#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2016 EDF R&D
+# Copyright (C) 2008-2017 EDF R&D
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects
+from daCore import BasicObjects, PlatformInfo
import numpy, scipy.optimize
# ==============================================================================
BasicObjects.Algorithm.__init__(self, "DERIVATIVEFREEOPTIMIZATION")
self.defineRequiredParameter(
name = "Minimizer",
- default = "POWELL",
+ default = "BOBYQA",
typecast = str,
message = "Minimiseur utilisé",
- listval = ["POWELL", "SIMPLEX", "COBYLA"],
+ listval = ["BOBYQA", "COBYLA", "NEWUOA", "POWELL", "SIMPLEX", "SUBPLEX"],
)
self.defineRequiredParameter(
name = "MaximumNumberOfSteps",
name = "MaximumNumberOfFunctionEvaluations",
default = 15000,
typecast = int,
- message = "Nombre maximal de d'évaluations de la fonction",
+ message = "Nombre maximal d'évaluations de la fonction",
minval = -1,
)
self.defineRequiredParameter(
)
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
- self._pre_run()
- if logging.getLogger().level < logging.WARNING:
- self.__disp = 1
- else:
- self.__disp = 0
- #
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
+ self._pre_run(Parameters)
#
- if self._parameters.has_key("Bounds") and (type(self._parameters["Bounds"]) is type([]) or type(self._parameters["Bounds"]) is type(())) and (len(self._parameters["Bounds"]) > 0):
- Bounds = self._parameters["Bounds"]
- logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
- else:
- Bounds = None
+ if not PlatformInfo.has_nlopt and not self._parameters["Minimizer"] in ["COBYLA", "POWELL", "SIMPLEX"]:
+ self._parameters["Minimizer"] = "SIMPLEX"
#
# Opérateurs
# ----------
# Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
Xini = numpy.ravel(Xb)
+ if len(Xini) < 2 and self._parameters["Minimizer"] == "NEWUOA":
+ raise ValueError("The minimizer %s can not be used when the optimisation state dimension is 1. Please choose another minimizer."%self._parameters["Minimizer"])
#
# Minimisation de la fonctionnelle
# --------------------------------
xtol = self._parameters["StateVariationTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
full_output = True,
- disp = self.__disp,
- )
- elif self._parameters["Minimizer"] == "SIMPLEX":
- Minimum, J_optimal, niter, nfeval, rc = scipy.optimize.fmin(
- func = CostFunction,
- x0 = Xini,
- args = (self._parameters["QualityCriterion"],),
- maxiter = self._parameters["MaximumNumberOfSteps"]-1,
- maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
- xtol = self._parameters["StateVariationTolerance"],
- ftol = self._parameters["CostDecrementTolerance"],
- full_output = True,
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
)
- elif self._parameters["Minimizer"] == "COBYLA":
+ elif self._parameters["Minimizer"] == "COBYLA" and not PlatformInfo.has_nlopt:
def make_constraints(bounds):
constraints = []
for (i,(a,b)) in enumerate(bounds):
upper = lambda x: b - x[i]
constraints = constraints + [lower] + [upper]
return constraints
- if Bounds is None:
+ if self._parameters["Bounds"] is None:
raise ValueError("Bounds have to be given for all axes as a list of lower/upper pairs!")
Minimum = scipy.optimize.fmin_cobyla(
func = CostFunction,
x0 = Xini,
- cons = make_constraints( Bounds ),
+ cons = make_constraints( self._parameters["Bounds"] ),
args = (self._parameters["QualityCriterion"],),
consargs = (), # To avoid extra-args
maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
rhobeg = 1.0,
rhoend = self._parameters["StateVariationTolerance"],
catol = 2.*self._parameters["StateVariationTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
+ )
+ elif self._parameters["Minimizer"] == "COBYLA" and PlatformInfo.has_nlopt:
+ import nlopt
+ opt = nlopt.opt(nlopt.LN_COBYLA, Xini.size)
+ def _f(_Xx, Grad):
+ # DFO, so no gradient
+ return CostFunction(_Xx, self._parameters["QualityCriterion"])
+ opt.set_min_objective(_f)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
+ lb = lub[:,0]
+ ub = lub[:,1]
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ opt.set_upper_bounds(ub)
+ opt.set_lower_bounds(lb)
+ opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
+ opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
+ opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
+ Minimum = opt.optimize( Xini )
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
+ elif self._parameters["Minimizer"] == "SIMPLEX" and not PlatformInfo.has_nlopt:
+ Minimum, J_optimal, niter, nfeval, rc = scipy.optimize.fmin(
+ func = CostFunction,
+ x0 = Xini,
+ args = (self._parameters["QualityCriterion"],),
+ maxiter = self._parameters["MaximumNumberOfSteps"]-1,
+ maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
+ xtol = self._parameters["StateVariationTolerance"],
+ ftol = self._parameters["CostDecrementTolerance"],
+ full_output = True,
+ disp = self._parameters["optdisp"],
)
+ elif self._parameters["Minimizer"] == "SIMPLEX" and PlatformInfo.has_nlopt:
+ import nlopt
+ opt = nlopt.opt(nlopt.LN_NELDERMEAD, Xini.size)
+ def _f(_Xx, Grad):
+ # DFO, so no gradient
+ return CostFunction(_Xx, self._parameters["QualityCriterion"])
+ opt.set_min_objective(_f)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
+ lb = lub[:,0]
+ ub = lub[:,1]
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ opt.set_upper_bounds(ub)
+ opt.set_lower_bounds(lb)
+ opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
+ opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
+ opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
+ Minimum = opt.optimize( Xini )
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
+ elif self._parameters["Minimizer"] == "BOBYQA" and PlatformInfo.has_nlopt:
+ import nlopt
+ opt = nlopt.opt(nlopt.LN_BOBYQA, Xini.size)
+ def _f(_Xx, Grad):
+ # DFO, so no gradient
+ return CostFunction(_Xx, self._parameters["QualityCriterion"])
+ opt.set_min_objective(_f)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
+ lb = lub[:,0]
+ ub = lub[:,1]
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ opt.set_upper_bounds(ub)
+ opt.set_lower_bounds(lb)
+ opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
+ opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
+ opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
+ Minimum = opt.optimize( Xini )
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
+ elif self._parameters["Minimizer"] == "NEWUOA" and PlatformInfo.has_nlopt:
+ import nlopt
+ opt = nlopt.opt(nlopt.LN_NEWUOA, Xini.size)
+ def _f(_Xx, Grad):
+ # DFO, so no gradient
+ return CostFunction(_Xx, self._parameters["QualityCriterion"])
+ opt.set_min_objective(_f)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
+ lb = lub[:,0]
+ ub = lub[:,1]
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ opt.set_upper_bounds(ub)
+ opt.set_lower_bounds(lb)
+ opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
+ opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
+ opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
+ Minimum = opt.optimize( Xini )
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
+ elif self._parameters["Minimizer"] == "SUBPLEX" and PlatformInfo.has_nlopt:
+ import nlopt
+ opt = nlopt.opt(nlopt.LN_SBPLX, Xini.size)
+ def _f(_Xx, Grad):
+ # DFO, so no gradient
+ return CostFunction(_Xx, self._parameters["QualityCriterion"])
+ opt.set_min_objective(_f)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
+ lb = lub[:,0]
+ ub = lub[:,1]
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ opt.set_upper_bounds(ub)
+ opt.set_lower_bounds(lb)
+ opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
+ opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
+ opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
+ Minimum = opt.optimize( Xini )
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
else:
raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
#
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
