gemseo / datasets

optimization_dataset module

A Dataset to store optimization histories.

class gemseo.datasets.optimization_dataset.OptimizationDataset(data=None, index=None, columns=None, dtype=None, copy=None, *, dataset_name='')[source]

Bases: Dataset

A Dataset to store optimization histories.

Warning

A Dataset behaves like any multi-index DataFrame but its instantiation using the constructor dataset = Dataset(data, ...) can lead to some inconsistencies (multi-index levels, index values, dtypes, …). Hence, the construction from the dedicated methods is recommended, e.g. dataset = Dataset(); dataset.add_variable("x", data).

Notes

The columns of a data structure (NumPy array, DataFrame, Dataset, …) are called features. The features of a Dataset include all the components of all the variables of all the groups.

Initialize self. See help(type(self)) for accurate signature.

Parameters:
  • data (ndarray | Iterable | dict | DataFrame | None) – See DataFrame.

  • index (Axes | None) – See DataFrame.

  • columns (Axes | None) – See DataFrame.

  • dtype (Dtype | None) – See DataFrame.

  • copy (bool | None) – See DataFrame.

  • dataset_name (str) –

    The name of the dataset.

    By default it is set to “”.

add_constraint_group(data, variable_names=(), variable_names_to_n_components=None)[source]

Add the data related to the constraint group.

Parameters:
  • data (DataType) – The data.

  • variable_names (StrColumnType) –

    The names of the variables. If empty, use DEFAULT_VARIABLE_NAME.

    By default it is set to ().

  • variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If variable_names is empty, this argument is not considered. If None, assume that all the variables have a single component.

Return type:

None

add_constraint_variable(variable_name, data, components=())[source]

Add data related to a constraint.

Parameters:
  • variable_name (str) – The name of the variable.

  • data (ndarray | Iterable[Any] | Any) – The data, either an array shaped as (n_entries, n_features), an array shaped as (n_entries,) that will be reshaped as (n_entries, 1) or a scalar that will be converted into an array shaped as (n_entries, 1).

  • components (int | Iterable[int]) –

    The components considered. If empty, use [0, ..., n_features].

    By default it is set to ().

Return type:

None

add_design_group(data, variable_names=(), variable_names_to_n_components=None)[source]

Add the data related to the design variable group.

Parameters:
  • data (DataType) – The data.

  • variable_names (StrColumnType) –

    The names of the variables. If empty, use DEFAULT_VARIABLE_NAME.

    By default it is set to ().

  • variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If variable_names is empty, this argument is not considered. If None, assume that all the variables have a single component.

Return type:

None

add_design_variable(variable_name, data, components=())[source]

Add data related to a design variable.

Parameters:
  • variable_name (str) – The name of the variable.

  • data (ndarray | Iterable[Any] | Any) – The data, either an array shaped as (n_entries, n_features), an array shaped as (n_entries,) that will be reshaped as (n_entries, 1) or a scalar that will be converted into an array shaped as (n_entries, 1).

  • components (int | Iterable[int]) –

    The components considered. If empty, use [0, ..., n_features].

    By default it is set to ().

Return type:

None

add_objective_group(data, variable_names=(), variable_names_to_n_components=None)[source]

Add the data related to the objective group.

Parameters:
  • data (DataType) – The data.

  • variable_names (StrColumnType) –

    The names of the variables. If empty, use DEFAULT_VARIABLE_NAME.

    By default it is set to ().

  • variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If variable_names is empty, this argument is not considered. If None, assume that all the variables have a single component.

Return type:

None

add_objective_variable(variable_name, data, components=())[source]

Add data related to an objective.

Parameters:
  • variable_name (str) – The name of the variable.

  • data (ndarray | Iterable[Any] | Any) – The data, either an array shaped as (n_entries, n_features), an array shaped as (n_entries,) that will be reshaped as (n_entries, 1) or a scalar that will be converted into an array shaped as (n_entries, 1).

  • components (int | Iterable[int]) –

    The components considered. If empty, use [0, ..., n_features].

    By default it is set to ().

Return type:

None

add_observable_group(data, variable_names=(), variable_names_to_n_components=None)[source]

Add the data related to the observable group.

Parameters:
  • data (DataType) – The data.

  • variable_names (StrColumnType) –

    The names of the variables. If empty, use DEFAULT_VARIABLE_NAME.

    By default it is set to ().

  • variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If variable_names is empty, this argument is not considered. If None, assume that all the variables have a single component.

Return type:

None

add_observable_variable(variable_name, data, components=())[source]

Add data related to an observable.

Parameters:
  • variable_name (str) – The name of the variable.

  • data (ndarray | Iterable[Any] | Any) – The data, either an array shaped as (n_entries, n_features), an array shaped as (n_entries,) that will be reshaped as (n_entries, 1) or a scalar that will be converted into an array shaped as (n_entries, 1).

  • components (int | Iterable[int]) –

    The components considered. If empty, use [0, ..., n_features].

    By default it is set to ().

Return type:

None

CONSTRAINT_GROUP: Final[str] = 'constraints'

The group name for the constraints of an OptimizationProblem.

DESIGN_GROUP: Final[str] = 'designs'

The group name for the design variables of an OptimizationProblem.

FUNCTION_GROUP: Final[str] = 'functions'

The group name for the functions of an OptimizationProblem.

OBJECTIVE_GROUP: Final[str] = 'objectives'

The group name for the objectives of an OptimizationProblem.

OBSERVABLE_GROUP: Final[str] = 'observables.'

The group name for the observables of an OptimizationProblem.

property constraint_dataset: OptimizationDataset

The view of the constraint dataset.

property constraint_names: list[str]

The names of the constraints.

Warning

The names are sorted with the Python function sorted.

property design_dataset: OptimizationDataset

The view of the design dataset.

property design_variable_names: list[str]

The names of the design variables.

Warning

The names are sorted with the Python function sorted.

property iterations: list[int]

The iterations.

misc: dict[str, Any]

Miscellaneous information specific to the dataset, and not to an entry.

property n_iterations: int

The number of iterations.

name: str

The name of the dataset.

property objective_dataset: OptimizationDataset

The view of the objective dataset.

property objective_names: list[str]

The names of the objectives.

Warning

The names are sorted with the Python function sorted.

property observable_dataset: OptimizationDataset

The view of the observable dataset.

property observable_names: list[str]

The names of the observables.

Warning

The names are sorted with the Python function sorted.

Examples using OptimizationDataset

Parameter space

Parameter space

Create an MDO Scenario

Create an MDO Scenario

GEMSEO in 10 minutes

GEMSEO in 10 minutes

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

The optimisation dataset

The optimisation dataset

ZvsXY

ZvsXY