#!/usr/local/bin/python ''' pyMMFD - A Python pyOpt interface to MMFD (part of NASA's ADS). Copyright (c) 2008-2011 by pyOpt Developers All rights reserved. Revision: 1.2 $Date: 21/06/2010 21:00$ Tested on: --------- Win32 with gfortran Linux with pathf95 Developers: ----------- - Dr. Ruben E. Perez (RP) - Mr. Peter Jansen (PJ) History ------- v. 1.0 - Initial Class Creation (RP, 2010) v. 1.1 - History support (PJ,RP, 2010) v. 1.2 - Gradient Class Support (PJ,RP, 2010) ''' __version__ = '$Revision: $' ''' To Do: - Include IOUT - Implement Informs ''' # ============================================================================= # MMFD Library # ============================================================================= try: import mmfd except: raise ImportError('MMFD shared library failed to import') #end # ============================================================================= # Standard Python modules # ============================================================================= import os, sys import copy, time # ============================================================================= # External Python modules # ============================================================================= import numpy # ============================================================================= # Extension modules # ============================================================================= from pyOpt import Optimizer from pyOpt import History from pyOpt import Gradient # ============================================================================= # Misc Definitions # ============================================================================= inf = 10.E+20 # define a value for infinity # ============================================================================= eps = 1.0 # define a value for machine precision while ((eps/2.0 + 1.0) > 1.0): eps = eps/2.0 #end eps = 2.0*eps #eps = math.ldexp(1,-52) # ============================================================================= # MMFD Optimizer Class # ============================================================================= class MMFD(Optimizer): ''' MMFD Optimizer Class - Inherited from Optimizer Abstract Class ''' def __init__(self, pll_type=None, *args, **kwargs): ''' MMFD Optimizer Class Initialization **Keyword arguments:** - pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None Documentation last updated: Feb. 16, 2010 - Peter W. Jansen ''' # if (pll_type == None): self.poa = False elif (pll_type.upper() == 'POA'): self.poa = True else: raise ValueError("pll_type must be either None or 'POA'") #end # name = 'MMFD' category = 'Local Optimizer' def_opts = { 'IOPT':[int,0], # Feasible Directions Approach (0 - MMFD, 1 - MFD) 'IONED':[int,0], # One-Dimensional Search Method (0,1,2,3) 'CT':[float,-3e-2], # Constraint Tolerance 'CTMIN':[float,4e-3], # Active Constraint Tolerance 'DABOBJ':[float,1e-3], # Objective Absolute Tolerance (DABOBJ*abs(f(x))) 'DELOBJ':[float,1e-3], # Objective Relative Tolerance 'THETAZ':[float,1e-1], # Push-Off Factor 'PMLT':[float,1e1], # Penalty multiplier for equality constraints 'ITMAX':[int,4e2], # Maximum Number of Iterations 'ITRMOP':[int,3], # consecutive Iterations Iterations for Convergence 'IPRINT':[int,2], # Print Control (0 - None, 1 - Final, 2 - Iters) 'IFILE':[str,'MMFD.out'], # Output File Name } informs = { } Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs) def __solve__(self, opt_problem={}, sens_type='FD', store_sol=True, store_hst=False, hot_start=False, disp_opts=False, sens_mode='', sens_step={}, *args, **kwargs): ''' Run Optimizer (Optimize Routine) **Keyword arguments:** - opt_problem -> INST: Optimization instance - sens_type -> STR/FUNC: Gradient type, *Default* = 'FD' - store_sol -> BOOL: Store solution in Optimization class flag, *Default* = True - disp_opts -> BOOL: Flag to display options in solution text, *Default* = False - store_hst -> BOOL/STR: Flag/filename to store optimization history, *Default* = False - hot_start -> BOOL/STR: Flag/filename to read optimization history, *Default* = False - sens_mode -> STR: Flag for parallel gradient calculation, *Default* = '' - sens_step -> FLOAT: Sensitivity setp size, *Default* = {} [corresponds to 1e-6 (FD), 1e-20(CS)] Additional arguments and keyword arguments are passed to the objective function call. Documentation last updated: February. 2, 2011 - Ruben E. Perez ''' # if ((self.poa) and (sens_mode.lower() == 'pgc')): raise NotImplementedError("pyMMFD - Current implementation only allows single level parallelization, either 'POA' or 'pgc'") #end if self.poa or (sens_mode.lower() == 'pgc'): try: import mpi4py from mpi4py import MPI except ImportError: print 'pyMMFD: Parallel objective Function Analysis requires mpi4py' #end comm = MPI.COMM_WORLD nproc = comm.Get_size() if (mpi4py.__version__[0] == '0'): Bcast = comm.Bcast elif (mpi4py.__version__[0] == '1'): Bcast = comm.bcast #end self.pll = True self.myrank = comm.Get_rank() else: self.pll = False self.myrank = 0 #end myrank = self.myrank # tmp_file = False def_fname = self.options['IFILE'][1].split('.')[0] if isinstance(store_hst,str): if isinstance(hot_start,str): if (myrank == 0): if (store_hst == hot_start): hos_file = History(hot_start, 'r', self) log_file = History(store_hst+'_tmp', 'w', self, opt_problem.name) tmp_file = True else: hos_file = History(hot_start, 'r', self) log_file = History(store_hst, 'w', self, opt_problem.name) #end #end self.sto_hst = True self.h_start = True elif hot_start: if (myrank == 0): hos_file = History(store_hst, 'r', self) log_file = History(store_hst+'_tmp', 'w', self, opt_problem.name) tmp_file = True #end self.sto_hst = True self.h_start = True else: if (myrank == 0): log_file = History(store_hst, 'w', self, opt_problem.name) #end self.sto_hst = True self.h_start = False #end elif store_hst: if isinstance(hot_start,str): if (hot_start == def_fname): if (myrank == 0): hos_file = History(hot_start, 'r', self) log_file = History(def_fname+'_tmp', 'w', self, opt_problem.name) tmp_file = True #end else: if (myrank == 0): hos_file = History(hot_start, 'r', self) log_file = History(def_fname, 'w', self, opt_problem.name) #end #end self.sto_hst = True self.h_start = True elif hot_start: if (myrank == 0): hos_file = History(def_fname, 'r', self) log_file = History(def_fname+'_tmp', 'w', self, opt_problem.name) tmp_file = True #end self.sto_hst = True self.h_start = True else: if (myrank == 0): log_file = History(def_fname, 'w', self, opt_problem.name) #end self.sto_hst = True self.h_start = False #end else: self.sto_hst = False self.h_start = False #end # gradient = Gradient(opt_problem, sens_type, sens_mode, sens_step, *args, **kwargs) #====================================================================== # MMFD - Objective/Constraint Values Function #====================================================================== def mmfdfun(nv,nc,x,f,g): # Variables Groups Handling if opt_problem.use_groups: xg = {} for group in group_ids.keys(): if (group_ids[group][1]-group_ids[group][0] == 1): xg[group] = x[group_ids[group][0]] else: xg[group] = x[group_ids[group][0]:group_ids[group][1]] #end #end xn = xg else: xn = x #end # Flush Output Files self.flushFiles() # Evaluate User Function fail = 0 if (myrank == 0): if self.h_start: [vals,hist_end] = hos_file.read(ident=['obj', 'con', 'fail']) if hist_end: self.h_start = False hos_file.close() else: [ff,gg,fail] = [vals['obj'][0][0],vals['con'][0],int(vals['fail'][0][0])] #end #end #end if self.pll: self.h_start = Bcast(self.h_start,root=0) #end if self.h_start and self.pll: [ff,gg,fail] = Bcast([ff,gg,fail],root=0) elif not self.h_start: [ff,gg,fail] = opt_problem.obj_fun(xn, *args, **kwargs) #end # Store History if (myrank == 0): if self.sto_hst: log_file.write(x,'x') log_file.write(ff,'obj') log_file.write(gg,'con') log_file.write(fail,'fail') #end #end # Objective Assigment if isinstance(ff,complex): f = ff.astype(float) else: f = ff #end # Constraints Assigment for i in xrange(len(opt_problem._constraints.keys())): if isinstance(gg[i],complex): g[i] = gg[i].astype(float) else: g[i] = gg[i] #end #end return f,g #====================================================================== # MMFD - Objective/Constraint Gradients Function #====================================================================== def mmfdgrd(nv,nc,x,f,g,df,dg): if self.h_start: if (myrank == 0): [vals,hist_end] = hos_file.read(ident=['grad_obj','grad_con']) if hist_end: self.h_start = False hos_file.close() else: dff = vals['grad_obj'][0].reshape((len(opt_problem._objectives.keys()),len(opt_problem._variables.keys()))) dgg = vals['grad_con'][0].reshape((len(opt_problem._constraints.keys()),len(opt_problem._variables.keys()))) #end #end if self.pll: self.h_start = Bcast(self.h_start,root=0) #end if self.h_start and self.pll: [dff,dgg] = Bcast([dff,dgg],root=0) #end #end if not self.h_start: # dff,dgg = gradient.getGrad(x, group_ids, [f], g, *args, **kwargs) #end # Store History if self.sto_hst and (myrank == 0): log_file.write(dff,'grad_obj') log_file.write(dgg,'grad_con') #end # Gradient Assignment for i in xrange(len(opt_problem._variables.keys())): df[i] = dff[0,i] for j in xrange(len(opt_problem._constraints.keys())): dg[i,j] = dgg[j,i] #end #end return df,dg # Variables Handling nvar = len(opt_problem._variables.keys()) xl = [] xu = [] xx = [] for key in opt_problem._variables.keys(): if (opt_problem._variables[key].type == 'c'): xl.append(opt_problem._variables[key].lower) xu.append(opt_problem._variables[key].upper) xx.append(opt_problem._variables[key].value) elif (opt_problem._variables[key].type == 'i'): raise IOError('MMFD cannot handle integer design variables') elif (opt_problem._variables[key].type == 'd'): raise IOError('MMFD cannot handle discrete design variables') #end #end xl = numpy.array(xl) xu = numpy.array(xu) xx = numpy.array(xx) # Variables Groups Handling group_ids = {} if opt_problem.use_groups: k = 0 for key in opt_problem._vargroups.keys(): group_len = len(opt_problem._vargroups[key]['ids']) group_ids[opt_problem._vargroups[key]['name']] = [k,k+group_len] k += group_len #end #end # Constraints Handling ncon = len(opt_problem._constraints.keys()) neqc = 0 gg = [] idg = [] if ncon > 0: for key in opt_problem._constraints.keys(): if opt_problem._constraints[key].type == 'i': idg.append(1) elif opt_problem._constraints[key].type == 'e': idg.append(-1) #end gg.append(opt_problem._constraints[key].value) #end #end gg = numpy.array(gg) idg = numpy.array(idg, numpy.int) # Objective Handling objfunc = opt_problem.obj_fun nobj = len(opt_problem._objectives.keys()) ff = [] for key in opt_problem._objectives.keys(): ff.append(opt_problem._objectives[key].value) #end ff = numpy.array(ff) # Setup argument list values ndv = numpy.array([nvar], numpy.int) ncn = numpy.array([ncon], numpy.int) if (self.options['IOPT'][1]>=0 and self.options['IOPT'][1]<=1): iopt = numpy.array([self.options['IOPT'][1]], numpy.int) else: raise IOError('Incorrect Feasible Directions Approach') #end if (self.options['IONED'][1]>=0 and self.options['IONED'][1]<=3): ioned = numpy.array([self.options['IONED'][1]], numpy.int) else: raise IOError('Incorrect One-Dimensional Search Method') #end if (myrank == 0): if (self.options['IPRINT'][1]>=0 and self.options['IPRINT'][1]<=2): iprint = numpy.array([self.options['IPRINT'][1]], numpy.int) else: raise IOError('Incorrect Output Level Setting') #end else: iprint = numpy.array([0], numpy.int) #end #iout = numpy.array([self.options['IOUT'][1]], numpy.int) ifile = self.options['IFILE'][1] if (iprint > 0): if os.path.isfile(ifile): os.remove(ifile) #end #end ct = numpy.array([self.options['CT'][1]], numpy.float) ctmin = numpy.array([self.options['CTMIN'][1]], numpy.float) finit,ginit = mmfdfun([],[],xx,ff,gg) dabobj = numpy.array([self.options['DABOBJ'][1]*finit], numpy.float) delobj = numpy.array([self.options['DELOBJ'][1]], numpy.float) thetaz = numpy.array([self.options['THETAZ'][1]], numpy.float) pmlt = numpy.array([self.options['PMLT'][1]], numpy.float) itmax = numpy.array([self.options['ITMAX'][1]], numpy.int) itrmop = numpy.array([self.options['ITRMOP'][1]], numpy.int) nrwk0 = 500 nrwk1 = 10*(2*nvar+ncon) nrwk2 = (ncon+2*nvar+3) nrwk3 = (ncon+2*nvar)*((ncon+2*nvar)/2+1) nrwkS = nrwk0 + nrwk1 + nrwk2 + nrwk3 nrwk = numpy.array([nrwkS], numpy.int) wk = numpy.zeros([nrwk], numpy.float) nriwk = numpy.array([nrwkS], numpy.int) iwk = numpy.zeros([nriwk], numpy.int) nfun = numpy.array([0], numpy.int) ngrd = numpy.array([0], numpy.int) # Run MMFD t0 = time.time() mmfd.mmfd(iopt,ioned,iprint,ndv,ncn,xx,xl,xu,ff,gg,idg, wk,nrwk,iwk,nriwk,ifile,ct,ctmin,dabobj,delobj,thetaz, pmlt,itmax,itrmop,nfun,ngrd,mmfdfun,mmfdgrd) sol_time = time.time() - t0 if (myrank == 0): if self.sto_hst: log_file.close() if tmp_file: hos_file.close() name = hos_file.filename os.remove(name+'.cue') os.remove(name+'.bin') os.rename(name+'_tmp.cue',name+'.cue') os.rename(name+'_tmp.bin',name+'.bin') #end #end #end if (iprint > 0): # mmfd.closeunit(self.options['IOUT'][1]) mmfd.closeunit(6) #end # Store Results sol_inform = {} sol_inform['value'] = [] sol_inform['text'] = {} if store_sol: sol_name = 'MMFD Solution to ' + opt_problem.name sol_options = copy.copy(self.options) if sol_options.has_key('defaults'): del sol_options['defaults'] #end sol_evals = nfun[0] + ngrd[0]*nvar sol_vars = copy.deepcopy(opt_problem._variables) i = 0 for key in sol_vars.keys(): sol_vars[key].value = xx[i] i += 1 #end sol_objs = copy.deepcopy(opt_problem._objectives) i = 0 for key in sol_objs.keys(): sol_objs[key].value = ff[i] i += 1 #end if ncon > 0: sol_cons = copy.deepcopy(opt_problem._constraints) i = 0 for key in sol_cons.keys(): sol_cons[key].value = gg[i] i += 1 #end else: sol_cons = {} #end sol_lambda = {} opt_problem.addSol(self.__class__.__name__, sol_name, objfunc, sol_time, sol_evals, sol_inform, sol_vars, sol_objs, sol_cons, sol_options, display_opts=disp_opts, Lambda=sol_lambda, Sensitivities=sens_type, myrank=myrank, arguments=args, **kwargs) #end return ff, xx, sol_inform def _on_setOption(self, name, value): ''' Set Optimizer Option Value (Optimizer Specific Routine) Documentation last updated: May. 07, 2008 - Ruben E. Perez ''' pass def _on_getOption(self, name): ''' Get Optimizer Option Value (Optimizer Specific Routine) Documentation last updated: May. 07, 2008 - Ruben E. Perez ''' pass def _on_getInform(self, infocode): ''' Get Optimizer Result Information (Optimizer Specific Routine) Keyword arguments: ----------------- id -> STRING: Option Name Documentation last updated: May. 07, 2008 - Ruben E. Perez ''' pass def _on_flushFiles(self): ''' Flush the Output Files (Optimizer Specific Routine) Documentation last updated: August. 09, 2009 - Ruben E. Perez ''' # iPrint = self.options['IPRINT'][1] if (iPrint > 0): #mmfd.pyflush(self.options['IOUT'][1]) mmfd.pyflush(6) #end #============================================================================== # MMFD Optimizer Test #============================================================================== if __name__ == '__main__': # Test MMFD print 'Testing ...' MMFD = MMFD() print MMFD