outcomes

Module for outcome classes.

class ema_workbench.em_framework.outcomes.ArrayOutcome(name, variable_name=None, function=None, shape=None, dtype=None)

Array Outcome class for n-dimensional arrays.

Parameters:

• name (str) – Name of the outcome.

• variable_name (str, optional) – if the name of the outcome in the underlying model is different from the name of the outcome, you can supply the variable name as an optional argument, if not provided, defaults to name

• function (callable, optional) – a callable to perform postprocessing on data retrieved from model

• shape ({tuple, None} optional) – must be used in conjunction with dtype. Enables pre-allocation of data structure for storing results.

• dtype (datatype, optional) – must be used in conjunction with shape. Enables pre-allocation of data structure for storing results.

name

Type:

str

kind

Type:

int

variable_name

Type:

str

function

Type:

callable

shape

Type:

tuple

dtype

Type:

datatype

classmethod from_disk(filename, archive)

Helper function for loading from disk.

Parameters:

• filename (str)

• archive (Tarfile)

process(values)

Process the values.

classmethod to_disk(values)

Helper function for writing outcome to disk.

Parameters:

values (ND array)

Returns:

• BytesIO

• filename

class ema_workbench.em_framework.outcomes.Constraint(name, function: Callable, parameter_names: list[str] | str | None = None, outcome_names: list[str] | str | None = None)

Constraints class that can be used when defining constrained optimization problems.

Parameters:

• name (str)

• parameter_names (str or collection of str)

• outcome_names (str or collection of str)

• function (callable)

name

Type:

str

function

The function should return the distance from the feasibility threshold, given the model outputs with a variable name. The distance should be 0 if the constraint is met.

Type:

callable

parameter_names

name(s) of the uncertain parameter(s) and/or lever parameter(s) to which the constraint applies

Type:

str, list of str

outcome_names

name(s) of the outcome(s) to which the constraint applies

Type:

str, list of str

process(values)

Process the values.

class ema_workbench.em_framework.outcomes.Outcome(name: str, kind: int = 0, variable_name: str | Sequence[str] | None = None, function: Callable[[...], Any] | None = None, shape: tuple[int, ...] | None = None, dtype: dtype | type | None = None)

Base Outcome class.

Parameters:

• name (str) – Name of the outcome.

• kind ({INFO, MINIMIZE, MAXIMIZE}, optional)

• variable_name (str, optional) – if the name of the outcome in the underlying model is different from the name of the outcome, you can supply the variable name as an optional argument, if not provided, defaults to name

• function (callable, optional) – a callable to perform postprocessing on data retrieved from model

• shape ({tuple, None} optional) – must be used in conjunction with dtype. Enables pre-allocation of data structure for storing results.

• dtype (datatype, optional) – must be used in conjunction with shape. Enables pre-allocation of data structure for storing results.

name

Type:

str

kind

Type:

int

variable_name

Type:

str

function

Type:

callable

shape

Type:

tuple

dtype

Type:

dataype

abstractmethod classmethod from_disk(filename: str, archive: TarFile) → ndarray

Helper function for loading from disk.

Parameters:

• filename (str)

• archive (Tarfile)

process(values: list[Any]) → Any

Process the values.

abstractmethod classmethod to_disk(values: ndarray | DataFrame) → tuple[BytesIO, str]

Helper function for writing outcome to disk.

Parameters:

values (obj) – data to store

Return type:

BytesIO

class ema_workbench.em_framework.outcomes.ScalarOutcome(name: str, kind: int = 0, variable_name: str | Sequence[str] | None = None, function: Callable[[...], Any] | None = None, dtype: dtype | type | None = None)

Scalar Outcome class.

Parameters:

• name (str) – Name of the outcome.

• kind ({INFO, MINIMIZE, MAXIMIZE}, optional)

• variable_name (str, optional) – if the name of the outcome in the underlying model is different from the name of the outcome, you can supply the variable name as an optional argument, if not provided, defaults to name

• function (callable, optional) – a callable to perform post processing on data retrieved from model

• dtype (datatype, optional) – Enables pre-allocation of data structure for storing results.

name

Type:

str

kind

Type:

int

variable_name

Type:

str

function

Type:

callable

shape

Type:

tuple

dtype

Type:

datatype

classmethod from_disk(filename, archive)

Helper function for loading from disk.

Parameters:

• filename (str)

• archive (Tarfile)

process(values)

Process the values.

classmethod to_disk(values)

Helper function for writing outcome to disk.

Parameters:

values (1D array)

Returns:

• BytesIO

• filename

class ema_workbench.em_framework.outcomes.TimeSeriesOutcome(name, variable_name=None, function=None, shape=None, dtype=None)

TimeSeries Outcome class for 1D arrays.

Parameters:

• name (str) – Name of the outcome.

• variable_name (str, optional) – if the name of the outcome in the underlying model is different from the name of the outcome, you can supply the variable name as an optional argument, if not provided, defaults to name

• function (callable, optional) – a callable to perform postprocessing on data retrieved from model

• shape ({tuple, None} optional) – must be used in conjunction with dtype. Enables pre-allocation of data structure for storing results.

• dtype (datatype, optional) – must be used in conjunction with shape. Enables pre-allocation of data structure for storing results.

name

Type:

str

kind

Type:

int

variable_name

Type:

str

function

Type:

callable

shape

Type:

tuple

dtype

Type:

datatype

Notes

Time series outcomes are currently assumed to be 1D arrays. If you are dealing with higher dimensional outputs (e.g., multiple replications resulting in 2D arrays), use ArrayOutcome instead.

classmethod from_disk(filename, archive)

Helper function for loading from disk.

Parameters:

• filename (str)

• archive (Tarfile)

classmethod to_disk(values)

Helper function for writing outcome to disk.

Parameters:

values (DataFrame)

Returns:

• StringIO

• filename