`WEB_LIST`
`algorithms`	Return the available algorithms.

Methods:

`check_distribution_name`(distribution_name)	Check that distribution is available.
`compute_phip_criteria`(samples)	Compute the phi^p criteria (see Morris & Mitchell, Exploratory designs for computational experiments, 1995)
`create_composed_distributions`()	Create a composed distribution from a list of distributions.
`create_distribution`([distribution_name])	Create a distribution for all design vectors and add it to the list of distributions.
`display_distributions_list`()	Display list of distributions use or that will be used for DOE design based on LHS or Monte-Carlo methods.
`driver_has_option`(option_key)	Checks if the option key exists.
`ensure_bounds`(orig_func[, normalize])	Project the design vector onto the design space before execution.
`evaluate_samples`([eval_jac, n_processes, …])	Evaluates all functions of optimization problem at the samples.
`execute`(problem[, algo_name])	Executes the driver.
`export_samples`(doe_output_file)	Export samples generated by DOE library to a csv file.
`filter_adapted_algorithms`(problem)	Filters the algorithms capable of solving the problem.
`finalize_iter_observer`()	Finalize the iteration observer.
`get_composed_distributions`()	Returns the composed distributions.
`get_distributions_list`()	Accessor for distributions list.
`get_optimum_from_database`([message, status])	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.
`init_iter_observer`(max_iter, message)	Initialize the iteration observer.
`init_options_grammar`(algo_name)	Initializes the options grammar.
`is_algo_requires_grad`(algo_name)	Returns True if the algorithm requires a gradient evaluation.
`is_algorithm_suited`(algo_dict, problem)	Checks if the algorithm is suited to the problem according to its algo dict.
`new_iteration_callback`()	Callback called at each new iteration, ie every time a design vector that is not already in the database is proposed by the optimizer.
`plot_distribution`(distribution[, show])	Plot the density PDF & the CDF (cumulative) of a given distribution.

ALGO_LIST = ['OT_SOBOL', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_AXIAL', 'OT_FACTORIAL', 'OT_MONTE_CARLO', 'OT_LHS', 'OT_LHSC', 'OT_OPT_LHS', 'OT_RANDOM', 'OT_FULLFACT', 'OT_COMPOSITE', 'OT_SOBOL_INDICES']¶

ANNEALING = 'annealing'¶

CENTER_KEYWORD = 'centers'¶

COMPLEX_STEP_METHOD = 'complex_step'¶

CRITERIA = {'C2': <class 'openturns.weightedexperiment.SpaceFillingC2'>, 'MinDist': <class 'openturns.weightedexperiment.SpaceFillingMinDist'>, 'PhiP': <class 'openturns.weightedexperiment.SpaceFillingPhiP'>}¶

CRITERION = 'criterion'¶

DESCRIPTION = 'description'¶

DESC_LIST = ['Sobol sequence implemented in openTURNS library', 'Haselgrove sequence implemented in openTURNS library', 'Reverse Halton sequence implemented in openTURNS library', 'Halton sequence implemented in openTURNS library', 'Faure sequence implemented in openTURNS library', 'Axial design implemented in openTURNS library', 'Factorial design implemented in openTURNS library', 'Monte Carlo sequence implemented in openTURNS library', 'Latin Hypercube Sampling implemented in openTURNS library', 'Centered Latin Hypercube Sampling implemented in openTURNS library', 'Optimal Latin Hypercube Sampling implemented in openTURNS library', 'Random sampling implemented in openTURNS library', 'Full factorial design implementedin openTURNS library', 'Composite design implemented in openTURNS library', 'Sobol indices']¶

DESIGN_ALGO_NAME = 'Design algorithm'¶

DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']¶

DIMENSION = 'dimension'¶

DISTRIBUTION_DEFAULT = 'Uniform'¶

DISTRIBUTION_KEYWORD = 'distribution_name'¶

DISTRIBUTION_LIST = ['Arcsine', 'Beta', 'Dirichlet', 'Normal', 'TruncatedNormal', 'Triangular', 'Trapezoidal', 'Uniform']¶

DOE_SETTINGS_OPTIONS = ['levels', 'distribution_name', 'mu', 'sigma', 'start', 'end', 'centers']¶

END_KEYWORD = 'end'¶

EQ_TOLERANCE = 'eq_tolerance'¶

EVAL_JAC = 'eval_jac'¶

FINITE_DIFF_METHOD = 'finite_differences'¶

HANDLE_EQ_CONS = 'handle_equality_constraints'¶

HANDLE_INEQ_CONS = 'handle_inequality_constraints'¶

INEQ_TOLERANCE = 'ineq_tolerance'¶

INTERNAL_NAME = 'internal_algo_name'¶

LEVEL_KEYWORD = 'levels'¶

LIB = 'lib'¶

MAX_DS_SIZE_PRINT = 40¶

MAX_TIME = 'max_time'¶

MEAN_KEYWORD = 'mu'¶

MIN_DIMS = 'min_dims'¶

NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'¶

N_PROCESSES = 'n_processes'¶

N_REPLICATES = 'n_replicates'¶

N_SAMPLES = 'n_samples'¶

OPTIONS_DIR = 'options'¶

OPTIONS_MAP = {}¶

OT_ARCSINE = 'Arcsine'¶

OT_AXIAL = 'OT_AXIAL'¶

OT_AXIAL_DESC = 'Axial design implemented in openTURNS library'¶

OT_AXIAL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Axial.html'¶

OT_BETA = 'Beta'¶

OT_COMPOSITE = 'OT_COMPOSITE'¶

OT_COMPOSITE_DESC = 'Composite design implemented in openTURNS library'¶

OT_COMPOSITE_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Composite.html'¶

OT_DIRICHLET = 'Dirichlet'¶

OT_DOC = 'http://openturns.github.io/openturns/master/user_manual/'¶

OT_FACTORIAL = 'OT_FACTORIAL'¶

OT_FACTORIAL_DESC = 'Factorial design implemented in openTURNS library'¶

OT_FACTORIAL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Factorial.html'¶

OT_FAURE = 'OT_FAURE'¶

OT_FAURE_DESC = 'Faure sequence implemented in openTURNS library'¶

OT_FAURE_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.FaureSequence.html'¶

OT_FULLFACT = 'OT_FULLFACT'¶

OT_FULLFACT_DESC = 'Full factorial design implementedin openTURNS library'¶

OT_FULLFACT_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Box.html'¶

OT_HALTON = 'OT_HALTON'¶

OT_HALTON_DESC = 'Halton sequence implemented in openTURNS library'¶

OT_HALTON_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaltonSequence.html'¶

OT_HASEL = 'OT_HASELGROVE'¶

OT_HASEL_DESC = 'Haselgrove sequence implemented in openTURNS library'¶

OT_HASEL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaselgroveSequence.html'¶

OT_LHS = 'OT_LHS'¶

OT_LHSC = 'OT_LHSC'¶

OT_LHSC_DESC = 'Centered Latin Hypercube Sampling implemented in openTURNS library'¶

OT_LHSC_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html'¶

OT_LHSO = 'OT_OPT_LHS'¶

OT_LHSO_DESC = 'Optimal Latin Hypercube Sampling implemented in openTURNS library'¶

OT_LHSO_WEB = 'https://openturns.github.io/openturns/master/examples/optimal_lhs.html'¶

OT_LHS_DESC = 'Latin Hypercube Sampling implemented in openTURNS library'¶

OT_LHS_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html'¶

OT_MC = 'OT_MONTE_CARLO'¶

OT_MC_DESC = 'Monte Carlo sequence implemented in openTURNS library'¶

OT_MC_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html'¶

OT_NORMAL = 'Normal'¶

OT_RANDOM = 'OT_RANDOM'¶

OT_RANDOM_DESC = 'Random sampling implemented in openTURNS library'¶

OT_RANDOM_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html'¶

OT_REVERSE_HALTON = 'OT_REVERSE_HALTON'¶

OT_REVERSE_HALTON_DESC = 'Reverse Halton sequence implemented in openTURNS library'¶

OT_REVERSE_HALTON_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.ReverseHaltonSequence.html'¶

OT_SOBOL = 'OT_SOBOL'¶

OT_SOBOL_DESC = 'Sobol sequence implemented in openTURNS library'¶

OT_SOBOL_INDICES = 'OT_SOBOL_INDICES'¶

OT_SOBOL_INDICES_DESC = 'Sobol indices'¶

OT_SOBOL_INDICES_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolIndicesAlgorithm.html'¶

OT_SOBOL_WEB = 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolSequence.html'¶

OT_TRAPEZOIDAL = 'Trapezoidal'¶

OT_TRIANGULAR = 'Triangular'¶

OT_TRUNCNORMAL = 'TruncatedNormal'¶

OT_UNIFORM = 'Uniform'¶

PHIP_CRITERIA = 'phi^p'¶

POSITIVE_CONSTRAINTS = 'positive_constraints'¶

PROBLEM_TYPE = 'problem_type'¶

REQUIRE_GRAD = 'require_grad'¶

ROUND_INTS_OPTION = 'round_ints'¶

SAMPLES_TAG = 'samples'¶

SEED = 'seed'¶

START_KEYWORD = 'start'¶

STD_KEYWORD = 'sigma'¶

TEMPERATURE = 'temperature'¶

TEMPERATURES = {'Geometric': <class 'openturns.weightedexperiment.GeometricProfile'>, 'Linear': <class 'openturns.weightedexperiment.LinearProfile'>}¶

USER_DEFINED_GRADIENT = 'user'¶

USE_DATABASE_OPTION = 'use_database'¶

WAIT_TIME_BETWEEN_SAMPLES = 'wait_time_between_samples'¶

WEBSITE = 'website'¶

WEB_LIST = ['http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaselgroveSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.ReverseHaltonSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.HaltonSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.FaureSequence.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Axial.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Factorial.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.LHS.html', 'https://openturns.github.io/openturns/master/examples/optimal_lhs.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.RandomGenerator.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Box.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.Composite.html', 'http://openturns.github.io/openturns/master/user_manual/_generated/openturns.SobolIndicesAlgorithm.html']¶

property algorithms¶: Return the available algorithms.

check_distribution_name(distribution_name)[source]¶

Check that distribution is available.

Parameters: distribution_name (string) – name of the distribution

static compute_phip_criteria(samples)¶

Compute the phi^p criteria (see Morris & Mitchell, Exploratory designs for computational experiments, 1995)

Parameters: samples – design variables list

create_composed_distributions()[source]¶: Create a composed distribution from a list of distributions.

create_distribution(distribution_name='Uniform', **options)[source]¶

Create a distribution for all design vectors and add it to the list of distributions.

Parameters

distribution_name (str) – name of the distribution (Default value = “Uniform”)
options (dict) – optional parameters
options – OT distributions options

display_distributions_list()[source]¶: Display list of distributions use or that will be used for DOE design based on LHS or Monte-Carlo methods.

driver_has_option(option_key)¶

Checks if the option key exists.

Parameters: option_key – the name of the option
Returns: True if the option is in the grammar

ensure_bounds(orig_func, normalize=True)¶

Project the design vector onto the design space before execution.

Parameters

orig_func – the original function
normalize – if True, use the normalized design space

Returns

the wrapped function

evaluate_samples(eval_jac=False, n_processes=1, wait_time_between_samples=0)¶

Evaluates all functions of optimization problem at the samples.

Parameters: eval_jac – if True, the jacobian is also evaluated (Default value = False)

execute(problem, algo_name=None, **options)¶

Executes the driver.

Parameters

problem – the problem to be solved
algo_name – name of the algorithm if None, use self.algo_name which may have been set by the factory (Default value = None)
options – the options dict for the algorithm

export_samples(doe_output_file)¶

Export samples generated by DOE library to a csv file.

Parameters: doe_output_file (string) – export file name

filter_adapted_algorithms(problem)¶

Filters the algorithms capable of solving the problem.

Parameters: problem – the opt_problem to be solved
Returns: the list of adapted algorithms names

finalize_iter_observer()¶: Finalize the iteration observer.

get_composed_distributions()[source]¶

Returns the composed distributions.

Returns: composed distributions
Return type: openturns.ComposedDistribution

get_distributions_list()[source]¶

Accessor for distributions list.

Returns: distribution list
Return type: list

get_optimum_from_database(message=None, status=None)¶

Retrieves the optimum from the database and builds an optimization result object from it.

Parameters

message – Default value = None)
status – Default value = None)

get_x0_and_bounds_vects(normalize_ds)¶

Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.

Parameters: normalize_ds – if True, normalizes all input vars that are not integers, according to design space normalization policy
Returns: x, lower bounds, 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

max_iter – maximum number of calls
message – message to display at the beginning

init_options_grammar(algo_name)¶

Initializes the options grammar.

Parameters: algo_name – name of the algorithm

is_algo_requires_grad(algo_name)¶

Returns True if the algorithm requires a gradient evaluation.

Parameters: algo_name – name of the algorithm

static is_algorithm_suited(algo_dict, problem)¶

Checks if the algorithm is suited to the problem according to its algo dict.

Parameters

algo_dict – the algorithm characteristics
problem – the opt_problem to be solved

new_iteration_callback()¶

Callback called at each new iteration, ie every time a design vector that is not already in the database is proposed by the optimizer.

Iterates the progress bar, implements the stop criteria.

static plot_distribution(distribution, show=False)[source]¶

Plot the density PDF & the CDF (cumulative) of a given distribution.

Parameters

distribution (openturns.Distribution) – the distribution to plot
show (bool) – show plot (Default value = False)