SubsetSelectionProblem#

class pybrops.breed.prot.sel.prob.SubsetSelectionProblem.SubsetSelectionProblem(ndecn, decn_space, decn_space_lower, decn_space_upper, nobj, obj_wt=None, obj_trans=None, obj_trans_kwargs=None, nineqcv=None, ineqcv_wt=None, ineqcv_trans=None, ineqcv_trans_kwargs=None, neqcv=None, eqcv_wt=None, eqcv_trans=None, eqcv_trans_kwargs=None, vtype=None, vars=None, elementwise=True, elementwise_func=<class 'pymoo.core.problem.ElementwiseEvaluationFunction'>, elementwise_runner=<pymoo.core.problem.LoopedElementwiseEvaluation object>, replace_nan_values_by=None, exclude_from_serialization=None, callback=None, strict=True, **kwargs)[source]#

Bases: SubsetProblem, SelectionProblem

Semi-abstract class representing selection problems in subset search spaces.

Constructor for SubsetSelectionProblem.

Parameters:

ndecn (Integral) – Number of decision variables.
decn_space (numpy.ndarray, None) – An array of shape (2,ndecn) defining the decision space. If None, do not set a decision space.
decn_space_lower (numpy.ndarray, Real, None) – An array of shape (ndecn,) containing lower limits for decision variables. If a Real is provided, construct an array of shape (ndecn,) containing the Real. If None, do not set a lower limit for the decision variables.
decn_space_upper (numpy.ndarray, Real, None) – An array of shape (ndecn,) containing upper limits for decision variables. If a Real is provided, construct an array of shape (ndecn,) containing the Real. If None, do not set a upper limit for the decision variables.
nobj (Integral) – Number of objectives.
obj_wt (numpy.ndarray) – Objective function weights.
obj_trans (Callable, None) – A transformation function transforming a latent space vector to an objective space vector. The transformation function must be of the form: obj_trans(x: numpy.ndarray, **kwargs) -> numpy.ndarray If None, use the identity transformation function: copy the latent space vector to the objective space vector.
obj_trans_kwargs (dict, None) – Keyword arguments for the latent space to objective space transformation function. If None, an empty dictionary is used.
nineqcv (Integral,) – Number of inequality constraints.
ineqcv_wt (numpy.ndarray,) – Inequality constraint violation weights.
ineqcv_trans (Callable, None) – A transformation function transforming a latent space vector to an inequality constraint violation vector. The transformation function must be of the form: ineqcv_trans(x: numpy.ndarray, **kwargs) -> numpy.ndarray If None, use the empty set transformation function: return an empty vector of length zero.
ineqcv_trans_kwargs (Optional[dict],) – Keyword arguments for the latent space to inequality constraint violation space transformation function. If None, an empty dictionary is used.
neqcv (Integral) – Number of equality constraints.
eqcv_wt (numpy.ndarray) – Equality constraint violation weights.
eqcv_trans (Callable, None) – A transformation function transforming a latent space vector to an equality constraint violation vector. The transformation function must be of the form: eqcv_trans(x: numpy.ndarray, **kwargs) -> numpy.ndarray If None, use the empty set transformation function: return an empty vector of length zero.
eqcv_trans_kwargs (dict, None) – Keyword arguments for the latent space to equality constraint violation space transformation function. If None, an empty dictionary is used.
vtype (type, None) – The variable type. So far, just used as a type hint. See PyMOO documentation.
vars (Sequence, None) – Variables provided in their explicit form. See PyMOO documentation.
elementwise (bool) – Whether the evaluation function should be run elementwise. See PyMOO documentation.
elementwise_func (type) – A class that creates the function that evaluates a single individual. See PyMOO documentation.
elementwise_runner (Callable) – A function that runs the function that evaluates a single individual. See PyMOO documentation.
replace_nan_values_by (Number, None) – Value for which to replace NaN values. See PyMOO documentation.
exclude_from_serialization (Iterable, None) – Attributes which are excluded from being serialized. See PyMOO documentation.
callback (Callable, None) – A callback function to be called after every evaluation. See PyMOO documentation.
strict (bool, default = True) – See PyMOO documentation.
kwargs (dict) – Additional keyword arguments used for cooperative inheritance. See PyMOO documentation.

Methods

`bounds`
`do`
`evalfn`	Evaluate a candidate solution for the given Problem.
`evaluate`
`has_bounds`
`has_constraints`
`ideal_point`
`latentfn`	Encode a candidate solution for the given Problem into an `l` dimensional latent evaluation space.
`nadir_point`
`name`
`pareto_front`
`pareto_set`

Attributes

`callback`	A callback function to be called after every evaluation.
`data`	Type of the variable to be evaluated.
`decn_space`	Decision space boundaries.
`decn_space_lower`	Lower boundary of the decision space.
`decn_space_upper`	Upper boundary of the decision space.
`elementwise`	Whether the evaluation function should be run elementwise.
`elementwise_func`	A class that creates the function that evaluates a single individual.
`elementwise_runner`	A function that runs the function that evaluates a single individual.
`eqcv_trans`	Function which transforms outputs from `latentfn` to equality constraint violation values.
`eqcv_trans_kwargs`	Keyword arguments for the latent space to equality constraint violation transformation function.
`eqcv_wt`	Equality constraint violation function weights.
`exclude_from_serialization`	attributes which are excluded from being serialized.
`ineqcv_trans`	Function which transforms outputs from `latentfn` to inequality constraint violation values.
`ineqcv_trans_kwargs`	Keyword arguments for the latent space to inequality constraint violation transformation function.
`ineqcv_wt`	Inequality constraint violation function weights.
`n_constr`
`n_eq_constr`	n_eq_constr.
`n_ieq_constr`	Number of inequality constraints.
`n_obj`	Number of objectives.
`n_var`	Number of decision variables.
`ndecn`	Number of decision variables.
`neqcv`	Number of equality constraint violations.
`nineqcv`	Number of inequality constraint violation functions.
`nlatent`	Size of the latent vector for the optimization problem.
`nobj`	Number of objectives.
`obj_trans`	Function which transforms outputs from `latentfn` to objective function values.
`obj_trans_kwargs`	Keyword arguments for the latent space to objective space transformation function.
`obj_wt`	Objective function weights.
`replace_nan_values_by`	replace_nan_values_by.
`strict`	Whether the shapes are checked strictly.
`vars`	Variables provided in their explicit form.
`vtype`	The variable type.
`xl`	Lower boundary of the decision space.
`xu`	Upper boundary of the decision space.

property callback: Callable | None#: A callback function to be called after every evaluation.

property data: dict#: Type of the variable to be evaluated.

property decn_space: ndarray | None#: Decision space boundaries.

property decn_space_lower: ndarray | None#: Lower boundary of the decision space.

property decn_space_upper: ndarray | None#: Upper boundary of the decision space.

property elementwise: bool#: Whether the evaluation function should be run elementwise.

property elementwise_func: type#: A class that creates the function that evaluates a single individual.

property elementwise_runner: Callable#: A function that runs the function that evaluates a single individual.

property eqcv_trans: Callable[[ndarray, ndarray, dict], ndarray]#: Function which transforms outputs from latentfn to equality constraint violation values.

property eqcv_trans_kwargs: dict#: Keyword arguments for the latent space to equality constraint violation transformation function.

property eqcv_wt: ndarray#: Equality constraint violation function weights.

evalfn(x, *args, **kwargs)#

Evaluate a candidate solution for the given Problem.

This calculates three vectors which are to be minimized:

\[\mathbf{v_{obj}} = \mathbf{w_{obj} \odot T_{obj}(L(x))} \ \mathbf{v_{ineqcv}} = \mathbf{w_{ineqcv} \odot T_{ineqcv}(L(x))} \ \mathbf{v_{eqcv}} = \mathbf{w_{eqcv} \odot T_{eqcv}(L(x))}\]

Parameters:

x (numpy.ndarray) – A candidate solution vector of shape (ndecn,).
args (tuple) – Additional non-keyword arguments.
kwargs (dict) – Additional keyword arguments.

Returns:

out – A tuple (obj, ineqcv, eqcv).

Where:

obj is a numpy.ndarray of shape (nobj,) that contains
objective function evaluations. This is equivalent to \(\mathbf{v_{obj}}\)
ineqcv is a numpy.ndarray of shape (nineqcv,) that contains
inequality constraint violation values. This is equivalent to \(\mathbf{v_{ineqcv}}\)
eqcv is a numpy.ndarray of shape (neqcv,) that contains
equality constraint violation values. This is equivalent to \(\mathbf{v_{eqcv}}\)

Return type:

tuple

property exclude_from_serialization: Iterable | None#: attributes which are excluded from being serialized.

property ineqcv_trans: Callable[[ndarray, ndarray, dict], ndarray]#: Function which transforms outputs from latentfn to inequality constraint violation values.

property ineqcv_trans_kwargs: dict#: Keyword arguments for the latent space to inequality constraint violation transformation function.

property ineqcv_wt: ndarray#: Inequality constraint violation function weights.

abstract latentfn(x, *args, **kwargs)#

Encode a candidate solution for the given Problem into an l dimensional latent evaluation space.

Parameters:

x (numpy.ndarray) – A candidate solution vector of shape (ndecn,).
args (tuple) – Additional non-keyword arguments.
kwargs (dict) – Additional keyword arguments.

Returns:

out – A numpy.ndarray of shape (l,) containing latent evaluation values.

Return type:

numpy.ndarray

property n_eq_constr: Integral#: n_eq_constr.

property n_ieq_constr: Integral#: Number of inequality constraints.

property n_obj: Integral#: Number of objectives.

property n_var: Integral#: Number of decision variables.

property ndecn: Integral#: Number of decision variables.

property neqcv: Integral#: Number of equality constraint violations.

property nineqcv: Integral#: Number of inequality constraint violation functions.

abstract property nlatent: Integral#: Size of the latent vector for the optimization problem.

property nobj: Integral#: Number of objectives.

property obj_trans: Callable[[ndarray, ndarray, dict], ndarray]#: Function which transforms outputs from latentfn to objective function values.

property obj_trans_kwargs: dict#: Keyword arguments for the latent space to objective space transformation function.

property obj_wt: ndarray#: Objective function weights.

property replace_nan_values_by: Real | None#: replace_nan_values_by.

property strict: bool#: Whether the shapes are checked strictly.

property vars: Container | None#: Variables provided in their explicit form.

property vtype: type | None#: The variable type. So far, just used as a type hint.

property xl: ndarray | None#: Lower boundary of the decision space.

property xu: ndarray | None#: Upper boundary of the decision space.