iwopy.wrappers.simple_objective.SimpleObjective

class iwopy.wrappers.simple_objective.SimpleObjective

Bases: Objective

A simple objective that assumes the same variables as defined by the problem.

Parameters:

• problem (iwopy.Problem) – The underlying optimization problem

• name (str) – The function name

• n_components (int) – The number of components

• maximize (bool or list of bool) – For each component, the maximization goal

• cnames (list of str, optional) – The names of the components

• bool (has_ana_derivs =) – Flag for analytical derivatives

__init__(problem, name='f', n_components=1, maximize=False, cnames=None, has_ana_derivs=True)

Methods

__init__(problem[, name, n_components, ...])
ana_deriv(vars_int, vars_float, var[, ...])	Calculates the analytic derivative, if possible.
calc_individual(vars_int, vars_float, ...[, ...])	Calculate values for a single individual of the underlying problem.
calc_population(vars_int, vars_float, ...[, ...])	Calculate values for all individuals of a population.
f(*x)	The function.
finalize([verbosity])	Finalize the object.
finalize_individual(vars_int, vars_float, ...)	Finalization, given the champion data.
finalize_population(vars_int, vars_float, ...)	Finalization, given the final population data.
g(var, *x, components)	The analytical derivative of the function f, df/dvar, if available.
initialize([verbosity])	Initialize the object.
maximize()	Returns flag for maximization of each component.
n_components()	Returns the number of components of the function.
rename_vars_float(varmap)	Rename float variables.
rename_vars_int(varmap)	Rename integer variables.
vardeps_float()	Gets the dependencies of all components on the function float variables
vardeps_int()	Gets the dependencies of all components on the function int variables

Attributes

component_names	The names of the components
has_ana_derivs	Returns analyical derivatives flag
initialized	Flag for finished initialization
n_vars_float	The number of float variables
n_vars_int	The number of int variables
var_names_float	The names of the float variables
var_names_int	The names of the integer variables

__init__(problem, name='f', n_components=1, maximize=False, cnames=None, has_ana_derivs=True)

ana_deriv(vars_int, vars_float, var, components=None)

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:

• vars_int (np.array) – The integer variable values, shape: (n_vars_int,)

• vars_float (np.array) – The float variable values, shape: (n_vars_float,)

• var (int) – The index of the differentiation float variable

• components (list of int) – The selected components, or None for all

Returns:

deriv – The derivative values, shape: (n_sel_components,)

Return type:

numpy.ndarray

calc_individual(vars_int, vars_float, problem_results, components=None)

Calculate values for a single individual of the underlying problem.

Parameters:

• vars_int (np.array) – The integer variable values, shape: (n_vars_int,)

• vars_float (np.array) – The float variable values, shape: (n_vars_float,)

• problem_results (Any) – The results of the variable application to the problem

• components (list of int, optional) – The selected components or None for all

Returns:

values – The component values, shape: (n_sel_components,)

Return type:

np.array

calc_population(vars_int, vars_float, problem_results, components=None)

Calculate values for all individuals of a population.

Parameters:

• vars_int (np.array) – The integer variable values, shape: (n_pop, n_vars_int)

• vars_float (np.array) – The float variable values, shape: (n_pop, n_vars_float)

• problem_results (Any) – The results of the variable application to the problem

• components (list of int, optional) – The selected components or None for all

Returns:

values – The component values, shape: (n_pop, n_sel_components,)

Return type:

np.array

property component_names

The names of the components

Returns:

names – The component names

Return type:

list of str

abstract f(*x)

The function.

Parameters:

x (tuple) – The int and float variables in that order. Variables are either scalars or numpy arrays in case of populations.

Returns:

result – For one component, a float, else a list of floats. For population results, a array with shape (n_pop,) in case of one component or a list of such arrays otherwise.

Return type:

float (or numpy.ndarray) or list of float (or numpy.ndarray)

finalize(verbosity=0)

Finalize the object.

Parameters:

verbosity (int) – The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)

Finalization, given the champion data.

Parameters:

• vars_int (np.array) – The optimal integer variable values, shape: (n_vars_int,)

• vars_float (np.array) – The optimal float variable values, shape: (n_vars_float,)

• problem_results (Any) – The results of the variable application to the problem

• verbosity (int) – The verbosity level, 0 = silent

Returns:

values – The component values, shape: (n_components,)

Return type:

np.array

finalize_population(vars_int, vars_float, problem_results, verbosity=1)

Finalization, given the final population data.

Parameters:

• vars_int (np.array) – The integer variable values of the final generation, shape: (n_pop, n_vars_int)

• vars_float (np.array) – The float variable values of the final generation, shape: (n_pop, n_vars_float)

• problem_results (Any) – The results of the variable application to the problem

• verbosity (int) – The verbosity level, 0 = silent

Returns:

values – The component values, shape: (n_pop, n_components)

Return type:

np.array

g(var, *x, components)

The analytical derivative of the function f, df/dvar, if available.

Parameters:

• var (int) – The index of the derivation varibable within the function float variables

• x (tuple) – The int and float variables in that order.

• components (list of int) – The selected components

Returns:

result – For one component, a float, else a list of floats. The length of list is 0 or 1 in case of single component, or n_sel_components otherwise.

Return type:

float or list of float

property has_ana_derivs

Returns analyical derivatives flag

Returns:

Analitical derivatives flag

Return type:

bool

initialize(verbosity=0)

Initialize the object.

Parameters:

verbosity (int) – The verbosity level, 0 = silent

property initialized

Flag for finished initialization

Returns:

True if initialization has been done

Return type:

bool

maximize()

Returns flag for maximization of each component.

Returns:

flags – Bool array for component maximization, shape: (n_components,)

Return type:

np.array

n_components()

Returns the number of components of the function.

Returns:

The number of components.

Return type:

int

property n_vars_float

The number of float variables

Returns:

n – The number of float variables

Return type:

int

property n_vars_int

The number of int variables

Returns:

n – The number of int variables

Return type:

int

rename_vars_float(varmap)

Rename float variables.

Parameters:

varmap (dict) – The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)

Rename integer variables.

Parameters:

varmap (dict) – The name mapping. Key: old name str, Value: new name str

property var_names_float

The names of the float variables

Returns:

names – The float variable names

Return type:

list of str

property var_names_int

The names of the integer variables

Returns:

names – The integer variable names

Return type:

list of str

vardeps_float()

Gets the dependencies of all components on the function float variables

Returns:

deps – The dependencies of components on function variables, shape: (n_components, n_vars_float)

Return type:

numpy.ndarray of bool

vardeps_int()

Gets the dependencies of all components on the function int variables

Returns:

deps – The dependencies of components on function variables, shape: (n_components, n_vars_int)

Return type:

numpy.ndarray of bool