# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Damien Guenot
# :author: Francois Gallard, refactoring
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""Optimization library wrappers base class."""
from __future__ import division, unicode_literals
import logging
from typing import Optional
from numpy import ndarray
from gemseo.algos.driver_lib import DriverLib
from gemseo.algos.stop_criteria import (
FtolReached,
XtolReached,
is_f_tol_reached,
is_x_tol_reached,
)
LOGGER = logging.getLogger(__name__)
[docs]class OptimizationLibrary(DriverLib):
"""Optimization library base class See DriverLib."""
MAX_ITER = "max_iter"
F_TOL_REL = "ftol_rel"
F_TOL_ABS = "ftol_abs"
X_TOL_REL = "xtol_rel"
X_TOL_ABS = "xtol_abs"
STOP_CRIT_NX = "stop_crit_n_x"
# Maximum step for the line search
LS_STEP_SIZE_MAX = "max_ls_step_size"
# Maximum number of line search steps (per iteration).
LS_STEP_NB_MAX = "max_ls_step_nb"
MAX_FUN_EVAL = "max_fun_eval"
MAX_TIME = "max_time"
PG_TOL = "pg_tol"
VERBOSE = "verbose"
def __init__(self):
super(OptimizationLibrary, self).__init__()
self._ftol_rel = 0.0
self._ftol_abs = 0.0
self._xtol_rel = 0.0
self._xtol_abs = 0.0
self._stop_crit_n_x = 3
def __algorithm_handles(self, algo_name, cstr_type):
"""Returns True if the algorithms handles cstr_type constraints.
:param algo_name : the name of the algo_name
:returns: True or False
"""
if algo_name not in self.lib_dict:
raise KeyError(
"Algorithm "
+ str(algo_name)
+ " not in library "
+ self.__class__.__name__
)
if cstr_type in self.lib_dict[algo_name]:
return self.lib_dict[algo_name][cstr_type]
return False
[docs] def algorithm_handles_eqcstr(self, algo_name):
"""Returns True if the algorithms handles equality constraints.
:param algo_name: the name of the algorithm
:returns: True or False
"""
return self.__algorithm_handles(algo_name, self.HANDLE_EQ_CONS)
[docs] def algorithm_handles_ineqcstr(self, algo_name):
"""Returns True if the algorithms handles inequality constraints.
:param algo_name: the name of the algorithm
:returns: True or False
"""
return self.__algorithm_handles(algo_name, self.HANDLE_INEQ_CONS)
[docs] def is_algo_requires_positive_cstr(self, algo_name):
"""Returns True if the algorithm requires positive constraints False otherwise.
:param algo_name: the name of the algorithm
:returns: True if constraints must be positive
:rtype: logical
"""
loc_dict = self.lib_dict[algo_name]
if self.POSITIVE_CONSTRAINTS in loc_dict:
return loc_dict[self.POSITIVE_CONSTRAINTS]
return False
def _check_constraints_handling(self, algo_name, problem):
"""Check if problem and algorithm are consistent for constraints handling."""
if problem.has_eq_constraints() and not self.algorithm_handles_eqcstr(
algo_name
):
raise ValueError(
"Requested optimization algorithm "
+ "%s can not handle equality constraints" % self.algo_name
)
if problem.has_ineq_constraints() and not self.algorithm_handles_ineqcstr(
algo_name
):
raise ValueError(
"Requested optimization algorithm "
+ " %s can not handle inequality constraints" % algo_name
)
[docs] def get_right_sign_constraints(self):
"""Transforms the problem constraints into their opposite sign counterpart if
the algorithm requires positive constraints."""
if self.problem.has_ineq_constraints() and self.is_algo_requires_positive_cstr(
self.algo_name
):
return [-cstr for cstr in self.problem.constraints]
return self.problem.constraints
def _run(self, **options):
"""Runs the algorithm, to be overloaded by subclasses.
:param options: the options dict for the algorithm,
see associated JSON file
"""
raise NotImplementedError()
def _pre_run(self, problem, algo_name, **options):
"""To be overriden by subclasses Specific method to be executed just before _run
method call.
:param problem: the problem to be solved
:param algo_name: name of the algorithm
:param options: the options dict for the algorithm,
see associated JSON file
"""
super(OptimizationLibrary, self)._pre_run(problem, algo_name, **options)
self._check_constraints_handling(algo_name, problem)
if self.MAX_ITER in options:
max_iter = options[self.MAX_ITER]
elif (
self.MAX_ITER in self.OPTIONS_MAP
and self.OPTIONS_MAP[self.MAX_ITER] in options
):
max_iter = options[self.OPTIONS_MAP[self.MAX_ITER]]
else:
raise ValueError("Could not determine the maximum number of iterations")
self._ftol_rel = options.get(self.F_TOL_REL, 0.0)
self._ftol_abs = options.get(self.F_TOL_ABS, 0.0)
self._xtol_rel = options.get(self.X_TOL_REL, 0.0)
self._xtol_abs = options.get(self.X_TOL_ABS, 0.0)
self._stop_crit_n_x = options.get(self.STOP_CRIT_NX, 3)
LOGGER.info("%s", problem)
if problem.design_space.dimension <= self.MAX_DS_SIZE_PRINT:
LOGGER.info("%s", problem.design_space)
self.init_iter_observer(max_iter, "Optimization")
problem.add_callback(self.new_iteration_callback)
eval_jac = self.is_algo_requires_grad(algo_name)
normalize = options.get(self.NORMALIZE_DESIGN_SPACE_OPTION, True)
# First, evaluate all functions at x_0. Some algorithms dont do this
self.problem.evaluate_functions(
eval_jac=eval_jac, eval_obj=True, normalize=normalize
)
[docs] @staticmethod
def is_algorithm_suited(algo_dict, problem):
"""Checks if the algorithm is suited to the problem according to its algo dict.
:param algo_dict: the algorithm characteristics
:param problem: the opt_problem to be solved
"""
if problem.has_eq_constraints() and (
(OptimizationLibrary.HANDLE_EQ_CONS not in algo_dict)
or not algo_dict[OptimizationLibrary.HANDLE_EQ_CONS]
):
return False
if problem.has_ineq_constraints() and (
(OptimizationLibrary.HANDLE_INEQ_CONS not in algo_dict)
or not algo_dict[OptimizationLibrary.HANDLE_INEQ_CONS]
):
return False
non_lin = problem.pb_type == problem.NON_LINEAR_PB
lin_alg = (
OptimizationLibrary.PROBLEM_TYPE in algo_dict
and algo_dict[OptimizationLibrary.PROBLEM_TYPE] == problem.LINEAR_PB
)
if non_lin and lin_alg:
return False
return True
[docs] def new_iteration_callback(
self, x_vect=None # type: Optional[ndarray]
): # type: (...) -> None
"""
Raises:
FtolReached: If the defined relative or absolute function
tolerance is reached.
XtolReached: If the defined relative or absolute x tolerance
is reached.
"""
# First check if the max_iter is reached and update the progress bar
super(OptimizationLibrary, self).new_iteration_callback(x_vect)
if is_f_tol_reached(
self.problem, self._ftol_rel, self._ftol_abs, self._stop_crit_n_x
):
raise FtolReached()
if is_x_tol_reached(
self.problem, self._xtol_rel, self._xtol_abs, self._stop_crit_n_x
):
raise XtolReached()
