def lambdacommamu(pop, Optimizer):
"""Selection function to employ a lambda,mu GA scheme
"""
fits = [ind.fitness for ind in pop]
minindex = [i for i in range(len(pop)) if pop[i].fitness==min(fits)]
try:
Optimizer.mark
except:
Optimizer.mark = len(pop)/2
parents = pop[0:Optimizer.mark]
offspring = pop[Optimizer.mark::]
if minindex < Optimizer.mark:
offspring.append(pop[minindex])
if len(offspring) < Optimizer.nindiv:
diff = Optimizer.nindiv-len(offspring)
if Optimizer.natural_selection_scheme=='elitism':
addins = get_best(parents,diff)
STR = 'Adding in '+repr(diff)+' lowest fitness parents\n'
else:
addins = selection_switch(parents, diff, Optimizer.natural_selection_scheme, Optimizer)
STR = 'Adding in '+repr(diff)+' parents based on natural selection\n'
for one in addins:
offspring.append(one)
elif len(offspring) > Optimizer.nindiv:
diff = len(offspring)-Optimizer.nindiv
if Optimizer.natural_selection_scheme=='elitism':
offspring = get_best(offspring,Optimizer.nindiv)
STR = 'Removing lowest '+repr(diff)+' fitness offspring\n'
else:
offspring = selection_switch(offspring, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
STR = 'Removing '+repr(diff)+' offspring by natural selection\n'
else:
STR = 'Number of offspring = {0}\n'.format(len(offspring))
return offspring, STR
def cost(pop, nkeep, Optimizer): """Selection function to order and select structures based on simple relative cost """ pop = get_best(pop,len(pop)) newpop = [] fitnesses = [ind.fitness for ind in pop] prob = [] cumprob = [] try: norms = [fit - pop[nkeep+1].fitness for fit in fitnesses[0:nkeep]] except: norms = [fit - pop[nkeep-1].fitness for fit in fitnesses[0:nkeep]] sumn = sum(norms) for i in range(nkeep): prob.append(norms[i] / sumn) cumprob.append(sum(prob)) prevcounter = [] for i in range(nkeep): rand = random.random() counter = 0 while cumprob[counter] < rand: counter += 1 if counter in prevcounter: a = random.choice(pop) newpop.append(a) prevcounter.append(a.index) else: newpop.append(pop[counter]) prevcounter.append(counter) return newpop
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 cost(pop, nkeep, Optimizer):
"""Selection function to order and select structures based on simple relative cost
"""
pop = get_best(pop, len(pop))
newpop = []
fitnesses = [ind.fitness for ind in pop]
prob = []
cumprob = []
try:
norms = [fit - pop[nkeep + 1].fitness for fit in fitnesses[0:nkeep]]
except:
norms = [fit - pop[nkeep - 1].fitness for fit in fitnesses[0:nkeep]]
sumn = sum(norms)
for i in range(nkeep):
prob.append(norms[i] / sumn)
cumprob.append(sum(prob))
prevcounter = []
for i in range(nkeep):
rand = random.random()
counter = 0
while cumprob[counter] < rand:
counter += 1
if counter in prevcounter:
a = random.choice(pop)
newpop.append(a)
prevcounter.append(a.index)
else:
newpop.append(pop[counter])
prevcounter.append(counter)
return newpop
def rank(pop, nkeep, Optimizer):
"""Selection function that chooses structures to survive and orders them based on their relative ranking
"""
pop = get_best(pop, len(pop))
newpop = []
prob = []
cumprob = []
for i in range(len(pop)):
rankprob = float((nkeep - i)) / float(sum(range(nkeep + 1)))
prob.append(rankprob)
cumprob.append(sum(prob))
prevcounter = None
for i in range(nkeep):
rand = random.random()
counter = 0
while cumprob[counter] < rand:
counter += 1
if prevcounter == counter:
while True:
a = random.choice(pop)
if a.index != prevcounter:
break
newpop.append(random.choice(pop))
prevcounter = a.index
else:
newpop.append(pop[counter])
prevcounter = counter
return newpop
def rank(pop, nkeep, Optimizer):
"""Selection function that chooses structures to survive and orders them based on their relative ranking
"""
pop = get_best(pop,len(pop))
newpop = []
prob = []
cumprob = []
for i in range(len(pop)):
rankprob = float((nkeep-i)) / float(sum(range(nkeep+1)))
prob.append(rankprob)
cumprob.append(sum(prob))
prevcounter = None
for i in range(nkeep):
rand = random.random()
counter = 0
while cumprob[counter] < rand:
counter += 1
if prevcounter==counter:
while True:
a = random.choice(pop)
if a.index != prevcounter:
break
newpop.append(random.choice(pop))
prevcounter = a.index
else:
newpop.append(pop[counter])
prevcounter = counter
return newpop
def random_pick(pop, nkeep, Optimizer): """Selection function that randomly choose structures from a population to survive. """ pop = get_best(pop,len(pop)) newpop = [] for i in range(nkeep): sel = random.choice(pop) newpop.append(sel) return newpop
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_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_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_stem.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 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 best(pop, nkeep, Optimizer=None):
"""Selection function to select the best individuals in a population
Inputs:
pop = list of Individual class structures
nkeep = number of Individuals to keep in the population
must be less than or equal to the number of individuals currently in the population
Optimizer = dummy placeholder
Outputs:
npop = population with desired number of individuals
"""
if nkeep <= len(pop):
npop = get_best(pop, nkeep)
else:
print 'WARNING: selection.best nkeep > pop. Returning pop'
npop = pop
return npop
def best(pop, nkeep, Optimizer=None):
"""Selection function to select the best individuals in a population
Inputs:
pop = list of Individual class structures
nkeep = number of Individuals to keep in the population
must be less than or equal to the number of individuals currently in the population
Optimizer = dummy placeholder
Outputs:
npop = population with desired number of individuals
"""
if nkeep <= len(pop):
npop = get_best(pop,nkeep)
else:
print 'WARNING: selection.best nkeep > pop. Returning pop'
npop = pop
return npop
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 energy_cluster(pop, Optimizer):
"""Use a k-means clustering approach to identify individuals for new population
"""
#Get coordinates for each individual
pts = [ind.energy for ind in pop]
#Identify spatial bounds
mins = min(pts)
maxs = max(pts)
STR=''
attemptcount=10
passflag=False
while attemptcount > 0:
#Select random initial centroid locations
cents = []
while len(cents) < Optimizer.nindiv:
a = random.choice(pts)
if a not in cents:
cents.append(a)
#Assign individual to closest centroid
clustlist = [[] for i in range(Optimizer.nindiv)]
for one in pts:
ds = []
for i in range(len(cents)):
d = abs(one - cents[i])
ds.append([d,i])
ds.sort()
loc = ds[0]
clustlist[loc[1]].append(one)
try:
count = 0
while True:
count += 1
#Calculate the centroid of the new cluster
centsnew = []
for one in clustlist:
if len(one) > 0:
m = [1.0]*len(one)
#cop = numpy.dot(m, one) / sum(m)
cop = [sum([es for es in one]) / sum(m)]
centsnew.append(cop)
else:
centsnew.append(one)
#Assign individuals to new centroids
clustlistn=[[] for i in range(Optimizer.nindiv)]
for one in pts:
ds=[]
for i in range(len(centsnew)):
d = abs(one - cents[i])
ds.append([d,i])
ds.sort()
loc = ds[0]
clustlistn[loc[1]].append(one)
if clustlistn==clustlist:
clustlist = clustlistn
centroids = centsnew
attemptcount=-10
passflag=True
break
else:
clustlist = clustlistn
except:
attemptcount-=1
STR+='WARNING: PREDATOR: K-means cluster difficulty\n'
print 'WARNING: PREDATOR: K-means cluster difficulty'
if passflag==True:
#Select one individual from each cluster
news = []
for one in clustlist:
#Random
news.append(random.choice(one))
#Minimum energy
#one.sort()
#news.append(one[0])
indices = []
for one in news:
for i in range(len(pts)):
if pts[i]==one:
indices.append(i)
break
npop = [pop[ind] for ind in indices]
else:
STR+='WARNING: PREDATOR: Population converging. Unable to find nindiv distinct clusters\n'
npop,str1 = Mutation_Dups(pop,Optimizer)
STR+=str1
#npop=pop
STR+='WARNING: PREDATOR: Chose new population based on Mutation-Dups predator\n'
pop = get_best(npop,len(npop))
return pop
def fingerprint_niche(pop, Optimizer):
"""Use a k-means clustering approach to identify individuals for new population"""
STR = ''
#Get coordinates for each individual
pts = []
for one in pop:
fpd = fingerprint_dist(pop[0].fingerprint,one.fingerprint)
pts.append([one.fitness - pop[0].fitness,fpd])
for one in pts:
if numpy.isnan(one[1]):
one[1] = 0
#Identify spatial bounds
mins = min(pts)
maxs = max(pts)
attemptcount=10
passflag=False
while attemptcount > 0:
#Select random initial centroid locations
cents = []
#for i in range(Optimizer.nindiv):
# cents.append([random.uniform(mins[0],maxs[0]),random.uniform(mins[1],maxs[1])])
while len(cents) < Optimizer.nindiv:
a = random.choice(pts)
if a not in cents:
cents.append(a)
#Assign individual to closest centroid
clustlist = [[] for i in range(Optimizer.nindiv)]
for one in pts:
ds = []
for i in range(len(cents)):
d = ((one[0] - cents[i][0])**2 + (one[1] - cents[i][1])**2)**0.5
ds.append([d,i])
ds = sorted(ds, key=lambda two: two[0])
loc = ds[0]
clustlist[loc[1]].append(one)
try:
count = 0
while True:
count += 1
#Calculate the centroid of the new cluster
centsnew = []
for one in clustlist:
if len(one) > 0:
m = [1.0]*len(one)
#cop = numpy.dot(m, one) / sum(m)
cop = [sum([es for es,fps in one]) / sum(m),sum([fps for es,fps in one]) / sum(m)]
centsnew.append(cop)
else:
centsnew.append(one)
#Assign individuals to new centroids
clustlistn=[[] for i in range(Optimizer.nindiv)]
for one in pts:
ds=[]
for i in range(len(centsnew)):
d = ((one[0] - centsnew[i][0])**2 + (one[1] - centsnew[i][1])**2)**0.5
ds.append([d,i])
ds = sorted(ds, key=lambda two: two[0])
loc = ds[0]
clustlistn[loc[1]].append(one)
if clustlistn==clustlist:
clustlist = clustlistn
centroids = centsnew
attemptcount=-10
passflag=True
break
else:
clustlist = clustlistn
except:
attemptcount-=1
STR+= 'WARNING: PREDATOR: K-means cluster difficulty\n'
if passflag==True:
#Select one individual from each cluster
news = []
for one in clustlist:
#Random
#npop.append(random.choice(one))
#Minimum energy
opts = sorted(one, key=lambda two: two[0])
news.append(opts[0])
indices = []
for one in news:
for i in range(len(pts)):
if pts[i]==one:
indices.append(i)
break
npop = [pop[ind] for ind in indices]
else:
STR+='WARNING: PREDATOR: Population converging. Unable to find nindiv distinct clusters\n'
npop = mutation_dups(pop,Optimizer)
#npop = pop
STR+='WARNING: PREDATOR: Chose new population based on Mutation-Dups predator\n'
pop = get_best(npop,len(npop))
return pop, STR
def generation_eval(self, pop):
global logger
emx = max(ind.energy for ind in pop)
emn = min(ind.energy for ind in pop)
for ind in pop:
ind.tenergymx = emx
ind.tenergymin = emn
#DEBUG: Write relaxed individual
if 'MA' in self.debug:
if self.generation > 0:
inp_out.write_xyz(self.debugfile,pop[self.nindiv][0],\
'First Relaxed Offspring '+repr(pop[self.nindiv-1].energy))
#DEBUG: Write relaxed ind in solid
if self.structure=='Defect' or self.structure=='Surface':
inp_out.write_xyz(self.debugfile,pop[self.nindiv].bulki,\
'First Relaxed bulki '+repr(pop[self.nindiv-1].energy))
sols = pop[self.nindiv][0].copy()
sols.extend(pop[self.nindiv].bulki)
inp_out.write_xyz(self.debugfile,sols,'First from Invalid-ind + Bulki '+\
repr(pop[self.nindiv].energy))
sols = pop[self.nindiv][0].copy()
sols.extend(pop[self.nindiv].bulko)
inp_out.write_xyz(self.debugfile,sols,\
'First from Invalid-ind + Bulko '+repr(pop[self.nindiv].energy))
if self.generation==0:
logger.info('Initializing Bests list')
self.BESTS = list()
if self.best_inds_list:
self.BESTS = tools.BestInds(pop,self.BESTS,self,writefile=True)
# Determine survival based on fitness predator
if 'lambda,mu' not in self.algorithm_type:
pop = tools.get_best(pop, len(pop))
if self.fingerprinting:
logger.info('Writing fingerprint files')
for one in pop:
self.fpfile.write(repr(fingerprinting.fingerprint_dist(
pop[0].fingerprint,one.fingerprint))+' '+repr(one.energy)+' ')
self.fpfile.write('\n')
self.fpminfile.write(repr(pop[0].fingerprint)+'\n')
self.fpminfile.write(repr(pop[0].energy)+'\n')
nevals = len(pop)/2
if self.generation !=0:
logger.info('Applying predator')
pop = predator_switch(pop,self)
else:
self.genrep = 0
self.minfit = 0
# Evaluate population
logger.info('Checking population for convergence')
self.check_pop(pop)
#Update general output tracking
if self.generation !=0:
histlist = []
for ind in pop:
histlist.append(ind.history_index)
self.Runtimes.append(time.time())
self.Evaluations.append(nevals)
cxsuccess = 0
mutsuccess = []
for one in histlist:
if '+' in one:
cxsuccess +=1
if 'm' in one:
mutsuccess.append(one)
self.CXs.append((self.cxattempts,cxsuccess))
mutslist = [[0,0] for one in self.mutation_options]
for one in mutsuccess:
for two, opt in self.mutattempts:
if one==two:
index = [ind for ind,value in enumerate(
self.mutation_options) if value==opt][0]
mutslist[index][1]+=1
for one,opt in self.mutattempts:
index = [ind for ind,value in enumerate(
self.mutation_options) if value==opt][0]
mutslist[index][0]+=1
self.Muts.append(mutslist)
self.output.write('\n----- Generation Stats -----\n')
self.output.write('Attempted Crossovers: ' + repr(self.cxattempts)+'\n')
self.output.write('Successful Crossovers: ' + repr(cxsuccess)+'\n')
self.output.write('Mutations:\n')
i=0
for opt in self.mutation_options:
self.output.write(' Attempted ' + opt + ' : ' + repr(mutslist[i][0]) + '\n')
self.output.write(' Successful ' + opt + ' : ' + repr(mutslist[i][1]) + '\n')
i+=1
self.generation += 1
# Set new index values
index1 = 0
for ind in pop:
ind.index = index1
index1+=1
index1 = 0
if not self.convergence:
try:
self.calc.clean()
if self.fixed_region:
self.static_calc.clean()
except:
pass
if self.lammps_keep_files:
try:
if self.parallel and ('Island_Method' not in self.algorithm_type):
nproc = MPI.COMM_WORLD.Get_size()
lmpfilepath = os.path.dirname(self.calc.tmp_dir)
for proc in range(nproc):
pth = os.path.join(lmpfilepath,'rank-{0}'.format(proc))
fls = [fl for fl in os.listdir(pth) if '.' not in fl]
for one in fls:
os.remove(pth+'/'+one)
else:
fls = [fl for fl in os.listdir(self.calc.tmp_dir) if '.' not in fl]
for one in fls:
os.remove(self.calc.tmp_dir+'/'+one)
except Exception, e:
logger.error('Print issue in removing files {0}.'.format(e),exc_info=True)
print str(e)
pass
STR += 'Predator: Adding duplicates back\n'
choice = random.choice(otherlist)
if choice.index not in nindices:
newpop.append(choice)
nindices.append(choice.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, str = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR += str
else:
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme,
Optimizer)
pop = get_best(pop, len(pop))
Optimizer.output.write(STR)
return pop
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
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:
newpop.append(choice)
nindices.append(choice.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,str = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
Optimizer.output.write(STR)
return pop
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_stem.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_stem.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 fitpred(pop,Optimizer):
"""Predator function to select best structures
"""
pop = get_best(pop,Optimizer.nindiv)
return pop, ''
def fingerprint_niche(pop, Optimizer):
"""Use a k-means clustering approach to identify individuals for new population"""
STR = ''
#Get coordinates for each individual
pts = []
for one in pop:
fpd = fingerprint_dist(pop[0].fingerprint, one.fingerprint)
pts.append([one.fitness - pop[0].fitness, fpd])
for one in pts:
if numpy.isnan(one[1]):
one[1] = 0
#Identify spatial bounds
mins = min(pts)
maxs = max(pts)
attemptcount = 10
passflag = False
while attemptcount > 0:
#Select random initial centroid locations
cents = []
#for i in range(Optimizer.nindiv):
# cents.append([random.uniform(mins[0],maxs[0]),random.uniform(mins[1],maxs[1])])
while len(cents) < Optimizer.nindiv:
a = random.choice(pts)
if a not in cents:
cents.append(a)
#Assign individual to closest centroid
clustlist = [[] for i in range(Optimizer.nindiv)]
for one in pts:
ds = []
for i in range(len(cents)):
d = ((one[0] - cents[i][0])**2 +
(one[1] - cents[i][1])**2)**0.5
ds.append([d, i])
ds = sorted(ds, key=lambda two: two[0])
loc = ds[0]
clustlist[loc[1]].append(one)
try:
count = 0
while True:
count += 1
#Calculate the centroid of the new cluster
centsnew = []
for one in clustlist:
if len(one) > 0:
m = [1.0] * len(one)
#cop = numpy.dot(m, one) / sum(m)
cop = [
sum([es for es, fps in one]) / sum(m),
sum([fps for es, fps in one]) / sum(m)
]
centsnew.append(cop)
else:
centsnew.append(one)
#Assign individuals to new centroids
clustlistn = [[] for i in range(Optimizer.nindiv)]
for one in pts:
ds = []
for i in range(len(centsnew)):
d = ((one[0] - centsnew[i][0])**2 +
(one[1] - centsnew[i][1])**2)**0.5
ds.append([d, i])
ds = sorted(ds, key=lambda two: two[0])
loc = ds[0]
clustlistn[loc[1]].append(one)
if clustlistn == clustlist:
clustlist = clustlistn
centroids = centsnew
attemptcount = -10
passflag = True
break
else:
clustlist = clustlistn
except:
attemptcount -= 1
STR += 'WARNING: PREDATOR: K-means cluster difficulty\n'
if passflag == True:
#Select one individual from each cluster
news = []
for one in clustlist:
#Random
#npop.append(random.choice(one))
#Minimum energy
opts = sorted(one, key=lambda two: two[0])
news.append(opts[0])
indices = []
for one in news:
for i in range(len(pts)):
if pts[i] == one:
indices.append(i)
break
npop = [pop[ind] for ind in indices]
else:
STR += 'WARNING: PREDATOR: Population converging. Unable to find nindiv distinct clusters\n'
npop = mutation_dups(pop, Optimizer)
#npop = pop
STR += 'WARNING: PREDATOR: Chose new population based on Mutation-Dups predator\n'
pop = get_best(npop, len(npop))
return pop, STR
def fitpred(pop, Optimizer):
"""Predator function to select best structures
"""
pop = get_best(pop, Optimizer.nindiv)
return pop, ''
