smt_optim.core package

class smt_optim.core.ConstraintConfig(constraint: list[Callable], type: str = 'less', value: float = 0, surrogate: Surrogate = None, surrogate_kwargs: dict | None = None)

Bases: object

Configuration of a constraint function used in the optimization problem.

This class stores a constraint callable(s) together with surrogate modeling information used to approximate the constraint during optimization.

constraint

List of constraint functions. Each callable must accept a decision variable vector x and return a scalar constraint value. The functions must be ordered in increasing level of fidelity.

Type:

list[Callable]

type

Specifies whether the feasible domain is defined when the constraint value is less, greater or equal to the configuration value.

Type:

{“less”, “greater”, “equal”}, default=”minimize”

value

Specifies the value defining the feasible domain.

Type:

float, default=0

surrogate

Surrogate model used to approximate the constraint function.

Type:

Surrogate or None, default=None

surrogate_kwargs

Optional keyword arguments passed to the surrogate model.

Type:

dict or None, default=None

constraint: list[Callable]

surrogate: Surrogate = None

surrogate_kwargs: dict | None = None

type: str = 'less'

value: float = 0

class smt_optim.core.Driver(problem, config, strategy, strategy_kwargs={})

Bases: object

call_loggers(state)

finalize()

iteration(state)

Perform an optimization iteration. An iteration consists of: - scaling all the training data - building the surrogate models - acquiring points to sample (and their associated fidelity level) - sampling the expensive-to-evaluate functions

Parameters:

state (State) – Optimization state on which to perform an iteration.

Returns:

Return optimization state on which an iteration was performed.

Return type:

State

make_res_dir(res_dir: str | None) → str | None

optimize()

Perform the optimization process which consists of: - generating a DoE if the initial dataset is empty - while no termination criteria is met, perform iterations

Returns:

optimization state on which the optimization process was performed.

Return type:

State

class smt_optim.core.DriverConfig(ctol: float = 0.0001, max_iter: int | None = None, max_budget: float = inf, max_time: float = inf, nt_init: int | None = None, xt_init: ndarray | None = None, results_dir: str | None = 'bo_result', verbose: bool = False, log_doe: bool = False, log_stats: bool = False, callback_func: list[Callable] | Callable | None = None, scaling: bool = True, seed: None = None)

Bases: object

Optimization driver configuration

max_iter

Maximum number of iteration

Type:

int

max_budget

Maximum budget before termination of the optimization process

Type:

float, default=inf

max_time

Maximum time before termination of the optimization process

Type:

float, default=inf

nt_init

Number of samples in the initial DoE

Type:

int

xt_init

Initial DoE to use. The Numpy array must be of shape (num_sample, num_dimension). By providing an initial DoE, the driver will not generate an initial DoE. Cannot be used with nt_init

Type:

list[np.ndarray]

results_dir

Name of the logging directory

Type:

str or None, default=None

verbose

Print optimization information.

Type:

bool, default=False

log_doe

Log the value of the function values as soon as they are sampled. The values are stored in a .csv file.

Type:

bool, default=False

log_stats

Log optimization statistics at the end of each iteration. The stats are store in a .jsonl file.

Type:

bool, default=False

scaling

Scale the data. The objective is standardized. The constraints are divided by their standard deviation. The design variables are normalized between 0 and 1.

Type:

bool, default=True

seed

Seed for experiment reproducibility

Type:

default=None

callback_func: list[Callable] | Callable | None = None

ctol: float = 0.0001

log_doe: bool = False

log_stats: bool = False

max_budget: float = inf

max_iter: int | None = None

max_time: float = inf

nt_init: int | None = None

results_dir: str | None = 'bo_result'

scaling: bool = True

seed: None = None

verbose: bool = False

xt_init: ndarray | None = None

class smt_optim.core.Evaluator(problem, res_path: str | None = None)

Bases: object

Evaluate the expensive-to-evaluate functions.

problem

Optimization problem.

Type:

Problem

res_path

Logging directory path.

Type:

str

log_sample(sample) → None

Log the sample data.

Parameters:

sample (Sample) – The sample to log.

Return type:

None

sample_func(infill: list[ndarray | None], state) → None

Sample the problem functions at requested query points.

Parameters:

• infill (list[np.ndarray | None]) – Query points. Each np.ndarray in the list corresponds to a fidelity level. The np.ndarray must have the shape (num_points, num_dim).

• state (State) – Optimization state.

Return type:

None

class smt_optim.core.ObjectiveConfig(objective: list[Callable], surrogate: type[Surrogate], type: str = 'minimize', surrogate_kwargs: dict | None = None)

Bases: object

Configuration of the objective function used in the optimization problem.

This class stores an objective callable(s) together with surrogate modeling information used to approximate the objective during optimization.

objective

List of an objective functions. Each callable must accept a decision variable vector x and return a scalar objective value. The functions must be ordered in increasing level of fidelity.

Type:

list[Callable]

type

Specifies whether the objective should be minimized or maximized.

Type:

{“minimize”, “maximize”}, default=”minimize”

surrogate

Surrogate model used to approximate the objective function.

Type:

Surrogate or None, default=None

surrogate_kwargs

Optional keyword arguments passed to the surrogate model.

Type:

dict or None, default=None

objective: list[Callable]

surrogate: type[Surrogate]

surrogate_kwargs: dict | None = None

type: str = 'minimize'

class smt_optim.core.OptimizationDataset

Bases: object

Store samples.

samples

Type:

list[Sample]

num_obj

Number of objectives

Type:

int

num_cstr

Number of constraints

Type:

int

num_fidelity

Number of fidelity levels

Type:

int

fidelities

Fidelity levels sorted in increasing order.

Type:

list

num_samples

Number of samples for each fidelity levels.

Type:

dict

add(sample: Sample)

export_as_dict() → dict

Exports the samples data as a dictionary. Each attribute corresponds to a key in the dictionary.

Returns:

Dictionary containing all sample data.

Return type:

dict

export_data(idx: int | list[int], lvl: int) → ndarray

get_by_fidelity(lvl: int)

class smt_optim.core.Problem(obj_configs: list, design_space, cstr_configs: list = [], costs: list[float] | None = None)

Bases: object

Problem configuration

num_dim

Number of dimensions.

Type:

int

num_obj

Number of objectives.

Type:

int

num_cstr

Number of constraints.

Type:

int

num_fidelity

Number of fidelities.

Type:

int

design_space

Problem design space.

Type:

np.ndarray

costs

Fidelity level costs.

Type:

list

obj_configs

Objective configurations.

Type:

list

obj_funcs

Objective functions.

Type:

list

cstr_configs

Constraint configurations.

Type:

list

cstr_funcs

Constraint functions.

Type:

list

class smt_optim.core.Sample(x: ndarray, fidelity: int, obj: ndarray | None, cstr: ndarray | None, eval_time: ndarray | None, metadata: dict = <factory>)

Bases: object

Store sample data.

x

Variable

Type:

np.ndarray

obj

Objective value(s). Array dimension: (num_obj,)

Type:

np.ndarray

cstr

Contraint value(s). Array dimension: (num_cstr,)

Type:

np.ndarray

eval_time

Evaluation times of each QoI. Array dimension: (num_obj+num_cstr,)

Type:

np.ndarray

metadata

Dictionary with sample metadata such as iter, budget and fidelity.

Type:

dict

cstr: ndarray | None

eval_time: ndarray | None

fidelity: int

metadata: dict

obj: ndarray | None

x: ndarray

class smt_optim.core.State(problem)

Bases: object

State of the optimization process at a given moment.

The optimization state holds information about the optimization process at a given moment.

Parameters:

problem (Problem) – The problem to be optimized.

problem

The problem to be optimized.

Type:

Problem

iter

Current iteration number.

Type:

int

budget

Current used budget.

Type:

float

bo_start

Start time of the optimization problem

Type:

float

bo_time

Elapsed time of the optimization driver.

Type:

float

obj_models

List containing the surrogate modeling the objective(s) function(s).

Type:

list[Surrogate]

cstr_models

List containing the surrogate modeling the constraint(s) function(s).

Type:

list[Surrogate]

cstr_types

List containing the constraint types.

Type:

list[str]

dataset

The dataset containing all samples from the expensive-to-evaluate functions.

Type:

OptimizationDataset

scaled_dataset

The scaled dataset.

Type:

OptimizationDataset

iter_log

Dictionary containing logging data.

Type:

dict

scale_dataset(unit_std: bool)

Scale data in the dataset. The scaled dataset is accessible using the `scaled_dataset`attribute.

build_models()

Builds the surrogate models based on the scale dataset.

get_best_sample()

Returns the best sample in the dataset.

build_models()

Builds the surrogate models.

Return type:

None

get_best_sample(ctol=0.0001, fidelity=-1)

Returns the best sample based on the objective function value.

Parameters:

• ctol (float, optional) – Tolerance for constraint violation. Default is 1e-4.

• fidelity (int, optional) – Fidelity level to consider. If -1, uses the highest fidelity. Default is -1.

Returns:

sample – The best sample based on the objective function value.

Return type:

Sample

scale_dataset(unit_std: bool = False)

Scales the dataset.

Parameters:

unit_std (bool, optional) – If True, normalize by standard deviation.

Return type:

None

Submodules

smt_optim.core.driver module

class smt_optim.core.driver.ConstraintConfig(constraint: list[Callable], type: str = 'less', value: float = 0, surrogate: Surrogate = None, surrogate_kwargs: dict | None = None)

Bases: object

Configuration of a constraint function used in the optimization problem.

This class stores a constraint callable(s) together with surrogate modeling information used to approximate the constraint during optimization.

constraint

List of constraint functions. Each callable must accept a decision variable vector x and return a scalar constraint value. The functions must be ordered in increasing level of fidelity.

Type:

list[Callable]

type

Specifies whether the feasible domain is defined when the constraint value is less, greater or equal to the configuration value.

Type:

{“less”, “greater”, “equal”}, default=”minimize”

value

Specifies the value defining the feasible domain.

Type:

float, default=0

surrogate

Surrogate model used to approximate the constraint function.

Type:

Surrogate or None, default=None

surrogate_kwargs

Optional keyword arguments passed to the surrogate model.

Type:

dict or None, default=None

constraint: list[Callable]

surrogate: Surrogate = None

surrogate_kwargs: dict | None = None

type: str = 'less'

value: float = 0

class smt_optim.core.driver.Driver(problem, config, strategy, strategy_kwargs={})

Bases: object

call_loggers(state)

finalize()

iteration(state)

Perform an optimization iteration. An iteration consists of: - scaling all the training data - building the surrogate models - acquiring points to sample (and their associated fidelity level) - sampling the expensive-to-evaluate functions

Parameters:

state (State) – Optimization state on which to perform an iteration.

Returns:

Return optimization state on which an iteration was performed.

Return type:

State

make_res_dir(res_dir: str | None) → str | None

optimize()

Perform the optimization process which consists of: - generating a DoE if the initial dataset is empty - while no termination criteria is met, perform iterations

Returns:

optimization state on which the optimization process was performed.

Return type:

State

class smt_optim.core.driver.DriverConfig(ctol: float = 0.0001, max_iter: int | None = None, max_budget: float = inf, max_time: float = inf, nt_init: int | None = None, xt_init: ndarray | None = None, results_dir: str | None = 'bo_result', verbose: bool = False, log_doe: bool = False, log_stats: bool = False, callback_func: list[Callable] | Callable | None = None, scaling: bool = True, seed: None = None)

Bases: object

Optimization driver configuration

max_iter

Maximum number of iteration

Type:

int

max_budget

Maximum budget before termination of the optimization process

Type:

float, default=inf

max_time

Maximum time before termination of the optimization process

Type:

float, default=inf

nt_init

Number of samples in the initial DoE

Type:

int

xt_init

Initial DoE to use. The Numpy array must be of shape (num_sample, num_dimension). By providing an initial DoE, the driver will not generate an initial DoE. Cannot be used with nt_init

Type:

list[np.ndarray]

results_dir

Name of the logging directory

Type:

str or None, default=None

verbose

Print optimization information.

Type:

bool, default=False

log_doe

Log the value of the function values as soon as they are sampled. The values are stored in a .csv file.

Type:

bool, default=False

log_stats

Log optimization statistics at the end of each iteration. The stats are store in a .jsonl file.

Type:

bool, default=False

scaling

Scale the data. The objective is standardized. The constraints are divided by their standard deviation. The design variables are normalized between 0 and 1.

Type:

bool, default=True

seed

Seed for experiment reproducibility

Type:

default=None

callback_func: list[Callable] | Callable | None = None

ctol: float = 0.0001

log_doe: bool = False

log_stats: bool = False

max_budget: float = inf

max_iter: int | None = None

max_time: float = inf

nt_init: int | None = None

results_dir: str | None = 'bo_result'

scaling: bool = True

seed: None = None

verbose: bool = False

xt_init: ndarray | None = None

class smt_optim.core.driver.ObjectiveConfig(objective: list[Callable], surrogate: type[Surrogate], type: str = 'minimize', surrogate_kwargs: dict | None = None)

Bases: object

Configuration of the objective function used in the optimization problem.

This class stores an objective callable(s) together with surrogate modeling information used to approximate the objective during optimization.

objective

List of an objective functions. Each callable must accept a decision variable vector x and return a scalar objective value. The functions must be ordered in increasing level of fidelity.

Type:

list[Callable]

type

Specifies whether the objective should be minimized or maximized.

Type:

{“minimize”, “maximize”}, default=”minimize”

surrogate

Surrogate model used to approximate the objective function.

Type:

Surrogate or None, default=None

surrogate_kwargs

Optional keyword arguments passed to the surrogate model.

Type:

dict or None, default=None

objective: list[Callable]

surrogate: type[Surrogate]

surrogate_kwargs: dict | None = None

type: str = 'minimize'

smt_optim.core.driver.check_bounds(x: ndarray, bounds: ndarray) → ndarray

Apply L1 correction to x point to make sure it’s within the problem’s bounds.

Parameters:

• x (np.ndarray) – Infill point.

• bounds (np.ndarray) – Problem boundaries.

Returns:

The bounds corrected infill point.

Return type:

np.ndarray

smt_optim.core.driver.compute_rscv(cstr_array: ndarray, cstr_config: list, g_tol: float = 0.0, h_tol: float = 0.0) → ndarray

smt_optim.core.driver.wrap_array(array: ndarray, factor: float | ndarray = 1.0, step: float | ndarray = 0.0) → ndarray

smt_optim.core.driver.wrap_func(func: Callable, factor: float = 1, step: float = 0) → Callable

Wrap function to return factor * (func - step).

Parameters:

• func (Callable) – Function to wrap.

• factor (float) – Multiplicative factor.

• step (float) – Additive factor.

Returns:

Wrapped function.

Return type:

Callable

smt_optim.core.problem module

class smt_optim.core.problem.Problem(obj_configs: list, design_space, cstr_configs: list = [], costs: list[float] | None = None)

Bases: object

Problem configuration

num_dim

Number of dimensions.

Type:

int

num_obj

Number of objectives.

Type:

int

num_cstr

Number of constraints.

Type:

int

num_fidelity

Number of fidelities.

Type:

int

design_space

Problem design space.

Type:

np.ndarray

costs

Fidelity level costs.

Type:

list

obj_configs

Objective configurations.

Type:

list

obj_funcs

Objective functions.

Type:

list

cstr_configs

Constraint configurations.

Type:

list

cstr_funcs

Constraint functions.

Type:

list

smt_optim.core.sample module

class smt_optim.core.sample.Evaluator(problem, res_path: str | None = None)

Bases: object

Evaluate the expensive-to-evaluate functions.

problem

Optimization problem.

Type:

Problem

res_path

Logging directory path.

Type:

str

log_sample(sample) → None

Log the sample data.

Parameters:

sample (Sample) – The sample to log.

Return type:

None

sample_func(infill: list[ndarray | None], state) → None

Sample the problem functions at requested query points.

Parameters:

• infill (list[np.ndarray | None]) – Query points. Each np.ndarray in the list corresponds to a fidelity level. The np.ndarray must have the shape (num_points, num_dim).

• state (State) – Optimization state.

Return type:

None

class smt_optim.core.sample.OptimizationDataset

Bases: object

Store samples.

samples

Type:

list[Sample]

num_obj

Number of objectives

Type:

int

num_cstr

Number of constraints

Type:

int

num_fidelity

Number of fidelity levels

Type:

int

fidelities

Fidelity levels sorted in increasing order.

Type:

list

num_samples

Number of samples for each fidelity levels.

Type:

dict

add(sample: Sample)

export_as_dict() → dict

Exports the samples data as a dictionary. Each attribute corresponds to a key in the dictionary.

Returns:

Dictionary containing all sample data.

Return type:

dict

export_data(idx: int | list[int], lvl: int) → ndarray

fidelities: list

get_by_fidelity(lvl: int)

num_cstr: int | None

num_fidelity: int

num_obj: int | None

num_samples: dict

samples: list[Sample]

class smt_optim.core.sample.Sample(x: ndarray, fidelity: int, obj: ndarray | None, cstr: ndarray | None, eval_time: ndarray | None, metadata: dict = <factory>)

Bases: object

Store sample data.

x

Variable

Type:

np.ndarray

obj

Objective value(s). Array dimension: (num_obj,)

Type:

np.ndarray

cstr

Contraint value(s). Array dimension: (num_cstr,)

Type:

np.ndarray

eval_time

Evaluation times of each QoI. Array dimension: (num_obj+num_cstr,)

Type:

np.ndarray

metadata

Dictionary with sample metadata such as iter, budget and fidelity.

Type:

dict

cstr: ndarray | None

eval_time: ndarray | None

fidelity: int

metadata: dict

obj: ndarray | None

x: ndarray

smt_optim.core.sample.sample_func(x_new: ndarray, func: Callable) → tuple[float, float]

Evaluates the function func value evaluated at x_new. Returns the function value and the elapsed time. If the function output is of type np.ndarray, converts it to a float.

Parameters:

• x_new (np.ndarry) – Point to sample.

• func – Function to sample.

Returns:

• float – Function value at x_new.

• float – Elapsed time for sampling the function.

smt_optim.core.state module

class smt_optim.core.state.State(problem)

Bases: object

State of the optimization process at a given moment.

The optimization state holds information about the optimization process at a given moment.

Parameters:

problem (Problem) – The problem to be optimized.

problem

The problem to be optimized.

Type:

Problem

iter

Current iteration number.

Type:

int

budget

Current used budget.

Type:

float

bo_start

Start time of the optimization problem

Type:

float

bo_time

Elapsed time of the optimization driver.

Type:

float

obj_models

List containing the surrogate modeling the objective(s) function(s).

Type:

list[Surrogate]

cstr_models

List containing the surrogate modeling the constraint(s) function(s).

Type:

list[Surrogate]

cstr_types

List containing the constraint types.

Type:

list[str]

dataset

The dataset containing all samples from the expensive-to-evaluate functions.

Type:

OptimizationDataset

scaled_dataset

The scaled dataset.

Type:

OptimizationDataset

iter_log

Dictionary containing logging data.

Type:

dict

scale_dataset(unit_std: bool)

Scale data in the dataset. The scaled dataset is accessible using the `scaled_dataset`attribute.

build_models()

Builds the surrogate models based on the scale dataset.

get_best_sample()

Returns the best sample in the dataset.

build_models()

Builds the surrogate models.

Return type:

None

get_best_sample(ctol=0.0001, fidelity=-1)

Returns the best sample based on the objective function value.

Parameters:

• ctol (float, optional) – Tolerance for constraint violation. Default is 1e-4.

• fidelity (int, optional) – Fidelity level to consider. If -1, uses the highest fidelity. Default is -1.

Returns:

sample – The best sample based on the objective function value.

Return type:

Sample

scale_dataset(unit_std: bool = False)

Scales the dataset.

Parameters:

unit_std (bool, optional) – If True, normalize by standard deviation.

Return type:

None