def init_local_vars(self):
''' '''
self._popsize=self.attrib('PopulationSize')
self.fit=ones(self._popsize)*-2
self.chroms=zeros((self._popsize, self._np), uint16)
self.breeder=Breeder(self)
self._npts=0
class GeneticAlgorithm(Optimizer):
'''Optimizer subclass that searches parameter space using a continuous replacement genetic algorithm with crossover, mutation and transposition operators.
MutationProbability: Mutation probability for breeding (Default .1)
CrossoverProbability: Crossover probablitiy (.7)
TrnasposeProbability: (default 0)
PopulationSize: number of units in the population (100)
Cull: if true, units with fitness values worse than this value
"die at birth". (False)
NewBlood : A percentage chance that any given new unit is generated randomly. (Default 0)
ChromosomeVariance: Determines the distribution of values in a new
chromosome. If 0 the distribution is uniform.
Otherwise it is normal with mean 0 and this
variance. (Default 0)
SelectionMethod: Method of parent selection. May be
"Proportional" or "Ordinal" (Default ordinal)
MinimumBreedInterval: The number of breeding events that must occur before
a given parent can be re-selected. (Default 0)
DistributerProfile: The port number for distributed evaluation.
default: "Local" - this (and any non-int value)
result in serial local evaluation)
MaxDuplicates: The maximum number of chromosomes in the population that
are allowed to be the same (5)
MutationRange: The fraction of the chromosome range that can be reached
via mutation. A value of 1.0 means that a mutation can lead to any
possible value. 0 always means that only single step mutations
are allowed. (default .5)
These algorithms always use continuous replacement, with the worst errors
"dying" first to allow room for new members of the population.
Alternately, if eval is the string "resume" and params is the name of a
directory, resume a GA stored in the indicated directory.
'''
_requiredAttributes = Optimizer._requiredAttributes+['MutationProbability', 'CrossoverProbability', 'PopulationSize']
_specialAttributes = Optimizer._specialAttributes+['Cull',
'CompeteToReplace',
'Death',
'NewBlood',
'TransposeProbability',
'SelectionMethod',
'MaxDuplicates',
'MutationRange',
'MinimumBreedInterval']
_guiConstructorInfo = {'CompeteToReplace':{'Name':'CompeteToReplace',
'Type':'List',
'Value':[True,
False]},
'SelectionMethod':{'Name':'SelectionMethod',
'Type':'List',
'Default':"Ordinal",
'Value':["Proportional",
"Ordinal"]},
'Death':{'Name':'Death',
'Type':'List',
'Default':"Worst",
'Value':['Worst',
'Select']}}
def init_local_vars(self):
''' '''
self._popsize=self.attrib('PopulationSize')
self.fit=ones(self._popsize)*-2
self.chroms=zeros((self._popsize, self._np), uint16)
self.breeder=Breeder(self)
self._npts=0
def resume(self, fname=None):
'''Resume a run from stored data. This is an alternative to self.prep. If fname isn't specified it defaults to self.attrib("File").'''
self.initialize_vars()
if not fname:
fname=self.attrib("File")
if not ast.verify(fname, self._storesize):
self.report("can't resume. %s is not an appropriate storage file" % fname)
self.prep()
return
self._store=ast.ArrayStore(fname, 'w')
self._nunits = len(self._store)
fit=self._store.getColumn(0)
if self.attrib('Maximize'):
bid=argmin(fit)
else:
bid=argmin(fit)
self._best=(fit[bid], bid)
dat=self._store.tail(self._popsize)
self.fit[:dat.shape[0]]=dat[:,0]
for i in range(dat.shape[0]):
self.chroms[i, :]=self._params.getIndex(dat[i,-self._np:])
self.report("Resume complete. %i units. Best %.4f, Mean %.4f" % (self._nunits, self._best[0], self.fit.mean()))
#print self.fit, self.chroms
def cleanup(self):
'''Write the current population to the end of the arraystore (required for correct resume behavior. This results in duplicate records that analysers may need to delete).'''
for i in range(self._popsize):
c=self._params.indextoPar(self.chroms[i,:])
self.record(c, (self.fit[i], (), ))
def randind(self):
'''Return a random chromosome. This is not the same as the random parameter set returned by self.random because it is a uint16 array of indexes into the parameter space, not a vector of actual parameter values'''
q=random.uniform(0, 1, self._np)
q=q*self._params._bins
q=minimum(q.astype(uint16), self._params._bins-1)
return q
def next(self):
'''Return the next set of parameters to be evaluated. If the initial population isn't established, or if the "NewBlood" probability occurs, returns random. Othewise, calls "breed" to generate new values. Note the the values returned by this function are indexes (ints) not raw parameters.'''
rc=False
if any(self.fit==-2):
rc=True
elif self.attrib('NewBlood') and random.uniform(0,1)<self.attrib('NewBlood'):
rc=True
if rc:
c=self.randind()
else:
c=self.breeder.newUnit()
return c
def evaluate(self, c):
dup=alltrue(self.chroms==c, 1)
fit=None
if any(dup):
fit=take(self.fit, nonzero1d(dup))
fit=compress(fit>-2, fit)
if self.attrib('MaxDuplicates') and fit.shape[0]>self.attrib('MaxDuplicates'):
return
elif fit.shape[0]>0:
fit=(fit[0], None)
c=self._params.indextoPar(c)
if fit==None:
self.general_eval(c, self.insertUnit)
else:
self.insertUnit(c, fit)
def insertUnit(self, chrom, fit):
'''Add a new unit to the population. Thread safe.'''
#print fit
try:
fv=fit[0]
except IndexError:
print "got eval error: %s" % (str(fit),)
return
try:
fv=float(fit[0])
except ValueError:
print "got eval error: %s" % (str(fit),)
return
self.lock.acquire()
try:
self._npts+=1
if not self._npts%200:
print "tried %i, %i viable, %.3f mean fit" % (self._npts, self._nunits, self.fit.mean())
ind=self.compete(fit[0])
if ind>=0:
self.fit[ind]=fit[0]
self.chroms[ind]=self._params.getIndex(chrom)
self._nunits=self.record(chrom, fit)
self.lock.release()
except:
self.lock.release()
#raise
self.report("Record failed")
def compete(self, fit):
'''Determine which unit is replaced by a new unit with fitness "fit". Return value is the index of the unit to "kill". If this value is <0, the new unit is killed and doesn't replace any existing unit. Not locally thread safe (usually called in a thread safe way from insertUnit).'''
if any(self.fit<0):
return argmin(self.fit)
elif fit<0:
return -1
elif self.attrib("Cull") and self.better(self.attrib("Cull"), fit):
return -1
if self.attrib("Death")=='Select':
ind=self.breeder.kill()
else:
if self.attrib('Maximize'):
ind=argmin(self.fit)
else:
ind=argmax(self.fit)
if self.attrib('CompeteToReplace'):
if self.better(self.fit[ind], fit):
ind=-1
return ind
