-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2016 EDF R&D
+# Copyright (C) 2008-2021 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"],
+ message = "Minimiseur utilisé",
+ 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 function",
+ message = "Nombre maximal d'évaluations de la fonction",
minval = -1,
)
self.defineRequiredParameter(
name = "StateVariationTolerance",
default = 1.e-4,
typecast = float,
- message = "Variation relative maximale de l'état lors de l'arrêt",
+ message = "Variation relative maximale de l'état lors de l'arrêt",
)
self.defineRequiredParameter(
name = "CostDecrementTolerance",
default = 1.e-7,
typecast = float,
- message = "Diminution relative minimale du cout lors de l'arrêt",
+ message = "Diminution relative minimale du cout lors de l'arrêt",
)
self.defineRequiredParameter(
name = "QualityCriterion",
default = "AugmentedWeightedLeastSquares",
typecast = str,
- message = "Critère de qualité utilisé",
+ message = "Critère de qualité utilisé",
listval = ["AugmentedWeightedLeastSquares","AWLS","DA",
"WeightedLeastSquares","WLS",
"LeastSquares","LS","L2",
name = "StoreInternalVariables",
default = False,
typecast = bool,
- message = "Stockage des variables internes ou intermédiaires du calcul",
+ message = "Stockage des variables internes ou intermédiaires du calcul",
)
self.defineRequiredParameter(
name = "StoreSupplementaryCalculations",
default = [],
typecast = tuple,
- message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["CurrentState", "CostFunctionJ", "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "IndexOfOptimum", "InnovationAtCurrentState", "BMA", "OMA", "OMB", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
+ "CurrentOptimum",
+ "CurrentState",
+ "IndexOfOptimum",
+ "Innovation",
+ "InnovationAtCurrentState",
+ "OMA",
+ "OMB",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
)
+ self.defineRequiredParameter( # Pas de type
+ name = "Bounds",
+ message = "Liste des valeurs de bornes",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B" ),
+ )
+ self.setAttributes(tags=(
+ "Optimization",
+ "NonLinear",
+ "MetaHeuristic",
+ ))
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
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
+ if not PlatformInfo.has_nlopt and not self._parameters["Minimizer"] in ["COBYLA", "POWELL", "SIMPLEX"]:
+ logging.warning("%s Minimization by SIMPLEX is forced because %s is unavailable (COBYLA, POWELL are also available)"%(self._name,self._parameters["Minimizer"]))
+ self._parameters["Minimizer"] = "SIMPLEX"
#
- # Opérateurs
+ # Opérateurs
# ----------
Hm = HO["Direct"].appliedTo
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
BI = B.getI()
RI = R.getI()
#
- # Définition de la fonction-coût
+ # Définition de la fonction-coût
# ------------------------------
def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"):
_X = numpy.asmatrix(numpy.ravel( x )).T
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
_Innovation = Y - _HX
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum"):
self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
- if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("InnovationAtCurrentState"):
self.StoredVariables["InnovationAtCurrentState"].store( _Innovation )
#
if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]:
#
J = float( Jb ) + float( Jo )
#
+ self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) )
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
- if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("IndexOfOptimum") or \
+ self._toStore("CurrentOptimum") or \
+ self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum"):
IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
- if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("IndexOfOptimum"):
self.StoredVariables["IndexOfOptimum"].store( IndexMin )
- if "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CurrentOptimum"):
self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
- if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
- if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CostFunctionJAtCurrentOptimum"):
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+ if self._toStore("CostFunctionJbAtCurrentOptimum"):
self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+ if self._toStore("CostFunctionJoAtCurrentOptimum"):
self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
- self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
return J
#
- # Point de démarrage de l'optimisation : Xini = Xb
+ # 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
# --------------------------------
x0 = Xini,
args = (self._parameters["QualityCriterion"],),
maxiter = self._parameters["MaximumNumberOfSteps"]-1,
- maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"]-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"] == "SIMPLEX":
+ elif self._parameters["Minimizer"] == "COBYLA" and not PlatformInfo.has_nlopt:
+ def make_constraints(bounds):
+ constraints = []
+ for (i,(a,b)) in enumerate(bounds):
+ lower = lambda x: x[i] - a
+ upper = lambda x: b - x[i]
+ constraints = constraints + [lower] + [upper]
+ return constraints
+ 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( 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._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"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ 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"]-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"] == "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"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ 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"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ 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"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ 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"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ 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"])
#
#
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if self._toStore("OMA" ) or \
+ self._toStore("SimulatedObservationAtOptimum"):
+ if self._toStore("SimulatedObservationAtCurrentState"):
HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin]
- elif "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ elif self._toStore("SimulatedObservationAtCurrentOptimum"):
HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
else:
HXa = Hm(Xa)
- #
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("Innovation") or \
+ self._toStore("OMB"):
+ d = Y - HXb
+ if self._toStore("Innovation"):
self.StoredVariables["Innovation"].store( numpy.ravel(d) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMB"):
self.StoredVariables["OMB"].store( numpy.ravel(d) )
- if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("BMA"):
self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
- if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA"):
self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
- if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtBackground"):
self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(Hm(Xb)) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtOptimum"):
self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
self._post_run()
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
