iwopy.core.opt_results¶

Classes¶

Module Contents¶

class iwopy.core.opt_results.MultiObjOptResults(problem: iwopy.core.problem.Problem, success: numpy.ndarray, vars_int: numpy.ndarray, vars_float: numpy.ndarray, objs: numpy.ndarray, cons: numpy.ndarray, problem_results: object | None)[source]¶

Container for optimization results for multi objective problems.

Parameters:¶

problem: iwopy.core.problem.Problem¶

The problem

success: numpy.ndarray¶

Optimization success

vars_int: numpy.ndarray¶

Pareto-optimal variables, shape: (n_pop, n_vars_int)

vars_float: numpy.ndarray¶

Pareto-optimal variables, shape: (n_pop, n_vars_float)

objs: numpy.ndarray¶

Pareto front objective function values, shape: (n_pop, n_objectives)

cons: numpy.ndarray¶

Parteo front Constraint values, shape: (n_pop, n_constraints)

problem_results: object | None¶

The results of the variable application to the problem

find_pareto_objmix(obj_weights: collections.abc.Sequence[float], max: bool = False) → int[source]¶

Find the point on the pareto front that approximates best the given weights of objectives

Parameters:¶

obj_weights: collections.abc.Sequence[float]¶

The weights of the objectives

max: bool = False¶

Find the maximal value of the weighted result (otherwise find the minimal value)

Returns:¶

index – The index in the pareto front results

plot_pareto(obj_0: int = 0, obj_1: int = 1, ax: matplotlib.axes.Axes | None = None, figsize: tuple[float, float] = (5, 5), s: float = 50, color_val: str = 'orange', color_ival: str = 'red', title: str | None = None) → matplotlib.axes.Axes[source]¶

Get figure that shows the pareto front

Parameters:¶

obj_0: int = 0¶

The objective on the x axis

obj_1: int = 1¶

The objective on the y axis

ax: matplotlib.axes.Axes | None = None¶

The axis to plot on

figsize: tuple[float, float] = (5, 5)¶

The figure size, if ax is not given

s: float = 50¶

Scatter point size

color_val: str = 'orange'¶

Color choice for valid points

color_ival: str = 'red'¶

Color choice for invalid points

title: str | None = None¶

The plot title

Returns:¶

ax – The plot axis

cnames¶

cons¶

objs¶

onames¶

pname¶

problem_results¶

success¶

vars_float¶

vars_int¶

vnames_float¶

vnames_int¶

class iwopy.core.opt_results.SingleObjOptResults(problem: iwopy.core.problem.Problem, success: bool, vars_int: numpy.ndarray | None, vars_float: numpy.ndarray | None, objs: numpy.ndarray | None, cons: numpy.ndarray | None, problem_results: object | None)[source]¶

Container for optimization results for single objective problems.

Parameters:¶

problem: iwopy.core.problem.Problem¶

The problem

success: bool¶

Optimization success

vars_int: numpy.ndarray | None¶

Optimal variables, shape: (n_vars_int,)

vars_float: numpy.ndarray | None¶

Optimal variables, shape: (n_vars_float,)

objs: numpy.ndarray | None¶

Optimal objective function value

cons: numpy.ndarray | None¶

Constraint values, shape: (n_constraints,)

problem_results: object | None¶

The results of the variable application to the problem

cnames¶

cons¶

objs¶

onames¶

pname¶

problem_results¶

success¶

vars_float¶

vars_int¶

vnames_float¶

vnames_int¶