import numpy as np
from abc import abstractmethod
from iwopy.core import Objective
[docs]
class SimpleObjective(Objective):
"""
A simple objective that assumes the
same variables as defined by the problem.
:group: wrappers
"""
[docs]
def __init__(
self,
problem,
name="f",
n_components=1,
maximize=False,
cnames=None,
has_ana_derivs=True,
):
"""
Constructor
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
has_ana_derivs = bool
Flag for analytical derivatives
"""
if cnames is not None and len(cnames) != n_components:
raise ValueError(
f"Wrong number of component names, found {len(cnames)}, expected {n_components}: {cnames}"
)
self._n_comps = n_components
super().__init__(
problem,
name,
vnames_int=problem.var_names_int(),
vnames_float=problem.var_names_float(),
cnames=cnames,
)
self._maxi = np.zeros(self._n_comps, dtype=bool)
self._maxi[:] = maximize
self._ana = has_ana_derivs
[docs]
@abstractmethod
def f(self, *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: float (or numpy.ndarray) or list of float (or numpy.ndarray)
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.
"""
pass
[docs]
def g(self, 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: float or list of float
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.
"""
pass
@property
def has_ana_derivs(self):
"""
Returns analyical derivatives flag
Returns
-------
bool :
Analitical derivatives flag
"""
return self._ana
[docs]
def maximize(self):
"""
Returns flag for maximization of each component.
Returns
-------
flags: np.array
Bool array for component maximization,
shape: (n_components,)
"""
return self._maxi
[docs]
def n_components(self):
"""
Returns the number of components of the
function.
Returns
-------
int:
The number of components.
"""
return self._n_comps
[docs]
def calc_individual(self, 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: np.array
The component values, shape: (n_sel_components,)
"""
results = np.array(self.f(*vars_int, *vars_float), dtype=np.float64)
return np.atleast_1d(results)
[docs]
def calc_population(self, 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: np.array
The component values, shape: (n_pop, n_sel_components,)
"""
varsi = [vars_int[:, vi] for vi in range(self.n_vars_int)]
varsf = [vars_float[:, vi] for vi in range(self.n_vars_float)]
results = self.f(*varsi, *varsf)
if self.n_components() == 1:
return np.atleast_1d(results)[:, None]
else:
return np.stack(results, axis=1)
[docs]
def ana_deriv(self, 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: numpy.ndarray
The derivative values, shape: (n_sel_components,)
"""
cmpnts = list(range(self.n_components())) if components is None else components
if self._ana:
results = np.atleast_1d(self.g(var, *vars_int, *vars_float, cmpnts))
else:
results = None
if results is None:
return super().ana_deriv(vars_int, vars_float, var, cmpnts)
else:
return np.atleast_1d(np.array(results, dtype=np.float64))