def fitpred_new(pop, Optimizer): """Predator function to identify similar structures based on energy and replace one with new structure. """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR += 'Predator: Removed total of ' + repr( len(fitlist) - len(nfitlist)) + ' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: if Optimizer.structure == 'Defect' or Optimizer.structure == 'Cluster': ind = gen_pop_box(Optimizer.atomlist, Optimizer.size) elif Optimizer.structure == 'Crystal': outts = gen_pop_box(Optimizer.atomlist, Optimizer.size, Optimizer.cell_shape_options) ind = outts[0] elif Optimizer.structure == 'Surface': mutopto = Optimizer.mutation_options Optimizer.mutation_options = ['Lattice_Alteration_rdrd'] topind = random.choice(pop)[0].copy() ind, scheme = moves_switch(topind, Optimizer) Optimizer.mutation_options = mutopto individ = Individual(ind) #CHECK THIS LATER!! MAY NEED TO ADD MORE PROPERTIES!! individ.energy = 1000 individ.fitness = 1000 newpop.append(individ) STR += 'Predator: Adding mutated duplicates to new pop history=' + individ.history_index + '\n' nindices.append(individ.index) nindices.sort() if Optimizer.natural_selection_scheme == 'fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer, ind, Optimizer.fpbin, Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index, n in enumerate(nindices) if n > Optimizer.nindiv - 1 ][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR += str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop, len(pop)) return pop, STR
def fitpred_bests(pop,Optimizer): """Predator function to identify similar structures based on energy and replace one with structure from BESTS List. """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) count = 0 while len(newpop) < Optimizer.nindiv: try: Optimizer.BESTS except: Optimizer.BESTS=[] if len(Optimizer.BESTS) > 0: idx = random.choice(range(len(Optimizer.BESTS))) newpop.append(Optimizer.BESTS[idx]) STR+='Predator: Adding in structure from Best List from position = {0} with fitness = {1}\n'.format(idx,Optimizer.BESTS[idx].fitness) newindices.append(len(pop)+count) count+=1 else: indiv = random.choice(otherlist).duplicate() indiv, scheme = moves_switch(indiv,Optimizer) indiv.energy = 1000 indiv.fitness = 1000 newpop.append(indiv) STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n' nindices.append(indiv.index) nindices.sort() if Optimizer.natural_selection_scheme=='fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR+=str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop,len(pop)) return pop, STR
def fitpred_new(pop,Optimizer): """Predator function to identify similar structures based on energy and replace one with new structure. """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: if Optimizer.structure=='Defect' or Optimizer.structure=='Cluster': ind=gen_pop_box(Optimizer.atomlist,Optimizer.size) elif Optimizer.structure=='Crystal': outts=gen_pop_box(Optimizer.atomlist,Optimizer.size,Optimizer.cell_shape_options) ind=outts[0] elif Optimizer.structure=='Surface': mutopto=Optimizer.mutation_options Optimizer.mutation_options=['Lattice_Alteration_rdrd'] topind=random.choice(pop)[0].copy() ind, scheme = moves_switch(topind,Optimizer) Optimizer.mutation_options=mutopto individ=Individual(ind) #CHECK THIS LATER!! MAY NEED TO ADD MORE PROPERTIES!! individ.energy=1000 individ.fitness=1000 newpop.append(individ) STR+='Predator: Adding mutated duplicates to new pop history='+individ.history_index+'\n' nindices.append(individ.index) nindices.sort() if Optimizer.natural_selection_scheme=='fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR+=str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop,len(pop)) return pop, STR
def mutation_dups_adapt_stem(pop, Optimizer): """Predator function that removes individuals based on fitness and mutates replacements """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: indiv = random.choice(otherlist).duplicate() indiv, scheme = moves_switch(indiv,Optimizer) indiv.energy = 1000 indiv.fitness = 1000 newpop.append(indiv) STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n' nindices.append(indiv.index) nindices.sort() if Optimizer.natural_selection_scheme=='fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR+=str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop,len(pop)) indiv = pop[0] if (indiv.fitness/indiv.energy <2.0): from MAST.structopt.tools.StemCalc import find_stem_coeff outs = find_stem_coeff(Optimizer,indiv) ind = outs[1] Optimizer.stem_coeff = outs[0] STR+='Readjusting STEM Coeff = {0}'.format(Optimizer.stem_coeff)) return pop, STR
def adapting(pop, Optimizer): """Function to provide an adapting fitness function for GA evaluation Input: pop = population consisting of list of Individual Class objects to be evaluated Optimizer = Optimizer class object with fitness parameters Output: pop = new population updated based on fitness evaluation *** needs work *** """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR += 'Predator: Removed total of ' + repr( len(fitlist) - len(nfitlist)) + ' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: Optimizer.output.write('Predator: Adding duplicates back') indiv = random.choice(otherlist) newpop.append(indiv) STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n' if Optimizer.natural_selection_scheme == 'fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer, ind, Optimizer.fpbin, Optimizer.fpcutoff) if genrep >= Optimizer.reqrep * Optimizer.adaptbegin: ofusslim = Optimizer.fusslimit nfusslim = ofusslim * math.exp(-Optimizer.adaptmultiplier * float( Optimizer.genrep) / float(Optimizer.reqrep)) Optimizer.fusslimit = nfusslim else: ofusslim = Optimizer.fusslimit pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop, len(pop)) Optimizer.fusslimit = ofusslim return pop, STR
def mutation_dups_zp(pop, Optimizer): """Predator function that selects individuals that are too similar based fitness and replaces them with a zero point rotation of the structure """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: indiv = random.choice(otherlist).duplicate() mutopts = Optimizer.mutation_options Optimizer.mutation_options = ['ZP_Rotation'] indiv = moves_switch(indiv, Optimizer) Optimizer.mutation_options = mutopts newpop.append(indiv) nindices.append(indiv.index) STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n' nindices.sort() if Optimizer.natural_selection_scheme=='FUSSF': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR+=str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop,len(pop)) return pop, STR
def mutation_dups_energy(pop, Optimizer): """Predator function that removes duplicates based on energy and replaces with mutations """ fitlist = [one.energy for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: indiv = random.choice(otherlist).duplicate() indiv, scheme = moves_switch(indiv,Optimizer) indiv.energy = 1000 indiv.fitness = 1000 newpop.append(indiv) STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n' nindices.append(indiv.index) nindices.sort() if Optimizer.natural_selection_scheme=='fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR+=str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop,len(pop)) return pop, STR
def adapting(pop, Optimizer): """Function to provide an adapting fitness function for GA evaluation Input: pop = population consisting of list of Individual Class objects to be evaluated Optimizer = Optimizer class object with fitness parameters Output: pop = new population updated based on fitness evaluation *** needs work *** """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: Optimizer.output.write('Predator: Adding duplicates back') indiv = random.choice(otherlist) newpop.append(indiv) STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n' if Optimizer.natural_selection_scheme=='fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if genrep >= Optimizer.reqrep*Optimizer.adaptbegin: ofusslim = Optimizer.fusslimit nfusslim = ofusslim*math.exp(-Optimizer.adaptmultiplier*float(Optimizer.genrep)/float(Optimizer.reqrep)) Optimizer.fusslimit = nfusslim else: ofusslim = Optimizer.fusslimit pop = selection_switch(newpop, Optimizer.nindiv,Optimizer.natural_selection_scheme,Optimizer) pop = get_best(pop,len(pop)) Optimizer.fusslimit=ofusslim return pop, STR
passflag = True except NameError, e: logger.warning('Specified predator not one of the available options. Please check documentation and spelling! Predator : {0}. {1}'.format(scheme,e), exc_info=True) passflag = False STR+='Specified predator not one of the available options. Please check documentation and spelling! Predator : '+repr(scheme) STR+=repr(e)+'\n' except Exception, e: logger.error('ERROR: Issue in Predator Scheme. Predator = {0}. {1}'.format(scheme,e), exc_info=True) print 'ERROR: Issue in Predator Scheme. Predator = '+repr(scheme) print e passflag = False STR+='' if not passflag: logger.warning('Issue in predator. Attempting basic Fitpred') fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR += 'Issue in predator. Attempting basic Fitpred\n' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: STR+='Predator: Adding duplicates back\n' choice = random.choice(otherlist) if choice.index not in nindices:
def mutation_dups_quench(pop, Optimizer): """Predator function that removes individuals based on fitness and mutates replacements Also quenches top individuals """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR+='Predator: Removed '+repr(pop[i].history_index)+'\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: indiv = random.choice(otherlist).duplicate() indiv, scheme = moves_switch(indiv,Optimizer) indiv.energy = 1000 indiv.fitness = 1000 newpop.append(indiv) STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n' nindices.append(indiv.index) nindices.sort() if Optimizer.natural_selection_scheme=='fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR+=str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop,len(pop)) if Optimizer.genrep >10: from MAST.structopt.moves.quench import quench import os olammpsvar = os.environ['LAMMPS_COMMAND'] try: from mpi4py import MPI if '-n' in olammpsvar: lcommand = olammpsvar.split('-n') lcommand[1]=lcommand[1].split() nproc = MPI.COMM_WORLD.Get_size() os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(lcommand[0],nproc,lcommand[1][1]) except: pass oqns2 = Optimizer.quench_n_steps_2 Optimizer.quench_n_steps_2 = 100000 opar = Optimizer.parallel Optimizer.parallel = False for i in range(3): pop[i] = quench(pop[i],Optimizer) Optimizer.quench_n_steps_2 = oqns2 os.environ['LAMMPS_COMMAND'] = olammpsvar Optimizer.parallel = opar return pop, STR
def mutation_dups_quench(pop, Optimizer): """Predator function that removes individuals based on fitness and mutates replacements Also quenches top individuals """ fitlist = [one.fitness for one in pop] nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin) STR = '' newpop = [] if len(nfitlist) != len(fitlist): STR += 'Predator: Removed total of ' + repr( len(fitlist) - len(nfitlist)) + ' from population\n' otherlist = [] for i in range(len(pop)): if i not in nindices: STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n' otherlist.append(pop[i]) else: newpop.append(pop[i]) while len(newpop) < Optimizer.nindiv: indiv = random.choice(otherlist).duplicate() indiv, scheme = moves_switch(indiv, Optimizer) indiv.energy = 1000 indiv.fitness = 1000 newpop.append(indiv) STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n' nindices.append(indiv.index) nindices.sort() if Optimizer.natural_selection_scheme == 'fussf': for ind in newpop: if ind.fingerprint == 0: ind.fingerprint = get_fingerprint(Optimizer, ind, Optimizer.fpbin, Optimizer.fpcutoff) if 'lambda,mu' in Optimizer.algorithm_type: try: mark = [ index for index, n in enumerate(nindices) if n > Optimizer.nindiv - 1 ][0] except: mark = Optimizer.nindiv Optimizer.mark = mark pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer) STR += str1 else: pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer) pop = get_best(pop, len(pop)) if Optimizer.genrep > 10: from MAST.structopt.moves.quench import quench import os olammpsvar = os.environ['LAMMPS_COMMAND'] try: from mpi4py import MPI if '-n' in olammpsvar: lcommand = olammpsvar.split('-n') lcommand[1] = lcommand[1].split() nproc = MPI.COMM_WORLD.Get_size() os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format( lcommand[0], nproc, lcommand[1][1]) except: pass oqns2 = Optimizer.quench_n_steps_2 Optimizer.quench_n_steps_2 = 100000 opar = Optimizer.parallel Optimizer.parallel = False for i in range(3): pop[i] = quench(pop[i], Optimizer) Optimizer.quench_n_steps_2 = oqns2 os.environ['LAMMPS_COMMAND'] = olammpsvar Optimizer.parallel = opar return pop, STR