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